From 3c13f9cde73d4c00c37ea4a931f65f0808dc5231 Mon Sep 17 00:00:00 2001
From: brifordwylie <brifordwylie@users.noreply.github.com>
Date: Sat, 21 Dec 2024 15:44:38 +0000
Subject: [PATCH] deploy: a57cb33b4f376ab9226b68b82ba3c7aa21fd7673

---
 404.html                                      |   20 +-
 admin/aws_service_limits/index.html           |   28 +-
 admin/base_docker_push/index.html             |   60 +-
 admin/dashboard_docker_push/index.html        |   56 +-
 admin/dashboard_s3_plugins/index.html         |   30 +-
 admin/dashboard_with_plugins/index.html       |   64 +-
 admin/pypi_release/index.html                 |   26 +-
 .../index.html                                |   42 +-
 api_classes/data_source/index.html            |  118 +-
 api_classes/df_store/index.html               |  124 +-
 api_classes/endpoint/index.html               |  110 +-
 api_classes/feature_set/index.html            |  118 +-
 api_classes/meta/index.html                   |  216 +-
 api_classes/model/index.html                  |   96 +-
 api_classes/monitor/index.html                |  138 +-
 api_classes/overview/index.html               |   52 +-
 api_classes/parameter_store/index.html        |  142 +-
 api_classes/pipelines/index.html              |  162 +-
 api_classes/views/index.html                  |  128 +-
 aws_setup/aws_access_management/index.html    |   26 +-
 aws_setup/aws_client_vpn/index.html           |   20 +-
 aws_setup/aws_setup/index.html                |   26 +-
 aws_setup/aws_tips_and_tricks/index.html      |   26 +-
 aws_setup/core_stack/index.html               |  163 +-
 aws_setup/dashboard_stack/index.html          |   40 +-
 aws_setup/domain_cert_setup/index.html        |   28 +-
 aws_setup/full_pipeline/index.html            |   46 +-
 aws_setup/iam_assume_role/index.html          | 3328 ++++++++++++++++
 aws_setup/sso_assume_role/index.html          | 3341 +++++++++++++++++
 blogs/aws_credentials_lock/index.html         |   24 +-
 blogs/eda/index.html                          |   30 +-
 blogs/htg/index.html                          |   26 +-
 blogs/index.html                              |   28 +-
 blogs/model_monitoring/index.html             |   20 +-
 blogs/residual_analysis/index.html            |   20 +-
 cached/cached_data_source/index.html          |  248 +-
 cached/cached_endpoint/index.html             |  192 +-
 cached/cached_feature_set/index.html          |  248 +-
 cached/cached_meta/index.html                 |  278 +-
 cached/cached_model/index.html                |  224 +-
 cached/overview/index.html                    |   42 +-
 cloudwatch/index.html                         |   48 +-
 core_classes/artifacts/artifact/index.html    |  634 ++--
 .../artifacts/athena_source/index.html        |  566 +--
 .../artifacts/data_source_abstract/index.html |  236 +-
 .../artifacts/endpoint_core/index.html        |  304 +-
 .../artifacts/feature_set_core/index.html     |  460 +--
 core_classes/artifacts/model_core/index.html  |  564 +--
 .../artifacts/monitor_core/index.html         |  182 +-
 core_classes/artifacts/overview/index.html    |   38 +-
 core_classes/overview/index.html              |   28 +-
 .../transforms/data_loaders_heavy/index.html  |  132 +-
 .../transforms/data_loaders_light/index.html  |  236 +-
 .../transforms/data_to_features/index.html    |  124 +-
 .../transforms/features_to_model/index.html   |  114 +-
 .../transforms/model_to_endpoint/index.html   |   62 +-
 core_classes/transforms/overview/index.html   |   24 +-
 .../transforms/pandas_transforms/index.html   |  987 ++---
 core_classes/transforms/transform/index.html  |  715 ++--
 .../views/computation_view/index.html         |   52 +-
 core_classes/views/display_view/index.html    |   52 +-
 core_classes/views/mdq_view/index.html        |   52 +-
 core_classes/views/overview/index.html        |   38 +-
 core_classes/views/training_view/index.html   |   54 +-
 .../dataframes/overview/index.html            |  244 +-
 data_algorithms/graphs/overview/index.html    |   60 +-
 data_algorithms/overview/index.html           |   24 +-
 data_algorithms/spark/overview/index.html     |   50 +-
 data_algorithms/sql/overview/index.html       |  108 +-
 enterprise/index.html                         |   66 +-
 enterprise/private_saas/index.html            |   24 +-
 enterprise/project_branding/index.html        |   26 +-
 enterprise/themes/index.html                  |   30 +-
 getting_started/index.html                    |   50 +-
 glue/index.html                               |  102 +-
 images/sageworks.png                          |  Bin 51137 -> 0 bytes
 ...ks_concepts.png => workbench_concepts.png} |  Bin
 index.html                                    |   42 +-
 lambda_layer/index.html                       |   82 +-
 misc/faq/index.html                           |   32 +-
 misc/general_info/index.html                  |   94 +-
 misc/scp_consulting/index.html                |   36 +-
 .../index.html                                |   30 +-
 objects.inv                                   |  Bin 10310 -> 10307 bytes
 plugins/index.html                            |   38 +-
 presentations/index.html                      |   60 +-
 release_notes/0_8_50/index.html               |   32 +-
 release_notes/0_8_55/index.html               |   32 +-
 release_notes/0_8_58/index.html               |   38 +-
 release_notes/0_8_60/index.html               |   28 +-
 release_notes/0_8_71/index.html               |   28 +-
 release_notes/0_8_74/index.html               |   30 +-
 release_notes/0_8_78/index.html               |   28 +-
 release_notes/archived/0_7_8/index.html       |   28 +-
 release_notes/archived/0_8_11/index.html      |   36 +-
 release_notes/archived/0_8_20/index.html      |   30 +-
 release_notes/archived/0_8_22/index.html      |   26 +-
 release_notes/archived/0_8_23/index.html      |   26 +-
 release_notes/archived/0_8_27/index.html      |   26 +-
 release_notes/archived/0_8_29/index.html      |   28 +-
 release_notes/archived/0_8_33/index.html      |   26 +-
 release_notes/archived/0_8_35/index.html      |   30 +-
 release_notes/archived/0_8_36/index.html      |   30 +-
 release_notes/archived/0_8_39/index.html      |   28 +-
 release_notes/archived/0_8_42/index.html      |   28 +-
 release_notes/archived/0_8_46/index.html      |   28 +-
 release_notes/archived/0_8_6/index.html       |   30 +-
 release_notes/archived/0_8_8/index.html       |   40 +-
 repl/index.html                               |   46 +-
 road_maps/0_9_0/index.html                    |   34 +-
 road_maps/0_9_5/index.html                    |   26 +-
 search/search_index.json                      |    2 +-
 sitemap.xml.gz                                |  Bin 127 -> 127 bytes
 themes/details/index.html                     |   24 +-
 themes/index.html                             |   28 +-
 115 files changed, 12335 insertions(+), 5535 deletions(-)
 rename admin/{sageworks_docker_for_lambdas => workbench_docker_for_lambdas}/index.html (98%)
 create mode 100644 aws_setup/iam_assume_role/index.html
 create mode 100644 aws_setup/sso_assume_role/index.html
 delete mode 100644 images/sageworks.png
 rename images/{sageworks_concepts.png => workbench_concepts.png} (100%)
 rename misc/{sageworks_classes_concepts => workbench_classes_concepts}/index.html (98%)

diff --git a/404.html b/404.html
index 69ed034d5..329b13885 100644
--- a/404.html
+++ b/404.html
@@ -16,7 +16,7 @@
     
     
       
-        <title>SageWorks</title>
+        <title>Workbench</title>
       
     
     
@@ -81,7 +81,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="/." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="/." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -95,7 +95,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -202,13 +202,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="/." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="/." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -284,7 +284,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1808,7 +1808,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1818,7 +1818,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3037,11 +3037,11 @@
   
   
     <li class="md-nav__item">
-      <a href="/admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="/admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
diff --git a/admin/aws_service_limits/index.html b/admin/aws_service_limits/index.html
index ae8bdcf7a..bb7a51acd 100644
--- a/admin/aws_service_limits/index.html
+++ b/admin/aws_service_limits/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS Service Limits - SageWorks</title>
+        <title>AWS Service Limits - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3115,11 +3115,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3215,10 +3215,10 @@
 <h1 id="aws-service-limits">AWS Service Limits</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>In general SageWorks works well, out of the box, with the standard set of limits for AWS accounts. SageWorks supports throttling, timeouts, and a broad set of AWS error handling routines for general purpose usage.</p>
-<p>When using SageWorks for <strong>large scale deployments</strong> there are a set of AWS Service limits that will need to be increased.</p>
+<p>In general Workbench works well, out of the box, with the standard set of limits for AWS accounts. Workbench supports throttling, timeouts, and a broad set of AWS error handling routines for general purpose usage.</p>
+<p>When using Workbench for <strong>large scale deployments</strong> there are a set of AWS Service limits that will need to be increased.</p>
 <h2 id="serverless-endpoints">ServerLess Endpoints</h2>
 <p>There are two serverless endpoint quotas that will need to be adjusted.</p>
 <ul>
@@ -3234,7 +3234,7 @@ <h2 id="parallel-featuregroup-creation">Parallel FeatureGroup Creation</h2>
 <p>Unfortunately this one is not adjustable through the AWS Service Quota console and you'll have to initiate an AWS Support ticket.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/admin/base_docker_push/index.html b/admin/base_docker_push/index.html
index 08bc54120..0237d891a 100644
--- a/admin/base_docker_push/index.html
+++ b/admin/base_docker_push/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Base Docker Push - SageWorks</title>
+        <title>Base Docker Push - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-base-docker-build-and-push" class="md-skip">
+        <a href="#workbench-base-docker-build-and-push" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -2975,9 +2975,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#update-sageworks-version" class="md-nav__link">
+  <a href="#update-workbench-version" class="md-nav__link">
     <span class="md-ellipsis">
-      Update SageWorks Version
+      Update Workbench Version
     </span>
   </a>
   
@@ -3160,11 +3160,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3216,9 +3216,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#update-sageworks-version" class="md-nav__link">
+  <a href="#update-workbench-version" class="md-nav__link">
     <span class="md-ellipsis">
-      Update SageWorks Version
+      Update Workbench Version
     </span>
   </a>
   
@@ -3302,18 +3302,18 @@
                   
 
 
-<h1 id="sageworks-base-docker-build-and-push">SageWorks Base Docker Build and Push</h1>
+<h1 id="workbench-base-docker-build-and-push">Workbench Base Docker Build and Push</h1>
 <p>Notes and information on how to do the Docker Builds and Push to AWS ECR.</p>
-<h3 id="update-sageworks-version">Update SageWorks Version</h3>
+<h3 id="update-workbench-version">Update Workbench Version</h3>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>vi Dockerfile
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
-</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a># Install latest Sageworks
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>RUN pip install --no-cache-dir &#39;sageworks[ml-tool,chem]&#39;==0.7.0
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a># Install latest Workbench
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>RUN pip install --no-cache-dir &#39;workbench[ml-tool,chem]&#39;==0.7.0
 </span></code></pre></div>
 <h3 id="build-the-docker-image">Build the Docker Image</h3>
 <p><strong>Note:</strong> For a client specific config file you'll need to copy it locally so that it's within Dockers 'build context'. If you're building the 'vanilla' open source Docker image, then you can use the <code>open_source_config.json</code> that's in the directory already.</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \
-</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>sageworks_base:v0_7_0_amd64 --platform linux/amd64 .
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>workbench_base:v0_7_0_amd64 --platform linux/amd64 .
 </span></code></pre></div>
 <h3 id="test-the-image-locally">Test the Image Locally</h3>
 <p>You have a <code>docker_local_base</code> alias in your <code>~/.zshrc</code> :)</p>
@@ -3323,23 +3323,23 @@ <h3 id="login-to-ecr">Login to ECR</h3>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>--password-stdin public.ecr.aws
 </span></code></pre></div>
 <h3 id="tagpush-the-image-to-aws-ecr">Tag/Push the Image to AWS ECR</h3>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>docker tag sageworks_base:v0_7_0_amd64 \
-</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>docker tag workbench_base:v0_7_0_amd64 \
+</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>docker push public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64
+<div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>docker push public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64
 </span></code></pre></div></p>
 <h3 id="update-the-latest-tag">Update the 'latest' tag</h3>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \
-</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>public.ecr.aws/m6i5k1r2/sageworks_base:latest
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>public.ecr.aws/m6i5k1r2/workbench_base:latest
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>docker push public.ecr.aws/m6i5k1r2/sageworks_base:latest
+<div class="language-text highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>docker push public.ecr.aws/m6i5k1r2/workbench_base:latest
 </span></code></pre></div></p>
 <h3 id="update-the-stable-tag">Update the 'stable' tag</h3>
 <p>This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :)</p>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \
-</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a>public.ecr.aws/m6i5k1r2/sageworks_base:stable
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \
+</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a>public.ecr.aws/m6i5k1r2/workbench_base:stable
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a>docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable
+<div class="language-text highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a>docker push public.ecr.aws/m6i5k1r2/workbench_base:stable
 </span></code></pre></div></p>
 <h3 id="test-the-ecr-image">Test the ECR Image</h3>
 <p>You have a <code>docker_ecr_base</code> alias in your <code>~/.zshrc</code> :)</p>
diff --git a/admin/dashboard_docker_push/index.html b/admin/dashboard_docker_push/index.html
index 22f1dc1ca..f6ac8213a 100644
--- a/admin/dashboard_docker_push/index.html
+++ b/admin/dashboard_docker_push/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Dashboard Docker Push - SageWorks</title>
+        <title>Dashboard Docker Push - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -2996,9 +2996,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#update-sageworks-version" class="md-nav__link">
+  <a href="#update-workbench-version" class="md-nav__link">
     <span class="md-ellipsis">
-      Update SageWorks Version
+      Update Workbench Version
     </span>
   </a>
   
@@ -3160,11 +3160,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3216,9 +3216,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#update-sageworks-version" class="md-nav__link">
+  <a href="#update-workbench-version" class="md-nav__link">
     <span class="md-ellipsis">
-      Update SageWorks Version
+      Update Workbench Version
     </span>
   </a>
   
@@ -3304,17 +3304,17 @@
 
 <h1 id="dashboard-docker-build-and-push">Dashboard Docker Build and Push</h1>
 <p>Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR.</p>
-<h3 id="update-sageworks-version">Update SageWorks Version</h3>
+<h3 id="update-workbench-version">Update Workbench Version</h3>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>cd applications/aws_dashboard
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>vi Dockerfile
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a># Install Sageworks (changes often)
-</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>RUN pip install --no-cache-dir sageworks==0.4.13 &lt;-- change this
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a># Install Workbench (changes often)
+</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>RUN pip install --no-cache-dir workbench==0.4.13 &lt;-- change this
 </span></code></pre></div>
 <h3 id="build-the-docker-image">Build the Docker Image</h3>
 <p><strong>Note:</strong> For a client specific config file you'll need to copy it locally so that it's within Dockers 'build context'. If you're building the 'vanilla' open source Docker image, then you can use the <code>open_source_config.json</code> that's in the directory already.</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \
-</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 .
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 .
 </span></code></pre></div>
 <p><strong>Docker with Custom Plugins:</strong> If you're using custom plugins you should visit our <a href="../dashboard_with_plugins/">Dashboard with Plugins</a>) page.</p>
 <h3 id="test-the-image-locally">Test the Image Locally</h3>
@@ -3325,23 +3325,23 @@ <h3 id="login-to-ecr">Login to ECR</h3>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>--password-stdin public.ecr.aws
 </span></code></pre></div>
 <h3 id="tagpush-the-image-to-aws-ecr">Tag/Push the Image to AWS ECR</h3>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>docker tag sageworks_dashboard:v0_4_13_amd64 \
-</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>docker tag workbench_dashboard:v0_4_13_amd64 \
+</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64
+<div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64
 </span></code></pre></div></p>
 <h3 id="update-the-latest-tag">Update the 'latest' tag</h3>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \
-</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>public.ecr.aws/m6i5k1r2/workbench_dashboard:latest
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest
+<div class="language-text highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest
 </span></code></pre></div></p>
 <h3 id="update-the-stable-tag">Update the 'stable' tag</h3>
 <p>This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :)</p>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \
-</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a>public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \
+</span><span id="__span-7-2"><a id="__codelineno-7-2" name="__codelineno-7-2" href="#__codelineno-7-2"></a>public.ecr.aws/m6i5k1r2/workbench_dashboard:stable
 </span></code></pre></div>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a>docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable
+<div class="language-text highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a>docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable
 </span></code></pre></div></p>
 <h3 id="test-the-ecr-image">Test the ECR Image</h3>
 <p>You have a <code>docker_ecr_dashboard</code> alias in your <code>~/.zshrc</code> :)</p>
diff --git a/admin/dashboard_s3_plugins/index.html b/admin/dashboard_s3_plugins/index.html
index 394188278..17d7e26a2 100644
--- a/admin/dashboard_s3_plugins/index.html
+++ b/admin/dashboard_s3_plugins/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Dashboard with S3 Plugins - SageWorks</title>
+        <title>Dashboard with S3 Plugins - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3139,11 +3139,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3263,9 +3263,9 @@
 <h1 id="deploying-s3-plugins-with-the-dashboard">Deploying S3 Plugins with the Dashboard</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>Notes and information on how to include S3 based plugins with your SageWorks Dashboard. </p>
+<p>Notes and information on how to include S3 based plugins with your Workbench Dashboard. </p>
 <p>Deploying your Dashboard plugins via an S3 bucket allows plugin developers to modify and improve plugins without a bunch of Docker builds, ECR, and CDK Deploy.</p>
 <p>On, the other hand when you have a 'final build' you might want to lock it in with a Docker image. If you'd like to bundle up your plugins in a Docker image and deploy please see <a href="../dashboard_with_plugins/">Dashboard with Plugins</a>.</p>
 <h2 id="check-your-dashboard">Check your Dashboard</h2>
@@ -3298,10 +3298,10 @@ <h2 id="restart-the-ecs-service">Restart the ECS Service</h2>
 <p><strong>Getting Cluster and Service Names</strong></p>
 <p>You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service.</p>
 <p>The cluster will be something like:</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>SageworksDashboard-SageworksCluster123456
+<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>WorkbenchDashboard-WorkbenchCluster123456
 </span></code></pre></div>
 <p>and the service will be something like:</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>SageworksDashboard-SageworksService789123
+<div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>WorkbenchDashboard-WorkbenchService789123
 </span></code></pre></div>
 <p>Anyway, find those two things and run this command below (<strong>Note:</strong> You probably need admin permisions)</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>aws ecs update-service --cluster your-cluster-name \
@@ -3312,7 +3312,7 @@ <h3 id="verify-new-plugin-changes">Verify new Plugin changes</h3>
 <p>Okay now that the ECS service has restarted (which can take a bit) you can now go to the Dashboard and test/verify that the changes you made now show up on the Dashboard.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/admin/dashboard_with_plugins/index.html b/admin/dashboard_with_plugins/index.html
index 1e91d99d5..1f5550451 100644
--- a/admin/dashboard_with_plugins/index.html
+++ b/admin/dashboard_with_plugins/index.html
@@ -12,7 +12,7 @@
         <link rel="prev" href="../dashboard_s3_plugins/">
       
       
-        <link rel="next" href="../sageworks_docker_for_lambdas/">
+        <link rel="next" href="../workbench_docker_for_lambdas/">
       
       
       <link rel="icon" href="../../assets/images/favicon.png">
@@ -20,7 +20,7 @@
     
     
       
-        <title>Dashboard with Docker Plugins - SageWorks</title>
+        <title>Dashboard with Docker Plugins - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3108,9 +3108,9 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#note-on-sageworks-configuration" class="md-nav__link">
+  <a href="#note-on-workbench-configuration" class="md-nav__link">
     <span class="md-ellipsis">
-      Note on SageWorks Configuration
+      Note on Workbench Configuration
     </span>
   </a>
   
@@ -3172,11 +3172,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3298,9 +3298,9 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#note-on-sageworks-configuration" class="md-nav__link">
+  <a href="#note-on-workbench-configuration" class="md-nav__link">
     <span class="md-ellipsis">
-      Note on SageWorks Configuration
+      Note on Workbench Configuration
     </span>
   </a>
   
@@ -3327,7 +3327,7 @@
 
 
 <h1 id="deploying-plugins-with-the-dashboard">Deploying Plugins with the Dashboard</h1>
-<p>Notes and information on how to include plugins with your SageWorks Dashboard.</p>
+<p>Notes and information on how to include plugins with your Workbench Dashboard.</p>
 <ul>
 <li><strong>ECR:</strong> AWS Elastic Container Registry (stores Docker images)</li>
 <li><strong>ECS:</strong> AWS Elastic Container Service (uses Docker images)</li>
@@ -3339,15 +3339,15 @@ <h3 id="install-docker">Install Docker</h3>
 </ul>
 <h2 id="build-the-docker-image">Build the Docker Image</h2>
 <p>If you don't already have a Dockerfile, here's one to get you started, just place this into your repo/directory that has the plugins. </p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a># Pull base sageworks dashboard image with specific tag (pick latest or stable)
-</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest
+<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a># Pull base workbench dashboard image with specific tag (pick latest or stable)
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a># Copy the plugin files into the Dashboard plugins dir
-</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>COPY ./sageworks_plugins /app/sageworks_plugins
-</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a>ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins
+</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>COPY ./workbench_plugins /app/workbench_plugins
+</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a>ENV WORKBENCH_PLUGINS=/app/workbench_plugins
 </span></code></pre></div>
 <p><strong>Note:</strong> Your plugins directory should looks like this</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>sageworks_plugins/
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>workbench_plugins/
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>   pages/
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>      my_plugin_page.py
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a>      ...
@@ -3359,10 +3359,10 @@ <h2 id="build-the-docker-image">Build the Docker Image</h2>
 </span><span id="__span-1-10"><a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a>      ...
 </span></code></pre></div>
 <h3 id="build-it">Build it</h3>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 .
+<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 .
 </span></code></pre></div>
 <h3 id="test-the-image-locally">Test the Image Locally</h3>
-<p>You'll need to use AWS Credentials for this, it's a bit complicated, please contact SageWorks Support <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>You'll need to use AWS Credentials for this, it's a bit complicated, please contact Workbench Support <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 <h3 id="login-to-your-ecr">Login to your ECR</h3>
 <p>Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR.</p>
 <p><strong>Note:</strong> This ECR should be <strong>private</strong> as your plugins are customized for specific business use cases.</p>
@@ -3374,25 +3374,25 @@ <h3 id="login-to-your-ecr">Login to your ECR</h3>
 </span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a>&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com
 </span></code></pre></div>
 <h3 id="tagpush-the-image-to-aws-ecr">Tag/Push the Image to AWS ECR</h3>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>docker tag my_sageworks_with_plugins:v1_0 \
-</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>docker tag my_workbench_with_plugins:v1_0 \
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0
 </span></code></pre></div>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>docker push \
-</span><span id="__span-6-2"><a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a>&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0
+</span><span id="__span-6-2"><a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a>&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0
 </span></code></pre></div></p>
 <h2 id="deploying-plugin-docker-image-to-aws">Deploying Plugin Docker Image to AWS</h2>
 <p>Okay now that you have your plugin Docker Image you can deploy to your AWS account:</p>
 <p><strong>Copy the Dashboard CDK files</strong></p>
 <p>This is cheesy but just copy all the CDK files into your repo/directory.</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/
+<div class="language-text highlight"><pre><span></span><code><span id="__span-7-1"><a id="__codelineno-7-1" name="__codelineno-7-1" href="#__codelineno-7-1"></a>cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/
 </span></code></pre></div>
 <p><strong>Change the Docker Image to Deploy</strong></p>
 <p>Now open up the <code>app.py</code> file and change this line to your Docker Image</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-8-1"><a id="__codelineno-8-1" name="__codelineno-8-1" href="#__codelineno-8-1"></a># When you want a different docker image change this line
-</span><span id="__span-8-2"><a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a>dashboard_image = &quot;public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64&quot;
+</span><span id="__span-8-2"><a id="__codelineno-8-2" name="__codelineno-8-2" href="#__codelineno-8-2"></a>dashboard_image = &quot;public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64&quot;
 </span></code></pre></div>
-<p>Make sure your <code>SAGEWORKS_CONFIG</code> is properly set, and run the following commands:</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-9-1"><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a>export SAGEWORKS_CONFIG=/Users/&lt;user_name&gt;/.sageworks/sageworks_config.json
+<p>Make sure your <code>WORKBENCH_CONFIG</code> is properly set, and run the following commands:</p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-9-1"><a id="__codelineno-9-1" name="__codelineno-9-1" href="#__codelineno-9-1"></a>export WORKBENCH_CONFIG=/Users/&lt;user_name&gt;/.workbench/workbench_config.json
 </span><span id="__span-9-2"><a id="__codelineno-9-2" name="__codelineno-9-2" href="#__codelineno-9-2"></a>cdk diff
 </span><span id="__span-9-3"><a id="__codelineno-9-3" name="__codelineno-9-3" href="#__codelineno-9-3"></a>cdk deploy
 </span></code></pre></div>
@@ -3404,8 +3404,8 @@ <h2 id="deploying-plugin-docker-image-to-aws">Deploying Plugin Docker Image to A
 </span><span id="__span-10-3"><a id="__codelineno-10-3" name="__codelineno-10-3" href="#__codelineno-10-3"></a>[+] &quot;Image&quot;: &quot;&lt;account&gt;.dkr.ecr.us-east-1/my-plugins:latest_456&quot;,
 </span></code></pre></div></p>
 </div>
-<h3 id="note-on-sageworks-configuration">Note on SageWorks Configuration</h3>
-<p>All Configuration is managed by the CDK Python Script and the <code>SAGEWORKS_CONFIG</code> ENV var. If you want to change things like <code>REDIS_HOST</code> or <code>SAGEWORKS_BUCKET</code> you should do that with a <code>sageworks.config</code> file and then point the <code>SAGEWORKS_CONFIG</code> ENV var to that file.</p>
+<h3 id="note-on-workbench-configuration">Note on Workbench Configuration</h3>
+<p>All Configuration is managed by the CDK Python Script and the <code>WORKBENCH_CONFIG</code> ENV var. If you want to change things like <code>REDIS_HOST</code> or <code>WORKBENCH_BUCKET</code> you should do that with a <code>workbench.config</code> file and then point the <code>WORKBENCH_CONFIG</code> ENV var to that file.</p>
 
 
 
diff --git a/admin/pypi_release/index.html b/admin/pypi_release/index.html
index bedcf7bea..34656e9d1 100644
--- a/admin/pypi_release/index.html
+++ b/admin/pypi_release/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>PyPI Release - SageWorks</title>
+        <title>PyPI Release - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3178,11 +3178,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3364,9 +3364,9 @@ <h3 id="setup-pypirc">Setup pypirc</h3>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="nv">password</span><span class="w"> </span><span class="o">=</span><span class="w"> </span>pypi-AgENdG...
 </span></code></pre></div>
 <h3 id="tox-background">Tox Background</h3>
-<p>Tox will install the SageMaker Sandbox package into a blank virtualenv and then execute all the tests against the newly installed package. So if everything goes okay, you know the pypi package installed fine and the tests (which puls from the installed <code>sageworks</code> package) also ran okay.</p>
+<p>Tox will install the SageMaker Sandbox package into a blank virtualenv and then execute all the tests against the newly installed package. So if everything goes okay, you know the pypi package installed fine and the tests (which puls from the installed <code>workbench</code> package) also ran okay.</p>
 <h3 id="make-sure-all-tests-pass">Make sure ALL tests pass</h3>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>sageworks
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>$<span class="w"> </span><span class="nb">cd</span><span class="w"> </span>workbench
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>$<span class="w"> </span>tox<span class="w"> </span>
 </span></code></pre></div>
 <p>If ALL the test above pass...</p>
@@ -3382,7 +3382,7 @@ <h3 id="create-the-test-pypi-release">Create the TEST PyPI Release</h3>
 </span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>twine<span class="w"> </span>upload<span class="w"> </span>dist/*<span class="w"> </span>-r<span class="w"> </span>testpypi
 </span></code></pre></div>
 <h3 id="install-the-test-pypi-release">Install the TEST PyPI Release</h3>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>pip<span class="w"> </span>install<span class="w"> </span>--index-url<span class="w"> </span>https://test.pypi.org/simple<span class="w"> </span>sageworks
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>pip<span class="w"> </span>install<span class="w"> </span>--index-url<span class="w"> </span>https://test.pypi.org/simple<span class="w"> </span>workbench
 </span></code></pre></div>
 <h3 id="create-the-real-pypi-release">Create the REAL PyPI Release</h3>
 <div class="language-bash highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>twine<span class="w"> </span>upload<span class="w"> </span>dist/*<span class="w"> </span>-r<span class="w"> </span>pypi
diff --git a/admin/sageworks_docker_for_lambdas/index.html b/admin/workbench_docker_for_lambdas/index.html
similarity index 98%
rename from admin/sageworks_docker_for_lambdas/index.html
rename to admin/workbench_docker_for_lambdas/index.html
index 082b453ec..393ea952b 100644
--- a/admin/sageworks_docker_for_lambdas/index.html
+++ b/admin/workbench_docker_for_lambdas/index.html
@@ -18,7 +18,7 @@
     
     
       
-        <title>SageWorks Docker for Lambdas - SageWorks</title>
+        <title>Workbench Docker for Lambdas - Workbench</title>
       
     
     
@@ -72,7 +72,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-docker-image-for-lambdas" class="md-skip">
+        <a href="#workbench-docker-image-for-lambdas" class="md-skip">
           Skip to content
         </a>
       
@@ -88,7 +88,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -102,13 +102,13 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
           <span class="md-ellipsis">
             
-              SageWorks Docker for Lambdas
+              Workbench Docker for Lambdas
             
           </span>
         </div>
@@ -209,13 +209,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -291,7 +291,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1815,7 +1815,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1825,7 +1825,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3061,7 +3061,7 @@
           
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3072,7 +3072,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3356,8 +3356,8 @@
                   
 
 
-<h1 id="sageworks-docker-image-for-lambdas">SageWorks Docker Image for Lambdas</h1>
-<p>Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks.</p>
+<h1 id="workbench-docker-image-for-lambdas">Workbench Docker Image for Lambdas</h1>
+<p>Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench.</p>
 <p>AWS, for some reason, does not allow Public ECRs to be used for Lambda Docker images. So you'll have to copy the Docker image into your private ECR. </p>
 <h2 id="creating-a-private-ecr">Creating a Private ECR</h2>
 <p>You only need to do this if you don't already have a private ECR.</p>
@@ -3365,21 +3365,21 @@ <h4 id="aws-console-to-create-private-ecr">AWS Console to create Private ECR</h4
 <ol>
 <li>Open the Amazon ECR console.</li>
 <li>Choose "Create repository".</li>
-<li>For "Repository name", enter <code>sageworks_base</code>.</li>
+<li>For "Repository name", enter <code>workbench_base</code>.</li>
 <li>Ensure "Private" is selected.</li>
 <li>Choose "Create repository".</li>
 </ol>
 <h4 id="command-line-to-create-private-ecr">Command Line to create Private ECR</h4>
 <p>Create the ECR repository using the AWS CLI:</p>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>aws<span class="w"> </span>ecr<span class="w"> </span>create-repository<span class="w"> </span>--repository-name<span class="w"> </span>sageworks_base<span class="w"> </span>--region<span class="w"> </span>&lt;region&gt;
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>aws<span class="w"> </span>ecr<span class="w"> </span>create-repository<span class="w"> </span>--repository-name<span class="w"> </span>workbench_base<span class="w"> </span>--region<span class="w"> </span>&lt;region&gt;
 </span></code></pre></div>
 <h2 id="pulling-docker-image-into-private-ecr">Pulling Docker Image into Private ECR</h2>
-<p><strong>Note: You'll only need to do this when you want to update the SageWorks Docker image</strong></p>
-<p><strong>Pull the SageWorks Public ECR Image</strong></p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest
+<p><strong>Note: You'll only need to do this when you want to update the Workbench Docker image</strong></p>
+<p><strong>Pull the Workbench Public ECR Image</strong></p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest
 </span></code></pre></div>
 <p><strong>Tag the image for your private ECR</strong></p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \
+<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>&lt;your-account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:latest
 </span></code></pre></div>
 <p><strong>Push the image to your private ECR</strong></p>
@@ -3389,7 +3389,7 @@ <h2 id="pulling-docker-image-into-private-ecr">Pulling Docker Image into Private
 </span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a>docker push &lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;
 </span></code></pre></div>
 <h3 id="using-the-docker-image-for-your-lambdas">Using the Docker Image for your Lambdas</h3>
-<p>Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs.</p>
+<p>Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs.</p>
 <h4 id="aws-console">AWS Console</h4>
 <ol>
 <li>Open the AWS Lambda console.</li>
diff --git a/api_classes/data_source/index.html b/api_classes/data_source/index.html
index e03cff286..caed83bd2 100644
--- a/api_classes/data_source/index.html
+++ b/api_classes/data_source/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>DataSource - SageWorks</title>
+        <title>DataSource - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -434,7 +434,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.data_source" class="md-nav__link">
+  <a href="#workbench.api.data_source" class="md-nav__link">
     <span class="md-ellipsis">
       data_source
     </span>
@@ -443,7 +443,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.data_source.DataSource" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource" class="md-nav__link">
     <span class="md-ellipsis">
       DataSource
     </span>
@@ -453,7 +453,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.data_source.DataSource.__init__" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -462,7 +462,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.data_source.DataSource.details" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -471,7 +471,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.data_source.DataSource.pull_dataframe" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.pull_dataframe" class="md-nav__link">
     <span class="md-ellipsis">
       pull_dataframe
     </span>
@@ -480,7 +480,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.data_source.DataSource.query" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.query" class="md-nav__link">
     <span class="md-ellipsis">
       query
     </span>
@@ -489,7 +489,7 @@
 </li>
         
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-  <a href="#sageworks.api.data_source.DataSource.to_features" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.to_features" class="md-nav__link">
     <span class="md-ellipsis">
       to_features
     </span>
@@ -512,9 +512,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-ui" class="md-nav__link">
+  <a href="#workbench-ui" class="md-nav__link">
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-      SageWorks UI
+      Workbench UI
     </span>
   </a>
   
@@ -1946,7 +1946,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1956,7 +1956,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3175,11 +3175,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3231,7 +3231,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.data_source" class="md-nav__link">
+  <a href="#workbench.api.data_source" class="md-nav__link">
     <span class="md-ellipsis">
       data_source
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@@ -3240,7 +3240,7 @@
 </li>
       
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+  <a href="#workbench.api.data_source.DataSource" class="md-nav__link">
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       DataSource
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@@ -3250,7 +3250,7 @@
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-  <a href="#sageworks.api.data_source.DataSource.__init__" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.__init__" class="md-nav__link">
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@@ -3259,7 +3259,7 @@
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+  <a href="#workbench.api.data_source.DataSource.details" class="md-nav__link">
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       details
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@@ -3268,7 +3268,7 @@
 </li>
         
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-  <a href="#sageworks.api.data_source.DataSource.pull_dataframe" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.pull_dataframe" class="md-nav__link">
     <span class="md-ellipsis">
       pull_dataframe
     </span>
@@ -3277,7 +3277,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.data_source.DataSource.query" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.query" class="md-nav__link">
     <span class="md-ellipsis">
       query
     </span>
@@ -3286,7 +3286,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.data_source.DataSource.to_features" class="md-nav__link">
+  <a href="#workbench.api.data_source.DataSource.to_features" class="md-nav__link">
     <span class="md-ellipsis">
       to_features
     </span>
@@ -3309,9 +3309,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-ui" class="md-nav__link">
+  <a href="#workbench-ui" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks UI
+      Workbench UI
     </span>
   </a>
   
@@ -3343,14 +3343,14 @@ <h1 id="datasource">DataSource</h1>
 
 
 
-<a id="sageworks.api.data_source"></a>
+<a id="workbench.api.data_source"></a>
     <div class="doc doc-contents first">
 
         <p>DataSource: Manages AWS Data Catalog creation and management.
 DataSources are set up so that can easily be queried with AWS Athena.
 All DataSources are run through a full set of Exploratory Data Analysis (EDA)
 techniques (data quality, distributions, stats, outliers, etc.) DataSources
-can be viewed and explored within the SageWorks Dashboard UI.</p>
+can be viewed and explored within the Workbench Dashboard UI.</p>
 
 
 
@@ -3372,7 +3372,7 @@ <h1 id="datasource">DataSource</h1>
 
 
 
-<h2 id="sageworks.api.data_source.DataSource" class="doc doc-heading">
+<h2 id="workbench.api.data_source.DataSource" class="doc doc-heading">
             <code>DataSource</code>
 
 
@@ -3381,10 +3381,10 @@ <h2 id="sageworks.api.data_source.DataSource" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.athena_source.AthenaSource" href="../../core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource">AthenaSource</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.athena_source.AthenaSource" href="../../core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource">AthenaSource</a></code></p>
 
 
-        <p>DataSource: SageWorks DataSource API Class</p>
+        <p>DataSource: Workbench DataSource API Class</p>
 
 
 <details class="common-usage" open>
@@ -3400,7 +3400,7 @@ <h2 id="sageworks.api.data_source.DataSource" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/data_source.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/data_source.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-22"> 22</a></span>
 <span class="normal"><a href="#__codelineno-0-23"> 23</a></span>
 <span class="normal"><a href="#__codelineno-0-24"> 24</a></span>
@@ -3553,7 +3553,7 @@ <h2 id="sageworks.api.data_source.DataSource" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-171">171</a></span>
 <span class="normal"><a href="#__codelineno-0-172">172</a></span>
 <span class="normal"><a href="#__codelineno-0-173">173</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="k">class</span> <span class="nc">DataSource</span><span class="p">(</span><span class="n">AthenaSource</span><span class="p">):</span>
-</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;DataSource: SageWorks DataSource API Class</span>
+</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;DataSource: Workbench DataSource API Class</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">        ```python</span>
@@ -3722,7 +3722,7 @@ <h2 id="sageworks.api.data_source.DataSource" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.data_source.DataSource.__init__" class="doc doc-heading">
+<h3 id="workbench.api.data_source.DataSource.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">source</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3796,7 +3796,7 @@ <h3 id="sageworks.api.data_source.DataSource.__init__" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-33">33</a></span>
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span>
@@ -3867,7 +3867,7 @@ <h3 id="sageworks.api.data_source.DataSource.__init__" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.data_source.DataSource.details" class="doc doc-heading">
+<h3 id="workbench.api.data_source.DataSource.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3901,7 +3901,7 @@ <h3 id="sageworks.api.data_source.DataSource.details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-65">65</a></span>
 <span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
@@ -3924,7 +3924,7 @@ <h3 id="sageworks.api.data_source.DataSource.details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.data_source.DataSource.pull_dataframe" class="doc doc-heading">
+<h3 id="workbench.api.data_source.DataSource.pull_dataframe" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pull_dataframe</span><span class="p">(</span><span class="n">include_aws_columns</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -3994,7 +3994,7 @@ <h3 id="sageworks.api.data_source.DataSource.pull_dataframe" class="doc doc-head
   <p>Obviously this is not recommended for large datasets :)</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-84"> 84</a></span>
 <span class="normal"><a href="#__codelineno-0-85"> 85</a></span>
 <span class="normal"><a href="#__codelineno-0-86"> 86</a></span>
@@ -4051,7 +4051,7 @@ <h3 id="sageworks.api.data_source.DataSource.pull_dataframe" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.data_source.DataSource.query" class="doc doc-heading">
+<h3 id="workbench.api.data_source.DataSource.query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span></code>
 
 </h3>
@@ -4116,7 +4116,7 @@ <h3 id="sageworks.api.data_source.DataSource.query" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-73">73</a></span>
 <span class="normal"><a href="#__codelineno-0-74">74</a></span>
 <span class="normal"><a href="#__codelineno-0-75">75</a></span>
@@ -4145,7 +4145,7 @@ <h3 id="sageworks.api.data_source.DataSource.query" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.data_source.DataSource.to_features" class="doc doc-heading">
+<h3 id="workbench.api.data_source.DataSource.to_features" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">to_features</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">id_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">event_time_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">one_hot_columns</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -4262,7 +4262,7 @@ <h3 id="sageworks.api.data_source.DataSource.to_features" class="doc doc-heading
       <tbody>
           <tr class="doc-section-item">
 <td><code>FeatureSet</code></td>            <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.api.feature_set.FeatureSet" href="../feature_set/#sageworks.api.feature_set.FeatureSet">FeatureSet</a>, None]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.api.feature_set.FeatureSet" href="../feature_set/#workbench.api.feature_set.FeatureSet">FeatureSet</a>, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -4274,7 +4274,7 @@ <h3 id="sageworks.api.data_source.DataSource.to_features" class="doc doc-heading
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-109">109</a></span>
 <span class="normal"><a href="#__codelineno-0-110">110</a></span>
 <span class="normal"><a href="#__codelineno-0-111">111</a></span>
@@ -4378,20 +4378,20 @@ <h3 id="sageworks.api.data_source.DataSource.to_features" class="doc doc-heading
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Create a DataSource from an S3 Path or File Path</strong></p>
-<div class="language-py highlight"><span class="filename">datasource_from_s3.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+<div class="language-py highlight"><span class="filename">datasource_from_s3.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Create a DataSource from an S3 Path (or a local file)</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">source_path</span> <span class="o">=</span> <span class="s2">&quot;s3://sageworks-public-data/common/abalone.csv&quot;</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">source_path</span> <span class="o">=</span> <span class="s2">&quot;s3://workbench-public-data/common/abalone.csv&quot;</span>
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="c1"># source_path = &quot;/full/path/to/local/file.csv&quot;</span>
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a>
 </span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="n">my_data</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="n">source_path</span><span class="p">)</span>
 </span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="nb">print</span><span class="p">(</span><span class="n">my_data</span><span class="o">.</span><span class="n">details</span><span class="p">())</span>
 </span></code></pre></div>
 <p><strong>Create a DataSource from a Pandas Dataframe</strong></p>
-<div class="language-py highlight"><span class="filename">datasource_from_df.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.utils.test_data_generator</span> <span class="kn">import</span> <span class="n">TestDataGenerator</span>
-</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+<div class="language-py highlight"><span class="filename">datasource_from_df.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.utils.test_data_generator</span> <span class="kn">import</span> <span class="n">TestDataGenerator</span>
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="c1"># Create a DataSource from a Pandas DataFrame</span>
 </span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a><span class="n">gen_data</span> <span class="o">=</span> <span class="n">TestDataGenerator</span><span class="p">()</span>
@@ -4401,8 +4401,8 @@ <h3 id="sageworks.api.data_source.DataSource.to_features" class="doc doc-heading
 </span><span id="__span-1-9"><a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a><span class="nb">print</span><span class="p">(</span><span class="n">test_data</span><span class="o">.</span><span class="n">details</span><span class="p">())</span>
 </span></code></pre></div>
 <p><strong>Query a DataSource</strong></p>
-<p>All SageWorks DataSources use AWS Athena, so any query that you can make with Athena is accessible through the DataSource API.</p>
-<div class="language-py highlight"><span class="filename">datasource_query.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+<p>All Workbench DataSources use AWS Athena, so any query that you can make with Athena is accessible through the DataSource API.</p>
+<div class="language-py highlight"><span class="filename">datasource_query.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="c1"># Grab a DataSource</span>
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="n">my_data</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="s2">&quot;abalone_data&quot;</span><span class="p">)</span>
@@ -4424,23 +4424,23 @@ <h3 id="sageworks.api.data_source.DataSource.to_features" class="doc doc-heading
 </span><span id="__span-3-7"><a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a><span class="mi">1</span>   <span class="n">I</span>   <span class="mf">0.150</span>     <span class="mf">0.100</span>   <span class="mf">0.025</span>         <span class="mf">0.015</span>          <span class="mf">0.0045</span>          <span class="mf">0.0040</span>        <span class="mf">0.0050</span>                      <span class="mi">2</span>
 </span></code></pre></div>
 <p><strong>Create a FeatureSet from a DataSource</strong></p>
-<div class="language-py highlight"><span class="filename">datasource_to_featureset.py</span><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+<div class="language-py highlight"><span class="filename">datasource_to_featureset.py</span><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
 </span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>
 </span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="c1"># Convert the Data Source to a Feature Set</span>
 </span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a><span class="n">test_data</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="s1">&#39;test_data&#39;</span><span class="p">)</span>
 </span><span id="__span-4-5"><a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a><span class="n">my_features</span> <span class="o">=</span> <span class="n">test_data</span><span class="o">.</span><span class="n">to_features</span><span class="p">(</span><span class="s2">&quot;test_features&quot;</span><span class="p">)</span>
 </span><span id="__span-4-6"><a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a><span class="nb">print</span><span class="p">(</span><span class="n">my_features</span><span class="o">.</span><span class="n">details</span><span class="p">())</span>
 </span></code></pre></div>
-<h2 id="sageworks-ui">SageWorks UI</h2>
-<p>Whenever a DataSource is created SageWorks performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p>
+<h2 id="workbench-ui">Workbench UI</h2>
+<p>Whenever a DataSource is created Workbench performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p>
 <figure style="width: 700px;">
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/9126bbe7-902e-409e-9caa-570b054b69e6"">
-<figcaption>SageWorks Dashboard: DataSources</figcaption>
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/9126bbe7-902e-409e-9caa-570b054b69e6"">
+<figcaption>Workbench Dashboard: DataSources</figcaption>
 </figure>
 
 <div class="admonition note">
 <p class="admonition-title">Not Finding a particular method?</p>
-<p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">SageWorks Core Classes</a></p>
+<p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">Workbench Core Classes</a></p>
 </div>
 
 
diff --git a/api_classes/df_store/index.html b/api_classes/df_store/index.html
index 147b7c0cd..506ebabab 100644
--- a/api_classes/df_store/index.html
+++ b/api_classes/df_store/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>DataFrame Store - SageWorks</title>
+        <title>DataFrame Store - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-dataframe-storage" class="md-skip">
+        <a href="#workbench-dataframe-storage" class="md-skip">
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@@ -90,7 +90,7 @@
 
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-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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@@ -104,7 +104,7 @@
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+            Workbench
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@@ -211,13 +211,13 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
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-    SageWorks
+    Workbench
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@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
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@@ -578,7 +578,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.df_store" class="md-nav__link">
+  <a href="#workbench.api.df_store" class="md-nav__link">
     <span class="md-ellipsis">
       df_store
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@@ -587,7 +587,7 @@
 </li>
       
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-  <a href="#sageworks.api.df_store.DFStore" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore" class="md-nav__link">
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       DFStore
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@@ -597,7 +597,7 @@
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-  <a href="#sageworks.api.df_store.DFStore.__init__" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
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@@ -606,7 +606,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.check" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.check" class="md-nav__link">
     <span class="md-ellipsis">
       check
     </span>
@@ -615,7 +615,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.delete" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
     </span>
@@ -624,7 +624,7 @@
 </li>
         
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-  <a href="#sageworks.api.df_store.DFStore.details" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
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@@ -633,7 +633,7 @@
 </li>
         
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-  <a href="#sageworks.api.df_store.DFStore.get" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.get" class="md-nav__link">
     <span class="md-ellipsis">
       get
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@@ -642,7 +642,7 @@
 </li>
         
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-  <a href="#sageworks.api.df_store.DFStore.last_modified" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.last_modified" class="md-nav__link">
     <span class="md-ellipsis">
       last_modified
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@@ -651,7 +651,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.list" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.list" class="md-nav__link">
     <span class="md-ellipsis">
       list
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@@ -660,7 +660,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.summary" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.summary" class="md-nav__link">
     <span class="md-ellipsis">
       summary
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@@ -669,7 +669,7 @@
 </li>
         
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+  <a href="#workbench.api.df_store.DFStore.upsert" class="md-nav__link">
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       upsert
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@@ -1991,7 +1991,7 @@
             
   
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-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3220,11 +3220,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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@@ -3294,7 +3294,7 @@
 </li>
       
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-  <a href="#sageworks.api.df_store" class="md-nav__link">
+  <a href="#workbench.api.df_store" class="md-nav__link">
     <span class="md-ellipsis">
       df_store
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+  <a href="#workbench.api.df_store.DFStore" class="md-nav__link">
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       DFStore
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@@ -3313,7 +3313,7 @@
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-  <a href="#sageworks.api.df_store.DFStore.__init__" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
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@@ -3322,7 +3322,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.check" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.check" class="md-nav__link">
     <span class="md-ellipsis">
       check
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@@ -3331,7 +3331,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.delete" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
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@@ -3340,7 +3340,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.details" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.details" class="md-nav__link">
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       details
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@@ -3349,7 +3349,7 @@
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+  <a href="#workbench.api.df_store.DFStore.get" class="md-nav__link">
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       get
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@@ -3358,7 +3358,7 @@
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     <span class="md-ellipsis">
       last_modified
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@@ -3367,7 +3367,7 @@
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-  <a href="#sageworks.api.df_store.DFStore.list" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.list" class="md-nav__link">
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       list
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@@ -3376,7 +3376,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.summary" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.summary" class="md-nav__link">
     <span class="md-ellipsis">
       summary
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@@ -3385,7 +3385,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.df_store.DFStore.upsert" class="md-nav__link">
+  <a href="#workbench.api.df_store.DFStore.upsert" class="md-nav__link">
     <span class="md-ellipsis">
       upsert
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@@ -3422,7 +3422,7 @@
                   
 
 
-<h1 id="sageworks-dataframe-storage">SageWorks DataFrame Storage</h1>
+<h1 id="workbench-dataframe-storage">Workbench DataFrame Storage</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Examples</p>
 <p>Examples of using the Parameter Storage class are listed at the bottom of this page <a href="#examples">Examples</a>.</p>
@@ -3437,7 +3437,7 @@ <h2 id="efficient-storage">Efficient Storage</h2>
 
 
 
-<a id="sageworks.api.df_store"></a>
+<a id="workbench.api.df_store"></a>
     <div class="doc doc-contents first">
 
         <p>DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy</p>
@@ -3462,7 +3462,7 @@ <h2 id="efficient-storage">Efficient Storage</h2>
 
 
 
-<h2 id="sageworks.api.df_store.DFStore" class="doc doc-heading">
+<h2 id="workbench.api.df_store.DFStore" class="doc doc-heading">
             <code>DFStore</code>
 
 
@@ -3471,7 +3471,7 @@ <h2 id="sageworks.api.df_store.DFStore" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.cloud_platform.aws.aws_df_store.AWSDFStore">AWSDFStore</span></code></p>
+              Bases: <code><span title="workbench.core.cloud_platform.aws.aws_df_store.AWSDFStore">AWSDFStore</span></code></p>
 
 
         <p>DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy</p>
@@ -3502,7 +3502,7 @@ <h2 id="sageworks.api.df_store.DFStore" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12"> 12</a></span>
 <span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
 <span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
@@ -3650,7 +3650,7 @@ <h2 id="sageworks.api.df_store.DFStore" class="doc doc-heading">
 </span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">            path_prefix (Union[str, None], optional): Add a path prefix to storage locations (Defaults to None)</span>
 </span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>
 </span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>        <span class="c1"># Initialize the SuperClass</span>
 </span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">path_prefix</span><span class="o">=</span><span class="n">path_prefix</span><span class="p">)</span>
@@ -3758,7 +3758,7 @@ <h2 id="sageworks.api.df_store.DFStore" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.__init__" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">path_prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -3800,7 +3800,7 @@ <h3 id="sageworks.api.df_store.DFStore.__init__" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-35">35</a></span>
 <span class="normal"><a href="#__codelineno-0-36">36</a></span>
 <span class="normal"><a href="#__codelineno-0-37">37</a></span>
@@ -3816,7 +3816,7 @@ <h3 id="sageworks.api.df_store.DFStore.__init__" class="doc doc-heading">
 </span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">        path_prefix (Union[str, None], optional): Add a path prefix to storage locations (Defaults to None)</span>
 </span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>
 </span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="c1"># Initialize the SuperClass</span>
 </span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">path_prefix</span><span class="o">=</span><span class="n">path_prefix</span><span class="p">)</span>
@@ -3829,7 +3829,7 @@ <h3 id="sageworks.api.df_store.DFStore.__init__" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.check" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">check</span><span class="p">(</span><span class="n">location</span><span class="p">)</span></code>
 
 </h3>
@@ -3894,7 +3894,7 @@ <h3 id="sageworks.api.df_store.DFStore.check" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-79">79</a></span>
 <span class="normal"><a href="#__codelineno-0-80">80</a></span>
 <span class="normal"><a href="#__codelineno-0-81">81</a></span>
@@ -3923,7 +3923,7 @@ <h3 id="sageworks.api.df_store.DFStore.check" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.delete" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">(</span><span class="n">location</span><span class="p">)</span></code>
 
 </h3>
@@ -3965,7 +3965,7 @@ <h3 id="sageworks.api.df_store.DFStore.delete" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-124">124</a></span>
 <span class="normal"><a href="#__codelineno-0-125">125</a></span>
 <span class="normal"><a href="#__codelineno-0-126">126</a></span>
@@ -3988,7 +3988,7 @@ <h3 id="sageworks.api.df_store.DFStore.delete" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.details" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="n">include_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4053,7 +4053,7 @@ <h3 id="sageworks.api.df_store.DFStore.details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-68">68</a></span>
 <span class="normal"><a href="#__codelineno-0-69">69</a></span>
 <span class="normal"><a href="#__codelineno-0-70">70</a></span>
@@ -4082,7 +4082,7 @@ <h3 id="sageworks.api.df_store.DFStore.details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.get" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.get" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get</span><span class="p">(</span><span class="n">location</span><span class="p">)</span></code>
 
 </h3>
@@ -4147,7 +4147,7 @@ <h3 id="sageworks.api.df_store.DFStore.get" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-90"> 90</a></span>
 <span class="normal"><a href="#__codelineno-0-91"> 91</a></span>
 <span class="normal"><a href="#__codelineno-0-92"> 92</a></span>
@@ -4182,7 +4182,7 @@ <h3 id="sageworks.api.df_store.DFStore.get" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.last_modified" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.last_modified" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">last_modified</span><span class="p">(</span><span class="n">location</span><span class="p">)</span></code>
 
 </h3>
@@ -4247,7 +4247,7 @@ <h3 id="sageworks.api.df_store.DFStore.last_modified" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-113">113</a></span>
 <span class="normal"><a href="#__codelineno-0-114">114</a></span>
 <span class="normal"><a href="#__codelineno-0-115">115</a></span>
@@ -4276,7 +4276,7 @@ <h3 id="sageworks.api.df_store.DFStore.last_modified" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.list" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.list" class="doc doc-heading">
             <code class="highlight language-python"><span class="nb">list</span><span class="p">(</span><span class="n">include_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4329,7 +4329,7 @@ <h3 id="sageworks.api.df_store.DFStore.list" class="doc doc-heading">
       <tbody>
           <tr class="doc-section-item">
 <td><code>list</code></td>            <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.api.df_store.DFStore.list" href="#sageworks.api.df_store.DFStore.list">list</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.api.df_store.DFStore.list" href="#workbench.api.df_store.DFStore.list">list</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -4341,7 +4341,7 @@ <h3 id="sageworks.api.df_store.DFStore.list" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-46">46</a></span>
 <span class="normal"><a href="#__codelineno-0-47">47</a></span>
 <span class="normal"><a href="#__codelineno-0-48">48</a></span>
@@ -4370,7 +4370,7 @@ <h3 id="sageworks.api.df_store.DFStore.list" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.summary" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="n">include_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4435,7 +4435,7 @@ <h3 id="sageworks.api.df_store.DFStore.summary" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-57">57</a></span>
 <span class="normal"><a href="#__codelineno-0-58">58</a></span>
 <span class="normal"><a href="#__codelineno-0-59">59</a></span>
@@ -4464,7 +4464,7 @@ <h3 id="sageworks.api.df_store.DFStore.summary" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.df_store.DFStore.upsert" class="doc doc-heading">
+<h3 id="workbench.api.df_store.DFStore.upsert" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">upsert</span><span class="p">(</span><span class="n">location</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span></code>
 
 </h3>
@@ -4522,7 +4522,7 @@ <h3 id="sageworks.api.df_store.DFStore.upsert" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/df_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/df_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-104">104</a></span>
 <span class="normal"><a href="#__codelineno-0-105">105</a></span>
 <span class="normal"><a href="#__codelineno-0-106">106</a></span>
@@ -4562,10 +4562,10 @@ <h3 id="sageworks.api.df_store.DFStore.upsert" class="doc doc-heading">
 </div><h2 id="examples">Examples</h2>
 <p>These example show how to use the <code>DFStore()</code> class to list, add, and get dataframes from AWS Storage.</p>
 <div class="admonition tip">
-<p class="admonition-title">SageWorks REPL</p>
-<p>If you'd like to experiment with listing, adding, and getting dataframe with the <code>DFStore()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">SageWorks REPL</a></p>
+<p class="admonition-title">Workbench REPL</p>
+<p>If you'd like to experiment with listing, adding, and getting dataframe with the <code>DFStore()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">Workbench REPL</a></p>
 </div>
-<div class="language-py highlight"><span class="filename">Using DataFrame Store</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.df_store</span> <span class="kn">import</span> <span class="n">DFStore</span>
+<div class="language-py highlight"><span class="filename">Using DataFrame Store</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.df_store</span> <span class="kn">import</span> <span class="n">DFStore</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">df_store</span> <span class="o">=</span> <span class="n">DFStore</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c1"># List DataFrames</span>
diff --git a/api_classes/endpoint/index.html b/api_classes/endpoint/index.html
index aac87d4a6..4968bb477 100644
--- a/api_classes/endpoint/index.html
+++ b/api_classes/endpoint/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Endpoint - SageWorks</title>
+        <title>Endpoint - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -497,7 +497,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint" class="md-nav__link">
+  <a href="#workbench.api.endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       endpoint
     </span>
@@ -506,7 +506,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       Endpoint
     </span>
@@ -516,7 +516,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.auto_inference" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.auto_inference" class="md-nav__link">
     <span class="md-ellipsis">
       auto_inference
     </span>
@@ -525,7 +525,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.details" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -534,7 +534,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.fast_inference" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.fast_inference" class="md-nav__link">
     <span class="md-ellipsis">
       fast_inference
     </span>
@@ -543,7 +543,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.inference" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.inference" class="md-nav__link">
     <span class="md-ellipsis">
       inference
     </span>
@@ -566,9 +566,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-ui" class="md-nav__link">
+  <a href="#workbench-ui" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks UI
+      Workbench UI
     </span>
   </a>
   
@@ -1937,7 +1937,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1947,7 +1947,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3166,11 +3166,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3222,7 +3222,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint" class="md-nav__link">
+  <a href="#workbench.api.endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       endpoint
     </span>
@@ -3231,7 +3231,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       Endpoint
     </span>
@@ -3241,7 +3241,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.auto_inference" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.auto_inference" class="md-nav__link">
     <span class="md-ellipsis">
       auto_inference
     </span>
@@ -3250,7 +3250,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.details" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3259,7 +3259,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.fast_inference" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.fast_inference" class="md-nav__link">
     <span class="md-ellipsis">
       fast_inference
     </span>
@@ -3268,7 +3268,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.endpoint.Endpoint.inference" class="md-nav__link">
+  <a href="#workbench.api.endpoint.Endpoint.inference" class="md-nav__link">
     <span class="md-ellipsis">
       inference
     </span>
@@ -3291,9 +3291,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-ui" class="md-nav__link">
+  <a href="#workbench-ui" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks UI
+      Workbench UI
     </span>
   </a>
   
@@ -3325,12 +3325,12 @@ <h1 id="endpoint">Endpoint</h1>
 
 
 
-<a id="sageworks.api.endpoint"></a>
+<a id="workbench.api.endpoint"></a>
     <div class="doc doc-contents first">
 
         <p>Endpoint: Manages AWS Endpoint creation and deployment.
 Endpoints are automatically set up and provisioned for deployment into AWS.
-Endpoints can be viewed in the AWS Sagemaker interfaces or in the SageWorks
+Endpoints can be viewed in the AWS Sagemaker interfaces or in the Workbench
 Dashboard UI, which provides additional model details and performance metrics</p>
 
 
@@ -3353,7 +3353,7 @@ <h1 id="endpoint">Endpoint</h1>
 
 
 
-<h2 id="sageworks.api.endpoint.Endpoint" class="doc doc-heading">
+<h2 id="workbench.api.endpoint.Endpoint" class="doc doc-heading">
             <code>Endpoint</code>
 
 
@@ -3362,10 +3362,10 @@ <h2 id="sageworks.api.endpoint.Endpoint" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.endpoint_core.EndpointCore" href="../../core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore">EndpointCore</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.endpoint_core.EndpointCore" href="../../core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore">EndpointCore</a></code></p>
 
 
-        <p>Endpoint: SageWorks Endpoint API Class</p>
+        <p>Endpoint: Workbench Endpoint API Class</p>
 
 
 <details class="common-usage" open>
@@ -3381,7 +3381,7 @@ <h2 id="sageworks.api.endpoint.Endpoint" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/endpoint.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/endpoint.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
 <span class="normal"><a href="#__codelineno-0-14">14</a></span>
@@ -3438,7 +3438,7 @@ <h2 id="sageworks.api.endpoint.Endpoint" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-65">65</a></span>
 <span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="k">class</span> <span class="nc">Endpoint</span><span class="p">(</span><span class="n">EndpointCore</span><span class="p">):</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Endpoint: SageWorks Endpoint API Class</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Endpoint: Workbench Endpoint API Class</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="sd">        ```python</span>
@@ -3511,7 +3511,7 @@ <h2 id="sageworks.api.endpoint.Endpoint" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.endpoint.Endpoint.auto_inference" class="doc doc-heading">
+<h3 id="workbench.api.endpoint.Endpoint.auto_inference" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">auto_inference</span><span class="p">(</span><span class="n">capture</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -3576,7 +3576,7 @@ <h3 id="sageworks.api.endpoint.Endpoint.auto_inference" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-44">44</a></span>
 <span class="normal"><a href="#__codelineno-0-45">45</a></span>
 <span class="normal"><a href="#__codelineno-0-46">46</a></span>
@@ -3605,7 +3605,7 @@ <h3 id="sageworks.api.endpoint.Endpoint.auto_inference" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.endpoint.Endpoint.details" class="doc doc-heading">
+<h3 id="workbench.api.endpoint.Endpoint.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3639,7 +3639,7 @@ <h3 id="sageworks.api.endpoint.Endpoint.details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-23">23</a></span>
 <span class="normal"><a href="#__codelineno-0-24">24</a></span>
 <span class="normal"><a href="#__codelineno-0-25">25</a></span>
@@ -3662,7 +3662,7 @@ <h3 id="sageworks.api.endpoint.Endpoint.details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.endpoint.Endpoint.fast_inference" class="doc doc-heading">
+<h3 id="workbench.api.endpoint.Endpoint.fast_inference" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">fast_inference</span><span class="p">(</span><span class="n">eval_df</span><span class="p">)</span></code>
 
 </h3>
@@ -3732,7 +3732,7 @@ <h3 id="sageworks.api.endpoint.Endpoint.fast_inference" class="doc doc-heading">
   <p>There's no sanity checks or error handling... just FAST Inference!</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-55">55</a></span>
 <span class="normal"><a href="#__codelineno-0-56">56</a></span>
 <span class="normal"><a href="#__codelineno-0-57">57</a></span>
@@ -3767,7 +3767,7 @@ <h3 id="sageworks.api.endpoint.Endpoint.fast_inference" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.endpoint.Endpoint.inference" class="doc doc-heading">
+<h3 id="workbench.api.endpoint.Endpoint.inference" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">inference</span><span class="p">(</span><span class="n">eval_df</span><span class="p">,</span> <span class="n">capture_uuid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">id_column</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -3864,7 +3864,7 @@ <h3 id="sageworks.api.endpoint.Endpoint.inference" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-31">31</a></span>
 <span class="normal"><a href="#__codelineno-0-32">32</a></span>
 <span class="normal"><a href="#__codelineno-0-33">33</a></span>
@@ -3911,14 +3911,14 @@ <h3 id="sageworks.api.endpoint.Endpoint.inference" class="doc doc-heading">
 
 </div><h2 id="examples">Examples</h2>
 <p><strong>Run Inference on an Endpoint</strong></p>
-<div class="language-py highlight"><span class="filename">endpoint_inference.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
-</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">from</span> <span class="nn">sageworks.api.model</span> <span class="kn">import</span> <span class="n">Model</span>
-</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">sageworks.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
+<div class="language-py highlight"><span class="filename">endpoint_inference.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">from</span> <span class="nn">workbench.api.model</span> <span class="kn">import</span> <span class="n">Model</span>
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">workbench.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="c1"># Grab an existing Endpoint</span>
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="n">endpoint</span> <span class="o">=</span> <span class="n">Endpoint</span><span class="p">(</span><span class="s2">&quot;abalone-regression-end&quot;</span><span class="p">)</span>
 </span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a>
-</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="c1"># SageWorks has full ML Pipeline provenance, so we can backtrack the inputs,</span>
+</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="c1"># Workbench has full ML Pipeline provenance, so we can backtrack the inputs,</span>
 </span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="c1"># get a DataFrame of data (not used for training) and run inference</span>
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="n">endpoint</span><span class="o">.</span><span class="n">get_input</span><span class="p">())</span>
 </span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="n">fs</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">get_input</span><span class="p">())</span>
@@ -3952,9 +3952,9 @@ <h3 id="sageworks.api.endpoint.Endpoint.inference" class="doc doc-heading">
 <strong>Endpoint Details</strong></p>
 <div class="admonition tip inline end">
 <p class="admonition-title">The details() method</p>
-<p>The <code>detail()</code> method on the Endpoint class provides a lot of useful information. All of the SageWorks classes have a <code>details()</code> method try it out!</p>
+<p>The <code>detail()</code> method on the Endpoint class provides a lot of useful information. All of the Workbench classes have a <code>details()</code> method try it out!</p>
 </div>
-<div class="language-py highlight"><span class="filename">endpoint_details.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
+<div class="language-py highlight"><span class="filename">endpoint_details.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">workbench.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="c1"># Get Endpoint and print out it&#39;s details</span>
@@ -3984,18 +3984,18 @@ <h3 id="sageworks.api.endpoint.Endpoint.inference" class="doc doc-heading">
 </span><span id="__span-3-20"><a id="__codelineno-3-20" name="__codelineno-3-20" href="#__codelineno-3-20"></a><span class="mi">497</span>                     <span class="mi">13</span>    <span class="mf">7.779886</span>
 </span><span id="__span-3-21"><a id="__codelineno-3-21" name="__codelineno-3-21" href="#__codelineno-3-21"></a><span class="mi">498</span>                     <span class="mi">12</span>   <span class="mf">14.718514</span>
 </span><span id="__span-3-22"><a id="__codelineno-3-22" name="__codelineno-3-22" href="#__codelineno-3-22"></a><span class="mi">499</span>                     <span class="mi">13</span>   <span class="mf">10.637320</span>
-</span><span id="__span-3-23"><a id="__codelineno-3-23" name="__codelineno-3-23" href="#__codelineno-3-23"></a> <span class="s1">&#39;sageworks_tags&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;abalone&#39;</span><span class="p">,</span> <span class="s1">&#39;regression&#39;</span><span class="p">],</span>
+</span><span id="__span-3-23"><a id="__codelineno-3-23" name="__codelineno-3-23" href="#__codelineno-3-23"></a> <span class="s1">&#39;workbench_tags&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;abalone&#39;</span><span class="p">,</span> <span class="s1">&#39;regression&#39;</span><span class="p">],</span>
 </span><span id="__span-3-24"><a id="__codelineno-3-24" name="__codelineno-3-24" href="#__codelineno-3-24"></a> <span class="s1">&#39;status&#39;</span><span class="p">:</span> <span class="s1">&#39;InService&#39;</span><span class="p">,</span>
 </span><span id="__span-3-25"><a id="__codelineno-3-25" name="__codelineno-3-25" href="#__codelineno-3-25"></a> <span class="s1">&#39;uuid&#39;</span><span class="p">:</span> <span class="s1">&#39;abalone-regression-end&#39;</span><span class="p">,</span>
 </span><span id="__span-3-26"><a id="__codelineno-3-26" name="__codelineno-3-26" href="#__codelineno-3-26"></a> <span class="s1">&#39;variant&#39;</span><span class="p">:</span> <span class="s1">&#39;AllTraffic&#39;</span><span class="p">}</span>
 </span></code></pre></div>
 <p><strong>Endpoint Metrics</strong></p>
-<div class="language-py highlight"><span class="filename">endpoint_metrics.py</span><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
+<div class="language-py highlight"><span class="filename">endpoint_metrics.py</span><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">from</span> <span class="nn">workbench.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
 </span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>
 </span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="c1"># Grab an existing Endpoint</span>
 </span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a><span class="n">endpoint</span> <span class="o">=</span> <span class="n">Endpoint</span><span class="p">(</span><span class="s2">&quot;abalone-regression-end&quot;</span><span class="p">)</span>
 </span><span id="__span-4-5"><a id="__codelineno-4-5" name="__codelineno-4-5" href="#__codelineno-4-5"></a>
-</span><span id="__span-4-6"><a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a><span class="c1"># SageWorks tracks both Model performance and Endpoint Metrics</span>
+</span><span id="__span-4-6"><a id="__codelineno-4-6" name="__codelineno-4-6" href="#__codelineno-4-6"></a><span class="c1"># Workbench tracks both Model performance and Endpoint Metrics</span>
 </span><span id="__span-4-7"><a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a><span class="n">model_metrics</span> <span class="o">=</span> <span class="n">endpoint</span><span class="o">.</span><span class="n">details</span><span class="p">()[</span><span class="s2">&quot;model_metrics&quot;</span><span class="p">]</span>
 </span><span id="__span-4-8"><a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a><span class="n">endpoint_metrics</span> <span class="o">=</span> <span class="n">endpoint</span><span class="o">.</span><span class="n">endpoint_metrics</span><span class="p">()</span>
 </span><span id="__span-4-9"><a id="__codelineno-4-9" name="__codelineno-4-9" href="#__codelineno-4-9"></a><span class="nb">print</span><span class="p">(</span><span class="n">model_metrics</span><span class="p">)</span>
@@ -4020,16 +4020,16 @@ <h3 id="sageworks.api.endpoint.Endpoint.inference" class="doc doc-heading">
 </span><span id="__span-5-16"><a id="__codelineno-5-16" name="__codelineno-5-16" href="#__codelineno-5-16"></a><span class="mi">62</span>          <span class="mf">0.0</span>          <span class="mf">0.00</span>             <span class="mf">0.00</span>            <span class="mf">0.00</span>                  <span class="mf">0.0</span>
 </span><span id="__span-5-17"><a id="__codelineno-5-17" name="__codelineno-5-17" href="#__codelineno-5-17"></a><span class="o">...</span>
 </span></code></pre></div>
-<h2 id="sageworks-ui">SageWorks UI</h2>
-<p>Running these few lines of code creates and deploys an AWS Endpoint. The Endpoint artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the SageWorks Dashboard UI. SageWorks will monitor the endpoint, plot invocations, latencies, and tracks error metrics.</p>
+<h2 id="workbench-ui">Workbench UI</h2>
+<p>Running these few lines of code creates and deploys an AWS Endpoint. The Endpoint artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the Workbench Dashboard UI. Workbench will monitor the endpoint, plot invocations, latencies, and tracks error metrics.</p>
 <figure>
-<img alt="sageworks_endpoints" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/b5eab741-2c23-4c5e-9495-15fd3ea8155c">
-<figcaption>SageWorks Dashboard: Endpoints</figcaption>
+<img alt="workbench_endpoints" src="https://github.com/SuperCowPowers/workbench/assets/4806709/b5eab741-2c23-4c5e-9495-15fd3ea8155c">
+<figcaption>Workbench Dashboard: Endpoints</figcaption>
 </figure>
 
 <div class="admonition note">
 <p class="admonition-title">Not Finding a particular method?</p>
-<p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">SageWorks Core Classes</a></p>
+<p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">Workbench Core Classes</a></p>
 </div>
 
 
diff --git a/api_classes/feature_set/index.html b/api_classes/feature_set/index.html
index aad7dab50..38b016b48 100644
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+++ b/api_classes/feature_set/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>FeatureSet - SageWorks</title>
+        <title>FeatureSet - Workbench</title>
       
     
     
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-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
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   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+    Workbench
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-    SageWorks REPL
+    Workbench REPL
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+  <a href="#workbench.api.feature_set" class="md-nav__link">
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@@ -464,7 +464,7 @@
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+  <a href="#workbench.api.feature_set.FeatureSet" class="md-nav__link">
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+  <a href="#workbench.api.feature_set.FeatureSet.details" class="md-nav__link">
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+  <a href="#workbench.api.feature_set.FeatureSet.pull_dataframe" class="md-nav__link">
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@@ -492,7 +492,7 @@
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+  <a href="#workbench.api.feature_set.FeatureSet.query" class="md-nav__link">
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+  <a href="#workbench.api.feature_set.FeatureSet.to_model" class="md-nav__link">
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+  <a href="#workbench-ui" class="md-nav__link">
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-      SageWorks UI
+      Workbench UI
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+    Workbench Enterprise
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+            Workbench Enterprise
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     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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           <li class="md-nav__item">
-  <a href="#sageworks.api.feature_set.FeatureSet.to_model" class="md-nav__link">
+  <a href="#workbench.api.feature_set.FeatureSet.to_model" class="md-nav__link">
     <span class="md-ellipsis">
       to_model
     </span>
@@ -3291,9 +3291,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-ui" class="md-nav__link">
+  <a href="#workbench-ui" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks UI
+      Workbench UI
     </span>
   </a>
   
@@ -3317,7 +3317,7 @@
 <h1 id="featureset">FeatureSet</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">FeatureSet Examples</p>
-<p>Examples of using the FeatureSet Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Feature Store and Feature Groups are quite complicated to set up manually but the SageWorks FeatureSet makes it a breeze!</p>
+<p>Examples of using the FeatureSet Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Feature Store and Feature Groups are quite complicated to set up manually but the Workbench FeatureSet makes it a breeze!</p>
 </div>
 
 
@@ -3325,14 +3325,14 @@ <h1 id="featureset">FeatureSet</h1>
 
 
 
-<a id="sageworks.api.feature_set"></a>
+<a id="workbench.api.feature_set"></a>
     <div class="doc doc-contents first">
 
         <p>FeatureSet: Manages AWS Feature Store/Group creation and management.
 FeatureSets are set up so they can easily be queried with AWS Athena.
 All FeatureSets are run through a full set of Exploratory Data Analysis (EDA)
 techniques (data quality, distributions, stats, outliers, etc.) FeatureSets
-can be viewed and explored within the SageWorks Dashboard UI.</p>
+can be viewed and explored within the Workbench Dashboard UI.</p>
 
 
 
@@ -3354,7 +3354,7 @@ <h1 id="featureset">FeatureSet</h1>
 
 
 
-<h2 id="sageworks.api.feature_set.FeatureSet" class="doc doc-heading">
+<h2 id="workbench.api.feature_set.FeatureSet" class="doc doc-heading">
             <code>FeatureSet</code>
 
 
@@ -3363,10 +3363,10 @@ <h2 id="sageworks.api.feature_set.FeatureSet" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.feature_set_core.FeatureSetCore" href="../../core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore">FeatureSetCore</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.feature_set_core.FeatureSetCore" href="../../core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore">FeatureSetCore</a></code></p>
 
 
-        <p>FeatureSet: SageWorks FeatureSet API Class</p>
+        <p>FeatureSet: Workbench FeatureSet API Class</p>
 
 
 <details class="common-usage" open>
@@ -3387,7 +3387,7 @@ <h2 id="sageworks.api.feature_set.FeatureSet" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/feature_set.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/feature_set.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
 <span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
 <span class="normal"><a href="#__codelineno-0-19"> 19</a></span>
@@ -3505,7 +3505,7 @@ <h2 id="sageworks.api.feature_set.FeatureSet" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-131">131</a></span>
 <span class="normal"><a href="#__codelineno-0-132">132</a></span>
 <span class="normal"><a href="#__codelineno-0-133">133</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="k">class</span> <span class="nc">FeatureSet</span><span class="p">(</span><span class="n">FeatureSetCore</span><span class="p">):</span>
-</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;FeatureSet: SageWorks FeatureSet API Class</span>
+</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;FeatureSet: Workbench FeatureSet API Class</span>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        ```python</span>
@@ -3580,7 +3580,7 @@ <h2 id="sageworks.api.feature_set.FeatureSet" class="doc doc-heading">
 </span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>
 </span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a><span class="sd">            name (str): The name of the Model to create</span>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">            model_type (ModelType): The type of model to create (See sageworks.model.ModelType)</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">            model_type (ModelType): The type of model to create (See workbench.model.ModelType)</span>
 </span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a><span class="sd">            tags (list, optional): Set the tags for the model.  If not given tags will be generated.</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a><span class="sd">            description (str, optional): Set the description for the model. If not give a description is generated.</span>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a><span class="sd">            feature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.</span>
@@ -3639,7 +3639,7 @@ <h2 id="sageworks.api.feature_set.FeatureSet" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.feature_set.FeatureSet.details" class="doc doc-heading">
+<h3 id="workbench.api.feature_set.FeatureSet.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3673,7 +3673,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-33">33</a></span>
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span>
@@ -3696,7 +3696,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.feature_set.FeatureSet.pull_dataframe" class="doc doc-heading">
+<h3 id="workbench.api.feature_set.FeatureSet.pull_dataframe" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pull_dataframe</span><span class="p">(</span><span class="n">include_aws_columns</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -3766,7 +3766,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.pull_dataframe" class="doc doc-head
   <p>Obviously this is not recommended for large datasets :)</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-52">52</a></span>
 <span class="normal"><a href="#__codelineno-0-53">53</a></span>
 <span class="normal"><a href="#__codelineno-0-54">54</a></span>
@@ -3821,7 +3821,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.pull_dataframe" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.feature_set.FeatureSet.query" class="doc doc-heading">
+<h3 id="workbench.api.feature_set.FeatureSet.query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3886,7 +3886,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.query" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-41">41</a></span>
 <span class="normal"><a href="#__codelineno-0-42">42</a></span>
 <span class="normal"><a href="#__codelineno-0-43">43</a></span>
@@ -3915,7 +3915,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.query" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
+<h3 id="workbench.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">to_model</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">model_type</span><span class="p">,</span> <span class="n">tags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">feature_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">target_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">scikit_model_class</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">model_import_str</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3926,7 +3926,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
         <p>Create a Model from the FeatureSet</p>
 <p>Args:</p>
 <div class="language-text highlight"><pre><span></span><code>name (str): The name of the Model to create
-model_type (ModelType): The type of model to create (See sageworks.model.ModelType)
+model_type (ModelType): The type of model to create (See workbench.model.ModelType)
 tags (list, optional): Set the tags for the model.  If not given tags will be generated.
 description (str, optional): Set the description for the model. If not give a description is generated.
 feature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.
@@ -3947,7 +3947,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
       <tbody>
           <tr class="doc-section-item">
 <td><code>Model</code></td>            <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.api.model.Model" href="../model/#sageworks.api.model.Model">Model</a>, None]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.api.model.Model" href="../model/#workbench.api.model.Model">Model</a>, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3959,7 +3959,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-76"> 76</a></span>
 <span class="normal"><a href="#__codelineno-0-77"> 77</a></span>
 <span class="normal"><a href="#__codelineno-0-78"> 78</a></span>
@@ -4034,7 +4034,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
 </span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>
 </span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a><span class="sd">        name (str): The name of the Model to create</span>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">        model_type (ModelType): The type of model to create (See sageworks.model.ModelType)</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">        model_type (ModelType): The type of model to create (See workbench.model.ModelType)</span>
 </span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a><span class="sd">        tags (list, optional): Set the tags for the model.  If not given tags will be generated.</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a><span class="sd">        description (str, optional): Set the description for the model. If not give a description is generated.</span>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a><span class="sd">        feature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.</span>
@@ -4097,9 +4097,9 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Create a FeatureSet from a Datasource</strong></p>
-<div class="language-py highlight"><span class="filename">datasource_to_featureset.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+<div class="language-py highlight"><span class="filename">datasource_to_featureset.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Convert the Data Source to a Feature Set</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">ds</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="s1">&#39;test_data&#39;</span><span class="p">)</span>
@@ -4107,7 +4107,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="nb">print</span><span class="p">(</span><span class="n">fs</span><span class="o">.</span><span class="n">details</span><span class="p">())</span>
 </span></code></pre></div>
 <p><strong>FeatureSet EDA Statistics</strong></p>
-<p><div class="language-py highlight"><span class="filename">featureset_eda.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<p><div class="language-py highlight"><span class="filename">featureset_eda.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="c1"># Grab a FeatureSet and pull some of the EDA Stats</span>
@@ -4146,8 +4146,8 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
 </span><span id="__span-2-17"><a id="__codelineno-2-17" name="__codelineno-2-17" href="#__codelineno-2-17"></a>&lt;lots of EDA data and statistics&gt;
 </span></code></pre></div>
 <p><strong>Query a FeatureSet</strong></p>
-<p>All SageWorks FeatureSet have an 'offline' store that uses AWS Athena, so any query that you can make with Athena is accessible through the FeatureSet API.</p>
-<div class="language-py highlight"><span class="filename">featureset_query.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<p>All Workbench FeatureSet have an 'offline' store that uses AWS Athena, so any query that you can make with Athena is accessible through the FeatureSet API.</p>
+<div class="language-py highlight"><span class="filename">featureset_query.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>
 </span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="c1"># Grab a FeatureSet</span>
 </span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="n">my_features</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="s2">&quot;abalone_features&quot;</span><span class="p">)</span>
@@ -4169,8 +4169,8 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
 </span><span id="__span-4-7"><a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a><span class="mi">1</span>   <span class="n">I</span>   <span class="mf">0.150</span>     <span class="mf">0.100</span>   <span class="mf">0.025</span>         <span class="mf">0.015</span>          <span class="mf">0.0045</span>          <span class="mf">0.0040</span>         <span class="mf">0.0050</span>                      <span class="mi">2</span>
 </span></code></pre></div>
 <p><strong>Create a Model from a FeatureSet</strong></p>
-<div class="language-py highlight"><span class="filename">featureset_to_model.py</span><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
-</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span> <span class="nn">sageworks.api.model</span> <span class="kn">import</span> <span class="n">ModelType</span>
+<div class="language-py highlight"><span class="filename">featureset_to_model.py</span><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span> <span class="nn">workbench.api.model</span> <span class="kn">import</span> <span class="n">ModelType</span>
 </span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
 </span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a>
 </span><span id="__span-5-5"><a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="c1"># Grab a FeatureSet</span>
@@ -4202,7 +4202,7 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
 </span><span id="__span-6-15"><a id="__codelineno-6-15" name="__codelineno-6-15" href="#__codelineno-6-15"></a>                            <span class="mi">4</span>   <span class="mf">127.881073</span>  <span class="mf">113.825005</span>
 </span><span id="__span-6-16"><a id="__codelineno-6-16" name="__codelineno-6-16" href="#__codelineno-6-16"></a>    <span class="o">...</span>
 </span><span id="__span-6-17"><a id="__codelineno-6-17" name="__codelineno-6-17" href="#__codelineno-6-17"></a> <span class="s1">&#39;response_types&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;text/csv&#39;</span><span class="p">],</span>
-</span><span id="__span-6-18"><a id="__codelineno-6-18" name="__codelineno-6-18" href="#__codelineno-6-18"></a> <span class="s1">&#39;sageworks_tags&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;test-model&#39;</span><span class="p">],</span>
+</span><span id="__span-6-18"><a id="__codelineno-6-18" name="__codelineno-6-18" href="#__codelineno-6-18"></a> <span class="s1">&#39;workbench_tags&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;test-model&#39;</span><span class="p">],</span>
 </span><span id="__span-6-19"><a id="__codelineno-6-19" name="__codelineno-6-19" href="#__codelineno-6-19"></a> <span class="s1">&#39;shapley_values&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
 </span><span id="__span-6-20"><a id="__codelineno-6-20" name="__codelineno-6-20" href="#__codelineno-6-20"></a> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
 </span><span id="__span-6-21"><a id="__codelineno-6-21" name="__codelineno-6-21" href="#__codelineno-6-21"></a> <span class="s1">&#39;status&#39;</span><span class="p">:</span> <span class="s1">&#39;Completed&#39;</span><span class="p">,</span>
@@ -4210,16 +4210,16 @@ <h3 id="sageworks.api.feature_set.FeatureSet.to_model" class="doc doc-heading">
 </span><span id="__span-6-23"><a id="__codelineno-6-23" name="__codelineno-6-23" href="#__codelineno-6-23"></a> <span class="s1">&#39;uuid&#39;</span><span class="p">:</span> <span class="s1">&#39;test-model&#39;</span><span class="p">,</span>
 </span><span id="__span-6-24"><a id="__codelineno-6-24" name="__codelineno-6-24" href="#__codelineno-6-24"></a> <span class="s1">&#39;version&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
 </span></code></pre></div>
-<h2 id="sageworks-ui">SageWorks UI</h2>
-<p>Whenever a FeatureSet is created SageWorks performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p>
+<h2 id="workbench-ui">Workbench UI</h2>
+<p>Whenever a FeatureSet is created Workbench performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p>
 <figure style="width: 700px;">
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/0b4103fe-2c33-4611-86df-ff659fad1a3b">
-<figcaption>SageWorks Dashboard: FeatureSets</figcaption>
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/0b4103fe-2c33-4611-86df-ff659fad1a3b">
+<figcaption>Workbench Dashboard: FeatureSets</figcaption>
 </figure>
 
 <div class="admonition note">
 <p class="admonition-title">Not Finding a particular method?</p>
-<p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">SageWorks Core Classes</a></p>
+<p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">Workbench Core Classes</a></p>
 </div>
 
 
diff --git a/api_classes/meta/index.html b/api_classes/meta/index.html
index 9f53a827b..2be2a5569 100644
--- a/api_classes/meta/index.html
+++ b/api_classes/meta/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Meta - SageWorks</title>
+        <title>Meta - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -413,7 +413,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.meta" class="md-nav__link">
+  <a href="#workbench.api.meta" class="md-nav__link">
     <span class="md-ellipsis">
       meta
     </span>
@@ -422,7 +422,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta" class="md-nav__link">
     <span class="md-ellipsis">
       Meta
     </span>
@@ -432,7 +432,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.account" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.account" class="md-nav__link">
     <span class="md-ellipsis">
       account
     </span>
@@ -441,7 +441,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.config" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.config" class="md-nav__link">
     <span class="md-ellipsis">
       config
     </span>
@@ -450,7 +450,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.data_source" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.data_source" class="md-nav__link">
     <span class="md-ellipsis">
       data_source
     </span>
@@ -459,7 +459,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.data_sources" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.data_sources" class="md-nav__link">
     <span class="md-ellipsis">
       data_sources
     </span>
@@ -468,7 +468,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.endpoint" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       endpoint
     </span>
@@ -477,7 +477,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.endpoints" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.endpoints" class="md-nav__link">
     <span class="md-ellipsis">
       endpoints
     </span>
@@ -486,7 +486,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.etl_jobs" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.etl_jobs" class="md-nav__link">
     <span class="md-ellipsis">
       etl_jobs
     </span>
@@ -495,7 +495,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.feature_set" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.feature_set" class="md-nav__link">
     <span class="md-ellipsis">
       feature_set
     </span>
@@ -504,7 +504,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.feature_sets" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.feature_sets" class="md-nav__link">
     <span class="md-ellipsis">
       feature_sets
     </span>
@@ -513,7 +513,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.glue_job" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.glue_job" class="md-nav__link">
     <span class="md-ellipsis">
       glue_job
     </span>
@@ -522,7 +522,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.incoming_data" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.incoming_data" class="md-nav__link">
     <span class="md-ellipsis">
       incoming_data
     </span>
@@ -531,7 +531,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.model" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.model" class="md-nav__link">
     <span class="md-ellipsis">
       model
     </span>
@@ -540,7 +540,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.models" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.models" class="md-nav__link">
     <span class="md-ellipsis">
       models
     </span>
@@ -549,7 +549,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.pipelines" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.pipelines" class="md-nav__link">
     <span class="md-ellipsis">
       pipelines
     </span>
@@ -558,7 +558,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.views" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.views" class="md-nav__link">
     <span class="md-ellipsis">
       views
     </span>
@@ -2027,7 +2027,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -2037,7 +2037,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3256,11 +3256,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3312,7 +3312,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.meta" class="md-nav__link">
+  <a href="#workbench.api.meta" class="md-nav__link">
     <span class="md-ellipsis">
       meta
     </span>
@@ -3321,7 +3321,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta" class="md-nav__link">
     <span class="md-ellipsis">
       Meta
     </span>
@@ -3331,7 +3331,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.account" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.account" class="md-nav__link">
     <span class="md-ellipsis">
       account
     </span>
@@ -3340,7 +3340,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.config" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.config" class="md-nav__link">
     <span class="md-ellipsis">
       config
     </span>
@@ -3349,7 +3349,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.data_source" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.data_source" class="md-nav__link">
     <span class="md-ellipsis">
       data_source
     </span>
@@ -3358,7 +3358,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.data_sources" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.data_sources" class="md-nav__link">
     <span class="md-ellipsis">
       data_sources
     </span>
@@ -3367,7 +3367,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.endpoint" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       endpoint
     </span>
@@ -3376,7 +3376,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.endpoints" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.endpoints" class="md-nav__link">
     <span class="md-ellipsis">
       endpoints
     </span>
@@ -3385,7 +3385,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.etl_jobs" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.etl_jobs" class="md-nav__link">
     <span class="md-ellipsis">
       etl_jobs
     </span>
@@ -3394,7 +3394,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.feature_set" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.feature_set" class="md-nav__link">
     <span class="md-ellipsis">
       feature_set
     </span>
@@ -3403,7 +3403,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.feature_sets" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.feature_sets" class="md-nav__link">
     <span class="md-ellipsis">
       feature_sets
     </span>
@@ -3412,7 +3412,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.glue_job" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.glue_job" class="md-nav__link">
     <span class="md-ellipsis">
       glue_job
     </span>
@@ -3421,7 +3421,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.incoming_data" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.incoming_data" class="md-nav__link">
     <span class="md-ellipsis">
       incoming_data
     </span>
@@ -3430,7 +3430,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.model" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.model" class="md-nav__link">
     <span class="md-ellipsis">
       model
     </span>
@@ -3439,7 +3439,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.models" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.models" class="md-nav__link">
     <span class="md-ellipsis">
       models
     </span>
@@ -3448,7 +3448,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.pipelines" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.pipelines" class="md-nav__link">
     <span class="md-ellipsis">
       pipelines
     </span>
@@ -3457,7 +3457,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.meta.Meta.views" class="md-nav__link">
+  <a href="#workbench.api.meta.Meta.views" class="md-nav__link">
     <span class="md-ellipsis">
       views
     </span>
@@ -3505,7 +3505,7 @@ <h1 id="meta">Meta</h1>
 
 
 
-<a id="sageworks.api.meta"></a>
+<a id="workbench.api.meta"></a>
     <div class="doc doc-contents first">
 
         <p>Meta: A class that provides high level information and summaries of Cloud Platform Artifacts.
@@ -3532,7 +3532,7 @@ <h1 id="meta">Meta</h1>
 
 
 
-<h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
+<h2 id="workbench.api.meta.Meta" class="doc doc-heading">
             <code>Meta</code>
 
 
@@ -3541,7 +3541,7 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.cloud_platform.cloud_meta.CloudMeta">CloudMeta</span></code></p>
+              Bases: <code><span title="workbench.core.cloud_platform.cloud_meta.CloudMeta">CloudMeta</span></code></p>
 
 
         <p>Meta: A class that provides metadata functionality for Cloud Platform Artifacts.</p>
@@ -3549,7 +3549,7 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 
 <details class="common-usage" open>
   <summary>Common Usage</summary>
-  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">Meta</span>
+  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">Meta</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">meta</span> <span class="o">=</span> <span class="n">Meta</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c1"># Get the AWS Account Info</span>
@@ -3578,7 +3578,7 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
 <span class="normal"><a href="#__codelineno-0-15"> 15</a></span>
 <span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
@@ -3759,7 +3759,7 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="sd">       ```python</span>
-</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">       from sageworks.api import Meta</span>
+</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">       from workbench.api import Meta</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">       meta = Meta()</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">       # Get the AWS Account Info</span>
@@ -3792,10 +3792,10 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">account</span><span class="p">()</span>
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="k">def</span> <span class="nf">config</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the current SageWorks Configuration</span>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the current Workbench Configuration</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a><span class="sd">        Returns:</span>
-</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="sd">            dict: The current SageWorks Configuration</span>
+</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="sd">            dict: The current Workbench Configuration</span>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">config</span><span class="p">()</span>
 </span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>
@@ -3823,11 +3823,11 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 </span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">data_sources</span><span class="p">()</span>
 </span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 </span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get a summary of the all the Views, for the given database, in AWS</span>
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="sd">            database (str, optional): Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="sd">            database (str, optional): Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>
 </span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="sd">            pd.DataFrame: A summary of all the Views, for the given database, in AWS</span>
@@ -3883,12 +3883,12 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 </span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">glue_job</span><span class="p">(</span><span class="n">job_name</span><span class="o">=</span><span class="n">job_name</span><span class="p">)</span>
 </span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a>
-</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>    <span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>    <span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the details of a specific Data Source</span>
 </span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>
 </span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a><span class="sd">            data_source_name (str): The name of the Data Source</span>
-</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a><span class="sd">            database (str, optional): The Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a><span class="sd">            database (str, optional): The Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>
 </span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a><span class="sd">            dict: The details of the Data Source (None if not found)</span>
@@ -3948,7 +3948,7 @@ <h2 id="sageworks.api.meta.Meta" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.account" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.account" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">account</span><span class="p">()</span></code>
 
 </h3>
@@ -3982,7 +3982,7 @@ <h3 id="sageworks.api.meta.Meta.account" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-43">43</a></span>
 <span class="normal"><a href="#__codelineno-0-44">44</a></span>
 <span class="normal"><a href="#__codelineno-0-45">45</a></span>
@@ -4005,7 +4005,7 @@ <h3 id="sageworks.api.meta.Meta.account" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.config" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.config" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">config</span><span class="p">()</span></code>
 
 </h3>
@@ -4013,7 +4013,7 @@ <h3 id="sageworks.api.meta.Meta.config" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Return the current SageWorks Configuration</p>
+        <p>Return the current Workbench Configuration</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -4031,7 +4031,7 @@ <h3 id="sageworks.api.meta.Meta.config" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The current SageWorks Configuration</p>
+                <p>The current Workbench Configuration</p>
               </div>
             </td>
           </tr>
@@ -4039,7 +4039,7 @@ <h3 id="sageworks.api.meta.Meta.config" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-51">51</a></span>
 <span class="normal"><a href="#__codelineno-0-52">52</a></span>
 <span class="normal"><a href="#__codelineno-0-53">53</a></span>
@@ -4047,10 +4047,10 @@ <h3 id="sageworks.api.meta.Meta.config" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-55">55</a></span>
 <span class="normal"><a href="#__codelineno-0-56">56</a></span>
 <span class="normal"><a href="#__codelineno-0-57">57</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a><span class="k">def</span> <span class="nf">config</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the current SageWorks Configuration</span>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the current Workbench Configuration</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="sd">        dict: The current SageWorks Configuration</span>
+</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="sd">        dict: The current Workbench Configuration</span>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">config</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
@@ -4062,8 +4062,8 @@ <h3 id="sageworks.api.meta.Meta.config" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.data_source" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">data_source</span><span class="p">(</span><span class="n">data_source_name</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.api.meta.Meta.data_source" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">data_source</span><span class="p">(</span><span class="n">data_source_name</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -4109,11 +4109,11 @@ <h3 id="sageworks.api.meta.Meta.data_source" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The Glue database. Defaults to 'sageworks'.</p>
+                <p>The Glue database. Defaults to 'workbench'.</p>
               </div>
             </td>
             <td>
-                  <code>&#39;sageworks&#39;</code>
+                  <code>&#39;workbench&#39;</code>
             </td>
           </tr>
       </tbody>
@@ -4143,7 +4143,7 @@ <h3 id="sageworks.api.meta.Meta.data_source" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-143">143</a></span>
 <span class="normal"><a href="#__codelineno-0-144">144</a></span>
 <span class="normal"><a href="#__codelineno-0-145">145</a></span>
@@ -4154,12 +4154,12 @@ <h3 id="sageworks.api.meta.Meta.data_source" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-150">150</a></span>
 <span class="normal"><a href="#__codelineno-0-151">151</a></span>
 <span class="normal"><a href="#__codelineno-0-152">152</a></span>
-<span class="normal"><a href="#__codelineno-0-153">153</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a><span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+<span class="normal"><a href="#__codelineno-0-153">153</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a><span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the details of a specific Data Source</span>
 </span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>
 </span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a><span class="sd">        data_source_name (str): The name of the Data Source</span>
-</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a><span class="sd">        database (str, optional): The Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a><span class="sd">        database (str, optional): The Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>
 </span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a><span class="sd">        dict: The details of the Data Source (None if not found)</span>
@@ -4174,7 +4174,7 @@ <h3 id="sageworks.api.meta.Meta.data_source" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.data_sources" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.data_sources" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">data_sources</span><span class="p">()</span></code>
 
 </h3>
@@ -4208,7 +4208,7 @@ <h3 id="sageworks.api.meta.Meta.data_sources" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-75">75</a></span>
 <span class="normal"><a href="#__codelineno-0-76">76</a></span>
 <span class="normal"><a href="#__codelineno-0-77">77</a></span>
@@ -4231,7 +4231,7 @@ <h3 id="sageworks.api.meta.Meta.data_sources" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.endpoint" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.endpoint" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">endpoint</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span></code>
 
 </h3>
@@ -4296,7 +4296,7 @@ <h3 id="sageworks.api.meta.Meta.endpoint" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-177">177</a></span>
 <span class="normal"><a href="#__codelineno-0-178">178</a></span>
 <span class="normal"><a href="#__codelineno-0-179">179</a></span>
@@ -4325,7 +4325,7 @@ <h3 id="sageworks.api.meta.Meta.endpoint" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.endpoints" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.endpoints" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">endpoints</span><span class="p">()</span></code>
 
 </h3>
@@ -4359,7 +4359,7 @@ <h3 id="sageworks.api.meta.Meta.endpoints" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-116">116</a></span>
 <span class="normal"><a href="#__codelineno-0-117">117</a></span>
 <span class="normal"><a href="#__codelineno-0-118">118</a></span>
@@ -4382,7 +4382,7 @@ <h3 id="sageworks.api.meta.Meta.endpoints" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.etl_jobs" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.etl_jobs" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">etl_jobs</span><span class="p">()</span></code>
 
 </h3>
@@ -4416,7 +4416,7 @@ <h3 id="sageworks.api.meta.Meta.etl_jobs" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-67">67</a></span>
 <span class="normal"><a href="#__codelineno-0-68">68</a></span>
 <span class="normal"><a href="#__codelineno-0-69">69</a></span>
@@ -4439,7 +4439,7 @@ <h3 id="sageworks.api.meta.Meta.etl_jobs" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.feature_set" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.feature_set" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">feature_set</span><span class="p">(</span><span class="n">feature_set_name</span><span class="p">)</span></code>
 
 </h3>
@@ -4504,7 +4504,7 @@ <h3 id="sageworks.api.meta.Meta.feature_set" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-155">155</a></span>
 <span class="normal"><a href="#__codelineno-0-156">156</a></span>
 <span class="normal"><a href="#__codelineno-0-157">157</a></span>
@@ -4533,7 +4533,7 @@ <h3 id="sageworks.api.meta.Meta.feature_set" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.feature_sets" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.feature_sets" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">feature_sets</span><span class="p">(</span><span class="n">details</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4598,7 +4598,7 @@ <h3 id="sageworks.api.meta.Meta.feature_sets" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-94"> 94</a></span>
 <span class="normal"><a href="#__codelineno-0-95"> 95</a></span>
 <span class="normal"><a href="#__codelineno-0-96"> 96</a></span>
@@ -4627,7 +4627,7 @@ <h3 id="sageworks.api.meta.Meta.feature_sets" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.glue_job" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.glue_job" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">glue_job</span><span class="p">(</span><span class="n">job_name</span><span class="p">)</span></code>
 
 </h3>
@@ -4692,7 +4692,7 @@ <h3 id="sageworks.api.meta.Meta.glue_job" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-132">132</a></span>
 <span class="normal"><a href="#__codelineno-0-133">133</a></span>
 <span class="normal"><a href="#__codelineno-0-134">134</a></span>
@@ -4721,7 +4721,7 @@ <h3 id="sageworks.api.meta.Meta.glue_job" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.incoming_data" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.incoming_data" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">incoming_data</span><span class="p">()</span></code>
 
 </h3>
@@ -4755,7 +4755,7 @@ <h3 id="sageworks.api.meta.Meta.incoming_data" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-59">59</a></span>
 <span class="normal"><a href="#__codelineno-0-60">60</a></span>
 <span class="normal"><a href="#__codelineno-0-61">61</a></span>
@@ -4778,7 +4778,7 @@ <h3 id="sageworks.api.meta.Meta.incoming_data" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.model" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.model" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">model</span><span class="p">(</span><span class="n">model_name</span><span class="p">)</span></code>
 
 </h3>
@@ -4843,7 +4843,7 @@ <h3 id="sageworks.api.meta.Meta.model" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-166">166</a></span>
 <span class="normal"><a href="#__codelineno-0-167">167</a></span>
 <span class="normal"><a href="#__codelineno-0-168">168</a></span>
@@ -4872,7 +4872,7 @@ <h3 id="sageworks.api.meta.Meta.model" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.models" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.models" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">models</span><span class="p">(</span><span class="n">details</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4937,7 +4937,7 @@ <h3 id="sageworks.api.meta.Meta.models" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-105">105</a></span>
 <span class="normal"><a href="#__codelineno-0-106">106</a></span>
 <span class="normal"><a href="#__codelineno-0-107">107</a></span>
@@ -4966,7 +4966,7 @@ <h3 id="sageworks.api.meta.Meta.models" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.pipelines" class="doc doc-heading">
+<h3 id="workbench.api.meta.Meta.pipelines" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pipelines</span><span class="p">()</span></code>
 
 </h3>
@@ -5000,7 +5000,7 @@ <h3 id="sageworks.api.meta.Meta.pipelines" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-124">124</a></span>
 <span class="normal"><a href="#__codelineno-0-125">125</a></span>
 <span class="normal"><a href="#__codelineno-0-126">126</a></span>
@@ -5023,8 +5023,8 @@ <h3 id="sageworks.api.meta.Meta.pipelines" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">views</span><span class="p">(</span><span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.api.meta.Meta.views" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">views</span><span class="p">(</span><span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -5054,11 +5054,11 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>Glue database. Defaults to 'sageworks'.</p>
+                <p>Glue database. Defaults to 'workbench'.</p>
               </div>
             </td>
             <td>
-                  <code>&#39;sageworks&#39;</code>
+                  <code>&#39;workbench&#39;</code>
             </td>
           </tr>
       </tbody>
@@ -5088,7 +5088,7 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-83">83</a></span>
 <span class="normal"><a href="#__codelineno-0-84">84</a></span>
 <span class="normal"><a href="#__codelineno-0-85">85</a></span>
@@ -5098,11 +5098,11 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-89">89</a></span>
 <span class="normal"><a href="#__codelineno-0-90">90</a></span>
 <span class="normal"><a href="#__codelineno-0-91">91</a></span>
-<span class="normal"><a href="#__codelineno-0-92">92</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+<span class="normal"><a href="#__codelineno-0-92">92</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 </span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get a summary of the all the Views, for the given database, in AWS</span>
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="sd">        database (str, optional): Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="sd">        database (str, optional): Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>
 </span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="sd">        pd.DataFrame: A summary of all the Views, for the given database, in AWS</span>
@@ -5132,10 +5132,10 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
 </div><h2 id="examples">Examples</h2>
 <p>These example show how to use the <code>Meta()</code> class to pull lists of artifacts from AWS. DataSources, FeatureSets, Models, Endpoints and more. If you're building a web interface plugin, the <strong>Meta</strong> class is a great place to start.</p>
 <div class="admonition tip">
-<p class="admonition-title">SageWorks REPL</p>
-<p>If you'd like to see <strong>exactly</strong> what data/details you get back from the <code>Meta()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">SageWorks REPL</a></p>
+<p class="admonition-title">Workbench REPL</p>
+<p>If you'd like to see <strong>exactly</strong> what data/details you get back from the <code>Meta()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">Workbench REPL</a></p>
 </div>
-<div class="language-py highlight"><span class="filename">Using SageWorks REPL</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">meta</span> <span class="o">=</span> <span class="n">Meta</span><span class="p">()</span>
+<div class="language-py highlight"><span class="filename">Using Workbench REPL</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">meta</span> <span class="o">=</span> <span class="n">Meta</span><span class="p">()</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">model_df</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">models</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">model_df</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>               <span class="n">Model</span> <span class="n">Group</span>   <span class="n">Health</span> <span class="n">Owner</span>  <span class="o">...</span>             <span class="n">Input</span>     <span class="n">Status</span>                <span class="n">Description</span>
@@ -5146,7 +5146,7 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
 </span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="p">[</span><span class="mi">3</span> <span class="n">rows</span> <span class="n">x</span> <span class="mi">10</span> <span class="n">columns</span><span class="p">]</span>
 </span></code></pre></div>
 <p><strong>List the Models in AWS</strong></p>
-<div class="language-py highlight"><span class="filename">meta_list_models.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">Meta</span>
+<div class="language-py highlight"><span class="filename">meta_list_models.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">Meta</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="c1"># Create our Meta Class and get a list of our Models</span>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="n">meta</span> <span class="o">=</span> <span class="n">Meta</span><span class="p">()</span>
@@ -5173,7 +5173,7 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
 </span><span id="__span-2-10"><a id="__codelineno-2-10" name="__codelineno-2-10" href="#__codelineno-2-10"></a><span class="n">abalone</span><span class="o">-</span><span class="n">regression</span>
 </span></code></pre></div>
 <p><strong>Getting Model Performance Metrics</strong></p>
-<div class="language-py highlight"><span class="filename">meta_model_metrics.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">Meta</span>
+<div class="language-py highlight"><span class="filename">meta_model_metrics.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">Meta</span>
 </span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>
 </span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="c1"># Create our Meta Class and get a list of our Models</span>
 </span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="n">meta</span> <span class="o">=</span> <span class="n">Meta</span><span class="p">()</span>
@@ -5187,7 +5187,7 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
 </span><span id="__span-3-12"><a id="__codelineno-3-12" name="__codelineno-3-12" href="#__codelineno-3-12"></a><span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">model_names</span><span class="p">[:</span><span class="mi">5</span><span class="p">]:</span>
 </span><span id="__span-3-13"><a id="__codelineno-3-13" name="__codelineno-3-13" href="#__codelineno-3-13"></a>    <span class="n">model_details</span> <span class="o">=</span> <span class="n">meta</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
 </span><span id="__span-3-14"><a id="__codelineno-3-14" name="__codelineno-3-14" href="#__codelineno-3-14"></a>    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Model: </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-3-15"><a id="__codelineno-3-15" name="__codelineno-3-15" href="#__codelineno-3-15"></a>    <span class="n">performance_metrics</span> <span class="o">=</span> <span class="n">model_details</span><span class="p">[</span><span class="s2">&quot;sageworks_meta&quot;</span><span class="p">][</span><span class="s2">&quot;sageworks_inference_metrics&quot;</span><span class="p">]</span>
+</span><span id="__span-3-15"><a id="__codelineno-3-15" name="__codelineno-3-15" href="#__codelineno-3-15"></a>    <span class="n">performance_metrics</span> <span class="o">=</span> <span class="n">model_details</span><span class="p">[</span><span class="s2">&quot;workbench_meta&quot;</span><span class="p">][</span><span class="s2">&quot;workbench_inference_metrics&quot;</span><span class="p">]</span>
 </span><span id="__span-3-16"><a id="__codelineno-3-16" name="__codelineno-3-16" href="#__codelineno-3-16"></a>    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Performance Metrics: </span><span class="si">{</span><span class="n">performance_metrics</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span></code></pre></div>
 <p><strong>Output</strong></p>
@@ -5207,7 +5207,7 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
 </span></code></pre></div>
 <p><strong>List the Endpoints in AWS</strong></p>
 <div class="language-py highlight"><span class="filename">meta_list_endpoints.py</span><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
-</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">Meta</span>
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">Meta</span>
 </span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>
 </span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="c1"># Create our Meta Class and get a list of our Endpoints</span>
 </span><span id="__span-5-5"><a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="n">meta</span> <span class="o">=</span> <span class="n">Meta</span><span class="p">()</span>
@@ -5232,7 +5232,7 @@ <h3 id="sageworks.api.meta.Meta.views" class="doc doc-heading">
 </span></code></pre></div>
 <div class="admonition note">
 <p class="admonition-title">Not Finding some particular AWS Data?</p>
-<p>The SageWorks Meta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>
+<p>The Workbench Meta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>
 </div>
 
 
diff --git a/api_classes/model/index.html b/api_classes/model/index.html
index 72b98b721..6633d8383 100644
--- a/api_classes/model/index.html
+++ b/api_classes/model/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Model - SageWorks</title>
+        <title>Model - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -476,7 +476,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.model" class="md-nav__link">
+  <a href="#workbench.api.model" class="md-nav__link">
     <span class="md-ellipsis">
       model
     </span>
@@ -485,7 +485,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.model.Model" class="md-nav__link">
+  <a href="#workbench.api.model.Model" class="md-nav__link">
     <span class="md-ellipsis">
       Model
     </span>
@@ -495,7 +495,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.model.Model.details" class="md-nav__link">
+  <a href="#workbench.api.model.Model.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -504,7 +504,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.model.Model.to_endpoint" class="md-nav__link">
+  <a href="#workbench.api.model.Model.to_endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       to_endpoint
     </span>
@@ -527,9 +527,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-ui" class="md-nav__link">
+  <a href="#workbench-ui" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks UI
+      Workbench UI
     </span>
   </a>
   
@@ -1919,7 +1919,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1929,7 +1929,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3148,11 +3148,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3204,7 +3204,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.model" class="md-nav__link">
+  <a href="#workbench.api.model" class="md-nav__link">
     <span class="md-ellipsis">
       model
     </span>
@@ -3213,7 +3213,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.model.Model" class="md-nav__link">
+  <a href="#workbench.api.model.Model" class="md-nav__link">
     <span class="md-ellipsis">
       Model
     </span>
@@ -3223,7 +3223,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.model.Model.details" class="md-nav__link">
+  <a href="#workbench.api.model.Model.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3232,7 +3232,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.model.Model.to_endpoint" class="md-nav__link">
+  <a href="#workbench.api.model.Model.to_endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       to_endpoint
     </span>
@@ -3255,9 +3255,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-ui" class="md-nav__link">
+  <a href="#workbench-ui" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks UI
+      Workbench UI
     </span>
   </a>
   
@@ -3281,7 +3281,7 @@
 <h1 id="model">Model</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Model Examples</p>
-<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p>
+<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p>
 </div>
 
 
@@ -3289,12 +3289,12 @@ <h1 id="model">Model</h1>
 
 
 
-<a id="sageworks.api.model"></a>
+<a id="workbench.api.model"></a>
     <div class="doc doc-contents first">
 
         <p>Model: Manages AWS Model Package/Group creation and management.</p>
 <p>Models are automatically set up and provisioned for deployment into AWS.
-Models can be viewed in the AWS Sagemaker interfaces or in the SageWorks
+Models can be viewed in the AWS Sagemaker interfaces or in the Workbench
 Dashboard UI, which provides additional model details and performance metrics</p>
 
 
@@ -3317,7 +3317,7 @@ <h1 id="model">Model</h1>
 
 
 
-<h2 id="sageworks.api.model.Model" class="doc doc-heading">
+<h2 id="workbench.api.model.Model" class="doc doc-heading">
             <code>Model</code>
 
 
@@ -3326,10 +3326,10 @@ <h2 id="sageworks.api.model.Model" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.model_core.ModelCore" href="../../core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore">ModelCore</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.model_core.ModelCore" href="../../core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore">ModelCore</a></code></p>
 
 
-        <p>Model: SageWorks Model API Class.</p>
+        <p>Model: Workbench Model API Class.</p>
 
 
 <details class="common-usage" open>
@@ -3345,7 +3345,7 @@ <h2 id="sageworks.api.model.Model" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/model.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/model.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-15">15</a></span>
 <span class="normal"><a href="#__codelineno-0-16">16</a></span>
 <span class="normal"><a href="#__codelineno-0-17">17</a></span>
@@ -3396,7 +3396,7 @@ <h2 id="sageworks.api.model.Model" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-62">62</a></span>
 <span class="normal"><a href="#__codelineno-0-63">63</a></span>
 <span class="normal"><a href="#__codelineno-0-64">64</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="k">class</span> <span class="nc">Model</span><span class="p">(</span><span class="n">ModelCore</span><span class="p">):</span>
-</span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Model: SageWorks Model API Class.</span>
+</span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Model: Workbench Model API Class.</span>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a>
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">        ```python</span>
@@ -3463,7 +3463,7 @@ <h2 id="sageworks.api.model.Model" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.model.Model.details" class="doc doc-heading">
+<h3 id="workbench.api.model.Model.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3497,7 +3497,7 @@ <h3 id="sageworks.api.model.Model.details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/model.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-26">26</a></span>
 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
 <span class="normal"><a href="#__codelineno-0-28">28</a></span>
@@ -3520,7 +3520,7 @@ <h3 id="sageworks.api.model.Model.details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
+<h3 id="workbench.api.model.Model.to_endpoint" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">to_endpoint</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">tags</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">serverless</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -3605,7 +3605,7 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
       <tbody>
           <tr class="doc-section-item">
 <td><code>Endpoint</code></td>            <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.api.endpoint.Endpoint" href="../endpoint/#sageworks.api.endpoint.Endpoint">Endpoint</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.api.endpoint.Endpoint" href="../endpoint/#workbench.api.endpoint.Endpoint">Endpoint</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3617,7 +3617,7 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/model.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span>
 <span class="normal"><a href="#__codelineno-0-36">36</a></span>
@@ -3701,10 +3701,10 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Create a Model from a FeatureSet</strong></p>
-<div class="language-py highlight"><span class="filename">featureset_to_model.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
-</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">from</span> <span class="nn">sageworks.api.model</span> <span class="kn">import</span> <span class="n">ModelType</span>
+<div class="language-py highlight"><span class="filename">featureset_to_model.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">from</span> <span class="nn">workbench.api.model</span> <span class="kn">import</span> <span class="n">ModelType</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="c1"># Grab a FeatureSet</span>
@@ -3735,7 +3735,7 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
 </span><span id="__span-1-15"><a id="__codelineno-1-15" name="__codelineno-1-15" href="#__codelineno-1-15"></a>                            <span class="mi">4</span>   <span class="mf">127.881073</span>  <span class="mf">113.825005</span>
 </span><span id="__span-1-16"><a id="__codelineno-1-16" name="__codelineno-1-16" href="#__codelineno-1-16"></a>    <span class="o">...</span>
 </span><span id="__span-1-17"><a id="__codelineno-1-17" name="__codelineno-1-17" href="#__codelineno-1-17"></a> <span class="s1">&#39;response_types&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;text/csv&#39;</span><span class="p">],</span>
-</span><span id="__span-1-18"><a id="__codelineno-1-18" name="__codelineno-1-18" href="#__codelineno-1-18"></a> <span class="s1">&#39;sageworks_tags&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;test-model&#39;</span><span class="p">],</span>
+</span><span id="__span-1-18"><a id="__codelineno-1-18" name="__codelineno-1-18" href="#__codelineno-1-18"></a> <span class="s1">&#39;workbench_tags&#39;</span><span class="p">:</span> <span class="p">[</span><span class="s1">&#39;test-model&#39;</span><span class="p">],</span>
 </span><span id="__span-1-19"><a id="__codelineno-1-19" name="__codelineno-1-19" href="#__codelineno-1-19"></a> <span class="s1">&#39;shapley_values&#39;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span>
 </span><span id="__span-1-20"><a id="__codelineno-1-20" name="__codelineno-1-20" href="#__codelineno-1-20"></a> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
 </span><span id="__span-1-21"><a id="__codelineno-1-21" name="__codelineno-1-21" href="#__codelineno-1-21"></a> <span class="s1">&#39;status&#39;</span><span class="p">:</span> <span class="s1">&#39;Completed&#39;</span><span class="p">,</span>
@@ -3744,7 +3744,7 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
 </span><span id="__span-1-24"><a id="__codelineno-1-24" name="__codelineno-1-24" href="#__codelineno-1-24"></a> <span class="s1">&#39;version&#39;</span><span class="p">:</span> <span class="mi">1</span><span class="p">}</span>
 </span></code></pre></div>
 <p><strong>Use a specific Scikit-Learn Model</strong></p>
-<p><div class="language-py highlight"><span class="filename">featureset_to_knn.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<p><div class="language-py highlight"><span class="filename">featureset_to_knn.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="c1"># Grab a FeatureSet</span>
@@ -3766,7 +3766,7 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
 </span><span id="__span-2-20"><a id="__codelineno-2-20" name="__codelineno-2-20" href="#__codelineno-2-20"></a><span class="n">pprint</span><span class="p">(</span><span class="n">my_model</span><span class="o">.</span><span class="n">details</span><span class="p">())</span>
 </span></code></pre></div>
 <strong>Another Scikit-Learn Example</strong></p>
-<div class="language-py highlight"><span class="filename">featureset_to_rfc.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<div class="language-py highlight"><span class="filename">featureset_to_rfc.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
 </span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a>
 </span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="c1"># Grab a FeatureSet</span>
@@ -3790,7 +3790,7 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
 <p class="admonition-title">Endpoint Costs</p>
 <p>Serverless endpoints are a great option, they have no AWS charges when not running. A <strong>realtime</strong> endpoint has less latency (no cold start) but AWS charges an hourly fee which can add up quickly!</p>
 </div>
-<div class="language-py highlight"><span class="filename">model_to_endpoint.py</span><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.model</span> <span class="kn">import</span> <span class="n">Model</span>
+<div class="language-py highlight"><span class="filename">model_to_endpoint.py</span><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="kn">from</span> <span class="nn">workbench.api.model</span> <span class="kn">import</span> <span class="n">Model</span>
 </span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>
 </span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a><span class="c1"># Grab the abalone regression Model</span>
 </span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="s2">&quot;abalone-regression&quot;</span><span class="p">)</span>
@@ -3802,7 +3802,7 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
 </span><span id="__span-4-10"><a id="__codelineno-4-10" name="__codelineno-4-10" href="#__codelineno-4-10"></a>                  <span class="n">serverless</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span></code></pre></div>
 <p><strong>Model Health Check and Metrics</strong></p>
-<div class="language-py highlight"><span class="filename">model_metrics.py</span><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.model</span> <span class="kn">import</span> <span class="n">Model</span>
+<div class="language-py highlight"><span class="filename">model_metrics.py</span><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">workbench.api.model</span> <span class="kn">import</span> <span class="n">Model</span>
 </span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>
 </span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="c1"># Grab the abalone-regression Model</span>
 </span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="s2">&quot;abalone-regression&quot;</span><span class="p">)</span>
@@ -3841,16 +3841,16 @@ <h3 id="sageworks.api.model.Model.to_endpoint" class="doc doc-heading">
 </span><span id="__span-6-17"><a id="__codelineno-6-17" name="__codelineno-6-17" href="#__codelineno-6-17"></a><span class="mi">498</span>                     <span class="mi">12</span>   <span class="mf">14.718514</span>
 </span><span id="__span-6-18"><a id="__codelineno-6-18" name="__codelineno-6-18" href="#__codelineno-6-18"></a><span class="mi">499</span>                     <span class="mi">13</span>   <span class="mf">10.637320</span>
 </span></code></pre></div>
-<h2 id="sageworks-ui">SageWorks UI</h2>
-<p>Running these few lines of code creates an AWS Model Package Group and an AWS Model Package. These model artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the SageWorks Dashboard UI.</p>
+<h2 id="workbench-ui">Workbench UI</h2>
+<p>Running these few lines of code creates an AWS Model Package Group and an AWS Model Package. These model artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the Workbench Dashboard UI.</p>
 <figure>
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/0c5cc2f8-bcc2-406d-a66e-32dcbad0cc25">
-<figcaption>SageWorks Dashboard: Models</figcaption>
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/0c5cc2f8-bcc2-406d-a66e-32dcbad0cc25">
+<figcaption>Workbench Dashboard: Models</figcaption>
 </figure>
 
 <div class="admonition note">
 <p class="admonition-title">Not Finding a particular method?</p>
-<p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">SageWorks Core Classes</a></p>
+<p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">Workbench Core Classes</a></p>
 </div>
 
 
diff --git a/api_classes/monitor/index.html b/api_classes/monitor/index.html
index bd35bde9e..2ad890cde 100644
--- a/api_classes/monitor/index.html
+++ b/api_classes/monitor/index.html
@@ -20,7 +20,7 @@
     
     
       
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+        <title>Monitor - Workbench</title>
       
     
     
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+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+    Workbench
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-    SageWorks REPL
+    Workbench REPL
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+  <a href="#workbench.api.monitor" class="md-nav__link">
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+  <a href="#workbench.api.monitor.Monitor.add_data_capture" class="md-nav__link">
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+  <a href="#workbench.api.monitor.Monitor.create_baseline" class="md-nav__link">
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 </li>
         
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+  <a href="#workbench.api.monitor.Monitor.create_monitoring_schedule" class="md-nav__link">
     <span class="md-ellipsis">
       create_monitoring_schedule
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@@ -648,7 +648,7 @@
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+  <a href="#workbench.api.monitor.Monitor.details" class="md-nav__link">
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@@ -657,7 +657,7 @@
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+  <a href="#workbench.api.monitor.Monitor.get_baseline" class="md-nav__link">
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       get_baseline
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@@ -666,7 +666,7 @@
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+  <a href="#workbench.api.monitor.Monitor.get_constraints" class="md-nav__link">
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       get_constraints
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+  <a href="#workbench.api.monitor.Monitor.get_latest_data_capture" class="md-nav__link">
     <span class="md-ellipsis">
       get_latest_data_capture
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@@ -684,7 +684,7 @@
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+  <a href="#workbench.api.monitor.Monitor.get_statistics" class="md-nav__link">
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       get_statistics
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@@ -693,7 +693,7 @@
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+  <a href="#workbench.api.monitor.Monitor.summary" class="md-nav__link">
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@@ -716,9 +716,9 @@
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+  <a href="#workbench-ui" class="md-nav__link">
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-      SageWorks UI
+      Workbench UI
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+    Workbench Enterprise
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-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3211,11 +3211,11 @@
   
   
     <li class="md-nav__item">
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+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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+  <a href="#workbench.api.monitor.Monitor" class="md-nav__link">
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+  <a href="#workbench.api.monitor.Monitor.summary" class="md-nav__link">
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@@ -3381,9 +3381,9 @@
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+  <a href="#workbench-ui" class="md-nav__link">
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-      SageWorks UI
+      Workbench UI
     </span>
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@@ -3415,12 +3415,12 @@ <h1 id="monitor">Monitor</h1>
 
 
 
-<a id="sageworks.api.monitor"></a>
+<a id="workbench.api.monitor"></a>
     <div class="doc doc-contents first">
 
         <p>Monitor: Manages AWS Endpoint Monitor creation and deployment.
 Endpoints Monitors are set up and provisioned for deployment into AWS.
-Monitors can be viewed in the AWS Sagemaker interfaces or in the SageWorks
+Monitors can be viewed in the AWS Sagemaker interfaces or in the Workbench
 Dashboard UI, which provides additional monitor details and performance metrics</p>
 
 
@@ -3443,7 +3443,7 @@ <h1 id="monitor">Monitor</h1>
 
 
 
-<h2 id="sageworks.api.monitor.Monitor" class="doc doc-heading">
+<h2 id="workbench.api.monitor.Monitor" class="doc doc-heading">
             <code>Monitor</code>
 
 
@@ -3452,10 +3452,10 @@ <h2 id="sageworks.api.monitor.Monitor" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.monitor_core.MonitorCore" href="../../core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore">MonitorCore</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.monitor_core.MonitorCore" href="../../core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore">MonitorCore</a></code></p>
 
 
-        <p>Monitor: SageWorks Monitor API Class</p>
+        <p>Monitor: Workbench Monitor API Class</p>
 
 
 <details class="common-usage" open>
@@ -3483,7 +3483,7 @@ <h2 id="sageworks.api.monitor.Monitor" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
 <span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
 <span class="normal"><a href="#__codelineno-0-15"> 15</a></span>
@@ -3588,7 +3588,7 @@ <h2 id="sageworks.api.monitor.Monitor" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-114">114</a></span>
 <span class="normal"><a href="#__codelineno-0-115">115</a></span>
 <span class="normal"><a href="#__codelineno-0-116">116</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="k">class</span> <span class="nc">Monitor</span><span class="p">(</span><span class="n">MonitorCore</span><span class="p">):</span>
-</span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Monitor: SageWorks Monitor API Class</span>
+</span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Monitor: Workbench Monitor API Class</span>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="sd">       ```</span>
@@ -3709,7 +3709,7 @@ <h2 id="sageworks.api.monitor.Monitor" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.add_data_capture" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.add_data_capture" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">add_data_capture</span><span class="p">(</span><span class="n">capture_percentage</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span></code>
 
 </h3>
@@ -3751,7 +3751,7 @@ <h3 id="sageworks.api.monitor.Monitor.add_data_capture" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-52">52</a></span>
 <span class="normal"><a href="#__codelineno-0-53">53</a></span>
 <span class="normal"><a href="#__codelineno-0-54">54</a></span>
@@ -3776,7 +3776,7 @@ <h3 id="sageworks.api.monitor.Monitor.add_data_capture" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.create_baseline" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.create_baseline" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create_baseline</span><span class="p">(</span><span class="n">recreate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -3826,7 +3826,7 @@ <h3 id="sageworks.api.monitor.Monitor.create_baseline" class="doc doc-heading">
 - statistics.json</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-61">61</a></span>
 <span class="normal"><a href="#__codelineno-0-62">62</a></span>
 <span class="normal"><a href="#__codelineno-0-63">63</a></span>
@@ -3861,7 +3861,7 @@ <h3 id="sageworks.api.monitor.Monitor.create_baseline" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.create_monitoring_schedule" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.create_monitoring_schedule" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create_monitoring_schedule</span><span class="p">(</span><span class="n">schedule</span><span class="o">=</span><span class="s1">&#39;hourly&#39;</span><span class="p">,</span> <span class="n">recreate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -3919,7 +3919,7 @@ <h3 id="sageworks.api.monitor.Monitor.create_monitoring_schedule" class="doc doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-75">75</a></span>
 <span class="normal"><a href="#__codelineno-0-76">76</a></span>
 <span class="normal"><a href="#__codelineno-0-77">77</a></span>
@@ -3946,7 +3946,7 @@ <h3 id="sageworks.api.monitor.Monitor.create_monitoring_schedule" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.details" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">()</span></code>
 
 </h3>
@@ -3980,7 +3980,7 @@ <h3 id="sageworks.api.monitor.Monitor.details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-44">44</a></span>
 <span class="normal"><a href="#__codelineno-0-45">45</a></span>
 <span class="normal"><a href="#__codelineno-0-46">46</a></span>
@@ -4003,7 +4003,7 @@ <h3 id="sageworks.api.monitor.Monitor.details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.get_baseline" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.get_baseline" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_baseline</span><span class="p">()</span></code>
 
 </h3>
@@ -4037,7 +4037,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_baseline" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-94"> 94</a></span>
 <span class="normal"><a href="#__codelineno-0-95"> 95</a></span>
 <span class="normal"><a href="#__codelineno-0-96"> 96</a></span>
@@ -4060,7 +4060,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_baseline" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.get_constraints" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.get_constraints" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_constraints</span><span class="p">()</span></code>
 
 </h3>
@@ -4094,7 +4094,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_constraints" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-102">102</a></span>
 <span class="normal"><a href="#__codelineno-0-103">103</a></span>
 <span class="normal"><a href="#__codelineno-0-104">104</a></span>
@@ -4117,7 +4117,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_constraints" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.get_latest_data_capture" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.get_latest_data_capture" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_latest_data_capture</span><span class="p">()</span></code>
 
 </h3>
@@ -4151,7 +4151,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_latest_data_capture" class="doc doc-he
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-85">85</a></span>
 <span class="normal"><a href="#__codelineno-0-86">86</a></span>
 <span class="normal"><a href="#__codelineno-0-87">87</a></span>
@@ -4176,7 +4176,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_latest_data_capture" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.get_statistics" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.get_statistics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_statistics</span><span class="p">()</span></code>
 
 </h3>
@@ -4210,7 +4210,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_statistics" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-110">110</a></span>
 <span class="normal"><a href="#__codelineno-0-111">111</a></span>
 <span class="normal"><a href="#__codelineno-0-112">112</a></span>
@@ -4233,7 +4233,7 @@ <h3 id="sageworks.api.monitor.Monitor.get_statistics" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.monitor.Monitor.summary" class="doc doc-heading">
+<h3 id="workbench.api.monitor.Monitor.summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">summary</span><span class="p">()</span></code>
 
 </h3>
@@ -4267,7 +4267,7 @@ <h3 id="sageworks.api.monitor.Monitor.summary" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/monitor.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/monitor.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-36">36</a></span>
 <span class="normal"><a href="#__codelineno-0-37">37</a></span>
 <span class="normal"><a href="#__codelineno-0-38">38</a></span>
@@ -4304,7 +4304,7 @@ <h3 id="sageworks.api.monitor.Monitor.summary" class="doc doc-heading">
 
 </div><h2 id="examples">Examples</h2>
 <p><strong>Initial Setup of the Endpoint Monitor</strong></p>
-<div class="language-py highlight"><span class="filename">monitor_setup.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.monitor</span> <span class="kn">import</span> <span class="n">Monitor</span>
+<div class="language-py highlight"><span class="filename">monitor_setup.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.monitor</span> <span class="kn">import</span> <span class="n">Monitor</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Create an Endpoint Monitor Class and perform initial Setup</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">endpoint_name</span> <span class="o">=</span> <span class="s2">&quot;abalone-regression-end-rt&quot;</span>
@@ -4320,8 +4320,8 @@ <h3 id="sageworks.api.monitor.Monitor.summary" class="doc doc-heading">
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a><span class="n">mon</span><span class="o">.</span><span class="n">create_monitoring_schedule</span><span class="p">(</span><span class="n">schedule</span><span class="o">=</span><span class="s2">&quot;hourly&quot;</span><span class="p">)</span>
 </span></code></pre></div>
 <p><strong>Pulling Information from an Existing Monitor</strong></p>
-<div class="language-py highlight"><span class="filename">monitor_usage.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.monitor</span> <span class="kn">import</span> <span class="n">Monitor</span>
-</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="kn">from</span> <span class="nn">sageworks.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
+<div class="language-py highlight"><span class="filename">monitor_usage.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.api.monitor</span> <span class="kn">import</span> <span class="n">Monitor</span>
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="kn">from</span> <span class="nn">workbench.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="c1"># Construct a Monitor Class in one of Two Ways</span>
 </span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a><span class="n">mon</span> <span class="o">=</span> <span class="n">Endpoint</span><span class="p">(</span><span class="s2">&quot;abalone-regression-end-rt&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">get_monitor</span><span class="p">()</span>
@@ -4346,16 +4346,16 @@ <h3 id="sageworks.api.monitor.Monitor.summary" class="doc doc-heading">
 </span><span id="__span-1-24"><a id="__codelineno-1-24" name="__codelineno-1-24" href="#__codelineno-1-24"></a><span class="n">input_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </span><span id="__span-1-25"><a id="__codelineno-1-25" name="__codelineno-1-25" href="#__codelineno-1-25"></a><span class="n">output_df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
 </span></code></pre></div>
-<h2 id="sageworks-ui">SageWorks UI</h2>
-<p>Running these few lines of code creates and deploys an AWS Endpoint Monitor. The Monitor status and outputs can be viewed in the Sagemaker Console interfaces or in the SageWorks Dashboard UI. SageWorks will use the monitor to track various metrics including Data Quality, Model Bias, etc...</p>
+<h2 id="workbench-ui">Workbench UI</h2>
+<p>Running these few lines of code creates and deploys an AWS Endpoint Monitor. The Monitor status and outputs can be viewed in the Sagemaker Console interfaces or in the Workbench Dashboard UI. Workbench will use the monitor to track various metrics including Data Quality, Model Bias, etc...</p>
 <figure>
-<img alt="sageworks_endpoints" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/b5eab741-2c23-4c5e-9495-15fd3ea8155c">
-<figcaption>SageWorks Dashboard: Endpoints</figcaption>
+<img alt="workbench_endpoints" src="https://github.com/SuperCowPowers/workbench/assets/4806709/b5eab741-2c23-4c5e-9495-15fd3ea8155c">
+<figcaption>Workbench Dashboard: Endpoints</figcaption>
 </figure>
 
 <div class="admonition note">
 <p class="admonition-title">Not Finding a particular method?</p>
-<p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">SageWorks Core Classes</a></p>
+<p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">Workbench Core Classes</a></p>
 </div>
 
 
diff --git a/api_classes/overview/index.html b/api_classes/overview/index.html
index 25ed2c3d9..e48adba73 100644
--- a/api_classes/overview/index.html
+++ b/api_classes/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -392,9 +392,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#welcome-to-the-sageworks-api-classes" class="md-nav__link">
+  <a href="#welcome-to-the-workbench-api-classes" class="md-nav__link">
     <span class="md-ellipsis">
-      Welcome to the SageWorks API Classes
+      Welcome to the Workbench API Classes
     </span>
   </a>
   
@@ -1877,7 +1877,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1887,7 +1887,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3106,11 +3106,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3162,9 +3162,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#welcome-to-the-sageworks-api-classes" class="md-nav__link">
+  <a href="#welcome-to-the-workbench-api-classes" class="md-nav__link">
     <span class="md-ellipsis">
-      Welcome to the SageWorks API Classes
+      Welcome to the Workbench API Classes
     </span>
   </a>
   
@@ -3197,10 +3197,10 @@
 <h1 id="overview">Overview</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Just Getting Started?</p>
-<p>You're in the right place, the SageWorks API Classes are the best way to get started with SageWorks!</p>
+<p>You're in the right place, the Workbench API Classes are the best way to get started with Workbench!</p>
 </div>
-<h2 id="welcome-to-the-sageworks-api-classes">Welcome to the SageWorks API Classes</h2>
-<p>These classes provide high-level APIs for the SageWorks package, they enable your team to build full AWS Machine Learning Pipelines. They handle all the details around updating and managing a complex set of AWS Services. Each class provides an essential component of the overall ML Pipline. Simply combine the classes to build production ready, AWS powered, machine learning pipelines. </p>
+<h2 id="welcome-to-the-workbench-api-classes">Welcome to the Workbench API Classes</h2>
+<p>These classes provide high-level APIs for the Workbench package, they enable your team to build full AWS Machine Learning Pipelines. They handle all the details around updating and managing a complex set of AWS Services. Each class provides an essential component of the overall ML Pipline. Simply combine the classes to build production ready, AWS powered, machine learning pipelines. </p>
 <ul>
 <li><strong><a href="../data_source/">DataSource</a>:</strong> Manages AWS Data Catalog and Athena</li>
 <li><strong><a href="../feature_set/">FeatureSet</a>:</strong> Manages AWS Feature Store and Feature Groups</li>
@@ -3208,15 +3208,15 @@ <h2 id="welcome-to-the-sageworks-api-classes">Welcome to the SageWorks API Class
 <li><strong><a href="../endpoint/">Endpoint</a>:</strong> Manages the deployment and invocations/inference on AWS Endpoints</li>
 <li><strong><a href="../monitor/">Monitor</a>:</strong> Manages the setup and deployment of AWS Endpoint Monitors</li>
 </ul>
-<p><img alt="ML Pipeline" src="../../images/sageworks_concepts.png" /></p>
+<p><img alt="ML Pipeline" src="../../images/workbench_concepts.png" /></p>
 <h2 id="example-ml-pipline">Example ML Pipline</h2>
-<div class="language-py highlight"><span class="filename">full_ml_pipeline.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
-</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
-</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">sageworks.api.model</span> <span class="kn">import</span> <span class="n">Model</span><span class="p">,</span> <span class="n">ModelType</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="kn">from</span> <span class="nn">sageworks.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
+<div class="language-py highlight"><span class="filename">full_ml_pipeline.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">workbench.api.model</span> <span class="kn">import</span> <span class="n">Model</span><span class="p">,</span> <span class="n">ModelType</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="kn">from</span> <span class="nn">workbench.api.endpoint</span> <span class="kn">import</span> <span class="n">Endpoint</span>
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="c1"># Create the abalone_data DataSource</span>
-</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="n">ds</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="s2">&quot;s3://sageworks-public-data/common/abalone.csv&quot;</span><span class="p">)</span>
+</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="n">ds</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="s2">&quot;s3://workbench-public-data/common/abalone.csv&quot;</span><span class="p">)</span>
 </span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a>
 </span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="c1"># Now create a FeatureSet</span>
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="n">ds</span><span class="o">.</span><span class="n">to_features</span><span class="p">(</span><span class="s2">&quot;abalone_features&quot;</span><span class="p">)</span>
@@ -3261,12 +3261,12 @@ <h2 id="example-ml-pipline">Example ML Pipline</h2>
 </span></code></pre></div>
 <div class="admonition success">
 <p class="admonition-title">Full AWS ML Pipeline Achievement Unlocked!</p>
-<p>Bing! You just built and deployed a full AWS Machine Learning Pipeline. You can now use the SageWorks Dashboard web interface to inspect your AWS artifacts. A comprehensive set of Exploratory Data Analysis techniques and Model Performance Metrics are available for your entire team to review, inspect and interact with.</p>
+<p>Bing! You just built and deployed a full AWS Machine Learning Pipeline. You can now use the Workbench Dashboard web interface to inspect your AWS artifacts. A comprehensive set of Exploratory Data Analysis techniques and Model Performance Metrics are available for your entire team to review, inspect and interact with.</p>
 </div>
-<p><img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/ed2ed1bd-e2d8-49a1-b350-b2e19e2b7832"></p>
+<p><img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/ed2ed1bd-e2d8-49a1-b350-b2e19e2b7832"></p>
 <div class="admonition note">
 <p class="admonition-title">Examples</p>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 </div>
 
 
diff --git a/api_classes/parameter_store/index.html b/api_classes/parameter_store/index.html
index a5e85ca2d..ed26bdb0c 100644
--- a/api_classes/parameter_store/index.html
+++ b/api_classes/parameter_store/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Parameter Store - SageWorks</title>
+        <title>Parameter Store - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-parameter-storage" class="md-skip">
+        <a href="#workbench-parameter-storage" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -539,7 +539,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store" class="md-nav__link">
+  <a href="#workbench.api.parameter_store" class="md-nav__link">
     <span class="md-ellipsis">
       parameter_store
     </span>
@@ -548,7 +548,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore" class="md-nav__link">
     <span class="md-ellipsis">
       ParameterStore
     </span>
@@ -558,7 +558,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.__init__" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -567,7 +567,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.__repr__" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.__repr__" class="md-nav__link">
     <span class="md-ellipsis">
       __repr__
     </span>
@@ -576,7 +576,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.delete" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
     </span>
@@ -585,7 +585,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.get" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.get" class="md-nav__link">
     <span class="md-ellipsis">
       get
     </span>
@@ -594,7 +594,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.list" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.list" class="md-nav__link">
     <span class="md-ellipsis">
       list
     </span>
@@ -603,7 +603,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.upsert" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.upsert" class="md-nav__link">
     <span class="md-ellipsis">
       upsert
     </span>
@@ -1955,7 +1955,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1965,7 +1965,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3184,11 +3184,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3240,7 +3240,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store" class="md-nav__link">
+  <a href="#workbench.api.parameter_store" class="md-nav__link">
     <span class="md-ellipsis">
       parameter_store
     </span>
@@ -3249,7 +3249,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore" class="md-nav__link">
     <span class="md-ellipsis">
       ParameterStore
     </span>
@@ -3259,7 +3259,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.__init__" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3268,7 +3268,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.__repr__" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.__repr__" class="md-nav__link">
     <span class="md-ellipsis">
       __repr__
     </span>
@@ -3277,7 +3277,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.delete" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
     </span>
@@ -3286,7 +3286,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.get" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.get" class="md-nav__link">
     <span class="md-ellipsis">
       get
     </span>
@@ -3295,7 +3295,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.list" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.list" class="md-nav__link">
     <span class="md-ellipsis">
       list
     </span>
@@ -3304,7 +3304,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.parameter_store.ParameterStore.upsert" class="md-nav__link">
+  <a href="#workbench.api.parameter_store.ParameterStore.upsert" class="md-nav__link">
     <span class="md-ellipsis">
       upsert
     </span>
@@ -3350,7 +3350,7 @@
                   
 
 
-<h1 id="sageworks-parameter-storage">SageWorks Parameter Storage</h1>
+<h1 id="workbench-parameter-storage">Workbench Parameter Storage</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Examples</p>
 <p>Examples of using the Parameter Storage class are listed at the bottom of this page <a href="#examples">Examples</a>.</p>
@@ -3361,10 +3361,10 @@ <h1 id="sageworks-parameter-storage">SageWorks Parameter Storage</h1>
 
 
 
-<a id="sageworks.api.parameter_store"></a>
+<a id="workbench.api.parameter_store"></a>
     <div class="doc doc-contents first">
 
-        <p>ParameterStore: Manages SageWorks parameters in AWS Systems Manager Parameter Store.</p>
+        <p>ParameterStore: Manages Workbench parameters in AWS Systems Manager Parameter Store.</p>
 
 
 
@@ -3386,7 +3386,7 @@ <h1 id="sageworks-parameter-storage">SageWorks Parameter Storage</h1>
 
 
 
-<h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
+<h2 id="workbench.api.parameter_store.ParameterStore" class="doc doc-heading">
             <code>ParameterStore</code>
 
 
@@ -3396,7 +3396,7 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
     <div class="doc doc-contents ">
 
 
-        <p>ParameterStore: Manages SageWorks parameters in AWS Systems Manager Parameter Store.</p>
+        <p>ParameterStore: Manages Workbench parameters in AWS Systems Manager Parameter Store.</p>
 
 
 <details class="common-usage" open>
@@ -3406,10 +3406,10 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># List Parameters</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">params</span><span class="o">.</span><span class="n">list</span><span class="p">()</span>
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>
-</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="p">[</span><span class="s1">&#39;/sageworks/abalone_info&#39;</span><span class="p">,</span>
-</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="s1">&#39;/sageworks/my_data&#39;</span><span class="p">,</span>
-</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a> <span class="s1">&#39;/sageworks/test&#39;</span><span class="p">,</span>
-</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a> <span class="s1">&#39;/sageworks/pipelines/my_pipeline&#39;</span><span class="p">]</span>
+</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="p">[</span><span class="s1">&#39;/workbench/abalone_info&#39;</span><span class="p">,</span>
+</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="s1">&#39;/workbench/my_data&#39;</span><span class="p">,</span>
+</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a> <span class="s1">&#39;/workbench/test&#39;</span><span class="p">,</span>
+</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a> <span class="s1">&#39;/workbench/pipelines/my_pipeline&#39;</span><span class="p">]</span>
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>
 </span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="c1"># Add Key</span>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="n">params</span><span class="o">.</span><span class="n">upsert</span><span class="p">(</span><span class="s2">&quot;key&quot;</span><span class="p">,</span> <span class="s2">&quot;value&quot;</span><span class="p">)</span>
@@ -3435,7 +3435,7 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/parameter_store.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/parameter_store.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
 <span class="normal"><a href="#__codelineno-0-15"> 15</a></span>
 <span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
@@ -3623,7 +3623,7 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-198">198</a></span>
 <span class="normal"><a href="#__codelineno-0-199">199</a></span>
 <span class="normal"><a href="#__codelineno-0-200">200</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="k">class</span> <span class="nc">ParameterStore</span><span class="p">:</span>
-</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;ParameterStore: Manages SageWorks parameters in AWS Systems Manager Parameter Store.</span>
+</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;ParameterStore: Manages Workbench parameters in AWS Systems Manager Parameter Store.</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="sd">        ```python</span>
@@ -3632,10 +3632,10 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        # List Parameters</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        params.list()</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">        [&#39;/sageworks/abalone_info&#39;,</span>
-</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">         &#39;/sageworks/my_data&#39;,</span>
-</span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">         &#39;/sageworks/test&#39;,</span>
-</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">         &#39;/sageworks/pipelines/my_pipeline&#39;]</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">        [&#39;/workbench/abalone_info&#39;,</span>
+</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">         &#39;/workbench/my_data&#39;,</span>
+</span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">         &#39;/workbench/test&#39;,</span>
+</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">         &#39;/workbench/pipelines/my_pipeline&#39;]</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">        # Add Key</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">        params.upsert(&quot;key&quot;, &quot;value&quot;)</span>
@@ -3658,9 +3658,9 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
 </span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;ParameterStore Init Method&quot;&quot;&quot;</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>        <span class="c1"># Initialize a SageWorks Session (to assume the SageWorks ExecutionRole)</span>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>        <span class="c1"># Initialize a Workbench Session (to assume the Workbench ExecutionRole)</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span> <span class="o">=</span> <span class="n">AWSSession</span><span class="p">()</span><span class="o">.</span><span class="n">boto3_session</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>        <span class="c1"># Create a Systems Manager (SSM) client for Parameter Store operations</span>
@@ -3770,7 +3770,7 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
 </span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>                <span class="c1"># Report on the size of the compressed value</span>
 </span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>                <span class="n">compressed_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">compressed_value</span><span class="p">)</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>                <span class="k">if</span> <span class="n">compressed_size</span> <span class="o">&gt;</span> <span class="mi">4096</span><span class="p">:</span>
-</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>                    <span class="n">doc_link</span> <span class="o">=</span> <span class="s2">&quot;https://supercowpowers.github.io/sageworks/api_classes/df_store&quot;</span>
+</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>                    <span class="n">doc_link</span> <span class="o">=</span> <span class="s2">&quot;https://supercowpowers.github.io/workbench/api_classes/df_store&quot;</span>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>                    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Compressed size </span><span class="si">{</span><span class="n">compressed_size</span><span class="si">}</span><span class="s2"> bytes, cannot store &gt; 4KB&quot;</span><span class="p">)</span>
 </span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>                    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;For larger data use the DFStore() class (</span><span class="si">{</span><span class="n">doc_link</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
 </span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>                    <span class="k">return</span>
@@ -3827,7 +3827,7 @@ <h2 id="sageworks.api.parameter_store.ParameterStore" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.parameter_store.ParameterStore.__init__" class="doc doc-heading">
+<h3 id="workbench.api.parameter_store.ParameterStore.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">()</span></code>
 
 </h3>
@@ -3838,7 +3838,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.__init__" class="doc doc-he
         <p>ParameterStore Init Method</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/parameter_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/parameter_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-48">48</a></span>
 <span class="normal"><a href="#__codelineno-0-49">49</a></span>
 <span class="normal"><a href="#__codelineno-0-50">50</a></span>
@@ -3849,9 +3849,9 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.__init__" class="doc doc-he
 <span class="normal"><a href="#__codelineno-0-55">55</a></span>
 <span class="normal"><a href="#__codelineno-0-56">56</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;ParameterStore Init Method&quot;&quot;&quot;</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="c1"># Initialize a SageWorks Session (to assume the SageWorks ExecutionRole)</span>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="c1"># Initialize a Workbench Session (to assume the Workbench ExecutionRole)</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span> <span class="o">=</span> <span class="n">AWSSession</span><span class="p">()</span><span class="o">.</span><span class="n">boto3_session</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>    <span class="c1"># Create a Systems Manager (SSM) client for Parameter Store operations</span>
@@ -3865,7 +3865,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.__init__" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.parameter_store.ParameterStore.__repr__" class="doc doc-heading">
+<h3 id="workbench.api.parameter_store.ParameterStore.__repr__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__repr__</span><span class="p">()</span></code>
 
 </h3>
@@ -3876,7 +3876,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.__repr__" class="doc doc-he
         <p>Return a string representation of the ParameterStore object.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/parameter_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/parameter_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-198">198</a></span>
 <span class="normal"><a href="#__codelineno-0-199">199</a></span>
 <span class="normal"><a href="#__codelineno-0-200">200</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-198"><a id="__codelineno-0-198" name="__codelineno-0-198"></a><span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -3891,7 +3891,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.__repr__" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.parameter_store.ParameterStore.delete" class="doc doc-heading">
+<h3 id="workbench.api.parameter_store.ParameterStore.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></code>
 
 </h3>
@@ -3933,7 +3933,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.delete" class="doc doc-head
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/parameter_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/parameter_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-185">185</a></span>
 <span class="normal"><a href="#__codelineno-0-186">186</a></span>
 <span class="normal"><a href="#__codelineno-0-187">187</a></span>
@@ -3966,7 +3966,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.delete" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.parameter_store.ParameterStore.get" class="doc doc-heading">
+<h3 id="workbench.api.parameter_store.ParameterStore.get" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">warn</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">decrypt</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -4051,7 +4051,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.get" class="doc doc-heading
       <tbody>
           <tr class="doc-section-item">
             <td>
-                  <code><span title="typing.Union">Union</span>[str, <a class="autorefs autorefs-internal" title="sageworks.api.parameter_store.ParameterStore.list" href="#sageworks.api.parameter_store.ParameterStore.list">list</a>, dict, None]</code>
+                  <code><span title="typing.Union">Union</span>[str, <a class="autorefs autorefs-internal" title="workbench.api.parameter_store.ParameterStore.list" href="#workbench.api.parameter_store.ParameterStore.list">list</a>, dict, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -4063,7 +4063,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.get" class="doc doc-heading
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/parameter_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/parameter_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-100">100</a></span>
 <span class="normal"><a href="#__codelineno-0-101">101</a></span>
 <span class="normal"><a href="#__codelineno-0-102">102</a></span>
@@ -4148,7 +4148,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.get" class="doc doc-heading
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.parameter_store.ParameterStore.list" class="doc doc-heading">
+<h3 id="workbench.api.parameter_store.ParameterStore.list" class="doc doc-heading">
             <code class="highlight language-python"><span class="nb">list</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -4201,7 +4201,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.list" class="doc doc-headin
       <tbody>
           <tr class="doc-section-item">
 <td><code>list</code></td>            <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.api.parameter_store.ParameterStore.list" href="#sageworks.api.parameter_store.ParameterStore.list">list</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.api.parameter_store.ParameterStore.list" href="#workbench.api.parameter_store.ParameterStore.list">list</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -4213,7 +4213,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.list" class="doc doc-headin
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/parameter_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/parameter_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-58">58</a></span>
 <span class="normal"><a href="#__codelineno-0-59">59</a></span>
 <span class="normal"><a href="#__codelineno-0-60">60</a></span>
@@ -4304,7 +4304,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.list" class="doc doc-headin
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.parameter_store.ParameterStore.upsert" class="doc doc-heading">
+<h3 id="workbench.api.parameter_store.ParameterStore.upsert" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">upsert</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -4347,7 +4347,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.upsert" class="doc doc-head
                 <code>value</code>
             </td>
             <td>
-                  <code>str | <a class="autorefs autorefs-internal" title="sageworks.api.parameter_store.ParameterStore.list" href="#sageworks.api.parameter_store.ParameterStore.list">list</a> | dict</code>
+                  <code>str | <a class="autorefs autorefs-internal" title="workbench.api.parameter_store.ParameterStore.list" href="#workbench.api.parameter_store.ParameterStore.list">list</a> | dict</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -4378,7 +4378,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.upsert" class="doc doc-head
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/parameter_store.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/parameter_store.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-139">139</a></span>
 <span class="normal"><a href="#__codelineno-0-140">140</a></span>
 <span class="normal"><a href="#__codelineno-0-141">141</a></span>
@@ -4446,7 +4446,7 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.upsert" class="doc doc-head
 </span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>            <span class="c1"># Report on the size of the compressed value</span>
 </span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>            <span class="n">compressed_size</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">compressed_value</span><span class="p">)</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>            <span class="k">if</span> <span class="n">compressed_size</span> <span class="o">&gt;</span> <span class="mi">4096</span><span class="p">:</span>
-</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>                <span class="n">doc_link</span> <span class="o">=</span> <span class="s2">&quot;https://supercowpowers.github.io/sageworks/api_classes/df_store&quot;</span>
+</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>                <span class="n">doc_link</span> <span class="o">=</span> <span class="s2">&quot;https://supercowpowers.github.io/workbench/api_classes/df_store&quot;</span>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Compressed size </span><span class="si">{</span><span class="n">compressed_size</span><span class="si">}</span><span class="s2"> bytes, cannot store &gt; 4KB&quot;</span><span class="p">)</span>
 </span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;For larger data use the DFStore() class (</span><span class="si">{</span><span class="n">doc_link</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
 </span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>                <span class="k">return</span>
@@ -4490,22 +4490,22 @@ <h3 id="sageworks.api.parameter_store.ParameterStore.upsert" class="doc doc-head
     </div>
 
 </div><h2 id="bypassing-the-4k-limit">Bypassing the 4k Limit</h2>
-<p>AWS Parameter Storage has a 4k limit on values, the SageWorks class bypasses this limit by detecting large values (strings, data, whatever) and compressing those on the fly. The decompressing is also handled automatically, so for larger data simply use the <code>add()</code> and <code>get()</code> methods and it will all <strong>just work</strong>.</p>
+<p>AWS Parameter Storage has a 4k limit on values, the Workbench class bypasses this limit by detecting large values (strings, data, whatever) and compressing those on the fly. The decompressing is also handled automatically, so for larger data simply use the <code>add()</code> and <code>get()</code> methods and it will all <strong>just work</strong>.</p>
 <h2 id="examples">Examples</h2>
 <p>These example show how to use the <code>ParameterStore()</code> class to list, add, and get parameters from the AWS Parameter Store Service.</p>
 <div class="admonition tip">
-<p class="admonition-title">SageWorks REPL</p>
-<p>If you'd like to experiment with listing, adding, and getting data with the <code>ParameterStore()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">SageWorks REPL</a></p>
+<p class="admonition-title">Workbench REPL</p>
+<p>If you'd like to experiment with listing, adding, and getting data with the <code>ParameterStore()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">Workbench REPL</a></p>
 </div>
-<div class="language-py highlight"><span class="filename">Using SageWorks REPL</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">params</span> <span class="o">=</span> <span class="n">ParameterStore</span><span class="p">()</span>
+<div class="language-py highlight"><span class="filename">Using Workbench REPL</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">params</span> <span class="o">=</span> <span class="n">ParameterStore</span><span class="p">()</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># List Parameters</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">params</span><span class="o">.</span><span class="n">list</span><span class="p">()</span>
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>
-</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="p">[</span><span class="s1">&#39;/sageworks/abalone_info&#39;</span><span class="p">,</span>
-</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="s1">&#39;/sageworks/my_data&#39;</span><span class="p">,</span>
-</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a> <span class="s1">&#39;/sageworks/test&#39;</span><span class="p">,</span>
-</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a> <span class="s1">&#39;/sageworks/pipelines/my_pipeline&#39;</span><span class="p">]</span>
+</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="p">[</span><span class="s1">&#39;/workbench/abalone_info&#39;</span><span class="p">,</span>
+</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a> <span class="s1">&#39;/workbench/my_data&#39;</span><span class="p">,</span>
+</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a> <span class="s1">&#39;/workbench/test&#39;</span><span class="p">,</span>
+</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a> <span class="s1">&#39;/workbench/pipelines/my_pipeline&#39;</span><span class="p">]</span>
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>
 </span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="c1"># Add Key</span>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="n">params</span><span class="o">.</span><span class="n">upsert</span><span class="p">(</span><span class="s2">&quot;key&quot;</span><span class="p">,</span> <span class="s2">&quot;value&quot;</span><span class="p">)</span>
diff --git a/api_classes/pipelines/index.html b/api_classes/pipelines/index.html
index 56516a65e..03de6813b 100644
--- a/api_classes/pipelines/index.html
+++ b/api_classes/pipelines/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Pipelines - SageWorks</title>
+        <title>Pipelines - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
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@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
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-    SageWorks
+    Workbench
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   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
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@@ -581,7 +581,7 @@
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         <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline" class="md-nav__link">
+  <a href="#workbench.api.pipeline" class="md-nav__link">
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       pipeline
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@@ -590,7 +590,7 @@
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+  <a href="#workbench.api.pipeline.log" class="md-nav__link">
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       log
     </span>
@@ -599,7 +599,7 @@
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+  <a href="#workbench.api.pipeline.Pipeline" class="md-nav__link">
     <span class="md-ellipsis">
       Pipeline
     </span>
@@ -609,7 +609,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.__init__" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
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@@ -618,7 +618,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.__repr__" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.__repr__" class="md-nav__link">
     <span class="md-ellipsis">
       __repr__
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@@ -627,7 +627,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.delete" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
     </span>
@@ -636,7 +636,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.details" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -645,7 +645,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.execute" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.execute" class="md-nav__link">
     <span class="md-ellipsis">
       execute
     </span>
@@ -654,7 +654,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.execute_partial" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.execute_partial" class="md-nav__link">
     <span class="md-ellipsis">
       execute_partial
     </span>
@@ -663,7 +663,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.health_check" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -672,7 +672,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.report_settable_fields" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.report_settable_fields" class="md-nav__link">
     <span class="md-ellipsis">
       report_settable_fields
     </span>
@@ -681,7 +681,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.set_input" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.set_input" class="md-nav__link">
     <span class="md-ellipsis">
       set_input
     </span>
@@ -690,7 +690,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.set_training_holdouts" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.set_training_holdouts" class="md-nav__link">
     <span class="md-ellipsis">
       set_training_holdouts
     </span>
@@ -699,7 +699,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.summary" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.summary" class="md-nav__link">
     <span class="md-ellipsis">
       summary
     </span>
@@ -2009,7 +2009,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -2019,7 +2019,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3238,11 +3238,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3294,7 +3294,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline" class="md-nav__link">
+  <a href="#workbench.api.pipeline" class="md-nav__link">
     <span class="md-ellipsis">
       pipeline
     </span>
@@ -3303,7 +3303,7 @@
 </li>
       
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-  <a href="#sageworks.api.pipeline.log" class="md-nav__link">
+  <a href="#workbench.api.pipeline.log" class="md-nav__link">
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       log
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@@ -3312,7 +3312,7 @@
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+  <a href="#workbench.api.pipeline.Pipeline" class="md-nav__link">
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+  <a href="#workbench.api.pipeline.Pipeline.__init__" class="md-nav__link">
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+  <a href="#workbench.api.pipeline.Pipeline.delete" class="md-nav__link">
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       delete
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           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.details" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.details" class="md-nav__link">
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       details
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@@ -3358,7 +3358,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.execute" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.execute" class="md-nav__link">
     <span class="md-ellipsis">
       execute
     </span>
@@ -3367,7 +3367,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.execute_partial" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.execute_partial" class="md-nav__link">
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       execute_partial
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@@ -3376,7 +3376,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.health_check" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -3385,7 +3385,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.report_settable_fields" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.report_settable_fields" class="md-nav__link">
     <span class="md-ellipsis">
       report_settable_fields
     </span>
@@ -3394,7 +3394,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.set_input" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.set_input" class="md-nav__link">
     <span class="md-ellipsis">
       set_input
     </span>
@@ -3403,7 +3403,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.set_training_holdouts" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.set_training_holdouts" class="md-nav__link">
     <span class="md-ellipsis">
       set_training_holdouts
     </span>
@@ -3412,7 +3412,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.api.pipeline.Pipeline.summary" class="md-nav__link">
+  <a href="#workbench.api.pipeline.Pipeline.summary" class="md-nav__link">
     <span class="md-ellipsis">
       summary
     </span>
@@ -3463,7 +3463,7 @@ <h1 id="pipelines">Pipelines</h1>
 <p class="admonition-title">Pipeline Examples</p>
 <p>Examples of using the Pipeline classes are listed at the bottom of this page <a href="#examples">Examples</a>.</p>
 </div>
-<p>Pipelines store sequences of SageWorks transforms. So if you have a nightly ML workflow you can capture that as a Pipeline. Here's an example pipeline:</p>
+<p>Pipelines store sequences of Workbench transforms. So if you have a nightly ML workflow you can capture that as a Pipeline. Here's an example pipeline:</p>
 <div class="language-py highlight"><span class="filename">nightly_sol_pipeline_v1.json</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="p">{</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>    <span class="s2">&quot;data_source&quot;</span><span class="p">:</span> <span class="p">{</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>         <span class="s2">&quot;name&quot;</span><span class="p">:</span> <span class="s2">&quot;nightly_data&quot;</span><span class="p">,</span>
@@ -3492,10 +3492,10 @@ <h1 id="pipelines">Pipelines</h1>
 
 
 
-<a id="sageworks.api.pipeline"></a>
+<a id="workbench.api.pipeline"></a>
     <div class="doc doc-contents first">
 
-        <p>Pipeline: Manages the details around a SageWorks Pipeline, including Execution</p>
+        <p>Pipeline: Manages the details around a Workbench Pipeline, including Execution</p>
 
 
 
@@ -3516,8 +3516,8 @@ <h1 id="pipelines">Pipelines</h1>
 
 
 
-<h2 id="sageworks.api.pipeline.log" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h2 id="workbench.api.pipeline.log" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-module-attribute"><code>module-attribute</code></small>
@@ -3529,7 +3529,7 @@ <h2 id="sageworks.api.pipeline.log" class="doc doc-heading">
     <div class="doc doc-contents ">
 
         <p>my_pipeline = Pipeline("aqsol_pipeline_v1")
-my_pipeline.set_input("s3://sageworks-public-data/comp_chem/aqsol_public_data.csv")
+my_pipeline.set_input("s3://workbench-public-data/comp_chem/aqsol_public_data.csv")
 my_pipeline.execute_partial(["model", "endpoint"])
 exit(0)</p>
     </div>
@@ -3541,7 +3541,7 @@ <h2 id="sageworks.api.pipeline.log" class="doc doc-heading">
 
 
 
-<h2 id="sageworks.api.pipeline.Pipeline" class="doc doc-heading">
+<h2 id="workbench.api.pipeline.Pipeline" class="doc doc-heading">
             <code>Pipeline</code>
 
 
@@ -3551,7 +3551,7 @@ <h2 id="sageworks.api.pipeline.Pipeline" class="doc doc-heading">
     <div class="doc doc-contents ">
 
 
-        <p>Pipeline: SageWorks Pipeline API Class</p>
+        <p>Pipeline: Workbench Pipeline API Class</p>
 
 
 <details class="common-usage" open>
@@ -3569,7 +3569,7 @@ <h2 id="sageworks.api.pipeline.Pipeline" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+                <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
 <span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
 <span class="normal"><a href="#__codelineno-0-15"> 15</a></span>
@@ -3693,7 +3693,7 @@ <h2 id="sageworks.api.pipeline.Pipeline" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-133">133</a></span>
 <span class="normal"><a href="#__codelineno-0-134">134</a></span>
 <span class="normal"><a href="#__codelineno-0-135">135</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="k">class</span> <span class="nc">Pipeline</span><span class="p">:</span>
-</span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Pipeline: SageWorks Pipeline API Class</span>
+</span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Pipeline: Workbench Pipeline API Class</span>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="sd">        ```python</span>
@@ -3707,11 +3707,11 @@ <h2 id="sageworks.api.pipeline.Pipeline" class="doc doc-heading">
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Pipeline Init Method&quot;&quot;&quot;</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">uuid</span> <span class="o">=</span> <span class="n">name</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>        <span class="c1"># Spin up a Parameter Store for Pipelines</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span> <span class="o">=</span> <span class="s2">&quot;/sageworks/pipelines&quot;</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span> <span class="o">=</span> <span class="s2">&quot;/workbench/pipelines&quot;</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">ParameterStore</span><span class="p">()</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">pipeline</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">prefix</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>
@@ -3833,7 +3833,7 @@ <h2 id="sageworks.api.pipeline.Pipeline" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.__init__" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="p">)</span></code>
 
 </h3>
@@ -3844,7 +3844,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.__init__" class="doc doc-heading">
         <p>Pipeline Init Method</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-26">26</a></span>
 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
 <span class="normal"><a href="#__codelineno-0-28">28</a></span>
@@ -3855,11 +3855,11 @@ <h3 id="sageworks.api.pipeline.Pipeline.__init__" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-33">33</a></span>
 <span class="normal"><a href="#__codelineno-0-34">34</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Pipeline Init Method&quot;&quot;&quot;</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">uuid</span> <span class="o">=</span> <span class="n">name</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>    <span class="c1"># Spin up a Parameter Store for Pipelines</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span> <span class="o">=</span> <span class="s2">&quot;/sageworks/pipelines&quot;</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">prefix</span> <span class="o">=</span> <span class="s2">&quot;/workbench/pipelines&quot;</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="n">ParameterStore</span><span class="p">()</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">pipeline</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">params</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">prefix</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -3871,7 +3871,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.__init__" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.__repr__" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.__repr__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__repr__</span><span class="p">()</span></code>
 
 </h3>
@@ -3905,7 +3905,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.__repr__" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-126">126</a></span>
 <span class="normal"><a href="#__codelineno-0-127">127</a></span>
 <span class="normal"><a href="#__codelineno-0-128">128</a></span>
@@ -3934,7 +3934,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.__repr__" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.delete" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">()</span></code>
 
 </h3>
@@ -3945,7 +3945,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.delete" class="doc doc-heading">
         <p>Pipeline Deletion</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-121">121</a></span>
 <span class="normal"><a href="#__codelineno-0-122">122</a></span>
 <span class="normal"><a href="#__codelineno-0-123">123</a></span>
@@ -3962,7 +3962,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.delete" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.details" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3996,7 +3996,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-44">44</a></span>
 <span class="normal"><a href="#__codelineno-0-45">45</a></span>
 <span class="normal"><a href="#__codelineno-0-46">46</a></span>
@@ -4019,7 +4019,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.execute" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.execute" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">execute</span><span class="p">()</span></code>
 
 </h3>
@@ -4053,7 +4053,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.execute" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-79">79</a></span>
 <span class="normal"><a href="#__codelineno-0-80">80</a></span>
 <span class="normal"><a href="#__codelineno-0-81">81</a></span>
@@ -4078,7 +4078,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.execute" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.execute_partial" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.execute_partial" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">execute_partial</span><span class="p">(</span><span class="n">subset</span><span class="p">)</span></code>
 
 </h3>
@@ -4143,7 +4143,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.execute_partial" class="doc doc-heading"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-88">88</a></span>
 <span class="normal"><a href="#__codelineno-0-89">89</a></span>
 <span class="normal"><a href="#__codelineno-0-90">90</a></span>
@@ -4174,7 +4174,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.execute_partial" class="doc doc-heading"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.health_check" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4208,7 +4208,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.health_check" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-52">52</a></span>
 <span class="normal"><a href="#__codelineno-0-53">53</a></span>
 <span class="normal"><a href="#__codelineno-0-54">54</a></span>
@@ -4231,7 +4231,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.health_check" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.report_settable_fields" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.report_settable_fields" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">report_settable_fields</span><span class="p">(</span><span class="n">pipeline</span><span class="o">=</span><span class="p">{},</span> <span class="n">path</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -4245,7 +4245,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.report_settable_fields" class="doc doc-h
 path (str): Current path to the key, used for nested dictionaries.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-100">100</a></span>
 <span class="normal"><a href="#__codelineno-0-101">101</a></span>
 <span class="normal"><a href="#__codelineno-0-102">102</a></span>
@@ -4294,7 +4294,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.report_settable_fields" class="doc doc-h
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.set_input" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.set_input" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_input</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">artifact</span><span class="o">=</span><span class="s1">&#39;data_source&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -4352,7 +4352,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.set_input" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-60">60</a></span>
 <span class="normal"><a href="#__codelineno-0-61">61</a></span>
 <span class="normal"><a href="#__codelineno-0-62">62</a></span>
@@ -4377,7 +4377,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.set_input" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.set_training_holdouts" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.set_training_holdouts" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_training_holdouts</span><span class="p">(</span><span class="n">id_column</span><span class="p">,</span> <span class="n">holdout_ids</span><span class="p">)</span></code>
 
 </h3>
@@ -4435,7 +4435,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.set_training_holdouts" class="doc doc-he
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-69">69</a></span>
 <span class="normal"><a href="#__codelineno-0-70">70</a></span>
 <span class="normal"><a href="#__codelineno-0-71">71</a></span>
@@ -4462,7 +4462,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.set_training_holdouts" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.api.pipeline.Pipeline.summary" class="doc doc-heading">
+<h3 id="workbench.api.pipeline.Pipeline.summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4496,7 +4496,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.summary" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/api/pipeline.py</code></summary>
+              <summary>Source code in <code>src/workbench/api/pipeline.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-36">36</a></span>
 <span class="normal"><a href="#__codelineno-0-37">37</a></span>
 <span class="normal"><a href="#__codelineno-0-38">38</a></span>
@@ -4533,8 +4533,8 @@ <h3 id="sageworks.api.pipeline.Pipeline.summary" class="doc doc-heading">
 
 </div><h2 id="examples">Examples</h2>
 <p><strong>Make a Pipeline</strong></p>
-<p>Pipelines are just JSON files (see <code>sageworks/examples/pipelines/</code>). You can copy one and make changes to fit your objects/use case, or if you have a set of SageWorks artifacts created you can 'backtrack' from the Endpoint and have it create the Pipeline for you.</p>
-<div class="language-py highlight"><span class="filename">pipeline_manager.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.pipeline_manager</span> <span class="kn">import</span> <span class="n">PipelineManager</span>
+<p>Pipelines are just JSON files (see <code>workbench/examples/pipelines/</code>). You can copy one and make changes to fit your objects/use case, or if you have a set of Workbench artifacts created you can 'backtrack' from the Endpoint and have it create the Pipeline for you.</p>
+<div class="language-py highlight"><span class="filename">pipeline_manager.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.api.pipeline_manager</span> <span class="kn">import</span> <span class="n">PipelineManager</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a> <span class="c1"># Create a PipelineManager</span>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="n">my_manager</span> <span class="o">=</span> <span class="n">PipelineManager</span><span class="p">()</span>
@@ -4555,7 +4555,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.summary" class="doc doc-heading">
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a>  <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="mi">445</span><span class="p">}]</span>
 </span></code></pre></div>
 <strong>Pipeline Details</strong></p>
-<div class="language-py highlight"><span class="filename">pipeline_details.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
+<div class="language-py highlight"><span class="filename">pipeline_details.py</span><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">workbench.api.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
 </span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>
 </span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="c1"># Retrieve an existing Pipeline</span>
 </span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="n">my_pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="s2">&quot;abalone_pipeline_v1&quot;</span><span class="p">)</span>
@@ -4571,7 +4571,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.summary" class="doc doc-heading">
 </span><span id="__span-4-7"><a id="__codelineno-4-7" name="__codelineno-4-7" href="#__codelineno-4-7"></a><span class="w">            </span><span class="nt">&quot;tags&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
 </span><span id="__span-4-8"><a id="__codelineno-4-8" name="__codelineno-4-8" href="#__codelineno-4-8"></a><span class="w">                </span><span class="s2">&quot;abalone_data&quot;</span>
 </span><span id="__span-4-9"><a id="__codelineno-4-9" name="__codelineno-4-9" href="#__codelineno-4-9"></a><span class="w">            </span><span class="p">],</span>
-</span><span id="__span-4-10"><a id="__codelineno-4-10" name="__codelineno-4-10" href="#__codelineno-4-10"></a><span class="w">            </span><span class="nt">&quot;input&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;/Users/briford/work/sageworks/data/abalone.csv&quot;</span>
+</span><span id="__span-4-10"><a id="__codelineno-4-10" name="__codelineno-4-10" href="#__codelineno-4-10"></a><span class="w">            </span><span class="nt">&quot;input&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;/Users/briford/work/workbench/data/abalone.csv&quot;</span>
 </span><span id="__span-4-11"><a id="__codelineno-4-11" name="__codelineno-4-11" href="#__codelineno-4-11"></a><span class="w">        </span><span class="p">},</span>
 </span><span id="__span-4-12"><a id="__codelineno-4-12" name="__codelineno-4-12" href="#__codelineno-4-12"></a><span class="w">        </span><span class="nt">&quot;feature_set&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
 </span><span id="__span-4-13"><a id="__codelineno-4-13" name="__codelineno-4-13" href="#__codelineno-4-13"></a><span class="w">            </span><span class="nt">&quot;name&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;abalone_features&quot;</span><span class="p">,</span>
@@ -4597,7 +4597,7 @@ <h3 id="sageworks.api.pipeline.Pipeline.summary" class="doc doc-heading">
 <p class="admonition-title">Pipeline Execution</p>
 <p>Executing the Pipeline is obviously the most important reason for creating one. If gives you a reproducible way to capture, inspect, and run the same ML pipeline on different data (nightly).</p>
 </div>
-<div class="language-py highlight"><span class="filename">pipeline_execution.py</span><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
+<div class="language-py highlight"><span class="filename">pipeline_execution.py</span><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">workbench.api.pipeline</span> <span class="kn">import</span> <span class="n">Pipeline</span>
 </span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>
 </span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a><span class="c1"># Retrieve an existing Pipeline</span>
 </span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="n">my_pipeline</span> <span class="o">=</span> <span class="n">Pipeline</span><span class="p">(</span><span class="s2">&quot;abalone_pipeline_v1&quot;</span><span class="p">)</span>
@@ -4628,10 +4628,10 @@ <h2 id="pipelines-advanced">Pipelines Advanced</h2>
 </span><span id="__span-6-15"><a id="__codelineno-6-15" name="__codelineno-6-15" href="#__codelineno-6-15"></a>        &quot;&quot;&quot;
 </span><span id="__span-6-16"><a id="__codelineno-6-16" name="__codelineno-6-16" href="#__codelineno-6-16"></a>        self.pipeline[&quot;feature_set&quot;][&quot;hold_out_ids&quot;] = id_list
 </span></code></pre></div>
-<p>Running a pipeline creates and deploys a set of SageWorks Artifacts, DataSource, FeatureSet, Model and Endpoint. These artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the SageWorks Dashboard UI.</p>
+<p>Running a pipeline creates and deploys a set of Workbench Artifacts, DataSource, FeatureSet, Model and Endpoint. These artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the Workbench Dashboard UI.</p>
 <div class="admonition note">
 <p class="admonition-title">Not Finding a particular method?</p>
-<p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">SageWorks Core Classes</a></p>
+<p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: <a href="../../core_classes/overview/">Workbench Core Classes</a></p>
 </div>
 
 
diff --git a/api_classes/views/index.html b/api_classes/views/index.html
index c1c14774c..fb264e13f 100644
--- a/api_classes/views/index.html
+++ b/api_classes/views/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Views - SageWorks</title>
+        <title>Views - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
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           <span class="md-ellipsis">
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+            Workbench
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   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
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-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
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@@ -518,7 +518,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.view" class="md-nav__link">
+  <a href="#workbench.core.views.view" class="md-nav__link">
     <span class="md-ellipsis">
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     </span>
@@ -527,7 +527,7 @@
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+  <a href="#workbench.core.views.view.View" class="md-nav__link">
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-  <a href="#sageworks.core.views.view.View.table" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.table" class="md-nav__link">
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       table
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@@ -546,7 +546,7 @@
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+  <a href="#workbench.core.views.view.View.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -555,7 +555,7 @@
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-  <a href="#sageworks.core.views.view.View.__repr__" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.__repr__" class="md-nav__link">
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       __repr__
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@@ -564,7 +564,7 @@
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-  <a href="#sageworks.core.views.view.View.column_details" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.column_details" class="md-nav__link">
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       column_details
     </span>
@@ -573,7 +573,7 @@
 </li>
         
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-  <a href="#sageworks.core.views.view.View.delete" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
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@@ -582,7 +582,7 @@
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+  <a href="#workbench.core.views.view.View.ensure_exists" class="md-nav__link">
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       ensure_exists
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@@ -591,7 +591,7 @@
 </li>
         
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+  <a href="#workbench.core.views.view.View.exists" class="md-nav__link">
     <span class="md-ellipsis">
       exists
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@@ -600,7 +600,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.view.View.pull_dataframe" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.pull_dataframe" class="md-nav__link">
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       pull_dataframe
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@@ -609,7 +609,7 @@
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+  <a href="#workbench.core.views.view.View.query" class="md-nav__link">
     <span class="md-ellipsis">
       query
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@@ -1973,7 +1973,7 @@
             
   
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-    SageWorks Enterprise
+    Workbench Enterprise
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@@ -1983,7 +1983,7 @@
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             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3202,11 +3202,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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@@ -3258,7 +3258,7 @@
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+  <a href="#workbench.core.views.view" class="md-nav__link">
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+  <a href="#workbench.core.views.view.View" class="md-nav__link">
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+  <a href="#workbench.core.views.view.View.table" class="md-nav__link">
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+  <a href="#workbench.core.views.view.View.__repr__" class="md-nav__link">
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+  <a href="#workbench.core.views.view.View.column_details" class="md-nav__link">
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-  <a href="#sageworks.core.views.view.View.exists" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.exists" class="md-nav__link">
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       exists
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@@ -3340,7 +3340,7 @@
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-  <a href="#sageworks.core.views.view.View.pull_dataframe" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.pull_dataframe" class="md-nav__link">
     <span class="md-ellipsis">
       pull_dataframe
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@@ -3349,7 +3349,7 @@
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-  <a href="#sageworks.core.views.view.View.query" class="md-nav__link">
+  <a href="#workbench.core.views.view.View.query" class="md-nav__link">
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       query
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@@ -3389,16 +3389,16 @@
 <h1 id="views">Views</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">View Examples</p>
-<p>Examples of using the Views classes to extend the functionality of SageWorks Artifacts are in the <a href="#examples">Examples</a> section at the bottom of this page. </p>
+<p>Examples of using the Views classes to extend the functionality of Workbench Artifacts are in the <a href="#examples">Examples</a> section at the bottom of this page. </p>
 </div>
-<p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing SageWorks Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p>
+<p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing Workbench Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p>
 
 
 <div class="doc doc-object doc-module">
 
 
 
-<a id="sageworks.core.views.view"></a>
+<a id="workbench.core.views.view"></a>
     <div class="doc doc-contents first">
 
         <p>View: Read from a view (training, display, etc) for DataSources and FeatureSets.</p>
@@ -3423,7 +3423,7 @@ <h1 id="views">Views</h1>
 
 
 
-<h2 id="sageworks.core.views.view.View" class="doc doc-heading">
+<h2 id="workbench.core.views.view.View" class="doc doc-heading">
             <code>View</code>
 
 
@@ -3463,7 +3463,7 @@ <h2 id="sageworks.core.views.view.View" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-15"> 15</a></span>
 <span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
 <span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
@@ -3752,7 +3752,7 @@ <h2 id="sageworks.core.views.view.View" class="doc doc-heading">
 </span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>    <span class="c1"># Class attributes</span>
-</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="n">meta</span> <span class="o">=</span> <span class="n">Meta</span><span class="p">()</span>
 </span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>
 </span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">artifact</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">DataSource</span><span class="p">,</span> <span class="n">FeatureSet</span><span class="p">],</span> <span class="n">view_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
@@ -3940,7 +3940,7 @@ <h2 id="sageworks.core.views.view.View" class="doc doc-heading">
 </span><span id="__span-0-227"><a id="__codelineno-0-227" name="__codelineno-0-227"></a><span class="sd">        Raises:</span>
 </span><span id="__span-0-228"><a id="__codelineno-0-228" name="__codelineno-0-228"></a><span class="sd">            ValueError: If the view type is not supported</span>
 </span><span id="__span-0-229"><a id="__codelineno-0-229" name="__codelineno-0-229"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-230"><a id="__codelineno-0-230" name="__codelineno-0-230"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span><span class="p">,</span> <span class="n">ComputationView</span><span class="p">,</span> <span class="n">TrainingView</span>
+</span><span id="__span-0-230"><a id="__codelineno-0-230" name="__codelineno-0-230"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span><span class="p">,</span> <span class="n">ComputationView</span><span class="p">,</span> <span class="n">TrainingView</span>
 </span><span id="__span-0-231"><a id="__codelineno-0-231" name="__codelineno-0-231"></a>
 </span><span id="__span-0-232"><a id="__codelineno-0-232" name="__codelineno-0-232"></a>        <span class="c1"># First if we&#39;re going to auto-create, we need to make sure the data source exists</span>
 </span><span id="__span-0-233"><a id="__codelineno-0-233" name="__codelineno-0-233"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
@@ -4004,7 +4004,7 @@ <h2 id="sageworks.core.views.view.View" class="doc doc-heading">
 
 
 
-<h3 id="sageworks.core.views.view.View.table" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.table" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">table</span><span class="p">:</span> <span class="nb">str</span></code>
 
   <span class="doc doc-labels">
@@ -4049,7 +4049,7 @@ <h3 id="sageworks.core.views.view.View.table" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.__init__" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">artifact</span><span class="p">,</span> <span class="n">view_name</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4076,7 +4076,7 @@ <h3 id="sageworks.core.views.view.View.__init__" class="doc doc-heading">
                 <code>artifact</code>
             </td>
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.api.DataSource" href="../data_source/#sageworks.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="sageworks.api.FeatureSet" href="../feature_set/#sageworks.api.feature_set.FeatureSet">FeatureSet</a>]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.api.DataSource" href="../data_source/#workbench.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="workbench.api.FeatureSet" href="../feature_set/#workbench.api.feature_set.FeatureSet">FeatureSet</a>]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -4107,7 +4107,7 @@ <h3 id="sageworks.core.views.view.View.__init__" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-45">45</a></span>
 <span class="normal"><a href="#__codelineno-0-46">46</a></span>
 <span class="normal"><a href="#__codelineno-0-47">47</a></span>
@@ -4208,7 +4208,7 @@ <h3 id="sageworks.core.views.view.View.__init__" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.__repr__" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.__repr__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__repr__</span><span class="p">()</span></code>
 
 </h3>
@@ -4219,7 +4219,7 @@ <h3 id="sageworks.core.views.view.View.__repr__" class="doc doc-heading">
         <p>Return a string representation of this object</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-264">264</a></span>
 <span class="normal"><a href="#__codelineno-0-265">265</a></span>
 <span class="normal"><a href="#__codelineno-0-266">266</a></span>
@@ -4254,7 +4254,7 @@ <h3 id="sageworks.core.views.view.View.__repr__" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.column_details" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.column_details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_details</span><span class="p">()</span></code>
 
 </h3>
@@ -4288,7 +4288,7 @@ <h3 id="sageworks.core.views.view.View.column_details" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-121">121</a></span>
 <span class="normal"><a href="#__codelineno-0-122">122</a></span>
 <span class="normal"><a href="#__codelineno-0-123">123</a></span>
@@ -4311,7 +4311,7 @@ <h3 id="sageworks.core.views.view.View.column_details" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.delete" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">()</span></code>
 
 </h3>
@@ -4322,7 +4322,7 @@ <h3 id="sageworks.core.views.view.View.delete" class="doc doc-heading">
         <p>Delete the database view (and supplemental data) if it exists.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-142">142</a></span>
 <span class="normal"><a href="#__codelineno-0-143">143</a></span>
 <span class="normal"><a href="#__codelineno-0-144">144</a></span>
@@ -4383,7 +4383,7 @@ <h3 id="sageworks.core.views.view.View.delete" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.ensure_exists" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.ensure_exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">ensure_exists</span><span class="p">()</span></code>
 
 </h3>
@@ -4394,7 +4394,7 @@ <h3 id="sageworks.core.views.view.View.ensure_exists" class="doc doc-heading">
         <p>Ensure if the view exists by making a query directly to the database. If it doesn't exist, create it</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-195">195</a></span>
 <span class="normal"><a href="#__codelineno-0-196">196</a></span>
 <span class="normal"><a href="#__codelineno-0-197">197</a></span>
@@ -4423,7 +4423,7 @@ <h3 id="sageworks.core.views.view.View.ensure_exists" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.exists" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">exists</span><span class="p">(</span><span class="n">skip_cache</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4467,7 +4467,7 @@ <h3 id="sageworks.core.views.view.View.exists" class="doc doc-heading">
     bool: True if the view exists, False otherwise.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-169">169</a></span>
 <span class="normal"><a href="#__codelineno-0-170">170</a></span>
 <span class="normal"><a href="#__codelineno-0-171">171</a></span>
@@ -4526,7 +4526,7 @@ <h3 id="sageworks.core.views.view.View.exists" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.pull_dataframe" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.pull_dataframe" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pull_dataframe</span><span class="p">(</span><span class="n">limit</span><span class="o">=</span><span class="mi">50000</span><span class="p">,</span> <span class="n">head</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4607,7 +4607,7 @@ <h3 id="sageworks.core.views.view.View.pull_dataframe" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-92"> 92</a></span>
 <span class="normal"><a href="#__codelineno-0-93"> 93</a></span>
 <span class="normal"><a href="#__codelineno-0-94"> 94</a></span>
@@ -4650,7 +4650,7 @@ <h3 id="sageworks.core.views.view.View.pull_dataframe" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.view.View.query" class="doc doc-heading">
+<h3 id="workbench.core.views.view.View.query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span></code>
 
 </h3>
@@ -4715,7 +4715,7 @@ <h3 id="sageworks.core.views.view.View.query" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-110">110</a></span>
 <span class="normal"><a href="#__codelineno-0-111">111</a></span>
 <span class="normal"><a href="#__codelineno-0-112">112</a></span>
@@ -4757,9 +4757,9 @@ <h3 id="sageworks.core.views.view.View.query" class="doc doc-heading">
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Listing Views</strong></p>
-<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Convert the Data Source to a Feature Set</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">test_data</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="s1">&#39;test_data&#39;</span><span class="p">)</span>
@@ -4767,7 +4767,7 @@ <h3 id="sageworks.core.views.view.View.query" class="doc doc-heading">
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="p">[</span><span class="s2">&quot;display&quot;</span><span class="p">,</span> <span class="s2">&quot;training&quot;</span><span class="p">,</span> <span class="s2">&quot;computation&quot;</span><span class="p">]</span>
 </span></code></pre></div>
 <p><strong>Getting a Particular View</strong></p>
-<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="n">fs</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="s1">&#39;test_features&#39;</span><span class="p">)</span>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a>
@@ -4782,8 +4782,8 @@ <h3 id="sageworks.core.views.view.View.query" class="doc doc-heading">
 </span><span id="__span-1-13"><a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a><span class="mi">0</span>   <span class="mi">58</span>  <span class="n">Person</span> <span class="mi">58</span>  <span class="mf">71.781227</span>  <span class="mf">275.088196</span>  <span class="mf">162053.140625</span>  
 </span></code></pre></div>
 <p><strong>View Queries</strong></p>
-<p>All SageWorks Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p>
-<div class="language-py highlight"><span class="filename">view_query.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<p>All Workbench Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p>
+<div class="language-py highlight"><span class="filename">view_query.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="c1"># Grab a FeatureSet View</span>
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="n">fs</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="s2">&quot;abalone_features&quot;</span><span class="p">)</span>
@@ -4807,9 +4807,9 @@ <h3 id="sageworks.core.views.view.View.query" class="doc doc-heading">
 </span></code></pre></div>
 <div class="admonition note">
 <p class="admonition-title">Classes to construct View</p>
-<p>The SageWorks Classes used to construct viewss are currently in 'Core'. So you can check out the documentation for those classes here: <a href="../../core_classes/views/overview/">SageWorks View Creators</a></p>
+<p>The Workbench Classes used to construct viewss are currently in 'Core'. So you can check out the documentation for those classes here: <a href="../../core_classes/views/overview/">Workbench View Creators</a></p>
 </div>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/aws_setup/aws_access_management/index.html b/aws_setup/aws_access_management/index.html
index a87f25c47..c8c6e47ab 100644
--- a/aws_setup/aws_access_management/index.html
+++ b/aws_setup/aws_access_management/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS Access Management - SageWorks</title>
+        <title>AWS Access Management - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3106,11 +3106,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3197,11 +3197,11 @@
 <h1 id="aws-acesss-management">AWS Acesss Management</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need AWS Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>This page gives an overview of how SageWorks sets up roles and policies in a granular way that provides 'least priviledge' and also provides a unified framework for AWS access management.</p>
+<p>This page gives an overview of how Workbench sets up roles and policies in a granular way that provides 'least priviledge' and also provides a unified framework for AWS access management.</p>
 <h2 id="conceptual-slide-deck">Conceptual Slide Deck</h2>
-<p><a href="https://docs.google.com/presentation/d/1_KwbaBsyBoiWW_8SEallHg8RMsi9FdK10dr2wwzo3CA/edit?usp=sharing">SageWorks AWS Acesss Management</a></p>
+<p><a href="https://docs.google.com/presentation/d/1_KwbaBsyBoiWW_8SEallHg8RMsi9FdK10dr2wwzo3CA/edit?usp=sharing">Workbench AWS Acesss Management</a></p>
 <h2 id="aws-resources">AWS Resources</h2>
 <ul>
 <li><a href="https://docs.aws.amazon.com/singlesignon/latest/userguide/what-is.html">AWS Identity Center</a></li>
diff --git a/aws_setup/aws_client_vpn/index.html b/aws_setup/aws_client_vpn/index.html
index a96c63002..0d584cdb7 100644
--- a/aws_setup/aws_client_vpn/index.html
+++ b/aws_setup/aws_client_vpn/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS Client VPN - SageWorks</title>
+        <title>AWS Client VPN - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3160,11 +3160,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
diff --git a/aws_setup/aws_setup/index.html b/aws_setup/aws_setup/index.html
index 5296a7e8e..294a7d00b 100644
--- a/aws_setup/aws_setup/index.html
+++ b/aws_setup/aws_setup/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS CLI/SSO Setup - SageWorks</title>
+        <title>AWS CLI/SSO Setup - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3151,11 +3151,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3287,7 +3287,7 @@
 <h1 id="aws-setup">AWS Setup</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need AWS Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
 <h2 id="get-some-information">Get some information</h2>
 <ul>
@@ -3351,10 +3351,10 @@ <h2 id="testing-your-new-aws-profile">Testing your new AWS Profile</h2>
 </span><span id="__span-7-3"><a id="__codelineno-7-3" name="__codelineno-7-3" href="#__codelineno-7-3"></a>2023-03-20 20:06:53 aws-athena-query-results-XXXYYY-us-west-2
 </span><span id="__span-7-4"><a id="__codelineno-7-4" name="__codelineno-7-4" href="#__codelineno-7-4"></a>2023-03-30 13:22:28 sagemaker-studio-XXXYYY-dbgyvq8ruka
 </span><span id="__span-7-5"><a id="__codelineno-7-5" name="__codelineno-7-5" href="#__codelineno-7-5"></a>2023-03-24 22:05:55 sagemaker-us-west-2-XXXYYY
-</span><span id="__span-7-6"><a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a>2023-04-30 13:43:29 scp-sageworks-artifacts
+</span><span id="__span-7-6"><a id="__codelineno-7-6" name="__codelineno-7-6" href="#__codelineno-7-6"></a>2023-04-30 13:43:29 scp-workbench-artifacts
 </span></code></pre></div>
 <h2 id="back-to-initial-setup">Back to Initial Setup</h2>
-<p>If you're doing the initial setup of SageWorks you should now go back and finish that process: <a href="../../getting_started/">Getting Started</a></p>
+<p>If you're doing the initial setup of Workbench you should now go back and finish that process: <a href="../../getting_started/">Getting Started</a></p>
 <h2 id="aws-resources">AWS Resources</h2>
 <ul>
 <li><a href="https://docs.aws.amazon.com/singlesignon/latest/userguide/what-is.html">AWS Identity Center</a></li>
diff --git a/aws_setup/aws_tips_and_tricks/index.html b/aws_setup/aws_tips_and_tricks/index.html
index f74e5d87c..2531701e0 100644
--- a/aws_setup/aws_tips_and_tricks/index.html
+++ b/aws_setup/aws_tips_and_tricks/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS Tips and Tricks - SageWorks</title>
+        <title>AWS Tips and Tricks - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3106,11 +3106,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3197,9 +3197,9 @@
 <h1 id="aws-tips-and-tricks">AWS Tips and Tricks</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need AWS Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>This page tries to give helpful guidance when setting up AWS Accounts, Users, and Groups. In general AWS can be a bit tricky to set up the first time. Feel free to use any material in this guide but we're more than happy to help clients get their AWS Setup ready to go for FREE. Below are some guides for setting up a new AWS account for SageWorks and also setting up SSO Users and Groups within AWS.</p>
+<p>This page tries to give helpful guidance when setting up AWS Accounts, Users, and Groups. In general AWS can be a bit tricky to set up the first time. Feel free to use any material in this guide but we're more than happy to help clients get their AWS Setup ready to go for FREE. Below are some guides for setting up a new AWS account for Workbench and also setting up SSO Users and Groups within AWS.</p>
 <h2 id="new-aws-account-with-aws-organizations-easy">New AWS Account (with AWS Organizations: easy)</h2>
 <ul>
 <li>If you already have an AWS Account you can activate the AWS Identity Center/Organization functionality.</li>
@@ -3247,7 +3247,7 @@ <h3 id="setting-up-groups">Setting up Groups</h3>
 <li>Add Policy: arn:aws:iam::aws:policy/job-function/AdministratorAccess</li>
 </ul>
 <p>See: <a href="https://docs.aws.amazon.com/singlesignon/latest/userguide/permissionsetsconcept.html">Permission Sets</a> for more details and instructions.</p>
-<p>Another benefit of creating groups is that you can include that group in 'Trust Policy (assume_role)' for the SageWorks-ExecutionRole (this gets deployed as part of the SageWorks AWS Stack). This means that the management of what SageWorks can do/see/read/write is completely done through the SageWorks-ExecutionRole.</p>
+<p>Another benefit of creating groups is that you can include that group in 'Trust Policy (assume_role)' for the Workbench-ExecutionRole (this gets deployed as part of the Workbench AWS Stack). This means that the management of what Workbench can do/see/read/write is completely done through the Workbench-ExecutionRole.</p>
 <h2 id="back-to-adding-user">Back to Adding User</h2>
 <p>Okay now that we have our groups set up we can go back to our original goal of adding a user. So here's the second panel with the groups and now we can hit 'Next'</p>
 <p><img width="800" alt="Screenshot 2023-05-03 at 9 31 49 AM" src="https://user-images.githubusercontent.com/4806709/235965818-fa44bb58-6e58-49df-93df-ba582148b3f4.png"></p>
diff --git a/aws_setup/core_stack/index.html b/aws_setup/core_stack/index.html
index 0545e51d0..923780b6f 100644
--- a/aws_setup/core_stack/index.html
+++ b/aws_setup/core_stack/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS Admin Initial Setup - SageWorks</title>
+        <title>AWS Admin Initial Setup - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-aws-stack" class="md-skip">
+        <a href="#workbench-aws-stack" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -2134,9 +2134,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#two-main-options-when-using-sageworks" class="md-nav__link">
+  <a href="#two-main-options-when-using-workbench" class="md-nav__link">
     <span class="md-ellipsis">
-      Two main options when using SageWorks
+      Two main options when using Workbench
     </span>
   </a>
   
@@ -2152,39 +2152,54 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#onboarding-sageworks-to-your-aws-account" class="md-nav__link">
+  <a href="#onboarding-workbench-to-your-aws-account" class="md-nav__link">
     <span class="md-ellipsis">
-      Onboarding SageWorks to your AWS Account
+      Onboarding Workbench to your AWS Account
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-uses-aws-python-cdk-for-deployments" class="md-nav__link">
+  <a href="#workbench-uses-aws-python-cdk-for-deployments" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks uses AWS Python CDK for Deployments
+      Workbench uses AWS Python CDK for Deployments
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#create-an-s3-bucket-for-sageworks" class="md-nav__link">
+  <a href="#create-an-s3-bucket-for-workbench" class="md-nav__link">
     <span class="md-ellipsis">
-      Create an S3 Bucket for SageWorks
+      Create an S3 Bucket for Workbench
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#deploying-the-sageworks-core-stack" class="md-nav__link">
+  <a href="#deploying-the-workbench-core-stack" class="md-nav__link">
     <span class="md-ellipsis">
-      Deploying the SageWorks Core Stack
+      Deploying the Workbench Core Stack
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Deploying the Workbench Core Stack">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#enable-users-to-assume-workbench-executionrole" class="md-nav__link">
+    <span class="md-ellipsis">
+      Enable Users to Assume Workbench-ExecutionRole
     </span>
   </a>
   
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
       
         <li class="md-nav__item">
@@ -3151,11 +3166,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3207,9 +3222,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#two-main-options-when-using-sageworks" class="md-nav__link">
+  <a href="#two-main-options-when-using-workbench" class="md-nav__link">
     <span class="md-ellipsis">
-      Two main options when using SageWorks
+      Two main options when using Workbench
     </span>
   </a>
   
@@ -3225,39 +3240,54 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#onboarding-sageworks-to-your-aws-account" class="md-nav__link">
+  <a href="#onboarding-workbench-to-your-aws-account" class="md-nav__link">
     <span class="md-ellipsis">
-      Onboarding SageWorks to your AWS Account
+      Onboarding Workbench to your AWS Account
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-uses-aws-python-cdk-for-deployments" class="md-nav__link">
+  <a href="#workbench-uses-aws-python-cdk-for-deployments" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks uses AWS Python CDK for Deployments
+      Workbench uses AWS Python CDK for Deployments
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#create-an-s3-bucket-for-sageworks" class="md-nav__link">
+  <a href="#create-an-s3-bucket-for-workbench" class="md-nav__link">
     <span class="md-ellipsis">
-      Create an S3 Bucket for SageWorks
+      Create an S3 Bucket for Workbench
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#deploying-the-sageworks-core-stack" class="md-nav__link">
+  <a href="#deploying-the-workbench-core-stack" class="md-nav__link">
     <span class="md-ellipsis">
-      Deploying the SageWorks Core Stack
+      Deploying the Workbench Core Stack
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Deploying the Workbench Core Stack">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#enable-users-to-assume-workbench-executionrole" class="md-nav__link">
+    <span class="md-ellipsis">
+      Enable Users to Assume Workbench-ExecutionRole
     </span>
   </a>
   
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
       
         <li class="md-nav__item">
@@ -3284,29 +3314,29 @@
                   
 
 
-<h1 id="sageworks-aws-stack">SageWorks AWS Stack</h1>
-<p>Welcome to the SageWorks AWS Setup Guide. SageWorks is deployed as an AWS <strong>Stack</strong> following the well architected system practices of AWS. </p>
+<h1 id="workbench-aws-stack">Workbench AWS Stack</h1>
+<p>Welcome to the Workbench AWS Setup Guide. Workbench is deployed as an AWS <strong>Stack</strong> following the well architected system practices of AWS. </p>
 <div class="admonition warning">
 <p class="admonition-title">AWS Setup can be a bit complex</p>
-<p>Setting up SageWorks with AWS can be a bit complex, but this only needs to be done ONCE for your entire company. The install uses standard CDK --&gt; AWS Stacks and SageWorks tries to make it straight forward. If you have any troubles at all feel free to contact us a <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> and we're happy to help you with AWS for FREE.</p>
+<p>Setting up Workbench with AWS can be a bit complex, but this only needs to be done ONCE for your entire company. The install uses standard CDK --&gt; AWS Stacks and Workbench tries to make it straight forward. If you have any troubles at all feel free to contact us a <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> and we're happy to help you with AWS for FREE.</p>
 </div>
-<h2 id="two-main-options-when-using-sageworks">Two main options when using SageWorks</h2>
+<h2 id="two-main-options-when-using-workbench">Two main options when using Workbench</h2>
 <ol>
-<li>Spin up a new AWS Account for the SageWorks Stacks (<a href="../aws_tips_and_tricks/">Make a New Account</a>)</li>
-<li>Deploy SageWorks Stacks into your existing AWS Account</li>
+<li>Spin up a new AWS Account for the Workbench Stacks (<a href="../aws_tips_and_tricks/">Make a New Account</a>)</li>
+<li>Deploy Workbench Stacks into your existing AWS Account</li>
 </ol>
 <p>Either of these options are fully supported, but we highly suggest a NEW account as it gives the following benefits:</p>
 <ul>
 <li><strong>AWS Data Isolation:</strong> Data Scientists will feel empowered to play in the sandbox without impacting production services.</li>
-<li><strong>AWS Cost Accounting:</strong> Monitor and Track all those new ML Pipelines that your team creates with SageWorks :)</li>
+<li><strong>AWS Cost Accounting:</strong> Monitor and Track all those new ML Pipelines that your team creates with Workbench :)</li>
 </ul>
 <h2 id="setting-up-users-and-groups">Setting up Users and Groups</h2>
 <p>If your AWS Account already has users and groups set up you can skip this but here's our recommendations on setting up <a href="../aws_tips_and_tricks/">SSO Users and Groups</a></p>
-<h2 id="onboarding-sageworks-to-your-aws-account">Onboarding SageWorks to your AWS Account</h2>
-<p>Pulling down the SageWorks Repo
-  <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>git clone https://github.com/SuperCowPowers/sageworks.git
+<h2 id="onboarding-workbench-to-your-aws-account">Onboarding Workbench to your AWS Account</h2>
+<p>Pulling down the Workbench Repo
+  <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>git clone https://github.com/SuperCowPowers/workbench.git
 </span></code></pre></div></p>
-<h2 id="sageworks-uses-aws-python-cdk-for-deployments">SageWorks uses AWS Python CDK for Deployments</h2>
+<h2 id="workbench-uses-aws-python-cdk-for-deployments">Workbench uses AWS Python CDK for Deployments</h2>
 <p>If you don't have AWS CDK already installed you can do these steps:</p>
 <p>Mac</p>
 <p><div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>brew install node 
@@ -3317,36 +3347,45 @@ <h2 id="sageworks-uses-aws-python-cdk-for-deployments">SageWorks uses AWS Python
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>sudo npm install -g aws-cdk
 </span></code></pre></div>
 For more information on Linux installs see <a href="https://www.digitalocean.com/community/tutorials/how-to-install-node-js-on-ubuntu-20-04">Digital Ocean NodeJS</a></p>
-<h2 id="create-an-s3-bucket-for-sageworks">Create an S3 Bucket for SageWorks</h2>
-<p>SageWorks pushes and pulls data from AWS, it will use this S3 Bucket for storage and processing. You should create a <strong>NEW</strong> S3 Bucket, we suggest a name like <code>&lt;company_name&gt;-sageworks</code></p>
-<h2 id="deploying-the-sageworks-core-stack">Deploying the SageWorks Core Stack</h2>
-<p>Do the initial setup/config here: <a href="../../getting_started/">Getting Started</a>. After you've done that come back to this section. For Stack Deployment additional things need to be added to your config file. The config file will be located in your home directory <code>~/.sageworks/sageworks_config.json</code>. Edit this file and add addition stuff for the deployment. Specifically there are two additional fields to be added (optional for both)</p>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>&quot;SAGEWORKS_SSO_GROUP&quot;: DataScientist (or whatever)
-</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>&quot;SAGEWORKS_ADDITIONAL_BUCKETS&quot;: &quot;bucket1, bucket2
+<h2 id="create-an-s3-bucket-for-workbench">Create an S3 Bucket for Workbench</h2>
+<p>Workbench pushes and pulls data from AWS, it will use this S3 Bucket for storage and processing. You should create a <strong>NEW</strong> S3 Bucket, we suggest a name like <code>&lt;company-name&gt;-workbench</code></p>
+<h2 id="deploying-the-workbench-core-stack">Deploying the Workbench Core Stack</h2>
+<p>This stack has the <code>Workbench-Execution-Role</code> and an associated role for AWS Glue Jobs.</p>
+<p>You'll need to set some environmental vars before deploying the stack.</p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>export WORKBENCH_BUCKET=name-of-workbench-bucket
+</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>export WORKBENCH_SSO_GROUP=DataScientist (or whatever)
+</span></code></pre></div>
+<p><strong>Optional ENV Vars</strong></p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a>export WORKBENCH_ADDITIONAL_BUCKETS=&lt;comma separated list of buckets&gt;
 </span></code></pre></div>
-These are optional but are set/used by most SageWorks users.</p>
 <div class="admonition note">
 <p class="admonition-title">AWS Stuff</p>
-<p>Activate your AWS Account that's used for SageWorks deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p>
+<p>Activate your AWS Account that's used for Workbench deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p>
 </div>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-4-1"><a id="__codelineno-4-1" name="__codelineno-4-1" href="#__codelineno-4-1"></a><span class="nb">cd</span><span class="w"> </span>sageworks/aws_setup/sageworks_core
-</span><span id="__span-4-2"><a id="__codelineno-4-2" name="__codelineno-4-2" href="#__codelineno-4-2"></a>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
-</span><span id="__span-4-3"><a id="__codelineno-4-3" name="__codelineno-4-3" href="#__codelineno-4-3"></a>cdk<span class="w"> </span>bootstrap
-</span><span id="__span-4-4"><a id="__codelineno-4-4" name="__codelineno-4-4" href="#__codelineno-4-4"></a>cdk<span class="w"> </span>deploy
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="nb">cd</span><span class="w"> </span>workbench/aws_setup/workbench_core
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
+</span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>cdk<span class="w"> </span>bootstrap
+</span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a>cdk<span class="w"> </span>deploy
 </span></code></pre></div>
+<h3 id="enable-users-to-assume-workbench-executionrole">Enable Users to Assume Workbench-ExecutionRole</h3>
+<p>Now that the <code>Workbench-ExecutionRole</code> has been deployed via AWS Stack. These guides walk you through setting up access for both SSO users and IAM users to assume the Workbench-ExecutionRole in your AWS account.</p>
+<ul>
+<li><a href="../sso_assume_role/">Set up SSO Users</a></li>
+<li><a href="../iam_assume_role/">Set up IAM Users</a> (not recommend, but contact us we'll help you out)</li>
+</ul>
 <h2 id="aws-account-setup-check">AWS Account Setup Check</h2>
-<p>After setting up SageWorks config/AWS Account you can run this test/checking script. If the results ends with <code>INFO AWS Account Clamp: AOK!</code> you're in good shape. If not feel free to contact us on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> and we'll get it straightened out for you :)</p>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>pip<span class="w"> </span>install<span class="w"> </span>sageworks<span class="w"> </span><span class="o">(</span><span class="k">if</span><span class="w"> </span>not<span class="w"> </span>already<span class="w"> </span>installed<span class="o">)</span>
-</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="nb">cd</span><span class="w"> </span>sageworks/aws_setup
-</span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>python<span class="w"> </span>aws_account_check.py
-</span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a>&lt;lot<span class="w"> </span>of<span class="w"> </span>print<span class="w"> </span>outs<span class="w"> </span><span class="k">for</span><span class="w"> </span>various<span class="w"> </span>checks&gt;
-</span><span id="__span-5-5"><a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a>INFO<span class="w"> </span>AWS<span class="w"> </span>Account<span class="w"> </span>Clamp:<span class="w"> </span>AOK!
+<p>After deploying the Workbench Core Stack and setting up users to assume that Role, you can run this test/checking script. If the results ends with <code>INFO AWS Account Clamp: AOK!</code> you're in good shape. If not feel free to contact us on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> and we'll get it straightened out for you :)</p>
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-6-1"><a id="__codelineno-6-1" name="__codelineno-6-1" href="#__codelineno-6-1"></a>pip<span class="w"> </span>install<span class="w"> </span>workbench<span class="w"> </span><span class="o">(</span><span class="k">if</span><span class="w"> </span>not<span class="w"> </span>already<span class="w"> </span>installed<span class="o">)</span>
+</span><span id="__span-6-2"><a id="__codelineno-6-2" name="__codelineno-6-2" href="#__codelineno-6-2"></a><span class="nb">cd</span><span class="w"> </span>workbench/aws_setup
+</span><span id="__span-6-3"><a id="__codelineno-6-3" name="__codelineno-6-3" href="#__codelineno-6-3"></a>python<span class="w"> </span>aws_account_check.py
+</span><span id="__span-6-4"><a id="__codelineno-6-4" name="__codelineno-6-4" href="#__codelineno-6-4"></a>&lt;lot<span class="w"> </span>of<span class="w"> </span>print<span class="w"> </span>outs<span class="w"> </span><span class="k">for</span><span class="w"> </span>various<span class="w"> </span>checks&gt;
+</span><span id="__span-6-5"><a id="__codelineno-6-5" name="__codelineno-6-5" href="#__codelineno-6-5"></a>INFO<span class="w"> </span>AWS<span class="w"> </span>Account<span class="w"> </span>Clamp:<span class="w"> </span>AOK!
 </span></code></pre></div>
 <div class="admonition success">
 <p class="admonition-title">Success</p>
-<p>Congratulations: SageWorks is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install sageworks</code> and start using the API.</p>
+<p>Congratulations: Workbench is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install workbench</code> and start using the API.</p>
 </div>
-<p>If you ran into any issues with this procedure please contact us via <a href="https://discord.gg/WHAJuz8sw8">Discord</a> or email <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> and the SCP team will provide <strong>free</strong> setup and support for new SageWorks users.</p>
+<p>If you ran into any issues with this procedure please contact us via <a href="https://discord.gg/WHAJuz8sw8">Discord</a> or email <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> and the SCP team will provide <strong>free</strong> setup and support for new Workbench users.</p>
 
 
 
diff --git a/aws_setup/dashboard_stack/index.html b/aws_setup/dashboard_stack/index.html
index 51f442a62..9eae5cc4c 100644
--- a/aws_setup/dashboard_stack/index.html
+++ b/aws_setup/dashboard_stack/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Dashboard Setup - SageWorks</title>
+        <title>Dashboard Setup - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#deploy-the-sageworks-dashboard-stack" class="md-skip">
+        <a href="#deploy-the-workbench-dashboard-stack" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3121,11 +3121,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3224,20 +3224,20 @@
                   
 
 
-<h1 id="deploy-the-sageworks-dashboard-stack">Deploy the SageWorks Dashboard Stack</h1>
+<h1 id="deploy-the-workbench-dashboard-stack">Deploy the Workbench Dashboard Stack</h1>
 <figure style="float: right; width: 300px;">
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4" style="padding-left: 10px; padding-top: -20px; ">
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4" style="padding-left: 10px; padding-top: -20px; ">
 </figure>
 
 <p>Deploying the Dashboard Stack is reasonably straight forward, it's the same approach as the <a href="../core_stack/">Core Stack</a> that you've already deployed.</p>
-<p>Please review the <a href="#stack-details">Stack Details</a> section to understand all the AWS components that are included and utilized in the SageWorks Dashboard Stack.</p>
+<p>Please review the <a href="#stack-details">Stack Details</a> section to understand all the AWS components that are included and utilized in the Workbench Dashboard Stack.</p>
 <h2 id="deploying-the-dashboard-stack">Deploying the Dashboard Stack</h2>
 <div class="admonition note">
 <p class="admonition-title">AWS Stuff</p>
-<p>Activate your AWS Account that's used for SageWorks deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p>
+<p>Activate your AWS Account that's used for Workbench deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p>
 </div>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nb">cd</span><span class="w"> </span>sageworks/aws_setup/sageworks_dashboard_full
-</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="nb">export</span><span class="w"> </span><span class="nv">SAGEWORKS_CONFIG</span><span class="o">=</span>/full/path/to/config.json
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="nb">cd</span><span class="w"> </span>workbench/aws_setup/workbench_dashboard_full
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="nb">export</span><span class="w"> </span><span class="nv">WORKBENCH_CONFIG</span><span class="o">=</span>/full/path/to/config.json
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>cdk<span class="w"> </span>bootstrap
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>cdk<span class="w"> </span>deploy
@@ -3261,10 +3261,10 @@ <h3 id="aws-stack-benefits">AWS Stack Benefits</h3>
 </ol>
 <div class="admonition warning">
 <p class="admonition-title">AWS Costs</p>
-<p>Deploying the SageWorks Dashboard does incur some monthly AWS costs. If you're on a tight budget you can deploy the 'lite' version of the Dashboard Stack.</p>
+<p>Deploying the Workbench Dashboard does incur some monthly AWS costs. If you're on a tight budget you can deploy the 'lite' version of the Dashboard Stack.</p>
 </div>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="nb">cd</span><span class="w"> </span>sageworks/aws_setup/sageworks_dashboard_lite
-</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="nb">export</span><span class="w"> </span><span class="nv">SAGEWORKS_CONFIG</span><span class="o">=</span>/full/path/to/config.json
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="nb">cd</span><span class="w"> </span>workbench/aws_setup/workbench_dashboard_lite
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="nb">export</span><span class="w"> </span><span class="nv">WORKBENCH_CONFIG</span><span class="o">=</span>/full/path/to/config.json
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>requirements.txt
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a>cdk<span class="w"> </span>bootstrap
 </span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a>cdk<span class="w"> </span>deploy
diff --git a/aws_setup/domain_cert_setup/index.html b/aws_setup/domain_cert_setup/index.html
index c85c73b86..d148d0302 100644
--- a/aws_setup/domain_cert_setup/index.html
+++ b/aws_setup/domain_cert_setup/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Domain/SSL Cert Setup - SageWorks</title>
+        <title>Domain/SSL Cert Setup - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3190,11 +3190,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3365,14 +3365,14 @@
 <h1 id="aws-domain-and-certificate-instructions">AWS Domain and Certificate Instructions</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need AWS Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
 <p>This page tries to give helpful guidance when setting up a new domain and SSL Certificate in your AWS Account.</p>
 <h2 id="new-domain">New Domain</h2>
-<p>You'll want the SageWorks Dashboard to have a domain for your companies internal use. Customers will typically use a domain like <code>&lt;company_name&gt;-ml-dashboard.com</code> but you are free to choose any domain you'd like.</p>
+<p>You'll want the Workbench Dashboard to have a domain for your companies internal use. Customers will typically use a domain like <code>&lt;company_name&gt;-ml-dashboard.com</code> but you are free to choose any domain you'd like.</p>
 <div class="admonition warning">
 <p class="admonition-title">Domains are tied to AWS Accounts</p>
-<p>When you create a new domain in AWS Route 53, that domain is tied to that AWS Account. You can do a cross account setup for domains but it's a bit more tricky. We recommend that each account where SageWorks gets deployed owns the domain for that Dashboard.</p>
+<p>When you create a new domain in AWS Route 53, that domain is tied to that AWS Account. You can do a cross account setup for domains but it's a bit more tricky. We recommend that each account where Workbench gets deployed owns the domain for that Dashboard.</p>
 </div>
 <h3 id="multiple-aws-accounts">Multiple AWS Accounts</h3>
 <p>Many customers will have a dev/stage/prod set of AWS accounts, if that the case then the best practice is to make a domain specific to each account. So for instance:</p>
@@ -3517,7 +3517,7 @@ <h3 id="certificate-states">Certificate States</h3>
 <p><strong>Inactive</strong>: This state indicates that the certificate is not currently in use.</p>
 </li>
 </ul>
-<p>The certificate status should obviously be in the <strong>Issued</strong> state, if not please contact SageWorks Support Team.</p>
+<p>The certificate status should obviously be in the <strong>Issued</strong> state, if not please contact Workbench Support Team.</p>
 <h2 id="retrieving-the-certificate-arn">Retrieving the Certificate ARN</h2>
 <ol>
 <li>
diff --git a/aws_setup/full_pipeline/index.html b/aws_setup/full_pipeline/index.html
index 5364df73f..b14d42d0a 100644
--- a/aws_setup/full_pipeline/index.html
+++ b/aws_setup/full_pipeline/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Full Pipeline Testing - SageWorks</title>
+        <title>Full Pipeline Testing - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -2113,9 +2113,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#run-the-sageworks-dashboard-local" class="md-nav__link">
+  <a href="#run-the-workbench-dashboard-local" class="md-nav__link">
     <span class="md-ellipsis">
-      Run the SageWorks Dashboard (Local)
+      Run the Workbench Dashboard (Local)
     </span>
   </a>
   
@@ -3097,11 +3097,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3153,9 +3153,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#run-the-sageworks-dashboard-local" class="md-nav__link">
+  <a href="#run-the-workbench-dashboard-local" class="md-nav__link">
     <span class="md-ellipsis">
-      Run the SageWorks Dashboard (Local)
+      Run the Workbench Dashboard (Local)
     </span>
   </a>
   
@@ -3177,10 +3177,10 @@
 
 
 <h1 id="testing-full-ml-pipeline">Testing Full ML Pipeline</h1>
-<p>Now that the core Sageworks AWS Stack has been deployed. Let's test out SageWorks by building a full entire AWS ML Pipeline from start to finish. The script <code>build_ml_pipeline.py</code> uses the SageWorks API to quickly and easily build an AWS Modeling Pipeline.</p>
+<p>Now that the core Workbench AWS Stack has been deployed. Let's test out Workbench by building a full entire AWS ML Pipeline from start to finish. The script <code>build_ml_pipeline.py</code> uses the Workbench API to quickly and easily build an AWS Modeling Pipeline.</p>
 <div class="admonition tip inline end">
 <p class="admonition-title">Taste the Awesome</p>
-<p>The SageWorks "hello world" builds a full AWS ML Pipeline. From S3 to deployed model and endpoint. If you have any troubles at all feel free to contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks email</a> or on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> and we're happy to help you for FREE.</p>
+<p>The Workbench "hello world" builds a full AWS ML Pipeline. From S3 to deployed model and endpoint. If you have any troubles at all feel free to contact us at <a href="mailto:workbench@supercowpowers.com">workbench email</a> or on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> and we're happy to help you for FREE.</p>
 </div>
 <ul>
 <li>DataLoader(abalone.csv) --&gt; DataSource</li>
@@ -3193,25 +3193,25 @@ <h1 id="testing-full-ml-pipeline">Testing Full ML Pipeline</h1>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>&lt;lot of building ML pipeline outputs&gt;
 </span></code></pre></div>
 After the script completes you will see that it's built out an AWS ML Pipeline and testing artifacts.</p>
-<h2 id="run-the-sageworks-dashboard-local">Run the SageWorks Dashboard (Local)</h2>
+<h2 id="run-the-workbench-dashboard-local">Run the Workbench Dashboard (Local)</h2>
 <div class="admonition tip inline end">
 <p class="admonition-title">Dashboard AWS Stack</p>
 <p>Deploying the Dashboard Stack is straight-forward and provides a robust AWS Web Server with Load Balancer, Elastic Container Service, VPC Networks, etc. (see <a href="../dashboard_stack/">AWS Dashboard Stack</a>)</p>
 </div>
-<p>For testing it's nice to run the Dashboard locally, but for longterm use the SageWorks Dashboard should be deployed as an AWS Stack. The deployed Stack allows everyone in the company to use, view, and interact with the AWS Machine Learning Artifacts created with SageWorks.</p>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>cd sageworks/application/aws_dashboard
+<p>For testing it's nice to run the Dashboard locally, but for longterm use the Workbench Dashboard should be deployed as an AWS Stack. The deployed Stack allows everyone in the company to use, view, and interact with the AWS Machine Learning Artifacts created with Workbench.</p>
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>cd workbench/application/aws_dashboard
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>./dashboard
 </span></code></pre></div>
 <strong>This will open a browser to http://localhost:8000</strong></p>
 <p><figure">
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4"></p>
-<figcaption>SageWorks Dashboard: AWS Pipelines in a Whole New Light!</figcaption>
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4"></p>
+<figcaption>Workbench Dashboard: AWS Pipelines in a Whole New Light!</figcaption>
 </figure>
 <div class="admonition success">
 <p class="admonition-title">Success</p>
-<p>Congratulations: SageWorks is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install sageworks</code> and start using the API.</p>
+<p>Congratulations: Workbench is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install workbench</code> and start using the API.</p>
 </div>
-<p>If you ran into any issues with this procedure please contact us via <a href="https://discord.gg/WHAJuz8sw8">Discord</a> or email <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> and the SCP team will provide <strong>free</strong> setup and support for new SageWorks users.</p>
+<p>If you ran into any issues with this procedure please contact us via <a href="https://discord.gg/WHAJuz8sw8">Discord</a> or email <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> and the SCP team will provide <strong>free</strong> setup and support for new Workbench users.</p>
 
 
 
diff --git a/aws_setup/iam_assume_role/index.html b/aws_setup/iam_assume_role/index.html
new file mode 100644
index 000000000..9ff28a438
--- /dev/null
+++ b/aws_setup/iam_assume_role/index.html
@@ -0,0 +1,3328 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
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+      <a href="../../blogs/residual_analysis/" class="md-nav__link">
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+  
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+  
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+    <li class="md-nav__item">
+      <a href="../../blogs/eda/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Exploratory Data Analysis(EDA)
+  </span>
+  
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+  
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+      <a href="../../blogs/htg/" class="md-nav__link">
+        
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+  
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+    </li>
+  
+
+              
+            
+              
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+  
+  
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+    <li class="md-nav__item">
+      <a href="../../blogs/model_monitoring/" class="md-nav__link">
+        
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+    </li>
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+        </nav>
+      
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+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/athena_source/" class="md-nav__link">
+        
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+    AthenaSource
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+  
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+    </li>
+  
+
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+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/feature_set_core/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    FeatureSetCore
+  </span>
+  
+
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+    </li>
+  
+
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+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/model_core/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    ModelCore
+  </span>
+  
+
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+    </li>
+  
+
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+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/endpoint_core/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    EndpointCore
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/monitor_core/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    MonitorCore
+  </span>
+  
+
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+    </li>
+  
+
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+              
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+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/artifact/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
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+  
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+    </li>
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+
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+        </nav>
+      
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+      <a href="../../core_classes/views/computation_view/" class="md-nav__link">
+        
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+    ComputationView
+  </span>
+  
+
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+    </li>
+  
+
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+  
+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/views/display_view/" class="md-nav__link">
+        
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+  </span>
+  
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+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/views/training_view/" class="md-nav__link">
+        
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+    TrainingView
+  </span>
+  
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+
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+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/views/mdq_view/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ModelDataQualityView
+  </span>
+  
+
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+    </li>
+  
+
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+            
+          </ul>
+        </nav>
+      
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+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
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+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/overview/" class="md-nav__link">
+        
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+      <a href="../../core_classes/transforms/data_loaders_light/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    Data Loaders Light
+  </span>
+  
+
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+    </li>
+  
+
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+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/data_loaders_heavy/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Data Loaders Heavy
+  </span>
+  
+
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+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/data_to_features/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Data To Features
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/features_to_model/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Features To Model
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/model_to_endpoint/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Model to Endpoint
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/pandas_transforms/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Pandas Transforms
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/transform/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Transform
+  </span>
+  
+
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+    </li>
+  
+
+              
+            
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+        </nav>
+      
+    </li>
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+  
+  
+    
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_17" >
+        
+          
+          <label class="md-nav__link" for="__nav_17" id="__nav_17_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    AWS Setup
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_17_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_17">
+            <span class="md-nav__icon md-icon"></span>
+            AWS Setup
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../aws_setup/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS CLI/SSO Setup
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../full_pipeline/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Full Pipeline Testing
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../core_stack/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Admin Initial Setup
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../dashboard_stack/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Dashboard Setup
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../domain_cert_setup/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Domain/SSL Cert Setup
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../aws_tips_and_tricks/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Tips and Tricks
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../aws_access_management/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Access Management
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../aws_client_vpn/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Client VPN
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_18" >
+        
+          
+          <label class="md-nav__link" for="__nav_18" id="__nav_18_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Road Maps
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_18_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_18">
+            <span class="md-nav__icon md-icon"></span>
+            Road Maps
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../road_maps/0_9_0/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.9.0
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../road_maps/0_9_5/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.9.5
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_19" >
+        
+          
+          <label class="md-nav__link" for="__nav_19" id="__nav_19_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Release Notes
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_19_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_19">
+            <span class="md-nav__icon md-icon"></span>
+            Release Notes
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_78/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.78
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_74/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.74
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_71/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.71
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_60/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.60
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_58/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.58
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_55/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.55
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_50/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.50
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_19_8" >
+        
+          
+          <label class="md-nav__link" for="__nav_19_8" id="__nav_19_8_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Archived
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_19_8_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_19_8">
+            <span class="md-nav__icon md-icon"></span>
+            Archived
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_46/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.46
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_42/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.42
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_39/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.39
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_36/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.36
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_35/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.35
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_33/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.33
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_29/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.29
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_27/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.27
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_23/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.23
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_22/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.22
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_20/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.20
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_11/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.11
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_8/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.8
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_6/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.6
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_7_8/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.7.8
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_20" >
+        
+          
+          <label class="md-nav__link" for="__nav_20" id="__nav_20_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Admin
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_20_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_20">
+            <span class="md-nav__icon md-icon"></span>
+            Admin
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../admin/aws_service_limits/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Service Limits
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../admin/pypi_release/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    PyPI Release
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../admin/base_docker_push/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Base Docker Push
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../admin/dashboard_docker_push/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Dashboard Docker Push
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../admin/dashboard_s3_plugins/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Dashboard with S3 Plugins
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../admin/dashboard_with_plugins/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Dashboard with Docker Plugins
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_20_7" >
+        
+          
+          <label class="md-nav__link" for="__nav_20_7" id="__nav_20_7_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Storage
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_20_7_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_20_7">
+            <span class="md-nav__icon md-icon"></span>
+            Storage
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Workbench Docker for Lambdas
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+  </ul>
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+            
+              
+              <div class="md-sidebar md-sidebar--secondary" data-md-component="sidebar" data-md-type="toc" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#prerequisites" class="md-nav__link">
+    <span class="md-ellipsis">
+      Prerequisites
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#steps-to-update-iam-user-permissions" class="md-nav__link">
+    <span class="md-ellipsis">
+      Steps to Update IAM User Permissions
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Steps to Update IAM User Permissions">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#1-log-in-to-the-aws-management-console" class="md-nav__link">
+    <span class="md-ellipsis">
+      1. Log in to the AWS Management Console
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#2-select-the-iam-user" class="md-nav__link">
+    <span class="md-ellipsis">
+      2. Select the IAM User
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#3-attach-an-inline-policy" class="md-nav__link">
+    <span class="md-ellipsis">
+      3. Attach an Inline Policy
+    </span>
+  </a>
+  
+</li>
+        
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+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#verifying-access-for-iam-users" class="md-nav__link">
+    <span class="md-ellipsis">
+      Verifying Access for IAM Users
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#troubleshooting" class="md-nav__link">
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+      Common Issues
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+        
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+                
+                  
+
+
+<h1 id="setting-up-iam-users-to-access-workbench-executionrole">Setting Up IAM Users to Access Workbench-ExecutionRole</h1>
+<p>This guide provides step-by-step instructions to configure IAM users to assume the <code>Workbench-ExecutionRole</code> in your AWS account.</p>
+<h2 id="prerequisites">Prerequisites</h2>
+<ul>
+<li><strong>Administrator permissions</strong> to update IAM users and policies.</li>
+<li>The <code>Workbench-ExecutionRole</code> must already be deployed via the Workbench AWS CDK stack.</li>
+</ul>
+<h2 id="steps-to-update-iam-user-permissions">Steps to Update IAM User Permissions</h2>
+<h3 id="1-log-in-to-the-aws-management-console">1. Log in to the AWS Management Console</h3>
+<ul>
+<li>Navigate to the <a href="https://console.aws.amazon.com/iam/">IAM Console</a>.</li>
+</ul>
+<h3 id="2-select-the-iam-user">2. Select the IAM User</h3>
+<ol>
+<li>In the left-hand menu, select <strong>Users</strong>.</li>
+<li>Locate and select the IAM user who needs access to the <code>Workbench-ExecutionRole</code>.</li>
+</ol>
+<h3 id="3-attach-an-inline-policy">3. Attach an Inline Policy</h3>
+<ol>
+<li>Navigate to the <strong>Permissions</strong> tab for the IAM user.</li>
+<li>Click <strong>Add inline policy</strong>.</li>
+<li>
+<p>Select the <strong>JSON</strong> editor and paste the following policy:</p>
+<div class="language-json highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="p">{</span>
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="w">    </span><span class="nt">&quot;Version&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;2012-10-17&quot;</span><span class="p">,</span>
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="w">    </span><span class="nt">&quot;Statement&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="w">        </span><span class="p">{</span>
+</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="w">            </span><span class="nt">&quot;Effect&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Allow&quot;</span><span class="p">,</span>
+</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="w">            </span><span class="nt">&quot;Action&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;sts:AssumeRole&quot;</span><span class="p">,</span>
+</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="w">            </span><span class="nt">&quot;Resource&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole&quot;</span>
+</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="w">        </span><span class="p">}</span>
+</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="w">    </span><span class="p">]</span>
+</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="p">}</span>
+</span></code></pre></div>
+<ul>
+<li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li>
+</ul>
+</li>
+<li>
+<p>Review and save the policy.</p>
+</li>
+</ol>
+<h2 id="verifying-access-for-iam-users">Verifying Access for IAM Users</h2>
+<ol>
+<li>Log in to the AWS Management Console as the IAM user.</li>
+<li>
+<p>Use the following CLI command to test access:</p>
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>aws<span class="w"> </span>sts<span class="w"> </span>assume-role<span class="w"> </span><span class="se">\</span>
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="w">    </span>--role-arn<span class="w"> </span>arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole<span class="w"> </span><span class="se">\</span>
+</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="w">    </span>--role-session-name<span class="w"> </span>TestSession
+</span></code></pre></div>
+<ul>
+<li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li>
+</ul>
+</li>
+<li>
+<p>If successful, you will receive temporary credentials for the <code>Workbench-ExecutionRole</code>.</p>
+</li>
+</ol>
+<h2 id="troubleshooting">Troubleshooting</h2>
+<h3 id="common-issues">Common Issues</h3>
+<ul>
+<li><strong>Permission Denied</strong>: Ensure the correct inline policy is attached to the IAM user.</li>
+<li><strong>Role Not Found</strong>: Verify that the <code>Workbench-ExecutionRole</code> has been deployed correctly.</li>
+</ul>
+<h3 id="contact-support">Contact Support</h3>
+<p>If you encounter issues, please contact your AWS administrator or reach out to the Workbench support team.</p>
+
+
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+      <a href="../../cached/cached_data_source/" class="md-nav__link">
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+    Cached DataSource
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+      <a href="../../cached/cached_model/" class="md-nav__link">
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+  
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+    Overview
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+    Support Phases (WIP)
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+      </a>
+    </li>
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+          </label>
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+  
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+    <li class="md-nav__item">
+      <a href="../../blogs/residual_analysis/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Residual Analysis
+  </span>
+  
+
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+  
+
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+            
+              
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+  
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+  
+    <li class="md-nav__item">
+      <a href="../../blogs/eda/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    Exploratory Data Analysis(EDA)
+  </span>
+  
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+    </li>
+  
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+  
+    <li class="md-nav__item">
+      <a href="../../blogs/htg/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
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+  </span>
+  
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+    </li>
+  
+
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+  
+  
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+    <li class="md-nav__item">
+      <a href="../../blogs/model_monitoring/" class="md-nav__link">
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+      
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+        
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+            
+  
+  <span class="md-ellipsis">
+    Artifacts
+  </span>
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+        
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+          <label class="md-nav__title" for="__nav_9_2">
+            <span class="md-nav__icon md-icon"></span>
+            Artifacts
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
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+                
+  
+  
+  
+  
+    <li class="md-nav__item">
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+        
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+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/athena_source/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AthenaSource
+  </span>
+  
+
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+    </li>
+  
+
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+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/feature_set_core/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    FeatureSetCore
+  </span>
+  
+
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+    </li>
+  
+
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+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/model_core/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    ModelCore
+  </span>
+  
+
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+    </li>
+  
+
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+            
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+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/endpoint_core/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    EndpointCore
+  </span>
+  
+
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+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/monitor_core/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    MonitorCore
+  </span>
+  
+
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+    </li>
+  
+
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+  
+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/artifacts/artifact/" class="md-nav__link">
+        
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+  
+
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+    </li>
+  
+
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+        </nav>
+      
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+      
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+    Views
+  </span>
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+        
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+            
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+        
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+    OverView
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+    <li class="md-nav__item">
+      <a href="../../core_classes/views/computation_view/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ComputationView
+  </span>
+  
+
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+    </li>
+  
+
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+            
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+                
+  
+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/views/display_view/" class="md-nav__link">
+        
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+    DisplayView
+  </span>
+  
+
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+    </li>
+  
+
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+    <li class="md-nav__item">
+      <a href="../../core_classes/views/training_view/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    TrainingView
+  </span>
+  
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+    </li>
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+
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+    <li class="md-nav__item">
+      <a href="../../core_classes/views/mdq_view/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    ModelDataQualityView
+  </span>
+  
+
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+    </li>
+  
+
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+        </nav>
+      
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+  
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+    Transforms
+  </span>
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+          </label>
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+          <label class="md-nav__title" for="__nav_9_4">
+            <span class="md-nav__icon md-icon"></span>
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+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
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+    <li class="md-nav__item">
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+        
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+      <a href="../../core_classes/transforms/data_loaders_light/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Data Loaders Light
+  </span>
+  
+
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+    </li>
+  
+
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+            
+              
+                
+  
+  
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+      <a href="../../core_classes/transforms/data_loaders_heavy/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Data Loaders Heavy
+  </span>
+  
+
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+    </li>
+  
+
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+            
+              
+                
+  
+  
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+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/data_to_features/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Data To Features
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/features_to_model/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Features To Model
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/model_to_endpoint/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Model to Endpoint
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
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+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/pandas_transforms/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Pandas Transforms
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../core_classes/transforms/transform/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Transform
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
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+    <li class="md-nav__item">
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+        
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+    </li>
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+    <li class="md-nav__item">
+      <a href="../../data_algorithms/spark/overview/" class="md-nav__link">
+        
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+  </span>
+  
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+    <li class="md-nav__item">
+      <a href="../../data_algorithms/sql/overview/" class="md-nav__link">
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+  </span>
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+    <li class="md-nav__item">
+      <a href="../../enterprise/private_saas/" class="md-nav__link">
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+  
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+        
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+    <li class="md-nav__item">
+      <a href="../../enterprise/project_branding/" class="md-nav__link">
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+    <li class="md-nav__item">
+      <a href="../../glue/" class="md-nav__link">
+        
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+  </span>
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+    <li class="md-nav__item">
+      <a href="../../lambda_layer/" class="md-nav__link">
+        
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+            
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+    <li class="md-nav__item">
+      <a href="../aws_setup/" class="md-nav__link">
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+    AWS CLI/SSO Setup
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+  
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+  
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+  
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+  
+    <li class="md-nav__item">
+      <a href="../full_pipeline/" class="md-nav__link">
+        
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+  </span>
+  
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+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../core_stack/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Admin Initial Setup
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../dashboard_stack/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    Dashboard Setup
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../domain_cert_setup/" class="md-nav__link">
+        
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+  <span class="md-ellipsis">
+    Domain/SSL Cert Setup
+  </span>
+  
+
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+    </li>
+  
+
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+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../aws_tips_and_tricks/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Tips and Tricks
+  </span>
+  
+
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+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../aws_access_management/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Access Management
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../aws_client_vpn/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    AWS Client VPN
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
+    
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+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_18" >
+        
+          
+          <label class="md-nav__link" for="__nav_18" id="__nav_18_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Road Maps
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_18_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_18">
+            <span class="md-nav__icon md-icon"></span>
+            Road Maps
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../road_maps/0_9_0/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.9.0
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../road_maps/0_9_5/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.9.5
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+          </ul>
+        </nav>
+      
+    </li>
+  
+
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+      
+      
+  
+  
+  
+  
+    
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+    <li class="md-nav__item md-nav__item--nested">
+      
+        
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+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_19" >
+        
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+            
+  
+  <span class="md-ellipsis">
+    Release Notes
+  </span>
+  
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+          </label>
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+          <label class="md-nav__title" for="__nav_19">
+            <span class="md-nav__icon md-icon"></span>
+            Release Notes
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
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+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_78/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.78
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_74/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.74
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_71/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.71
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_60/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.60
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_58/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.58
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_55/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.55
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/0_8_50/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.50
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    
+    
+    
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+        
+        
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_19_8" >
+        
+          
+          <label class="md-nav__link" for="__nav_19_8" id="__nav_19_8_label" tabindex="0">
+            
+  
+  <span class="md-ellipsis">
+    Archived
+  </span>
+  
+
+            <span class="md-nav__icon md-icon"></span>
+          </label>
+        
+        <nav class="md-nav" data-md-level="2" aria-labelledby="__nav_19_8_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_19_8">
+            <span class="md-nav__icon md-icon"></span>
+            Archived
+          </label>
+          <ul class="md-nav__list" data-md-scrollfix>
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_46/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.46
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_42/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.42
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_39/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.39
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_36/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.36
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_35/" class="md-nav__link">
+        
+  
+  <span class="md-ellipsis">
+    0.8.35
+  </span>
+  
+
+      </a>
+    </li>
+  
+
+              
+            
+              
+                
+  
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../../release_notes/archived/0_8_33/" class="md-nav__link">
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+                  <div class="md-sidebar__inner">
+                    
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#prerequisites" class="md-nav__link">
+    <span class="md-ellipsis">
+      Prerequisites
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#steps-to-update-sso-permissions" class="md-nav__link">
+    <span class="md-ellipsis">
+      Steps to Update SSO Permissions
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Steps to Update SSO Permissions">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#1-log-in-to-the-aws-sso-console" class="md-nav__link">
+    <span class="md-ellipsis">
+      1. Log in to the AWS SSO Console
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#2-find-the-relevant-permission-set" class="md-nav__link">
+    <span class="md-ellipsis">
+      2. Find the Relevant Permission Set
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#3-edit-the-permission-set" class="md-nav__link">
+    <span class="md-ellipsis">
+      3. Edit the Permission Set
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#4-save-changes" class="md-nav__link">
+    <span class="md-ellipsis">
+      4. Save Changes
+    </span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#verifying-access-for-sso-users" class="md-nav__link">
+    <span class="md-ellipsis">
+      Verifying Access for SSO Users
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#troubleshooting" class="md-nav__link">
+    <span class="md-ellipsis">
+      Troubleshooting
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Troubleshooting">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#common-issues" class="md-nav__link">
+    <span class="md-ellipsis">
+      Common Issues
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#contact-support" class="md-nav__link">
+    <span class="md-ellipsis">
+      Contact Support
+    </span>
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+</li>
+      
+    </ul>
+  
+</nav>
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+            
+          
+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset">
+                
+                  
+
+
+<h1 id="setting-up-sso-users-to-access-workbench-executionrole">Setting Up SSO Users to Access Workbench-ExecutionRole</h1>
+<p>This guide provides step-by-step instructions to configure AWS SSO users to assume the <code>Workbench-ExecutionRole</code> in your AWS account.</p>
+<h2 id="prerequisites">Prerequisites</h2>
+<ul>
+<li>Access to the <strong>management account</strong> in your AWS Organization.</li>
+<li><strong>Administrative permissions</strong> to modify AWS SSO permission sets.</li>
+<li>The <code>Workbench-ExecutionRole</code> must already be deployed via the Workbench AWS CDK stack.</li>
+</ul>
+<h2 id="steps-to-update-sso-permissions">Steps to Update SSO Permissions</h2>
+<h3 id="1-log-in-to-the-aws-sso-console">1. Log in to the AWS SSO Console</h3>
+<ul>
+<li>Login to your 'main' organization AWS Account.</li>
+<li>Go to the IAM Identity Center</li>
+</ul>
+<h3 id="2-find-the-relevant-permission-set">2. Find the Relevant Permission Set</h3>
+<ul>
+<li>In the left menu, select <strong>Permission Sets</strong>.</li>
+<li>Locate the permission set used by the group needing access (e.g., <code>DataScientist</code> or another relevant group).</li>
+</ul>
+<h3 id="3-edit-the-permission-set">3. Edit the Permission Set</h3>
+<ol>
+<li>Select the permission set</li>
+<li>Scroll down to <strong>Inline policy</strong> and click the the Edit button.</li>
+<li>
+<p>Add an <strong>inline policy</strong> with the following content:</p>
+<div class="language-json highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="p">{</span>
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="w">    </span><span class="nt">&quot;Version&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;2012-10-17&quot;</span><span class="p">,</span>
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="w">    </span><span class="nt">&quot;Statement&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="w">        </span><span class="p">{</span>
+</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="w">            </span><span class="nt">&quot;Effect&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;Allow&quot;</span><span class="p">,</span>
+</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="w">            </span><span class="nt">&quot;Action&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;sts:AssumeRole&quot;</span><span class="p">,</span>
+</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="w">            </span><span class="nt">&quot;Resource&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole&quot;</span>
+</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="w">        </span><span class="p">}</span>
+</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="w">    </span><span class="p">]</span>
+</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="p">}</span>
+</span></code></pre></div>
+<ul>
+<li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li>
+</ul>
+</li>
+</ol>
+<h3 id="4-save-changes">4. Save Changes</h3>
+<ul>
+<li>Save the updated permission set.</li>
+<li>AWS SSO will automatically propagate the changes to all users in the associated group.</li>
+</ul>
+<h2 id="verifying-access-for-sso-users">Verifying Access for SSO Users</h2>
+<ol>
+<li>Log in to the AWS Management Console as a user in the configured group.</li>
+<li>
+<p>Use the following CLI command to test access:</p>
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>aws<span class="w"> </span>sts<span class="w"> </span>assume-role<span class="w"> </span><span class="se">\</span>
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="w">    </span>--role-arn<span class="w"> </span>arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole<span class="w"> </span><span class="se">\</span>
+</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="w">    </span>--role-session-name<span class="w"> </span>TestSession
+</span></code></pre></div>
+<ul>
+<li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li>
+</ul>
+</li>
+<li>
+<p>If successful, you will receive temporary credentials for the <code>Workbench-ExecutionRole</code>.</p>
+</li>
+</ol>
+<h2 id="troubleshooting">Troubleshooting</h2>
+<h3 id="common-issues">Common Issues</h3>
+<ul>
+<li><strong>Permission Denied</strong>: Ensure the correct permission set is updated.</li>
+<li><strong>Role Not Found</strong>: Verify that the <code>Workbench-ExecutionRole</code> has been deployed correctly.</li>
+</ul>
+<h3 id="contact-support">Contact Support</h3>
+<p>If you encounter issues, please contact your AWS administrator or reach out to the Workbench support team.</p>
+
+
+
+
+
+
+
+
+
+
+
+
+                
+              </article>
+            </div>
+          
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diff --git a/blogs/aws_credentials_lock/index.html b/blogs/aws_credentials_lock/index.html
index 3739537c6..c308dc6a3 100644
--- a/blogs/aws_credentials_lock/index.html
+++ b/blogs/aws_credentials_lock/index.html
@@ -16,7 +16,7 @@
     
     
       
-        <title>AWS Credentials Lock - SageWorks</title>
+        <title>AWS Credentials Lock - Workbench</title>
       
     
     
@@ -86,7 +86,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
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-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -100,7 +100,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
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-            SageWorks
+            Workbench
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-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -289,7 +289,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1813,7 +1813,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1823,7 +1823,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3042,11 +3042,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3151,7 +3151,7 @@
 <h1 id="aws-credentials-lock">AWS Credentials Lock</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">AWS Curious?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
 <h2 id="tbd">TBD</h2>
 <h2 id="threading-lock">Threading Lock</h2>
@@ -3170,7 +3170,7 @@ <h2 id="error">Error</h2>
 </span></code></pre></div>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/blogs/eda/index.html b/blogs/eda/index.html
index 0982b132c..df7e2dc5c 100644
--- a/blogs/eda/index.html
+++ b/blogs/eda/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Exploratory Data Analysis(EDA) - SageWorks</title>
+        <title>Exploratory Data Analysis(EDA) - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1868,7 +1868,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1878,7 +1878,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3097,11 +3097,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3178,17 +3178,17 @@
 
 <h1 id="exploratory-data-analysis">Exploratory Data Analysis</h1>
 <div class="admonition tip inline end">
-<p class="admonition-title">SageWorks EDS</p>
-<p>The SageWorks toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components <a href="../../aws_setup/dashboard_stack/">Dashboard</a>.</p>
+<p class="admonition-title">Workbench EDS</p>
+<p>The Workbench toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components <a href="../../aws_setup/dashboard_stack/">Dashboard</a>.</p>
 </div>
-<p>The SageWorks framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p>
+<p>The Workbench framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p>
 <ul>
 <li>TBD</li>
 <li>TBD2</li>
 </ul>
 <figure style="float: right; width: 400px;">
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4">
-<figcaption>SageWorks Exploratory Data Analysis</figcaption>
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4">
+<figcaption>Workbench Exploratory Data Analysis</figcaption>
 </figure>
 
 <h2 id="additional-resources">Additional Resources</h2>
diff --git a/blogs/htg/index.html b/blogs/htg/index.html
index 68052e142..e659518a6 100644
--- a/blogs/htg/index.html
+++ b/blogs/htg/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>High Target Gradients (HTG) - SageWorks</title>
+        <title>High Target Gradients (HTG) - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1868,7 +1868,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1878,7 +1878,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3097,11 +3097,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3178,10 +3178,10 @@
 
 <h1 id="eda-high-target-gradients">EDA: High Target Gradients</h1>
 <div class="admonition tip inline end">
-<p class="admonition-title">SageWorks EDS</p>
-<p>The SageWorks toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components <a href="../../aws_setup/dashboard_stack/">Dashboard</a>.</p>
+<p class="admonition-title">Workbench EDS</p>
+<p>The Workbench toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components <a href="../../aws_setup/dashboard_stack/">Dashboard</a>.</p>
 </div>
-<p>The SageWorks framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p>
+<p>The Workbench framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p>
 <ul>
 <li>TBD</li>
 <li>TBD2</li>
diff --git a/blogs/index.html b/blogs/index.html
index b8ea71985..b11d57386 100644
--- a/blogs/index.html
+++ b/blogs/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-blogs" class="md-skip">
+        <a href="#workbench-blogs" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1886,7 +1886,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1896,7 +1896,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3115,11 +3115,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3212,10 +3212,10 @@
                   
 
 
-<h1 id="sageworks-blogs">SageWorks Blogs</h1>
+<h1 id="workbench-blogs">Workbench Blogs</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Just Getting Started?</p>
-<p>The SageWorks Blogs is a great way to see what's possible with SageWorks. Also if you're ready to jump in the <a href="../api_classes/overview/">API Classes</a> will give you details on the SageWorks ML Pipeline Classes.</p>
+<p>The Workbench Blogs is a great way to see what's possible with Workbench. Also if you're ready to jump in the <a href="../api_classes/overview/">API Classes</a> will give you details on the Workbench ML Pipeline Classes.</p>
 </div>
 <h2 id="latest-blogs">Latest Blogs</h2>
 <ul>
@@ -3228,7 +3228,7 @@ <h2 id="stupid-yet-interesting">Stupid yet Interesting</h2>
 </ul>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/blogs/model_monitoring/index.html b/blogs/model_monitoring/index.html
index ba0b4280a..761a4a041 100644
--- a/blogs/model_monitoring/index.html
+++ b/blogs/model_monitoring/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Model Monitoring - SageWorks</title>
+        <title>Model Monitoring - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1829,7 +1829,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1839,7 +1839,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3058,11 +3058,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
diff --git a/blogs/residual_analysis/index.html b/blogs/residual_analysis/index.html
index 39be876ba..419954451 100644
--- a/blogs/residual_analysis/index.html
+++ b/blogs/residual_analysis/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Residual Analysis - SageWorks</title>
+        <title>Residual Analysis - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1864,7 +1864,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1874,7 +1874,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3093,11 +3093,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
diff --git a/cached/cached_data_source/index.html b/cached/cached_data_source/index.html
index 7e21d78d1..eeb8d527d 100644
--- a/cached/cached_data_source/index.html
+++ b/cached/cached_data_source/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Cached DataSource - SageWorks</title>
+        <title>Cached DataSource - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -707,7 +707,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source" class="md-nav__link">
     <span class="md-ellipsis">
       cached_data_source
     </span>
@@ -716,7 +716,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource" class="md-nav__link">
     <span class="md-ellipsis">
       CachedDataSource
     </span>
@@ -726,7 +726,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.__init__" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -735,7 +735,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.details" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -744,7 +744,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.health_check" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -753,27 +753,27 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.smart_sample" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      smart_sample
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.smart_sample" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.summary" class="md-nav__link">
     <span class="md-ellipsis">
-      smart_sample
+      summary
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.summary" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      summary
+      workbench_meta
     </span>
   </a>
   
@@ -1946,7 +1946,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1956,7 +1956,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3175,11 +3175,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3231,7 +3231,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source" class="md-nav__link">
     <span class="md-ellipsis">
       cached_data_source
     </span>
@@ -3240,7 +3240,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource" class="md-nav__link">
     <span class="md-ellipsis">
       CachedDataSource
     </span>
@@ -3250,7 +3250,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.__init__" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3259,7 +3259,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.details" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3268,7 +3268,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.health_check" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -3277,27 +3277,27 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.smart_sample" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      smart_sample
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.smart_sample" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.summary" class="md-nav__link">
     <span class="md-ellipsis">
-      smart_sample
+      summary
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_data_source.CachedDataSource.summary" class="md-nav__link">
+  <a href="#workbench.cached.cached_data_source.CachedDataSource.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      summary
+      workbench_meta
     </span>
   </a>
   
@@ -3335,7 +3335,7 @@
 <h1 id="cacheddatasource">CachedDataSource</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Model Examples</p>
-<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p>
+<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p>
 </div>
 
 
@@ -3343,10 +3343,10 @@ <h1 id="cacheddatasource">CachedDataSource</h1>
 
 
 
-<a id="sageworks.cached.cached_data_source"></a>
+<a id="workbench.cached.cached_data_source"></a>
     <div class="doc doc-contents first">
 
-        <p>CachedDataSource: Caches the method results for SageWorks DataSources</p>
+        <p>CachedDataSource: Caches the method results for Workbench DataSources</p>
 
 
 
@@ -3368,7 +3368,7 @@ <h1 id="cacheddatasource">CachedDataSource</h1>
 
 
 
-<h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-heading">
+<h2 id="workbench.cached.cached_data_source.CachedDataSource" class="doc doc-heading">
             <code>CachedDataSource</code>
 
 
@@ -3377,10 +3377,10 @@ <h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-hea
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.athena_source.AthenaSource" href="../../core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource">AthenaSource</a></code></p>
+              Bases: <code><span title="workbench.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.athena_source.AthenaSource" href="../../core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource">AthenaSource</a></code></p>
 
 
-        <p>CachedDataSource: Caches the method results for SageWorks DataSources</p>
+        <p>CachedDataSource: Caches the method results for Workbench DataSources</p>
 <p>Note: Cached method values may lag underlying DataSource changes.</p>
 
 
@@ -3389,7 +3389,7 @@ <h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-hea
   <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">my_data</span> <span class="o">=</span> <span class="n">CachedDataSource</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">my_data</span><span class="o">.</span><span class="n">details</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">my_data</span><span class="o">.</span><span class="n">health_check</span><span class="p">()</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_data</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_data</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span></code></pre></div>
 </details>
 
@@ -3398,7 +3398,7 @@ <h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-hea
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/cached/cached_data_source.py</code></summary>
+                <summary>Source code in <code>src/workbench/cached/cached_data_source.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
 <span class="normal"><a href="#__codelineno-0-14">14</a></span>
@@ -3460,7 +3460,7 @@ <h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-hea
 <span class="normal"><a href="#__codelineno-0-70">70</a></span>
 <span class="normal"><a href="#__codelineno-0-71">71</a></span>
 <span class="normal"><a href="#__codelineno-0-72">72</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="k">class</span> <span class="nc">CachedDataSource</span><span class="p">(</span><span class="n">CachedArtifactMixin</span><span class="p">,</span> <span class="n">AthenaSource</span><span class="p">):</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedDataSource: Caches the method results for SageWorks DataSources</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedDataSource: Caches the method results for Workbench DataSources</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="sd">    Note: Cached method values may lag underlying DataSource changes.</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a>
@@ -3469,11 +3469,11 @@ <h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-hea
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">        my_data = CachedDataSource(name)</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        my_data.details()</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        my_data.health_check()</span>
-</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        my_data.sageworks_meta()</span>
+</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        my_data.workbench_meta()</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">        ```</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
-</span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">):</span>
+</span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;CachedDataSource Initialization&quot;&quot;&quot;</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="n">AthenaSource</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="o">=</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="n">database</span><span class="p">,</span> <span class="n">use_cached_meta</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>
@@ -3505,13 +3505,13 @@ <h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-hea
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>
 </span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>    <span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>    <span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the SageWorks Metadata for this DataSource.</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>    <span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the Workbench Metadata for this DataSource.</span>
 </span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>
 </span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">        Returns:</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">            Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">            Union[dict, None]: Dictionary of Workbench metadata for this Artifact</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>
 </span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>    <span class="k">def</span> <span class="nf">smart_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 </span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the Smart Sample for this DataSource.</span>
@@ -3538,8 +3538,8 @@ <h2 id="sageworks.cached.cached_data_source.CachedDataSource" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_data_source.CachedDataSource.__init__" class="doc doc-heading">
-            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.cached.cached_data_source.CachedDataSource.__init__" class="doc doc-heading">
+            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -3549,10 +3549,10 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.__init__" class="do
         <p>CachedDataSource Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-26">26</a></span>
 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
-<span class="normal"><a href="#__codelineno-0-28">28</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">):</span>
+<span class="normal"><a href="#__codelineno-0-28">28</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedDataSource Initialization&quot;&quot;&quot;</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="n">AthenaSource</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="o">=</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="n">database</span><span class="p">,</span> <span class="n">use_cached_meta</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -3564,7 +3564,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.__init__" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_data_source.CachedDataSource.details" class="doc doc-heading">
+<h3 id="workbench.cached.cached_data_source.CachedDataSource.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3598,7 +3598,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.details" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-39">39</a></span>
 <span class="normal"><a href="#__codelineno-0-40">40</a></span>
 <span class="normal"><a href="#__codelineno-0-41">41</a></span>
@@ -3623,7 +3623,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.details" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_data_source.CachedDataSource.health_check" class="doc doc-heading">
+<h3 id="workbench.cached.cached_data_source.CachedDataSource.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3657,7 +3657,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.health_check" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-48">48</a></span>
 <span class="normal"><a href="#__codelineno-0-49">49</a></span>
 <span class="normal"><a href="#__codelineno-0-50">50</a></span>
@@ -3682,66 +3682,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.health_check" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_data_source.CachedDataSource.sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">sageworks_meta</span><span class="p">()</span></code>
-
-</h3>
-
-
-    <div class="doc doc-contents ">
-
-        <p>Retrieve the SageWorks Metadata for this DataSource.</p>
-
-
-    <p><span class="doc-section-title">Returns:</span></p>
-    <table>
-      <thead>
-        <tr>
-          <th>Type</th>
-          <th>Description</th>
-        </tr>
-      </thead>
-      <tbody>
-          <tr class="doc-section-item">
-            <td>
-                  <code><span title="typing.Union">Union</span>[dict, None]</code>
-            </td>
-            <td>
-              <div class="doc-md-description">
-                <p>Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</p>
-              </div>
-            </td>
-          </tr>
-      </tbody>
-    </table>
-
-            <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_data_source.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-57">57</a></span>
-<span class="normal"><a href="#__codelineno-0-58">58</a></span>
-<span class="normal"><a href="#__codelineno-0-59">59</a></span>
-<span class="normal"><a href="#__codelineno-0-60">60</a></span>
-<span class="normal"><a href="#__codelineno-0-61">61</a></span>
-<span class="normal"><a href="#__codelineno-0-62">62</a></span>
-<span class="normal"><a href="#__codelineno-0-63">63</a></span>
-<span class="normal"><a href="#__codelineno-0-64">64</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a><span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the SageWorks Metadata for this DataSource.</span>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>
-</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">        Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
-</span></code></pre></div></td></tr></table></div>
-            </details>
-    </div>
-
-</div>
-
-<div class="doc doc-object doc-function">
-
-
-<h3 id="sageworks.cached.cached_data_source.CachedDataSource.smart_sample" class="doc doc-heading">
+<h3 id="workbench.cached.cached_data_source.CachedDataSource.smart_sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">smart_sample</span><span class="p">()</span></code>
 
 </h3>
@@ -3775,7 +3716,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.smart_sample" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
 <span class="normal"><a href="#__codelineno-0-68">68</a></span>
@@ -3798,7 +3739,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.smart_sample" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_data_source.CachedDataSource.summary" class="doc doc-heading">
+<h3 id="workbench.cached.cached_data_source.CachedDataSource.summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3832,7 +3773,7 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.summary" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-30">30</a></span>
 <span class="normal"><a href="#__codelineno-0-31">31</a></span>
 <span class="normal"><a href="#__codelineno-0-32">32</a></span>
@@ -3854,6 +3795,65 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.summary" class="doc
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+<h3 id="workbench.cached.cached_data_source.CachedDataSource.workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">workbench_meta</span><span class="p">()</span></code>
+
+</h3>
+
+
+    <div class="doc doc-contents ">
+
+        <p>Retrieve the Workbench Metadata for this DataSource.</p>
+
+
+    <p><span class="doc-section-title">Returns:</span></p>
+    <table>
+      <thead>
+        <tr>
+          <th>Type</th>
+          <th>Description</th>
+        </tr>
+      </thead>
+      <tbody>
+          <tr class="doc-section-item">
+            <td>
+                  <code><span title="typing.Union">Union</span>[dict, None]</code>
+            </td>
+            <td>
+              <div class="doc-md-description">
+                <p>Union[dict, None]: Dictionary of Workbench metadata for this Artifact</p>
+              </div>
+            </td>
+          </tr>
+      </tbody>
+    </table>
+
+            <details class="quote">
+              <summary>Source code in <code>src/workbench/cached/cached_data_source.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-57">57</a></span>
+<span class="normal"><a href="#__codelineno-0-58">58</a></span>
+<span class="normal"><a href="#__codelineno-0-59">59</a></span>
+<span class="normal"><a href="#__codelineno-0-60">60</a></span>
+<span class="normal"><a href="#__codelineno-0-61">61</a></span>
+<span class="normal"><a href="#__codelineno-0-62">62</a></span>
+<span class="normal"><a href="#__codelineno-0-63">63</a></span>
+<span class="normal"><a href="#__codelineno-0-64">64</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a><span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the Workbench Metadata for this DataSource.</span>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>
+</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">        Union[dict, None]: Dictionary of Workbench metadata for this Artifact</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
+</span></code></pre></div></td></tr></table></div>
+            </details>
+    </div>
+
+</div>
+
 
 
   </div>
@@ -3870,9 +3870,9 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.summary" class="doc
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Pull DataSource Details</strong></p>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_data_source</span> <span class="kn">import</span> <span class="n">CachedDataSource</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_data_source</span> <span class="kn">import</span> <span class="n">CachedDataSource</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Grab a DataSource</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">ds</span> <span class="o">=</span> <span class="n">CachedDataSource</span><span class="p">(</span><span class="s2">&quot;abalone_data&quot;</span><span class="p">)</span>
@@ -3884,13 +3884,13 @@ <h3 id="sageworks.cached.cached_data_source.CachedDataSource.summary" class="doc
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>
 </span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="p">{</span><span class="s1">&#39;uuid&#39;</span><span class="p">:</span> <span class="s1">&#39;abalone_data&#39;</span><span class="p">,</span>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a> <span class="s1">&#39;health_tags&#39;</span><span class="p">:</span> <span class="p">[],</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a> <span class="s1">&#39;aws_arn&#39;</span><span class="p">:</span> <span class="s1">&#39;arn:aws:glue:x:table/sageworks/abalone_data&#39;</span><span class="p">,</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a> <span class="s1">&#39;aws_arn&#39;</span><span class="p">:</span> <span class="s1">&#39;arn:aws:glue:x:table/workbench/abalone_data&#39;</span><span class="p">,</span>
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-</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a> <span class="s1">&#39;sageworks_health_tags&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
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+</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a> <span class="s1">&#39;workbench_correlations&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;length&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;diameter&#39;</span><span class="p">:</span> <span class="mf">0.9868115846024996</span><span class="p">,</span>
 </span></code></pre></div>
 
 
diff --git a/cached/cached_endpoint/index.html b/cached/cached_endpoint/index.html
index 9a9513c60..d3f061436 100644
--- a/cached/cached_endpoint/index.html
+++ b/cached/cached_endpoint/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Cached Endpoint - SageWorks</title>
+        <title>Cached Endpoint - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+            Workbench
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@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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-    SageWorks
+    Workbench
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-    SageWorks REPL
+    Workbench REPL
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@@ -770,7 +770,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_endpoint" class="md-nav__link">
+  <a href="#workbench.cached.cached_endpoint" class="md-nav__link">
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@@ -779,7 +779,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_endpoint.CachedEndpoint" class="md-nav__link">
+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint" class="md-nav__link">
     <span class="md-ellipsis">
       CachedEndpoint
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@@ -789,7 +789,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_endpoint.CachedEndpoint.__init__" class="md-nav__link">
+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
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@@ -798,7 +798,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_endpoint.CachedEndpoint.details" class="md-nav__link">
+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -807,7 +807,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_endpoint.CachedEndpoint.endpoint_metrics" class="md-nav__link">
+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.endpoint_metrics" class="md-nav__link">
     <span class="md-ellipsis">
       endpoint_metrics
     </span>
@@ -816,7 +816,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_endpoint.CachedEndpoint.health_check" class="md-nav__link">
+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -825,18 +825,18 @@
 </li>
         
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+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.summary" class="md-nav__link">
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+      summary
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   </a>
   
 </li>
         
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+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.workbench_meta" class="md-nav__link">
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-      summary
+      workbench_meta
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-    SageWorks Enterprise
+    Workbench Enterprise
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@@ -1956,7 +1956,7 @@
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           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3175,11 +3175,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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+  <a href="#workbench.cached.cached_endpoint" class="md-nav__link">
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+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint" class="md-nav__link">
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+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.__init__" class="md-nav__link">
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@@ -3268,7 +3268,7 @@
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+  <a href="#workbench.cached.cached_endpoint.CachedEndpoint.endpoint_metrics" class="md-nav__link">
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@@ -3286,18 +3286,18 @@
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@@ -3335,7 +3335,7 @@
 <h1 id="cachedendpoint">CachedEndpoint</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Model Examples</p>
-<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p>
+<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p>
 </div>
 
 
@@ -3343,10 +3343,10 @@ <h1 id="cachedendpoint">CachedEndpoint</h1>
 
 
 
-<a id="sageworks.cached.cached_endpoint"></a>
+<a id="workbench.cached.cached_endpoint"></a>
     <div class="doc doc-contents first">
 
-        <p>CachedEndpoint: Caches the method results for SageWorks Endpoints</p>
+        <p>CachedEndpoint: Caches the method results for Workbench Endpoints</p>
 
 
 
@@ -3368,7 +3368,7 @@ <h1 id="cachedendpoint">CachedEndpoint</h1>
 
 
 
-<h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading">
+<h2 id="workbench.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading">
             <code>CachedEndpoint</code>
 
 
@@ -3377,10 +3377,10 @@ <h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading"
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.endpoint_core.EndpointCore" href="../../core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore">EndpointCore</a></code></p>
+              Bases: <code><span title="workbench.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.endpoint_core.EndpointCore" href="../../core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore">EndpointCore</a></code></p>
 
 
-        <p>CachedEndpoint: Caches the method results for SageWorks Endpoints</p>
+        <p>CachedEndpoint: Caches the method results for Workbench Endpoints</p>
 <p>Note: Cached method values may lag underlying Endpoint changes.</p>
 
 
@@ -3389,7 +3389,7 @@ <h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading"
   <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">my_endpoint</span> <span class="o">=</span> <span class="n">CachedEndpoint</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">my_endpoint</span><span class="o">.</span><span class="n">details</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">my_endpoint</span><span class="o">.</span><span class="n">health_check</span><span class="p">()</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_endpoint</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_endpoint</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span></code></pre></div>
 </details>
 
@@ -3398,7 +3398,7 @@ <h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading"
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/cached/cached_endpoint.py</code></summary>
+                <summary>Source code in <code>src/workbench/cached/cached_endpoint.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-10">10</a></span>
 <span class="normal"><a href="#__codelineno-0-11">11</a></span>
 <span class="normal"><a href="#__codelineno-0-12">12</a></span>
@@ -3461,7 +3461,7 @@ <h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading"
 <span class="normal"><a href="#__codelineno-0-69">69</a></span>
 <span class="normal"><a href="#__codelineno-0-70">70</a></span>
 <span class="normal"><a href="#__codelineno-0-71">71</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10"></a><span class="k">class</span> <span class="nc">CachedEndpoint</span><span class="p">(</span><span class="n">CachedArtifactMixin</span><span class="p">,</span> <span class="n">EndpointCore</span><span class="p">):</span>
-</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedEndpoint: Caches the method results for SageWorks Endpoints</span>
+</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedEndpoint: Caches the method results for Workbench Endpoints</span>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a>
 </span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="sd">    Note: Cached method values may lag underlying Endpoint changes.</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a>
@@ -3470,7 +3470,7 @@ <h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading"
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="sd">        my_endpoint = CachedEndpoint(name)</span>
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="sd">        my_endpoint.details()</span>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">        my_endpoint.health_check()</span>
-</span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        my_endpoint.sageworks_meta()</span>
+</span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        my_endpoint.workbench_meta()</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        ```</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>
@@ -3506,13 +3506,13 @@ <h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading"
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>    <span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>    <span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>    <span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</span>
 </span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>
 </span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a><span class="sd">            str: The Enumerated Model Type</span>
 </span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>
 </span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
 </span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>    <span class="k">def</span> <span class="nf">endpoint_metrics</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
@@ -3540,7 +3540,7 @@ <h2 id="sageworks.cached.cached_endpoint.CachedEndpoint" class="doc doc-heading"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.__init__" class="doc doc-heading">
+<h3 id="workbench.cached.cached_endpoint.CachedEndpoint.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">endpoint_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -3551,7 +3551,7 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.__init__" class="doc doc
         <p>CachedEndpoint Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-24">24</a></span>
 <span class="normal"><a href="#__codelineno-0-25">25</a></span>
 <span class="normal"><a href="#__codelineno-0-26">26</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">endpoint_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
@@ -3566,7 +3566,7 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.__init__" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.details" class="doc doc-heading">
+<h3 id="workbench.cached.cached_endpoint.CachedEndpoint.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3600,7 +3600,7 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.details" class="doc doc-
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-37">37</a></span>
 <span class="normal"><a href="#__codelineno-0-38">38</a></span>
 <span class="normal"><a href="#__codelineno-0-39">39</a></span>
@@ -3625,7 +3625,7 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.details" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.endpoint_metrics" class="doc doc-heading">
+<h3 id="workbench.cached.cached_endpoint.CachedEndpoint.endpoint_metrics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">endpoint_metrics</span><span class="p">()</span></code>
 
 </h3>
@@ -3659,7 +3659,7 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.endpoint_metrics" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-64">64</a></span>
 <span class="normal"><a href="#__codelineno-0-65">65</a></span>
 <span class="normal"><a href="#__codelineno-0-66">66</a></span>
@@ -3684,7 +3684,7 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.endpoint_metrics" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.health_check" class="doc doc-heading">
+<h3 id="workbench.cached.cached_endpoint.CachedEndpoint.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3718,7 +3718,7 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.health_check" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-46">46</a></span>
 <span class="normal"><a href="#__codelineno-0-47">47</a></span>
 <span class="normal"><a href="#__codelineno-0-48">48</a></span>
@@ -3743,15 +3743,15 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.health_check" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">sageworks_meta</span><span class="p">()</span></code>
+<h3 id="workbench.cached.cached_endpoint.CachedEndpoint.summary" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p>
+        <p>Retrieve the CachedEndpoint Details.</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -3764,12 +3764,12 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.sageworks_meta" class="d
       </thead>
       <tbody>
           <tr class="doc-section-item">
-<td><code>str</code></td>            <td>
-                  <code><span title="typing.Union">Union</span>[str, None]</code>
+<td><code>dict</code></td>            <td>
+                  <code>dict</code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The Enumerated Model Type</p>
+                <p>A dictionary of details about the CachedEndpoint</p>
               </div>
             </td>
           </tr>
@@ -3777,22 +3777,22 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.sageworks_meta" class="d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_endpoint.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-55">55</a></span>
-<span class="normal"><a href="#__codelineno-0-56">56</a></span>
-<span class="normal"><a href="#__codelineno-0-57">57</a></span>
-<span class="normal"><a href="#__codelineno-0-58">58</a></span>
-<span class="normal"><a href="#__codelineno-0-59">59</a></span>
-<span class="normal"><a href="#__codelineno-0-60">60</a></span>
-<span class="normal"><a href="#__codelineno-0-61">61</a></span>
-<span class="normal"><a href="#__codelineno-0-62">62</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a><span class="sd">        str: The Enumerated Model Type</span>
-</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+              <summary>Source code in <code>src/workbench/cached/cached_endpoint.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-28">28</a></span>
+<span class="normal"><a href="#__codelineno-0-29">29</a></span>
+<span class="normal"><a href="#__codelineno-0-30">30</a></span>
+<span class="normal"><a href="#__codelineno-0-31">31</a></span>
+<span class="normal"><a href="#__codelineno-0-32">32</a></span>
+<span class="normal"><a href="#__codelineno-0-33">33</a></span>
+<span class="normal"><a href="#__codelineno-0-34">34</a></span>
+<span class="normal"><a href="#__codelineno-0-35">35</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
+</span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+</span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the CachedEndpoint Details.</span>
+</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">        dict: A dictionary of details about the CachedEndpoint</span>
+</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -3802,15 +3802,15 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.sageworks_meta" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.summary" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<h3 id="workbench.cached.cached_endpoint.CachedEndpoint.workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">workbench_meta</span><span class="p">()</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Retrieve the CachedEndpoint Details.</p>
+        <p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -3823,12 +3823,12 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.summary" class="doc doc-
       </thead>
       <tbody>
           <tr class="doc-section-item">
-<td><code>dict</code></td>            <td>
-                  <code>dict</code>
+<td><code>str</code></td>            <td>
+                  <code><span title="typing.Union">Union</span>[str, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>A dictionary of details about the CachedEndpoint</p>
+                <p>The Enumerated Model Type</p>
               </div>
             </td>
           </tr>
@@ -3836,22 +3836,22 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.summary" class="doc doc-
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_endpoint.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-28">28</a></span>
-<span class="normal"><a href="#__codelineno-0-29">29</a></span>
-<span class="normal"><a href="#__codelineno-0-30">30</a></span>
-<span class="normal"><a href="#__codelineno-0-31">31</a></span>
-<span class="normal"><a href="#__codelineno-0-32">32</a></span>
-<span class="normal"><a href="#__codelineno-0-33">33</a></span>
-<span class="normal"><a href="#__codelineno-0-34">34</a></span>
-<span class="normal"><a href="#__codelineno-0-35">35</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the CachedEndpoint Details.</span>
-</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">        dict: A dictionary of details about the CachedEndpoint</span>
-</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+              <summary>Source code in <code>src/workbench/cached/cached_endpoint.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-55">55</a></span>
+<span class="normal"><a href="#__codelineno-0-56">56</a></span>
+<span class="normal"><a href="#__codelineno-0-57">57</a></span>
+<span class="normal"><a href="#__codelineno-0-58">58</a></span>
+<span class="normal"><a href="#__codelineno-0-59">59</a></span>
+<span class="normal"><a href="#__codelineno-0-60">60</a></span>
+<span class="normal"><a href="#__codelineno-0-61">61</a></span>
+<span class="normal"><a href="#__codelineno-0-62">62</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
+</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a><span class="sd">        str: The Enumerated Model Type</span>
+</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -3874,9 +3874,9 @@ <h3 id="sageworks.cached.cached_endpoint.CachedEndpoint.summary" class="doc doc-
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Get Endpoint Details</strong></p>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_endpoint</span> <span class="kn">import</span> <span class="n">CachedEndpoint</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_endpoint</span> <span class="kn">import</span> <span class="n">CachedEndpoint</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Grab an Endpoint</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">end</span> <span class="o">=</span> <span class="n">CachedEndpoint</span><span class="p">(</span><span class="s2">&quot;abalone-regression&quot;</span><span class="p">)</span>
diff --git a/cached/cached_feature_set/index.html b/cached/cached_feature_set/index.html
index b9b0cfd13..0a17e3540 100644
--- a/cached/cached_feature_set/index.html
+++ b/cached/cached_feature_set/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Cached FeatureSet - SageWorks</title>
+        <title>Cached FeatureSet - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -728,7 +728,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set" class="md-nav__link">
     <span class="md-ellipsis">
       cached_feature_set
     </span>
@@ -737,7 +737,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet" class="md-nav__link">
     <span class="md-ellipsis">
       CachedFeatureSet
     </span>
@@ -747,7 +747,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.__init__" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -756,7 +756,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.details" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -765,7 +765,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.health_check" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -774,27 +774,27 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.smart_sample" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      smart_sample
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.smart_sample" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.summary" class="md-nav__link">
     <span class="md-ellipsis">
-      smart_sample
+      summary
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.summary" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      summary
+      workbench_meta
     </span>
   </a>
   
@@ -1946,7 +1946,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1956,7 +1956,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3175,11 +3175,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3231,7 +3231,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set" class="md-nav__link">
     <span class="md-ellipsis">
       cached_feature_set
     </span>
@@ -3240,7 +3240,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet" class="md-nav__link">
     <span class="md-ellipsis">
       CachedFeatureSet
     </span>
@@ -3250,7 +3250,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.__init__" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3259,7 +3259,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.details" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3268,7 +3268,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.health_check" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -3277,27 +3277,27 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.smart_sample" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      smart_sample
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.smart_sample" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.summary" class="md-nav__link">
     <span class="md-ellipsis">
-      smart_sample
+      summary
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_feature_set.CachedFeatureSet.summary" class="md-nav__link">
+  <a href="#workbench.cached.cached_feature_set.CachedFeatureSet.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      summary
+      workbench_meta
     </span>
   </a>
   
@@ -3335,7 +3335,7 @@
 <h1 id="cachedfeatureset">CachedFeatureSet</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Model Examples</p>
-<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p>
+<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p>
 </div>
 
 
@@ -3343,10 +3343,10 @@ <h1 id="cachedfeatureset">CachedFeatureSet</h1>
 
 
 
-<a id="sageworks.cached.cached_feature_set"></a>
+<a id="workbench.cached.cached_feature_set"></a>
     <div class="doc doc-contents first">
 
-        <p>CachedFeatureSet: Caches the method results for SageWorks FeatureSets</p>
+        <p>CachedFeatureSet: Caches the method results for Workbench FeatureSets</p>
 
 
 
@@ -3368,7 +3368,7 @@ <h1 id="cachedfeatureset">CachedFeatureSet</h1>
 
 
 
-<h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-heading">
+<h2 id="workbench.cached.cached_feature_set.CachedFeatureSet" class="doc doc-heading">
             <code>CachedFeatureSet</code>
 
 
@@ -3377,10 +3377,10 @@ <h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-hea
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.feature_set_core.FeatureSetCore" href="../../core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore">FeatureSetCore</a></code></p>
+              Bases: <code><span title="workbench.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.feature_set_core.FeatureSetCore" href="../../core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore">FeatureSetCore</a></code></p>
 
 
-        <p>CachedFeatureSet: Caches the method results for SageWorks FeatureSets</p>
+        <p>CachedFeatureSet: Caches the method results for Workbench FeatureSets</p>
 <p>Note: Cached method values may lag underlying FeatureSet changes.</p>
 
 
@@ -3389,7 +3389,7 @@ <h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-hea
   <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">my_features</span> <span class="o">=</span> <span class="n">CachedFeatureSet</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">my_features</span><span class="o">.</span><span class="n">details</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">my_features</span><span class="o">.</span><span class="n">health_check</span><span class="p">()</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_features</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_features</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span></code></pre></div>
 </details>
 
@@ -3398,7 +3398,7 @@ <h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-hea
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/cached/cached_feature_set.py</code></summary>
+                <summary>Source code in <code>src/workbench/cached/cached_feature_set.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
 <span class="normal"><a href="#__codelineno-0-14">14</a></span>
@@ -3461,7 +3461,7 @@ <h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-hea
 <span class="normal"><a href="#__codelineno-0-71">71</a></span>
 <span class="normal"><a href="#__codelineno-0-72">72</a></span>
 <span class="normal"><a href="#__codelineno-0-73">73</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="k">class</span> <span class="nc">CachedFeatureSet</span><span class="p">(</span><span class="n">CachedArtifactMixin</span><span class="p">,</span> <span class="n">FeatureSetCore</span><span class="p">):</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedFeatureSet: Caches the method results for SageWorks FeatureSets</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedFeatureSet: Caches the method results for Workbench FeatureSets</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="sd">    Note: Cached method values may lag underlying FeatureSet changes.</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a>
@@ -3470,11 +3470,11 @@ <h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-hea
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">        my_features = CachedFeatureSet(name)</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        my_features.details()</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        my_features.health_check()</span>
-</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        my_features.sageworks_meta()</span>
+</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        my_features.workbench_meta()</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">        ```</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
-</span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_set_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">):</span>
+</span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_set_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;CachedFeatureSet Initialization&quot;&quot;&quot;</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="n">FeatureSetCore</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_set_uuid</span><span class="o">=</span><span class="n">feature_set_uuid</span><span class="p">,</span> <span class="n">use_cached_meta</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>
@@ -3506,13 +3506,13 @@ <h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-hea
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>
 </span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>    <span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>    <span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the SageWorks Metadata for this DataSource.</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>    <span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the Workbench Metadata for this DataSource.</span>
 </span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>
 </span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">        Returns:</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">            Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">            Union[dict, None]: Dictionary of Workbench metadata for this Artifact</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>
 </span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>    <span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
 </span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>    <span class="k">def</span> <span class="nf">smart_sample</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
@@ -3540,8 +3540,8 @@ <h2 id="sageworks.cached.cached_feature_set.CachedFeatureSet" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.__init__" class="doc doc-heading">
-            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">feature_set_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.cached.cached_feature_set.CachedFeatureSet.__init__" class="doc doc-heading">
+            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">feature_set_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -3551,10 +3551,10 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.__init__" class="do
         <p>CachedFeatureSet Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-26">26</a></span>
 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
-<span class="normal"><a href="#__codelineno-0-28">28</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_set_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">):</span>
+<span class="normal"><a href="#__codelineno-0-28">28</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_set_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedFeatureSet Initialization&quot;&quot;&quot;</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="n">FeatureSetCore</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_set_uuid</span><span class="o">=</span><span class="n">feature_set_uuid</span><span class="p">,</span> <span class="n">use_cached_meta</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -3566,7 +3566,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.__init__" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.details" class="doc doc-heading">
+<h3 id="workbench.cached.cached_feature_set.CachedFeatureSet.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3600,7 +3600,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.details" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-39">39</a></span>
 <span class="normal"><a href="#__codelineno-0-40">40</a></span>
 <span class="normal"><a href="#__codelineno-0-41">41</a></span>
@@ -3625,7 +3625,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.details" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.health_check" class="doc doc-heading">
+<h3 id="workbench.cached.cached_feature_set.CachedFeatureSet.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3659,7 +3659,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.health_check" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-48">48</a></span>
 <span class="normal"><a href="#__codelineno-0-49">49</a></span>
 <span class="normal"><a href="#__codelineno-0-50">50</a></span>
@@ -3684,66 +3684,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.health_check" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">sageworks_meta</span><span class="p">()</span></code>
-
-</h3>
-
-
-    <div class="doc doc-contents ">
-
-        <p>Retrieve the SageWorks Metadata for this DataSource.</p>
-
-
-    <p><span class="doc-section-title">Returns:</span></p>
-    <table>
-      <thead>
-        <tr>
-          <th>Type</th>
-          <th>Description</th>
-        </tr>
-      </thead>
-      <tbody>
-          <tr class="doc-section-item">
-            <td>
-                  <code><span title="typing.Union">Union</span>[str, None]</code>
-            </td>
-            <td>
-              <div class="doc-md-description">
-                <p>Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</p>
-              </div>
-            </td>
-          </tr>
-      </tbody>
-    </table>
-
-            <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_feature_set.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-57">57</a></span>
-<span class="normal"><a href="#__codelineno-0-58">58</a></span>
-<span class="normal"><a href="#__codelineno-0-59">59</a></span>
-<span class="normal"><a href="#__codelineno-0-60">60</a></span>
-<span class="normal"><a href="#__codelineno-0-61">61</a></span>
-<span class="normal"><a href="#__codelineno-0-62">62</a></span>
-<span class="normal"><a href="#__codelineno-0-63">63</a></span>
-<span class="normal"><a href="#__codelineno-0-64">64</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a><span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the SageWorks Metadata for this DataSource.</span>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>
-</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">        Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
-</span></code></pre></div></td></tr></table></div>
-            </details>
-    </div>
-
-</div>
-
-<div class="doc doc-object doc-function">
-
-
-<h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.smart_sample" class="doc doc-heading">
+<h3 id="workbench.cached.cached_feature_set.CachedFeatureSet.smart_sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">smart_sample</span><span class="p">()</span></code>
 
 </h3>
@@ -3777,7 +3718,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.smart_sample" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
 <span class="normal"><a href="#__codelineno-0-68">68</a></span>
@@ -3802,7 +3743,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.smart_sample" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.summary" class="doc doc-heading">
+<h3 id="workbench.cached.cached_feature_set.CachedFeatureSet.summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3836,7 +3777,7 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.summary" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_feature_set.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_feature_set.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-30">30</a></span>
 <span class="normal"><a href="#__codelineno-0-31">31</a></span>
 <span class="normal"><a href="#__codelineno-0-32">32</a></span>
@@ -3858,6 +3799,65 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.summary" class="doc
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+<h3 id="workbench.cached.cached_feature_set.CachedFeatureSet.workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">workbench_meta</span><span class="p">()</span></code>
+
+</h3>
+
+
+    <div class="doc doc-contents ">
+
+        <p>Retrieve the Workbench Metadata for this DataSource.</p>
+
+
+    <p><span class="doc-section-title">Returns:</span></p>
+    <table>
+      <thead>
+        <tr>
+          <th>Type</th>
+          <th>Description</th>
+        </tr>
+      </thead>
+      <tbody>
+          <tr class="doc-section-item">
+            <td>
+                  <code><span title="typing.Union">Union</span>[str, None]</code>
+            </td>
+            <td>
+              <div class="doc-md-description">
+                <p>Union[dict, None]: Dictionary of Workbench metadata for this Artifact</p>
+              </div>
+            </td>
+          </tr>
+      </tbody>
+    </table>
+
+            <details class="quote">
+              <summary>Source code in <code>src/workbench/cached/cached_feature_set.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-57">57</a></span>
+<span class="normal"><a href="#__codelineno-0-58">58</a></span>
+<span class="normal"><a href="#__codelineno-0-59">59</a></span>
+<span class="normal"><a href="#__codelineno-0-60">60</a></span>
+<span class="normal"><a href="#__codelineno-0-61">61</a></span>
+<span class="normal"><a href="#__codelineno-0-62">62</a></span>
+<span class="normal"><a href="#__codelineno-0-63">63</a></span>
+<span class="normal"><a href="#__codelineno-0-64">64</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a><span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the Workbench Metadata for this DataSource.</span>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>
+</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">        Union[dict, None]: Dictionary of Workbench metadata for this Artifact</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
+</span></code></pre></div></td></tr></table></div>
+            </details>
+    </div>
+
+</div>
+
 
 
   </div>
@@ -3874,9 +3874,9 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.summary" class="doc
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Pull FeatureSet Details</strong></p>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_feature_set</span> <span class="kn">import</span> <span class="n">CachedFeatureSet</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_feature_set</span> <span class="kn">import</span> <span class="n">CachedFeatureSet</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Grab a FeatureSet</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">fs</span> <span class="o">=</span> <span class="n">CachedFeatureSet</span><span class="p">(</span><span class="s2">&quot;abalone_features&quot;</span><span class="p">)</span>
@@ -3888,13 +3888,13 @@ <h3 id="sageworks.cached.cached_feature_set.CachedFeatureSet.summary" class="doc
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>
 </span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="p">{</span><span class="s1">&#39;uuid&#39;</span><span class="p">:</span> <span class="s1">&#39;abalone_features&#39;</span><span class="p">,</span>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a> <span class="s1">&#39;health_tags&#39;</span><span class="p">:</span> <span class="p">[],</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a> <span class="s1">&#39;aws_arn&#39;</span><span class="p">:</span> <span class="s1">&#39;arn:aws:glue:x:table/sageworks/abalone_data&#39;</span><span class="p">,</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a> <span class="s1">&#39;aws_arn&#39;</span><span class="p">:</span> <span class="s1">&#39;arn:aws:glue:x:table/workbench/abalone_data&#39;</span><span class="p">,</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a> <span class="s1">&#39;size&#39;</span><span class="p">:</span> <span class="mf">0.070272</span><span class="p">,</span>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15" href="#__codelineno-0-15"></a> <span class="s1">&#39;created&#39;</span><span class="p">:</span> <span class="s1">&#39;2024-11-09T20:42:34.000Z&#39;</span><span class="p">,</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a> <span class="s1">&#39;modified&#39;</span><span class="p">:</span> <span class="s1">&#39;2024-11-10T19:57:52.000Z&#39;</span><span class="p">,</span>
-</span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a> <span class="s1">&#39;input&#39;</span><span class="p">:</span> <span class="s1">&#39;s3://sageworks-public-data/common/aBaLone.CSV&#39;</span><span class="p">,</span>
-</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a> <span class="s1">&#39;sageworks_health_tags&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
-</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a> <span class="s1">&#39;sageworks_correlations&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;length&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;diameter&#39;</span><span class="p">:</span> <span class="mf">0.9868115846024996</span><span class="p">,</span>
+</span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a> <span class="s1">&#39;input&#39;</span><span class="p">:</span> <span class="s1">&#39;s3://workbench-public-data/common/aBaLone.CSV&#39;</span><span class="p">,</span>
+</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a> <span class="s1">&#39;workbench_health_tags&#39;</span><span class="p">:</span> <span class="s1">&#39;&#39;</span><span class="p">,</span>
+</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a> <span class="s1">&#39;workbench_correlations&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;length&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;diameter&#39;</span><span class="p">:</span> <span class="mf">0.9868115846024996</span><span class="p">,</span>
 </span></code></pre></div>
 
 
diff --git a/cached/cached_meta/index.html b/cached/cached_meta/index.html
index 7f1b3d295..4f9f0e5a1 100644
--- a/cached/cached_meta/index.html
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@@ -20,7 +20,7 @@
     
     
       
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     <li class="md-nav__item">
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-    SageWorks Docker for Lambdas
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       clear_meta_cache
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       close
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     <span class="md-ellipsis">
       config
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@@ -3448,7 +3448,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.data_source" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.data_source" class="md-nav__link">
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       data_source
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@@ -3457,7 +3457,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.data_sources" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.data_sources" class="md-nav__link">
     <span class="md-ellipsis">
       data_sources
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@@ -3466,7 +3466,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.endpoint" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       endpoint
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@@ -3475,7 +3475,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.endpoints" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.endpoints" class="md-nav__link">
     <span class="md-ellipsis">
       endpoints
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@@ -3484,7 +3484,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.etl_jobs" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.etl_jobs" class="md-nav__link">
     <span class="md-ellipsis">
       etl_jobs
     </span>
@@ -3493,7 +3493,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.feature_set" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.feature_set" class="md-nav__link">
     <span class="md-ellipsis">
       feature_set
     </span>
@@ -3502,7 +3502,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.feature_sets" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.feature_sets" class="md-nav__link">
     <span class="md-ellipsis">
       feature_sets
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@@ -3511,7 +3511,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.glue_job" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.glue_job" class="md-nav__link">
     <span class="md-ellipsis">
       glue_job
     </span>
@@ -3520,7 +3520,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.incoming_data" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.incoming_data" class="md-nav__link">
     <span class="md-ellipsis">
       incoming_data
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@@ -3529,7 +3529,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.list_meta_cache" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.list_meta_cache" class="md-nav__link">
     <span class="md-ellipsis">
       list_meta_cache
     </span>
@@ -3538,7 +3538,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.model" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.model" class="md-nav__link">
     <span class="md-ellipsis">
       model
     </span>
@@ -3547,7 +3547,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.models" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.models" class="md-nav__link">
     <span class="md-ellipsis">
       models
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@@ -3556,7 +3556,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.CachedMeta.views" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.CachedMeta.views" class="md-nav__link">
     <span class="md-ellipsis">
       views
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@@ -3570,7 +3570,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_meta.cache_result" class="md-nav__link">
+  <a href="#workbench.cached.cached_meta.cache_result" class="md-nav__link">
     <span class="md-ellipsis">
       cache_result
     </span>
@@ -3613,7 +3613,7 @@ <h1 id="cachedmeta">CachedMeta</h1>
 
 
 
-<a id="sageworks.cached.cached_meta"></a>
+<a id="workbench.cached.cached_meta"></a>
     <div class="doc doc-contents first">
 
         <p>CachedMeta: A class that provides caching for the Meta() class</p>
@@ -3638,7 +3638,7 @@ <h1 id="cachedmeta">CachedMeta</h1>
 
 
 
-<h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
+<h2 id="workbench.cached.cached_meta.CachedMeta" class="doc doc-heading">
             <code>CachedMeta</code>
 
 
@@ -3647,7 +3647,7 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.cloud_platform.cloud_meta.CloudMeta">CloudMeta</span></code></p>
+              Bases: <code><span title="workbench.core.cloud_platform.cloud_meta.CloudMeta">CloudMeta</span></code></p>
 
 
         <p>CachedMeta: Singleton class for caching metadata functionality.</p>
@@ -3655,7 +3655,7 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 
 <details class="common-usage" open>
   <summary>Common Usage</summary>
-  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
+  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">meta</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c1"># Get the AWS Account Info</span>
@@ -3683,7 +3683,7 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+                <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-48"> 48</a></span>
 <span class="normal"><a href="#__codelineno-0-49"> 49</a></span>
 <span class="normal"><a href="#__codelineno-0-50"> 50</a></span>
@@ -3935,7 +3935,7 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="sd">       ```python</span>
-</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="sd">       from sageworks.cached.cached_meta import CachedMeta</span>
+</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="sd">       from workbench.cached.cached_meta import CachedMeta</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a><span class="sd">       meta = CachedMeta()</span>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="sd">       # Get the AWS Account Info</span>
@@ -3970,13 +3970,13 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_initialized&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initialized</span><span class="p">:</span>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>            <span class="k">return</span>  <span class="c1"># Prevent reinitialization</span>
 </span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>
-</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Initializing CachedMeta...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 </span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>
 </span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>        <span class="c1"># Create both our Meta Cache and Fresh Cache (tracks if data is stale)</span>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">meta_cache</span> <span class="o">=</span> <span class="n">SageWorksCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta&quot;</span><span class="p">)</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">fresh_cache</span> <span class="o">=</span> <span class="n">SageWorksCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta_fresh&quot;</span><span class="p">,</span> <span class="n">expire</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>  <span class="c1"># 90-second expiration</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">meta_cache</span> <span class="o">=</span> <span class="n">WorkbenchCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta&quot;</span><span class="p">)</span>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">fresh_cache</span> <span class="o">=</span> <span class="n">WorkbenchCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta_fresh&quot;</span><span class="p">,</span> <span class="n">expire</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>  <span class="c1"># 90-second expiration</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>        <span class="c1"># Create a ThreadPoolExecutor for refreshing stale data</span>
 </span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">thread_pool</span> <span class="o">=</span> <span class="n">ThreadPoolExecutor</span><span class="p">(</span><span class="n">max_workers</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
@@ -4007,10 +4007,10 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 </span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a>
 </span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a>    <span class="nd">@cache_result</span>
 </span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>    <span class="k">def</span> <span class="nf">config</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the current SageWorks Configuration</span>
+</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the current Workbench Configuration</span>
 </span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a>
 </span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a><span class="sd">        Returns:</span>
-</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="sd">            dict: The current SageWorks Configuration</span>
+</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="sd">            dict: The current Workbench Configuration</span>
 </span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">config</span><span class="p">()</span>
 </span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>
@@ -4042,11 +4042,11 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 </span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">data_sources</span><span class="p">()</span>
 </span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a>
 </span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>    <span class="nd">@cache_result</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>    <span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>    <span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get a summary of the all the Views, for the given database, in AWS</span>
 </span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a><span class="sd">            database (str, optional): Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a><span class="sd">            database (str, optional): Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>
 </span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a><span class="sd">            pd.DataFrame: A summary of all the Views, for the given database, in AWS</span>
@@ -4099,12 +4099,12 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 </span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">glue_job</span><span class="p">(</span><span class="n">job_name</span><span class="o">=</span><span class="n">job_name</span><span class="p">)</span>
 </span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>
 </span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>    <span class="nd">@cache_result</span>
-</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>    <span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>    <span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the details of a specific Data Source</span>
 </span><span id="__span-0-219"><a id="__codelineno-0-219" name="__codelineno-0-219"></a>
 </span><span id="__span-0-220"><a id="__codelineno-0-220" name="__codelineno-0-220"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-221"><a id="__codelineno-0-221" name="__codelineno-0-221"></a><span class="sd">            data_source_name (str): The name of the Data Source</span>
-</span><span id="__span-0-222"><a id="__codelineno-0-222" name="__codelineno-0-222"></a><span class="sd">            database (str, optional): The Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-222"><a id="__codelineno-0-222" name="__codelineno-0-222"></a><span class="sd">            database (str, optional): The Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-223"><a id="__codelineno-0-223" name="__codelineno-0-223"></a>
 </span><span id="__span-0-224"><a id="__codelineno-0-224" name="__codelineno-0-224"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-225"><a id="__codelineno-0-225" name="__codelineno-0-225"></a><span class="sd">            dict: The details of the Data Source (None if not found)</span>
@@ -4195,7 +4195,7 @@ <h2 id="sageworks.cached.cached_meta.CachedMeta" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.__del__" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.__del__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__del__</span><span class="p">()</span></code>
 
 </h3>
@@ -4206,7 +4206,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.__del__" class="doc doc-heading"
         <p>Destructor to shut down the thread pool gracefully.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-278">278</a></span>
 <span class="normal"><a href="#__codelineno-0-279">279</a></span>
 <span class="normal"><a href="#__codelineno-0-280">280</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-278"><a id="__codelineno-0-278" name="__codelineno-0-278"></a><span class="k">def</span> <span class="fm">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -4221,7 +4221,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.__del__" class="doc doc-heading"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.__init__" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">()</span></code>
 
 </h3>
@@ -4232,7 +4232,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.__init__" class="doc doc-heading
         <p>CachedMeta Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-83"> 83</a></span>
 <span class="normal"><a href="#__codelineno-0-84"> 84</a></span>
 <span class="normal"><a href="#__codelineno-0-85"> 85</a></span>
@@ -4255,13 +4255,13 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.__init__" class="doc doc-heading
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;_initialized&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_initialized</span><span class="p">:</span>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>        <span class="k">return</span>  <span class="c1"># Prevent reinitialization</span>
 </span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>
-</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Initializing CachedMeta...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a>    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
 </span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>
 </span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>    <span class="c1"># Create both our Meta Cache and Fresh Cache (tracks if data is stale)</span>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">meta_cache</span> <span class="o">=</span> <span class="n">SageWorksCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta&quot;</span><span class="p">)</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">fresh_cache</span> <span class="o">=</span> <span class="n">SageWorksCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta_fresh&quot;</span><span class="p">,</span> <span class="n">expire</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>  <span class="c1"># 90-second expiration</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">meta_cache</span> <span class="o">=</span> <span class="n">WorkbenchCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta&quot;</span><span class="p">)</span>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">fresh_cache</span> <span class="o">=</span> <span class="n">WorkbenchCache</span><span class="p">(</span><span class="n">prefix</span><span class="o">=</span><span class="s2">&quot;meta_fresh&quot;</span><span class="p">,</span> <span class="n">expire</span><span class="o">=</span><span class="mi">90</span><span class="p">)</span>  <span class="c1"># 90-second expiration</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>    <span class="c1"># Create a ThreadPoolExecutor for refreshing stale data</span>
 </span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">thread_pool</span> <span class="o">=</span> <span class="n">ThreadPoolExecutor</span><span class="p">(</span><span class="n">max_workers</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
@@ -4277,7 +4277,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.__init__" class="doc doc-heading
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.account" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.account" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">account</span><span class="p">()</span></code>
 
 </h3>
@@ -4311,7 +4311,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.account" class="doc doc-heading"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-114">114</a></span>
 <span class="normal"><a href="#__codelineno-0-115">115</a></span>
 <span class="normal"><a href="#__codelineno-0-116">116</a></span>
@@ -4336,7 +4336,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.account" class="doc doc-heading"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.check" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">check</span><span class="p">()</span></code>
 
 </h3>
@@ -4347,7 +4347,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.check" class="doc doc-heading">
         <p>Check if our underlying caches are working</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-102">102</a></span>
 <span class="normal"><a href="#__codelineno-0-103">103</a></span>
 <span class="normal"><a href="#__codelineno-0-104">104</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a><span class="k">def</span> <span class="nf">check</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -4362,7 +4362,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.check" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.clear_meta_cache" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.clear_meta_cache" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">clear_meta_cache</span><span class="p">()</span></code>
 
 </h3>
@@ -4373,7 +4373,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.clear_meta_cache" class="doc doc
         <p>Clear the current Meta Cache</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-110">110</a></span>
 <span class="normal"><a href="#__codelineno-0-111">111</a></span>
 <span class="normal"><a href="#__codelineno-0-112">112</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a><span class="k">def</span> <span class="nf">clear_meta_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -4388,7 +4388,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.clear_meta_cache" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.close" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.close" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">close</span><span class="p">()</span></code>
 
 </h3>
@@ -4399,7 +4399,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.close" class="doc doc-heading">
         <p>Explicitly close the thread pool, if needed.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-282">282</a></span>
 <span class="normal"><a href="#__codelineno-0-283">283</a></span>
 <span class="normal"><a href="#__codelineno-0-284">284</a></span>
@@ -4428,7 +4428,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.close" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.config" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.config" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">config</span><span class="p">()</span></code>
 
 </h3>
@@ -4436,7 +4436,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.config" class="doc doc-heading">
 
     <div class="doc doc-contents ">
 
-        <p>Return the current SageWorks Configuration</p>
+        <p>Return the current Workbench Configuration</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -4454,7 +4454,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.config" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The current SageWorks Configuration</p>
+                <p>The current Workbench Configuration</p>
               </div>
             </td>
           </tr>
@@ -4462,7 +4462,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.config" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-123">123</a></span>
 <span class="normal"><a href="#__codelineno-0-124">124</a></span>
 <span class="normal"><a href="#__codelineno-0-125">125</a></span>
@@ -4472,10 +4472,10 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.config" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-129">129</a></span>
 <span class="normal"><a href="#__codelineno-0-130">130</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a><span class="nd">@cache_result</span>
 </span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a><span class="k">def</span> <span class="nf">config</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the current SageWorks Configuration</span>
+</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the current Workbench Configuration</span>
 </span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a>
 </span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="sd">        dict: The current SageWorks Configuration</span>
+</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="sd">        dict: The current Workbench Configuration</span>
 </span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">config</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
@@ -4487,8 +4487,8 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.config" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.data_source" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">data_source</span><span class="p">(</span><span class="n">data_source_name</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.cached.cached_meta.CachedMeta.data_source" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">data_source</span><span class="p">(</span><span class="n">data_source_name</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -4534,11 +4534,11 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.data_source" class="doc doc-head
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The Glue database. Defaults to 'sageworks'.</p>
+                <p>The Glue database. Defaults to 'workbench'.</p>
               </div>
             </td>
             <td>
-                  <code>&#39;sageworks&#39;</code>
+                  <code>&#39;workbench&#39;</code>
             </td>
           </tr>
       </tbody>
@@ -4568,7 +4568,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.data_source" class="doc doc-head
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-216">216</a></span>
 <span class="normal"><a href="#__codelineno-0-217">217</a></span>
 <span class="normal"><a href="#__codelineno-0-218">218</a></span>
@@ -4581,12 +4581,12 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.data_source" class="doc doc-head
 <span class="normal"><a href="#__codelineno-0-225">225</a></span>
 <span class="normal"><a href="#__codelineno-0-226">226</a></span>
 <span class="normal"><a href="#__codelineno-0-227">227</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a><span class="nd">@cache_result</span>
-</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a><span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a><span class="k">def</span> <span class="nf">data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the details of a specific Data Source</span>
 </span><span id="__span-0-219"><a id="__codelineno-0-219" name="__codelineno-0-219"></a>
 </span><span id="__span-0-220"><a id="__codelineno-0-220" name="__codelineno-0-220"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-221"><a id="__codelineno-0-221" name="__codelineno-0-221"></a><span class="sd">        data_source_name (str): The name of the Data Source</span>
-</span><span id="__span-0-222"><a id="__codelineno-0-222" name="__codelineno-0-222"></a><span class="sd">        database (str, optional): The Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-222"><a id="__codelineno-0-222" name="__codelineno-0-222"></a><span class="sd">        database (str, optional): The Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-223"><a id="__codelineno-0-223" name="__codelineno-0-223"></a>
 </span><span id="__span-0-224"><a id="__codelineno-0-224" name="__codelineno-0-224"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-225"><a id="__codelineno-0-225" name="__codelineno-0-225"></a><span class="sd">        dict: The details of the Data Source (None if not found)</span>
@@ -4601,7 +4601,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.data_source" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.data_sources" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.data_sources" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">data_sources</span><span class="p">()</span></code>
 
 </h3>
@@ -4635,7 +4635,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.data_sources" class="doc doc-hea
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-150">150</a></span>
 <span class="normal"><a href="#__codelineno-0-151">151</a></span>
 <span class="normal"><a href="#__codelineno-0-152">152</a></span>
@@ -4660,7 +4660,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.data_sources" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.endpoint" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.endpoint" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">endpoint</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span></code>
 
 </h3>
@@ -4725,7 +4725,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.endpoint" class="doc doc-heading
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-253">253</a></span>
 <span class="normal"><a href="#__codelineno-0-254">254</a></span>
 <span class="normal"><a href="#__codelineno-0-255">255</a></span>
@@ -4756,7 +4756,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.endpoint" class="doc doc-heading
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.endpoints" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.endpoints" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">endpoints</span><span class="p">()</span></code>
 
 </h3>
@@ -4790,7 +4790,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.endpoints" class="doc doc-headin
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-195">195</a></span>
 <span class="normal"><a href="#__codelineno-0-196">196</a></span>
 <span class="normal"><a href="#__codelineno-0-197">197</a></span>
@@ -4815,7 +4815,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.endpoints" class="doc doc-headin
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.etl_jobs" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.etl_jobs" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">etl_jobs</span><span class="p">()</span></code>
 
 </h3>
@@ -4849,7 +4849,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.etl_jobs" class="doc doc-heading
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-141">141</a></span>
 <span class="normal"><a href="#__codelineno-0-142">142</a></span>
 <span class="normal"><a href="#__codelineno-0-143">143</a></span>
@@ -4874,7 +4874,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.etl_jobs" class="doc doc-heading
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.feature_set" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.feature_set" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">feature_set</span><span class="p">(</span><span class="n">feature_set_name</span><span class="p">)</span></code>
 
 </h3>
@@ -4939,7 +4939,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.feature_set" class="doc doc-head
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-229">229</a></span>
 <span class="normal"><a href="#__codelineno-0-230">230</a></span>
 <span class="normal"><a href="#__codelineno-0-231">231</a></span>
@@ -4970,7 +4970,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.feature_set" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.feature_sets" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.feature_sets" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">feature_sets</span><span class="p">(</span><span class="n">details</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5035,7 +5035,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.feature_sets" class="doc doc-hea
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-171">171</a></span>
 <span class="normal"><a href="#__codelineno-0-172">172</a></span>
 <span class="normal"><a href="#__codelineno-0-173">173</a></span>
@@ -5066,7 +5066,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.feature_sets" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.glue_job" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.glue_job" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">glue_job</span><span class="p">(</span><span class="n">job_name</span><span class="p">)</span></code>
 
 </h3>
@@ -5131,7 +5131,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.glue_job" class="doc doc-heading
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-204">204</a></span>
 <span class="normal"><a href="#__codelineno-0-205">205</a></span>
 <span class="normal"><a href="#__codelineno-0-206">206</a></span>
@@ -5162,7 +5162,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.glue_job" class="doc doc-heading
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.incoming_data" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.incoming_data" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">incoming_data</span><span class="p">()</span></code>
 
 </h3>
@@ -5196,7 +5196,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.incoming_data" class="doc doc-he
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-132">132</a></span>
 <span class="normal"><a href="#__codelineno-0-133">133</a></span>
 <span class="normal"><a href="#__codelineno-0-134">134</a></span>
@@ -5221,7 +5221,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.incoming_data" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.list_meta_cache" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.list_meta_cache" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">list_meta_cache</span><span class="p">()</span></code>
 
 </h3>
@@ -5232,7 +5232,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.list_meta_cache" class="doc doc-
         <p>List the current Meta Cache</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-106">106</a></span>
 <span class="normal"><a href="#__codelineno-0-107">107</a></span>
 <span class="normal"><a href="#__codelineno-0-108">108</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a><span class="k">def</span> <span class="nf">list_meta_cache</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -5247,7 +5247,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.list_meta_cache" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.model" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.model" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">model</span><span class="p">(</span><span class="n">model_name</span><span class="p">)</span></code>
 
 </h3>
@@ -5312,7 +5312,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.model" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-241">241</a></span>
 <span class="normal"><a href="#__codelineno-0-242">242</a></span>
 <span class="normal"><a href="#__codelineno-0-243">243</a></span>
@@ -5343,7 +5343,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.model" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.models" class="doc doc-heading">
+<h3 id="workbench.cached.cached_meta.CachedMeta.models" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">models</span><span class="p">(</span><span class="n">details</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5408,7 +5408,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.models" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-183">183</a></span>
 <span class="normal"><a href="#__codelineno-0-184">184</a></span>
 <span class="normal"><a href="#__codelineno-0-185">185</a></span>
@@ -5439,8 +5439,8 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.models" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_meta.CachedMeta.views" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">views</span><span class="p">(</span><span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.cached.cached_meta.CachedMeta.views" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">views</span><span class="p">(</span><span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -5470,11 +5470,11 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.views" class="doc doc-heading">
             </td>
             <td>
               <div class="doc-md-description">
-                <p>Glue database. Defaults to 'sageworks'.</p>
+                <p>Glue database. Defaults to 'workbench'.</p>
               </div>
             </td>
             <td>
-                  <code>&#39;sageworks&#39;</code>
+                  <code>&#39;workbench&#39;</code>
             </td>
           </tr>
       </tbody>
@@ -5504,7 +5504,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.views" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-159">159</a></span>
 <span class="normal"><a href="#__codelineno-0-160">160</a></span>
 <span class="normal"><a href="#__codelineno-0-161">161</a></span>
@@ -5516,11 +5516,11 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.views" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-167">167</a></span>
 <span class="normal"><a href="#__codelineno-0-168">168</a></span>
 <span class="normal"><a href="#__codelineno-0-169">169</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a><span class="nd">@cache_result</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a><span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a><span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get a summary of the all the Views, for the given database, in AWS</span>
 </span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a><span class="sd">        database (str, optional): Glue database. Defaults to &#39;sageworks&#39;.</span>
+</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a><span class="sd">        database (str, optional): Glue database. Defaults to &#39;workbench&#39;.</span>
 </span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>
 </span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a><span class="sd">        pd.DataFrame: A summary of all the Views, for the given database, in AWS</span>
@@ -5544,7 +5544,7 @@ <h3 id="sageworks.cached.cached_meta.CachedMeta.views" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
+<h2 id="workbench.cached.cached_meta.cache_result" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">cache_result</span><span class="p">(</span><span class="n">method</span><span class="p">)</span></code>
 
 </h2>
@@ -5555,7 +5555,7 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
         <p>Decorator to cache method results in meta_cache</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_meta.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_meta.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-18">18</a></span>
 <span class="normal"><a href="#__codelineno-0-19">19</a></span>
 <span class="normal"><a href="#__codelineno-0-20">20</a></span>
@@ -5592,7 +5592,7 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>        <span class="n">cache_key</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="o">.</span><span class="n">_flatten_redis_key</span><span class="p">(</span><span class="n">method</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>        <span class="c1"># Check for fresh data, spawn thread to refresh if stale</span>
-</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>        <span class="k">if</span> <span class="n">SageWorksCache</span><span class="o">.</span><span class="n">refresh_enabled</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">fresh_cache</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">cache_key</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>        <span class="k">if</span> <span class="n">WorkbenchCache</span><span class="o">.</span><span class="n">refresh_enabled</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">fresh_cache</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">cache_key</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Async: Metadata for </span><span class="si">{</span><span class="n">cache_key</span><span class="si">}</span><span class="s2"> refresh thread started...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">fresh_cache</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">cache_key</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>  <span class="c1"># Mark as refreshed</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>
@@ -5626,10 +5626,10 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
 </div><h2 id="examples">Examples</h2>
 <p>These example show how to use the <code>CachedMeta()</code> class to pull lists of artifacts from AWS. DataSources, FeatureSets, Models, Endpoints and more. If you're building a web interface plugin, the <strong>CachedMeta</strong> class is a great place to start.</p>
 <div class="admonition tip">
-<p class="admonition-title">SageWorks REPL</p>
-<p>If you'd like to see <strong>exactly</strong> what data/details you get back from the <code>CachedMeta()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">SageWorks REPL</a></p>
+<p class="admonition-title">Workbench REPL</p>
+<p>If you'd like to see <strong>exactly</strong> what data/details you get back from the <code>CachedMeta()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! <a href="../../repl/">Workbench REPL</a></p>
 </div>
-<div class="language-py highlight"><span class="filename">Using SageWorks REPL</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">CachedMeta</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="p">()</span>
+<div class="language-py highlight"><span class="filename">Using Workbench REPL</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">CachedMeta</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="p">()</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">model_df</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="o">.</span><span class="n">models</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">model_df</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>               <span class="n">Model</span> <span class="n">Group</span>   <span class="n">Health</span> <span class="n">Owner</span>  <span class="o">...</span>             <span class="n">Input</span>     <span class="n">Status</span>                <span class="n">Description</span>
@@ -5640,7 +5640,7 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
 </span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="p">[</span><span class="mi">3</span> <span class="n">rows</span> <span class="n">x</span> <span class="mi">10</span> <span class="n">columns</span><span class="p">]</span>
 </span></code></pre></div>
 <p><strong>List the Models in AWS</strong></p>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="c1"># Create our CachedMeta Class and get a list of our Models</span>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="n">CachedMeta</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="p">()</span>
@@ -5667,7 +5667,7 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
 </span><span id="__span-2-10"><a id="__codelineno-2-10" name="__codelineno-2-10" href="#__codelineno-2-10"></a><span class="n">abalone</span><span class="o">-</span><span class="n">regression</span>
 </span></code></pre></div>
 <p><strong>Getting Model Performance Metrics</strong></p>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
 </span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>
 </span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="c1"># Create our CachedMeta Class and get a list of our Models</span>
 </span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="n">CachedMeta</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="p">()</span>
@@ -5681,7 +5681,7 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
 </span><span id="__span-3-12"><a id="__codelineno-3-12" name="__codelineno-3-12" href="#__codelineno-3-12"></a><span class="k">for</span> <span class="n">name</span> <span class="ow">in</span> <span class="n">model_names</span><span class="p">[:</span><span class="mi">5</span><span class="p">]:</span>
 </span><span id="__span-3-13"><a id="__codelineno-3-13" name="__codelineno-3-13" href="#__codelineno-3-13"></a>    <span class="n">model_details</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
 </span><span id="__span-3-14"><a id="__codelineno-3-14" name="__codelineno-3-14" href="#__codelineno-3-14"></a>    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n\n</span><span class="s2">Model: </span><span class="si">{</span><span class="n">name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-3-15"><a id="__codelineno-3-15" name="__codelineno-3-15" href="#__codelineno-3-15"></a>    <span class="n">performance_metrics</span> <span class="o">=</span> <span class="n">model_details</span><span class="p">[</span><span class="s2">&quot;sageworks_CachedMeta&quot;</span><span class="p">][</span><span class="s2">&quot;sageworks_inference_metrics&quot;</span><span class="p">]</span>
+</span><span id="__span-3-15"><a id="__codelineno-3-15" name="__codelineno-3-15" href="#__codelineno-3-15"></a>    <span class="n">performance_metrics</span> <span class="o">=</span> <span class="n">model_details</span><span class="p">[</span><span class="s2">&quot;workbench_CachedMeta&quot;</span><span class="p">][</span><span class="s2">&quot;workbench_inference_metrics&quot;</span><span class="p">]</span>
 </span><span id="__span-3-16"><a id="__codelineno-3-16" name="__codelineno-3-16" href="#__codelineno-3-16"></a>    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\t</span><span class="s2">Performance Metrics: </span><span class="si">{</span><span class="n">performance_metrics</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span></code></pre></div>
 <p><strong>Output</strong></p>
@@ -5701,7 +5701,7 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
 </span></code></pre></div>
 <p><strong>List the Endpoints in AWS</strong></p>
 <div class="language-python highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
-</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
+</span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_meta</span> <span class="kn">import</span> <span class="n">CachedMeta</span>
 </span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>
 </span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a><span class="c1"># Create our CachedMeta Class and get a list of our Endpoints</span>
 </span><span id="__span-5-5"><a id="__codelineno-5-5" name="__codelineno-5-5" href="#__codelineno-5-5"></a><span class="n">CachedMeta</span> <span class="o">=</span> <span class="n">CachedMeta</span><span class="p">()</span>
@@ -5726,7 +5726,7 @@ <h2 id="sageworks.cached.cached_meta.cache_result" class="doc doc-heading">
 </span></code></pre></div>
 <div class="admonition note">
 <p class="admonition-title">Not Finding some particular AWS Data?</p>
-<p>The SageWorks CachedMeta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>
+<p>The Workbench CachedMeta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>
 </div>
 
 
diff --git a/cached/cached_model/index.html b/cached/cached_model/index.html
index 90c9be260..ac1be5653 100644
--- a/cached/cached_model/index.html
+++ b/cached/cached_model/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Cached Model - SageWorks</title>
+        <title>Cached Model - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -749,7 +749,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model" class="md-nav__link">
+  <a href="#workbench.cached.cached_model" class="md-nav__link">
     <span class="md-ellipsis">
       cached_model
     </span>
@@ -758,7 +758,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel" class="md-nav__link">
     <span class="md-ellipsis">
       CachedModel
     </span>
@@ -768,7 +768,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.__init__" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -777,7 +777,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.confusion_matrix" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.confusion_matrix" class="md-nav__link">
     <span class="md-ellipsis">
       confusion_matrix
     </span>
@@ -786,7 +786,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.details" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -795,7 +795,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.get_endpoint_inference_path" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.get_endpoint_inference_path" class="md-nav__link">
     <span class="md-ellipsis">
       get_endpoint_inference_path
     </span>
@@ -804,7 +804,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.get_inference_metrics" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.get_inference_metrics" class="md-nav__link">
     <span class="md-ellipsis">
       get_inference_metrics
     </span>
@@ -813,7 +813,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.get_inference_predictions" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.get_inference_predictions" class="md-nav__link">
     <span class="md-ellipsis">
       get_inference_predictions
     </span>
@@ -822,7 +822,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.health_check" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -831,7 +831,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.list_inference_runs" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.list_inference_runs" class="md-nav__link">
     <span class="md-ellipsis">
       list_inference_runs
     </span>
@@ -840,18 +840,18 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.summary" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      summary
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.summary" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      summary
+      workbench_meta
     </span>
   </a>
   
@@ -1982,7 +1982,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1992,7 +1992,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3211,11 +3211,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3267,7 +3267,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model" class="md-nav__link">
+  <a href="#workbench.cached.cached_model" class="md-nav__link">
     <span class="md-ellipsis">
       cached_model
     </span>
@@ -3276,7 +3276,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel" class="md-nav__link">
     <span class="md-ellipsis">
       CachedModel
     </span>
@@ -3286,7 +3286,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.__init__" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3295,7 +3295,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.confusion_matrix" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.confusion_matrix" class="md-nav__link">
     <span class="md-ellipsis">
       confusion_matrix
     </span>
@@ -3304,7 +3304,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.details" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3313,7 +3313,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.get_endpoint_inference_path" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.get_endpoint_inference_path" class="md-nav__link">
     <span class="md-ellipsis">
       get_endpoint_inference_path
     </span>
@@ -3322,7 +3322,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.get_inference_metrics" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.get_inference_metrics" class="md-nav__link">
     <span class="md-ellipsis">
       get_inference_metrics
     </span>
@@ -3331,7 +3331,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.get_inference_predictions" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.get_inference_predictions" class="md-nav__link">
     <span class="md-ellipsis">
       get_inference_predictions
     </span>
@@ -3340,7 +3340,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.health_check" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -3349,7 +3349,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.list_inference_runs" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.list_inference_runs" class="md-nav__link">
     <span class="md-ellipsis">
       list_inference_runs
     </span>
@@ -3358,18 +3358,18 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.summary" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      summary
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.cached.cached_model.CachedModel.summary" class="md-nav__link">
+  <a href="#workbench.cached.cached_model.CachedModel.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      summary
+      workbench_meta
     </span>
   </a>
   
@@ -3407,7 +3407,7 @@
 <h1 id="cachedmodel">CachedModel</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Model Examples</p>
-<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p>
+<p>Examples of using the Model Class are in the <a href="#examples">Examples</a> section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p>
 </div>
 
 
@@ -3415,10 +3415,10 @@ <h1 id="cachedmodel">CachedModel</h1>
 
 
 
-<a id="sageworks.cached.cached_model"></a>
+<a id="workbench.cached.cached_model"></a>
     <div class="doc doc-contents first">
 
-        <p>CachedModel: Caches the method results for SageWorks Models</p>
+        <p>CachedModel: Caches the method results for Workbench Models</p>
 
 
 
@@ -3440,7 +3440,7 @@ <h1 id="cachedmodel">CachedModel</h1>
 
 
 
-<h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
+<h2 id="workbench.cached.cached_model.CachedModel" class="doc doc-heading">
             <code>CachedModel</code>
 
 
@@ -3449,10 +3449,10 @@ <h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.model_core.ModelCore" href="../../core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore">ModelCore</a></code></p>
+              Bases: <code><span title="workbench.core.artifacts.cached_artifact_mixin.CachedArtifactMixin">CachedArtifactMixin</span></code>, <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.model_core.ModelCore" href="../../core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore">ModelCore</a></code></p>
 
 
-        <p>CachedModel: Caches the method results for SageWorks Models</p>
+        <p>CachedModel: Caches the method results for Workbench Models</p>
 <p>Note: Cached method values may lag underlying Model changes.</p>
 
 
@@ -3461,7 +3461,7 @@ <h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
   <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">my_model</span> <span class="o">=</span> <span class="n">CachedModel</span><span class="p">(</span><span class="n">name</span><span class="p">)</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">my_model</span><span class="o">.</span><span class="n">details</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">my_model</span><span class="o">.</span><span class="n">health_check</span><span class="p">()</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_model</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">my_model</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span></code></pre></div>
 </details>
 
@@ -3470,7 +3470,7 @@ <h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+                <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-11"> 11</a></span>
 <span class="normal"><a href="#__codelineno-0-12"> 12</a></span>
 <span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
@@ -3583,7 +3583,7 @@ <h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-120">120</a></span>
 <span class="normal"><a href="#__codelineno-0-121">121</a></span>
 <span class="normal"><a href="#__codelineno-0-122">122</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11"></a><span class="k">class</span> <span class="nc">CachedModel</span><span class="p">(</span><span class="n">CachedArtifactMixin</span><span class="p">,</span> <span class="n">ModelCore</span><span class="p">):</span>
-</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedModel: Caches the method results for SageWorks Models</span>
+</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CachedModel: Caches the method results for Workbench Models</span>
 </span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="sd">    Note: Cached method values may lag underlying Model changes.</span>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a>
@@ -3592,7 +3592,7 @@ <h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="sd">        my_model = CachedModel(name)</span>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">        my_model.details()</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        my_model.health_check()</span>
-</span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        my_model.sageworks_meta()</span>
+</span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        my_model.workbench_meta()</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        ```</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>
@@ -3628,13 +3628,13 @@ <h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>    <span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>    <span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>    <span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</span>
 </span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>
 </span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">            str: The Enumerated Model Type</span>
 </span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>
 </span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>    <span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
 </span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>    <span class="k">def</span> <span class="nf">get_endpoint_inference_path</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
@@ -3712,7 +3712,7 @@ <h2 id="sageworks.cached.cached_model.CachedModel" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.__init__" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -3723,7 +3723,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.__init__" class="doc doc-headi
         <p>CachedModel Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-25">25</a></span>
 <span class="normal"><a href="#__codelineno-0-26">26</a></span>
 <span class="normal"><a href="#__codelineno-0-27">27</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
@@ -3738,7 +3738,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.__init__" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.confusion_matrix" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.confusion_matrix" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;latest&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -3803,7 +3803,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.confusion_matrix" class="doc d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-112">112</a></span>
 <span class="normal"><a href="#__codelineno-0-113">113</a></span>
 <span class="normal"><a href="#__codelineno-0-114">114</a></span>
@@ -3834,7 +3834,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.confusion_matrix" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.details" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3868,7 +3868,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.details" class="doc doc-headin
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-38">38</a></span>
 <span class="normal"><a href="#__codelineno-0-39">39</a></span>
 <span class="normal"><a href="#__codelineno-0-40">40</a></span>
@@ -3893,7 +3893,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.details" class="doc doc-headin
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.get_endpoint_inference_path" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.get_endpoint_inference_path" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_endpoint_inference_path</span><span class="p">()</span></code>
 
 </h3>
@@ -3927,7 +3927,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.get_endpoint_inference_path" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-65">65</a></span>
 <span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
@@ -3952,7 +3952,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.get_endpoint_inference_path" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.get_inference_metrics" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.get_inference_metrics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_inference_metrics</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;latest&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -4017,7 +4017,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.get_inference_metrics" class="
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-83">83</a></span>
 <span class="normal"><a href="#__codelineno-0-84">84</a></span>
 <span class="normal"><a href="#__codelineno-0-85">85</a></span>
@@ -4048,7 +4048,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.get_inference_metrics" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.get_inference_predictions" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.get_inference_predictions" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_inference_predictions</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;auto_inference&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -4113,7 +4113,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.get_inference_predictions" cla
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-95"> 95</a></span>
 <span class="normal"><a href="#__codelineno-0-96"> 96</a></span>
 <span class="normal"><a href="#__codelineno-0-97"> 97</a></span>
@@ -4154,7 +4154,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.get_inference_predictions" cla
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.health_check" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4188,7 +4188,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.health_check" class="doc doc-h
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-47">47</a></span>
 <span class="normal"><a href="#__codelineno-0-48">48</a></span>
 <span class="normal"><a href="#__codelineno-0-49">49</a></span>
@@ -4213,7 +4213,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.health_check" class="doc doc-h
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.list_inference_runs" class="doc doc-heading">
+<h3 id="workbench.cached.cached_model.CachedModel.list_inference_runs" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">list_inference_runs</span><span class="p">()</span></code>
 
 </h3>
@@ -4247,7 +4247,7 @@ <h3 id="sageworks.cached.cached_model.CachedModel.list_inference_runs" class="do
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-74">74</a></span>
 <span class="normal"><a href="#__codelineno-0-75">75</a></span>
 <span class="normal"><a href="#__codelineno-0-76">76</a></span>
@@ -4272,15 +4272,15 @@ <h3 id="sageworks.cached.cached_model.CachedModel.list_inference_runs" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">sageworks_meta</span><span class="p">()</span></code>
+<h3 id="workbench.cached.cached_model.CachedModel.summary" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p>
+        <p>Retrieve the CachedModel Details.</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -4293,12 +4293,12 @@ <h3 id="sageworks.cached.cached_model.CachedModel.sageworks_meta" class="doc doc
       </thead>
       <tbody>
           <tr class="doc-section-item">
-<td><code>str</code></td>            <td>
-                  <code><span title="typing.Union">Union</span>[str, None]</code>
+<td><code>dict</code></td>            <td>
+                  <code>dict</code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The Enumerated Model Type</p>
+                <p>A dictionary of details about the CachedModel</p>
               </div>
             </td>
           </tr>
@@ -4306,22 +4306,22 @@ <h3 id="sageworks.cached.cached_model.CachedModel.sageworks_meta" class="doc doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-56">56</a></span>
-<span class="normal"><a href="#__codelineno-0-57">57</a></span>
-<span class="normal"><a href="#__codelineno-0-58">58</a></span>
-<span class="normal"><a href="#__codelineno-0-59">59</a></span>
-<span class="normal"><a href="#__codelineno-0-60">60</a></span>
-<span class="normal"><a href="#__codelineno-0-61">61</a></span>
-<span class="normal"><a href="#__codelineno-0-62">62</a></span>
-<span class="normal"><a href="#__codelineno-0-63">63</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">        str: The Enumerated Model Type</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-29">29</a></span>
+<span class="normal"><a href="#__codelineno-0-30">30</a></span>
+<span class="normal"><a href="#__codelineno-0-31">31</a></span>
+<span class="normal"><a href="#__codelineno-0-32">32</a></span>
+<span class="normal"><a href="#__codelineno-0-33">33</a></span>
+<span class="normal"><a href="#__codelineno-0-34">34</a></span>
+<span class="normal"><a href="#__codelineno-0-35">35</a></span>
+<span class="normal"><a href="#__codelineno-0-36">36</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
+</span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the CachedModel Details.</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>
+</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">        dict: A dictionary of details about the CachedModel</span>
+</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4331,15 +4331,15 @@ <h3 id="sageworks.cached.cached_model.CachedModel.sageworks_meta" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.cached.cached_model.CachedModel.summary" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<h3 id="workbench.cached.cached_model.CachedModel.workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">workbench_meta</span><span class="p">()</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Retrieve the CachedModel Details.</p>
+        <p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -4352,12 +4352,12 @@ <h3 id="sageworks.cached.cached_model.CachedModel.summary" class="doc doc-headin
       </thead>
       <tbody>
           <tr class="doc-section-item">
-<td><code>dict</code></td>            <td>
-                  <code>dict</code>
+<td><code>str</code></td>            <td>
+                  <code><span title="typing.Union">Union</span>[str, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>A dictionary of details about the CachedModel</p>
+                <p>The Enumerated Model Type</p>
               </div>
             </td>
           </tr>
@@ -4365,22 +4365,22 @@ <h3 id="sageworks.cached.cached_model.CachedModel.summary" class="doc doc-headin
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/cached/cached_model.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-29">29</a></span>
-<span class="normal"><a href="#__codelineno-0-30">30</a></span>
-<span class="normal"><a href="#__codelineno-0-31">31</a></span>
-<span class="normal"><a href="#__codelineno-0-32">32</a></span>
-<span class="normal"><a href="#__codelineno-0-33">33</a></span>
-<span class="normal"><a href="#__codelineno-0-34">34</a></span>
-<span class="normal"><a href="#__codelineno-0-35">35</a></span>
-<span class="normal"><a href="#__codelineno-0-36">36</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
-</span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the CachedModel Details.</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>
-</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">        dict: A dictionary of details about the CachedModel</span>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">summary</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+              <summary>Source code in <code>src/workbench/cached/cached_model.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-56">56</a></span>
+<span class="normal"><a href="#__codelineno-0-57">57</a></span>
+<span class="normal"><a href="#__codelineno-0-58">58</a></span>
+<span class="normal"><a href="#__codelineno-0-59">59</a></span>
+<span class="normal"><a href="#__codelineno-0-60">60</a></span>
+<span class="normal"><a href="#__codelineno-0-61">61</a></span>
+<span class="normal"><a href="#__codelineno-0-62">62</a></span>
+<span class="normal"><a href="#__codelineno-0-63">63</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="nd">@CachedArtifactMixin</span><span class="o">.</span><span class="n">cache_result</span>
+</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="sd">        str: The Enumerated Model Type</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>    <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4403,9 +4403,9 @@ <h3 id="sageworks.cached.cached_model.CachedModel.summary" class="doc doc-headin
     </div>
 
 </div><h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Pull Inference Run</strong></p>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.cached.cached_model</span> <span class="kn">import</span> <span class="n">CachedModel</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.cached.cached_model</span> <span class="kn">import</span> <span class="n">CachedModel</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Grab a Model</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">model</span> <span class="o">=</span> <span class="n">CachedModel</span><span class="p">(</span><span class="s2">&quot;abalone-regression&quot;</span><span class="p">)</span>
diff --git a/cached/overview/index.html b/cached/overview/index.html
index d9a0a41a4..62f01e4df 100644
--- a/cached/overview/index.html
+++ b/cached/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -665,9 +665,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#welcome-to-the-sageworks-cached-classes" class="md-nav__link">
+  <a href="#welcome-to-the-workbench-cached-classes" class="md-nav__link">
     <span class="md-ellipsis">
-      Welcome to the SageWorks Cached Classes
+      Welcome to the Workbench Cached Classes
     </span>
   </a>
   
@@ -1868,7 +1868,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1878,7 +1878,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3097,11 +3097,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3153,9 +3153,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#welcome-to-the-sageworks-cached-classes" class="md-nav__link">
+  <a href="#welcome-to-the-workbench-cached-classes" class="md-nav__link">
     <span class="md-ellipsis">
-      Welcome to the SageWorks Cached Classes
+      Welcome to the Workbench Cached Classes
     </span>
   </a>
   
@@ -3181,18 +3181,18 @@ <h1 id="caching-overview">Caching Overview</h1>
 <p class="admonition-title">Caching is Crazy</p>
 <p>Yes, but it's a necessary evil for Web Interfaces. AWS APIs (boto3, Sagemaker) often takes multiple seconds to respond and will often throttle requests if spammed. So for quicker response and less spamming we're using Cached Classes for any Web Interface work.</p>
 </div>
-<h2 id="welcome-to-the-sageworks-cached-classes">Welcome to the SageWorks Cached Classes</h2>
-<p>These classes provide caching for the for the most used SageWorks classes. They transparently handle all the details around retrieving and caching results from the underlying classes.</p>
+<h2 id="welcome-to-the-workbench-cached-classes">Welcome to the Workbench Cached Classes</h2>
+<p>These classes provide caching for the for the most used Workbench classes. They transparently handle all the details around retrieving and caching results from the underlying classes.</p>
 <ul>
 <li><strong><a href="../cached_meta/">CachedMeta</a>:</strong> Manages lists of Artifacts (get all models, endpoints, etc).</li>
-<li><strong><a href="../cached_data_source/">CachedDataSource</a>:</strong> Caches the method results for SageWorks DataSource.</li>
-<li><strong><a href="../cached_feature_set/">CachedFeatureSet</a>:</strong> Caches the method results for SageWorks FeatureSets.</li>
-<li><strong><a href="../cached_model/">CachedModel</a>:</strong> Caches the method results for SageWorks Models.</li>
-<li><strong><a href="../cached_endpoint/">CachedEndpoint</a>:</strong> Caches the method results for SageWorks Endpoints.</li>
+<li><strong><a href="../cached_data_source/">CachedDataSource</a>:</strong> Caches the method results for Workbench DataSource.</li>
+<li><strong><a href="../cached_feature_set/">CachedFeatureSet</a>:</strong> Caches the method results for Workbench FeatureSets.</li>
+<li><strong><a href="../cached_model/">CachedModel</a>:</strong> Caches the method results for Workbench Models.</li>
+<li><strong><a href="../cached_endpoint/">CachedEndpoint</a>:</strong> Caches the method results for Workbench Endpoints.</li>
 </ul>
 <div class="admonition note">
 <p class="admonition-title">Examples</p>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 </div>
 
 
diff --git a/cloudwatch/index.html b/cloudwatch/index.html
index 819ac5933..087629e58 100644
--- a/cloudwatch/index.html
+++ b/cloudwatch/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS CloudWatch - SageWorks</title>
+        <title>AWS CloudWatch - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-cloudwatch" class="md-skip">
+        <a href="#workbench-cloudwatch" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3131,11 +3131,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3246,18 +3246,18 @@
                   
 
 
-<h1 id="sageworks-cloudwatch">SageWorks CloudWatch</h1>
+<h1 id="workbench-cloudwatch">Workbench CloudWatch</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. As part of this we're including CloudWatch log forwarding/aggregation for any service using the SageWorks API (Dashboard, Glue, Lambda, Notebook, Laptop, etc).</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. As part of this we're including CloudWatch log forwarding/aggregation for any service using the Workbench API (Dashboard, Glue, Lambda, Notebook, Laptop, etc).</p>
 <h3 id="log-groups-and-streams">Log Groups and Streams</h3>
-<p>The SageWorks logging setup includes the addition of a CloudWatch 'Handler' that forwards all log messages to the <code>SageWorksLogGroup</code></p>
+<p>The Workbench logging setup includes the addition of a CloudWatch 'Handler' that forwards all log messages to the <code>WorkbenchLogGroup</code></p>
 <p><strong>Individual Streams</strong></p>
-<p>Each process running SageWorks will get a unique individual stream.</p>
+<p>Each process running Workbench will get a unique individual stream.</p>
 <ul>
-<li><strong>ecs/DashBoard*</strong> (any logs from SageWorks Dashboard)</li>
+<li><strong>ecs/DashBoard*</strong> (any logs from Workbench Dashboard)</li>
 <li><strong>glue/*</strong> (logs from Glue jobs)</li>
 <li><strong>lambda/*</strong> (logs from Lambda jobs)</li>
 <li><strong>docker/*</strong> (logs from Docker containers)</li>
@@ -3267,9 +3267,9 @@ <h3 id="log-groups-and-streams">Log Groups and Streams</h3>
 <h3 id="aws-cloudwatch-made-easy">AWS CloudWatch made Easy</h3>
 <div class="admonition tip inline end">
 <p class="admonition-title">Logs in Easy Mode</p>
-<p>The SageWorks <code>cloud_watch</code> command line tool gives you access to important logs without the hassle. Automatic display of important event and the context around those events.</p>
+<p>The Workbench <code>cloud_watch</code> command line tool gives you access to important logs without the hassle. Automatic display of important event and the context around those events.</p>
 </div>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>pip install sageworks
+<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>pip install workbench
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>cloud_watch
 </span></code></pre></div>
 <p>The <code>cloud_watch</code> script will automatically show the interesting (WARNING and CRITICAL) messages from any source within the last hour. There are lots of options to the script, just use <code>--help</code> to see options and descriptions.</p>
@@ -3297,15 +3297,15 @@ <h3 id="aws-cloudwatch-made-easy">AWS CloudWatch made Easy</h3>
 </span><span id="__span-2-19"><a id="__codelineno-2-19" name="__codelineno-2-19" href="#__codelineno-2-19"></a>cloud_watch --log-level ERROR --stream Dashboard
 </span></code></pre></div>
 <p>These options can be used in combination and try out the other options to make the perfect log search :)</p>
-<p><img alt="sageworks cloud_watch" src="https://github.com/user-attachments/assets/820817de-8f32-47e8-98dc-f3d3f415b2ea"></p>
+<p><img alt="workbench cloud_watch" src="https://github.com/user-attachments/assets/820817de-8f32-47e8-98dc-f3d3f415b2ea"></p>
 <h3 id="more-information">More Information</h3>
-<p>Check out our presentation on <a href="https://docs.google.com/presentation/d/1Jtoo7LXWBSF2xCpn9BNLQlnAtN2vIELCzn_-XMu9GAI/edit?usp=sharing">SageWorks CloudWatch</a></p>
+<p>Check out our presentation on <a href="https://docs.google.com/presentation/d/1Jtoo7LXWBSF2xCpn9BNLQlnAtN2vIELCzn_-XMu9GAI/edit?usp=sharing">Workbench CloudWatch</a></p>
 <h3 id="access-through-aws-console">Access through AWS Console</h3>
-<p>Since we're leveraging AWS functionality you can always use the AWS console to look/investigate the logs. In the AWS console go to <strong>CloudWatch... Log Groups... SageWorksLogGroup</strong></p>
-<p><img alt="sageworks log group" src="https://github.com/user-attachments/assets/a7778232-08db-4950-952c-dd8de650bae8"></p>
+<p>Since we're leveraging AWS functionality you can always use the AWS console to look/investigate the logs. In the AWS console go to <strong>CloudWatch... Log Groups... WorkbenchLogGroup</strong></p>
+<p><img alt="workbench log group" src="https://github.com/user-attachments/assets/a7778232-08db-4950-952c-dd8de650bae8"></p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/core_classes/artifacts/artifact/index.html b/core_classes/artifacts/artifact/index.html
index df1e568bf..c59d2d157 100644
--- a/core_classes/artifacts/artifact/index.html
+++ b/core_classes/artifacts/artifact/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Artifact - SageWorks</title>
+        <title>Artifact - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
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   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+    Workbench
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   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
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+  <a href="#workbench.core.artifacts.artifact" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.__init__" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.__repr__" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.add_tag" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.aws_url" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.delete" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.delete_metadata" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.details" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.expected_meta" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.get_input" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.health_check" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.refresh_meta" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.remove_workbench_meta" class="md-nav__link">
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+      remove_workbench_tag
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+      set_input
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+      set_owner
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+  <a href="#workbench.core.artifacts.artifact.Artifact.set_status" class="md-nav__link">
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+      set_status
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+      size
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+  <a href="#workbench.core.artifacts.artifact.Artifact.summary" class="md-nav__link">
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-      size
+      summary
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-  <a href="#sageworks.core.artifacts.artifact.Artifact.summary" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.upsert_workbench_meta" class="md-nav__link">
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+      upsert_workbench_meta
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+  <a href="#workbench.core.artifacts.artifact.Artifact.workbench_meta" class="md-nav__link">
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+      workbench_meta
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@@ -2191,7 +2191,7 @@
             
   
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-    SageWorks Enterprise
+    Workbench Enterprise
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-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3420,11 +3420,11 @@
   
   
     <li class="md-nav__item">
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+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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@@ -3476,7 +3476,7 @@
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+  <a href="#workbench.core.artifacts.artifact" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.artifact.Artifact.aws_url" class="md-nav__link">
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     </span>
@@ -3558,7 +3558,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.created" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.created" class="md-nav__link">
     <span class="md-ellipsis">
       created
     </span>
@@ -3567,7 +3567,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.delete" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
     </span>
@@ -3576,7 +3576,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.delete_metadata" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.delete_metadata" class="md-nav__link">
     <span class="md-ellipsis">
       delete_metadata
     </span>
@@ -3585,7 +3585,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.details" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3594,7 +3594,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.exists" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.exists" class="md-nav__link">
     <span class="md-ellipsis">
       exists
     </span>
@@ -3603,7 +3603,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.expected_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.expected_meta" class="md-nav__link">
     <span class="md-ellipsis">
       expected_meta
     </span>
@@ -3612,7 +3612,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.generate_valid_name" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.generate_valid_name" class="md-nav__link">
     <span class="md-ellipsis">
       generate_valid_name
     </span>
@@ -3621,7 +3621,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.get_input" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.get_input" class="md-nav__link">
     <span class="md-ellipsis">
       get_input
     </span>
@@ -3630,7 +3630,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.get_owner" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.get_owner" class="md-nav__link">
     <span class="md-ellipsis">
       get_owner
     </span>
@@ -3639,7 +3639,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.get_status" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.get_status" class="md-nav__link">
     <span class="md-ellipsis">
       get_status
     </span>
@@ -3648,7 +3648,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.get_tags" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.get_tags" class="md-nav__link">
     <span class="md-ellipsis">
       get_tags
     </span>
@@ -3657,7 +3657,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.hash" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.hash" class="md-nav__link">
     <span class="md-ellipsis">
       hash
     </span>
@@ -3666,7 +3666,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.health_check" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -3675,7 +3675,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.is_name_valid" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.is_name_valid" class="md-nav__link">
     <span class="md-ellipsis">
       is_name_valid
     </span>
@@ -3684,7 +3684,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.modified" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.modified" class="md-nav__link">
     <span class="md-ellipsis">
       modified
     </span>
@@ -3693,7 +3693,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.onboard" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.onboard" class="md-nav__link">
     <span class="md-ellipsis">
       onboard
     </span>
@@ -3702,7 +3702,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.ready" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.ready" class="md-nav__link">
     <span class="md-ellipsis">
       ready
     </span>
@@ -3711,7 +3711,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.refresh_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.refresh_meta" class="md-nav__link">
     <span class="md-ellipsis">
       refresh_meta
     </span>
@@ -3720,81 +3720,81 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.remove_sageworks_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.remove_workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      remove_sageworks_meta
+      remove_workbench_meta
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.remove_sageworks_tag" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.remove_workbench_tag" class="md-nav__link">
     <span class="md-ellipsis">
-      remove_sageworks_tag
+      remove_workbench_tag
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.set_input" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      set_input
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.set_input" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.set_owner" class="md-nav__link">
     <span class="md-ellipsis">
-      set_input
+      set_owner
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.set_owner" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.set_status" class="md-nav__link">
     <span class="md-ellipsis">
-      set_owner
+      set_status
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.set_status" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.size" class="md-nav__link">
     <span class="md-ellipsis">
-      set_status
+      size
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.size" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.summary" class="md-nav__link">
     <span class="md-ellipsis">
-      size
+      summary
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.summary" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.upsert_workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      summary
+      upsert_workbench_meta
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.artifact.Artifact.upsert_sageworks_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.artifact.Artifact.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      upsert_sageworks_meta
+      workbench_meta
     </span>
   </a>
   
@@ -3825,17 +3825,17 @@ <h1 id="artifact">Artifact</h1>
 <p class="admonition-title">API Classes</p>
 <p>Found a method here you want to use? The <a href="../../../api_classes/overview/">API Classes</a> have method pass-through so just call the method on the any class that inherits from the <strong>Artifact</strong> Class and voilà it works the same.</p>
 </div>
-<p>The SageWorks Artifact class is a base/abstract class that defines API implemented by all the child classes (DataSource, FeatureSet, Model, Endpoint).</p>
+<p>The Workbench Artifact class is a base/abstract class that defines API implemented by all the child classes (DataSource, FeatureSet, Model, Endpoint).</p>
 
 
 <div class="doc doc-object doc-module">
 
 
 
-<a id="sageworks.core.artifacts.artifact"></a>
+<a id="workbench.core.artifacts.artifact"></a>
     <div class="doc doc-contents first">
 
-        <p>Artifact: Abstract Base Class for all Artifact classes in SageWorks.
+        <p>Artifact: Abstract Base Class for all Artifact classes in Workbench.
 Artifacts simply reflect and aggregate one or more AWS Services</p>
 
 
@@ -3858,7 +3858,7 @@ <h1 id="artifact">Artifact</h1>
 
 
 
-<h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.artifact.Artifact" class="doc doc-heading">
             <code>Artifact</code>
 
 
@@ -3870,7 +3870,7 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
               Bases: <code><span title="abc.ABC">ABC</span></code></p>
 
 
-        <p>Artifact: Abstract Base Class for all Artifact classes in SageWorks</p>
+        <p>Artifact: Abstract Base Class for all Artifact classes in Workbench</p>
 
 
 
@@ -3878,7 +3878,7 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
 <span class="normal"><a href="#__codelineno-0-19"> 19</a></span>
 <span class="normal"><a href="#__codelineno-0-20"> 20</a></span>
@@ -4316,17 +4316,17 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-452">452</a></span>
 <span class="normal"><a href="#__codelineno-0-453">453</a></span>
 <span class="normal"><a href="#__codelineno-0-454">454</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="k">class</span> <span class="nc">Artifact</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
-</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Artifact: Abstract Base Class for all Artifact classes in SageWorks&quot;&quot;&quot;</span>
+</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Artifact: Abstract Base Class for all Artifact classes in Workbench&quot;&quot;&quot;</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a>    <span class="c1"># Class-level shared resources</span>
-</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a>    <span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a>    <span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>    <span class="c1"># Config Manager</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>    <span class="n">cm</span> <span class="o">=</span> <span class="n">ConfigManager</span><span class="p">()</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="k">if</span> <span class="ow">not</span> <span class="n">cm</span><span class="o">.</span><span class="n">config_okay</span><span class="p">():</span>
-</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>        <span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">&quot;SageWorks Configuration Incomplete...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>        <span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">&quot;Run the &#39;sageworks&#39; command and follow the prompts...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>        <span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">&quot;Workbench Configuration Incomplete...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>        <span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">&quot;Run the &#39;workbench&#39; command and follow the prompts...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>        <span class="k">raise</span> <span class="n">FatalConfigError</span><span class="p">()</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>
 </span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>    <span class="c1"># AWS Account Clamp</span>
@@ -4337,17 +4337,17 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>    <span class="n">aws_region</span> <span class="o">=</span> <span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">region</span>
 </span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a>
 </span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a>    <span class="c1"># Setup Bucket Paths</span>
-</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a>    <span class="n">sageworks_bucket</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_config</span><span class="p">(</span><span class="s2">&quot;SAGEWORKS_BUCKET&quot;</span><span class="p">)</span>
-</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>    <span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">sageworks_bucket</span><span class="si">}</span><span class="s2">/data-sources&quot;</span>
-</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="n">feature_sets_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">sageworks_bucket</span><span class="si">}</span><span class="s2">/feature-sets&quot;</span>
-</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="n">models_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">sageworks_bucket</span><span class="si">}</span><span class="s2">/models&quot;</span>
-</span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>    <span class="n">endpoints_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">sageworks_bucket</span><span class="si">}</span><span class="s2">/endpoints&quot;</span>
+</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a>    <span class="n">workbench_bucket</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_config</span><span class="p">(</span><span class="s2">&quot;WORKBENCH_BUCKET&quot;</span><span class="p">)</span>
+</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>    <span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">workbench_bucket</span><span class="si">}</span><span class="s2">/data-sources&quot;</span>
+</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="n">feature_sets_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">workbench_bucket</span><span class="si">}</span><span class="s2">/feature-sets&quot;</span>
+</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="n">models_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">workbench_bucket</span><span class="si">}</span><span class="s2">/models&quot;</span>
+</span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>    <span class="n">endpoints_s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="n">workbench_bucket</span><span class="si">}</span><span class="s2">/endpoints&quot;</span>
 </span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>
 </span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>    <span class="c1"># Delimiter for storing lists in AWS Tags</span>
 </span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>    <span class="n">tag_delimiter</span> <span class="o">=</span> <span class="s2">&quot;::&quot;</span>
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="c1"># Grab our Dataframe Storage</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="n">df_cache</span> <span class="o">=</span> <span class="n">DFStore</span><span class="p">(</span><span class="n">path_prefix</span><span class="o">=</span><span class="s2">&quot;/sageworks/dataframe_cache&quot;</span><span class="p">)</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="n">df_cache</span> <span class="o">=</span> <span class="n">DFStore</span><span class="p">(</span><span class="n">path_prefix</span><span class="o">=</span><span class="s2">&quot;/workbench/dataframe_cache&quot;</span><span class="p">)</span>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">use_cached_meta</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Initialize the Artifact Base Class</span>
@@ -4427,11 +4427,11 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Does the Artifact exist? Can we connect to it?&quot;&quot;&quot;</span>
 </span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a>        <span class="k">pass</span>
 </span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>
-</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>    <span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the SageWorks specific metadata for this Artifact</span>
+</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>    <span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the Workbench specific metadata for this Artifact</span>
 </span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>
 </span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a><span class="sd">        Returns:</span>
-</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a><span class="sd">            Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</span>
+</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a><span class="sd">            Union[dict, None]: Dictionary of Workbench metadata for this Artifact</span>
 </span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>
 </span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a><span class="sd">        Note: This functionality will work for FeatureSets, Models, and Endpoints</span>
 </span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a><span class="sd">              but not for DataSources and Graphs, those classes need to override this method.</span>
@@ -4445,7 +4445,7 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>
 </span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>        <span class="c1"># If an artifact has additional expected metadata override this method</span>
-</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">]</span>
+</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">]</span>
 </span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>
 </span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a>    <span class="nd">@abstractmethod</span>
 </span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a>    <span class="k">def</span> <span class="nf">refresh_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -4459,7 +4459,7 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>        <span class="k">try</span><span class="p">:</span>
 </span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>            <span class="c1"># Check for the expected metadata</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>            <span class="n">expected_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">expected_meta</span><span class="p">()</span>
-</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>            <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>            <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>            <span class="n">ready</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">existing_meta</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">issuperset</span><span class="p">(</span><span class="n">expected_meta</span><span class="p">)</span>
 </span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>            <span class="k">if</span> <span class="n">ready</span><span class="p">:</span>
 </span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>                <span class="k">return</span> <span class="kc">True</span>
@@ -4472,7 +4472,7 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-172"><a id="__codelineno-0-172" name="__codelineno-0-172"></a>
 </span><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a>    <span class="nd">@abstractmethod</span>
 </span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a>    <span class="k">def</span> <span class="nf">onboard</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Onboard this Artifact into SageWorks</span>
+</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Onboard this Artifact into Workbench</span>
 </span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a><span class="sd">            bool: True if the Artifact was successfully onboarded, False otherwise</span>
 </span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a><span class="sd">        &quot;&quot;&quot;</span>
@@ -4523,8 +4523,8 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-223"><a id="__codelineno-0-223" name="__codelineno-0-223"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Delete this artifact including all related AWS objects&quot;&quot;&quot;</span>
 </span><span id="__span-0-224"><a id="__codelineno-0-224" name="__codelineno-0-224"></a>        <span class="k">pass</span>
 </span><span id="__span-0-225"><a id="__codelineno-0-225" name="__codelineno-0-225"></a>
-</span><span id="__span-0-226"><a id="__codelineno-0-226" name="__codelineno-0-226"></a>    <span class="k">def</span> <span class="nf">upsert_sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-227"><a id="__codelineno-0-227" name="__codelineno-0-227"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Add SageWorks specific metadata to this Artifact</span>
+</span><span id="__span-0-226"><a id="__codelineno-0-226" name="__codelineno-0-226"></a>    <span class="k">def</span> <span class="nf">upsert_workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-227"><a id="__codelineno-0-227" name="__codelineno-0-227"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Add Workbench specific metadata to this Artifact</span>
 </span><span id="__span-0-228"><a id="__codelineno-0-228" name="__codelineno-0-228"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-229"><a id="__codelineno-0-229" name="__codelineno-0-229"></a><span class="sd">            new_meta (dict): Dictionary of NEW metadata to add</span>
 </span><span id="__span-0-230"><a id="__codelineno-0-230" name="__codelineno-0-230"></a><span class="sd">        Note:</span>
@@ -4545,8 +4545,8 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-245"><a id="__codelineno-0-245" name="__codelineno-0-245"></a>        <span class="k">except</span> <span class="ne">Exception</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
 </span><span id="__span-0-246"><a id="__codelineno-0-246" name="__codelineno-0-246"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error adding metadata to </span><span class="si">{</span><span class="n">aws_arn</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="n">e</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-247"><a id="__codelineno-0-247" name="__codelineno-0-247"></a>
-</span><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a>    <span class="k">def</span> <span class="nf">remove_sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key_to_remove</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Remove SageWorks specific metadata from this Artifact</span>
+</span><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a>    <span class="k">def</span> <span class="nf">remove_workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key_to_remove</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Remove Workbench specific metadata from this Artifact</span>
 </span><span id="__span-0-250"><a id="__codelineno-0-250" name="__codelineno-0-250"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-251"><a id="__codelineno-0-251" name="__codelineno-0-251"></a><span class="sd">            key_to_remove (str): The metadata key to remove</span>
 </span><span id="__span-0-252"><a id="__codelineno-0-252" name="__codelineno-0-252"></a><span class="sd">        Note:</span>
@@ -4558,7 +4558,7 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-258"><a id="__codelineno-0-258" name="__codelineno-0-258"></a>        <span class="k">if</span> <span class="n">aws_arn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
 </span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ARN is None for </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">!&quot;</span><span class="p">)</span>
 </span><span id="__span-0-260"><a id="__codelineno-0-260" name="__codelineno-0-260"></a>            <span class="k">return</span>
-</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Removing SageWorks Metadata </span><span class="si">{</span><span class="n">key_to_remove</span><span class="si">}</span><span class="s2"> for Artifact: </span><span class="si">{</span><span class="n">aws_arn</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Removing Workbench Metadata </span><span class="si">{</span><span class="n">key_to_remove</span><span class="si">}</span><span class="s2"> for Artifact: </span><span class="si">{</span><span class="n">aws_arn</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-262"><a id="__codelineno-0-262" name="__codelineno-0-262"></a>        <span class="n">sagemaker_delete_tag</span><span class="p">(</span><span class="n">aws_arn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span><span class="p">,</span> <span class="n">key_to_remove</span><span class="p">)</span>
 </span><span id="__span-0-263"><a id="__codelineno-0-263" name="__codelineno-0-263"></a>
 </span><span id="__span-0-264"><a id="__codelineno-0-264" name="__codelineno-0-264"></a>    <span class="k">def</span> <span class="nf">get_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;user&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">:</span>
@@ -4569,23 +4569,23 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-269"><a id="__codelineno-0-269" name="__codelineno-0-269"></a><span class="sd">            list[str]: List of tags for this artifact</span>
 </span><span id="__span-0-270"><a id="__codelineno-0-270" name="__codelineno-0-270"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-271"><a id="__codelineno-0-271" name="__codelineno-0-271"></a>        <span class="k">if</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;user&quot;</span><span class="p">:</span>
-</span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>            <span class="n">user_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">)</span>
+</span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>            <span class="n">user_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">)</span>
 </span><span id="__span-0-273"><a id="__codelineno-0-273" name="__codelineno-0-273"></a>            <span class="k">return</span> <span class="n">user_tags</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="p">)</span> <span class="k">if</span> <span class="n">user_tags</span> <span class="k">else</span> <span class="p">[]</span>
 </span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>
 </span><span id="__span-0-275"><a id="__codelineno-0-275" name="__codelineno-0-275"></a>        <span class="c1"># Grab our health tags</span>
-</span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>        <span class="n">health_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">)</span>
+</span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>        <span class="n">health_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">)</span>
 </span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a>
 </span><span id="__span-0-278"><a id="__codelineno-0-278" name="__codelineno-0-278"></a>        <span class="c1"># If we don&#39;t have health tags, create the storage and return an empty list</span>
 </span><span id="__span-0-279"><a id="__codelineno-0-279" name="__codelineno-0-279"></a>        <span class="k">if</span> <span class="n">health_tags</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-280"><a id="__codelineno-0-280" name="__codelineno-0-280"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> creating sageworks_health_tags storage...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-281"><a id="__codelineno-0-281" name="__codelineno-0-281"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">})</span>
+</span><span id="__span-0-280"><a id="__codelineno-0-280" name="__codelineno-0-280"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> creating workbench_health_tags storage...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-281"><a id="__codelineno-0-281" name="__codelineno-0-281"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">})</span>
 </span><span id="__span-0-282"><a id="__codelineno-0-282" name="__codelineno-0-282"></a>            <span class="k">return</span> <span class="p">[]</span>
 </span><span id="__span-0-283"><a id="__codelineno-0-283" name="__codelineno-0-283"></a>
 </span><span id="__span-0-284"><a id="__codelineno-0-284" name="__codelineno-0-284"></a>        <span class="c1"># Otherwise, return the health tags</span>
 </span><span id="__span-0-285"><a id="__codelineno-0-285" name="__codelineno-0-285"></a>        <span class="k">return</span> <span class="n">health_tags</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="p">)</span> <span class="k">if</span> <span class="n">health_tags</span> <span class="k">else</span> <span class="p">[]</span>
 </span><span id="__span-0-286"><a id="__codelineno-0-286" name="__codelineno-0-286"></a>
 </span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>    <span class="k">def</span> <span class="nf">set_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tags</span><span class="p">):</span>
-</span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)})</span>
+</span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)})</span>
 </span><span id="__span-0-289"><a id="__codelineno-0-289" name="__codelineno-0-289"></a>
 </span><span id="__span-0-290"><a id="__codelineno-0-290" name="__codelineno-0-290"></a>    <span class="k">def</span> <span class="nf">add_tag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;user&quot;</span><span class="p">):</span>
 </span><span id="__span-0-291"><a id="__codelineno-0-291" name="__codelineno-0-291"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Add a tag for this artifact, ensuring no duplicates and maintaining order.</span>
@@ -4598,11 +4598,11 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-298"><a id="__codelineno-0-298" name="__codelineno-0-298"></a>            <span class="n">current_tags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span>
 </span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>            <span class="n">combined_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">current_tags</span><span class="p">)</span>
 </span><span id="__span-0-300"><a id="__codelineno-0-300" name="__codelineno-0-300"></a>            <span class="k">if</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;user&quot;</span><span class="p">:</span>
-</span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
+</span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span><span id="__span-0-302"><a id="__codelineno-0-302" name="__codelineno-0-302"></a>            <span class="k">else</span><span class="p">:</span>
-</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
+</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span><span id="__span-0-304"><a id="__codelineno-0-304" name="__codelineno-0-304"></a>
-</span><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a>    <span class="k">def</span> <span class="nf">remove_sageworks_tag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;user&quot;</span><span class="p">):</span>
+</span><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a>    <span class="k">def</span> <span class="nf">remove_workbench_tag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;user&quot;</span><span class="p">):</span>
 </span><span id="__span-0-306"><a id="__codelineno-0-306" name="__codelineno-0-306"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Remove a tag from this artifact if it exists.</span>
 </span><span id="__span-0-307"><a id="__codelineno-0-307" name="__codelineno-0-307"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a><span class="sd">            tag (str): Tag to remove from this artifact</span>
@@ -4613,27 +4613,27 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-313"><a id="__codelineno-0-313" name="__codelineno-0-313"></a>            <span class="n">current_tags</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span>
 </span><span id="__span-0-314"><a id="__codelineno-0-314" name="__codelineno-0-314"></a>            <span class="n">combined_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">current_tags</span><span class="p">)</span>
 </span><span id="__span-0-315"><a id="__codelineno-0-315" name="__codelineno-0-315"></a>            <span class="k">if</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;user&quot;</span><span class="p">:</span>
-</span><span id="__span-0-316"><a id="__codelineno-0-316" name="__codelineno-0-316"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
+</span><span id="__span-0-316"><a id="__codelineno-0-316" name="__codelineno-0-316"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span><span id="__span-0-317"><a id="__codelineno-0-317" name="__codelineno-0-317"></a>            <span class="k">elif</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;health&quot;</span><span class="p">:</span>
-</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
+</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span><span id="__span-0-319"><a id="__codelineno-0-319" name="__codelineno-0-319"></a>
 </span><span id="__span-0-320"><a id="__codelineno-0-320" name="__codelineno-0-320"></a>    <span class="c1"># Syntactic sugar for health tags</span>
 </span><span id="__span-0-321"><a id="__codelineno-0-321" name="__codelineno-0-321"></a>    <span class="k">def</span> <span class="nf">get_health_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-322"><a id="__codelineno-0-322" name="__codelineno-0-322"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_tags</span><span class="p">(</span><span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;health&quot;</span><span class="p">)</span>
 </span><span id="__span-0-323"><a id="__codelineno-0-323" name="__codelineno-0-323"></a>
 </span><span id="__span-0-324"><a id="__codelineno-0-324" name="__codelineno-0-324"></a>    <span class="k">def</span> <span class="nf">set_health_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tags</span><span class="p">):</span>
-</span><span id="__span-0-325"><a id="__codelineno-0-325" name="__codelineno-0-325"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)})</span>
+</span><span id="__span-0-325"><a id="__codelineno-0-325" name="__codelineno-0-325"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)})</span>
 </span><span id="__span-0-326"><a id="__codelineno-0-326" name="__codelineno-0-326"></a>
 </span><span id="__span-0-327"><a id="__codelineno-0-327" name="__codelineno-0-327"></a>    <span class="k">def</span> <span class="nf">add_health_tag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">):</span>
 </span><span id="__span-0-328"><a id="__codelineno-0-328" name="__codelineno-0-328"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">add_tag</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;health&quot;</span><span class="p">)</span>
 </span><span id="__span-0-329"><a id="__codelineno-0-329" name="__codelineno-0-329"></a>
 </span><span id="__span-0-330"><a id="__codelineno-0-330" name="__codelineno-0-330"></a>    <span class="k">def</span> <span class="nf">remove_health_tag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">):</span>
-</span><span id="__span-0-331"><a id="__codelineno-0-331" name="__codelineno-0-331"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">remove_sageworks_tag</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;health&quot;</span><span class="p">)</span>
+</span><span id="__span-0-331"><a id="__codelineno-0-331" name="__codelineno-0-331"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">remove_workbench_tag</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;health&quot;</span><span class="p">)</span>
 </span><span id="__span-0-332"><a id="__codelineno-0-332" name="__codelineno-0-332"></a>
 </span><span id="__span-0-333"><a id="__codelineno-0-333" name="__codelineno-0-333"></a>    <span class="c1"># Owner of this artifact</span>
 </span><span id="__span-0-334"><a id="__codelineno-0-334" name="__codelineno-0-334"></a>    <span class="k">def</span> <span class="nf">get_owner</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-335"><a id="__codelineno-0-335" name="__codelineno-0-335"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the owner of this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-336"><a id="__codelineno-0-336" name="__codelineno-0-336"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_owner&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-336"><a id="__codelineno-0-336" name="__codelineno-0-336"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_owner&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span><span id="__span-0-337"><a id="__codelineno-0-337" name="__codelineno-0-337"></a>
 </span><span id="__span-0-338"><a id="__codelineno-0-338" name="__codelineno-0-338"></a>    <span class="k">def</span> <span class="nf">set_owner</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">owner</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
 </span><span id="__span-0-339"><a id="__codelineno-0-339" name="__codelineno-0-339"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the owner of this artifact</span>
@@ -4641,11 +4641,11 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-341"><a id="__codelineno-0-341" name="__codelineno-0-341"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-342"><a id="__codelineno-0-342" name="__codelineno-0-342"></a><span class="sd">            owner (str): Owner to set for this artifact</span>
 </span><span id="__span-0-343"><a id="__codelineno-0-343" name="__codelineno-0-343"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-344"><a id="__codelineno-0-344" name="__codelineno-0-344"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_owner&quot;</span><span class="p">:</span> <span class="n">owner</span><span class="p">})</span>
+</span><span id="__span-0-344"><a id="__codelineno-0-344" name="__codelineno-0-344"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_owner&quot;</span><span class="p">:</span> <span class="n">owner</span><span class="p">})</span>
 </span><span id="__span-0-345"><a id="__codelineno-0-345" name="__codelineno-0-345"></a>
 </span><span id="__span-0-346"><a id="__codelineno-0-346" name="__codelineno-0-346"></a>    <span class="k">def</span> <span class="nf">get_input</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-347"><a id="__codelineno-0-347" name="__codelineno-0-347"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the input data for this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-348"><a id="__codelineno-0-348" name="__codelineno-0-348"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-348"><a id="__codelineno-0-348" name="__codelineno-0-348"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span><span id="__span-0-349"><a id="__codelineno-0-349" name="__codelineno-0-349"></a>
 </span><span id="__span-0-350"><a id="__codelineno-0-350" name="__codelineno-0-350"></a>    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_data</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
 </span><span id="__span-0-351"><a id="__codelineno-0-351" name="__codelineno-0-351"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the input data for this artifact</span>
@@ -4657,18 +4657,18 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-357"><a id="__codelineno-0-357" name="__codelineno-0-357"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-358"><a id="__codelineno-0-358" name="__codelineno-0-358"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">: Setting input to </span><span class="si">{</span><span class="n">input_data</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-359"><a id="__codelineno-0-359" name="__codelineno-0-359"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Be careful with this! It breaks automatic provenance of the artifact!&quot;</span><span class="p">)</span>
-</span><span id="__span-0-360"><a id="__codelineno-0-360" name="__codelineno-0-360"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="n">input_data</span><span class="p">})</span>
+</span><span id="__span-0-360"><a id="__codelineno-0-360" name="__codelineno-0-360"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="n">input_data</span><span class="p">})</span>
 </span><span id="__span-0-361"><a id="__codelineno-0-361" name="__codelineno-0-361"></a>
 </span><span id="__span-0-362"><a id="__codelineno-0-362" name="__codelineno-0-362"></a>    <span class="k">def</span> <span class="nf">get_status</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-363"><a id="__codelineno-0-363" name="__codelineno-0-363"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the status for this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-364"><a id="__codelineno-0-364" name="__codelineno-0-364"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-364"><a id="__codelineno-0-364" name="__codelineno-0-364"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span><span id="__span-0-365"><a id="__codelineno-0-365" name="__codelineno-0-365"></a>
 </span><span id="__span-0-366"><a id="__codelineno-0-366" name="__codelineno-0-366"></a>    <span class="k">def</span> <span class="nf">set_status</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">status</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
 </span><span id="__span-0-367"><a id="__codelineno-0-367" name="__codelineno-0-367"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the status for this artifact</span>
 </span><span id="__span-0-368"><a id="__codelineno-0-368" name="__codelineno-0-368"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-369"><a id="__codelineno-0-369" name="__codelineno-0-369"></a><span class="sd">            status (str): Status to set for this artifact</span>
 </span><span id="__span-0-370"><a id="__codelineno-0-370" name="__codelineno-0-370"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-371"><a id="__codelineno-0-371" name="__codelineno-0-371"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">:</span> <span class="n">status</span><span class="p">})</span>
+</span><span id="__span-0-371"><a id="__codelineno-0-371" name="__codelineno-0-371"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">:</span> <span class="n">status</span><span class="p">})</span>
 </span><span id="__span-0-372"><a id="__codelineno-0-372" name="__codelineno-0-372"></a>
 </span><span id="__span-0-373"><a id="__codelineno-0-373" name="__codelineno-0-373"></a>    <span class="k">def</span> <span class="nf">health_check</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 </span><span id="__span-0-374"><a id="__codelineno-0-374" name="__codelineno-0-374"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform a health check on this artifact</span>
@@ -4696,8 +4696,8 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-396"><a id="__codelineno-0-396" name="__codelineno-0-396"></a>            <span class="s2">&quot;modified&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">modified</span><span class="p">(),</span>
 </span><span id="__span-0-397"><a id="__codelineno-0-397" name="__codelineno-0-397"></a>            <span class="s2">&quot;input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">(),</span>
 </span><span id="__span-0-398"><a id="__codelineno-0-398" name="__codelineno-0-398"></a>        <span class="p">}</span>
-</span><span id="__span-0-399"><a id="__codelineno-0-399" name="__codelineno-0-399"></a>        <span class="c1"># Combine the sageworks metadata with the basic metadata</span>
-</span><span id="__span-0-400"><a id="__codelineno-0-400" name="__codelineno-0-400"></a>        <span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">basic</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()}</span>
+</span><span id="__span-0-399"><a id="__codelineno-0-399" name="__codelineno-0-399"></a>        <span class="c1"># Combine the workbench metadata with the basic metadata</span>
+</span><span id="__span-0-400"><a id="__codelineno-0-400" name="__codelineno-0-400"></a>        <span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">basic</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()}</span>
 </span><span id="__span-0-401"><a id="__codelineno-0-401" name="__codelineno-0-401"></a>
 </span><span id="__span-0-402"><a id="__codelineno-0-402" name="__codelineno-0-402"></a>    <span class="k">def</span> <span class="fm">__repr__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-403"><a id="__codelineno-0-403" name="__codelineno-0-403"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;String representation of this artifact</span>
@@ -4718,8 +4718,8 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 </span><span id="__span-0-418"><a id="__codelineno-0-418" name="__codelineno-0-418"></a>            <span class="s2">&quot;created&quot;</span><span class="p">,</span>
 </span><span id="__span-0-419"><a id="__codelineno-0-419" name="__codelineno-0-419"></a>            <span class="s2">&quot;modified&quot;</span><span class="p">,</span>
 </span><span id="__span-0-420"><a id="__codelineno-0-420" name="__codelineno-0-420"></a>            <span class="s2">&quot;input&quot;</span><span class="p">,</span>
-</span><span id="__span-0-421"><a id="__codelineno-0-421" name="__codelineno-0-421"></a>            <span class="s2">&quot;sageworks_status&quot;</span><span class="p">,</span>
-</span><span id="__span-0-422"><a id="__codelineno-0-422" name="__codelineno-0-422"></a>            <span class="s2">&quot;sageworks_tags&quot;</span><span class="p">,</span>
+</span><span id="__span-0-421"><a id="__codelineno-0-421" name="__codelineno-0-421"></a>            <span class="s2">&quot;workbench_status&quot;</span><span class="p">,</span>
+</span><span id="__span-0-422"><a id="__codelineno-0-422" name="__codelineno-0-422"></a>            <span class="s2">&quot;workbench_tags&quot;</span><span class="p">,</span>
 </span><span id="__span-0-423"><a id="__codelineno-0-423" name="__codelineno-0-423"></a>        <span class="p">]</span>
 </span><span id="__span-0-424"><a id="__codelineno-0-424" name="__codelineno-0-424"></a>        <span class="n">summary_items</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;  </span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="n">key</span><span class="p">)</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">summary_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">display_keys</span><span class="p">]</span>
 </span><span id="__span-0-425"><a id="__codelineno-0-425" name="__codelineno-0-425"></a>        <span class="n">summary_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot;,</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">summary_items</span><span class="p">)</span>
@@ -4770,7 +4770,7 @@ <h2 id="sageworks.core.artifacts.artifact.Artifact" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.__init__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">uuid</span><span class="p">,</span> <span class="n">use_cached_meta</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4828,7 +4828,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.__init__" class="doc doc-head
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-52">52</a></span>
 <span class="normal"><a href="#__codelineno-0-53">53</a></span>
 <span class="normal"><a href="#__codelineno-0-54">54</a></span>
@@ -4863,7 +4863,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.__init__" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.__post_init__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.__post_init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">__post_init__</span><span class="p">()</span></code>
 
 </h3>
@@ -4874,7 +4874,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.__post_init__" class="doc doc
         <p>Artifact Post Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
 <span class="normal"><a href="#__codelineno-0-68">68</a></span>
@@ -4925,7 +4925,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.__post_init__" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.__repr__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.__repr__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__repr__</span><span class="p">()</span></code>
 
 </h3>
@@ -4959,7 +4959,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.__repr__" class="doc doc-head
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-402">402</a></span>
 <span class="normal"><a href="#__codelineno-0-403">403</a></span>
 <span class="normal"><a href="#__codelineno-0-404">404</a></span>
@@ -5003,8 +5003,8 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.__repr__" class="doc doc-head
 </span><span id="__span-0-418"><a id="__codelineno-0-418" name="__codelineno-0-418"></a>        <span class="s2">&quot;created&quot;</span><span class="p">,</span>
 </span><span id="__span-0-419"><a id="__codelineno-0-419" name="__codelineno-0-419"></a>        <span class="s2">&quot;modified&quot;</span><span class="p">,</span>
 </span><span id="__span-0-420"><a id="__codelineno-0-420" name="__codelineno-0-420"></a>        <span class="s2">&quot;input&quot;</span><span class="p">,</span>
-</span><span id="__span-0-421"><a id="__codelineno-0-421" name="__codelineno-0-421"></a>        <span class="s2">&quot;sageworks_status&quot;</span><span class="p">,</span>
-</span><span id="__span-0-422"><a id="__codelineno-0-422" name="__codelineno-0-422"></a>        <span class="s2">&quot;sageworks_tags&quot;</span><span class="p">,</span>
+</span><span id="__span-0-421"><a id="__codelineno-0-421" name="__codelineno-0-421"></a>        <span class="s2">&quot;workbench_status&quot;</span><span class="p">,</span>
+</span><span id="__span-0-422"><a id="__codelineno-0-422" name="__codelineno-0-422"></a>        <span class="s2">&quot;workbench_tags&quot;</span><span class="p">,</span>
 </span><span id="__span-0-423"><a id="__codelineno-0-423" name="__codelineno-0-423"></a>    <span class="p">]</span>
 </span><span id="__span-0-424"><a id="__codelineno-0-424" name="__codelineno-0-424"></a>    <span class="n">summary_items</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;  </span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="n">key</span><span class="p">)</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="nb">repr</span><span class="p">(</span><span class="n">value</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">summary_dict</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">display_keys</span><span class="p">]</span>
 </span><span id="__span-0-425"><a id="__codelineno-0-425" name="__codelineno-0-425"></a>    <span class="n">summary_str</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="si">}</span><span class="s2">: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="se">\n</span><span class="s2">&quot;</span> <span class="o">+</span> <span class="s2">&quot;,</span><span class="se">\n</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">summary_items</span><span class="p">)</span>
@@ -5018,7 +5018,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.__repr__" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.add_tag" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.add_tag" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">add_tag</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s1">&#39;user&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -5032,7 +5032,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.add_tag" class="doc doc-headi
     tag_type (str): Type of tag to add (user or health)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-290">290</a></span>
 <span class="normal"><a href="#__codelineno-0-291">291</a></span>
 <span class="normal"><a href="#__codelineno-0-292">292</a></span>
@@ -5057,9 +5057,9 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.add_tag" class="doc doc-headi
 </span><span id="__span-0-298"><a id="__codelineno-0-298" name="__codelineno-0-298"></a>        <span class="n">current_tags</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span>
 </span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>        <span class="n">combined_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">current_tags</span><span class="p">)</span>
 </span><span id="__span-0-300"><a id="__codelineno-0-300" name="__codelineno-0-300"></a>        <span class="k">if</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;user&quot;</span><span class="p">:</span>
-</span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
+</span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span><span id="__span-0-302"><a id="__codelineno-0-302" name="__codelineno-0-302"></a>        <span class="k">else</span><span class="p">:</span>
-</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
+</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5069,7 +5069,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.add_tag" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.arn" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.arn" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">arn</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5084,7 +5084,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.arn" class="doc doc-heading">
         <p>AWS ARN (Amazon Resource Name) for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-206">206</a></span>
 <span class="normal"><a href="#__codelineno-0-207">207</a></span>
 <span class="normal"><a href="#__codelineno-0-208">208</a></span>
@@ -5101,7 +5101,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.arn" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.aws_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.aws_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_meta</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5116,7 +5116,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.aws_meta" class="doc doc-head
         <p>Get the full AWS metadata for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-216">216</a></span>
 <span class="normal"><a href="#__codelineno-0-217">217</a></span>
 <span class="normal"><a href="#__codelineno-0-218">218</a></span>
@@ -5133,7 +5133,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.aws_meta" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.aws_url" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.aws_url" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_url</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5148,7 +5148,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.aws_url" class="doc doc-headi
         <p>AWS console/web interface for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-211">211</a></span>
 <span class="normal"><a href="#__codelineno-0-212">212</a></span>
 <span class="normal"><a href="#__codelineno-0-213">213</a></span>
@@ -5165,7 +5165,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.aws_url" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.created" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.created" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">created</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5180,7 +5180,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.created" class="doc doc-headi
         <p>Return the datetime when this artifact was created</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-191">191</a></span>
 <span class="normal"><a href="#__codelineno-0-192">192</a></span>
 <span class="normal"><a href="#__codelineno-0-193">193</a></span>
@@ -5197,7 +5197,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.created" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5212,7 +5212,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.delete" class="doc doc-headin
         <p>Delete this artifact including all related AWS objects</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-221">221</a></span>
 <span class="normal"><a href="#__codelineno-0-222">222</a></span>
 <span class="normal"><a href="#__codelineno-0-223">223</a></span>
@@ -5229,7 +5229,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.delete" class="doc doc-headin
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.delete_metadata" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.delete_metadata" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete_metadata</span><span class="p">(</span><span class="n">key_to_delete</span><span class="p">)</span></code>
 
 </h3>
@@ -5242,7 +5242,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.delete_metadata" class="doc d
     key_to_delete (str): Metadata key to delete</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-428">428</a></span>
 <span class="normal"><a href="#__codelineno-0-429">429</a></span>
 <span class="normal"><a href="#__codelineno-0-430">430</a></span>
@@ -5305,7 +5305,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.delete_metadata" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5320,7 +5320,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.details" class="doc doc-headi
         <p>Additional Details about this Artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-181">181</a></span>
 <span class="normal"><a href="#__codelineno-0-182">182</a></span>
 <span class="normal"><a href="#__codelineno-0-183">183</a></span>
@@ -5337,7 +5337,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.details" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.exists" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">exists</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5352,7 +5352,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.exists" class="doc doc-headin
         <p>Does the Artifact exist? Can we connect to it?</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-125">125</a></span>
 <span class="normal"><a href="#__codelineno-0-126">126</a></span>
 <span class="normal"><a href="#__codelineno-0-127">127</a></span>
@@ -5369,7 +5369,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.exists" class="doc doc-headin
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.expected_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.expected_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">expected_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -5382,7 +5382,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.expected_meta" class="doc doc
     list[str]: List of expected metadata keys</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-141">141</a></span>
 <span class="normal"><a href="#__codelineno-0-142">142</a></span>
 <span class="normal"><a href="#__codelineno-0-143">143</a></span>
@@ -5397,7 +5397,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.expected_meta" class="doc doc
 </span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>
 </span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>    <span class="c1"># If an artifact has additional expected metadata override this method</span>
-</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>    <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">]</span>
+</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>    <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">]</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5407,7 +5407,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.expected_meta" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.generate_valid_name" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.generate_valid_name" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">generate_valid_name</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="n">lower_case</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5508,7 +5508,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.generate_valid_name" class="d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-106">106</a></span>
 <span class="normal"><a href="#__codelineno-0-107">107</a></span>
 <span class="normal"><a href="#__codelineno-0-108">108</a></span>
@@ -5553,7 +5553,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.generate_valid_name" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.get_input" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.get_input" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_input</span><span class="p">()</span></code>
 
 </h3>
@@ -5564,12 +5564,12 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_input" class="doc doc-hea
         <p>Get the input data for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-346">346</a></span>
 <span class="normal"><a href="#__codelineno-0-347">347</a></span>
 <span class="normal"><a href="#__codelineno-0-348">348</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-346"><a id="__codelineno-0-346" name="__codelineno-0-346"></a><span class="k">def</span> <span class="nf">get_input</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-347"><a id="__codelineno-0-347" name="__codelineno-0-347"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the input data for this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-348"><a id="__codelineno-0-348" name="__codelineno-0-348"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-348"><a id="__codelineno-0-348" name="__codelineno-0-348"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5579,7 +5579,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_input" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.get_owner" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.get_owner" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_owner</span><span class="p">()</span></code>
 
 </h3>
@@ -5590,12 +5590,12 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_owner" class="doc doc-hea
         <p>Get the owner of this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-334">334</a></span>
 <span class="normal"><a href="#__codelineno-0-335">335</a></span>
 <span class="normal"><a href="#__codelineno-0-336">336</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-334"><a id="__codelineno-0-334" name="__codelineno-0-334"></a><span class="k">def</span> <span class="nf">get_owner</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-335"><a id="__codelineno-0-335" name="__codelineno-0-335"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the owner of this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-336"><a id="__codelineno-0-336" name="__codelineno-0-336"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_owner&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-336"><a id="__codelineno-0-336" name="__codelineno-0-336"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_owner&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5605,7 +5605,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_owner" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.get_status" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.get_status" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_status</span><span class="p">()</span></code>
 
 </h3>
@@ -5616,12 +5616,12 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_status" class="doc doc-he
         <p>Get the status for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-362">362</a></span>
 <span class="normal"><a href="#__codelineno-0-363">363</a></span>
 <span class="normal"><a href="#__codelineno-0-364">364</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-362"><a id="__codelineno-0-362" name="__codelineno-0-362"></a><span class="k">def</span> <span class="nf">get_status</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-363"><a id="__codelineno-0-363" name="__codelineno-0-363"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the status for this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-364"><a id="__codelineno-0-364" name="__codelineno-0-364"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-364"><a id="__codelineno-0-364" name="__codelineno-0-364"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5631,7 +5631,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_status" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.get_tags" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.get_tags" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_tags</span><span class="p">(</span><span class="n">tag_type</span><span class="o">=</span><span class="s1">&#39;user&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -5646,7 +5646,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_tags" class="doc doc-head
     list[str]: List of tags for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-264">264</a></span>
 <span class="normal"><a href="#__codelineno-0-265">265</a></span>
 <span class="normal"><a href="#__codelineno-0-266">266</a></span>
@@ -5676,16 +5676,16 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_tags" class="doc doc-head
 </span><span id="__span-0-269"><a id="__codelineno-0-269" name="__codelineno-0-269"></a><span class="sd">        list[str]: List of tags for this artifact</span>
 </span><span id="__span-0-270"><a id="__codelineno-0-270" name="__codelineno-0-270"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-271"><a id="__codelineno-0-271" name="__codelineno-0-271"></a>    <span class="k">if</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;user&quot;</span><span class="p">:</span>
-</span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>        <span class="n">user_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">)</span>
+</span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>        <span class="n">user_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">)</span>
 </span><span id="__span-0-273"><a id="__codelineno-0-273" name="__codelineno-0-273"></a>        <span class="k">return</span> <span class="n">user_tags</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="p">)</span> <span class="k">if</span> <span class="n">user_tags</span> <span class="k">else</span> <span class="p">[]</span>
 </span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>
 </span><span id="__span-0-275"><a id="__codelineno-0-275" name="__codelineno-0-275"></a>    <span class="c1"># Grab our health tags</span>
-</span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>    <span class="n">health_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">)</span>
+</span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>    <span class="n">health_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">)</span>
 </span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a>
 </span><span id="__span-0-278"><a id="__codelineno-0-278" name="__codelineno-0-278"></a>    <span class="c1"># If we don&#39;t have health tags, create the storage and return an empty list</span>
 </span><span id="__span-0-279"><a id="__codelineno-0-279" name="__codelineno-0-279"></a>    <span class="k">if</span> <span class="n">health_tags</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-280"><a id="__codelineno-0-280" name="__codelineno-0-280"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> creating sageworks_health_tags storage...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-281"><a id="__codelineno-0-281" name="__codelineno-0-281"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">})</span>
+</span><span id="__span-0-280"><a id="__codelineno-0-280" name="__codelineno-0-280"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> creating workbench_health_tags storage...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-281"><a id="__codelineno-0-281" name="__codelineno-0-281"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">:</span> <span class="s2">&quot;&quot;</span><span class="p">})</span>
 </span><span id="__span-0-282"><a id="__codelineno-0-282" name="__codelineno-0-282"></a>        <span class="k">return</span> <span class="p">[]</span>
 </span><span id="__span-0-283"><a id="__codelineno-0-283" name="__codelineno-0-283"></a>
 </span><span id="__span-0-284"><a id="__codelineno-0-284" name="__codelineno-0-284"></a>    <span class="c1"># Otherwise, return the health tags</span>
@@ -5699,7 +5699,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.get_tags" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.hash" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.hash" class="doc doc-heading">
             <code class="highlight language-python"><span class="nb">hash</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5714,7 +5714,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.hash" class="doc doc-heading"
         <p>Return the hash of this artifact, useful for content validation</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-201">201</a></span>
 <span class="normal"><a href="#__codelineno-0-202">202</a></span>
 <span class="normal"><a href="#__codelineno-0-203">203</a></span>
@@ -5731,7 +5731,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.hash" class="doc doc-heading"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.health_check" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">()</span></code>
 
 </h3>
@@ -5744,7 +5744,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.health_check" class="doc doc-
     list[str]: List of health issues</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-373">373</a></span>
 <span class="normal"><a href="#__codelineno-0-374">374</a></span>
 <span class="normal"><a href="#__codelineno-0-375">375</a></span>
@@ -5777,7 +5777,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.health_check" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.is_name_valid" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.is_name_valid" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">is_name_valid</span><span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">delimiter</span><span class="o">=</span><span class="s1">&#39;_&#39;</span><span class="p">,</span> <span class="n">lower_case</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5878,7 +5878,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.is_name_valid" class="doc doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-88"> 88</a></span>
 <span class="normal"><a href="#__codelineno-0-89"> 89</a></span>
 <span class="normal"><a href="#__codelineno-0-90"> 90</a></span>
@@ -5921,7 +5921,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.is_name_valid" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.modified" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.modified" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">modified</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5936,7 +5936,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.modified" class="doc doc-head
         <p>Return the datetime when this artifact was last modified</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-196">196</a></span>
 <span class="normal"><a href="#__codelineno-0-197">197</a></span>
 <span class="normal"><a href="#__codelineno-0-198">198</a></span>
@@ -5953,7 +5953,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.modified" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.onboard" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.onboard" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">onboard</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5965,12 +5965,12 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.onboard" class="doc doc-headi
 
     <div class="doc doc-contents ">
 
-        <p>Onboard this Artifact into SageWorks
+        <p>Onboard this Artifact into Workbench
 Returns:
     bool: True if the Artifact was successfully onboarded, False otherwise</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-173">173</a></span>
 <span class="normal"><a href="#__codelineno-0-174">174</a></span>
 <span class="normal"><a href="#__codelineno-0-175">175</a></span>
@@ -5979,7 +5979,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.onboard" class="doc doc-headi
 <span class="normal"><a href="#__codelineno-0-178">178</a></span>
 <span class="normal"><a href="#__codelineno-0-179">179</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a><span class="nd">@abstractmethod</span>
 </span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a><span class="k">def</span> <span class="nf">onboard</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
-</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Onboard this Artifact into SageWorks</span>
+</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Onboard this Artifact into Workbench</span>
 </span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a><span class="sd">        bool: True if the Artifact was successfully onboarded, False otherwise</span>
 </span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a><span class="sd">    &quot;&quot;&quot;</span>
@@ -5993,7 +5993,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.onboard" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.ready" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.ready" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">ready</span><span class="p">()</span></code>
 
 </h3>
@@ -6004,7 +6004,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.ready" class="doc doc-heading
         <p>Is the Artifact ready? Is initial setup complete and expected metadata populated?</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-155">155</a></span>
 <span class="normal"><a href="#__codelineno-0-156">156</a></span>
 <span class="normal"><a href="#__codelineno-0-157">157</a></span>
@@ -6028,7 +6028,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.ready" class="doc doc-heading
 </span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>    <span class="k">try</span><span class="p">:</span>
 </span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>        <span class="c1"># Check for the expected metadata</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>        <span class="n">expected_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">expected_meta</span><span class="p">()</span>
-</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>        <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>        <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>        <span class="n">ready</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">existing_meta</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">issuperset</span><span class="p">(</span><span class="n">expected_meta</span><span class="p">)</span>
 </span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>        <span class="k">if</span> <span class="n">ready</span><span class="p">:</span>
 </span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>            <span class="k">return</span> <span class="kc">True</span>
@@ -6047,7 +6047,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.ready" class="doc doc-heading
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.refresh_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.refresh_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">refresh_meta</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -6062,7 +6062,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.refresh_meta" class="doc doc-
         <p>Refresh the Artifact's metadata</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-150">150</a></span>
 <span class="normal"><a href="#__codelineno-0-151">151</a></span>
 <span class="normal"><a href="#__codelineno-0-152">152</a></span>
@@ -6079,15 +6079,15 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.refresh_meta" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">remove_sageworks_meta</span><span class="p">(</span><span class="n">key_to_remove</span><span class="p">)</span></code>
+<h3 id="workbench.core.artifacts.artifact.Artifact.remove_workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">remove_workbench_meta</span><span class="p">(</span><span class="n">key_to_remove</span><span class="p">)</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Remove SageWorks specific metadata from this Artifact
+        <p>Remove Workbench specific metadata from this Artifact
 Args:
     key_to_remove (str): The metadata key to remove
 Note:
@@ -6095,7 +6095,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_meta" class=
     but not for DataSources. The DataSource class overrides this method.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-248">248</a></span>
 <span class="normal"><a href="#__codelineno-0-249">249</a></span>
 <span class="normal"><a href="#__codelineno-0-250">250</a></span>
@@ -6110,8 +6110,8 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_meta" class=
 <span class="normal"><a href="#__codelineno-0-259">259</a></span>
 <span class="normal"><a href="#__codelineno-0-260">260</a></span>
 <span class="normal"><a href="#__codelineno-0-261">261</a></span>
-<span class="normal"><a href="#__codelineno-0-262">262</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a><span class="k">def</span> <span class="nf">remove_sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key_to_remove</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Remove SageWorks specific metadata from this Artifact</span>
+<span class="normal"><a href="#__codelineno-0-262">262</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a><span class="k">def</span> <span class="nf">remove_workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key_to_remove</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Remove Workbench specific metadata from this Artifact</span>
 </span><span id="__span-0-250"><a id="__codelineno-0-250" name="__codelineno-0-250"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-251"><a id="__codelineno-0-251" name="__codelineno-0-251"></a><span class="sd">        key_to_remove (str): The metadata key to remove</span>
 </span><span id="__span-0-252"><a id="__codelineno-0-252" name="__codelineno-0-252"></a><span class="sd">    Note:</span>
@@ -6123,7 +6123,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_meta" class=
 </span><span id="__span-0-258"><a id="__codelineno-0-258" name="__codelineno-0-258"></a>    <span class="k">if</span> <span class="n">aws_arn</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
 </span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;ARN is None for </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">!&quot;</span><span class="p">)</span>
 </span><span id="__span-0-260"><a id="__codelineno-0-260" name="__codelineno-0-260"></a>        <span class="k">return</span>
-</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Removing SageWorks Metadata </span><span class="si">{</span><span class="n">key_to_remove</span><span class="si">}</span><span class="s2"> for Artifact: </span><span class="si">{</span><span class="n">aws_arn</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Removing Workbench Metadata </span><span class="si">{</span><span class="n">key_to_remove</span><span class="si">}</span><span class="s2"> for Artifact: </span><span class="si">{</span><span class="n">aws_arn</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-262"><a id="__codelineno-0-262" name="__codelineno-0-262"></a>    <span class="n">sagemaker_delete_tag</span><span class="p">(</span><span class="n">aws_arn</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span><span class="p">,</span> <span class="n">key_to_remove</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
@@ -6134,8 +6134,8 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_meta" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_tag" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">remove_sageworks_tag</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s1">&#39;user&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.core.artifacts.artifact.Artifact.remove_workbench_tag" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">remove_workbench_tag</span><span class="p">(</span><span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s1">&#39;user&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -6148,7 +6148,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_tag" class="
     tag_type (str): Type of tag to remove (user or health)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-305">305</a></span>
 <span class="normal"><a href="#__codelineno-0-306">306</a></span>
 <span class="normal"><a href="#__codelineno-0-307">307</a></span>
@@ -6162,7 +6162,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_tag" class="
 <span class="normal"><a href="#__codelineno-0-315">315</a></span>
 <span class="normal"><a href="#__codelineno-0-316">316</a></span>
 <span class="normal"><a href="#__codelineno-0-317">317</a></span>
-<span class="normal"><a href="#__codelineno-0-318">318</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a><span class="k">def</span> <span class="nf">remove_sageworks_tag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;user&quot;</span><span class="p">):</span>
+<span class="normal"><a href="#__codelineno-0-318">318</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a><span class="k">def</span> <span class="nf">remove_workbench_tag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">tag_type</span><span class="o">=</span><span class="s2">&quot;user&quot;</span><span class="p">):</span>
 </span><span id="__span-0-306"><a id="__codelineno-0-306" name="__codelineno-0-306"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Remove a tag from this artifact if it exists.</span>
 </span><span id="__span-0-307"><a id="__codelineno-0-307" name="__codelineno-0-307"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a><span class="sd">        tag (str): Tag to remove from this artifact</span>
@@ -6173,77 +6173,9 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.remove_sageworks_tag" class="
 </span><span id="__span-0-313"><a id="__codelineno-0-313" name="__codelineno-0-313"></a>        <span class="n">current_tags</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">tag</span><span class="p">)</span>
 </span><span id="__span-0-314"><a id="__codelineno-0-314" name="__codelineno-0-314"></a>        <span class="n">combined_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">current_tags</span><span class="p">)</span>
 </span><span id="__span-0-315"><a id="__codelineno-0-315" name="__codelineno-0-315"></a>        <span class="k">if</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;user&quot;</span><span class="p">:</span>
-</span><span id="__span-0-316"><a id="__codelineno-0-316" name="__codelineno-0-316"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
+</span><span id="__span-0-316"><a id="__codelineno-0-316" name="__codelineno-0-316"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span><span id="__span-0-317"><a id="__codelineno-0-317" name="__codelineno-0-317"></a>        <span class="k">elif</span> <span class="n">tag_type</span> <span class="o">==</span> <span class="s2">&quot;health&quot;</span><span class="p">:</span>
-</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
-</span></code></pre></div></td></tr></table></div>
-            </details>
-    </div>
-
-</div>
-
-<div class="doc doc-object doc-function">
-
-
-<h3 id="sageworks.core.artifacts.artifact.Artifact.sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">sageworks_meta</span><span class="p">()</span></code>
-
-</h3>
-
-
-    <div class="doc doc-contents ">
-
-        <p>Get the SageWorks specific metadata for this Artifact</p>
-
-
-    <p><span class="doc-section-title">Returns:</span></p>
-    <table>
-      <thead>
-        <tr>
-          <th>Type</th>
-          <th>Description</th>
-        </tr>
-      </thead>
-      <tbody>
-          <tr class="doc-section-item">
-            <td>
-                  <code><span title="typing.Union">Union</span>[dict, None]</code>
-            </td>
-            <td>
-              <div class="doc-md-description">
-                <p>Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</p>
-              </div>
-            </td>
-          </tr>
-      </tbody>
-    </table>
-
-
-<details class="note" open>
-  <summary>This functionality will work for FeatureSets, Models, and Endpoints</summary>
-  <p>but not for DataSources and Graphs, those classes need to override this method.</p>
-</details>
-            <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-130">130</a></span>
-<span class="normal"><a href="#__codelineno-0-131">131</a></span>
-<span class="normal"><a href="#__codelineno-0-132">132</a></span>
-<span class="normal"><a href="#__codelineno-0-133">133</a></span>
-<span class="normal"><a href="#__codelineno-0-134">134</a></span>
-<span class="normal"><a href="#__codelineno-0-135">135</a></span>
-<span class="normal"><a href="#__codelineno-0-136">136</a></span>
-<span class="normal"><a href="#__codelineno-0-137">137</a></span>
-<span class="normal"><a href="#__codelineno-0-138">138</a></span>
-<span class="normal"><a href="#__codelineno-0-139">139</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a><span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
-</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the SageWorks specific metadata for this Artifact</span>
-</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>
-</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a><span class="sd">    Returns:</span>
-</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a><span class="sd">        Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</span>
-</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>
-</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a><span class="sd">    Note: This functionality will work for FeatureSets, Models, and Endpoints</span>
-</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a><span class="sd">          but not for DataSources and Graphs, those classes need to override this method.</span>
-</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get_aws_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arn</span><span class="p">())</span>
+</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_health_tags&quot;</span><span class="p">:</span> <span class="n">combined_tags</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6253,7 +6185,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.sageworks_meta" class="doc do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.set_input" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.set_input" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_input</span><span class="p">(</span><span class="n">input_data</span><span class="p">)</span></code>
 
 </h3>
@@ -6297,7 +6229,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_input" class="doc doc-hea
     This breaks the official provenance of the artifact, so use with caution.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-350">350</a></span>
 <span class="normal"><a href="#__codelineno-0-351">351</a></span>
 <span class="normal"><a href="#__codelineno-0-352">352</a></span>
@@ -6318,7 +6250,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_input" class="doc doc-hea
 </span><span id="__span-0-357"><a id="__codelineno-0-357" name="__codelineno-0-357"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-358"><a id="__codelineno-0-358" name="__codelineno-0-358"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">: Setting input to </span><span class="si">{</span><span class="n">input_data</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-359"><a id="__codelineno-0-359" name="__codelineno-0-359"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Be careful with this! It breaks automatic provenance of the artifact!&quot;</span><span class="p">)</span>
-</span><span id="__span-0-360"><a id="__codelineno-0-360" name="__codelineno-0-360"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="n">input_data</span><span class="p">})</span>
+</span><span id="__span-0-360"><a id="__codelineno-0-360" name="__codelineno-0-360"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="n">input_data</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6328,7 +6260,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_input" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.set_owner" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.set_owner" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_owner</span><span class="p">(</span><span class="n">owner</span><span class="p">)</span></code>
 
 </h3>
@@ -6370,7 +6302,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_owner" class="doc doc-hea
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-338">338</a></span>
 <span class="normal"><a href="#__codelineno-0-339">339</a></span>
 <span class="normal"><a href="#__codelineno-0-340">340</a></span>
@@ -6383,7 +6315,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_owner" class="doc doc-hea
 </span><span id="__span-0-341"><a id="__codelineno-0-341" name="__codelineno-0-341"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-342"><a id="__codelineno-0-342" name="__codelineno-0-342"></a><span class="sd">        owner (str): Owner to set for this artifact</span>
 </span><span id="__span-0-343"><a id="__codelineno-0-343" name="__codelineno-0-343"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-344"><a id="__codelineno-0-344" name="__codelineno-0-344"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_owner&quot;</span><span class="p">:</span> <span class="n">owner</span><span class="p">})</span>
+</span><span id="__span-0-344"><a id="__codelineno-0-344" name="__codelineno-0-344"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_owner&quot;</span><span class="p">:</span> <span class="n">owner</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6393,7 +6325,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_owner" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.set_status" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.set_status" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_status</span><span class="p">(</span><span class="n">status</span><span class="p">)</span></code>
 
 </h3>
@@ -6406,7 +6338,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_status" class="doc doc-he
     status (str): Status to set for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-366">366</a></span>
 <span class="normal"><a href="#__codelineno-0-367">367</a></span>
 <span class="normal"><a href="#__codelineno-0-368">368</a></span>
@@ -6417,7 +6349,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_status" class="doc doc-he
 </span><span id="__span-0-368"><a id="__codelineno-0-368" name="__codelineno-0-368"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-369"><a id="__codelineno-0-369" name="__codelineno-0-369"></a><span class="sd">        status (str): Status to set for this artifact</span>
 </span><span id="__span-0-370"><a id="__codelineno-0-370" name="__codelineno-0-370"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-371"><a id="__codelineno-0-371" name="__codelineno-0-371"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">:</span> <span class="n">status</span><span class="p">})</span>
+</span><span id="__span-0-371"><a id="__codelineno-0-371" name="__codelineno-0-371"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">:</span> <span class="n">status</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6427,7 +6359,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.set_status" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.size" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.size" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">size</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -6442,7 +6374,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.size" class="doc doc-heading"
         <p>Return the size of this artifact in MegaBytes</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-186">186</a></span>
 <span class="normal"><a href="#__codelineno-0-187">187</a></span>
 <span class="normal"><a href="#__codelineno-0-188">188</a></span>
@@ -6459,7 +6391,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.size" class="doc doc-heading"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.summary" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.artifact.Artifact.summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">summary</span><span class="p">()</span></code>
 
 </h3>
@@ -6472,7 +6404,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.summary" class="doc doc-headi
 which is implemented by the specific Artifact class</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-386">386</a></span>
 <span class="normal"><a href="#__codelineno-0-387">387</a></span>
 <span class="normal"><a href="#__codelineno-0-388">388</a></span>
@@ -6500,8 +6432,8 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.summary" class="doc doc-headi
 </span><span id="__span-0-396"><a id="__codelineno-0-396" name="__codelineno-0-396"></a>        <span class="s2">&quot;modified&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">modified</span><span class="p">(),</span>
 </span><span id="__span-0-397"><a id="__codelineno-0-397" name="__codelineno-0-397"></a>        <span class="s2">&quot;input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">(),</span>
 </span><span id="__span-0-398"><a id="__codelineno-0-398" name="__codelineno-0-398"></a>    <span class="p">}</span>
-</span><span id="__span-0-399"><a id="__codelineno-0-399" name="__codelineno-0-399"></a>    <span class="c1"># Combine the sageworks metadata with the basic metadata</span>
-</span><span id="__span-0-400"><a id="__codelineno-0-400" name="__codelineno-0-400"></a>    <span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">basic</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()}</span>
+</span><span id="__span-0-399"><a id="__codelineno-0-399" name="__codelineno-0-399"></a>    <span class="c1"># Combine the workbench metadata with the basic metadata</span>
+</span><span id="__span-0-400"><a id="__codelineno-0-400" name="__codelineno-0-400"></a>    <span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">basic</span><span class="p">,</span> <span class="o">**</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()}</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6511,15 +6443,15 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.summary" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.artifact.Artifact.upsert_sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">upsert_sageworks_meta</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)</span></code>
+<h3 id="workbench.core.artifacts.artifact.Artifact.upsert_workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">upsert_workbench_meta</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Add SageWorks specific metadata to this Artifact
+        <p>Add Workbench specific metadata to this Artifact
 Args:
     new_meta (dict): Dictionary of NEW metadata to add
 Note:
@@ -6527,7 +6459,7 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.upsert_sageworks_meta" class=
     but not for DataSources. The DataSource class overrides this method.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/artifact.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-226">226</a></span>
 <span class="normal"><a href="#__codelineno-0-227">227</a></span>
 <span class="normal"><a href="#__codelineno-0-228">228</a></span>
@@ -6548,8 +6480,8 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.upsert_sageworks_meta" class=
 <span class="normal"><a href="#__codelineno-0-243">243</a></span>
 <span class="normal"><a href="#__codelineno-0-244">244</a></span>
 <span class="normal"><a href="#__codelineno-0-245">245</a></span>
-<span class="normal"><a href="#__codelineno-0-246">246</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-226"><a id="__codelineno-0-226" name="__codelineno-0-226"></a><span class="k">def</span> <span class="nf">upsert_sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-227"><a id="__codelineno-0-227" name="__codelineno-0-227"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Add SageWorks specific metadata to this Artifact</span>
+<span class="normal"><a href="#__codelineno-0-246">246</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-226"><a id="__codelineno-0-226" name="__codelineno-0-226"></a><span class="k">def</span> <span class="nf">upsert_workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-227"><a id="__codelineno-0-227" name="__codelineno-0-227"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Add Workbench specific metadata to this Artifact</span>
 </span><span id="__span-0-228"><a id="__codelineno-0-228" name="__codelineno-0-228"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-229"><a id="__codelineno-0-229" name="__codelineno-0-229"></a><span class="sd">        new_meta (dict): Dictionary of NEW metadata to add</span>
 </span><span id="__span-0-230"><a id="__codelineno-0-230" name="__codelineno-0-230"></a><span class="sd">    Note:</span>
@@ -6575,6 +6507,74 @@ <h3 id="sageworks.core.artifacts.artifact.Artifact.upsert_sageworks_meta" class=
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+<h3 id="workbench.core.artifacts.artifact.Artifact.workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">workbench_meta</span><span class="p">()</span></code>
+
+</h3>
+
+
+    <div class="doc doc-contents ">
+
+        <p>Get the Workbench specific metadata for this Artifact</p>
+
+
+    <p><span class="doc-section-title">Returns:</span></p>
+    <table>
+      <thead>
+        <tr>
+          <th>Type</th>
+          <th>Description</th>
+        </tr>
+      </thead>
+      <tbody>
+          <tr class="doc-section-item">
+            <td>
+                  <code><span title="typing.Union">Union</span>[dict, None]</code>
+            </td>
+            <td>
+              <div class="doc-md-description">
+                <p>Union[dict, None]: Dictionary of Workbench metadata for this Artifact</p>
+              </div>
+            </td>
+          </tr>
+      </tbody>
+    </table>
+
+
+<details class="note" open>
+  <summary>This functionality will work for FeatureSets, Models, and Endpoints</summary>
+  <p>but not for DataSources and Graphs, those classes need to override this method.</p>
+</details>
+            <details class="quote">
+              <summary>Source code in <code>src/workbench/core/artifacts/artifact.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-130">130</a></span>
+<span class="normal"><a href="#__codelineno-0-131">131</a></span>
+<span class="normal"><a href="#__codelineno-0-132">132</a></span>
+<span class="normal"><a href="#__codelineno-0-133">133</a></span>
+<span class="normal"><a href="#__codelineno-0-134">134</a></span>
+<span class="normal"><a href="#__codelineno-0-135">135</a></span>
+<span class="normal"><a href="#__codelineno-0-136">136</a></span>
+<span class="normal"><a href="#__codelineno-0-137">137</a></span>
+<span class="normal"><a href="#__codelineno-0-138">138</a></span>
+<span class="normal"><a href="#__codelineno-0-139">139</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a><span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
+</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the Workbench specific metadata for this Artifact</span>
+</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>
+</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a><span class="sd">    Returns:</span>
+</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a><span class="sd">        Union[dict, None]: Dictionary of Workbench metadata for this Artifact</span>
+</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>
+</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a><span class="sd">    Note: This functionality will work for FeatureSets, Models, and Endpoints</span>
+</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a><span class="sd">          but not for DataSources and Graphs, those classes need to override this method.</span>
+</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get_aws_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">arn</span><span class="p">())</span>
+</span></code></pre></div></td></tr></table></div>
+            </details>
+    </div>
+
+</div>
+
 
 
   </div>
diff --git a/core_classes/artifacts/athena_source/index.html b/core_classes/artifacts/athena_source/index.html
index 2a712113c..c623f01e8 100644
--- a/core_classes/artifacts/athena_source/index.html
+++ b/core_classes/artifacts/athena_source/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AthenaSource - SageWorks</title>
+        <title>AthenaSource - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1143,7 +1143,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source" class="md-nav__link">
     <span class="md-ellipsis">
       athena_source
     </span>
@@ -1152,7 +1152,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource" class="md-nav__link">
     <span class="md-ellipsis">
       AthenaSource
     </span>
@@ -1162,7 +1162,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.column_types" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.column_types" class="md-nav__link">
     <span class="md-ellipsis">
       column_types
     </span>
@@ -1171,7 +1171,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.columns" class="md-nav__link">
     <span class="md-ellipsis">
       columns
     </span>
@@ -1180,7 +1180,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.__init__" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1189,7 +1189,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.arn" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.arn" class="md-nav__link">
     <span class="md-ellipsis">
       arn
     </span>
@@ -1198,7 +1198,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.athena_test_query" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.athena_test_query" class="md-nav__link">
     <span class="md-ellipsis">
       athena_test_query
     </span>
@@ -1207,7 +1207,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.aws_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.aws_meta" class="md-nav__link">
     <span class="md-ellipsis">
       aws_meta
     </span>
@@ -1216,7 +1216,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.aws_url" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.aws_url" class="md-nav__link">
     <span class="md-ellipsis">
       aws_url
     </span>
@@ -1225,7 +1225,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.column_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.column_stats" class="md-nav__link">
     <span class="md-ellipsis">
       column_stats
     </span>
@@ -1234,7 +1234,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.correlations" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.correlations" class="md-nav__link">
     <span class="md-ellipsis">
       correlations
     </span>
@@ -1243,7 +1243,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.created" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.created" class="md-nav__link">
     <span class="md-ellipsis">
       created
     </span>
@@ -1252,7 +1252,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.database_query" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.database_query" class="md-nav__link">
     <span class="md-ellipsis">
       database_query
     </span>
@@ -1261,7 +1261,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.delete" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
     </span>
@@ -1270,7 +1270,7 @@
 </li>
         
           <li class="md-nav__item">
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           <li class="md-nav__item">
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           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.details" class="md-nav__link">
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           <li class="md-nav__item">
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           <li class="md-nav__item">
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           <li class="md-nav__item">
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           <li class="md-nav__item">
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           <li class="md-nav__item">
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@@ -1351,7 +1351,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.num_rows" class="md-nav__link">
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     <span class="md-ellipsis">
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.outliers" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.outliers" class="md-nav__link">
     <span class="md-ellipsis">
       outliers
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@@ -1369,7 +1369,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.query" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.query" class="md-nav__link">
     <span class="md-ellipsis">
       query
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.refresh_meta" class="md-nav__link">
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       refresh_meta
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@@ -1387,7 +1387,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.s3_storage_location" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.s3_storage_location" class="md-nav__link">
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       s3_storage_location
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 </li>
         
           <li class="md-nav__item">
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+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.sample" class="md-nav__link">
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 </li>
         
           <li class="md-nav__item">
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           <li class="md-nav__item">
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-      upsert_sageworks_meta
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.value_counts" class="md-nav__link">
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     <span class="md-ellipsis">
-      value_counts
+      workbench_meta
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   </a>
   
@@ -2182,7 +2182,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -2192,7 +2192,7 @@
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           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3411,11 +3411,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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@@ -3467,7 +3467,7 @@
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       athena_source
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       AthenaSource
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@@ -3486,7 +3486,7 @@
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           <li class="md-nav__item">
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           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.columns" class="md-nav__link">
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       columns
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 </li>
         
           <li class="md-nav__item">
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           <li class="md-nav__item">
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       arn
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           <li class="md-nav__item">
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       aws_meta
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           <li class="md-nav__item">
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       aws_url
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@@ -3549,7 +3549,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.column_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.column_stats" class="md-nav__link">
     <span class="md-ellipsis">
       column_stats
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@@ -3558,7 +3558,7 @@
 </li>
         
           <li class="md-nav__item">
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       correlations
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@@ -3567,7 +3567,7 @@
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           <li class="md-nav__item">
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     <span class="md-ellipsis">
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 </li>
         
           <li class="md-nav__item">
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       database_query
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@@ -3585,7 +3585,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.delete" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.delete_views" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.delete_views" class="md-nav__link">
     <span class="md-ellipsis">
       delete_views
     </span>
@@ -3603,7 +3603,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.descriptive_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.descriptive_stats" class="md-nav__link">
     <span class="md-ellipsis">
       descriptive_stats
     </span>
@@ -3612,7 +3612,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.details" class="md-nav__link">
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       details
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 </li>
         
           <li class="md-nav__item">
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     <span class="md-ellipsis">
       execute_statement
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@@ -3630,7 +3630,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.exists" class="md-nav__link">
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.hash" class="md-nav__link">
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       hash
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@@ -3648,7 +3648,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.managed_delete" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.managed_delete" class="md-nav__link">
     <span class="md-ellipsis">
       managed_delete
     </span>
@@ -3657,7 +3657,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.modified" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.modified" class="md-nav__link">
     <span class="md-ellipsis">
       modified
     </span>
@@ -3666,7 +3666,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.num_columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.num_columns" class="md-nav__link">
     <span class="md-ellipsis">
       num_columns
     </span>
@@ -3675,7 +3675,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.num_rows" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.num_rows" class="md-nav__link">
     <span class="md-ellipsis">
       num_rows
     </span>
@@ -3684,7 +3684,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.outliers" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.outliers" class="md-nav__link">
     <span class="md-ellipsis">
       outliers
     </span>
@@ -3693,7 +3693,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.query" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.query" class="md-nav__link">
     <span class="md-ellipsis">
       query
     </span>
@@ -3702,7 +3702,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.refresh_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.refresh_meta" class="md-nav__link">
     <span class="md-ellipsis">
       refresh_meta
     </span>
@@ -3711,7 +3711,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.s3_storage_location" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.s3_storage_location" class="md-nav__link">
     <span class="md-ellipsis">
       s3_storage_location
     </span>
@@ -3720,63 +3720,63 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.sageworks_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.sample" class="md-nav__link">
     <span class="md-ellipsis">
-      sageworks_meta
+      sample
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   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.sample" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.size" class="md-nav__link">
     <span class="md-ellipsis">
-      sample
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   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.size" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.smart_sample" class="md-nav__link">
     <span class="md-ellipsis">
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     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.smart_sample" class="md-nav__link">
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     <span class="md-ellipsis">
-      smart_sample
+      table_hash
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.table_hash" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.upsert_workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      table_hash
+      upsert_workbench_meta
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.upsert_sageworks_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.value_counts" class="md-nav__link">
     <span class="md-ellipsis">
-      upsert_sageworks_meta
+      value_counts
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.athena_source.AthenaSource.value_counts" class="md-nav__link">
+  <a href="#workbench.core.artifacts.athena_source.AthenaSource.workbench_meta" class="md-nav__link">
     <span class="md-ellipsis">
-      value_counts
+      workbench_meta
     </span>
   </a>
   
@@ -3813,10 +3813,10 @@ <h1 id="athenasource">AthenaSource</h1>
 
 
 
-<a id="sageworks.core.artifacts.athena_source"></a>
+<a id="workbench.core.artifacts.athena_source"></a>
     <div class="doc doc-contents first">
 
-        <p>AthenaSource: SageWorks Data Source accessible through Athena</p>
+        <p>AthenaSource: Workbench Data Source accessible through Athena</p>
 
 
 
@@ -3838,7 +3838,7 @@ <h1 id="athenasource">AthenaSource</h1>
 
 
 
-<h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.athena_source.AthenaSource" class="doc doc-heading">
             <code>AthenaSource</code>
 
 
@@ -3847,15 +3847,15 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" href="../data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract">DataSourceAbstract</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.data_source_abstract.DataSourceAbstract" href="../data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract">DataSourceAbstract</a></code></p>
 
 
-        <p>AthenaSource: SageWorks Data Source accessible through Athena</p>
+        <p>AthenaSource: Workbench Data Source accessible through Athena</p>
 
 
 <details class="common-usage" open>
   <summary>Common Usage</summary>
-  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">my_data</span> <span class="o">=</span> <span class="n">AthenaSource</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="n">my_data</span> <span class="o">=</span> <span class="n">AthenaSource</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">my_data</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">my_data</span><span class="o">.</span><span class="n">details</span><span class="p">()</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">df</span> <span class="o">=</span> <span class="n">my_data</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;select * from </span><span class="si">{</span><span class="n">data_uuid</span><span class="si">}</span><span class="s2"> limit 5&quot;</span><span class="p">)</span>
@@ -3867,7 +3867,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-23"> 23</a></span>
 <span class="normal"><a href="#__codelineno-0-24"> 24</a></span>
 <span class="normal"><a href="#__codelineno-0-25"> 25</a></span>
@@ -4409,23 +4409,23 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 <span class="normal"><a href="#__codelineno-0-561">561</a></span>
 <span class="normal"><a href="#__codelineno-0-562">562</a></span>
 <span class="normal"><a href="#__codelineno-0-563">563</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="k">class</span> <span class="nc">AthenaSource</span><span class="p">(</span><span class="n">DataSourceAbstract</span><span class="p">):</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;AthenaSource: SageWorks Data Source accessible through Athena</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;AthenaSource: Workbench Data Source accessible through Athena</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">        ```python</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">        my_data = AthenaSource(data_uuid, database=&quot;sageworks&quot;)</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">        my_data = AthenaSource(data_uuid, database=&quot;workbench&quot;)</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">        my_data.summary()</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">        my_data.details()</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">        df = my_data.query(f&quot;select * from {data_uuid} limit 5&quot;)</span>
 </span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">        ```</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;AthenaSource Initialization</span>
 </span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>
 </span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">            data_uuid (str): Name of Athena Table</span>
-</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">            database (str): Athena Database Name (default: sageworks)</span>
+</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">            database (str): Athena Database Name (default: workbench)</span>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>        <span class="c1"># Ensure the data_uuid is a valid name/id</span>
 </span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">is_name_valid</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">)</span>
@@ -4456,20 +4456,20 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>        <span class="c1"># Are we able to pull AWS Metadata for this table_name?&quot;&quot;&quot;</span>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>        <span class="c1"># Do we have a valid data_source_meta?</span>
 </span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>        <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;data_source_meta&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;AthenaSource </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2"> not found in SageWorks Metadata...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;AthenaSource </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2"> not found in Workbench Metadata...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>            <span class="k">return</span> <span class="kc">False</span>
 </span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>        <span class="k">return</span> <span class="kc">True</span>
 </span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>
 </span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>    <span class="k">def</span> <span class="nf">arn</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;AWS ARN (Amazon Resource Name) for this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>        <span class="c1"># Grab our SageWorks Role Manager, get our AWS account id, and region for ARN creation</span>
+</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>        <span class="c1"># Grab our Workbench Role Manager, get our AWS account id, and region for ARN creation</span>
 </span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>        <span class="n">account_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">account_id</span>
 </span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>        <span class="n">region</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">region</span>
 </span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>        <span class="n">arn</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;arn:aws:glue:</span><span class="si">{</span><span class="n">region</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">account_id</span><span class="si">}</span><span class="s2">:table/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2">&quot;</span>
 </span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>        <span class="k">return</span> <span class="n">arn</span>
 </span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the SageWorks specific metadata for this Artifact&quot;&quot;&quot;</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the Workbench specific metadata for this Artifact&quot;&quot;&quot;</span>
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>        <span class="c1"># Sanity Check if we have invalid AWS Metadata</span>
 </span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source_meta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
@@ -4480,22 +4480,22 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">&quot;Malformed Artifact! Delete this Artifact and recreate it!&quot;</span><span class="p">)</span>
 </span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>            <span class="k">return</span> <span class="p">{}</span>
 </span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>
-</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>        <span class="c1"># Get the SageWorks Metadata from the &#39;Parameters&#39; section of the DataSource Metadata</span>
+</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>        <span class="c1"># Get the Workbench Metadata from the &#39;Parameters&#39; section of the DataSource Metadata</span>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>        <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source_meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;Parameters&quot;</span><span class="p">,</span> <span class="p">{})</span>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>        <span class="k">return</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">decode_value</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;sageworks&quot;</span> <span class="ow">in</span> <span class="n">key</span><span class="p">}</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>        <span class="k">return</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">decode_value</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;workbench&quot;</span> <span class="ow">in</span> <span class="n">key</span><span class="p">}</span>
 </span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a>
-</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a>    <span class="k">def</span> <span class="nf">upsert_sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Add SageWorks specific metadata to this Artifact</span>
+</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a>    <span class="k">def</span> <span class="nf">upsert_workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Add Workbench specific metadata to this Artifact</span>
 </span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>
 </span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a><span class="sd">            new_meta (dict): Dictionary of new metadata to add</span>
 </span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Upserting SageWorks Metadata </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)[:</span><span class="mi">50</span><span class="p">]</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Upserting Workbench Metadata </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)[:</span><span class="mi">50</span><span class="p">]</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a>
-</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>        <span class="c1"># Give a warning message for keys that don&#39;t start with sageworks_</span>
+</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>        <span class="c1"># Give a warning message for keys that don&#39;t start with workbench_</span>
 </span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a>        <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">new_meta</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>            <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;sageworks_&quot;</span><span class="p">):</span>
-</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Append &#39;sageworks_&#39; to key names to avoid overwriting AWS meta data&quot;</span><span class="p">)</span>
+</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>            <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;workbench_&quot;</span><span class="p">):</span>
+</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Append &#39;workbench_&#39; to key names to avoid overwriting AWS meta data&quot;</span><span class="p">)</span>
 </span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>
 </span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>        <span class="c1"># Now convert any non-string values to JSON strings</span>
 </span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">new_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
@@ -4544,8 +4544,8 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-156"><a id="__codelineno-0-156" name="__codelineno-0-156"></a>
 </span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a>    <span class="k">def</span> <span class="nf">aws_url</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;The AWS URL for looking at/querying this data source&quot;&quot;&quot;</span>
-</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>        <span class="n">sageworks_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>        <span class="k">return</span> <span class="n">sageworks_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>        <span class="n">workbench_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>        <span class="k">return</span> <span class="n">workbench_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>
 </span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>    <span class="k">def</span> <span class="nf">created</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">datetime</span><span class="p">:</span>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the datetime when this artifact was created&quot;&quot;&quot;</span>
@@ -4562,13 +4562,13 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a>
 </span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a>    <span class="k">def</span> <span class="nf">table_hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the table hash for this AthenaSource&quot;&quot;&quot;</span>
-</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>        <span class="n">s3_scratch</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span><span class="si">}</span><span class="s2">/temp/athena_output&quot;</span>
+</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>        <span class="n">s3_scratch</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span><span class="si">}</span><span class="s2">/temp/athena_output&quot;</span>
 </span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a>        <span class="k">return</span> <span class="n">compute_athena_table_hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span> <span class="n">s3_scratch</span><span class="p">)</span>
 </span><span id="__span-0-179"><a id="__codelineno-0-179" name="__codelineno-0-179"></a>
 </span><span id="__span-0-180"><a id="__codelineno-0-180" name="__codelineno-0-180"></a>    <span class="k">def</span> <span class="nf">num_rows</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 </span><span id="__span-0-181"><a id="__codelineno-0-181" name="__codelineno-0-181"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the number of rows for this Data Source&quot;&quot;&quot;</span>
-</span><span id="__span-0-182"><a id="__codelineno-0-182" name="__codelineno-0-182"></a>        <span class="n">count_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;select count(*) AS sageworks_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="si">}</span><span class="s1">&quot;.&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">)</span>
-</span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a>        <span class="k">return</span> <span class="n">count_df</span><span class="p">[</span><span class="s2">&quot;sageworks_count&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">count_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span>
+</span><span id="__span-0-182"><a id="__codelineno-0-182" name="__codelineno-0-182"></a>        <span class="n">count_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;select count(*) AS workbench_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="si">}</span><span class="s1">&quot;.&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">)</span>
+</span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a>        <span class="k">return</span> <span class="n">count_df</span><span class="p">[</span><span class="s2">&quot;workbench_count&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">count_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span>
 </span><span id="__span-0-184"><a id="__codelineno-0-184" name="__codelineno-0-184"></a>
 </span><span id="__span-0-185"><a id="__codelineno-0-185" name="__codelineno-0-185"></a>    <span class="k">def</span> <span class="nf">num_columns</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 </span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the number of columns for this Data Source&quot;&quot;&quot;</span>
@@ -4672,7 +4672,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-284"><a id="__codelineno-0-284" name="__codelineno-0-284"></a>
 </span><span id="__span-0-285"><a id="__codelineno-0-285" name="__codelineno-0-285"></a>    <span class="k">def</span> <span class="nf">athena_test_query</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-286"><a id="__codelineno-0-286" name="__codelineno-0-286"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Validate that Athena Queries are working&quot;&quot;&quot;</span>
-</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>        <span class="n">query</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;select count(*) as sageworks_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span>
+</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>        <span class="n">query</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;select count(*) as workbench_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span>
 </span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>        <span class="n">df</span> <span class="o">=</span> <span class="n">wr</span><span class="o">.</span><span class="n">athena</span><span class="o">.</span><span class="n">read_sql_query</span><span class="p">(</span>
 </span><span id="__span-0-289"><a id="__codelineno-0-289" name="__codelineno-0-289"></a>            <span class="n">sql</span><span class="o">=</span><span class="n">query</span><span class="p">,</span>
 </span><span id="__span-0-290"><a id="__codelineno-0-290" name="__codelineno-0-290"></a>            <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="p">,</span>
@@ -4695,7 +4695,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-307"><a id="__codelineno-0-307" name="__codelineno-0-307"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a>
 </span><span id="__span-0-309"><a id="__codelineno-0-309" name="__codelineno-0-309"></a>        <span class="c1"># First check if we have already computed the descriptive stats</span>
-</span><span id="__span-0-310"><a id="__codelineno-0-310" name="__codelineno-0-310"></a>        <span class="n">stat_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_descriptive_stats&quot;</span><span class="p">)</span>
+</span><span id="__span-0-310"><a id="__codelineno-0-310" name="__codelineno-0-310"></a>        <span class="n">stat_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_descriptive_stats&quot;</span><span class="p">)</span>
 </span><span id="__span-0-311"><a id="__codelineno-0-311" name="__codelineno-0-311"></a>        <span class="k">if</span> <span class="n">stat_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-312"><a id="__codelineno-0-312" name="__codelineno-0-312"></a>            <span class="k">return</span> <span class="n">stat_dict</span>
 </span><span id="__span-0-313"><a id="__codelineno-0-313" name="__codelineno-0-313"></a>
@@ -4703,7 +4703,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-315"><a id="__codelineno-0-315" name="__codelineno-0-315"></a>        <span class="n">stat_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">descriptive_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span><span id="__span-0-316"><a id="__codelineno-0-316" name="__codelineno-0-316"></a>
 </span><span id="__span-0-317"><a id="__codelineno-0-317" name="__codelineno-0-317"></a>        <span class="c1"># Push the descriptive stat data into our DataSource Metadata</span>
-</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_descriptive_stats&quot;</span><span class="p">:</span> <span class="n">stat_dict</span><span class="p">})</span>
+</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_descriptive_stats&quot;</span><span class="p">:</span> <span class="n">stat_dict</span><span class="p">})</span>
 </span><span id="__span-0-319"><a id="__codelineno-0-319" name="__codelineno-0-319"></a>
 </span><span id="__span-0-320"><a id="__codelineno-0-320" name="__codelineno-0-320"></a>        <span class="c1"># Return the descriptive stats</span>
 </span><span id="__span-0-321"><a id="__codelineno-0-321" name="__codelineno-0-321"></a>        <span class="k">return</span> <span class="n">stat_dict</span>
@@ -4783,7 +4783,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-395"><a id="__codelineno-0-395" name="__codelineno-0-395"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-396"><a id="__codelineno-0-396" name="__codelineno-0-396"></a>
 </span><span id="__span-0-397"><a id="__codelineno-0-397" name="__codelineno-0-397"></a>        <span class="c1"># First check if we have already computed the correlations</span>
-</span><span id="__span-0-398"><a id="__codelineno-0-398" name="__codelineno-0-398"></a>        <span class="n">correlations_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_correlations&quot;</span><span class="p">)</span>
+</span><span id="__span-0-398"><a id="__codelineno-0-398" name="__codelineno-0-398"></a>        <span class="n">correlations_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_correlations&quot;</span><span class="p">)</span>
 </span><span id="__span-0-399"><a id="__codelineno-0-399" name="__codelineno-0-399"></a>        <span class="k">if</span> <span class="n">correlations_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-400"><a id="__codelineno-0-400" name="__codelineno-0-400"></a>            <span class="k">return</span> <span class="n">correlations_dict</span>
 </span><span id="__span-0-401"><a id="__codelineno-0-401" name="__codelineno-0-401"></a>
@@ -4791,7 +4791,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-403"><a id="__codelineno-0-403" name="__codelineno-0-403"></a>        <span class="n">correlations_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">correlations</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span><span id="__span-0-404"><a id="__codelineno-0-404" name="__codelineno-0-404"></a>
 </span><span id="__span-0-405"><a id="__codelineno-0-405" name="__codelineno-0-405"></a>        <span class="c1"># Push the correlation data into our DataSource Metadata</span>
-</span><span id="__span-0-406"><a id="__codelineno-0-406" name="__codelineno-0-406"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_correlations&quot;</span><span class="p">:</span> <span class="n">correlations_dict</span><span class="p">})</span>
+</span><span id="__span-0-406"><a id="__codelineno-0-406" name="__codelineno-0-406"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_correlations&quot;</span><span class="p">:</span> <span class="n">correlations_dict</span><span class="p">})</span>
 </span><span id="__span-0-407"><a id="__codelineno-0-407" name="__codelineno-0-407"></a>
 </span><span id="__span-0-408"><a id="__codelineno-0-408" name="__codelineno-0-408"></a>        <span class="c1"># Return the correlation data</span>
 </span><span id="__span-0-409"><a id="__codelineno-0-409" name="__codelineno-0-409"></a>        <span class="k">return</span> <span class="n">correlations_dict</span>
@@ -4812,7 +4812,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-424"><a id="__codelineno-0-424" name="__codelineno-0-424"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-425"><a id="__codelineno-0-425" name="__codelineno-0-425"></a>
 </span><span id="__span-0-426"><a id="__codelineno-0-426" name="__codelineno-0-426"></a>        <span class="c1"># First check if we have already computed the column stats</span>
-</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>        <span class="n">columns_stats_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_column_stats&quot;</span><span class="p">)</span>
+</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>        <span class="n">columns_stats_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_column_stats&quot;</span><span class="p">)</span>
 </span><span id="__span-0-428"><a id="__codelineno-0-428" name="__codelineno-0-428"></a>        <span class="k">if</span> <span class="n">columns_stats_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-429"><a id="__codelineno-0-429" name="__codelineno-0-429"></a>            <span class="k">return</span> <span class="n">columns_stats_dict</span>
 </span><span id="__span-0-430"><a id="__codelineno-0-430" name="__codelineno-0-430"></a>
@@ -4820,7 +4820,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-432"><a id="__codelineno-0-432" name="__codelineno-0-432"></a>        <span class="n">column_stats_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">column_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">recompute</span><span class="o">=</span><span class="n">recompute</span><span class="p">)</span>
 </span><span id="__span-0-433"><a id="__codelineno-0-433" name="__codelineno-0-433"></a>
 </span><span id="__span-0-434"><a id="__codelineno-0-434" name="__codelineno-0-434"></a>        <span class="c1"># Push the column stats data into our DataSource Metadata</span>
-</span><span id="__span-0-435"><a id="__codelineno-0-435" name="__codelineno-0-435"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_column_stats&quot;</span><span class="p">:</span> <span class="n">column_stats_dict</span><span class="p">})</span>
+</span><span id="__span-0-435"><a id="__codelineno-0-435" name="__codelineno-0-435"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_column_stats&quot;</span><span class="p">:</span> <span class="n">column_stats_dict</span><span class="p">})</span>
 </span><span id="__span-0-436"><a id="__codelineno-0-436" name="__codelineno-0-436"></a>
 </span><span id="__span-0-437"><a id="__codelineno-0-437" name="__codelineno-0-437"></a>        <span class="c1"># Return the column stats data</span>
 </span><span id="__span-0-438"><a id="__codelineno-0-438" name="__codelineno-0-438"></a>        <span class="k">return</span> <span class="n">column_stats_dict</span>
@@ -4838,7 +4838,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-450"><a id="__codelineno-0-450" name="__codelineno-0-450"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-451"><a id="__codelineno-0-451" name="__codelineno-0-451"></a>
 </span><span id="__span-0-452"><a id="__codelineno-0-452" name="__codelineno-0-452"></a>        <span class="c1"># First check if we have already computed the value counts</span>
-</span><span id="__span-0-453"><a id="__codelineno-0-453" name="__codelineno-0-453"></a>        <span class="n">value_counts_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_value_counts&quot;</span><span class="p">)</span>
+</span><span id="__span-0-453"><a id="__codelineno-0-453" name="__codelineno-0-453"></a>        <span class="n">value_counts_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_value_counts&quot;</span><span class="p">)</span>
 </span><span id="__span-0-454"><a id="__codelineno-0-454" name="__codelineno-0-454"></a>        <span class="k">if</span> <span class="n">value_counts_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-455"><a id="__codelineno-0-455" name="__codelineno-0-455"></a>            <span class="k">return</span> <span class="n">value_counts_dict</span>
 </span><span id="__span-0-456"><a id="__codelineno-0-456" name="__codelineno-0-456"></a>
@@ -4846,7 +4846,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a>        <span class="n">value_count_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a>
 </span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a>        <span class="c1"># Push the value_count data into our DataSource Metadata</span>
-</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_value_counts&quot;</span><span class="p">:</span> <span class="n">value_count_dict</span><span class="p">})</span>
+</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_value_counts&quot;</span><span class="p">:</span> <span class="n">value_count_dict</span><span class="p">})</span>
 </span><span id="__span-0-462"><a id="__codelineno-0-462" name="__codelineno-0-462"></a>
 </span><span id="__span-0-463"><a id="__codelineno-0-463" name="__codelineno-0-463"></a>        <span class="c1"># Return the value_count data</span>
 </span><span id="__span-0-464"><a id="__codelineno-0-464" name="__codelineno-0-464"></a>        <span class="k">return</span> <span class="n">value_count_dict</span>
@@ -4879,7 +4879,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-491"><a id="__codelineno-0-491" name="__codelineno-0-491"></a>
 </span><span id="__span-0-492"><a id="__codelineno-0-492" name="__codelineno-0-492"></a>        <span class="c1"># Push the aws_url data into our DataSource Metadata</span>
 </span><span id="__span-0-493"><a id="__codelineno-0-493" name="__codelineno-0-493"></a>        <span class="c1"># FIXME: We need to revisit this but doing an upsert just for aws_url is silly</span>
-</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>        <span class="c1"># self.upsert_sageworks_meta({&quot;sageworks_details&quot;: {&quot;aws_url&quot;: details[&quot;aws_url&quot;]}})</span>
+</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>        <span class="c1"># self.upsert_workbench_meta({&quot;workbench_details&quot;: {&quot;aws_url&quot;: details[&quot;aws_url&quot;]}})</span>
 </span><span id="__span-0-495"><a id="__codelineno-0-495" name="__codelineno-0-495"></a>
 </span><span id="__span-0-496"><a id="__codelineno-0-496" name="__codelineno-0-496"></a>        <span class="c1"># Convert any datetime fields to ISO-8601 strings</span>
 </span><span id="__span-0-497"><a id="__codelineno-0-497" name="__codelineno-0-497"></a>        <span class="n">details</span> <span class="o">=</span> <span class="n">convert_all_to_iso8601</span><span class="p">(</span><span class="n">details</span><span class="p">)</span>
@@ -4901,12 +4901,12 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-513"><a id="__codelineno-0-513" name="__codelineno-0-513"></a>        <span class="n">AthenaSource</span><span class="o">.</span><span class="n">managed_delete</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="p">)</span>
 </span><span id="__span-0-514"><a id="__codelineno-0-514" name="__codelineno-0-514"></a>
 </span><span id="__span-0-515"><a id="__codelineno-0-515" name="__codelineno-0-515"></a>    <span class="nd">@classmethod</span>
-</span><span id="__span-0-516"><a id="__codelineno-0-516" name="__codelineno-0-516"></a>    <span class="k">def</span> <span class="nf">managed_delete</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">):</span>
+</span><span id="__span-0-516"><a id="__codelineno-0-516" name="__codelineno-0-516"></a>    <span class="k">def</span> <span class="nf">managed_delete</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-517"><a id="__codelineno-0-517" name="__codelineno-0-517"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects</span>
 </span><span id="__span-0-518"><a id="__codelineno-0-518" name="__codelineno-0-518"></a>
 </span><span id="__span-0-519"><a id="__codelineno-0-519" name="__codelineno-0-519"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-520"><a id="__codelineno-0-520" name="__codelineno-0-520"></a><span class="sd">            data_source_name (str): Name of DataSource (AthenaSource)</span>
-</span><span id="__span-0-521"><a id="__codelineno-0-521" name="__codelineno-0-521"></a><span class="sd">            database (str): Athena Database Name (default: sageworks)</span>
+</span><span id="__span-0-521"><a id="__codelineno-0-521" name="__codelineno-0-521"></a><span class="sd">            database (str): Athena Database Name (default: workbench)</span>
 </span><span id="__span-0-522"><a id="__codelineno-0-522" name="__codelineno-0-522"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-523"><a id="__codelineno-0-523" name="__codelineno-0-523"></a>        <span class="n">table</span> <span class="o">=</span> <span class="n">data_source_name</span>  <span class="c1"># The table name is the same as the data_source_name</span>
 </span><span id="__span-0-524"><a id="__codelineno-0-524" name="__codelineno-0-524"></a>
@@ -4946,7 +4946,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 </span><span id="__span-0-558"><a id="__codelineno-0-558" name="__codelineno-0-558"></a><span class="sd">            table (str): Name of Athena Table</span>
 </span><span id="__span-0-559"><a id="__codelineno-0-559" name="__codelineno-0-559"></a><span class="sd">            database (str): Athena Database Name</span>
 </span><span id="__span-0-560"><a id="__codelineno-0-560" name="__codelineno-0-560"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-561"><a id="__codelineno-0-561" name="__codelineno-0-561"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
+</span><span id="__span-0-561"><a id="__codelineno-0-561" name="__codelineno-0-561"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
 </span><span id="__span-0-562"><a id="__codelineno-0-562" name="__codelineno-0-562"></a>
 </span><span id="__span-0-563"><a id="__codelineno-0-563" name="__codelineno-0-563"></a>        <span class="n">delete_views_and_supplemental_data</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">database</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -4966,7 +4966,7 @@ <h2 id="sageworks.core.artifacts.athena_source.AthenaSource" class="doc doc-head
 
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.column_types" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.column_types" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_types</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span></code>
 
   <span class="doc doc-labels">
@@ -4987,7 +4987,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.column_types" class=
 
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">columns</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span></code>
 
   <span class="doc doc-labels">
@@ -5009,8 +5009,8 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.columns" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.__init__" class="doc doc-heading">
-            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.__init__" class="doc doc-heading">
+            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
 
@@ -5056,18 +5056,18 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.__init__" class="doc
             </td>
             <td>
               <div class="doc-md-description">
-                <p>Athena Database Name (default: sageworks)</p>
+                <p>Athena Database Name (default: workbench)</p>
               </div>
             </td>
             <td>
-                  <code>&#39;sageworks&#39;</code>
+                  <code>&#39;workbench&#39;</code>
             </td>
           </tr>
       </tbody>
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-35">35</a></span>
 <span class="normal"><a href="#__codelineno-0-36">36</a></span>
 <span class="normal"><a href="#__codelineno-0-37">37</a></span>
@@ -5092,12 +5092,12 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.__init__" class="doc
 <span class="normal"><a href="#__codelineno-0-56">56</a></span>
 <span class="normal"><a href="#__codelineno-0-57">57</a></span>
 <span class="normal"><a href="#__codelineno-0-58">58</a></span>
-<span class="normal"><a href="#__codelineno-0-59">59</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="normal"><a href="#__codelineno-0-59">59</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;AthenaSource Initialization</span>
 </span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>
 </span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">        data_uuid (str): Name of Athena Table</span>
-</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">        database (str): Athena Database Name (default: sageworks)</span>
+</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">        database (str): Athena Database Name (default: workbench)</span>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="c1"># Ensure the data_uuid is a valid name/id</span>
 </span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">is_name_valid</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">)</span>
@@ -5126,7 +5126,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.__init__" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.arn" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.arn" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">arn</span><span class="p">()</span></code>
 
 </h3>
@@ -5137,7 +5137,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.arn" class="doc doc-
         <p>AWS ARN (Amazon Resource Name) for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-75">75</a></span>
 <span class="normal"><a href="#__codelineno-0-76">76</a></span>
 <span class="normal"><a href="#__codelineno-0-77">77</a></span>
@@ -5146,7 +5146,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.arn" class="doc doc-
 <span class="normal"><a href="#__codelineno-0-80">80</a></span>
 <span class="normal"><a href="#__codelineno-0-81">81</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a><span class="k">def</span> <span class="nf">arn</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;AWS ARN (Amazon Resource Name) for this artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>    <span class="c1"># Grab our SageWorks Role Manager, get our AWS account id, and region for ARN creation</span>
+</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>    <span class="c1"># Grab our Workbench Role Manager, get our AWS account id, and region for ARN creation</span>
 </span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>    <span class="n">account_id</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">account_id</span>
 </span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>    <span class="n">region</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">region</span>
 </span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>    <span class="n">arn</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;arn:aws:glue:</span><span class="si">{</span><span class="n">region</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="n">account_id</span><span class="si">}</span><span class="s2">:table/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2">&quot;</span>
@@ -5160,7 +5160,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.arn" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.athena_test_query" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.athena_test_query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">athena_test_query</span><span class="p">()</span></code>
 
 </h3>
@@ -5171,7 +5171,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.athena_test_query" c
         <p>Validate that Athena Queries are working</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-285">285</a></span>
 <span class="normal"><a href="#__codelineno-0-286">286</a></span>
 <span class="normal"><a href="#__codelineno-0-287">287</a></span>
@@ -5184,7 +5184,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.athena_test_query" c
 <span class="normal"><a href="#__codelineno-0-294">294</a></span>
 <span class="normal"><a href="#__codelineno-0-295">295</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-285"><a id="__codelineno-0-285" name="__codelineno-0-285"></a><span class="k">def</span> <span class="nf">athena_test_query</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-286"><a id="__codelineno-0-286" name="__codelineno-0-286"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Validate that Athena Queries are working&quot;&quot;&quot;</span>
-</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>    <span class="n">query</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;select count(*) as sageworks_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span>
+</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>    <span class="n">query</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">&#39;select count(*) as workbench_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span>
 </span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>    <span class="n">df</span> <span class="o">=</span> <span class="n">wr</span><span class="o">.</span><span class="n">athena</span><span class="o">.</span><span class="n">read_sql_query</span><span class="p">(</span>
 </span><span id="__span-0-289"><a id="__codelineno-0-289" name="__codelineno-0-289"></a>        <span class="n">sql</span><span class="o">=</span><span class="n">query</span><span class="p">,</span>
 </span><span id="__span-0-290"><a id="__codelineno-0-290" name="__codelineno-0-290"></a>        <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="p">,</span>
@@ -5202,7 +5202,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.athena_test_query" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.aws_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.aws_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -5213,7 +5213,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.aws_meta" class="doc
         <p>Get the FULL AWS metadata for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-153">153</a></span>
 <span class="normal"><a href="#__codelineno-0-154">154</a></span>
 <span class="normal"><a href="#__codelineno-0-155">155</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-153"><a id="__codelineno-0-153" name="__codelineno-0-153"></a><span class="k">def</span> <span class="nf">aws_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
@@ -5228,7 +5228,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.aws_meta" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.aws_url" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.aws_url" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_url</span><span class="p">()</span></code>
 
 </h3>
@@ -5239,14 +5239,14 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.aws_url" class="doc
         <p>The AWS URL for looking at/querying this data source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-157">157</a></span>
 <span class="normal"><a href="#__codelineno-0-158">158</a></span>
 <span class="normal"><a href="#__codelineno-0-159">159</a></span>
 <span class="normal"><a href="#__codelineno-0-160">160</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a><span class="k">def</span> <span class="nf">aws_url</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;The AWS URL for looking at/querying this data source&quot;&quot;&quot;</span>
-</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>    <span class="n">sageworks_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>    <span class="k">return</span> <span class="n">sageworks_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>    <span class="n">workbench_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>    <span class="k">return</span> <span class="n">workbench_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5256,7 +5256,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.aws_url" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.column_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.column_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_stats</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5335,7 +5335,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.column_stats" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-411">411</a></span>
 <span class="normal"><a href="#__codelineno-0-412">412</a></span>
 <span class="normal"><a href="#__codelineno-0-413">413</a></span>
@@ -5379,7 +5379,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.column_stats" class=
 </span><span id="__span-0-424"><a id="__codelineno-0-424" name="__codelineno-0-424"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-425"><a id="__codelineno-0-425" name="__codelineno-0-425"></a>
 </span><span id="__span-0-426"><a id="__codelineno-0-426" name="__codelineno-0-426"></a>    <span class="c1"># First check if we have already computed the column stats</span>
-</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>    <span class="n">columns_stats_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_column_stats&quot;</span><span class="p">)</span>
+</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>    <span class="n">columns_stats_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_column_stats&quot;</span><span class="p">)</span>
 </span><span id="__span-0-428"><a id="__codelineno-0-428" name="__codelineno-0-428"></a>    <span class="k">if</span> <span class="n">columns_stats_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-429"><a id="__codelineno-0-429" name="__codelineno-0-429"></a>        <span class="k">return</span> <span class="n">columns_stats_dict</span>
 </span><span id="__span-0-430"><a id="__codelineno-0-430" name="__codelineno-0-430"></a>
@@ -5387,7 +5387,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.column_stats" class=
 </span><span id="__span-0-432"><a id="__codelineno-0-432" name="__codelineno-0-432"></a>    <span class="n">column_stats_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">column_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">recompute</span><span class="o">=</span><span class="n">recompute</span><span class="p">)</span>
 </span><span id="__span-0-433"><a id="__codelineno-0-433" name="__codelineno-0-433"></a>
 </span><span id="__span-0-434"><a id="__codelineno-0-434" name="__codelineno-0-434"></a>    <span class="c1"># Push the column stats data into our DataSource Metadata</span>
-</span><span id="__span-0-435"><a id="__codelineno-0-435" name="__codelineno-0-435"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_column_stats&quot;</span><span class="p">:</span> <span class="n">column_stats_dict</span><span class="p">})</span>
+</span><span id="__span-0-435"><a id="__codelineno-0-435" name="__codelineno-0-435"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_column_stats&quot;</span><span class="p">:</span> <span class="n">column_stats_dict</span><span class="p">})</span>
 </span><span id="__span-0-436"><a id="__codelineno-0-436" name="__codelineno-0-436"></a>
 </span><span id="__span-0-437"><a id="__codelineno-0-437" name="__codelineno-0-437"></a>    <span class="c1"># Return the column stats data</span>
 </span><span id="__span-0-438"><a id="__codelineno-0-438" name="__codelineno-0-438"></a>    <span class="k">return</span> <span class="n">column_stats_dict</span>
@@ -5400,7 +5400,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.column_stats" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.correlations" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.correlations" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">correlations</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5467,7 +5467,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.correlations" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-385">385</a></span>
 <span class="normal"><a href="#__codelineno-0-386">386</a></span>
 <span class="normal"><a href="#__codelineno-0-387">387</a></span>
@@ -5505,7 +5505,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.correlations" class=
 </span><span id="__span-0-395"><a id="__codelineno-0-395" name="__codelineno-0-395"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-396"><a id="__codelineno-0-396" name="__codelineno-0-396"></a>
 </span><span id="__span-0-397"><a id="__codelineno-0-397" name="__codelineno-0-397"></a>    <span class="c1"># First check if we have already computed the correlations</span>
-</span><span id="__span-0-398"><a id="__codelineno-0-398" name="__codelineno-0-398"></a>    <span class="n">correlations_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_correlations&quot;</span><span class="p">)</span>
+</span><span id="__span-0-398"><a id="__codelineno-0-398" name="__codelineno-0-398"></a>    <span class="n">correlations_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_correlations&quot;</span><span class="p">)</span>
 </span><span id="__span-0-399"><a id="__codelineno-0-399" name="__codelineno-0-399"></a>    <span class="k">if</span> <span class="n">correlations_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-400"><a id="__codelineno-0-400" name="__codelineno-0-400"></a>        <span class="k">return</span> <span class="n">correlations_dict</span>
 </span><span id="__span-0-401"><a id="__codelineno-0-401" name="__codelineno-0-401"></a>
@@ -5513,7 +5513,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.correlations" class=
 </span><span id="__span-0-403"><a id="__codelineno-0-403" name="__codelineno-0-403"></a>    <span class="n">correlations_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">correlations</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span><span id="__span-0-404"><a id="__codelineno-0-404" name="__codelineno-0-404"></a>
 </span><span id="__span-0-405"><a id="__codelineno-0-405" name="__codelineno-0-405"></a>    <span class="c1"># Push the correlation data into our DataSource Metadata</span>
-</span><span id="__span-0-406"><a id="__codelineno-0-406" name="__codelineno-0-406"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_correlations&quot;</span><span class="p">:</span> <span class="n">correlations_dict</span><span class="p">})</span>
+</span><span id="__span-0-406"><a id="__codelineno-0-406" name="__codelineno-0-406"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_correlations&quot;</span><span class="p">:</span> <span class="n">correlations_dict</span><span class="p">})</span>
 </span><span id="__span-0-407"><a id="__codelineno-0-407" name="__codelineno-0-407"></a>
 </span><span id="__span-0-408"><a id="__codelineno-0-408" name="__codelineno-0-408"></a>    <span class="c1"># Return the correlation data</span>
 </span><span id="__span-0-409"><a id="__codelineno-0-409" name="__codelineno-0-409"></a>    <span class="k">return</span> <span class="n">correlations_dict</span>
@@ -5526,7 +5526,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.correlations" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.created" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.created" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">created</span><span class="p">()</span></code>
 
 </h3>
@@ -5537,7 +5537,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.created" class="doc
         <p>Return the datetime when this artifact was created</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-162">162</a></span>
 <span class="normal"><a href="#__codelineno-0-163">163</a></span>
 <span class="normal"><a href="#__codelineno-0-164">164</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a><span class="k">def</span> <span class="nf">created</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">datetime</span><span class="p">:</span>
@@ -5552,7 +5552,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.created" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.database_query" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.database_query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">database_query</span><span class="p">(</span><span class="n">database</span><span class="p">,</span> <span class="n">query</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5637,7 +5637,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.database_query" clas
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-212">212</a></span>
 <span class="normal"><a href="#__codelineno-0-213">213</a></span>
 <span class="normal"><a href="#__codelineno-0-214">214</a></span>
@@ -5698,7 +5698,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.database_query" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">()</span></code>
 
 </h3>
@@ -5709,7 +5709,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.delete" class="doc d
         <p>Instance Method: Delete the AWS Data Catalog Table and S3 Storage Objects</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-505">505</a></span>
 <span class="normal"><a href="#__codelineno-0-506">506</a></span>
 <span class="normal"><a href="#__codelineno-0-507">507</a></span>
@@ -5736,7 +5736,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.delete" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.delete_views" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.delete_views" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete_views</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">database</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5798,7 +5798,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.delete_views" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-553">553</a></span>
 <span class="normal"><a href="#__codelineno-0-554">554</a></span>
 <span class="normal"><a href="#__codelineno-0-555">555</a></span>
@@ -5817,7 +5817,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.delete_views" class=
 </span><span id="__span-0-558"><a id="__codelineno-0-558" name="__codelineno-0-558"></a><span class="sd">        table (str): Name of Athena Table</span>
 </span><span id="__span-0-559"><a id="__codelineno-0-559" name="__codelineno-0-559"></a><span class="sd">        database (str): Athena Database Name</span>
 </span><span id="__span-0-560"><a id="__codelineno-0-560" name="__codelineno-0-560"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-561"><a id="__codelineno-0-561" name="__codelineno-0-561"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
+</span><span id="__span-0-561"><a id="__codelineno-0-561" name="__codelineno-0-561"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
 </span><span id="__span-0-562"><a id="__codelineno-0-562" name="__codelineno-0-562"></a>
 </span><span id="__span-0-563"><a id="__codelineno-0-563" name="__codelineno-0-563"></a>    <span class="n">delete_views_and_supplemental_data</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">database</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -5829,7 +5829,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.delete_views" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.descriptive_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.descriptive_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">descriptive_stats</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5896,7 +5896,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.descriptive_stats" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-297">297</a></span>
 <span class="normal"><a href="#__codelineno-0-298">298</a></span>
 <span class="normal"><a href="#__codelineno-0-299">299</a></span>
@@ -5934,7 +5934,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.descriptive_stats" c
 </span><span id="__span-0-307"><a id="__codelineno-0-307" name="__codelineno-0-307"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a>
 </span><span id="__span-0-309"><a id="__codelineno-0-309" name="__codelineno-0-309"></a>    <span class="c1"># First check if we have already computed the descriptive stats</span>
-</span><span id="__span-0-310"><a id="__codelineno-0-310" name="__codelineno-0-310"></a>    <span class="n">stat_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_descriptive_stats&quot;</span><span class="p">)</span>
+</span><span id="__span-0-310"><a id="__codelineno-0-310" name="__codelineno-0-310"></a>    <span class="n">stat_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_descriptive_stats&quot;</span><span class="p">)</span>
 </span><span id="__span-0-311"><a id="__codelineno-0-311" name="__codelineno-0-311"></a>    <span class="k">if</span> <span class="n">stat_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-312"><a id="__codelineno-0-312" name="__codelineno-0-312"></a>        <span class="k">return</span> <span class="n">stat_dict</span>
 </span><span id="__span-0-313"><a id="__codelineno-0-313" name="__codelineno-0-313"></a>
@@ -5942,7 +5942,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.descriptive_stats" c
 </span><span id="__span-0-315"><a id="__codelineno-0-315" name="__codelineno-0-315"></a>    <span class="n">stat_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">descriptive_stats</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span><span id="__span-0-316"><a id="__codelineno-0-316" name="__codelineno-0-316"></a>
 </span><span id="__span-0-317"><a id="__codelineno-0-317" name="__codelineno-0-317"></a>    <span class="c1"># Push the descriptive stat data into our DataSource Metadata</span>
-</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_descriptive_stats&quot;</span><span class="p">:</span> <span class="n">stat_dict</span><span class="p">})</span>
+</span><span id="__span-0-318"><a id="__codelineno-0-318" name="__codelineno-0-318"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_descriptive_stats&quot;</span><span class="p">:</span> <span class="n">stat_dict</span><span class="p">})</span>
 </span><span id="__span-0-319"><a id="__codelineno-0-319" name="__codelineno-0-319"></a>
 </span><span id="__span-0-320"><a id="__codelineno-0-320" name="__codelineno-0-320"></a>    <span class="c1"># Return the descriptive stats</span>
 </span><span id="__span-0-321"><a id="__codelineno-0-321" name="__codelineno-0-321"></a>    <span class="k">return</span> <span class="n">stat_dict</span>
@@ -5955,7 +5955,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.descriptive_stats" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6020,7 +6020,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.details" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-466">466</a></span>
 <span class="normal"><a href="#__codelineno-0-467">467</a></span>
 <span class="normal"><a href="#__codelineno-0-468">468</a></span>
@@ -6086,7 +6086,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.details" class="doc
 </span><span id="__span-0-491"><a id="__codelineno-0-491" name="__codelineno-0-491"></a>
 </span><span id="__span-0-492"><a id="__codelineno-0-492" name="__codelineno-0-492"></a>    <span class="c1"># Push the aws_url data into our DataSource Metadata</span>
 </span><span id="__span-0-493"><a id="__codelineno-0-493" name="__codelineno-0-493"></a>    <span class="c1"># FIXME: We need to revisit this but doing an upsert just for aws_url is silly</span>
-</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>    <span class="c1"># self.upsert_sageworks_meta({&quot;sageworks_details&quot;: {&quot;aws_url&quot;: details[&quot;aws_url&quot;]}})</span>
+</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>    <span class="c1"># self.upsert_workbench_meta({&quot;workbench_details&quot;: {&quot;aws_url&quot;: details[&quot;aws_url&quot;]}})</span>
 </span><span id="__span-0-495"><a id="__codelineno-0-495" name="__codelineno-0-495"></a>
 </span><span id="__span-0-496"><a id="__codelineno-0-496" name="__codelineno-0-496"></a>    <span class="c1"># Convert any datetime fields to ISO-8601 strings</span>
 </span><span id="__span-0-497"><a id="__codelineno-0-497" name="__codelineno-0-497"></a>    <span class="n">details</span> <span class="o">=</span> <span class="n">convert_all_to_iso8601</span><span class="p">(</span><span class="n">details</span><span class="p">)</span>
@@ -6105,7 +6105,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.details" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.execute_statement" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.execute_statement" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">execute_statement</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">silence_errors</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6163,7 +6163,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.execute_statement" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-239">239</a></span>
 <span class="normal"><a href="#__codelineno-0-240">240</a></span>
 <span class="normal"><a href="#__codelineno-0-241">241</a></span>
@@ -6254,7 +6254,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.execute_statement" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.exists" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">exists</span><span class="p">()</span></code>
 
 </h3>
@@ -6265,7 +6265,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.exists" class="doc d
         <p>Validation Checks for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-65">65</a></span>
 <span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
@@ -6280,7 +6280,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.exists" class="doc d
 </span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>    <span class="c1"># Are we able to pull AWS Metadata for this table_name?&quot;&quot;&quot;</span>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>    <span class="c1"># Do we have a valid data_source_meta?</span>
 </span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>    <span class="k">if</span> <span class="nb">getattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;data_source_meta&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;AthenaSource </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2"> not found in SageWorks Metadata...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;AthenaSource </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2"> not found in Workbench Metadata...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>        <span class="k">return</span> <span class="kc">False</span>
 </span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>    <span class="k">return</span> <span class="kc">True</span>
 </span></code></pre></div></td></tr></table></div>
@@ -6292,7 +6292,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.exists" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.hash" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.hash" class="doc doc-heading">
             <code class="highlight language-python"><span class="nb">hash</span><span class="p">()</span></code>
 
 </h3>
@@ -6303,7 +6303,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.hash" class="doc doc
         <p>Get the hash for the set of Parquet files used for this Artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-170">170</a></span>
 <span class="normal"><a href="#__codelineno-0-171">171</a></span>
 <span class="normal"><a href="#__codelineno-0-172">172</a></span>
@@ -6320,8 +6320,8 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.hash" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.managed_delete" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">managed_delete</span><span class="p">(</span><span class="n">data_source_name</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.managed_delete" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">managed_delete</span><span class="p">(</span><span class="n">data_source_name</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
       <small class="doc doc-label doc-label-classmethod"><code>classmethod</code></small>
@@ -6371,18 +6371,18 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.managed_delete" clas
             </td>
             <td>
               <div class="doc-md-description">
-                <p>Athena Database Name (default: sageworks)</p>
+                <p>Athena Database Name (default: workbench)</p>
               </div>
             </td>
             <td>
-                  <code>&#39;sageworks&#39;</code>
+                  <code>&#39;workbench&#39;</code>
             </td>
           </tr>
       </tbody>
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-515">515</a></span>
 <span class="normal"><a href="#__codelineno-0-516">516</a></span>
 <span class="normal"><a href="#__codelineno-0-517">517</a></span>
@@ -6420,12 +6420,12 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.managed_delete" clas
 <span class="normal"><a href="#__codelineno-0-549">549</a></span>
 <span class="normal"><a href="#__codelineno-0-550">550</a></span>
 <span class="normal"><a href="#__codelineno-0-551">551</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-515"><a id="__codelineno-0-515" name="__codelineno-0-515"></a><span class="nd">@classmethod</span>
-</span><span id="__span-0-516"><a id="__codelineno-0-516" name="__codelineno-0-516"></a><span class="k">def</span> <span class="nf">managed_delete</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">):</span>
+</span><span id="__span-0-516"><a id="__codelineno-0-516" name="__codelineno-0-516"></a><span class="k">def</span> <span class="nf">managed_delete</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">data_source_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-517"><a id="__codelineno-0-517" name="__codelineno-0-517"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects</span>
 </span><span id="__span-0-518"><a id="__codelineno-0-518" name="__codelineno-0-518"></a>
 </span><span id="__span-0-519"><a id="__codelineno-0-519" name="__codelineno-0-519"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-520"><a id="__codelineno-0-520" name="__codelineno-0-520"></a><span class="sd">        data_source_name (str): Name of DataSource (AthenaSource)</span>
-</span><span id="__span-0-521"><a id="__codelineno-0-521" name="__codelineno-0-521"></a><span class="sd">        database (str): Athena Database Name (default: sageworks)</span>
+</span><span id="__span-0-521"><a id="__codelineno-0-521" name="__codelineno-0-521"></a><span class="sd">        database (str): Athena Database Name (default: workbench)</span>
 </span><span id="__span-0-522"><a id="__codelineno-0-522" name="__codelineno-0-522"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-523"><a id="__codelineno-0-523" name="__codelineno-0-523"></a>    <span class="n">table</span> <span class="o">=</span> <span class="n">data_source_name</span>  <span class="c1"># The table name is the same as the data_source_name</span>
 </span><span id="__span-0-524"><a id="__codelineno-0-524" name="__codelineno-0-524"></a>
@@ -6465,7 +6465,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.managed_delete" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.modified" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.modified" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">modified</span><span class="p">()</span></code>
 
 </h3>
@@ -6476,7 +6476,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.modified" class="doc
         <p>Return the datetime when this artifact was last modified</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-166">166</a></span>
 <span class="normal"><a href="#__codelineno-0-167">167</a></span>
 <span class="normal"><a href="#__codelineno-0-168">168</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a><span class="k">def</span> <span class="nf">modified</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">datetime</span><span class="p">:</span>
@@ -6491,7 +6491,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.modified" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.num_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.num_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">num_columns</span><span class="p">()</span></code>
 
 </h3>
@@ -6502,7 +6502,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.num_columns" class="
         <p>Return the number of columns for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-185">185</a></span>
 <span class="normal"><a href="#__codelineno-0-186">186</a></span>
 <span class="normal"><a href="#__codelineno-0-187">187</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-185"><a id="__codelineno-0-185" name="__codelineno-0-185"></a><span class="k">def</span> <span class="nf">num_columns</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
@@ -6517,7 +6517,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.num_columns" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.num_rows" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.num_rows" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">num_rows</span><span class="p">()</span></code>
 
 </h3>
@@ -6528,14 +6528,14 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.num_rows" class="doc
         <p>Return the number of rows for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-180">180</a></span>
 <span class="normal"><a href="#__codelineno-0-181">181</a></span>
 <span class="normal"><a href="#__codelineno-0-182">182</a></span>
 <span class="normal"><a href="#__codelineno-0-183">183</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-180"><a id="__codelineno-0-180" name="__codelineno-0-180"></a><span class="k">def</span> <span class="nf">num_rows</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
 </span><span id="__span-0-181"><a id="__codelineno-0-181" name="__codelineno-0-181"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the number of rows for this Data Source&quot;&quot;&quot;</span>
-</span><span id="__span-0-182"><a id="__codelineno-0-182" name="__codelineno-0-182"></a>    <span class="n">count_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;select count(*) AS sageworks_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="si">}</span><span class="s1">&quot;.&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">)</span>
-</span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a>    <span class="k">return</span> <span class="n">count_df</span><span class="p">[</span><span class="s2">&quot;sageworks_count&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">count_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span>
+</span><span id="__span-0-182"><a id="__codelineno-0-182" name="__codelineno-0-182"></a>    <span class="n">count_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;select count(*) AS workbench_count from &quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="si">}</span><span class="s1">&quot;.&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s1">&quot;&#39;</span><span class="p">)</span>
+</span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a>    <span class="k">return</span> <span class="n">count_df</span><span class="p">[</span><span class="s2">&quot;workbench_count&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="k">if</span> <span class="n">count_df</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="mi">0</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6545,7 +6545,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.num_rows" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.outliers" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.outliers" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">outliers</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">use_stddev</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6632,7 +6632,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.outliers" class="doc
 The scale parameter can be adjusted to change the IQR multiplier</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-334">334</a></span>
 <span class="normal"><a href="#__codelineno-0-335">335</a></span>
 <span class="normal"><a href="#__codelineno-0-336">336</a></span>
@@ -6679,7 +6679,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.outliers" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.query" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span></code>
 
 </h3>
@@ -6744,7 +6744,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.query" class="doc do
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-199">199</a></span>
 <span class="normal"><a href="#__codelineno-0-200">200</a></span>
 <span class="normal"><a href="#__codelineno-0-201">201</a></span>
@@ -6777,7 +6777,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.query" class="doc do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.refresh_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.refresh_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">refresh_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -6788,7 +6788,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.refresh_meta" class=
         <p>Refresh our internal AWS Broker catalog metadata</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-61">61</a></span>
 <span class="normal"><a href="#__codelineno-0-62">62</a></span>
 <span class="normal"><a href="#__codelineno-0-63">63</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="k">def</span> <span class="nf">refresh_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -6803,7 +6803,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.refresh_meta" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.s3_storage_location" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.s3_storage_location" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">s3_storage_location</span><span class="p">()</span></code>
 
 </h3>
@@ -6814,7 +6814,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.s3_storage_location"
         <p>Get the S3 Storage Location for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-281">281</a></span>
 <span class="normal"><a href="#__codelineno-0-282">282</a></span>
 <span class="normal"><a href="#__codelineno-0-283">283</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-281"><a id="__codelineno-0-281" name="__codelineno-0-281"></a><span class="k">def</span> <span class="nf">s3_storage_location</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
@@ -6829,57 +6829,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.s3_storage_location"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">sageworks_meta</span><span class="p">()</span></code>
-
-</h3>
-
-
-    <div class="doc doc-contents ">
-
-        <p>Get the SageWorks specific metadata for this Artifact</p>
-
-            <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-83">83</a></span>
-<span class="normal"><a href="#__codelineno-0-84">84</a></span>
-<span class="normal"><a href="#__codelineno-0-85">85</a></span>
-<span class="normal"><a href="#__codelineno-0-86">86</a></span>
-<span class="normal"><a href="#__codelineno-0-87">87</a></span>
-<span class="normal"><a href="#__codelineno-0-88">88</a></span>
-<span class="normal"><a href="#__codelineno-0-89">89</a></span>
-<span class="normal"><a href="#__codelineno-0-90">90</a></span>
-<span class="normal"><a href="#__codelineno-0-91">91</a></span>
-<span class="normal"><a href="#__codelineno-0-92">92</a></span>
-<span class="normal"><a href="#__codelineno-0-93">93</a></span>
-<span class="normal"><a href="#__codelineno-0-94">94</a></span>
-<span class="normal"><a href="#__codelineno-0-95">95</a></span>
-<span class="normal"><a href="#__codelineno-0-96">96</a></span>
-<span class="normal"><a href="#__codelineno-0-97">97</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="k">def</span> <span class="nf">sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
-</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the SageWorks specific metadata for this Artifact&quot;&quot;&quot;</span>
-</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>
-</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>    <span class="c1"># Sanity Check if we have invalid AWS Metadata</span>
-</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source_meta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
-</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;DataSource </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> doesn&#39;t appear to exist...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a>        <span class="k">else</span><span class="p">:</span>
-</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unable to get AWS Metadata for </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">&quot;Malformed Artifact! Delete this Artifact and recreate it!&quot;</span><span class="p">)</span>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>        <span class="k">return</span> <span class="p">{}</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>
-</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>    <span class="c1"># Get the SageWorks Metadata from the &#39;Parameters&#39; section of the DataSource Metadata</span>
-</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>    <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source_meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;Parameters&quot;</span><span class="p">,</span> <span class="p">{})</span>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>    <span class="k">return</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">decode_value</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;sageworks&quot;</span> <span class="ow">in</span> <span class="n">key</span><span class="p">}</span>
-</span></code></pre></div></td></tr></table></div>
-            </details>
-    </div>
-
-</div>
-
-<div class="doc doc-object doc-function">
-
-
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.sample" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">sample</span><span class="p">()</span></code>
 
 </h3>
@@ -6913,7 +6863,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.sample" class="doc d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-323">323</a></span>
 <span class="normal"><a href="#__codelineno-0-324">324</a></span>
 <span class="normal"><a href="#__codelineno-0-325">325</a></span>
@@ -6942,7 +6892,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.sample" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.size" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.size" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">size</span><span class="p">()</span></code>
 
 </h3>
@@ -6953,7 +6903,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.size" class="doc doc
         <p>Return the size of this data in MegaBytes</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-147">147</a></span>
 <span class="normal"><a href="#__codelineno-0-148">148</a></span>
 <span class="normal"><a href="#__codelineno-0-149">149</a></span>
@@ -6972,7 +6922,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.size" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.smart_sample" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.smart_sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">smart_sample</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7037,7 +6987,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.smart_sample" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-354">354</a></span>
 <span class="normal"><a href="#__codelineno-0-355">355</a></span>
 <span class="normal"><a href="#__codelineno-0-356">356</a></span>
@@ -7106,7 +7056,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.smart_sample" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.table_hash" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.table_hash" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">table_hash</span><span class="p">()</span></code>
 
 </h3>
@@ -7117,13 +7067,13 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.table_hash" class="d
         <p>Get the table hash for this AthenaSource</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-175">175</a></span>
 <span class="normal"><a href="#__codelineno-0-176">176</a></span>
 <span class="normal"><a href="#__codelineno-0-177">177</a></span>
 <span class="normal"><a href="#__codelineno-0-178">178</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a><span class="k">def</span> <span class="nf">table_hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the table hash for this AthenaSource&quot;&quot;&quot;</span>
-</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>    <span class="n">s3_scratch</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span><span class="si">}</span><span class="s2">/temp/athena_output&quot;</span>
+</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>    <span class="n">s3_scratch</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;s3://</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span><span class="si">}</span><span class="s2">/temp/athena_output&quot;</span>
 </span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a>    <span class="k">return</span> <span class="n">compute_athena_table_hash</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">database</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span> <span class="n">s3_scratch</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
@@ -7134,15 +7084,15 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.table_hash" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.upsert_sageworks_meta" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">upsert_sageworks_meta</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)</span></code>
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.upsert_workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">upsert_workbench_meta</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Add SageWorks specific metadata to this Artifact</p>
+        <p>Add Workbench specific metadata to this Artifact</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -7176,7 +7126,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.upsert_sageworks_met
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-99"> 99</a></span>
 <span class="normal"><a href="#__codelineno-0-100">100</a></span>
 <span class="normal"><a href="#__codelineno-0-101">101</a></span>
@@ -7223,18 +7173,18 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.upsert_sageworks_met
 <span class="normal"><a href="#__codelineno-0-142">142</a></span>
 <span class="normal"><a href="#__codelineno-0-143">143</a></span>
 <span class="normal"><a href="#__codelineno-0-144">144</a></span>
-<span class="normal"><a href="#__codelineno-0-145">145</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="k">def</span> <span class="nf">upsert_sageworks_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Add SageWorks specific metadata to this Artifact</span>
+<span class="normal"><a href="#__codelineno-0-145">145</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="k">def</span> <span class="nf">upsert_workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">new_meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Add Workbench specific metadata to this Artifact</span>
 </span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>
 </span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a><span class="sd">        new_meta (dict): Dictionary of new metadata to add</span>
 </span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Upserting SageWorks Metadata </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)[:</span><span class="mi">50</span><span class="p">]</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Upserting Workbench Metadata </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">:</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">new_meta</span><span class="p">)[:</span><span class="mi">50</span><span class="p">]</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a>
-</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>    <span class="c1"># Give a warning message for keys that don&#39;t start with sageworks_</span>
+</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>    <span class="c1"># Give a warning message for keys that don&#39;t start with workbench_</span>
 </span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a>    <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">new_meta</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;sageworks_&quot;</span><span class="p">):</span>
-</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Append &#39;sageworks_&#39; to key names to avoid overwriting AWS meta data&quot;</span><span class="p">)</span>
+</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="n">key</span><span class="o">.</span><span class="n">startswith</span><span class="p">(</span><span class="s2">&quot;workbench_&quot;</span><span class="p">):</span>
+</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Append &#39;workbench_&#39; to key names to avoid overwriting AWS meta data&quot;</span><span class="p">)</span>
 </span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>
 </span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>    <span class="c1"># Now convert any non-string values to JSON strings</span>
 </span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">new_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
@@ -7279,7 +7229,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.upsert_sageworks_met
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.athena_source.AthenaSource.value_counts" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.value_counts" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">value_counts</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7346,7 +7296,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.value_counts" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/athena_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-440">440</a></span>
 <span class="normal"><a href="#__codelineno-0-441">441</a></span>
 <span class="normal"><a href="#__codelineno-0-442">442</a></span>
@@ -7384,7 +7334,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.value_counts" class=
 </span><span id="__span-0-450"><a id="__codelineno-0-450" name="__codelineno-0-450"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-451"><a id="__codelineno-0-451" name="__codelineno-0-451"></a>
 </span><span id="__span-0-452"><a id="__codelineno-0-452" name="__codelineno-0-452"></a>    <span class="c1"># First check if we have already computed the value counts</span>
-</span><span id="__span-0-453"><a id="__codelineno-0-453" name="__codelineno-0-453"></a>    <span class="n">value_counts_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_value_counts&quot;</span><span class="p">)</span>
+</span><span id="__span-0-453"><a id="__codelineno-0-453" name="__codelineno-0-453"></a>    <span class="n">value_counts_dict</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_value_counts&quot;</span><span class="p">)</span>
 </span><span id="__span-0-454"><a id="__codelineno-0-454" name="__codelineno-0-454"></a>    <span class="k">if</span> <span class="n">value_counts_dict</span> <span class="ow">and</span> <span class="ow">not</span> <span class="n">recompute</span><span class="p">:</span>
 </span><span id="__span-0-455"><a id="__codelineno-0-455" name="__codelineno-0-455"></a>        <span class="k">return</span> <span class="n">value_counts_dict</span>
 </span><span id="__span-0-456"><a id="__codelineno-0-456" name="__codelineno-0-456"></a>
@@ -7392,7 +7342,7 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.value_counts" class=
 </span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a>    <span class="n">value_count_dict</span> <span class="o">=</span> <span class="n">sql</span><span class="o">.</span><span class="n">value_counts</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a>
 </span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a>    <span class="c1"># Push the value_count data into our DataSource Metadata</span>
-</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_value_counts&quot;</span><span class="p">:</span> <span class="n">value_count_dict</span><span class="p">})</span>
+</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_value_counts&quot;</span><span class="p">:</span> <span class="n">value_count_dict</span><span class="p">})</span>
 </span><span id="__span-0-462"><a id="__codelineno-0-462" name="__codelineno-0-462"></a>
 </span><span id="__span-0-463"><a id="__codelineno-0-463" name="__codelineno-0-463"></a>    <span class="c1"># Return the value_count data</span>
 </span><span id="__span-0-464"><a id="__codelineno-0-464" name="__codelineno-0-464"></a>    <span class="k">return</span> <span class="n">value_count_dict</span>
@@ -7402,6 +7352,56 @@ <h3 id="sageworks.core.artifacts.athena_source.AthenaSource.value_counts" class=
 
 </div>
 
+<div class="doc doc-object doc-function">
+
+
+<h3 id="workbench.core.artifacts.athena_source.AthenaSource.workbench_meta" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">workbench_meta</span><span class="p">()</span></code>
+
+</h3>
+
+
+    <div class="doc doc-contents ">
+
+        <p>Get the Workbench specific metadata for this Artifact</p>
+
+            <details class="quote">
+              <summary>Source code in <code>src/workbench/core/artifacts/athena_source.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-83">83</a></span>
+<span class="normal"><a href="#__codelineno-0-84">84</a></span>
+<span class="normal"><a href="#__codelineno-0-85">85</a></span>
+<span class="normal"><a href="#__codelineno-0-86">86</a></span>
+<span class="normal"><a href="#__codelineno-0-87">87</a></span>
+<span class="normal"><a href="#__codelineno-0-88">88</a></span>
+<span class="normal"><a href="#__codelineno-0-89">89</a></span>
+<span class="normal"><a href="#__codelineno-0-90">90</a></span>
+<span class="normal"><a href="#__codelineno-0-91">91</a></span>
+<span class="normal"><a href="#__codelineno-0-92">92</a></span>
+<span class="normal"><a href="#__codelineno-0-93">93</a></span>
+<span class="normal"><a href="#__codelineno-0-94">94</a></span>
+<span class="normal"><a href="#__codelineno-0-95">95</a></span>
+<span class="normal"><a href="#__codelineno-0-96">96</a></span>
+<span class="normal"><a href="#__codelineno-0-97">97</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="k">def</span> <span class="nf">workbench_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
+</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the Workbench specific metadata for this Artifact&quot;&quot;&quot;</span>
+</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>
+</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>    <span class="c1"># Sanity Check if we have invalid AWS Metadata</span>
+</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source_meta</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">exists</span><span class="p">():</span>
+</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;DataSource </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> doesn&#39;t appear to exist...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a>        <span class="k">else</span><span class="p">:</span>
+</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unable to get AWS Metadata for </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="s2">&quot;Malformed Artifact! Delete this Artifact and recreate it!&quot;</span><span class="p">)</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>        <span class="k">return</span> <span class="p">{}</span>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>
+</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>    <span class="c1"># Get the Workbench Metadata from the &#39;Parameters&#39; section of the DataSource Metadata</span>
+</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>    <span class="n">params</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source_meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;Parameters&quot;</span><span class="p">,</span> <span class="p">{})</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>    <span class="k">return</span> <span class="p">{</span><span class="n">key</span><span class="p">:</span> <span class="n">decode_value</span><span class="p">(</span><span class="n">value</span><span class="p">)</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">params</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="s2">&quot;workbench&quot;</span> <span class="ow">in</span> <span class="n">key</span><span class="p">}</span>
+</span></code></pre></div></td></tr></table></div>
+            </details>
+    </div>
+
+</div>
+
 
 
   </div>
diff --git a/core_classes/artifacts/data_source_abstract/index.html b/core_classes/artifacts/data_source_abstract/index.html
index 104ed37f8..cf4da0076 100644
--- a/core_classes/artifacts/data_source_abstract/index.html
+++ b/core_classes/artifacts/data_source_abstract/index.html
@@ -16,7 +16,7 @@
     
     
       
-        <title>DataSource Abstract - SageWorks</title>
+        <title>DataSource Abstract - Workbench</title>
       
     
     
@@ -86,7 +86,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -100,7 +100,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -207,13 +207,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -289,7 +289,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1813,7 +1813,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1823,7 +1823,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3042,11 +3042,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3098,7 +3098,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract" class="md-nav__link">
     <span class="md-ellipsis">
       data_source_abstract
     </span>
@@ -3107,7 +3107,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract" class="md-nav__link">
     <span class="md-ellipsis">
       DataSourceAbstract
     </span>
@@ -3117,7 +3117,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_types" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_types" class="md-nav__link">
     <span class="md-ellipsis">
       column_types
     </span>
@@ -3126,7 +3126,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.columns" class="md-nav__link">
     <span class="md-ellipsis">
       columns
     </span>
@@ -3135,7 +3135,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.database" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.database" class="md-nav__link">
     <span class="md-ellipsis">
       database
     </span>
@@ -3144,7 +3144,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.table" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.table" class="md-nav__link">
     <span class="md-ellipsis">
       table
     </span>
@@ -3153,7 +3153,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.__init__" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3162,7 +3162,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_details" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_details" class="md-nav__link">
     <span class="md-ellipsis">
       column_details
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@@ -3171,7 +3171,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_stats" class="md-nav__link">
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       column_stats
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@@ -3180,7 +3180,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.correlations" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.correlations" class="md-nav__link">
     <span class="md-ellipsis">
       correlations
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@@ -3189,7 +3189,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.descriptive_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.descriptive_stats" class="md-nav__link">
     <span class="md-ellipsis">
       descriptive_stats
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@@ -3198,7 +3198,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.details" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.details" class="md-nav__link">
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.execute_statement" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.execute_statement" class="md-nav__link">
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@@ -3216,7 +3216,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.expected_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.expected_meta" class="md-nav__link">
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@@ -3225,7 +3225,7 @@
 </li>
         
           <li class="md-nav__item">
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@@ -3234,7 +3234,7 @@
 </li>
         
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-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_rows" class="md-nav__link">
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@@ -3261,7 +3261,7 @@
 </li>
         
           <li class="md-nav__item">
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@@ -3270,7 +3270,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.query" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.query" class="md-nav__link">
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@@ -3288,7 +3288,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.recompute_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.recompute_stats" class="md-nav__link">
     <span class="md-ellipsis">
       recompute_stats
     </span>
@@ -3297,7 +3297,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.sample" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.sample" class="md-nav__link">
     <span class="md-ellipsis">
       sample
     </span>
@@ -3306,7 +3306,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_computation_columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.set_computation_columns" class="md-nav__link">
     <span class="md-ellipsis">
       set_computation_columns
     </span>
@@ -3315,7 +3315,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_display_columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.set_display_columns" class="md-nav__link">
     <span class="md-ellipsis">
       set_display_columns
     </span>
@@ -3324,7 +3324,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.smart_sample" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.smart_sample" class="md-nav__link">
     <span class="md-ellipsis">
       smart_sample
     </span>
@@ -3333,7 +3333,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.value_counts" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.value_counts" class="md-nav__link">
     <span class="md-ellipsis">
       value_counts
     </span>
@@ -3342,7 +3342,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.view" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.view" class="md-nav__link">
     <span class="md-ellipsis">
       view
     </span>
@@ -3351,7 +3351,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.views" class="md-nav__link">
+  <a href="#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.views" class="md-nav__link">
     <span class="md-ellipsis">
       views
     </span>
@@ -3391,7 +3391,7 @@ <h1 id="datasource-abstract">DataSource Abstract</h1>
 
 
 
-<a id="sageworks.core.artifacts.data_source_abstract"></a>
+<a id="workbench.core.artifacts.data_source_abstract"></a>
     <div class="doc doc-contents first">
 
         <p>DataSourceAbstract: Abstract Base Class for all data sources (S3: CSV, JSONL, Parquet, RDS, etc)</p>
@@ -3416,7 +3416,7 @@ <h1 id="datasource-abstract">DataSource Abstract</h1>
 
 
 
-<h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract" class="doc doc-heading">
             <code>DataSourceAbstract</code>
 
 
@@ -3425,7 +3425,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.artifact.Artifact" href="../artifact/#sageworks.core.artifacts.artifact.Artifact">Artifact</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.artifact.Artifact" href="../artifact/#workbench.core.artifacts.artifact.Artifact">Artifact</a></code></p>
 
 
 
@@ -3434,7 +3434,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
 <span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
 <span class="normal"><a href="#__codelineno-0-19"> 19</a></span>
@@ -3739,11 +3739,11 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 <span class="normal"><a href="#__codelineno-0-318">318</a></span>
 <span class="normal"><a href="#__codelineno-0-319">319</a></span>
 <span class="normal"><a href="#__codelineno-0-320">320</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="k">class</span> <span class="nc">DataSourceAbstract</span><span class="p">(</span><span class="n">Artifact</span><span class="p">):</span>
-</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;DataSourceAbstract: Abstract Base Class for all data sources</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">            data_uuid(str): The UUID for this Data Source</span>
-</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">            database(str): The database to use for this Data Source (default: sageworks)</span>
+</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">            database(str): The database to use for this Data Source (default: workbench)</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>        <span class="c1"># Call superclass init</span>
@@ -3804,7 +3804,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 </span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>
 </span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>    <span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 </span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the views for this Data Source&quot;&quot;&quot;</span>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
 </span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>        <span class="k">return</span> <span class="n">list_views</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>
@@ -3815,7 +3815,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 </span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a><span class="sd">            pd.DataFrame: A DataFrame for the specified view</span>
 </span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">View</span>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">View</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>        <span class="k">return</span> <span class="n">View</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">view_name</span><span class="p">)</span>
 </span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>
@@ -3833,7 +3833,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 </span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">monitor</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Display View/Computation mismatch: </span><span class="si">{</span><span class="n">mismatch_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>
 </span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Display Columns...</span><span class="si">{</span><span class="n">diplay_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
+</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
 </span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>
 </span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>        <span class="c1"># Create a NEW display view</span>
 </span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>        <span class="n">DisplayView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="n">c_view</span><span class="o">.</span><span class="n">table</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">diplay_columns</span><span class="p">)</span>
@@ -3846,7 +3846,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 </span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="sd">            recompute_stats (bool): Recomputes all the stats for this Data Source (default: True)</span>
 </span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Computation Columns...</span><span class="si">{</span><span class="n">computation_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
+</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
 </span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a>
 </span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a>        <span class="c1"># Create a NEW computation view</span>
 </span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a>        <span class="n">ComputationView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">computation_columns</span><span class="p">)</span>
@@ -3855,7 +3855,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 </span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>
 </span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>    <span class="k">def</span> <span class="nf">_create_display_view</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal: Create the Display View for this DataSource&quot;&quot;&quot;</span>
-</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">View</span>
+</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">View</span>
 </span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>
 </span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a>        <span class="n">View</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;display&quot;</span><span class="p">)</span>
 </span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>
@@ -3974,11 +3974,11 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 </span><span id="__span-0-250"><a id="__codelineno-0-250" name="__codelineno-0-250"></a>        <span class="c1"># For DataSources, we expect to see the following metadata</span>
 </span><span id="__span-0-251"><a id="__codelineno-0-251" name="__codelineno-0-251"></a>        <span class="n">expected_meta</span> <span class="o">=</span> <span class="p">[</span>
 </span><span id="__span-0-252"><a id="__codelineno-0-252" name="__codelineno-0-252"></a>            <span class="c1"># FIXME: Revisit this</span>
-</span><span id="__span-0-253"><a id="__codelineno-0-253" name="__codelineno-0-253"></a>            <span class="c1"># &quot;sageworks_details&quot;,</span>
-</span><span id="__span-0-254"><a id="__codelineno-0-254" name="__codelineno-0-254"></a>            <span class="s2">&quot;sageworks_descriptive_stats&quot;</span><span class="p">,</span>
-</span><span id="__span-0-255"><a id="__codelineno-0-255" name="__codelineno-0-255"></a>            <span class="s2">&quot;sageworks_value_counts&quot;</span><span class="p">,</span>
-</span><span id="__span-0-256"><a id="__codelineno-0-256" name="__codelineno-0-256"></a>            <span class="s2">&quot;sageworks_correlations&quot;</span><span class="p">,</span>
-</span><span id="__span-0-257"><a id="__codelineno-0-257" name="__codelineno-0-257"></a>            <span class="s2">&quot;sageworks_column_stats&quot;</span><span class="p">,</span>
+</span><span id="__span-0-253"><a id="__codelineno-0-253" name="__codelineno-0-253"></a>            <span class="c1"># &quot;workbench_details&quot;,</span>
+</span><span id="__span-0-254"><a id="__codelineno-0-254" name="__codelineno-0-254"></a>            <span class="s2">&quot;workbench_descriptive_stats&quot;</span><span class="p">,</span>
+</span><span id="__span-0-255"><a id="__codelineno-0-255" name="__codelineno-0-255"></a>            <span class="s2">&quot;workbench_value_counts&quot;</span><span class="p">,</span>
+</span><span id="__span-0-256"><a id="__codelineno-0-256" name="__codelineno-0-256"></a>            <span class="s2">&quot;workbench_correlations&quot;</span><span class="p">,</span>
+</span><span id="__span-0-257"><a id="__codelineno-0-257" name="__codelineno-0-257"></a>            <span class="s2">&quot;workbench_column_stats&quot;</span><span class="p">,</span>
 </span><span id="__span-0-258"><a id="__codelineno-0-258" name="__codelineno-0-258"></a>        <span class="p">]</span>
 </span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>        <span class="k">return</span> <span class="n">expected_meta</span>
 </span><span id="__span-0-260"><a id="__codelineno-0-260" name="__codelineno-0-260"></a>
@@ -4059,7 +4059,7 @@ <h2 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract" class=
 
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_types" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_types" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_types</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span></code>
 
   <span class="doc doc-labels">
@@ -4081,7 +4081,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_
 
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">columns</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span></code>
 
   <span class="doc doc-labels">
@@ -4103,7 +4103,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.columns
 
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.database" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.database" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">database</span><span class="p">:</span> <span class="nb">str</span></code>
 
   <span class="doc doc-labels">
@@ -4124,7 +4124,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.databas
 
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.table" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.table" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">table</span><span class="p">:</span> <span class="nb">str</span></code>
 
   <span class="doc doc-labels">
@@ -4146,8 +4146,8 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.table"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.__init__" class="doc doc-heading">
-            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;sageworks&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.__init__" class="doc doc-heading">
+            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">database</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
 
@@ -4157,10 +4157,10 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.__init_
         <p>DataSourceAbstract: Abstract Base Class for all data sources
 Args:
     data_uuid(str): The UUID for this Data Source
-    database(str): The database to use for this Data Source (default: sageworks)</p>
+    database(str): The database to use for this Data Source (default: workbench)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-18">18</a></span>
 <span class="normal"><a href="#__codelineno-0-19">19</a></span>
 <span class="normal"><a href="#__codelineno-0-20">20</a></span>
@@ -4173,11 +4173,11 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.__init_
 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
 <span class="normal"><a href="#__codelineno-0-28">28</a></span>
 <span class="normal"><a href="#__codelineno-0-29">29</a></span>
-<span class="normal"><a href="#__codelineno-0-30">30</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+<span class="normal"><a href="#__codelineno-0-30">30</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">database</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;DataSourceAbstract: Abstract Base Class for all data sources</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        data_uuid(str): The UUID for this Data Source</span>
-</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        database(str): The database to use for this Data Source (default: sageworks)</span>
+</span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        database(str): The database to use for this Data Source (default: workbench)</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>    <span class="c1"># Call superclass init</span>
@@ -4195,7 +4195,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.__init_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_details</span><span class="p">()</span></code>
 
 </h3>
@@ -4229,7 +4229,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-73">73</a></span>
 <span class="normal"><a href="#__codelineno-0-74">74</a></span>
 <span class="normal"><a href="#__codelineno-0-75">75</a></span>
@@ -4252,7 +4252,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_stats</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4275,7 +4275,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_
       ...}</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-211">211</a></span>
 <span class="normal"><a href="#__codelineno-0-212">212</a></span>
 <span class="normal"><a href="#__codelineno-0-213">213</a></span>
@@ -4310,7 +4310,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.correlations" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.correlations" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">correlations</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4381,7 +4381,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.correla
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-225">225</a></span>
 <span class="normal"><a href="#__codelineno-0-226">226</a></span>
 <span class="normal"><a href="#__codelineno-0-227">227</a></span>
@@ -4416,7 +4416,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.correla
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.descriptive_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.descriptive_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">descriptive_stats</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4437,7 +4437,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.descrip
           'col2': ...}</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-163">163</a></span>
 <span class="normal"><a href="#__codelineno-0-164">164</a></span>
 <span class="normal"><a href="#__codelineno-0-165">165</a></span>
@@ -4468,7 +4468,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.descrip
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">()</span></code>
 
 </h3>
@@ -4479,7 +4479,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.details
         <p>Additional Details about this DataSourceAbstract Artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-239">239</a></span>
 <span class="normal"><a href="#__codelineno-0-240">240</a></span>
 <span class="normal"><a href="#__codelineno-0-241">241</a></span>
@@ -4502,7 +4502,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.details
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.execute_statement" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.execute_statement" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">execute_statement</span><span class="p">(</span><span class="n">query</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4519,7 +4519,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.execute
     query(str): The SQL statement to execute</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-146">146</a></span>
 <span class="normal"><a href="#__codelineno-0-147">147</a></span>
 <span class="normal"><a href="#__codelineno-0-148">148</a></span>
@@ -4542,7 +4542,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.execute
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.expected_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.expected_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">expected_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -4553,7 +4553,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.expecte
         <p>DataSources have quite a bit of expected Metadata for EDA displays</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-247">247</a></span>
 <span class="normal"><a href="#__codelineno-0-248">248</a></span>
 <span class="normal"><a href="#__codelineno-0-249">249</a></span>
@@ -4572,11 +4572,11 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.expecte
 </span><span id="__span-0-250"><a id="__codelineno-0-250" name="__codelineno-0-250"></a>    <span class="c1"># For DataSources, we expect to see the following metadata</span>
 </span><span id="__span-0-251"><a id="__codelineno-0-251" name="__codelineno-0-251"></a>    <span class="n">expected_meta</span> <span class="o">=</span> <span class="p">[</span>
 </span><span id="__span-0-252"><a id="__codelineno-0-252" name="__codelineno-0-252"></a>        <span class="c1"># FIXME: Revisit this</span>
-</span><span id="__span-0-253"><a id="__codelineno-0-253" name="__codelineno-0-253"></a>        <span class="c1"># &quot;sageworks_details&quot;,</span>
-</span><span id="__span-0-254"><a id="__codelineno-0-254" name="__codelineno-0-254"></a>        <span class="s2">&quot;sageworks_descriptive_stats&quot;</span><span class="p">,</span>
-</span><span id="__span-0-255"><a id="__codelineno-0-255" name="__codelineno-0-255"></a>        <span class="s2">&quot;sageworks_value_counts&quot;</span><span class="p">,</span>
-</span><span id="__span-0-256"><a id="__codelineno-0-256" name="__codelineno-0-256"></a>        <span class="s2">&quot;sageworks_correlations&quot;</span><span class="p">,</span>
-</span><span id="__span-0-257"><a id="__codelineno-0-257" name="__codelineno-0-257"></a>        <span class="s2">&quot;sageworks_column_stats&quot;</span><span class="p">,</span>
+</span><span id="__span-0-253"><a id="__codelineno-0-253" name="__codelineno-0-253"></a>        <span class="c1"># &quot;workbench_details&quot;,</span>
+</span><span id="__span-0-254"><a id="__codelineno-0-254" name="__codelineno-0-254"></a>        <span class="s2">&quot;workbench_descriptive_stats&quot;</span><span class="p">,</span>
+</span><span id="__span-0-255"><a id="__codelineno-0-255" name="__codelineno-0-255"></a>        <span class="s2">&quot;workbench_value_counts&quot;</span><span class="p">,</span>
+</span><span id="__span-0-256"><a id="__codelineno-0-256" name="__codelineno-0-256"></a>        <span class="s2">&quot;workbench_correlations&quot;</span><span class="p">,</span>
+</span><span id="__span-0-257"><a id="__codelineno-0-257" name="__codelineno-0-257"></a>        <span class="s2">&quot;workbench_column_stats&quot;</span><span class="p">,</span>
 </span><span id="__span-0-258"><a id="__codelineno-0-258" name="__codelineno-0-258"></a>    <span class="p">]</span>
 </span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>    <span class="k">return</span> <span class="n">expected_meta</span>
 </span></code></pre></div></td></tr></table></div>
@@ -4588,7 +4588,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.expecte
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.get_database" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.get_database" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_database</span><span class="p">()</span></code>
 
 </h3>
@@ -4599,7 +4599,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.get_dat
         <p>Get the database for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-36">36</a></span>
 <span class="normal"><a href="#__codelineno-0-37">37</a></span>
 <span class="normal"><a href="#__codelineno-0-38">38</a></span>
@@ -4616,7 +4616,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.get_dat
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.num_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">num_columns</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -4631,7 +4631,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_col
         <p>Return the number of columns for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-56">56</a></span>
 <span class="normal"><a href="#__codelineno-0-57">57</a></span>
 <span class="normal"><a href="#__codelineno-0-58">58</a></span>
@@ -4648,7 +4648,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_col
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_rows" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.num_rows" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">num_rows</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -4663,7 +4663,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_row
         <p>Return the number of rows for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-51">51</a></span>
 <span class="normal"><a href="#__codelineno-0-52">52</a></span>
 <span class="normal"><a href="#__codelineno-0-53">53</a></span>
@@ -4680,7 +4680,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_row
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.onboard" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.onboard" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">onboard</span><span class="p">()</span></code>
 
 </h3>
@@ -4714,7 +4714,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.onboard
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-276">276</a></span>
 <span class="normal"><a href="#__codelineno-0-277">277</a></span>
 <span class="normal"><a href="#__codelineno-0-278">278</a></span>
@@ -4769,7 +4769,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.onboard
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.outliers" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.outliers" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">outliers</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4844,7 +4844,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.outlier
 The scale parameter can be adjusted to change the IQR multiplier</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-175">175</a></span>
 <span class="normal"><a href="#__codelineno-0-176">176</a></span>
 <span class="normal"><a href="#__codelineno-0-177">177</a></span>
@@ -4883,7 +4883,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.outlier
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.query" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4900,7 +4900,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.query"
     query(str): The SQL query to execute</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-138">138</a></span>
 <span class="normal"><a href="#__codelineno-0-139">139</a></span>
 <span class="normal"><a href="#__codelineno-0-140">140</a></span>
@@ -4923,7 +4923,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.query"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.ready" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.ready" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">ready</span><span class="p">()</span></code>
 
 </h3>
@@ -4934,7 +4934,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.ready"
         <p>Is the DataSource ready?</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-261">261</a></span>
 <span class="normal"><a href="#__codelineno-0-262">262</a></span>
 <span class="normal"><a href="#__codelineno-0-263">263</a></span>
@@ -4971,7 +4971,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.ready"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.recompute_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.recompute_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">recompute_stats</span><span class="p">()</span></code>
 
 </h3>
@@ -5005,7 +5005,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.recompu
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-300">300</a></span>
 <span class="normal"><a href="#__codelineno-0-301">301</a></span>
 <span class="normal"><a href="#__codelineno-0-302">302</a></span>
@@ -5056,7 +5056,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.recompu
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.sample" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">sample</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5094,7 +5094,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.sample"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-154">154</a></span>
 <span class="normal"><a href="#__codelineno-0-155">155</a></span>
 <span class="normal"><a href="#__codelineno-0-156">156</a></span>
@@ -5119,7 +5119,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.sample"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_computation_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.set_computation_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_computation_columns</span><span class="p">(</span><span class="n">computation_columns</span><span class="p">,</span> <span class="n">recompute_stats</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -5177,7 +5177,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_com
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-117">117</a></span>
 <span class="normal"><a href="#__codelineno-0-118">118</a></span>
 <span class="normal"><a href="#__codelineno-0-119">119</a></span>
@@ -5199,7 +5199,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_com
 </span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="sd">        recompute_stats (bool): Recomputes all the stats for this Data Source (default: True)</span>
 </span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Computation Columns...</span><span class="si">{</span><span class="n">computation_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
+</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
 </span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a>
 </span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a>    <span class="c1"># Create a NEW computation view</span>
 </span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a>    <span class="n">ComputationView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">computation_columns</span><span class="p">)</span>
@@ -5214,7 +5214,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_com
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_display_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.set_display_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_display_columns</span><span class="p">(</span><span class="n">diplay_columns</span><span class="p">)</span></code>
 
 </h3>
@@ -5256,7 +5256,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_dis
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-98"> 98</a></span>
 <span class="normal"><a href="#__codelineno-0-99"> 99</a></span>
 <span class="normal"><a href="#__codelineno-0-100">100</a></span>
@@ -5288,7 +5288,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_dis
 </span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">monitor</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Display View/Computation mismatch: </span><span class="si">{</span><span class="n">mismatch_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>
 </span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Display Columns...</span><span class="si">{</span><span class="n">diplay_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
+</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
 </span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>
 </span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>    <span class="c1"># Create a NEW display view</span>
 </span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>    <span class="n">DisplayView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="n">c_view</span><span class="o">.</span><span class="n">table</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">diplay_columns</span><span class="p">)</span>
@@ -5301,7 +5301,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_dis
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.smart_sample" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.smart_sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">smart_sample</span><span class="p">()</span></code>
 
   <span class="doc doc-labels">
@@ -5318,7 +5318,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.smart_s
     pd.DataFrame: A combined DataFrame of sample data + outliers</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-191">191</a></span>
 <span class="normal"><a href="#__codelineno-0-192">192</a></span>
 <span class="normal"><a href="#__codelineno-0-193">193</a></span>
@@ -5341,7 +5341,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.smart_s
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.value_counts" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.value_counts" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">value_counts</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5362,7 +5362,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.value_c
           'col2': ...}</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-199">199</a></span>
 <span class="normal"><a href="#__codelineno-0-200">200</a></span>
 <span class="normal"><a href="#__codelineno-0-201">201</a></span>
@@ -5393,7 +5393,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.value_c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.view" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.view" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">view</span><span class="p">(</span><span class="n">view_name</span><span class="p">)</span></code>
 
 </h3>
@@ -5408,7 +5408,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.view" c
     pd.DataFrame: A DataFrame for the specified view</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-87">87</a></span>
 <span class="normal"><a href="#__codelineno-0-88">88</a></span>
 <span class="normal"><a href="#__codelineno-0-89">89</a></span>
@@ -5425,7 +5425,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.view" c
 </span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a><span class="sd">        pd.DataFrame: A DataFrame for the specified view</span>
 </span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">View</span>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">View</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>    <span class="k">return</span> <span class="n">View</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">view_name</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -5437,7 +5437,7 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.view" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.views" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.data_source_abstract.DataSourceAbstract.views" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">views</span><span class="p">()</span></code>
 
 </h3>
@@ -5448,14 +5448,14 @@ <h3 id="sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.views"
         <p>Return the views for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-81">81</a></span>
 <span class="normal"><a href="#__codelineno-0-82">82</a></span>
 <span class="normal"><a href="#__codelineno-0-83">83</a></span>
 <span class="normal"><a href="#__codelineno-0-84">84</a></span>
 <span class="normal"><a href="#__codelineno-0-85">85</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a><span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 </span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the views for this Data Source&quot;&quot;&quot;</span>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
 </span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>    <span class="k">return</span> <span class="n">list_views</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
diff --git a/core_classes/artifacts/endpoint_core/index.html b/core_classes/artifacts/endpoint_core/index.html
index ab0d1b4b3..cc325b152 100644
--- a/core_classes/artifacts/endpoint_core/index.html
+++ b/core_classes/artifacts/endpoint_core/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>EndpointCore - SageWorks</title>
+        <title>EndpointCore - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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     <span class="md-ellipsis">
       onboard_with_args
     </span>
@@ -3675,7 +3675,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.endpoint_core.EndpointCore.refresh_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.endpoint_core.EndpointCore.refresh_meta" class="md-nav__link">
     <span class="md-ellipsis">
       refresh_meta
     </span>
@@ -3684,7 +3684,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.endpoint_core.EndpointCore.regression_metrics" class="md-nav__link">
+  <a href="#workbench.core.artifacts.endpoint_core.EndpointCore.regression_metrics" class="md-nav__link">
     <span class="md-ellipsis">
       regression_metrics
     </span>
@@ -3693,7 +3693,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.endpoint_core.EndpointCore.residuals" class="md-nav__link">
+  <a href="#workbench.core.artifacts.endpoint_core.EndpointCore.residuals" class="md-nav__link">
     <span class="md-ellipsis">
       residuals
     </span>
@@ -3702,7 +3702,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.endpoint_core.EndpointCore.set_input" class="md-nav__link">
+  <a href="#workbench.core.artifacts.endpoint_core.EndpointCore.set_input" class="md-nav__link">
     <span class="md-ellipsis">
       set_input
     </span>
@@ -3711,7 +3711,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.endpoint_core.EndpointCore.size" class="md-nav__link">
+  <a href="#workbench.core.artifacts.endpoint_core.EndpointCore.size" class="md-nav__link">
     <span class="md-ellipsis">
       size
     </span>
@@ -3720,7 +3720,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.endpoint_core.EndpointCore.validate_proba_columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.endpoint_core.EndpointCore.validate_proba_columns" class="md-nav__link">
     <span class="md-ellipsis">
       validate_proba_columns
     </span>
@@ -3759,10 +3759,10 @@ <h1 id="endpointcore">EndpointCore</h1>
 
 
 
-<a id="sageworks.core.artifacts.endpoint_core"></a>
+<a id="workbench.core.artifacts.endpoint_core"></a>
     <div class="doc doc-contents first">
 
-        <p>EndpointCore: SageWorks EndpointCore Class</p>
+        <p>EndpointCore: Workbench EndpointCore Class</p>
 
 
 
@@ -3784,7 +3784,7 @@ <h1 id="endpointcore">EndpointCore</h1>
 
 
 
-<h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.endpoint_core.EndpointCore" class="doc doc-heading">
             <code>EndpointCore</code>
 
 
@@ -3793,10 +3793,10 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.artifact.Artifact" href="../artifact/#sageworks.core.artifacts.artifact.Artifact">Artifact</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.artifact.Artifact" href="../artifact/#workbench.core.artifacts.artifact.Artifact">Artifact</a></code></p>
 
 
-        <p>EndpointCore: SageWorks EndpointCore Class</p>
+        <p>EndpointCore: Workbench EndpointCore Class</p>
 
 
 <details class="common-usage" open>
@@ -3814,7 +3814,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-51"> 51</a></span>
 <span class="normal"><a href="#__codelineno-0-52"> 52</a></span>
 <span class="normal"><a href="#__codelineno-0-53"> 53</a></span>
@@ -4694,7 +4694,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 <span class="normal"><a href="#__codelineno-0-927">927</a></span>
 <span class="normal"><a href="#__codelineno-0-928">928</a></span>
 <span class="normal"><a href="#__codelineno-0-929">929</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a><span class="k">class</span> <span class="nc">EndpointCore</span><span class="p">(</span><span class="n">Artifact</span><span class="p">):</span>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;EndpointCore: SageWorks EndpointCore Class</span>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;EndpointCore: Workbench EndpointCore Class</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="sd">        ```python</span>
@@ -4710,7 +4710,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 </span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;EndpointCore Initialization</span>
 </span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>
 </span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a><span class="sd">            endpoint_uuid (str): Name of Endpoint in SageWorks</span>
+</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a><span class="sd">            endpoint_uuid (str): Name of Endpoint in Workbench</span>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>
 </span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>        <span class="c1"># Make sure the endpoint_uuid is a valid name</span>
@@ -4823,7 +4823,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 </span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a>
 </span><span id="__span-0-179"><a id="__codelineno-0-179" name="__codelineno-0-179"></a>    <span class="k">def</span> <span class="nf">get_monitor</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-180"><a id="__codelineno-0-180" name="__codelineno-0-180"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the MonitorCore class for this endpoint&quot;&quot;&quot;</span>
-</span><span id="__span-0-181"><a id="__codelineno-0-181" name="__codelineno-0-181"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.artifacts.monitor_core</span> <span class="kn">import</span> <span class="n">MonitorCore</span>
+</span><span id="__span-0-181"><a id="__codelineno-0-181" name="__codelineno-0-181"></a>        <span class="kn">from</span> <span class="nn">workbench.core.artifacts.monitor_core</span> <span class="kn">import</span> <span class="n">MonitorCore</span>
 </span><span id="__span-0-182"><a id="__codelineno-0-182" name="__codelineno-0-182"></a>
 </span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a>        <span class="k">return</span> <span class="n">MonitorCore</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">endpoint_name</span><span class="p">)</span>
 </span><span id="__span-0-184"><a id="__codelineno-0-184" name="__codelineno-0-184"></a>
@@ -4857,7 +4857,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 </span><span id="__span-0-212"><a id="__codelineno-0-212" name="__codelineno-0-212"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-213"><a id="__codelineno-0-213" name="__codelineno-0-213"></a><span class="sd">            Optional[str]: The hash for the internal model used by this endpoint</span>
 </span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>        <span class="kn">from</span> <span class="nn">sageworks.utils.endpoint_utils</span> <span class="kn">import</span> <span class="n">get_model_data_url</span>  <span class="c1"># Avoid circular import</span>
+</span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>        <span class="kn">from</span> <span class="nn">workbench.utils.endpoint_utils</span> <span class="kn">import</span> <span class="n">get_model_data_url</span>  <span class="c1"># Avoid circular import</span>
 </span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>
 </span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>        <span class="n">model_url</span> <span class="o">=</span> <span class="n">get_model_data_url</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">endpoint_config_name</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a>        <span class="k">return</span> <span class="n">compute_s3_object_hash</span><span class="p">(</span><span class="n">model_url</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
@@ -4945,7 +4945,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 </span><span id="__span-0-300"><a id="__codelineno-0-300" name="__codelineno-0-300"></a>        <span class="c1"># Set the status to onboarding</span>
 </span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">set_status</span><span class="p">(</span><span class="s2">&quot;onboarding&quot;</span><span class="p">)</span>
 </span><span id="__span-0-302"><a id="__codelineno-0-302" name="__codelineno-0-302"></a>
-</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="n">input_model</span><span class="p">})</span>
+</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="n">input_model</span><span class="p">})</span>
 </span><span id="__span-0-304"><a id="__codelineno-0-304" name="__codelineno-0-304"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">model_name</span> <span class="o">=</span> <span class="n">input_model</span>
 </span><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a>
 </span><span id="__span-0-306"><a id="__codelineno-0-306" name="__codelineno-0-306"></a>        <span class="c1"># Remove the needs_onboard tag</span>
@@ -5475,7 +5475,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 </span><span id="__span-0-830"><a id="__codelineno-0-830" name="__codelineno-0-830"></a>        <span class="c1"># Okay we&#39;re going to allow this to be set</span>
 </span><span id="__span-0-831"><a id="__codelineno-0-831" name="__codelineno-0-831"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">: Setting input to </span><span class="si">{</span><span class="nb">input</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-832"><a id="__codelineno-0-832" name="__codelineno-0-832"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Be careful with this! It breaks automatic provenance of the artifact!&quot;</span><span class="p">)</span>
-</span><span id="__span-0-833"><a id="__codelineno-0-833" name="__codelineno-0-833"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
+</span><span id="__span-0-833"><a id="__codelineno-0-833" name="__codelineno-0-833"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
 </span><span id="__span-0-834"><a id="__codelineno-0-834" name="__codelineno-0-834"></a>
 </span><span id="__span-0-835"><a id="__codelineno-0-835" name="__codelineno-0-835"></a>    <span class="k">def</span> <span class="nf">delete</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-836"><a id="__codelineno-0-836" name="__codelineno-0-836"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot; &quot;Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint&quot;&quot;&quot;</span>
@@ -5590,7 +5590,7 @@ <h2 id="sageworks.core.artifacts.endpoint_core.EndpointCore" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.__init__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">endpoint_uuid</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -5621,7 +5621,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.__init__" class="doc
             </td>
             <td>
               <div class="doc-md-description">
-                <p>Name of Endpoint in SageWorks</p>
+                <p>Name of Endpoint in Workbench</p>
               </div>
             </td>
             <td>
@@ -5632,7 +5632,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.__init__" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-64"> 64</a></span>
 <span class="normal"><a href="#__codelineno-0-65"> 65</a></span>
 <span class="normal"><a href="#__codelineno-0-66"> 66</a></span>
@@ -5684,7 +5684,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.__init__" class="doc
 </span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;EndpointCore Initialization</span>
 </span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>
 </span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a><span class="sd">        endpoint_uuid (str): Name of Endpoint in SageWorks</span>
+</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a><span class="sd">        endpoint_uuid (str): Name of Endpoint in Workbench</span>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>
 </span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>    <span class="c1"># Make sure the endpoint_uuid is a valid name</span>
@@ -5737,7 +5737,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.__init__" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.add_data_capture" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.add_data_capture" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">add_data_capture</span><span class="p">()</span></code>
 
 </h3>
@@ -5748,7 +5748,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.add_data_capture" cl
         <p>Add data capture to the endpoint</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-175">175</a></span>
 <span class="normal"><a href="#__codelineno-0-176">176</a></span>
 <span class="normal"><a href="#__codelineno-0-177">177</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a><span class="k">def</span> <span class="nf">add_data_capture</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -5763,7 +5763,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.add_data_capture" cl
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.arn" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.arn" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">arn</span><span class="p">()</span></code>
 
 </h3>
@@ -5774,7 +5774,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.arn" class="doc doc-
         <p>AWS ARN (Amazon Resource Name) for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-193">193</a></span>
 <span class="normal"><a href="#__codelineno-0-194">194</a></span>
 <span class="normal"><a href="#__codelineno-0-195">195</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-193"><a id="__codelineno-0-193" name="__codelineno-0-193"></a><span class="k">def</span> <span class="nf">arn</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
@@ -5789,7 +5789,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.arn" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.auto_inference" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.auto_inference" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">auto_inference</span><span class="p">(</span><span class="n">capture</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5831,7 +5831,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.auto_inference" clas
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-317">317</a></span>
 <span class="normal"><a href="#__codelineno-0-318">318</a></span>
 <span class="normal"><a href="#__codelineno-0-319">319</a></span>
@@ -5888,7 +5888,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.auto_inference" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.aws_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.aws_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -5899,7 +5899,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.aws_meta" class="doc
         <p>Get ALL the AWS metadata for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-189">189</a></span>
 <span class="normal"><a href="#__codelineno-0-190">190</a></span>
 <span class="normal"><a href="#__codelineno-0-191">191</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a><span class="k">def</span> <span class="nf">aws_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
@@ -5914,7 +5914,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.aws_meta" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.aws_url" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.aws_url" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_url</span><span class="p">()</span></code>
 
 </h3>
@@ -5925,7 +5925,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.aws_url" class="doc
         <p>The AWS URL for looking at/querying this data source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-197">197</a></span>
 <span class="normal"><a href="#__codelineno-0-198">198</a></span>
 <span class="normal"><a href="#__codelineno-0-199">199</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-197"><a id="__codelineno-0-197" name="__codelineno-0-197"></a><span class="k">def</span> <span class="nf">aws_url</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -5940,7 +5940,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.aws_url" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.classification_metrics" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.classification_metrics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">classification_metrics</span><span class="p">(</span><span class="n">target_column</span><span class="p">,</span> <span class="n">prediction_df</span><span class="p">)</span></code>
 
 </h3>
@@ -6021,7 +6021,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.classification_metri
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-712">712</a></span>
 <span class="normal"><a href="#__codelineno-0-713">713</a></span>
 <span class="normal"><a href="#__codelineno-0-714">714</a></span>
@@ -6160,7 +6160,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.classification_metri
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.created" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.created" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">created</span><span class="p">()</span></code>
 
 </h3>
@@ -6171,7 +6171,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.created" class="doc
         <p>Return the datetime when this artifact was created</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-201">201</a></span>
 <span class="normal"><a href="#__codelineno-0-202">202</a></span>
 <span class="normal"><a href="#__codelineno-0-203">203</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-201"><a id="__codelineno-0-201" name="__codelineno-0-201"></a><span class="k">def</span> <span class="nf">created</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">datetime</span><span class="p">:</span>
@@ -6186,7 +6186,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.created" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">()</span></code>
 
 </h3>
@@ -6197,7 +6197,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.delete" class="doc d
         <p>"Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-835">835</a></span>
 <span class="normal"><a href="#__codelineno-0-836">836</a></span>
 <span class="normal"><a href="#__codelineno-0-837">837</a></span>
@@ -6220,7 +6220,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.delete" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.delete_endpoint_models" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.delete_endpoint_models" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete_endpoint_models</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -6266,7 +6266,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.delete_endpoint_mode
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-901">901</a></span>
 <span class="normal"><a href="#__codelineno-0-902">902</a></span>
 <span class="normal"><a href="#__codelineno-0-903">903</a></span>
@@ -6333,7 +6333,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.delete_endpoint_mode
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6348,7 +6348,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.details" class="doc
     dict(dict): A dictionary of details about this Endpoint</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-242">242</a></span>
 <span class="normal"><a href="#__codelineno-0-243">243</a></span>
 <span class="normal"><a href="#__codelineno-0-244">244</a></span>
@@ -6413,7 +6413,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.details" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.endpoint_metrics" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.endpoint_metrics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">endpoint_metrics</span><span class="p">()</span></code>
 
 </h3>
@@ -6447,7 +6447,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.endpoint_metrics" cl
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-220">220</a></span>
 <span class="normal"><a href="#__codelineno-0-221">221</a></span>
 <span class="normal"><a href="#__codelineno-0-222">222</a></span>
@@ -6498,7 +6498,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.endpoint_metrics" cl
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.exists" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">exists</span><span class="p">()</span></code>
 
 </h3>
@@ -6509,7 +6509,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.exists" class="doc d
         <p>Does the feature_set_name exist in the AWS Metadata?</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-117">117</a></span>
 <span class="normal"><a href="#__codelineno-0-118">118</a></span>
 <span class="normal"><a href="#__codelineno-0-119">119</a></span>
@@ -6530,7 +6530,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.exists" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.fast_inference" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.fast_inference" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">fast_inference</span><span class="p">(</span><span class="n">eval_df</span><span class="p">)</span></code>
 
 </h3>
@@ -6600,7 +6600,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.fast_inference" clas
   <p>There's no sanity checks or error handling... just FAST Inference!</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-400">400</a></span>
 <span class="normal"><a href="#__codelineno-0-401">401</a></span>
 <span class="normal"><a href="#__codelineno-0-402">402</a></span>
@@ -6635,7 +6635,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.fast_inference" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.generate_confusion_matrix" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.generate_confusion_matrix" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">generate_confusion_matrix</span><span class="p">(</span><span class="n">target_column</span><span class="p">,</span> <span class="n">prediction_df</span><span class="p">)</span></code>
 
 </h3>
@@ -6651,7 +6651,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.generate_confusion_m
     pd.DataFrame: DataFrame with the confusion matrix</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-778">778</a></span>
 <span class="normal"><a href="#__codelineno-0-779">779</a></span>
 <span class="normal"><a href="#__codelineno-0-780">780</a></span>
@@ -6726,7 +6726,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.generate_confusion_m
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.get_monitor" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.get_monitor" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_monitor</span><span class="p">()</span></code>
 
 </h3>
@@ -6737,14 +6737,14 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.get_monitor" class="
         <p>Get the MonitorCore class for this endpoint</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-179">179</a></span>
 <span class="normal"><a href="#__codelineno-0-180">180</a></span>
 <span class="normal"><a href="#__codelineno-0-181">181</a></span>
 <span class="normal"><a href="#__codelineno-0-182">182</a></span>
 <span class="normal"><a href="#__codelineno-0-183">183</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-179"><a id="__codelineno-0-179" name="__codelineno-0-179"></a><span class="k">def</span> <span class="nf">get_monitor</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-180"><a id="__codelineno-0-180" name="__codelineno-0-180"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the MonitorCore class for this endpoint&quot;&quot;&quot;</span>
-</span><span id="__span-0-181"><a id="__codelineno-0-181" name="__codelineno-0-181"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.artifacts.monitor_core</span> <span class="kn">import</span> <span class="n">MonitorCore</span>
+</span><span id="__span-0-181"><a id="__codelineno-0-181" name="__codelineno-0-181"></a>    <span class="kn">from</span> <span class="nn">workbench.core.artifacts.monitor_core</span> <span class="kn">import</span> <span class="n">MonitorCore</span>
 </span><span id="__span-0-182"><a id="__codelineno-0-182" name="__codelineno-0-182"></a>
 </span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a>    <span class="k">return</span> <span class="n">MonitorCore</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">endpoint_name</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -6756,7 +6756,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.get_monitor" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.hash" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.hash" class="doc doc-heading">
             <code class="highlight language-python"><span class="nb">hash</span><span class="p">()</span></code>
 
 </h3>
@@ -6790,7 +6790,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.hash" class="doc doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-209">209</a></span>
 <span class="normal"><a href="#__codelineno-0-210">210</a></span>
 <span class="normal"><a href="#__codelineno-0-211">211</a></span>
@@ -6806,7 +6806,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.hash" class="doc doc
 </span><span id="__span-0-212"><a id="__codelineno-0-212" name="__codelineno-0-212"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-213"><a id="__codelineno-0-213" name="__codelineno-0-213"></a><span class="sd">        Optional[str]: The hash for the internal model used by this endpoint</span>
 </span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>    <span class="kn">from</span> <span class="nn">sageworks.utils.endpoint_utils</span> <span class="kn">import</span> <span class="n">get_model_data_url</span>  <span class="c1"># Avoid circular import</span>
+</span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>    <span class="kn">from</span> <span class="nn">workbench.utils.endpoint_utils</span> <span class="kn">import</span> <span class="n">get_model_data_url</span>  <span class="c1"># Avoid circular import</span>
 </span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>
 </span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>    <span class="n">model_url</span> <span class="o">=</span> <span class="n">get_model_data_url</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">endpoint_config_name</span><span class="p">(),</span> <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a>    <span class="k">return</span> <span class="n">compute_s3_object_hash</span><span class="p">(</span><span class="n">model_url</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
@@ -6819,7 +6819,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.hash" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.health_check" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">()</span></code>
 
 </h3>
@@ -6853,7 +6853,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.health_check" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-124">124</a></span>
 <span class="normal"><a href="#__codelineno-0-125">125</a></span>
 <span class="normal"><a href="#__codelineno-0-126">126</a></span>
@@ -6946,7 +6946,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.health_check" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.inference" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.inference" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">inference</span><span class="p">(</span><span class="n">eval_df</span><span class="p">,</span> <span class="n">capture_uuid</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">id_column</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -7048,7 +7048,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.inference" class="do
   <p>If capture=True inference/performance metrics are written to S3 Endpoint Inference Folder</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-342">342</a></span>
 <span class="normal"><a href="#__codelineno-0-343">343</a></span>
 <span class="normal"><a href="#__codelineno-0-344">344</a></span>
@@ -7171,7 +7171,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.inference" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.is_serverless" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.is_serverless" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">is_serverless</span><span class="p">()</span></code>
 
 </h3>
@@ -7205,7 +7205,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.is_serverless" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-167">167</a></span>
 <span class="normal"><a href="#__codelineno-0-168">168</a></span>
 <span class="normal"><a href="#__codelineno-0-169">169</a></span>
@@ -7228,7 +7228,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.is_serverless" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.managed_delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.managed_delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">managed_delete</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -7243,7 +7243,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.managed_delete" clas
         <p>Delete the Endpoint and associated resources if it exists</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-843">843</a></span>
 <span class="normal"><a href="#__codelineno-0-844">844</a></span>
 <span class="normal"><a href="#__codelineno-0-845">845</a></span>
@@ -7366,7 +7366,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.managed_delete" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.modified" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.modified" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">modified</span><span class="p">()</span></code>
 
 </h3>
@@ -7377,7 +7377,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.modified" class="doc
         <p>Return the datetime when this artifact was last modified</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-205">205</a></span>
 <span class="normal"><a href="#__codelineno-0-206">206</a></span>
 <span class="normal"><a href="#__codelineno-0-207">207</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-205"><a id="__codelineno-0-205" name="__codelineno-0-205"></a><span class="k">def</span> <span class="nf">modified</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">datetime</span><span class="p">:</span>
@@ -7392,7 +7392,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.modified" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.onboard" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.onboard" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">onboard</span><span class="p">(</span><span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7407,7 +7407,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.onboard" class="doc
     bool: True if the Endpoint is successfully onboarded, False otherwise</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-271">271</a></span>
 <span class="normal"><a href="#__codelineno-0-272">272</a></span>
 <span class="normal"><a href="#__codelineno-0-273">273</a></span>
@@ -7456,7 +7456,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.onboard" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.onboard_with_args" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.onboard_with_args" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">onboard_with_args</span><span class="p">(</span><span class="n">input_model</span><span class="p">)</span></code>
 
 </h3>
@@ -7500,7 +7500,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.onboard_with_args" c
     bool: True if the Endpoint is successfully onboarded, False otherwise</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-292">292</a></span>
 <span class="normal"><a href="#__codelineno-0-293">293</a></span>
 <span class="normal"><a href="#__codelineno-0-294">294</a></span>
@@ -7535,7 +7535,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.onboard_with_args" c
 </span><span id="__span-0-300"><a id="__codelineno-0-300" name="__codelineno-0-300"></a>    <span class="c1"># Set the status to onboarding</span>
 </span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">set_status</span><span class="p">(</span><span class="s2">&quot;onboarding&quot;</span><span class="p">)</span>
 </span><span id="__span-0-302"><a id="__codelineno-0-302" name="__codelineno-0-302"></a>
-</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="n">input_model</span><span class="p">})</span>
+</span><span id="__span-0-303"><a id="__codelineno-0-303" name="__codelineno-0-303"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="n">input_model</span><span class="p">})</span>
 </span><span id="__span-0-304"><a id="__codelineno-0-304" name="__codelineno-0-304"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">model_name</span> <span class="o">=</span> <span class="n">input_model</span>
 </span><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a>
 </span><span id="__span-0-306"><a id="__codelineno-0-306" name="__codelineno-0-306"></a>    <span class="c1"># Remove the needs_onboard tag</span>
@@ -7557,7 +7557,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.onboard_with_args" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.refresh_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.refresh_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">refresh_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -7568,7 +7568,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.refresh_meta" class=
         <p>Refresh the Artifact's metadata</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-113">113</a></span>
 <span class="normal"><a href="#__codelineno-0-114">114</a></span>
 <span class="normal"><a href="#__codelineno-0-115">115</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a><span class="k">def</span> <span class="nf">refresh_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -7583,7 +7583,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.refresh_meta" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.regression_metrics" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.regression_metrics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">regression_metrics</span><span class="p">(</span><span class="n">target_column</span><span class="p">,</span> <span class="n">prediction_df</span><span class="p">)</span></code>
 
 </h3>
@@ -7599,7 +7599,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.regression_metrics"
     pd.DataFrame: DataFrame with the performance metrics</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-631">631</a></span>
 <span class="normal"><a href="#__codelineno-0-632">632</a></span>
 <span class="normal"><a href="#__codelineno-0-633">633</a></span>
@@ -7682,7 +7682,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.regression_metrics"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.residuals" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.residuals" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">residuals</span><span class="p">(</span><span class="n">target_column</span><span class="p">,</span> <span class="n">prediction_df</span><span class="p">)</span></code>
 
 </h3>
@@ -7698,7 +7698,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.residuals" class="do
     pd.DataFrame: DataFrame with two new columns called 'residuals' and 'residuals_abs'</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-669">669</a></span>
 <span class="normal"><a href="#__codelineno-0-670">670</a></span>
 <span class="normal"><a href="#__codelineno-0-671">671</a></span>
@@ -7757,7 +7757,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.residuals" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.set_input" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.set_input" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_input</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7817,7 +7817,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.set_input" class="do
     We're going to not allow this to be used for Models</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-817">817</a></span>
 <span class="normal"><a href="#__codelineno-0-818">818</a></span>
 <span class="normal"><a href="#__codelineno-0-819">819</a></span>
@@ -7850,7 +7850,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.set_input" class="do
 </span><span id="__span-0-830"><a id="__codelineno-0-830" name="__codelineno-0-830"></a>    <span class="c1"># Okay we&#39;re going to allow this to be set</span>
 </span><span id="__span-0-831"><a id="__codelineno-0-831" name="__codelineno-0-831"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">: Setting input to </span><span class="si">{</span><span class="nb">input</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-832"><a id="__codelineno-0-832" name="__codelineno-0-832"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Be careful with this! It breaks automatic provenance of the artifact!&quot;</span><span class="p">)</span>
-</span><span id="__span-0-833"><a id="__codelineno-0-833" name="__codelineno-0-833"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
+</span><span id="__span-0-833"><a id="__codelineno-0-833" name="__codelineno-0-833"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -7860,7 +7860,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.set_input" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.size" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.size" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">size</span><span class="p">()</span></code>
 
 </h3>
@@ -7871,7 +7871,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.size" class="doc doc
         <p>Return the size of this data in MegaBytes</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-185">185</a></span>
 <span class="normal"><a href="#__codelineno-0-186">186</a></span>
 <span class="normal"><a href="#__codelineno-0-187">187</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-185"><a id="__codelineno-0-185" name="__codelineno-0-185"></a><span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -7886,7 +7886,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.size" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.validate_proba_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.endpoint_core.EndpointCore.validate_proba_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">validate_proba_columns</span><span class="p">(</span><span class="n">prediction_df</span><span class="p">,</span> <span class="n">class_labels</span><span class="p">,</span> <span class="n">guessing</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -7964,7 +7964,7 @@ <h3 id="sageworks.core.artifacts.endpoint_core.EndpointCore.validate_proba_colum
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-695">695</a></span>
 <span class="normal"><a href="#__codelineno-0-696">696</a></span>
 <span class="normal"><a href="#__codelineno-0-697">697</a></span>
diff --git a/core_classes/artifacts/feature_set_core/index.html b/core_classes/artifacts/feature_set_core/index.html
index 62943f268..917e21eca 100644
--- a/core_classes/artifacts/feature_set_core/index.html
+++ b/core_classes/artifacts/feature_set_core/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>FeatureSetCore - SageWorks</title>
+        <title>FeatureSetCore - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1164,7 +1164,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core" class="md-nav__link">
     <span class="md-ellipsis">
       feature_set_core
     </span>
@@ -1173,7 +1173,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="md-nav__link">
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.create_s3_training_data" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.create_s3_training_data" class="md-nav__link">
     <span class="md-ellipsis">
       create_s3_training_data
     </span>
@@ -3693,7 +3693,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.created" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.created" class="md-nav__link">
     <span class="md-ellipsis">
       created
     </span>
@@ -3702,7 +3702,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.delete" class="md-nav__link">
     <span class="md-ellipsis">
       delete
     </span>
@@ -3711,7 +3711,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete_views" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.delete_views" class="md-nav__link">
     <span class="md-ellipsis">
       delete_views
     </span>
@@ -3720,7 +3720,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.descriptive_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.descriptive_stats" class="md-nav__link">
     <span class="md-ellipsis">
       descriptive_stats
     </span>
@@ -3729,7 +3729,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.details" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3738,7 +3738,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.exists" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.exists" class="md-nav__link">
     <span class="md-ellipsis">
       exists
     </span>
@@ -3747,7 +3747,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_data_source" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.get_data_source" class="md-nav__link">
     <span class="md-ellipsis">
       get_data_source
     </span>
@@ -3756,7 +3756,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_feature_store" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.get_feature_store" class="md-nav__link">
     <span class="md-ellipsis">
       get_feature_store
     </span>
@@ -3765,7 +3765,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_training_data" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.get_training_data" class="md-nav__link">
     <span class="md-ellipsis">
       get_training_data
     </span>
@@ -3774,7 +3774,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.hash" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.hash" class="md-nav__link">
     <span class="md-ellipsis">
       hash
     </span>
@@ -3783,7 +3783,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.health_check" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -3792,7 +3792,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.managed_delete" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.managed_delete" class="md-nav__link">
     <span class="md-ellipsis">
       managed_delete
     </span>
@@ -3801,7 +3801,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.modified" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.modified" class="md-nav__link">
     <span class="md-ellipsis">
       modified
     </span>
@@ -3810,7 +3810,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.num_columns" class="md-nav__link">
     <span class="md-ellipsis">
       num_columns
     </span>
@@ -3819,7 +3819,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_rows" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.num_rows" class="md-nav__link">
     <span class="md-ellipsis">
       num_rows
     </span>
@@ -3828,7 +3828,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.onboard" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.onboard" class="md-nav__link">
     <span class="md-ellipsis">
       onboard
     </span>
@@ -3837,7 +3837,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.outliers" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.outliers" class="md-nav__link">
     <span class="md-ellipsis">
       outliers
     </span>
@@ -3846,7 +3846,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.query" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.query" class="md-nav__link">
     <span class="md-ellipsis">
       query
     </span>
@@ -3855,7 +3855,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.ready" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.ready" class="md-nav__link">
     <span class="md-ellipsis">
       ready
     </span>
@@ -3864,7 +3864,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats" class="md-nav__link">
     <span class="md-ellipsis">
       recompute_stats
     </span>
@@ -3873,7 +3873,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta" class="md-nav__link">
     <span class="md-ellipsis">
       refresh_meta
     </span>
@@ -3882,7 +3882,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.sample" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.sample" class="md-nav__link">
     <span class="md-ellipsis">
       sample
     </span>
@@ -3891,7 +3891,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_computation_columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.set_computation_columns" class="md-nav__link">
     <span class="md-ellipsis">
       set_computation_columns
     </span>
@@ -3900,7 +3900,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_display_columns" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.set_display_columns" class="md-nav__link">
     <span class="md-ellipsis">
       set_display_columns
     </span>
@@ -3909,7 +3909,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_training_holdouts" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.set_training_holdouts" class="md-nav__link">
     <span class="md-ellipsis">
       set_training_holdouts
     </span>
@@ -3918,7 +3918,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.size" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.size" class="md-nav__link">
     <span class="md-ellipsis">
       size
     </span>
@@ -3927,7 +3927,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.smart_sample" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.smart_sample" class="md-nav__link">
     <span class="md-ellipsis">
       smart_sample
     </span>
@@ -3936,7 +3936,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query" class="md-nav__link">
     <span class="md-ellipsis">
       snapshot_query
     </span>
@@ -3945,7 +3945,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.table_hash" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.table_hash" class="md-nav__link">
     <span class="md-ellipsis">
       table_hash
     </span>
@@ -3954,7 +3954,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.value_counts" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.value_counts" class="md-nav__link">
     <span class="md-ellipsis">
       value_counts
     </span>
@@ -3963,7 +3963,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.view" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.view" class="md-nav__link">
     <span class="md-ellipsis">
       view
     </span>
@@ -3972,7 +3972,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.feature_set_core.FeatureSetCore.views" class="md-nav__link">
+  <a href="#workbench.core.artifacts.feature_set_core.FeatureSetCore.views" class="md-nav__link">
     <span class="md-ellipsis">
       views
     </span>
@@ -4011,10 +4011,10 @@ <h1 id="featuresetcore">FeatureSetCore</h1>
 
 
 
-<a id="sageworks.core.artifacts.feature_set_core"></a>
+<a id="workbench.core.artifacts.feature_set_core"></a>
     <div class="doc doc-contents first">
 
-        <p>FeatureSet: SageWorks Feature Set accessible through Athena</p>
+        <p>FeatureSet: Workbench Feature Set accessible through Athena</p>
 
 
 
@@ -4036,7 +4036,7 @@ <h1 id="featuresetcore">FeatureSetCore</h1>
 
 
 
-<h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc-heading">
             <code>FeatureSetCore</code>
 
 
@@ -4045,10 +4045,10 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.artifact.Artifact" href="../artifact/#sageworks.core.artifacts.artifact.Artifact">Artifact</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.artifact.Artifact" href="../artifact/#workbench.core.artifacts.artifact.Artifact">Artifact</a></code></p>
 
 
-        <p>FeatureSetCore: SageWorks FeatureSetCore Class</p>
+        <p>FeatureSetCore: Workbench FeatureSetCore Class</p>
 
 
 <details class="common-usage" open>
@@ -4064,7 +4064,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-28"> 28</a></span>
 <span class="normal"><a href="#__codelineno-0-29"> 29</a></span>
 <span class="normal"><a href="#__codelineno-0-30"> 30</a></span>
@@ -4647,7 +4647,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 <span class="normal"><a href="#__codelineno-0-607">607</a></span>
 <span class="normal"><a href="#__codelineno-0-608">608</a></span>
 <span class="normal"><a href="#__codelineno-0-609">609</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="k">class</span> <span class="nc">FeatureSetCore</span><span class="p">(</span><span class="n">Artifact</span><span class="p">):</span>
-</span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;FeatureSetCore: SageWorks FeatureSetCore Class</span>
+</span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;FeatureSetCore: Workbench FeatureSetCore Class</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">        ```python</span>
@@ -4683,9 +4683,9 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">event_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;EventTimeFeatureName&quot;</span><span class="p">]</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>
 </span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>            <span class="c1"># Pull Athena and S3 Storage information from metadata</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">athena_table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;sageworks_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_table&quot;</span><span class="p">]</span>
-</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">athena_database</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;sageworks_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_database&quot;</span><span class="p">]</span>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">s3_storage</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;sageworks_meta&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;s3_storage&quot;</span><span class="p">)</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">athena_table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;workbench_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_table&quot;</span><span class="p">]</span>
+</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">athena_database</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;workbench_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_database&quot;</span><span class="p">]</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">s3_storage</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;workbench_meta&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;s3_storage&quot;</span><span class="p">)</span>
 </span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>            <span class="c1"># Create our internal DataSource (hardcoded to Athena for now)</span>
 </span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span> <span class="o">=</span> <span class="n">AthenaSource</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">athena_table</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">athena_database</span><span class="p">)</span>
@@ -4778,7 +4778,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a>
 </span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a>    <span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 </span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the views for this Data Source&quot;&quot;&quot;</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>
 </span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>        <span class="k">return</span> <span class="n">list_views</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="p">)</span>
 </span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>
@@ -4789,7 +4789,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a><span class="sd">            pd.DataFrame: A DataFrame for the specified view</span>
 </span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">View</span>
+</span><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">View</span>
 </span><span id="__span-0-172"><a id="__codelineno-0-172" name="__codelineno-0-172"></a>
 </span><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a>        <span class="k">return</span> <span class="n">View</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">view_name</span><span class="p">)</span>
 </span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a>
@@ -4807,7 +4807,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">monitor</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Display View/Computation mismatch: </span><span class="si">{</span><span class="n">mismatch_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-187"><a id="__codelineno-0-187" name="__codelineno-0-187"></a>
 </span><span id="__span-0-188"><a id="__codelineno-0-188" name="__codelineno-0-188"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Display Columns...</span><span class="si">{</span><span class="n">diplay_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
+</span><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
 </span><span id="__span-0-190"><a id="__codelineno-0-190" name="__codelineno-0-190"></a>
 </span><span id="__span-0-191"><a id="__codelineno-0-191" name="__codelineno-0-191"></a>        <span class="c1"># Create a NEW display view</span>
 </span><span id="__span-0-192"><a id="__codelineno-0-192" name="__codelineno-0-192"></a>        <span class="n">DisplayView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="n">c_view</span><span class="o">.</span><span class="n">table</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">diplay_columns</span><span class="p">)</span>
@@ -4820,7 +4820,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-199"><a id="__codelineno-0-199" name="__codelineno-0-199"></a><span class="sd">            reset_display (bool): Also reset the display columns to match (default: True)</span>
 </span><span id="__span-0-200"><a id="__codelineno-0-200" name="__codelineno-0-200"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-201"><a id="__codelineno-0-201" name="__codelineno-0-201"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Computation Columns...</span><span class="si">{</span><span class="n">computation_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-202"><a id="__codelineno-0-202" name="__codelineno-0-202"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
+</span><span id="__span-0-202"><a id="__codelineno-0-202" name="__codelineno-0-202"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
 </span><span id="__span-0-203"><a id="__codelineno-0-203" name="__codelineno-0-203"></a>
 </span><span id="__span-0-204"><a id="__codelineno-0-204" name="__codelineno-0-204"></a>        <span class="c1"># Create a NEW computation view</span>
 </span><span id="__span-0-205"><a id="__codelineno-0-205" name="__codelineno-0-205"></a>        <span class="n">ComputationView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">computation_columns</span><span class="p">)</span>
@@ -4854,8 +4854,8 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-233"><a id="__codelineno-0-233" name="__codelineno-0-233"></a>
 </span><span id="__span-0-234"><a id="__codelineno-0-234" name="__codelineno-0-234"></a>    <span class="k">def</span> <span class="nf">aws_url</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-235"><a id="__codelineno-0-235" name="__codelineno-0-235"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;The AWS URL for looking at/querying the underlying data source&quot;&quot;&quot;</span>
-</span><span id="__span-0-236"><a id="__codelineno-0-236" name="__codelineno-0-236"></a>        <span class="n">sageworks_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
-</span><span id="__span-0-237"><a id="__codelineno-0-237" name="__codelineno-0-237"></a>        <span class="k">return</span> <span class="n">sageworks_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-236"><a id="__codelineno-0-236" name="__codelineno-0-236"></a>        <span class="n">workbench_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
+</span><span id="__span-0-237"><a id="__codelineno-0-237" name="__codelineno-0-237"></a>        <span class="k">return</span> <span class="n">workbench_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span><span id="__span-0-238"><a id="__codelineno-0-238" name="__codelineno-0-238"></a>
 </span><span id="__span-0-239"><a id="__codelineno-0-239" name="__codelineno-0-239"></a>    <span class="k">def</span> <span class="nf">created</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">datetime</span><span class="p">:</span>
 </span><span id="__span-0-240"><a id="__codelineno-0-240" name="__codelineno-0-240"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the datetime when this artifact was created&quot;&quot;&quot;</span>
@@ -4915,7 +4915,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-294"><a id="__codelineno-0-294" name="__codelineno-0-294"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-295"><a id="__codelineno-0-295" name="__codelineno-0-295"></a><span class="sd">            pd.DataFrame: The training data for this FeatureSet</span>
 </span><span id="__span-0-296"><a id="__codelineno-0-296" name="__codelineno-0-296"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-297"><a id="__codelineno-0-297" name="__codelineno-0-297"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views.view</span> <span class="kn">import</span> <span class="n">View</span>
+</span><span id="__span-0-297"><a id="__codelineno-0-297" name="__codelineno-0-297"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views.view</span> <span class="kn">import</span> <span class="n">View</span>
 </span><span id="__span-0-298"><a id="__codelineno-0-298" name="__codelineno-0-298"></a>
 </span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>        <span class="k">return</span> <span class="n">View</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;training&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">pull_dataframe</span><span class="p">()</span>
 </span><span id="__span-0-300"><a id="__codelineno-0-300" name="__codelineno-0-300"></a>
@@ -4956,9 +4956,9 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-335"><a id="__codelineno-0-335" name="__codelineno-0-335"></a>        <span class="n">details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
 </span><span id="__span-0-336"><a id="__codelineno-0-336" name="__codelineno-0-336"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;aws_url&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_url</span><span class="p">()</span>
 </span><span id="__span-0-337"><a id="__codelineno-0-337" name="__codelineno-0-337"></a>
-</span><span id="__span-0-338"><a id="__codelineno-0-338" name="__codelineno-0-338"></a>        <span class="c1"># Store the AWS URL in the SageWorks Metadata</span>
+</span><span id="__span-0-338"><a id="__codelineno-0-338" name="__codelineno-0-338"></a>        <span class="c1"># Store the AWS URL in the Workbench Metadata</span>
 </span><span id="__span-0-339"><a id="__codelineno-0-339" name="__codelineno-0-339"></a>        <span class="c1"># FIXME: We need to revisit this but doing an upsert just for aws_url is silly</span>
-</span><span id="__span-0-340"><a id="__codelineno-0-340" name="__codelineno-0-340"></a>        <span class="c1"># self.upsert_sageworks_meta({&quot;aws_url&quot;: details[&quot;aws_url&quot;]})</span>
+</span><span id="__span-0-340"><a id="__codelineno-0-340" name="__codelineno-0-340"></a>        <span class="c1"># self.upsert_workbench_meta({&quot;aws_url&quot;: details[&quot;aws_url&quot;]})</span>
 </span><span id="__span-0-341"><a id="__codelineno-0-341" name="__codelineno-0-341"></a>
 </span><span id="__span-0-342"><a id="__codelineno-0-342" name="__codelineno-0-342"></a>        <span class="c1"># Now get a summary of the underlying DataSource</span>
 </span><span id="__span-0-343"><a id="__codelineno-0-343" name="__codelineno-0-343"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;storage_summary&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
@@ -4970,9 +4970,9 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-349"><a id="__codelineno-0-349" name="__codelineno-0-349"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;num_rows&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_rows</span><span class="p">()</span>
 </span><span id="__span-0-350"><a id="__codelineno-0-350" name="__codelineno-0-350"></a>
 </span><span id="__span-0-351"><a id="__codelineno-0-351" name="__codelineno-0-351"></a>        <span class="c1"># Additional Details</span>
-</span><span id="__span-0-352"><a id="__codelineno-0-352" name="__codelineno-0-352"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_status</span><span class="p">()</span>
-</span><span id="__span-0-353"><a id="__codelineno-0-353" name="__codelineno-0-353"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
-</span><span id="__span-0-354"><a id="__codelineno-0-354" name="__codelineno-0-354"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_tags</span><span class="p">())</span>
+</span><span id="__span-0-352"><a id="__codelineno-0-352" name="__codelineno-0-352"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_status</span><span class="p">()</span>
+</span><span id="__span-0-353"><a id="__codelineno-0-353" name="__codelineno-0-353"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+</span><span id="__span-0-354"><a id="__codelineno-0-354" name="__codelineno-0-354"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_tags</span><span class="p">())</span>
 </span><span id="__span-0-355"><a id="__codelineno-0-355" name="__codelineno-0-355"></a>
 </span><span id="__span-0-356"><a id="__codelineno-0-356" name="__codelineno-0-356"></a>        <span class="c1"># Underlying Storage Details</span>
 </span><span id="__span-0-357"><a id="__codelineno-0-357" name="__codelineno-0-357"></a>        <span class="n">details</span><span class="p">[</span><span class="s2">&quot;storage_type&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;athena&quot;</span>  <span class="c1"># TODO: Add RDS support</span>
@@ -5065,7 +5065,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-444"><a id="__codelineno-0-444" name="__codelineno-0-444"></a><span class="sd">            id_column (str): The name of the id column.</span>
 </span><span id="__span-0-445"><a id="__codelineno-0-445" name="__codelineno-0-445"></a><span class="sd">            holdout_ids (list[str]): The list of holdout ids.</span>
 </span><span id="__span-0-446"><a id="__codelineno-0-446" name="__codelineno-0-446"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-447"><a id="__codelineno-0-447" name="__codelineno-0-447"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">TrainingView</span>
+</span><span id="__span-0-447"><a id="__codelineno-0-447" name="__codelineno-0-447"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">TrainingView</span>
 </span><span id="__span-0-448"><a id="__codelineno-0-448" name="__codelineno-0-448"></a>
 </span><span id="__span-0-449"><a id="__codelineno-0-449" name="__codelineno-0-449"></a>        <span class="c1"># Create a NEW training view</span>
 </span><span id="__span-0-450"><a id="__codelineno-0-450" name="__codelineno-0-450"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Training Holdouts: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">holdout_ids</span><span class="p">)</span><span class="si">}</span><span class="s2"> ids...&quot;</span><span class="p">)</span>
@@ -5079,7 +5079,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a><span class="sd">            table (str): Name of Athena Table</span>
 </span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a><span class="sd">            database (str): Athena Database Name</span>
 </span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>        <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
+</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>        <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
 </span><span id="__span-0-462"><a id="__codelineno-0-462" name="__codelineno-0-462"></a>
 </span><span id="__span-0-463"><a id="__codelineno-0-463" name="__codelineno-0-463"></a>        <span class="n">delete_views_and_supplemental_data</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">database</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
 </span><span id="__span-0-464"><a id="__codelineno-0-464" name="__codelineno-0-464"></a>
@@ -5186,7 +5186,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 </span><span id="__span-0-565"><a id="__codelineno-0-565" name="__codelineno-0-565"></a>
 </span><span id="__span-0-566"><a id="__codelineno-0-566" name="__codelineno-0-566"></a>        <span class="c1"># Check the expected metadata for the FeatureSet</span>
 </span><span id="__span-0-567"><a id="__codelineno-0-567" name="__codelineno-0-567"></a>        <span class="n">expected_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">expected_meta</span><span class="p">()</span>
-</span><span id="__span-0-568"><a id="__codelineno-0-568" name="__codelineno-0-568"></a>        <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-568"><a id="__codelineno-0-568" name="__codelineno-0-568"></a>        <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-569"><a id="__codelineno-0-569" name="__codelineno-0-569"></a>        <span class="n">feature_set_ready</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">existing_meta</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">issuperset</span><span class="p">(</span><span class="n">expected_meta</span><span class="p">)</span>
 </span><span id="__span-0-570"><a id="__codelineno-0-570" name="__codelineno-0-570"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="n">feature_set_ready</span><span class="p">:</span>
 </span><span id="__span-0-571"><a id="__codelineno-0-571" name="__codelineno-0-571"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;FeatureSet </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> is not ready!&quot;</span><span class="p">)</span>
@@ -5245,7 +5245,7 @@ <h2 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore" class="doc doc
 
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_types" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.column_types" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_types</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span></code>
 
   <span class="doc doc-labels">
@@ -5266,7 +5266,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_types" c
 
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">columns</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span></code>
 
   <span class="doc doc-labels">
@@ -5287,7 +5287,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.columns" class=
 
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.table" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.table" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">table</span><span class="p">:</span> <span class="nb">str</span></code>
 
   <span class="doc doc-labels">
@@ -5309,7 +5309,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.table" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.__init__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">feature_set_uuid</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -5351,7 +5351,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.__init__" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-39">39</a></span>
 <span class="normal"><a href="#__codelineno-0-40">40</a></span>
 <span class="normal"><a href="#__codelineno-0-41">41</a></span>
@@ -5418,9 +5418,9 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.__init__" class
 </span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">event_time</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;EventTimeFeatureName&quot;</span><span class="p">]</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>
 </span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="c1"># Pull Athena and S3 Storage information from metadata</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">athena_table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;sageworks_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_table&quot;</span><span class="p">]</span>
-</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">athena_database</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;sageworks_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_database&quot;</span><span class="p">]</span>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">s3_storage</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;sageworks_meta&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;s3_storage&quot;</span><span class="p">)</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">athena_table</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;workbench_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_table&quot;</span><span class="p">]</span>
+</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">athena_database</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;workbench_meta&quot;</span><span class="p">][</span><span class="s2">&quot;athena_database&quot;</span><span class="p">]</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">s3_storage</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">feature_meta</span><span class="p">[</span><span class="s2">&quot;workbench_meta&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;s3_storage&quot;</span><span class="p">)</span>
 </span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>        <span class="c1"># Create our internal DataSource (hardcoded to Athena for now)</span>
 </span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span> <span class="o">=</span> <span class="n">AthenaSource</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">athena_table</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">athena_database</span><span class="p">)</span>
@@ -5442,7 +5442,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.__init__" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.anomalies" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.anomalies" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">anomalies</span><span class="p">()</span></code>
 
 </h3>
@@ -5455,7 +5455,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.anomalies" clas
     pd.DataFrame: A dataframe of anomalies from the underlying DataSource</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-507">507</a></span>
 <span class="normal"><a href="#__codelineno-0-508">508</a></span>
 <span class="normal"><a href="#__codelineno-0-509">509</a></span>
@@ -5490,7 +5490,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.anomalies" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.arn" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.arn" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">arn</span><span class="p">()</span></code>
 
 </h3>
@@ -5501,7 +5501,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.arn" class="doc
         <p>AWS ARN (Amazon Resource Name) for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-122">122</a></span>
 <span class="normal"><a href="#__codelineno-0-123">123</a></span>
 <span class="normal"><a href="#__codelineno-0-124">124</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="k">def</span> <span class="nf">arn</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
@@ -5516,7 +5516,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.arn" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.aws_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -5527,7 +5527,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_meta" class
         <p>Get ALL the AWS metadata for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-118">118</a></span>
 <span class="normal"><a href="#__codelineno-0-119">119</a></span>
 <span class="normal"><a href="#__codelineno-0-120">120</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a><span class="k">def</span> <span class="nf">aws_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
@@ -5542,7 +5542,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_meta" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_url" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.aws_url" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_url</span><span class="p">()</span></code>
 
 </h3>
@@ -5553,14 +5553,14 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_url" class=
         <p>The AWS URL for looking at/querying the underlying data source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-234">234</a></span>
 <span class="normal"><a href="#__codelineno-0-235">235</a></span>
 <span class="normal"><a href="#__codelineno-0-236">236</a></span>
 <span class="normal"><a href="#__codelineno-0-237">237</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-234"><a id="__codelineno-0-234" name="__codelineno-0-234"></a><span class="k">def</span> <span class="nf">aws_url</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-235"><a id="__codelineno-0-235" name="__codelineno-0-235"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;The AWS URL for looking at/querying the underlying data source&quot;&quot;&quot;</span>
-</span><span id="__span-0-236"><a id="__codelineno-0-236" name="__codelineno-0-236"></a>    <span class="n">sageworks_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
-</span><span id="__span-0-237"><a id="__codelineno-0-237" name="__codelineno-0-237"></a>    <span class="k">return</span> <span class="n">sageworks_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
+</span><span id="__span-0-236"><a id="__codelineno-0-236" name="__codelineno-0-236"></a>    <span class="n">workbench_details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_details&quot;</span><span class="p">,</span> <span class="p">{})</span>
+</span><span id="__span-0-237"><a id="__codelineno-0-237" name="__codelineno-0-237"></a>    <span class="k">return</span> <span class="n">workbench_details</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;aws_url&quot;</span><span class="p">,</span> <span class="s2">&quot;unknown&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5570,7 +5570,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_url" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.column_details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_details</span><span class="p">()</span></code>
 
 </h3>
@@ -5610,7 +5610,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_details"
 types, so we need to overlay that type information on top of the DataSource type information</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-140">140</a></span>
 <span class="normal"><a href="#__codelineno-0-141">141</a></span>
 <span class="normal"><a href="#__codelineno-0-142">142</a></span>
@@ -5653,7 +5653,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_details"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.column_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">column_stats</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5672,7 +5672,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_stats" c
       ...}</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-539">539</a></span>
 <span class="normal"><a href="#__codelineno-0-540">540</a></span>
 <span class="normal"><a href="#__codelineno-0-541">541</a></span>
@@ -5723,7 +5723,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_stats" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.correlations" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.correlations" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">correlations</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -5738,7 +5738,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.correlations" c
     dict: A dictionary of correlations for the numeric columns</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-530">530</a></span>
 <span class="normal"><a href="#__codelineno-0-531">531</a></span>
 <span class="normal"><a href="#__codelineno-0-532">532</a></span>
@@ -5763,7 +5763,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.correlations" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.create_s3_training_data" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.create_s3_training_data" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create_s3_training_data</span><span class="p">()</span></code>
 
 </h3>
@@ -5778,7 +5778,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.create_s3_train
     str: The full path/file for the CSV file created by Feature Store create_dataset()</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-267">267</a></span>
 <span class="normal"><a href="#__codelineno-0-268">268</a></span>
 <span class="normal"><a href="#__codelineno-0-269">269</a></span>
@@ -5833,7 +5833,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.create_s3_train
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.created" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.created" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">created</span><span class="p">()</span></code>
 
 </h3>
@@ -5844,7 +5844,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.created" class=
         <p>Return the datetime when this artifact was created</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-239">239</a></span>
 <span class="normal"><a href="#__codelineno-0-240">240</a></span>
 <span class="normal"><a href="#__codelineno-0-241">241</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-239"><a id="__codelineno-0-239" name="__codelineno-0-239"></a><span class="k">def</span> <span class="nf">created</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">datetime</span><span class="p">:</span>
@@ -5859,7 +5859,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.created" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">()</span></code>
 
 </h3>
@@ -5870,7 +5870,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete" class="
         <p>Instance Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-367">367</a></span>
 <span class="normal"><a href="#__codelineno-0-368">368</a></span>
 <span class="normal"><a href="#__codelineno-0-369">369</a></span>
@@ -5895,7 +5895,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete_views" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.delete_views" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete_views</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">database</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5957,7 +5957,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete_views" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-453">453</a></span>
 <span class="normal"><a href="#__codelineno-0-454">454</a></span>
 <span class="normal"><a href="#__codelineno-0-455">455</a></span>
@@ -5976,7 +5976,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete_views" c
 </span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a><span class="sd">        table (str): Name of Athena Table</span>
 </span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a><span class="sd">        database (str): Athena Database Name</span>
 </span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
+</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">delete_views_and_supplemental_data</span>
 </span><span id="__span-0-462"><a id="__codelineno-0-462" name="__codelineno-0-462"></a>
 </span><span id="__span-0-463"><a id="__codelineno-0-463" name="__codelineno-0-463"></a>    <span class="n">delete_views_and_supplemental_data</span><span class="p">(</span><span class="n">table</span><span class="p">,</span> <span class="n">database</span><span class="p">,</span> <span class="bp">cls</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -5988,7 +5988,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete_views" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.descriptive_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.descriptive_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">descriptive_stats</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6003,7 +6003,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.descriptive_sta
     dict: A dictionary of descriptive stats for the numeric columns</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-465">465</a></span>
 <span class="normal"><a href="#__codelineno-0-466">466</a></span>
 <span class="normal"><a href="#__codelineno-0-467">467</a></span>
@@ -6028,7 +6028,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.descriptive_sta
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6093,7 +6093,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.details" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-324">324</a></span>
 <span class="normal"><a href="#__codelineno-0-325">325</a></span>
 <span class="normal"><a href="#__codelineno-0-326">326</a></span>
@@ -6149,9 +6149,9 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.details" class=
 </span><span id="__span-0-335"><a id="__codelineno-0-335" name="__codelineno-0-335"></a>    <span class="n">details</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
 </span><span id="__span-0-336"><a id="__codelineno-0-336" name="__codelineno-0-336"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;aws_url&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_url</span><span class="p">()</span>
 </span><span id="__span-0-337"><a id="__codelineno-0-337" name="__codelineno-0-337"></a>
-</span><span id="__span-0-338"><a id="__codelineno-0-338" name="__codelineno-0-338"></a>    <span class="c1"># Store the AWS URL in the SageWorks Metadata</span>
+</span><span id="__span-0-338"><a id="__codelineno-0-338" name="__codelineno-0-338"></a>    <span class="c1"># Store the AWS URL in the Workbench Metadata</span>
 </span><span id="__span-0-339"><a id="__codelineno-0-339" name="__codelineno-0-339"></a>    <span class="c1"># FIXME: We need to revisit this but doing an upsert just for aws_url is silly</span>
-</span><span id="__span-0-340"><a id="__codelineno-0-340" name="__codelineno-0-340"></a>    <span class="c1"># self.upsert_sageworks_meta({&quot;aws_url&quot;: details[&quot;aws_url&quot;]})</span>
+</span><span id="__span-0-340"><a id="__codelineno-0-340" name="__codelineno-0-340"></a>    <span class="c1"># self.upsert_workbench_meta({&quot;aws_url&quot;: details[&quot;aws_url&quot;]})</span>
 </span><span id="__span-0-341"><a id="__codelineno-0-341" name="__codelineno-0-341"></a>
 </span><span id="__span-0-342"><a id="__codelineno-0-342" name="__codelineno-0-342"></a>    <span class="c1"># Now get a summary of the underlying DataSource</span>
 </span><span id="__span-0-343"><a id="__codelineno-0-343" name="__codelineno-0-343"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;storage_summary&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
@@ -6163,9 +6163,9 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.details" class=
 </span><span id="__span-0-349"><a id="__codelineno-0-349" name="__codelineno-0-349"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;num_rows&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_rows</span><span class="p">()</span>
 </span><span id="__span-0-350"><a id="__codelineno-0-350" name="__codelineno-0-350"></a>
 </span><span id="__span-0-351"><a id="__codelineno-0-351" name="__codelineno-0-351"></a>    <span class="c1"># Additional Details</span>
-</span><span id="__span-0-352"><a id="__codelineno-0-352" name="__codelineno-0-352"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_status</span><span class="p">()</span>
-</span><span id="__span-0-353"><a id="__codelineno-0-353" name="__codelineno-0-353"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
-</span><span id="__span-0-354"><a id="__codelineno-0-354" name="__codelineno-0-354"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_tags</span><span class="p">())</span>
+</span><span id="__span-0-352"><a id="__codelineno-0-352" name="__codelineno-0-352"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_status</span><span class="p">()</span>
+</span><span id="__span-0-353"><a id="__codelineno-0-353" name="__codelineno-0-353"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_input</span><span class="p">()</span>
+</span><span id="__span-0-354"><a id="__codelineno-0-354" name="__codelineno-0-354"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_tags</span><span class="p">())</span>
 </span><span id="__span-0-355"><a id="__codelineno-0-355" name="__codelineno-0-355"></a>
 </span><span id="__span-0-356"><a id="__codelineno-0-356" name="__codelineno-0-356"></a>    <span class="c1"># Underlying Storage Details</span>
 </span><span id="__span-0-357"><a id="__codelineno-0-357" name="__codelineno-0-357"></a>    <span class="n">details</span><span class="p">[</span><span class="s2">&quot;storage_type&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;athena&quot;</span>  <span class="c1"># TODO: Add RDS support</span>
@@ -6186,7 +6186,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.details" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.exists" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">exists</span><span class="p">()</span></code>
 
 </h3>
@@ -6197,7 +6197,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.exists" class="
         <p>Does the feature_set_name exist in the AWS Metadata?</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-91">91</a></span>
 <span class="normal"><a href="#__codelineno-0-92">92</a></span>
 <span class="normal"><a href="#__codelineno-0-93">93</a></span>
@@ -6218,7 +6218,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.exists" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_data_source" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.get_data_source" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_data_source</span><span class="p">()</span></code>
 
 </h3>
@@ -6229,7 +6229,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_data_source
         <p>Return the underlying DataSource object</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-256">256</a></span>
 <span class="normal"><a href="#__codelineno-0-257">257</a></span>
 <span class="normal"><a href="#__codelineno-0-258">258</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-256"><a id="__codelineno-0-256" name="__codelineno-0-256"></a><span class="k">def</span> <span class="nf">get_data_source</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">DataSourceFactory</span><span class="p">:</span>
@@ -6244,7 +6244,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_data_source
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_feature_store" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.get_feature_store" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_feature_store</span><span class="p">()</span></code>
 
 </h3>
@@ -6257,7 +6257,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_feature_sto
 See: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-260">260</a></span>
 <span class="normal"><a href="#__codelineno-0-261">261</a></span>
 <span class="normal"><a href="#__codelineno-0-262">262</a></span>
@@ -6278,7 +6278,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_feature_sto
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_training_data" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.get_training_data" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_training_data</span><span class="p">()</span></code>
 
 </h3>
@@ -6312,7 +6312,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_training_da
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-291">291</a></span>
 <span class="normal"><a href="#__codelineno-0-292">292</a></span>
 <span class="normal"><a href="#__codelineno-0-293">293</a></span>
@@ -6327,7 +6327,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_training_da
 </span><span id="__span-0-294"><a id="__codelineno-0-294" name="__codelineno-0-294"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-295"><a id="__codelineno-0-295" name="__codelineno-0-295"></a><span class="sd">        pd.DataFrame: The training data for this FeatureSet</span>
 </span><span id="__span-0-296"><a id="__codelineno-0-296" name="__codelineno-0-296"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-297"><a id="__codelineno-0-297" name="__codelineno-0-297"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views.view</span> <span class="kn">import</span> <span class="n">View</span>
+</span><span id="__span-0-297"><a id="__codelineno-0-297" name="__codelineno-0-297"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views.view</span> <span class="kn">import</span> <span class="n">View</span>
 </span><span id="__span-0-298"><a id="__codelineno-0-298" name="__codelineno-0-298"></a>
 </span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>    <span class="k">return</span> <span class="n">View</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;training&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">pull_dataframe</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
@@ -6339,7 +6339,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_training_da
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.hash" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.hash" class="doc doc-heading">
             <code class="highlight language-python"><span class="nb">hash</span><span class="p">()</span></code>
 
 </h3>
@@ -6350,7 +6350,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.hash" class="do
         <p>Return the hash for the set of Parquet files for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-248">248</a></span>
 <span class="normal"><a href="#__codelineno-0-249">249</a></span>
 <span class="normal"><a href="#__codelineno-0-250">250</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a><span class="k">def</span> <span class="nf">hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
@@ -6365,7 +6365,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.hash" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.health_check" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">()</span></code>
 
 </h3>
@@ -6399,7 +6399,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.health_check" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-98"> 98</a></span>
 <span class="normal"><a href="#__codelineno-0-99"> 99</a></span>
 <span class="normal"><a href="#__codelineno-0-100">100</a></span>
@@ -6446,7 +6446,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.health_check" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.managed_delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.managed_delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">managed_delete</span><span class="p">(</span><span class="n">feature_set_name</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -6492,7 +6492,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.managed_delete"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-376">376</a></span>
 <span class="normal"><a href="#__codelineno-0-377">377</a></span>
 <span class="normal"><a href="#__codelineno-0-378">378</a></span>
@@ -6581,7 +6581,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.managed_delete"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.modified" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.modified" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">modified</span><span class="p">()</span></code>
 
 </h3>
@@ -6592,7 +6592,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.modified" class
         <p>Return the datetime when this artifact was last modified</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-243">243</a></span>
 <span class="normal"><a href="#__codelineno-0-244">244</a></span>
 <span class="normal"><a href="#__codelineno-0-245">245</a></span>
@@ -6609,7 +6609,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.modified" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.num_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">num_columns</span><span class="p">()</span></code>
 
 </h3>
@@ -6620,7 +6620,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_columns" cl
         <p>Return the number of columns of the Feature Set</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-212">212</a></span>
 <span class="normal"><a href="#__codelineno-0-213">213</a></span>
 <span class="normal"><a href="#__codelineno-0-214">214</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-212"><a id="__codelineno-0-212" name="__codelineno-0-212"></a><span class="k">def</span> <span class="nf">num_columns</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
@@ -6635,7 +6635,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_columns" cl
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_rows" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.num_rows" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">num_rows</span><span class="p">()</span></code>
 
 </h3>
@@ -6646,7 +6646,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_rows" class
         <p>Return the number of rows of the internal DataSource</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-216">216</a></span>
 <span class="normal"><a href="#__codelineno-0-217">217</a></span>
 <span class="normal"><a href="#__codelineno-0-218">218</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a><span class="k">def</span> <span class="nf">num_rows</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
@@ -6661,7 +6661,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_rows" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.onboard" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.onboard" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">onboard</span><span class="p">()</span></code>
 
 </h3>
@@ -6672,7 +6672,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.onboard" class=
         <p>This is a BLOCKING method that will onboard the FeatureSet (make it ready)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-577">577</a></span>
 <span class="normal"><a href="#__codelineno-0-578">578</a></span>
 <span class="normal"><a href="#__codelineno-0-579">579</a></span>
@@ -6729,7 +6729,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.onboard" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.outliers" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.outliers" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">outliers</span><span class="p">(</span><span class="n">scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6748,7 +6748,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.outliers" class
     The scale parameter can be adjusted to change the IQR multiplier</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-483">483</a></span>
 <span class="normal"><a href="#__codelineno-0-484">484</a></span>
 <span class="normal"><a href="#__codelineno-0-485">485</a></span>
@@ -6781,7 +6781,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.outliers" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.query" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">query</span><span class="p">(</span><span class="n">query</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -6862,7 +6862,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.query" class="d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-220">220</a></span>
 <span class="normal"><a href="#__codelineno-0-221">221</a></span>
 <span class="normal"><a href="#__codelineno-0-222">222</a></span>
@@ -6897,7 +6897,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.query" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.ready" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.ready" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">ready</span><span class="p">()</span></code>
 
 </h3>
@@ -6910,7 +6910,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.ready" class="d
    check both to see if the FeatureSet is ready.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-561">561</a></span>
 <span class="normal"><a href="#__codelineno-0-562">562</a></span>
 <span class="normal"><a href="#__codelineno-0-563">563</a></span>
@@ -6932,7 +6932,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.ready" class="d
 </span><span id="__span-0-565"><a id="__codelineno-0-565" name="__codelineno-0-565"></a>
 </span><span id="__span-0-566"><a id="__codelineno-0-566" name="__codelineno-0-566"></a>    <span class="c1"># Check the expected metadata for the FeatureSet</span>
 </span><span id="__span-0-567"><a id="__codelineno-0-567" name="__codelineno-0-567"></a>    <span class="n">expected_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">expected_meta</span><span class="p">()</span>
-</span><span id="__span-0-568"><a id="__codelineno-0-568" name="__codelineno-0-568"></a>    <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span>
+</span><span id="__span-0-568"><a id="__codelineno-0-568" name="__codelineno-0-568"></a>    <span class="n">existing_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span>
 </span><span id="__span-0-569"><a id="__codelineno-0-569" name="__codelineno-0-569"></a>    <span class="n">feature_set_ready</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">existing_meta</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span><span class="o">.</span><span class="n">issuperset</span><span class="p">(</span><span class="n">expected_meta</span><span class="p">)</span>
 </span><span id="__span-0-570"><a id="__codelineno-0-570" name="__codelineno-0-570"></a>    <span class="k">if</span> <span class="ow">not</span> <span class="n">feature_set_ready</span><span class="p">:</span>
 </span><span id="__span-0-571"><a id="__codelineno-0-571" name="__codelineno-0-571"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;FeatureSet </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2"> is not ready!&quot;</span><span class="p">)</span>
@@ -6949,7 +6949,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.ready" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">recompute_stats</span><span class="p">()</span></code>
 
 </h3>
@@ -6960,7 +6960,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats
         <p>This is a BLOCKING method that will recompute the stats for the FeatureSet</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-602">602</a></span>
 <span class="normal"><a href="#__codelineno-0-603">603</a></span>
 <span class="normal"><a href="#__codelineno-0-604">604</a></span>
@@ -6985,7 +6985,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">refresh_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -6996,7 +6996,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta" c
         <p>Internal: Refresh our internal AWS Feature Store metadata</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-86">86</a></span>
 <span class="normal"><a href="#__codelineno-0-87">87</a></span>
 <span class="normal"><a href="#__codelineno-0-88">88</a></span>
@@ -7013,7 +7013,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.sample" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">sample</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7028,7 +7028,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.sample" class="
     pd.DataFrame: A sample of the data from the underlying DataSource</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-474">474</a></span>
 <span class="normal"><a href="#__codelineno-0-475">475</a></span>
 <span class="normal"><a href="#__codelineno-0-476">476</a></span>
@@ -7053,7 +7053,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.sample" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_computation_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.set_computation_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_computation_columns</span><span class="p">(</span><span class="n">computation_columns</span><span class="p">,</span> <span class="n">reset_display</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -7111,7 +7111,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_computation
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-194">194</a></span>
 <span class="normal"><a href="#__codelineno-0-195">195</a></span>
 <span class="normal"><a href="#__codelineno-0-196">196</a></span>
@@ -7136,7 +7136,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_computation
 </span><span id="__span-0-199"><a id="__codelineno-0-199" name="__codelineno-0-199"></a><span class="sd">        reset_display (bool): Also reset the display columns to match (default: True)</span>
 </span><span id="__span-0-200"><a id="__codelineno-0-200" name="__codelineno-0-200"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-201"><a id="__codelineno-0-201" name="__codelineno-0-201"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Computation Columns...</span><span class="si">{</span><span class="n">computation_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-202"><a id="__codelineno-0-202" name="__codelineno-0-202"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
+</span><span id="__span-0-202"><a id="__codelineno-0-202" name="__codelineno-0-202"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">ComputationView</span>
 </span><span id="__span-0-203"><a id="__codelineno-0-203" name="__codelineno-0-203"></a>
 </span><span id="__span-0-204"><a id="__codelineno-0-204" name="__codelineno-0-204"></a>    <span class="c1"># Create a NEW computation view</span>
 </span><span id="__span-0-205"><a id="__codelineno-0-205" name="__codelineno-0-205"></a>    <span class="n">ComputationView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">computation_columns</span><span class="p">)</span>
@@ -7154,7 +7154,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_computation
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_display_columns" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.set_display_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_display_columns</span><span class="p">(</span><span class="n">diplay_columns</span><span class="p">)</span></code>
 
 </h3>
@@ -7196,7 +7196,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_display_col
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-175">175</a></span>
 <span class="normal"><a href="#__codelineno-0-176">176</a></span>
 <span class="normal"><a href="#__codelineno-0-177">177</a></span>
@@ -7228,7 +7228,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_display_col
 </span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">monitor</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Display View/Computation mismatch: </span><span class="si">{</span><span class="n">mismatch_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-187"><a id="__codelineno-0-187" name="__codelineno-0-187"></a>
 </span><span id="__span-0-188"><a id="__codelineno-0-188" name="__codelineno-0-188"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Display Columns...</span><span class="si">{</span><span class="n">diplay_columns</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
+</span><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">DisplayView</span>
 </span><span id="__span-0-190"><a id="__codelineno-0-190" name="__codelineno-0-190"></a>
 </span><span id="__span-0-191"><a id="__codelineno-0-191" name="__codelineno-0-191"></a>    <span class="c1"># Create a NEW display view</span>
 </span><span id="__span-0-192"><a id="__codelineno-0-192" name="__codelineno-0-192"></a>    <span class="n">DisplayView</span><span class="o">.</span><span class="n">create</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="n">c_view</span><span class="o">.</span><span class="n">table</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="n">diplay_columns</span><span class="p">)</span>
@@ -7241,7 +7241,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_display_col
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_training_holdouts" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.set_training_holdouts" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_training_holdouts</span><span class="p">(</span><span class="n">id_column</span><span class="p">,</span> <span class="n">holdout_ids</span><span class="p">)</span></code>
 
 </h3>
@@ -7299,7 +7299,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_training_ho
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-440">440</a></span>
 <span class="normal"><a href="#__codelineno-0-441">441</a></span>
 <span class="normal"><a href="#__codelineno-0-442">442</a></span>
@@ -7318,7 +7318,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_training_ho
 </span><span id="__span-0-444"><a id="__codelineno-0-444" name="__codelineno-0-444"></a><span class="sd">        id_column (str): The name of the id column.</span>
 </span><span id="__span-0-445"><a id="__codelineno-0-445" name="__codelineno-0-445"></a><span class="sd">        holdout_ids (list[str]): The list of holdout ids.</span>
 </span><span id="__span-0-446"><a id="__codelineno-0-446" name="__codelineno-0-446"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-447"><a id="__codelineno-0-447" name="__codelineno-0-447"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">TrainingView</span>
+</span><span id="__span-0-447"><a id="__codelineno-0-447" name="__codelineno-0-447"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">TrainingView</span>
 </span><span id="__span-0-448"><a id="__codelineno-0-448" name="__codelineno-0-448"></a>
 </span><span id="__span-0-449"><a id="__codelineno-0-449" name="__codelineno-0-449"></a>    <span class="c1"># Create a NEW training view</span>
 </span><span id="__span-0-450"><a id="__codelineno-0-450" name="__codelineno-0-450"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Setting Training Holdouts: </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">holdout_ids</span><span class="p">)</span><span class="si">}</span><span class="s2"> ids...&quot;</span><span class="p">)</span>
@@ -7332,7 +7332,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_training_ho
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.size" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.size" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">size</span><span class="p">()</span></code>
 
 </h3>
@@ -7343,7 +7343,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.size" class="do
         <p>Return the size of the internal DataSource in MegaBytes</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-126">126</a></span>
 <span class="normal"><a href="#__codelineno-0-127">127</a></span>
 <span class="normal"><a href="#__codelineno-0-128">128</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a><span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -7358,7 +7358,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.size" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.smart_sample" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.smart_sample" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">smart_sample</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7423,7 +7423,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.smart_sample" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-496">496</a></span>
 <span class="normal"><a href="#__codelineno-0-497">497</a></span>
 <span class="normal"><a href="#__codelineno-0-498">498</a></span>
@@ -7452,7 +7452,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.smart_sample" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">snapshot_query</span><span class="p">(</span><span class="n">table_name</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -7517,7 +7517,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-301">301</a></span>
 <span class="normal"><a href="#__codelineno-0-302">302</a></span>
 <span class="normal"><a href="#__codelineno-0-303">303</a></span>
@@ -7570,7 +7570,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.table_hash" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.table_hash" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">table_hash</span><span class="p">()</span></code>
 
 </h3>
@@ -7581,7 +7581,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.table_hash" cla
         <p>Return the hash for the Athena table</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-252">252</a></span>
 <span class="normal"><a href="#__codelineno-0-253">253</a></span>
 <span class="normal"><a href="#__codelineno-0-254">254</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-252"><a id="__codelineno-0-252" name="__codelineno-0-252"></a><span class="k">def</span> <span class="nf">table_hash</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
@@ -7596,7 +7596,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.table_hash" cla
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.value_counts" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.value_counts" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">value_counts</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7611,7 +7611,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.value_counts" c
     dict: A dictionary of value counts for the string columns</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-521">521</a></span>
 <span class="normal"><a href="#__codelineno-0-522">522</a></span>
 <span class="normal"><a href="#__codelineno-0-523">523</a></span>
@@ -7636,7 +7636,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.value_counts" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.view" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.view" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">view</span><span class="p">(</span><span class="n">view_name</span><span class="p">)</span></code>
 
 </h3>
@@ -7651,7 +7651,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.view" class="do
     pd.DataFrame: A DataFrame for the specified view</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-164">164</a></span>
 <span class="normal"><a href="#__codelineno-0-165">165</a></span>
 <span class="normal"><a href="#__codelineno-0-166">166</a></span>
@@ -7668,7 +7668,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.view" class="do
 </span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a><span class="sd">        pd.DataFrame: A DataFrame for the specified view</span>
 </span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views</span> <span class="kn">import</span> <span class="n">View</span>
+</span><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views</span> <span class="kn">import</span> <span class="n">View</span>
 </span><span id="__span-0-172"><a id="__codelineno-0-172" name="__codelineno-0-172"></a>
 </span><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a>    <span class="k">return</span> <span class="n">View</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">view_name</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -7680,7 +7680,7 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.view" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.views" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.feature_set_core.FeatureSetCore.views" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">views</span><span class="p">()</span></code>
 
 </h3>
@@ -7691,14 +7691,14 @@ <h3 id="sageworks.core.artifacts.feature_set_core.FeatureSetCore.views" class="d
         <p>Return the views for this Data Source</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-158">158</a></span>
 <span class="normal"><a href="#__codelineno-0-159">159</a></span>
 <span class="normal"><a href="#__codelineno-0-160">160</a></span>
 <span class="normal"><a href="#__codelineno-0-161">161</a></span>
 <span class="normal"><a href="#__codelineno-0-162">162</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a><span class="k">def</span> <span class="nf">views</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 </span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the views for this Data Source&quot;&quot;&quot;</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>    <span class="kn">from</span> <span class="nn">sageworks.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>    <span class="kn">from</span> <span class="nn">workbench.core.views.view_utils</span> <span class="kn">import</span> <span class="n">list_views</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>
 </span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>    <span class="k">return</span> <span class="n">list_views</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_source</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
diff --git a/core_classes/artifacts/model_core/index.html b/core_classes/artifacts/model_core/index.html
index f2b99e4df..41bccc27d 100644
--- a/core_classes/artifacts/model_core/index.html
+++ b/core_classes/artifacts/model_core/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>ModelCore - SageWorks</title>
+        <title>ModelCore - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1185,7 +1185,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core" class="md-nav__link">
     <span class="md-ellipsis">
       model_core
     </span>
@@ -1194,7 +1194,7 @@
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-  <a href="#sageworks.core.artifacts.model_core.ModelCore.delete_inference_run" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.delete_inference_run" class="md-nav__link">
     <span class="md-ellipsis">
       delete_inference_run
     </span>
@@ -3711,7 +3711,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.details" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.details" class="md-nav__link">
     <span class="md-ellipsis">
       details
     </span>
@@ -3720,7 +3720,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.endpoints" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.endpoints" class="md-nav__link">
     <span class="md-ellipsis">
       endpoints
     </span>
@@ -3729,7 +3729,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.exists" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.exists" class="md-nav__link">
     <span class="md-ellipsis">
       exists
     </span>
@@ -3738,7 +3738,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.expected_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.expected_meta" class="md-nav__link">
     <span class="md-ellipsis">
       expected_meta
     </span>
@@ -3747,7 +3747,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.features" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.features" class="md-nav__link">
     <span class="md-ellipsis">
       features
     </span>
@@ -3756,7 +3756,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.get_endpoint_inference_path" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.get_endpoint_inference_path" class="md-nav__link">
     <span class="md-ellipsis">
       get_endpoint_inference_path
     </span>
@@ -3765,7 +3765,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.get_inference_metadata" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.get_inference_metadata" class="md-nav__link">
     <span class="md-ellipsis">
       get_inference_metadata
     </span>
@@ -3774,7 +3774,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.get_inference_metrics" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.get_inference_metrics" class="md-nav__link">
     <span class="md-ellipsis">
       get_inference_metrics
     </span>
@@ -3783,7 +3783,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.get_inference_predictions" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.get_inference_predictions" class="md-nav__link">
     <span class="md-ellipsis">
       get_inference_predictions
     </span>
@@ -3792,7 +3792,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.get_model_data_url" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.get_model_data_url" class="md-nav__link">
     <span class="md-ellipsis">
       get_model_data_url
     </span>
@@ -3801,7 +3801,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.get_pipeline" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.get_pipeline" class="md-nav__link">
     <span class="md-ellipsis">
       get_pipeline
     </span>
@@ -3810,7 +3810,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.group_arn" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.group_arn" class="md-nav__link">
     <span class="md-ellipsis">
       group_arn
     </span>
@@ -3819,7 +3819,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.hash" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.hash" class="md-nav__link">
     <span class="md-ellipsis">
       hash
     </span>
@@ -3828,7 +3828,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.health_check" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.health_check" class="md-nav__link">
     <span class="md-ellipsis">
       health_check
     </span>
@@ -3837,7 +3837,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.is_model_unknown" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.is_model_unknown" class="md-nav__link">
     <span class="md-ellipsis">
       is_model_unknown
     </span>
@@ -3846,7 +3846,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.latest_model_object" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.latest_model_object" class="md-nav__link">
     <span class="md-ellipsis">
       latest_model_object
     </span>
@@ -3855,7 +3855,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.list_inference_runs" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.list_inference_runs" class="md-nav__link">
     <span class="md-ellipsis">
       list_inference_runs
     </span>
@@ -3864,7 +3864,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.managed_delete" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.managed_delete" class="md-nav__link">
     <span class="md-ellipsis">
       managed_delete
     </span>
@@ -3873,7 +3873,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.model_package_arn" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.model_package_arn" class="md-nav__link">
     <span class="md-ellipsis">
       model_package_arn
     </span>
@@ -3882,7 +3882,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.modified" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.modified" class="md-nav__link">
     <span class="md-ellipsis">
       modified
     </span>
@@ -3891,7 +3891,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.onboard" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.onboard" class="md-nav__link">
     <span class="md-ellipsis">
       onboard
     </span>
@@ -3900,7 +3900,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.onboard_with_args" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.onboard_with_args" class="md-nav__link">
     <span class="md-ellipsis">
       onboard_with_args
     </span>
@@ -3909,7 +3909,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.refresh_meta" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.refresh_meta" class="md-nav__link">
     <span class="md-ellipsis">
       refresh_meta
     </span>
@@ -3918,7 +3918,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.register_endpoint" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.register_endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       register_endpoint
     </span>
@@ -3927,7 +3927,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.remove_endpoint" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.remove_endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       remove_endpoint
     </span>
@@ -3936,7 +3936,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.set_class_labels" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.set_class_labels" class="md-nav__link">
     <span class="md-ellipsis">
       set_class_labels
     </span>
@@ -3945,7 +3945,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.set_features" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.set_features" class="md-nav__link">
     <span class="md-ellipsis">
       set_features
     </span>
@@ -3954,7 +3954,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.set_input" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.set_input" class="md-nav__link">
     <span class="md-ellipsis">
       set_input
     </span>
@@ -3963,7 +3963,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.set_pipeline" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.set_pipeline" class="md-nav__link">
     <span class="md-ellipsis">
       set_pipeline
     </span>
@@ -3972,7 +3972,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.set_target" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.set_target" class="md-nav__link">
     <span class="md-ellipsis">
       set_target
     </span>
@@ -3981,7 +3981,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.shapley_values" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.shapley_values" class="md-nav__link">
     <span class="md-ellipsis">
       shapley_values
     </span>
@@ -3990,7 +3990,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.size" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.size" class="md-nav__link">
     <span class="md-ellipsis">
       size
     </span>
@@ -3999,7 +3999,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelCore.target" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelCore.target" class="md-nav__link">
     <span class="md-ellipsis">
       target
     </span>
@@ -4013,7 +4013,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.model_core.ModelType" class="md-nav__link">
+  <a href="#workbench.core.artifacts.model_core.ModelType" class="md-nav__link">
     <span class="md-ellipsis">
       ModelType
     </span>
@@ -4047,10 +4047,10 @@ <h1 id="modelcore">ModelCore</h1>
 
 
 
-<a id="sageworks.core.artifacts.model_core"></a>
+<a id="workbench.core.artifacts.model_core"></a>
     <div class="doc doc-contents first">
 
-        <p>ModelCore: SageWorks ModelCore Class</p>
+        <p>ModelCore: Workbench ModelCore Class</p>
 
 
 
@@ -4072,7 +4072,7 @@ <h1 id="modelcore">ModelCore</h1>
 
 
 
-<h2 id="sageworks.core.artifacts.model_core.InferenceImage" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.model_core.InferenceImage" class="doc doc-heading">
             <code>InferenceImage</code>
 
 
@@ -4090,7 +4090,7 @@ <h2 id="sageworks.core.artifacts.model_core.InferenceImage" class="doc doc-headi
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-38">38</a></span>
 <span class="normal"><a href="#__codelineno-0-39">39</a></span>
 <span class="normal"><a href="#__codelineno-0-40">40</a></span>
@@ -4179,7 +4179,7 @@ <h2 id="sageworks.core.artifacts.model_core.InferenceImage" class="doc doc-headi
 
 
 
-<h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.model_core.ModelCore" class="doc doc-heading">
             <code>ModelCore</code>
 
 
@@ -4188,10 +4188,10 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.artifact.Artifact" href="../artifact/#sageworks.core.artifacts.artifact.Artifact">Artifact</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.artifact.Artifact" href="../artifact/#workbench.core.artifacts.artifact.Artifact">Artifact</a></code></p>
 
 
-        <p>ModelCore: SageWorks ModelCore Class</p>
+        <p>ModelCore: Workbench ModelCore Class</p>
 
 
 <details class="common-usage" open>
@@ -4207,7 +4207,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-71">  71</a></span>
 <span class="normal"><a href="#__codelineno-0-72">  72</a></span>
 <span class="normal"><a href="#__codelineno-0-73">  73</a></span>
@@ -5147,7 +5147,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-1007">1007</a></span>
 <span class="normal"><a href="#__codelineno-0-1008">1008</a></span>
 <span class="normal"><a href="#__codelineno-0-1009">1009</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a><span class="k">class</span> <span class="nc">ModelCore</span><span class="p">(</span><span class="n">Artifact</span><span class="p">):</span>
-</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;ModelCore: SageWorks ModelCore Class</span>
+</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;ModelCore: Workbench ModelCore Class</span>
 </span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>
 </span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a><span class="sd">        ```python</span>
@@ -5160,7 +5160,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">model_type</span><span class="p">:</span> <span class="n">ModelType</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 </span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;ModelCore Initialization</span>
 </span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a><span class="sd">            model_uuid (str): Name of Model in SageWorks.</span>
+</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a><span class="sd">            model_uuid (str): Name of Model in Workbench.</span>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a><span class="sd">            model_type (ModelType, optional): Set this for newly created Models. Defaults to None.</span>
 </span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="sd">            **kwargs: Additional keyword arguments</span>
 </span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a><span class="sd">        &quot;&quot;&quot;</span>
@@ -5261,7 +5261,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-183"><a id="__codelineno-0-183" name="__codelineno-0-183"></a>        <span class="k">return</span> <span class="n">health_issues</span>
 </span><span id="__span-0-184"><a id="__codelineno-0-184" name="__codelineno-0-184"></a>
 </span><span id="__span-0-185"><a id="__codelineno-0-185" name="__codelineno-0-185"></a>    <span class="k">def</span> <span class="nf">latest_model_object</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">SagemakerModel</span><span class="p">:</span>
-</span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the latest AWS Sagemaker Model object for this SageWorks Model</span>
+</span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the latest AWS Sagemaker Model object for this Workbench Model</span>
 </span><span id="__span-0-187"><a id="__codelineno-0-187" name="__codelineno-0-187"></a>
 </span><span id="__span-0-188"><a id="__codelineno-0-188" name="__codelineno-0-188"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a><span class="sd">           sagemaker.model.Model: AWS Sagemaker Model object</span>
@@ -5282,7 +5282,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-204"><a id="__codelineno-0-204" name="__codelineno-0-204"></a>            <span class="k">return</span> <span class="p">[]</span>
 </span><span id="__span-0-205"><a id="__codelineno-0-205" name="__codelineno-0-205"></a>
 </span><span id="__span-0-206"><a id="__codelineno-0-206" name="__codelineno-0-206"></a>        <span class="c1"># Check if we have model training metrics in our metadata</span>
-</span><span id="__span-0-207"><a id="__codelineno-0-207" name="__codelineno-0-207"></a>        <span class="n">have_model_training</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span>
+</span><span id="__span-0-207"><a id="__codelineno-0-207" name="__codelineno-0-207"></a>        <span class="n">have_model_training</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span>
 </span><span id="__span-0-208"><a id="__codelineno-0-208" name="__codelineno-0-208"></a>
 </span><span id="__span-0-209"><a id="__codelineno-0-209" name="__codelineno-0-209"></a>        <span class="c1"># Now grab the list of directories from our inference path</span>
 </span><span id="__span-0-210"><a id="__codelineno-0-210" name="__codelineno-0-210"></a>        <span class="n">inference_runs</span> <span class="o">=</span> <span class="p">[]</span>
@@ -5334,13 +5334,13 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-256"><a id="__codelineno-0-256" name="__codelineno-0-256"></a>
 </span><span id="__span-0-257"><a id="__codelineno-0-257" name="__codelineno-0-257"></a>        <span class="c1"># Grab the metrics captured during model training (could return None)</span>
 </span><span id="__span-0-258"><a id="__codelineno-0-258" name="__codelineno-0-258"></a>        <span class="k">if</span> <span class="n">capture_uuid</span> <span class="o">==</span> <span class="s2">&quot;model_training&quot;</span><span class="p">:</span>
-</span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>            <span class="c1"># Sanity check the sageworks metadata</span>
-</span><span id="__span-0-260"><a id="__codelineno-0-260" name="__codelineno-0-260"></a>            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>                <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no sageworks_meta(). Either onboard() or delete this model!&quot;</span>
+</span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>            <span class="c1"># Sanity check the workbench metadata</span>
+</span><span id="__span-0-260"><a id="__codelineno-0-260" name="__codelineno-0-260"></a>            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>                <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no workbench_meta(). Either onboard() or delete this model!&quot;</span>
 </span><span id="__span-0-262"><a id="__codelineno-0-262" name="__codelineno-0-262"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-263"><a id="__codelineno-0-263" name="__codelineno-0-263"></a>                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-264"><a id="__codelineno-0-264" name="__codelineno-0-264"></a>
-</span><span id="__span-0-265"><a id="__codelineno-0-265" name="__codelineno-0-265"></a>            <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">)</span>
+</span><span id="__span-0-265"><a id="__codelineno-0-265" name="__codelineno-0-265"></a>            <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">)</span>
 </span><span id="__span-0-266"><a id="__codelineno-0-266" name="__codelineno-0-266"></a>            <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span> <span class="k">if</span> <span class="n">metrics</span> <span class="k">else</span> <span class="kc">None</span>
 </span><span id="__span-0-267"><a id="__codelineno-0-267" name="__codelineno-0-267"></a>
 </span><span id="__span-0-268"><a id="__codelineno-0-268" name="__codelineno-0-268"></a>        <span class="k">else</span><span class="p">:</span>  <span class="c1"># Specific capture_uuid (could return None)</span>
@@ -5361,20 +5361,20 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-283"><a id="__codelineno-0-283" name="__codelineno-0-283"></a><span class="sd">            pd.DataFrame: DataFrame of the Confusion Matrix (might be None)</span>
 </span><span id="__span-0-284"><a id="__codelineno-0-284" name="__codelineno-0-284"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-285"><a id="__codelineno-0-285" name="__codelineno-0-285"></a>
-</span><span id="__span-0-286"><a id="__codelineno-0-286" name="__codelineno-0-286"></a>        <span class="c1"># Sanity check the sageworks metadata</span>
-</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>            <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no sageworks_meta(). Either onboard() or delete this model!&quot;</span>
+</span><span id="__span-0-286"><a id="__codelineno-0-286" name="__codelineno-0-286"></a>        <span class="c1"># Sanity check the workbench metadata</span>
+</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+</span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>            <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no workbench_meta(). Either onboard() or delete this model!&quot;</span>
 </span><span id="__span-0-289"><a id="__codelineno-0-289" name="__codelineno-0-289"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-290"><a id="__codelineno-0-290" name="__codelineno-0-290"></a>            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-291"><a id="__codelineno-0-291" name="__codelineno-0-291"></a>
-</span><span id="__span-0-292"><a id="__codelineno-0-292" name="__codelineno-0-292"></a>        <span class="c1"># Grab the metrics from the SageWorks Metadata (try inference first, then training)</span>
+</span><span id="__span-0-292"><a id="__codelineno-0-292" name="__codelineno-0-292"></a>        <span class="c1"># Grab the metrics from the Workbench Metadata (try inference first, then training)</span>
 </span><span id="__span-0-293"><a id="__codelineno-0-293" name="__codelineno-0-293"></a>        <span class="k">if</span> <span class="n">capture_uuid</span> <span class="o">==</span> <span class="s2">&quot;latest&quot;</span><span class="p">:</span>
 </span><span id="__span-0-294"><a id="__codelineno-0-294" name="__codelineno-0-294"></a>            <span class="n">cm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="s2">&quot;auto_inference&quot;</span><span class="p">)</span>
 </span><span id="__span-0-295"><a id="__codelineno-0-295" name="__codelineno-0-295"></a>            <span class="k">return</span> <span class="n">cm</span> <span class="k">if</span> <span class="n">cm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="s2">&quot;model_training&quot;</span><span class="p">)</span>
 </span><span id="__span-0-296"><a id="__codelineno-0-296" name="__codelineno-0-296"></a>
 </span><span id="__span-0-297"><a id="__codelineno-0-297" name="__codelineno-0-297"></a>        <span class="c1"># Grab the confusion matrix captured during model training (could return None)</span>
 </span><span id="__span-0-298"><a id="__codelineno-0-298" name="__codelineno-0-298"></a>        <span class="k">if</span> <span class="n">capture_uuid</span> <span class="o">==</span> <span class="s2">&quot;model_training&quot;</span><span class="p">:</span>
-</span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>            <span class="n">cm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">)</span>
+</span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>            <span class="n">cm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">)</span>
 </span><span id="__span-0-300"><a id="__codelineno-0-300" name="__codelineno-0-300"></a>            <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span> <span class="k">if</span> <span class="n">cm</span> <span class="k">else</span> <span class="kc">None</span>
 </span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>
 </span><span id="__span-0-302"><a id="__codelineno-0-302" name="__codelineno-0-302"></a>        <span class="k">else</span><span class="p">:</span>  <span class="c1"># Specific capture_uuid</span>
@@ -5402,7 +5402,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-324"><a id="__codelineno-0-324" name="__codelineno-0-324"></a>        <span class="c1"># Okay we&#39;re going to allow this to be set</span>
 </span><span id="__span-0-325"><a id="__codelineno-0-325" name="__codelineno-0-325"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">: Setting input to </span><span class="si">{</span><span class="nb">input</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-326"><a id="__codelineno-0-326" name="__codelineno-0-326"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Be careful with this! It breaks automatic provenance of the artifact!&quot;</span><span class="p">)</span>
-</span><span id="__span-0-327"><a id="__codelineno-0-327" name="__codelineno-0-327"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
+</span><span id="__span-0-327"><a id="__codelineno-0-327" name="__codelineno-0-327"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
 </span><span id="__span-0-328"><a id="__codelineno-0-328" name="__codelineno-0-328"></a>
 </span><span id="__span-0-329"><a id="__codelineno-0-329" name="__codelineno-0-329"></a>    <span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
 </span><span id="__span-0-330"><a id="__codelineno-0-330" name="__codelineno-0-330"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the size of this data in MegaBytes&quot;&quot;&quot;</span>
@@ -5466,9 +5466,9 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-388"><a id="__codelineno-0-388" name="__codelineno-0-388"></a><span class="sd">            endpoint_name (str): Name of the endpoint</span>
 </span><span id="__span-0-389"><a id="__codelineno-0-389" name="__codelineno-0-389"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-390"><a id="__codelineno-0-390" name="__codelineno-0-390"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Registering Endpoint </span><span class="si">{</span><span class="n">endpoint_name</span><span class="si">}</span><span class="s2"> with Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-391"><a id="__codelineno-0-391" name="__codelineno-0-391"></a>        <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
+</span><span id="__span-0-391"><a id="__codelineno-0-391" name="__codelineno-0-391"></a>        <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
 </span><span id="__span-0-392"><a id="__codelineno-0-392" name="__codelineno-0-392"></a>        <span class="n">registered_endpoints</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span>
-</span><span id="__span-0-393"><a id="__codelineno-0-393" name="__codelineno-0-393"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
+</span><span id="__span-0-393"><a id="__codelineno-0-393" name="__codelineno-0-393"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
 </span><span id="__span-0-394"><a id="__codelineno-0-394" name="__codelineno-0-394"></a>
 </span><span id="__span-0-395"><a id="__codelineno-0-395" name="__codelineno-0-395"></a>        <span class="c1"># Remove any health tags</span>
 </span><span id="__span-0-396"><a id="__codelineno-0-396" name="__codelineno-0-396"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">remove_health_tag</span><span class="p">(</span><span class="s2">&quot;no_endpoint&quot;</span><span class="p">)</span>
@@ -5484,9 +5484,9 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-406"><a id="__codelineno-0-406" name="__codelineno-0-406"></a><span class="sd">            endpoint_name (str): Name of the endpoint</span>
 </span><span id="__span-0-407"><a id="__codelineno-0-407" name="__codelineno-0-407"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-408"><a id="__codelineno-0-408" name="__codelineno-0-408"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Removing Endpoint </span><span class="si">{</span><span class="n">endpoint_name</span><span class="si">}</span><span class="s2"> from Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-409"><a id="__codelineno-0-409" name="__codelineno-0-409"></a>        <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
+</span><span id="__span-0-409"><a id="__codelineno-0-409" name="__codelineno-0-409"></a>        <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
 </span><span id="__span-0-410"><a id="__codelineno-0-410" name="__codelineno-0-410"></a>        <span class="n">registered_endpoints</span><span class="o">.</span><span class="n">discard</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span>
-</span><span id="__span-0-411"><a id="__codelineno-0-411" name="__codelineno-0-411"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
+</span><span id="__span-0-411"><a id="__codelineno-0-411" name="__codelineno-0-411"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
 </span><span id="__span-0-412"><a id="__codelineno-0-412" name="__codelineno-0-412"></a>
 </span><span id="__span-0-413"><a id="__codelineno-0-413" name="__codelineno-0-413"></a>        <span class="c1"># If we have NO endpionts, then set a health tags</span>
 </span><span id="__span-0-414"><a id="__codelineno-0-414" name="__codelineno-0-414"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="n">registered_endpoints</span><span class="p">:</span>
@@ -5502,7 +5502,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-424"><a id="__codelineno-0-424" name="__codelineno-0-424"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-425"><a id="__codelineno-0-425" name="__codelineno-0-425"></a><span class="sd">            list[str]: List of registered endpoints</span>
 </span><span id="__span-0-426"><a id="__codelineno-0-426" name="__codelineno-0-426"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[])</span>
+</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[])</span>
 </span><span id="__span-0-428"><a id="__codelineno-0-428" name="__codelineno-0-428"></a>
 </span><span id="__span-0-429"><a id="__codelineno-0-429" name="__codelineno-0-429"></a>    <span class="k">def</span> <span class="nf">get_endpoint_inference_path</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-430"><a id="__codelineno-0-430" name="__codelineno-0-430"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the S3 Path for the Inference Data</span>
@@ -5512,7 +5512,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-434"><a id="__codelineno-0-434" name="__codelineno-0-434"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-435"><a id="__codelineno-0-435" name="__codelineno-0-435"></a>
 </span><span id="__span-0-436"><a id="__codelineno-0-436" name="__codelineno-0-436"></a>        <span class="c1"># Look for any Registered Endpoints</span>
-</span><span id="__span-0-437"><a id="__codelineno-0-437" name="__codelineno-0-437"></a>        <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">)</span>
+</span><span id="__span-0-437"><a id="__codelineno-0-437" name="__codelineno-0-437"></a>        <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">)</span>
 </span><span id="__span-0-438"><a id="__codelineno-0-438" name="__codelineno-0-438"></a>
 </span><span id="__span-0-439"><a id="__codelineno-0-439" name="__codelineno-0-439"></a>        <span class="c1"># Note: We may have 0 to N endpoints, so we find the one with the most recent artifacts</span>
 </span><span id="__span-0-440"><a id="__codelineno-0-440" name="__codelineno-0-440"></a>        <span class="k">if</span> <span class="n">registered_endpoints</span><span class="p">:</span>
@@ -5536,7 +5536,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a><span class="sd">            target_column (str): Target column for this Model</span>
 </span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_target&quot;</span><span class="p">:</span> <span class="n">target_column</span><span class="p">})</span>
+</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_target&quot;</span><span class="p">:</span> <span class="n">target_column</span><span class="p">})</span>
 </span><span id="__span-0-462"><a id="__codelineno-0-462" name="__codelineno-0-462"></a>
 </span><span id="__span-0-463"><a id="__codelineno-0-463" name="__codelineno-0-463"></a>    <span class="k">def</span> <span class="nf">set_features</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">feature_columns</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]):</span>
 </span><span id="__span-0-464"><a id="__codelineno-0-464" name="__codelineno-0-464"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the features for this Model</span>
@@ -5544,7 +5544,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-466"><a id="__codelineno-0-466" name="__codelineno-0-466"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-467"><a id="__codelineno-0-467" name="__codelineno-0-467"></a><span class="sd">            feature_columns (list[str]): List of feature columns</span>
 </span><span id="__span-0-468"><a id="__codelineno-0-468" name="__codelineno-0-468"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-469"><a id="__codelineno-0-469" name="__codelineno-0-469"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_features&quot;</span><span class="p">:</span> <span class="n">feature_columns</span><span class="p">})</span>
+</span><span id="__span-0-469"><a id="__codelineno-0-469" name="__codelineno-0-469"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_features&quot;</span><span class="p">:</span> <span class="n">feature_columns</span><span class="p">})</span>
 </span><span id="__span-0-470"><a id="__codelineno-0-470" name="__codelineno-0-470"></a>
 </span><span id="__span-0-471"><a id="__codelineno-0-471" name="__codelineno-0-471"></a>    <span class="k">def</span> <span class="nf">target</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-472"><a id="__codelineno-0-472" name="__codelineno-0-472"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the target for this Model (if supervised, else None)</span>
@@ -5552,7 +5552,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-474"><a id="__codelineno-0-474" name="__codelineno-0-474"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-475"><a id="__codelineno-0-475" name="__codelineno-0-475"></a><span class="sd">            str: Target column for this Model (if supervised, else None)</span>
 </span><span id="__span-0-476"><a id="__codelineno-0-476" name="__codelineno-0-476"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-477"><a id="__codelineno-0-477" name="__codelineno-0-477"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_model_target&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
+</span><span id="__span-0-477"><a id="__codelineno-0-477" name="__codelineno-0-477"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_model_target&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
 </span><span id="__span-0-478"><a id="__codelineno-0-478" name="__codelineno-0-478"></a>
 </span><span id="__span-0-479"><a id="__codelineno-0-479" name="__codelineno-0-479"></a>    <span class="k">def</span> <span class="nf">features</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-480"><a id="__codelineno-0-480" name="__codelineno-0-480"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return a list of features used for this Model</span>
@@ -5560,7 +5560,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-482"><a id="__codelineno-0-482" name="__codelineno-0-482"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-483"><a id="__codelineno-0-483" name="__codelineno-0-483"></a><span class="sd">            list[str]: List of features used for this Model</span>
 </span><span id="__span-0-484"><a id="__codelineno-0-484" name="__codelineno-0-484"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-485"><a id="__codelineno-0-485" name="__codelineno-0-485"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_model_features&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
+</span><span id="__span-0-485"><a id="__codelineno-0-485" name="__codelineno-0-485"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_model_features&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
 </span><span id="__span-0-486"><a id="__codelineno-0-486" name="__codelineno-0-486"></a>
 </span><span id="__span-0-487"><a id="__codelineno-0-487" name="__codelineno-0-487"></a>    <span class="k">def</span> <span class="nf">class_labels</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-488"><a id="__codelineno-0-488" name="__codelineno-0-488"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the class labels for this Model (if it&#39;s a classifier)</span>
@@ -5569,7 +5569,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-491"><a id="__codelineno-0-491" name="__codelineno-0-491"></a><span class="sd">            list[str]: List of class labels</span>
 </span><span id="__span-0-492"><a id="__codelineno-0-492" name="__codelineno-0-492"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-493"><a id="__codelineno-0-493" name="__codelineno-0-493"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="n">ModelType</span><span class="o">.</span><span class="n">CLASSIFIER</span><span class="p">:</span>
-</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;class_labels&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
+</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>            <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;class_labels&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
 </span><span id="__span-0-495"><a id="__codelineno-0-495" name="__codelineno-0-495"></a>        <span class="k">else</span><span class="p">:</span>
 </span><span id="__span-0-496"><a id="__codelineno-0-496" name="__codelineno-0-496"></a>            <span class="k">return</span> <span class="kc">None</span>
 </span><span id="__span-0-497"><a id="__codelineno-0-497" name="__codelineno-0-497"></a>
@@ -5580,7 +5580,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-502"><a id="__codelineno-0-502" name="__codelineno-0-502"></a><span class="sd">            labels (list[str]): List of class labels</span>
 </span><span id="__span-0-503"><a id="__codelineno-0-503" name="__codelineno-0-503"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-504"><a id="__codelineno-0-504" name="__codelineno-0-504"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="n">ModelType</span><span class="o">.</span><span class="n">CLASSIFIER</span><span class="p">:</span>
-</span><span id="__span-0-505"><a id="__codelineno-0-505" name="__codelineno-0-505"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;class_labels&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">})</span>
+</span><span id="__span-0-505"><a id="__codelineno-0-505" name="__codelineno-0-505"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;class_labels&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">})</span>
 </span><span id="__span-0-506"><a id="__codelineno-0-506" name="__codelineno-0-506"></a>        <span class="k">else</span><span class="p">:</span>
 </span><span id="__span-0-507"><a id="__codelineno-0-507" name="__codelineno-0-507"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> is not a classifier!&quot;</span><span class="p">)</span>
 </span><span id="__span-0-508"><a id="__codelineno-0-508" name="__codelineno-0-508"></a>
@@ -5639,7 +5639,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-561"><a id="__codelineno-0-561" name="__codelineno-0-561"></a>    <span class="c1"># Pipeline for this model</span>
 </span><span id="__span-0-562"><a id="__codelineno-0-562" name="__codelineno-0-562"></a>    <span class="k">def</span> <span class="nf">get_pipeline</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-563"><a id="__codelineno-0-563" name="__codelineno-0-563"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the pipeline for this model&quot;&quot;&quot;</span>
-</span><span id="__span-0-564"><a id="__codelineno-0-564" name="__codelineno-0-564"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_pipeline&quot;</span><span class="p">)</span>
+</span><span id="__span-0-564"><a id="__codelineno-0-564" name="__codelineno-0-564"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_pipeline&quot;</span><span class="p">)</span>
 </span><span id="__span-0-565"><a id="__codelineno-0-565" name="__codelineno-0-565"></a>
 </span><span id="__span-0-566"><a id="__codelineno-0-566" name="__codelineno-0-566"></a>    <span class="k">def</span> <span class="nf">set_pipeline</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pipeline</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
 </span><span id="__span-0-567"><a id="__codelineno-0-567" name="__codelineno-0-567"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the pipeline for this model</span>
@@ -5647,7 +5647,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-569"><a id="__codelineno-0-569" name="__codelineno-0-569"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-570"><a id="__codelineno-0-570" name="__codelineno-0-570"></a><span class="sd">            pipeline (str): Pipeline that was used to create this model</span>
 </span><span id="__span-0-571"><a id="__codelineno-0-571" name="__codelineno-0-571"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-572"><a id="__codelineno-0-572" name="__codelineno-0-572"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_pipeline&quot;</span><span class="p">:</span> <span class="n">pipeline</span><span class="p">})</span>
+</span><span id="__span-0-572"><a id="__codelineno-0-572" name="__codelineno-0-572"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_pipeline&quot;</span><span class="p">:</span> <span class="n">pipeline</span><span class="p">})</span>
 </span><span id="__span-0-573"><a id="__codelineno-0-573" name="__codelineno-0-573"></a>
 </span><span id="__span-0-574"><a id="__codelineno-0-574" name="__codelineno-0-574"></a>    <span class="k">def</span> <span class="nf">expected_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
 </span><span id="__span-0-575"><a id="__codelineno-0-575" name="__codelineno-0-575"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Metadata we expect to see for this Model when it&#39;s ready</span>
@@ -5655,7 +5655,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-577"><a id="__codelineno-0-577" name="__codelineno-0-577"></a><span class="sd">            list[str]: List of expected metadata keys</span>
 </span><span id="__span-0-578"><a id="__codelineno-0-578" name="__codelineno-0-578"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-579"><a id="__codelineno-0-579" name="__codelineno-0-579"></a>        <span class="c1"># Our current list of expected metadata, we can add to this as needed</span>
-</span><span id="__span-0-580"><a id="__codelineno-0-580" name="__codelineno-0-580"></a>        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">,</span> <span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">,</span> <span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">]</span>
+</span><span id="__span-0-580"><a id="__codelineno-0-580" name="__codelineno-0-580"></a>        <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">,</span> <span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">,</span> <span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">]</span>
 </span><span id="__span-0-581"><a id="__codelineno-0-581" name="__codelineno-0-581"></a>
 </span><span id="__span-0-582"><a id="__codelineno-0-582" name="__codelineno-0-582"></a>    <span class="k">def</span> <span class="nf">is_model_unknown</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
 </span><span id="__span-0-583"><a id="__codelineno-0-583" name="__codelineno-0-583"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Is the Model Type unknown?&quot;&quot;&quot;</span>
@@ -5857,18 +5857,18 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-779"><a id="__codelineno-0-779" name="__codelineno-0-779"></a>    <span class="k">def</span> <span class="nf">_set_model_type</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_type</span><span class="p">:</span> <span class="n">ModelType</span><span class="p">):</span>
 </span><span id="__span-0-780"><a id="__codelineno-0-780" name="__codelineno-0-780"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal: Set the Model Type for this Model&quot;&quot;&quot;</span>
 </span><span id="__span-0-781"><a id="__codelineno-0-781" name="__codelineno-0-781"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">=</span> <span class="n">model_type</span>
-</span><span id="__span-0-782"><a id="__codelineno-0-782" name="__codelineno-0-782"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_type&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="o">.</span><span class="n">value</span><span class="p">})</span>
+</span><span id="__span-0-782"><a id="__codelineno-0-782" name="__codelineno-0-782"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_type&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="o">.</span><span class="n">value</span><span class="p">})</span>
 </span><span id="__span-0-783"><a id="__codelineno-0-783" name="__codelineno-0-783"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">remove_health_tag</span><span class="p">(</span><span class="s2">&quot;model_type_unknown&quot;</span><span class="p">)</span>
 </span><span id="__span-0-784"><a id="__codelineno-0-784" name="__codelineno-0-784"></a>
 </span><span id="__span-0-785"><a id="__codelineno-0-785" name="__codelineno-0-785"></a>    <span class="k">def</span> <span class="nf">_get_model_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">ModelType</span><span class="p">:</span>
-</span><span id="__span-0-786"><a id="__codelineno-0-786" name="__codelineno-0-786"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal: Query the SageWorks Metadata to get the model type</span>
+</span><span id="__span-0-786"><a id="__codelineno-0-786" name="__codelineno-0-786"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal: Query the Workbench Metadata to get the model type</span>
 </span><span id="__span-0-787"><a id="__codelineno-0-787" name="__codelineno-0-787"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-788"><a id="__codelineno-0-788" name="__codelineno-0-788"></a><span class="sd">            ModelType: The ModelType of this Model</span>
 </span><span id="__span-0-789"><a id="__codelineno-0-789" name="__codelineno-0-789"></a><span class="sd">        Notes:</span>
 </span><span id="__span-0-790"><a id="__codelineno-0-790" name="__codelineno-0-790"></a><span class="sd">            This is an internal method that should not be called directly</span>
 </span><span id="__span-0-791"><a id="__codelineno-0-791" name="__codelineno-0-791"></a><span class="sd">            Use the model_type attribute instead</span>
 </span><span id="__span-0-792"><a id="__codelineno-0-792" name="__codelineno-0-792"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-793"><a id="__codelineno-0-793" name="__codelineno-0-793"></a>        <span class="n">model_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_model_type&quot;</span><span class="p">)</span>
+</span><span id="__span-0-793"><a id="__codelineno-0-793" name="__codelineno-0-793"></a>        <span class="n">model_type</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_model_type&quot;</span><span class="p">)</span>
 </span><span id="__span-0-794"><a id="__codelineno-0-794" name="__codelineno-0-794"></a>        <span class="k">try</span><span class="p">:</span>
 </span><span id="__span-0-795"><a id="__codelineno-0-795" name="__codelineno-0-795"></a>            <span class="k">return</span> <span class="n">ModelType</span><span class="p">(</span><span class="n">model_type</span><span class="p">)</span>
 </span><span id="__span-0-796"><a id="__codelineno-0-796" name="__codelineno-0-796"></a>        <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
@@ -5877,7 +5877,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-799"><a id="__codelineno-0-799" name="__codelineno-0-799"></a>
 </span><span id="__span-0-800"><a id="__codelineno-0-800" name="__codelineno-0-800"></a>    <span class="k">def</span> <span class="nf">_load_training_metrics</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-801"><a id="__codelineno-0-801" name="__codelineno-0-801"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal: Retrieve the training metrics and Confusion Matrix for this model</span>
-</span><span id="__span-0-802"><a id="__codelineno-0-802" name="__codelineno-0-802"></a><span class="sd">                     and load the data into the SageWorks Metadata</span>
+</span><span id="__span-0-802"><a id="__codelineno-0-802" name="__codelineno-0-802"></a><span class="sd">                     and load the data into the Workbench Metadata</span>
 </span><span id="__span-0-803"><a id="__codelineno-0-803" name="__codelineno-0-803"></a>
 </span><span id="__span-0-804"><a id="__codelineno-0-804" name="__codelineno-0-804"></a><span class="sd">        Notes:</span>
 </span><span id="__span-0-805"><a id="__codelineno-0-805" name="__codelineno-0-805"></a><span class="sd">            This may or may not exist based on whether we have access to TrainingJobAnalytics</span>
@@ -5886,7 +5886,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-808"><a id="__codelineno-0-808" name="__codelineno-0-808"></a>            <span class="n">df</span> <span class="o">=</span> <span class="n">TrainingJobAnalytics</span><span class="p">(</span><span class="n">training_job_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">training_job_name</span><span class="p">)</span><span class="o">.</span><span class="n">dataframe</span><span class="p">()</span>
 </span><span id="__span-0-809"><a id="__codelineno-0-809" name="__codelineno-0-809"></a>            <span class="k">if</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
 </span><span id="__span-0-810"><a id="__codelineno-0-810" name="__codelineno-0-810"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No training job metrics found for </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">training_job_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-811"><a id="__codelineno-0-811" name="__codelineno-0-811"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">})</span>
+</span><span id="__span-0-811"><a id="__codelineno-0-811" name="__codelineno-0-811"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">})</span>
 </span><span id="__span-0-812"><a id="__codelineno-0-812" name="__codelineno-0-812"></a>                <span class="k">return</span>
 </span><span id="__span-0-813"><a id="__codelineno-0-813" name="__codelineno-0-813"></a>            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="ow">in</span> <span class="p">[</span><span class="n">ModelType</span><span class="o">.</span><span class="n">REGRESSOR</span><span class="p">,</span> <span class="n">ModelType</span><span class="o">.</span><span class="n">QUANTILE_REGRESSOR</span><span class="p">]:</span>
 </span><span id="__span-0-814"><a id="__codelineno-0-814" name="__codelineno-0-814"></a>                <span class="k">if</span> <span class="s2">&quot;timestamp&quot;</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
@@ -5895,30 +5895,30 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-817"><a id="__codelineno-0-817" name="__codelineno-0-817"></a>                <span class="c1"># We&#39;re going to pivot the DataFrame to get the desired structure</span>
 </span><span id="__span-0-818"><a id="__codelineno-0-818" name="__codelineno-0-818"></a>                <span class="n">reg_metrics_df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">set_index</span><span class="p">(</span><span class="s2">&quot;metric_name&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">T</span>
 </span><span id="__span-0-819"><a id="__codelineno-0-819" name="__codelineno-0-819"></a>
-</span><span id="__span-0-820"><a id="__codelineno-0-820" name="__codelineno-0-820"></a>                <span class="c1"># Store and return the metrics in the SageWorks Metadata</span>
-</span><span id="__span-0-821"><a id="__codelineno-0-821" name="__codelineno-0-821"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">(</span>
-</span><span id="__span-0-822"><a id="__codelineno-0-822" name="__codelineno-0-822"></a>                    <span class="p">{</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">:</span> <span class="n">reg_metrics_df</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span> <span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">}</span>
+</span><span id="__span-0-820"><a id="__codelineno-0-820" name="__codelineno-0-820"></a>                <span class="c1"># Store and return the metrics in the Workbench Metadata</span>
+</span><span id="__span-0-821"><a id="__codelineno-0-821" name="__codelineno-0-821"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">(</span>
+</span><span id="__span-0-822"><a id="__codelineno-0-822" name="__codelineno-0-822"></a>                    <span class="p">{</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">:</span> <span class="n">reg_metrics_df</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span> <span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">}</span>
 </span><span id="__span-0-823"><a id="__codelineno-0-823" name="__codelineno-0-823"></a>                <span class="p">)</span>
 </span><span id="__span-0-824"><a id="__codelineno-0-824" name="__codelineno-0-824"></a>                <span class="k">return</span>
 </span><span id="__span-0-825"><a id="__codelineno-0-825" name="__codelineno-0-825"></a>
 </span><span id="__span-0-826"><a id="__codelineno-0-826" name="__codelineno-0-826"></a>        <span class="k">except</span> <span class="p">(</span><span class="ne">KeyError</span><span class="p">,</span> <span class="n">botocore</span><span class="o">.</span><span class="n">exceptions</span><span class="o">.</span><span class="n">ClientError</span><span class="p">):</span>
 </span><span id="__span-0-827"><a id="__codelineno-0-827" name="__codelineno-0-827"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;No training job metrics found for </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">training_job_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-828"><a id="__codelineno-0-828" name="__codelineno-0-828"></a>            <span class="c1"># Store and return the metrics in the SageWorks Metadata</span>
-</span><span id="__span-0-829"><a id="__codelineno-0-829" name="__codelineno-0-829"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">})</span>
+</span><span id="__span-0-828"><a id="__codelineno-0-828" name="__codelineno-0-828"></a>            <span class="c1"># Store and return the metrics in the Workbench Metadata</span>
+</span><span id="__span-0-829"><a id="__codelineno-0-829" name="__codelineno-0-829"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">:</span> <span class="kc">None</span><span class="p">})</span>
 </span><span id="__span-0-830"><a id="__codelineno-0-830" name="__codelineno-0-830"></a>            <span class="k">return</span>
 </span><span id="__span-0-831"><a id="__codelineno-0-831" name="__codelineno-0-831"></a>
 </span><span id="__span-0-832"><a id="__codelineno-0-832" name="__codelineno-0-832"></a>        <span class="c1"># We need additional processing for classification metrics</span>
 </span><span id="__span-0-833"><a id="__codelineno-0-833" name="__codelineno-0-833"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="n">ModelType</span><span class="o">.</span><span class="n">CLASSIFIER</span><span class="p">:</span>
 </span><span id="__span-0-834"><a id="__codelineno-0-834" name="__codelineno-0-834"></a>            <span class="n">metrics_df</span><span class="p">,</span> <span class="n">cm_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_process_classification_metrics</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-835"><a id="__codelineno-0-835" name="__codelineno-0-835"></a>
-</span><span id="__span-0-836"><a id="__codelineno-0-836" name="__codelineno-0-836"></a>            <span class="c1"># Store and return the metrics in the SageWorks Metadata</span>
-</span><span id="__span-0-837"><a id="__codelineno-0-837" name="__codelineno-0-837"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">(</span>
-</span><span id="__span-0-838"><a id="__codelineno-0-838" name="__codelineno-0-838"></a>                <span class="p">{</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">:</span> <span class="n">metrics_df</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span> <span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">:</span> <span class="n">cm_df</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()}</span>
+</span><span id="__span-0-836"><a id="__codelineno-0-836" name="__codelineno-0-836"></a>            <span class="c1"># Store and return the metrics in the Workbench Metadata</span>
+</span><span id="__span-0-837"><a id="__codelineno-0-837" name="__codelineno-0-837"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">(</span>
+</span><span id="__span-0-838"><a id="__codelineno-0-838" name="__codelineno-0-838"></a>                <span class="p">{</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">:</span> <span class="n">metrics_df</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(),</span> <span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">:</span> <span class="n">cm_df</span><span class="o">.</span><span class="n">to_dict</span><span class="p">()}</span>
 </span><span id="__span-0-839"><a id="__codelineno-0-839" name="__codelineno-0-839"></a>            <span class="p">)</span>
 </span><span id="__span-0-840"><a id="__codelineno-0-840" name="__codelineno-0-840"></a>
 </span><span id="__span-0-841"><a id="__codelineno-0-841" name="__codelineno-0-841"></a>    <span class="k">def</span> <span class="nf">_load_inference_metrics</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">capture_uuid</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;auto_inference&quot;</span><span class="p">):</span>
 </span><span id="__span-0-842"><a id="__codelineno-0-842" name="__codelineno-0-842"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Internal: Retrieve the inference model metrics for this model</span>
-</span><span id="__span-0-843"><a id="__codelineno-0-843" name="__codelineno-0-843"></a><span class="sd">                     and load the data into the SageWorks Metadata</span>
+</span><span id="__span-0-843"><a id="__codelineno-0-843" name="__codelineno-0-843"></a><span class="sd">                     and load the data into the Workbench Metadata</span>
 </span><span id="__span-0-844"><a id="__codelineno-0-844" name="__codelineno-0-844"></a>
 </span><span id="__span-0-845"><a id="__codelineno-0-845" name="__codelineno-0-845"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-846"><a id="__codelineno-0-846" name="__codelineno-0-846"></a><span class="sd">            capture_uuid (str, optional): A specific capture_uuid (default: &quot;auto_inference&quot;)</span>
@@ -5928,9 +5928,9 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 </span><span id="__span-0-850"><a id="__codelineno-0-850" name="__codelineno-0-850"></a>        <span class="n">s3_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">endpoint_inference_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">capture_uuid</span><span class="si">}</span><span class="s2">/inference_metrics.csv&quot;</span>
 </span><span id="__span-0-851"><a id="__codelineno-0-851" name="__codelineno-0-851"></a>        <span class="n">inference_metrics</span> <span class="o">=</span> <span class="n">pull_s3_data</span><span class="p">(</span><span class="n">s3_path</span><span class="p">)</span>
 </span><span id="__span-0-852"><a id="__codelineno-0-852" name="__codelineno-0-852"></a>
-</span><span id="__span-0-853"><a id="__codelineno-0-853" name="__codelineno-0-853"></a>        <span class="c1"># Store data into the SageWorks Metadata</span>
+</span><span id="__span-0-853"><a id="__codelineno-0-853" name="__codelineno-0-853"></a>        <span class="c1"># Store data into the Workbench Metadata</span>
 </span><span id="__span-0-854"><a id="__codelineno-0-854" name="__codelineno-0-854"></a>        <span class="n">metrics_storage</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">inference_metrics</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">inference_metrics</span><span class="o">.</span><span class="n">to_dict</span><span class="p">(</span><span class="s2">&quot;records&quot;</span><span class="p">)</span>
-</span><span id="__span-0-855"><a id="__codelineno-0-855" name="__codelineno-0-855"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_inference_metrics&quot;</span><span class="p">:</span> <span class="n">metrics_storage</span><span class="p">})</span>
+</span><span id="__span-0-855"><a id="__codelineno-0-855" name="__codelineno-0-855"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_inference_metrics&quot;</span><span class="p">:</span> <span class="n">metrics_storage</span><span class="p">})</span>
 </span><span id="__span-0-856"><a id="__codelineno-0-856" name="__codelineno-0-856"></a>
 </span><span id="__span-0-857"><a id="__codelineno-0-857" name="__codelineno-0-857"></a>    <span class="k">def</span> <span class="nf">get_inference_metadata</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">capture_uuid</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;auto_inference&quot;</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
 </span><span id="__span-0-858"><a id="__codelineno-0-858" name="__codelineno-0-858"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Retrieve the inference metadata for this model</span>
@@ -6103,7 +6103,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelCore" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.__init__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">model_uuid</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -6113,12 +6113,12 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.__init__" class="doc doc-h
 
         <p>ModelCore Initialization
 Args:
-    model_uuid (str): Name of Model in SageWorks.
+    model_uuid (str): Name of Model in Workbench.
     model_type (ModelType, optional): Set this for newly created Models. Defaults to None.
     **kwargs: Additional keyword arguments</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-82"> 82</a></span>
 <span class="normal"><a href="#__codelineno-0-83"> 83</a></span>
 <span class="normal"><a href="#__codelineno-0-84"> 84</a></span>
@@ -6177,7 +6177,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.__init__" class="doc doc-h
 <span class="normal"><a href="#__codelineno-0-137">137</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">model_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">model_type</span><span class="p">:</span> <span class="n">ModelType</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
 </span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;ModelCore Initialization</span>
 </span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a><span class="sd">        model_uuid (str): Name of Model in SageWorks.</span>
+</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a><span class="sd">        model_uuid (str): Name of Model in Workbench.</span>
 </span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a><span class="sd">        model_type (ModelType, optional): Set this for newly created Models. Defaults to None.</span>
 </span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="sd">        **kwargs: Additional keyword arguments</span>
 </span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a><span class="sd">    &quot;&quot;&quot;</span>
@@ -6239,7 +6239,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.__init__" class="doc doc-h
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.arn" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.arn" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">arn</span><span class="p">()</span></code>
 
 </h3>
@@ -6250,7 +6250,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.arn" class="doc doc-headin
         <p>AWS ARN (Amazon Resource Name) for the Model Package Group</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-337">337</a></span>
 <span class="normal"><a href="#__codelineno-0-338">338</a></span>
 <span class="normal"><a href="#__codelineno-0-339">339</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-337"><a id="__codelineno-0-337" name="__codelineno-0-337"></a><span class="k">def</span> <span class="nf">arn</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
@@ -6265,7 +6265,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.arn" class="doc doc-headin
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.aws_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.aws_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -6276,7 +6276,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.aws_meta" class="doc doc-h
         <p>Get ALL the AWS metadata for this artifact</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-333">333</a></span>
 <span class="normal"><a href="#__codelineno-0-334">334</a></span>
 <span class="normal"><a href="#__codelineno-0-335">335</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-333"><a id="__codelineno-0-333" name="__codelineno-0-333"></a><span class="k">def</span> <span class="nf">aws_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
@@ -6291,7 +6291,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.aws_meta" class="doc doc-h
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.aws_url" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.aws_url" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">aws_url</span><span class="p">()</span></code>
 
 </h3>
@@ -6302,7 +6302,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.aws_url" class="doc doc-he
         <p>The AWS URL for looking at/querying this model</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-359">359</a></span>
 <span class="normal"><a href="#__codelineno-0-360">360</a></span>
 <span class="normal"><a href="#__codelineno-0-361">361</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-359"><a id="__codelineno-0-359" name="__codelineno-0-359"></a><span class="k">def</span> <span class="nf">aws_url</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -6317,7 +6317,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.aws_url" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.class_labels" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.class_labels" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">class_labels</span><span class="p">()</span></code>
 
 </h3>
@@ -6351,7 +6351,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.class_labels" class="doc d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-487">487</a></span>
 <span class="normal"><a href="#__codelineno-0-488">488</a></span>
 <span class="normal"><a href="#__codelineno-0-489">489</a></span>
@@ -6368,7 +6368,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.class_labels" class="doc d
 </span><span id="__span-0-491"><a id="__codelineno-0-491" name="__codelineno-0-491"></a><span class="sd">        list[str]: List of class labels</span>
 </span><span id="__span-0-492"><a id="__codelineno-0-492" name="__codelineno-0-492"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-493"><a id="__codelineno-0-493" name="__codelineno-0-493"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="n">ModelType</span><span class="o">.</span><span class="n">CLASSIFIER</span><span class="p">:</span>
-</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;class_labels&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
+</span><span id="__span-0-494"><a id="__codelineno-0-494" name="__codelineno-0-494"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;class_labels&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
 </span><span id="__span-0-495"><a id="__codelineno-0-495" name="__codelineno-0-495"></a>    <span class="k">else</span><span class="p">:</span>
 </span><span id="__span-0-496"><a id="__codelineno-0-496" name="__codelineno-0-496"></a>        <span class="k">return</span> <span class="kc">None</span>
 </span></code></pre></div></td></tr></table></div>
@@ -6380,7 +6380,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.class_labels" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.confusion_matrix" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.confusion_matrix" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">confusion_matrix</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;latest&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -6424,7 +6424,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.confusion_matrix" class="d
     pd.DataFrame: DataFrame of the Confusion Matrix (might be None)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-277">277</a></span>
 <span class="normal"><a href="#__codelineno-0-278">278</a></span>
 <span class="normal"><a href="#__codelineno-0-279">279</a></span>
@@ -6466,20 +6466,20 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.confusion_matrix" class="d
 </span><span id="__span-0-283"><a id="__codelineno-0-283" name="__codelineno-0-283"></a><span class="sd">        pd.DataFrame: DataFrame of the Confusion Matrix (might be None)</span>
 </span><span id="__span-0-284"><a id="__codelineno-0-284" name="__codelineno-0-284"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-285"><a id="__codelineno-0-285" name="__codelineno-0-285"></a>
-</span><span id="__span-0-286"><a id="__codelineno-0-286" name="__codelineno-0-286"></a>    <span class="c1"># Sanity check the sageworks metadata</span>
-</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>        <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no sageworks_meta(). Either onboard() or delete this model!&quot;</span>
+</span><span id="__span-0-286"><a id="__codelineno-0-286" name="__codelineno-0-286"></a>    <span class="c1"># Sanity check the workbench metadata</span>
+</span><span id="__span-0-287"><a id="__codelineno-0-287" name="__codelineno-0-287"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+</span><span id="__span-0-288"><a id="__codelineno-0-288" name="__codelineno-0-288"></a>        <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no workbench_meta(). Either onboard() or delete this model!&quot;</span>
 </span><span id="__span-0-289"><a id="__codelineno-0-289" name="__codelineno-0-289"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-290"><a id="__codelineno-0-290" name="__codelineno-0-290"></a>        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-291"><a id="__codelineno-0-291" name="__codelineno-0-291"></a>
-</span><span id="__span-0-292"><a id="__codelineno-0-292" name="__codelineno-0-292"></a>    <span class="c1"># Grab the metrics from the SageWorks Metadata (try inference first, then training)</span>
+</span><span id="__span-0-292"><a id="__codelineno-0-292" name="__codelineno-0-292"></a>    <span class="c1"># Grab the metrics from the Workbench Metadata (try inference first, then training)</span>
 </span><span id="__span-0-293"><a id="__codelineno-0-293" name="__codelineno-0-293"></a>    <span class="k">if</span> <span class="n">capture_uuid</span> <span class="o">==</span> <span class="s2">&quot;latest&quot;</span><span class="p">:</span>
 </span><span id="__span-0-294"><a id="__codelineno-0-294" name="__codelineno-0-294"></a>        <span class="n">cm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="s2">&quot;auto_inference&quot;</span><span class="p">)</span>
 </span><span id="__span-0-295"><a id="__codelineno-0-295" name="__codelineno-0-295"></a>        <span class="k">return</span> <span class="n">cm</span> <span class="k">if</span> <span class="n">cm</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="bp">self</span><span class="o">.</span><span class="n">confusion_matrix</span><span class="p">(</span><span class="s2">&quot;model_training&quot;</span><span class="p">)</span>
 </span><span id="__span-0-296"><a id="__codelineno-0-296" name="__codelineno-0-296"></a>
 </span><span id="__span-0-297"><a id="__codelineno-0-297" name="__codelineno-0-297"></a>    <span class="c1"># Grab the confusion matrix captured during model training (could return None)</span>
 </span><span id="__span-0-298"><a id="__codelineno-0-298" name="__codelineno-0-298"></a>    <span class="k">if</span> <span class="n">capture_uuid</span> <span class="o">==</span> <span class="s2">&quot;model_training&quot;</span><span class="p">:</span>
-</span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>        <span class="n">cm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">)</span>
+</span><span id="__span-0-299"><a id="__codelineno-0-299" name="__codelineno-0-299"></a>        <span class="n">cm</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">)</span>
 </span><span id="__span-0-300"><a id="__codelineno-0-300" name="__codelineno-0-300"></a>        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">cm</span><span class="p">)</span> <span class="k">if</span> <span class="n">cm</span> <span class="k">else</span> <span class="kc">None</span>
 </span><span id="__span-0-301"><a id="__codelineno-0-301" name="__codelineno-0-301"></a>
 </span><span id="__span-0-302"><a id="__codelineno-0-302" name="__codelineno-0-302"></a>    <span class="k">else</span><span class="p">:</span>  <span class="c1"># Specific capture_uuid</span>
@@ -6499,7 +6499,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.confusion_matrix" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.container_image" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.container_image" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">container_image</span><span class="p">()</span></code>
 
 </h3>
@@ -6510,7 +6510,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.container_image" class="do
         <p>Container Image for the Latest Model Package</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-355">355</a></span>
 <span class="normal"><a href="#__codelineno-0-356">356</a></span>
 <span class="normal"><a href="#__codelineno-0-357">357</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-355"><a id="__codelineno-0-355" name="__codelineno-0-355"></a><span class="k">def</span> <span class="nf">container_image</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
@@ -6525,7 +6525,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.container_image" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.container_info" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.container_info" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">container_info</span><span class="p">()</span></code>
 
 </h3>
@@ -6536,7 +6536,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.container_info" class="doc
         <p>Container Info for the Latest Model Package</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-351">351</a></span>
 <span class="normal"><a href="#__codelineno-0-352">352</a></span>
 <span class="normal"><a href="#__codelineno-0-353">353</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-351"><a id="__codelineno-0-351" name="__codelineno-0-351"></a><span class="k">def</span> <span class="nf">container_info</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">dict</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
@@ -6551,7 +6551,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.container_info" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.created" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.created" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">created</span><span class="p">()</span></code>
 
 </h3>
@@ -6562,7 +6562,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.created" class="doc doc-he
         <p>Return the datetime when this artifact was created</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-363">363</a></span>
 <span class="normal"><a href="#__codelineno-0-364">364</a></span>
 <span class="normal"><a href="#__codelineno-0-365">365</a></span>
@@ -6581,7 +6581,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.created" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete</span><span class="p">()</span></code>
 
 </h3>
@@ -6592,7 +6592,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.delete" class="doc doc-hea
         <p>Delete the Model Packages and the Model Group</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-729">729</a></span>
 <span class="normal"><a href="#__codelineno-0-730">730</a></span>
 <span class="normal"><a href="#__codelineno-0-731">731</a></span>
@@ -6615,7 +6615,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.delete" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.delete_inference_run" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.delete_inference_run" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">delete_inference_run</span><span class="p">(</span><span class="n">inference_run_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -6657,7 +6657,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.delete_inference_run" clas
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-220">220</a></span>
 <span class="normal"><a href="#__codelineno-0-221">221</a></span>
 <span class="normal"><a href="#__codelineno-0-222">222</a></span>
@@ -6706,7 +6706,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.delete_inference_run" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">(</span><span class="n">recompute</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -6721,7 +6721,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.details" class="doc doc-he
     dict: Dictionary of details about this Model</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-509">509</a></span>
 <span class="normal"><a href="#__codelineno-0-510">510</a></span>
 <span class="normal"><a href="#__codelineno-0-511">511</a></span>
@@ -6832,7 +6832,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.details" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.endpoints" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.endpoints" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">endpoints</span><span class="p">()</span></code>
 
 </h3>
@@ -6866,7 +6866,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.endpoints" class="doc doc-
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-421">421</a></span>
 <span class="normal"><a href="#__codelineno-0-422">422</a></span>
 <span class="normal"><a href="#__codelineno-0-423">423</a></span>
@@ -6879,7 +6879,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.endpoints" class="doc doc-
 </span><span id="__span-0-424"><a id="__codelineno-0-424" name="__codelineno-0-424"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-425"><a id="__codelineno-0-425" name="__codelineno-0-425"></a><span class="sd">        list[str]: List of registered endpoints</span>
 </span><span id="__span-0-426"><a id="__codelineno-0-426" name="__codelineno-0-426"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[])</span>
+</span><span id="__span-0-427"><a id="__codelineno-0-427" name="__codelineno-0-427"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[])</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6889,7 +6889,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.endpoints" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.exists" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">exists</span><span class="p">()</span></code>
 
 </h3>
@@ -6900,7 +6900,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.exists" class="doc doc-hea
         <p>Does the model metadata exist in the AWS Metadata?</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-146">146</a></span>
 <span class="normal"><a href="#__codelineno-0-147">147</a></span>
 <span class="normal"><a href="#__codelineno-0-148">148</a></span>
@@ -6921,7 +6921,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.exists" class="doc doc-hea
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.expected_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.expected_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">expected_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -6934,7 +6934,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.expected_meta" class="doc
     list[str]: List of expected metadata keys</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-574">574</a></span>
 <span class="normal"><a href="#__codelineno-0-575">575</a></span>
 <span class="normal"><a href="#__codelineno-0-576">576</a></span>
@@ -6947,7 +6947,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.expected_meta" class="doc
 </span><span id="__span-0-577"><a id="__codelineno-0-577" name="__codelineno-0-577"></a><span class="sd">        list[str]: List of expected metadata keys</span>
 </span><span id="__span-0-578"><a id="__codelineno-0-578" name="__codelineno-0-578"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-579"><a id="__codelineno-0-579" name="__codelineno-0-579"></a>    <span class="c1"># Our current list of expected metadata, we can add to this as needed</span>
-</span><span id="__span-0-580"><a id="__codelineno-0-580" name="__codelineno-0-580"></a>    <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;sageworks_status&quot;</span><span class="p">,</span> <span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">,</span> <span class="s2">&quot;sageworks_training_cm&quot;</span><span class="p">]</span>
+</span><span id="__span-0-580"><a id="__codelineno-0-580" name="__codelineno-0-580"></a>    <span class="k">return</span> <span class="p">[</span><span class="s2">&quot;workbench_status&quot;</span><span class="p">,</span> <span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">,</span> <span class="s2">&quot;workbench_training_cm&quot;</span><span class="p">]</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -6957,7 +6957,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.expected_meta" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.features" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.features" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">features</span><span class="p">()</span></code>
 
 </h3>
@@ -6991,7 +6991,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.features" class="doc doc-h
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-479">479</a></span>
 <span class="normal"><a href="#__codelineno-0-480">480</a></span>
 <span class="normal"><a href="#__codelineno-0-481">481</a></span>
@@ -7004,7 +7004,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.features" class="doc doc-h
 </span><span id="__span-0-482"><a id="__codelineno-0-482" name="__codelineno-0-482"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-483"><a id="__codelineno-0-483" name="__codelineno-0-483"></a><span class="sd">        list[str]: List of features used for this Model</span>
 </span><span id="__span-0-484"><a id="__codelineno-0-484" name="__codelineno-0-484"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-485"><a id="__codelineno-0-485" name="__codelineno-0-485"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_model_features&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
+</span><span id="__span-0-485"><a id="__codelineno-0-485" name="__codelineno-0-485"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_model_features&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -7014,7 +7014,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.features" class="doc doc-h
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.get_endpoint_inference_path" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.get_endpoint_inference_path" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_endpoint_inference_path</span><span class="p">()</span></code>
 
 </h3>
@@ -7048,7 +7048,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_endpoint_inference_pat
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-429">429</a></span>
 <span class="normal"><a href="#__codelineno-0-430">430</a></span>
 <span class="normal"><a href="#__codelineno-0-431">431</a></span>
@@ -7081,7 +7081,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_endpoint_inference_pat
 </span><span id="__span-0-434"><a id="__codelineno-0-434" name="__codelineno-0-434"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-435"><a id="__codelineno-0-435" name="__codelineno-0-435"></a>
 </span><span id="__span-0-436"><a id="__codelineno-0-436" name="__codelineno-0-436"></a>    <span class="c1"># Look for any Registered Endpoints</span>
-</span><span id="__span-0-437"><a id="__codelineno-0-437" name="__codelineno-0-437"></a>    <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">)</span>
+</span><span id="__span-0-437"><a id="__codelineno-0-437" name="__codelineno-0-437"></a>    <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">)</span>
 </span><span id="__span-0-438"><a id="__codelineno-0-438" name="__codelineno-0-438"></a>
 </span><span id="__span-0-439"><a id="__codelineno-0-439" name="__codelineno-0-439"></a>    <span class="c1"># Note: We may have 0 to N endpoints, so we find the one with the most recent artifacts</span>
 </span><span id="__span-0-440"><a id="__codelineno-0-440" name="__codelineno-0-440"></a>    <span class="k">if</span> <span class="n">registered_endpoints</span><span class="p">:</span>
@@ -7107,7 +7107,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_endpoint_inference_pat
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_metadata" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.get_inference_metadata" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_inference_metadata</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;auto_inference&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -7174,7 +7174,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_metadata" cl
     Basically when Endpoint inference was run, name of the dataset, the MD5, etc</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-857">857</a></span>
 <span class="normal"><a href="#__codelineno-0-858">858</a></span>
 <span class="normal"><a href="#__codelineno-0-859">859</a></span>
@@ -7257,7 +7257,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_metadata" cl
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_metrics" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.get_inference_metrics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_inference_metrics</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;latest&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -7306,7 +7306,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_metrics" cla
   <p>If a capture_uuid isn't specified this will try to return something reasonable</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-241">241</a></span>
 <span class="normal"><a href="#__codelineno-0-242">242</a></span>
 <span class="normal"><a href="#__codelineno-0-243">243</a></span>
@@ -7359,13 +7359,13 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_metrics" cla
 </span><span id="__span-0-256"><a id="__codelineno-0-256" name="__codelineno-0-256"></a>
 </span><span id="__span-0-257"><a id="__codelineno-0-257" name="__codelineno-0-257"></a>    <span class="c1"># Grab the metrics captured during model training (could return None)</span>
 </span><span id="__span-0-258"><a id="__codelineno-0-258" name="__codelineno-0-258"></a>    <span class="k">if</span> <span class="n">capture_uuid</span> <span class="o">==</span> <span class="s2">&quot;model_training&quot;</span><span class="p">:</span>
-</span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>        <span class="c1"># Sanity check the sageworks metadata</span>
-</span><span id="__span-0-260"><a id="__codelineno-0-260" name="__codelineno-0-260"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
-</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>            <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no sageworks_meta(). Either onboard() or delete this model!&quot;</span>
+</span><span id="__span-0-259"><a id="__codelineno-0-259" name="__codelineno-0-259"></a>        <span class="c1"># Sanity check the workbench metadata</span>
+</span><span id="__span-0-260"><a id="__codelineno-0-260" name="__codelineno-0-260"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+</span><span id="__span-0-261"><a id="__codelineno-0-261" name="__codelineno-0-261"></a>            <span class="n">error_msg</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> has no workbench_meta(). Either onboard() or delete this model!&quot;</span>
 </span><span id="__span-0-262"><a id="__codelineno-0-262" name="__codelineno-0-262"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">critical</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-263"><a id="__codelineno-0-263" name="__codelineno-0-263"></a>            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="n">error_msg</span><span class="p">)</span>
 </span><span id="__span-0-264"><a id="__codelineno-0-264" name="__codelineno-0-264"></a>
-</span><span id="__span-0-265"><a id="__codelineno-0-265" name="__codelineno-0-265"></a>        <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">)</span>
+</span><span id="__span-0-265"><a id="__codelineno-0-265" name="__codelineno-0-265"></a>        <span class="n">metrics</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">)</span>
 </span><span id="__span-0-266"><a id="__codelineno-0-266" name="__codelineno-0-266"></a>        <span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">metrics</span><span class="p">)</span> <span class="k">if</span> <span class="n">metrics</span> <span class="k">else</span> <span class="kc">None</span>
 </span><span id="__span-0-267"><a id="__codelineno-0-267" name="__codelineno-0-267"></a>
 </span><span id="__span-0-268"><a id="__codelineno-0-268" name="__codelineno-0-268"></a>    <span class="k">else</span><span class="p">:</span>  <span class="c1"># Specific capture_uuid (could return None)</span>
@@ -7385,7 +7385,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_metrics" cla
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_predictions" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.get_inference_predictions" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_inference_predictions</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;auto_inference&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -7450,7 +7450,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_predictions"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-895">895</a></span>
 <span class="normal"><a href="#__codelineno-0-896">896</a></span>
 <span class="normal"><a href="#__codelineno-0-897">897</a></span>
@@ -7507,7 +7507,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_inference_predictions"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.get_model_data_url" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.get_model_data_url" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_model_data_url</span><span class="p">()</span></code>
 
 </h3>
@@ -7541,7 +7541,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_model_data_url" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-717">717</a></span>
 <span class="normal"><a href="#__codelineno-0-718">718</a></span>
 <span class="normal"><a href="#__codelineno-0-719">719</a></span>
@@ -7572,7 +7572,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_model_data_url" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.get_pipeline" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.get_pipeline" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_pipeline</span><span class="p">()</span></code>
 
 </h3>
@@ -7583,12 +7583,12 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_pipeline" class="doc d
         <p>Get the pipeline for this model</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-562">562</a></span>
 <span class="normal"><a href="#__codelineno-0-563">563</a></span>
 <span class="normal"><a href="#__codelineno-0-564">564</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-562"><a id="__codelineno-0-562" name="__codelineno-0-562"></a><span class="k">def</span> <span class="nf">get_pipeline</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">str</span><span class="p">:</span>
 </span><span id="__span-0-563"><a id="__codelineno-0-563" name="__codelineno-0-563"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the pipeline for this model&quot;&quot;&quot;</span>
-</span><span id="__span-0-564"><a id="__codelineno-0-564" name="__codelineno-0-564"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_pipeline&quot;</span><span class="p">)</span>
+</span><span id="__span-0-564"><a id="__codelineno-0-564" name="__codelineno-0-564"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_pipeline&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -7598,7 +7598,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.get_pipeline" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.group_arn" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.group_arn" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">group_arn</span><span class="p">()</span></code>
 
 </h3>
@@ -7609,7 +7609,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.group_arn" class="doc doc-
         <p>AWS ARN (Amazon Resource Name) for the Model Package Group</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-341">341</a></span>
 <span class="normal"><a href="#__codelineno-0-342">342</a></span>
 <span class="normal"><a href="#__codelineno-0-343">343</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-341"><a id="__codelineno-0-341" name="__codelineno-0-341"></a><span class="k">def</span> <span class="nf">group_arn</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Union</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="kc">None</span><span class="p">]:</span>
@@ -7624,7 +7624,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.group_arn" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.hash" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.hash" class="doc doc-heading">
             <code class="highlight language-python"><span class="nb">hash</span><span class="p">()</span></code>
 
 </h3>
@@ -7658,7 +7658,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.hash" class="doc doc-headi
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-375">375</a></span>
 <span class="normal"><a href="#__codelineno-0-376">376</a></span>
 <span class="normal"><a href="#__codelineno-0-377">377</a></span>
@@ -7683,7 +7683,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.hash" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.health_check" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.health_check" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">health_check</span><span class="p">()</span></code>
 
 </h3>
@@ -7696,7 +7696,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.health_check" class="doc d
     list[str]: List of health issues</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-153">153</a></span>
 <span class="normal"><a href="#__codelineno-0-154">154</a></span>
 <span class="normal"><a href="#__codelineno-0-155">155</a></span>
@@ -7767,7 +7767,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.health_check" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.is_model_unknown" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.is_model_unknown" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">is_model_unknown</span><span class="p">()</span></code>
 
 </h3>
@@ -7778,7 +7778,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.is_model_unknown" class="d
         <p>Is the Model Type unknown?</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-582">582</a></span>
 <span class="normal"><a href="#__codelineno-0-583">583</a></span>
 <span class="normal"><a href="#__codelineno-0-584">584</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-582"><a id="__codelineno-0-582" name="__codelineno-0-582"></a><span class="k">def</span> <span class="nf">is_model_unknown</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">bool</span><span class="p">:</span>
@@ -7793,7 +7793,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.is_model_unknown" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.latest_model_object" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.latest_model_object" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">latest_model_object</span><span class="p">()</span></code>
 
 </h3>
@@ -7801,7 +7801,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.latest_model_object" class
 
     <div class="doc doc-contents ">
 
-        <p>Return the latest AWS Sagemaker Model object for this SageWorks Model</p>
+        <p>Return the latest AWS Sagemaker Model object for this Workbench Model</p>
 
 
     <p><span class="doc-section-title">Returns:</span></p>
@@ -7827,7 +7827,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.latest_model_object" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-185">185</a></span>
 <span class="normal"><a href="#__codelineno-0-186">186</a></span>
 <span class="normal"><a href="#__codelineno-0-187">187</a></span>
@@ -7837,7 +7837,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.latest_model_object" class
 <span class="normal"><a href="#__codelineno-0-191">191</a></span>
 <span class="normal"><a href="#__codelineno-0-192">192</a></span>
 <span class="normal"><a href="#__codelineno-0-193">193</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-185"><a id="__codelineno-0-185" name="__codelineno-0-185"></a><span class="k">def</span> <span class="nf">latest_model_object</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">SagemakerModel</span><span class="p">:</span>
-</span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the latest AWS Sagemaker Model object for this SageWorks Model</span>
+</span><span id="__span-0-186"><a id="__codelineno-0-186" name="__codelineno-0-186"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Return the latest AWS Sagemaker Model object for this Workbench Model</span>
 </span><span id="__span-0-187"><a id="__codelineno-0-187" name="__codelineno-0-187"></a>
 </span><span id="__span-0-188"><a id="__codelineno-0-188" name="__codelineno-0-188"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-189"><a id="__codelineno-0-189" name="__codelineno-0-189"></a><span class="sd">       sagemaker.model.Model: AWS Sagemaker Model object</span>
@@ -7854,7 +7854,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.latest_model_object" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.list_inference_runs" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.list_inference_runs" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">list_inference_runs</span><span class="p">()</span></code>
 
 </h3>
@@ -7888,7 +7888,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.list_inference_runs" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-195">195</a></span>
 <span class="normal"><a href="#__codelineno-0-196">196</a></span>
 <span class="normal"><a href="#__codelineno-0-197">197</a></span>
@@ -7924,7 +7924,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.list_inference_runs" class
 </span><span id="__span-0-204"><a id="__codelineno-0-204" name="__codelineno-0-204"></a>        <span class="k">return</span> <span class="p">[]</span>
 </span><span id="__span-0-205"><a id="__codelineno-0-205" name="__codelineno-0-205"></a>
 </span><span id="__span-0-206"><a id="__codelineno-0-206" name="__codelineno-0-206"></a>    <span class="c1"># Check if we have model training metrics in our metadata</span>
-</span><span id="__span-0-207"><a id="__codelineno-0-207" name="__codelineno-0-207"></a>    <span class="n">have_model_training</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_training_metrics&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span>
+</span><span id="__span-0-207"><a id="__codelineno-0-207" name="__codelineno-0-207"></a>    <span class="n">have_model_training</span> <span class="o">=</span> <span class="kc">True</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_training_metrics&quot;</span><span class="p">)</span> <span class="k">else</span> <span class="kc">False</span>
 </span><span id="__span-0-208"><a id="__codelineno-0-208" name="__codelineno-0-208"></a>
 </span><span id="__span-0-209"><a id="__codelineno-0-209" name="__codelineno-0-209"></a>    <span class="c1"># Now grab the list of directories from our inference path</span>
 </span><span id="__span-0-210"><a id="__codelineno-0-210" name="__codelineno-0-210"></a>    <span class="n">inference_runs</span> <span class="o">=</span> <span class="p">[]</span>
@@ -7945,7 +7945,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.list_inference_runs" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.managed_delete" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.managed_delete" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">managed_delete</span><span class="p">(</span><span class="n">model_group_name</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -7991,7 +7991,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.managed_delete" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-737">737</a></span>
 <span class="normal"><a href="#__codelineno-0-738">738</a></span>
 <span class="normal"><a href="#__codelineno-0-739">739</a></span>
@@ -8082,7 +8082,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.managed_delete" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.model_package_arn" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.model_package_arn" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">model_package_arn</span><span class="p">()</span></code>
 
 </h3>
@@ -8093,7 +8093,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.model_package_arn" class="
         <p>AWS ARN (Amazon Resource Name) for the Latest Model Package (within the Group)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-345">345</a></span>
 <span class="normal"><a href="#__codelineno-0-346">346</a></span>
 <span class="normal"><a href="#__codelineno-0-347">347</a></span>
@@ -8112,7 +8112,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.model_package_arn" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.modified" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.modified" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">modified</span><span class="p">()</span></code>
 
 </h3>
@@ -8123,7 +8123,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.modified" class="doc doc-h
         <p>Return the datetime when this artifact was last modified</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-369">369</a></span>
 <span class="normal"><a href="#__codelineno-0-370">370</a></span>
 <span class="normal"><a href="#__codelineno-0-371">371</a></span>
@@ -8142,7 +8142,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.modified" class="doc doc-h
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.onboard" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.onboard" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">onboard</span><span class="p">(</span><span class="n">ask_everything</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -8207,7 +8207,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.onboard" class="doc doc-he
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-603">603</a></span>
 <span class="normal"><a href="#__codelineno-0-604">604</a></span>
 <span class="normal"><a href="#__codelineno-0-605">605</a></span>
@@ -8344,7 +8344,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.onboard" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.onboard_with_args" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.onboard_with_args" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">onboard_with_args</span><span class="p">(</span><span class="n">model_type</span><span class="p">,</span> <span class="n">target_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">feature_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">endpoints</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">owner</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -8371,7 +8371,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.onboard_with_args" class="
                 <code>model_type</code>
             </td>
             <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.core.artifacts.model_core.ModelType" href="#sageworks.core.artifacts.model_core.ModelType">ModelType</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.core.artifacts.model_core.ModelType" href="#workbench.core.artifacts.model_core.ModelType">ModelType</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -8452,7 +8452,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.onboard_with_args" class="
     bool: True if the Model is successfully onboarded, False otherwise</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-668">668</a></span>
 <span class="normal"><a href="#__codelineno-0-669">669</a></span>
 <span class="normal"><a href="#__codelineno-0-670">670</a></span>
@@ -8557,7 +8557,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.onboard_with_args" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.refresh_meta" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.refresh_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">refresh_meta</span><span class="p">()</span></code>
 
 </h3>
@@ -8568,7 +8568,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.refresh_meta" class="doc d
         <p>Refresh the Artifact's metadata</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-139">139</a></span>
 <span class="normal"><a href="#__codelineno-0-140">140</a></span>
 <span class="normal"><a href="#__codelineno-0-141">141</a></span>
@@ -8589,7 +8589,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.refresh_meta" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.register_endpoint" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.register_endpoint" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">register_endpoint</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span></code>
 
 </h3>
@@ -8631,7 +8631,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.register_endpoint" class="
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-384">384</a></span>
 <span class="normal"><a href="#__codelineno-0-385">385</a></span>
 <span class="normal"><a href="#__codelineno-0-386">386</a></span>
@@ -8655,9 +8655,9 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.register_endpoint" class="
 </span><span id="__span-0-388"><a id="__codelineno-0-388" name="__codelineno-0-388"></a><span class="sd">        endpoint_name (str): Name of the endpoint</span>
 </span><span id="__span-0-389"><a id="__codelineno-0-389" name="__codelineno-0-389"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-390"><a id="__codelineno-0-390" name="__codelineno-0-390"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Registering Endpoint </span><span class="si">{</span><span class="n">endpoint_name</span><span class="si">}</span><span class="s2"> with Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-391"><a id="__codelineno-0-391" name="__codelineno-0-391"></a>    <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
+</span><span id="__span-0-391"><a id="__codelineno-0-391" name="__codelineno-0-391"></a>    <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
 </span><span id="__span-0-392"><a id="__codelineno-0-392" name="__codelineno-0-392"></a>    <span class="n">registered_endpoints</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span>
-</span><span id="__span-0-393"><a id="__codelineno-0-393" name="__codelineno-0-393"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
+</span><span id="__span-0-393"><a id="__codelineno-0-393" name="__codelineno-0-393"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
 </span><span id="__span-0-394"><a id="__codelineno-0-394" name="__codelineno-0-394"></a>
 </span><span id="__span-0-395"><a id="__codelineno-0-395" name="__codelineno-0-395"></a>    <span class="c1"># Remove any health tags</span>
 </span><span id="__span-0-396"><a id="__codelineno-0-396" name="__codelineno-0-396"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">remove_health_tag</span><span class="p">(</span><span class="s2">&quot;no_endpoint&quot;</span><span class="p">)</span>
@@ -8674,7 +8674,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.register_endpoint" class="
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.remove_endpoint" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.remove_endpoint" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">remove_endpoint</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span></code>
 
 </h3>
@@ -8716,7 +8716,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.remove_endpoint" class="do
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-402">402</a></span>
 <span class="normal"><a href="#__codelineno-0-403">403</a></span>
 <span class="normal"><a href="#__codelineno-0-404">404</a></span>
@@ -8741,9 +8741,9 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.remove_endpoint" class="do
 </span><span id="__span-0-406"><a id="__codelineno-0-406" name="__codelineno-0-406"></a><span class="sd">        endpoint_name (str): Name of the endpoint</span>
 </span><span id="__span-0-407"><a id="__codelineno-0-407" name="__codelineno-0-407"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-408"><a id="__codelineno-0-408" name="__codelineno-0-408"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Removing Endpoint </span><span class="si">{</span><span class="n">endpoint_name</span><span class="si">}</span><span class="s2"> from Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-409"><a id="__codelineno-0-409" name="__codelineno-0-409"></a>    <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
+</span><span id="__span-0-409"><a id="__codelineno-0-409" name="__codelineno-0-409"></a>    <span class="n">registered_endpoints</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">,</span> <span class="p">[]))</span>
 </span><span id="__span-0-410"><a id="__codelineno-0-410" name="__codelineno-0-410"></a>    <span class="n">registered_endpoints</span><span class="o">.</span><span class="n">discard</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">)</span>
-</span><span id="__span-0-411"><a id="__codelineno-0-411" name="__codelineno-0-411"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
+</span><span id="__span-0-411"><a id="__codelineno-0-411" name="__codelineno-0-411"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_registered_endpoints&quot;</span><span class="p">:</span> <span class="nb">list</span><span class="p">(</span><span class="n">registered_endpoints</span><span class="p">)})</span>
 </span><span id="__span-0-412"><a id="__codelineno-0-412" name="__codelineno-0-412"></a>
 </span><span id="__span-0-413"><a id="__codelineno-0-413" name="__codelineno-0-413"></a>    <span class="c1"># If we have NO endpionts, then set a health tags</span>
 </span><span id="__span-0-414"><a id="__codelineno-0-414" name="__codelineno-0-414"></a>    <span class="k">if</span> <span class="ow">not</span> <span class="n">registered_endpoints</span><span class="p">:</span>
@@ -8761,7 +8761,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.remove_endpoint" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.set_class_labels" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.set_class_labels" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_class_labels</span><span class="p">(</span><span class="n">labels</span><span class="p">)</span></code>
 
 </h3>
@@ -8803,7 +8803,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_class_labels" class="d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-498">498</a></span>
 <span class="normal"><a href="#__codelineno-0-499">499</a></span>
 <span class="normal"><a href="#__codelineno-0-500">500</a></span>
@@ -8820,7 +8820,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_class_labels" class="d
 </span><span id="__span-0-502"><a id="__codelineno-0-502" name="__codelineno-0-502"></a><span class="sd">        labels (list[str]): List of class labels</span>
 </span><span id="__span-0-503"><a id="__codelineno-0-503" name="__codelineno-0-503"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-504"><a id="__codelineno-0-504" name="__codelineno-0-504"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_type</span> <span class="o">==</span> <span class="n">ModelType</span><span class="o">.</span><span class="n">CLASSIFIER</span><span class="p">:</span>
-</span><span id="__span-0-505"><a id="__codelineno-0-505" name="__codelineno-0-505"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;class_labels&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">})</span>
+</span><span id="__span-0-505"><a id="__codelineno-0-505" name="__codelineno-0-505"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;class_labels&quot;</span><span class="p">:</span> <span class="n">labels</span><span class="p">})</span>
 </span><span id="__span-0-506"><a id="__codelineno-0-506" name="__codelineno-0-506"></a>    <span class="k">else</span><span class="p">:</span>
 </span><span id="__span-0-507"><a id="__codelineno-0-507" name="__codelineno-0-507"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">model_name</span><span class="si">}</span><span class="s2"> is not a classifier!&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
@@ -8832,7 +8832,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_class_labels" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.set_features" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.set_features" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_features</span><span class="p">(</span><span class="n">feature_columns</span><span class="p">)</span></code>
 
 </h3>
@@ -8874,7 +8874,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_features" class="doc d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-463">463</a></span>
 <span class="normal"><a href="#__codelineno-0-464">464</a></span>
 <span class="normal"><a href="#__codelineno-0-465">465</a></span>
@@ -8887,7 +8887,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_features" class="doc d
 </span><span id="__span-0-466"><a id="__codelineno-0-466" name="__codelineno-0-466"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-467"><a id="__codelineno-0-467" name="__codelineno-0-467"></a><span class="sd">        feature_columns (list[str]): List of feature columns</span>
 </span><span id="__span-0-468"><a id="__codelineno-0-468" name="__codelineno-0-468"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-469"><a id="__codelineno-0-469" name="__codelineno-0-469"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_features&quot;</span><span class="p">:</span> <span class="n">feature_columns</span><span class="p">})</span>
+</span><span id="__span-0-469"><a id="__codelineno-0-469" name="__codelineno-0-469"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_features&quot;</span><span class="p">:</span> <span class="n">feature_columns</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -8897,7 +8897,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_features" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.set_input" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.set_input" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_input</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">force</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -8957,7 +8957,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_input" class="doc doc-
     We're going to not allow this to be used for Models</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-311">311</a></span>
 <span class="normal"><a href="#__codelineno-0-312">312</a></span>
 <span class="normal"><a href="#__codelineno-0-313">313</a></span>
@@ -8990,7 +8990,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_input" class="doc doc-
 </span><span id="__span-0-324"><a id="__codelineno-0-324" name="__codelineno-0-324"></a>    <span class="c1"># Okay we&#39;re going to allow this to be set</span>
 </span><span id="__span-0-325"><a id="__codelineno-0-325" name="__codelineno-0-325"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">uuid</span><span class="si">}</span><span class="s2">: Setting input to </span><span class="si">{</span><span class="nb">input</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-326"><a id="__codelineno-0-326" name="__codelineno-0-326"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="s2">&quot;Be careful with this! It breaks automatic provenance of the artifact!&quot;</span><span class="p">)</span>
-</span><span id="__span-0-327"><a id="__codelineno-0-327" name="__codelineno-0-327"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
+</span><span id="__span-0-327"><a id="__codelineno-0-327" name="__codelineno-0-327"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="nb">input</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -9000,7 +9000,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_input" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.set_pipeline" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.set_pipeline" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_pipeline</span><span class="p">(</span><span class="n">pipeline</span><span class="p">)</span></code>
 
 </h3>
@@ -9042,7 +9042,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_pipeline" class="doc d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-566">566</a></span>
 <span class="normal"><a href="#__codelineno-0-567">567</a></span>
 <span class="normal"><a href="#__codelineno-0-568">568</a></span>
@@ -9055,7 +9055,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_pipeline" class="doc d
 </span><span id="__span-0-569"><a id="__codelineno-0-569" name="__codelineno-0-569"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-570"><a id="__codelineno-0-570" name="__codelineno-0-570"></a><span class="sd">        pipeline (str): Pipeline that was used to create this model</span>
 </span><span id="__span-0-571"><a id="__codelineno-0-571" name="__codelineno-0-571"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-572"><a id="__codelineno-0-572" name="__codelineno-0-572"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_pipeline&quot;</span><span class="p">:</span> <span class="n">pipeline</span><span class="p">})</span>
+</span><span id="__span-0-572"><a id="__codelineno-0-572" name="__codelineno-0-572"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_pipeline&quot;</span><span class="p">:</span> <span class="n">pipeline</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -9065,7 +9065,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_pipeline" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.set_target" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.set_target" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_target</span><span class="p">(</span><span class="n">target_column</span><span class="p">)</span></code>
 
 </h3>
@@ -9107,7 +9107,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_target" class="doc doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-455">455</a></span>
 <span class="normal"><a href="#__codelineno-0-456">456</a></span>
 <span class="normal"><a href="#__codelineno-0-457">457</a></span>
@@ -9120,7 +9120,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_target" class="doc doc
 </span><span id="__span-0-458"><a id="__codelineno-0-458" name="__codelineno-0-458"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-459"><a id="__codelineno-0-459" name="__codelineno-0-459"></a><span class="sd">        target_column (str): Target column for this Model</span>
 </span><span id="__span-0-460"><a id="__codelineno-0-460" name="__codelineno-0-460"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_target&quot;</span><span class="p">:</span> <span class="n">target_column</span><span class="p">})</span>
+</span><span id="__span-0-461"><a id="__codelineno-0-461" name="__codelineno-0-461"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_target&quot;</span><span class="p">:</span> <span class="n">target_column</span><span class="p">})</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -9130,7 +9130,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.set_target" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.shapley_values" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.shapley_values" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">shapley_values</span><span class="p">(</span><span class="n">capture_uuid</span><span class="o">=</span><span class="s1">&#39;auto_inference&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -9200,7 +9200,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.shapley_values" class="doc
   <p>This may or may not exist based on whether an Endpoint ran Shapley</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-978"> 978</a></span>
 <span class="normal"><a href="#__codelineno-0-979"> 979</a></span>
 <span class="normal"><a href="#__codelineno-0-980"> 980</a></span>
@@ -9273,7 +9273,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.shapley_values" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.size" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.size" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">size</span><span class="p">()</span></code>
 
 </h3>
@@ -9284,7 +9284,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.size" class="doc doc-headi
         <p>Return the size of this data in MegaBytes</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-329">329</a></span>
 <span class="normal"><a href="#__codelineno-0-330">330</a></span>
 <span class="normal"><a href="#__codelineno-0-331">331</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-329"><a id="__codelineno-0-329" name="__codelineno-0-329"></a><span class="k">def</span> <span class="nf">size</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span><span class="p">:</span>
@@ -9299,7 +9299,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.size" class="doc doc-headi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.model_core.ModelCore.target" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.model_core.ModelCore.target" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">target</span><span class="p">()</span></code>
 
 </h3>
@@ -9333,7 +9333,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.target" class="doc doc-hea
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-471">471</a></span>
 <span class="normal"><a href="#__codelineno-0-472">472</a></span>
 <span class="normal"><a href="#__codelineno-0-473">473</a></span>
@@ -9346,7 +9346,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.target" class="doc doc-hea
 </span><span id="__span-0-474"><a id="__codelineno-0-474" name="__codelineno-0-474"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-475"><a id="__codelineno-0-475" name="__codelineno-0-475"></a><span class="sd">        str: Target column for this Model (if supervised, else None)</span>
 </span><span id="__span-0-476"><a id="__codelineno-0-476" name="__codelineno-0-476"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-477"><a id="__codelineno-0-477" name="__codelineno-0-477"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;sageworks_model_target&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
+</span><span id="__span-0-477"><a id="__codelineno-0-477" name="__codelineno-0-477"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_meta</span><span class="p">()</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;workbench_model_target&quot;</span><span class="p">)</span>  <span class="c1"># Returns None if not found</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -9365,7 +9365,7 @@ <h3 id="sageworks.core.artifacts.model_core.ModelCore.target" class="doc doc-hea
 
 
 
-<h2 id="sageworks.core.artifacts.model_core.ModelType" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.model_core.ModelType" class="doc doc-heading">
             <code>ModelType</code>
 
 
@@ -9377,7 +9377,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelType" class="doc doc-heading">
               Bases: <code><span title="enum.Enum">Enum</span></code></p>
 
 
-        <p>Enumerated Types for SageWorks Model Types</p>
+        <p>Enumerated Types for Workbench Model Types</p>
 
 
 
@@ -9385,7 +9385,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelType" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/model_core.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/model_core.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-24">24</a></span>
 <span class="normal"><a href="#__codelineno-0-25">25</a></span>
 <span class="normal"><a href="#__codelineno-0-26">26</a></span>
@@ -9398,7 +9398,7 @@ <h2 id="sageworks.core.artifacts.model_core.ModelType" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-33">33</a></span>
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="k">class</span> <span class="nc">ModelType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
-</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Enumerated Types for SageWorks Model Types&quot;&quot;&quot;</span>
+</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Enumerated Types for Workbench Model Types&quot;&quot;&quot;</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>    <span class="n">CLASSIFIER</span> <span class="o">=</span> <span class="s2">&quot;classifier&quot;</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="n">REGRESSOR</span> <span class="o">=</span> <span class="s2">&quot;regressor&quot;</span>
diff --git a/core_classes/artifacts/monitor_core/index.html b/core_classes/artifacts/monitor_core/index.html
index 8725cdb2e..2e1fc42d3 100644
--- a/core_classes/artifacts/monitor_core/index.html
+++ b/core_classes/artifacts/monitor_core/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>MonitorCore - SageWorks</title>
+        <title>MonitorCore - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
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-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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@@ -104,7 +104,7 @@
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         <div class="md-header__topic">
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@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+    Workbench
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@@ -293,7 +293,7 @@
         
   
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+    Workbench REPL
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@@ -1227,7 +1227,7 @@
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+  <a href="#workbench.core.artifacts.monitor_core" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.__init__" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.__repr__" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.add_data_capture" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.baseline_exists" class="md-nav__link">
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 </li>
         
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.create_baseline" class="md-nav__link">
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@@ -1291,7 +1291,7 @@
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.create_monitoring_schedule" class="md-nav__link">
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-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.details" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.details" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_baseline" class="md-nav__link">
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 </li>
         
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-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.get_constraints" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_constraints" class="md-nav__link">
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@@ -1327,7 +1327,7 @@
 </li>
         
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_latest_data_capture" class="md-nav__link">
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-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.get_statistics" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_statistics" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.last_run_details" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.monitoring_schedule_exists" class="md-nav__link">
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       monitoring_schedule_exists
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.process_captured_data" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.setup_alerts" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.summary" class="md-nav__link">
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-    SageWorks Enterprise
+    Workbench Enterprise
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@@ -2048,7 +2048,7 @@
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-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3267,11 +3267,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3323,7 +3323,7 @@
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         <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.monitor_core" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core" class="md-nav__link">
     <span class="md-ellipsis">
       monitor_core
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@@ -3332,7 +3332,7 @@
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore" class="md-nav__link">
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       MonitorCore
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-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.__init__" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.__init__" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.__repr__" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.baseline_exists" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.create_baseline" class="md-nav__link">
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@@ -3387,7 +3387,7 @@
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-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.create_monitoring_schedule" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.create_monitoring_schedule" class="md-nav__link">
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-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.details" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.details" class="md-nav__link">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_baseline" class="md-nav__link">
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           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.get_constraints" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_constraints" class="md-nav__link">
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 </li>
         
           <li class="md-nav__item">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_latest_data_capture" class="md-nav__link">
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@@ -3432,7 +3432,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.get_statistics" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.get_statistics" class="md-nav__link">
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@@ -3441,7 +3441,7 @@
 </li>
         
           <li class="md-nav__item">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.is_data_capture_configured" class="md-nav__link">
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@@ -3450,7 +3450,7 @@
 </li>
         
           <li class="md-nav__item">
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       monitoring_schedule_exists
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@@ -3468,7 +3468,7 @@
 </li>
         
           <li class="md-nav__item">
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+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.process_captured_data" class="md-nav__link">
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.setup_alerts" class="md-nav__link">
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       setup_alerts
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@@ -3486,7 +3486,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.artifacts.monitor_core.MonitorCore.summary" class="md-nav__link">
+  <a href="#workbench.core.artifacts.monitor_core.MonitorCore.summary" class="md-nav__link">
     <span class="md-ellipsis">
       summary
     </span>
@@ -3525,7 +3525,7 @@ <h1 id="monitorcore">MonitorCore</h1>
 
 
 
-<a id="sageworks.core.artifacts.monitor_core"></a>
+<a id="workbench.core.artifacts.monitor_core"></a>
     <div class="doc doc-contents first">
 
         <p>MonitorCore class for monitoring SageMaker endpoints</p>
@@ -3550,7 +3550,7 @@ <h1 id="monitorcore">MonitorCore</h1>
 
 
 
-<h2 id="sageworks.core.artifacts.monitor_core.MonitorCore" class="doc doc-heading">
+<h2 id="workbench.core.artifacts.monitor_core.MonitorCore" class="doc doc-heading">
             <code>MonitorCore</code>
 
 
@@ -3566,7 +3566,7 @@ <h2 id="sageworks.core.artifacts.monitor_core.MonitorCore" class="doc doc-headin
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-24"> 24</a></span>
 <span class="normal"><a href="#__codelineno-0-25"> 25</a></span>
 <span class="normal"><a href="#__codelineno-0-26"> 26</a></span>
@@ -3993,7 +3993,7 @@ <h2 id="sageworks.core.artifacts.monitor_core.MonitorCore" class="doc doc-headin
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">            instance_type (str): Instance type to use for monitoring. Defaults to &quot;ml.t3.large&quot;.</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">                                 Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">endpoint_name</span> <span class="o">=</span> <span class="n">endpoint_name</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">endpoint</span> <span class="o">=</span> <span class="n">EndpointCore</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">endpoint_name</span><span class="p">)</span>
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>
@@ -4011,8 +4011,8 @@ <h2 id="sageworks.core.artifacts.monitor_core.MonitorCore" class="doc doc-headin
 </span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">statistics_json_file</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">baseline_dir</span><span class="si">}</span><span class="s2">/statistics.json&quot;</span>
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="c1"># Initialize the DefaultModelMonitor</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_sageworks_execution_role_arn</span><span class="p">()</span>
-</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">model_monitor</span> <span class="o">=</span> <span class="n">DefaultModelMonitor</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">,</span> <span class="n">instance_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">instance_type</span><span class="p">)</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_workbench_execution_role_arn</span><span class="p">()</span>
+</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">model_monitor</span> <span class="o">=</span> <span class="n">DefaultModelMonitor</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">,</span> <span class="n">instance_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">instance_type</span><span class="p">)</span>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>    <span class="k">def</span> <span class="nf">summary</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">:</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Return the summary of information about the endpoint monitor</span>
@@ -4422,7 +4422,7 @@ <h2 id="sageworks.core.artifacts.monitor_core.MonitorCore" class="doc doc-headin
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__init__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">endpoint_name</span><span class="p">,</span> <span class="n">instance_type</span><span class="o">=</span><span class="s1">&#39;ml.t3.large&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -4437,7 +4437,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__init__" class="doc d
                          Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-25">25</a></span>
 <span class="normal"><a href="#__codelineno-0-26">26</a></span>
 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
@@ -4471,7 +4471,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__init__" class="doc d
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">        instance_type (str): Instance type to use for monitoring. Defaults to &quot;ml.t3.large&quot;.</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">                             Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">endpoint_name</span> <span class="o">=</span> <span class="n">endpoint_name</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">endpoint</span> <span class="o">=</span> <span class="n">EndpointCore</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">endpoint_name</span><span class="p">)</span>
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>
@@ -4489,8 +4489,8 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__init__" class="doc d
 </span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">statistics_json_file</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">baseline_dir</span><span class="si">}</span><span class="s2">/statistics.json&quot;</span>
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="c1"># Initialize the DefaultModelMonitor</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_sageworks_execution_role_arn</span><span class="p">()</span>
-</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">model_monitor</span> <span class="o">=</span> <span class="n">DefaultModelMonitor</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">,</span> <span class="n">instance_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">instance_type</span><span class="p">)</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_workbench_execution_role_arn</span><span class="p">()</span>
+</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">model_monitor</span> <span class="o">=</span> <span class="n">DefaultModelMonitor</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">,</span> <span class="n">instance_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">instance_type</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4500,7 +4500,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__init__" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__repr__" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.__repr__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__repr__</span><span class="p">()</span></code>
 
 </h3>
@@ -4534,7 +4534,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__repr__" class="doc d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-76">76</a></span>
 <span class="normal"><a href="#__codelineno-0-77">77</a></span>
 <span class="normal"><a href="#__codelineno-0-78">78</a></span>
@@ -4563,7 +4563,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.__repr__" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.add_data_capture" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.add_data_capture" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">add_data_capture</span><span class="p">(</span><span class="n">capture_percentage</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span></code>
 
 </h3>
@@ -4605,7 +4605,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.add_data_capture" clas
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-165">165</a></span>
 <span class="normal"><a href="#__codelineno-0-166">166</a></span>
 <span class="normal"><a href="#__codelineno-0-167">167</a></span>
@@ -4696,7 +4696,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.add_data_capture" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.baseline_exists" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.baseline_exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">baseline_exists</span><span class="p">()</span></code>
 
 </h3>
@@ -4730,7 +4730,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.baseline_exists" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-294">294</a></span>
 <span class="normal"><a href="#__codelineno-0-295">295</a></span>
 <span class="normal"><a href="#__codelineno-0-296">296</a></span>
@@ -4759,7 +4759,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.baseline_exists" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.create_baseline" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.create_baseline" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create_baseline</span><span class="p">(</span><span class="n">recreate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4777,7 +4777,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.create_baseline" class
     - statistics.json</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-305">305</a></span>
 <span class="normal"><a href="#__codelineno-0-306">306</a></span>
 <span class="normal"><a href="#__codelineno-0-307">307</a></span>
@@ -4846,7 +4846,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.create_baseline" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.create_monitoring_schedule" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.create_monitoring_schedule" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create_monitoring_schedule</span><span class="p">(</span><span class="n">schedule</span><span class="o">=</span><span class="s1">&#39;hourly&#39;</span><span class="p">,</span> <span class="n">recreate</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4860,7 +4860,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.create_monitoring_sche
     recreate (bool): If True, recreate the monitoring schedule even if it already exists.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-382">382</a></span>
 <span class="normal"><a href="#__codelineno-0-383">383</a></span>
 <span class="normal"><a href="#__codelineno-0-384">384</a></span>
@@ -4959,7 +4959,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.create_monitoring_sche
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">details</span><span class="p">()</span></code>
 
 </h3>
@@ -4993,7 +4993,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.details" class="doc do
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-110">110</a></span>
 <span class="normal"><a href="#__codelineno-0-111">111</a></span>
 <span class="normal"><a href="#__codelineno-0-112">112</a></span>
@@ -5110,7 +5110,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.details" class="doc do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_baseline" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.get_baseline" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_baseline</span><span class="p">()</span></code>
 
 </h3>
@@ -5144,7 +5144,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_baseline" class="d
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-336">336</a></span>
 <span class="normal"><a href="#__codelineno-0-337">337</a></span>
 <span class="normal"><a href="#__codelineno-0-338">338</a></span>
@@ -5177,7 +5177,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_baseline" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_constraints" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.get_constraints" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_constraints</span><span class="p">()</span></code>
 
 </h3>
@@ -5211,7 +5211,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_constraints" class
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-349">349</a></span>
 <span class="normal"><a href="#__codelineno-0-350">350</a></span>
 <span class="normal"><a href="#__codelineno-0-351">351</a></span>
@@ -5234,7 +5234,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_constraints" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_latest_data_capture" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.get_latest_data_capture" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_latest_data_capture</span><span class="p">()</span></code>
 
 </h3>
@@ -5268,7 +5268,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_latest_data_captur
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-228">228</a></span>
 <span class="normal"><a href="#__codelineno-0-229">229</a></span>
 <span class="normal"><a href="#__codelineno-0-230">230</a></span>
@@ -5319,7 +5319,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_latest_data_captur
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_statistics" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.get_statistics" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_statistics</span><span class="p">()</span></code>
 
 </h3>
@@ -5353,7 +5353,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_statistics" class=
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-357">357</a></span>
 <span class="normal"><a href="#__codelineno-0-358">358</a></span>
 <span class="normal"><a href="#__codelineno-0-359">359</a></span>
@@ -5376,7 +5376,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.get_statistics" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.is_data_capture_configured" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.is_data_capture_configured" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">is_data_capture_configured</span><span class="p">(</span><span class="n">capture_percentage</span><span class="p">)</span></code>
 
 </h3>
@@ -5391,7 +5391,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.is_data_capture_config
     bool: True if data capture is already configured, False otherwise.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-207">207</a></span>
 <span class="normal"><a href="#__codelineno-0-208">208</a></span>
 <span class="normal"><a href="#__codelineno-0-209">209</a></span>
@@ -5440,7 +5440,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.is_data_capture_config
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.last_run_details" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.last_run_details" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">last_run_details</span><span class="p">()</span></code>
 
 </h3>
@@ -5474,7 +5474,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.last_run_details" clas
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-87"> 87</a></span>
 <span class="normal"><a href="#__codelineno-0-88"> 88</a></span>
 <span class="normal"><a href="#__codelineno-0-89"> 89</a></span>
@@ -5527,7 +5527,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.last_run_details" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.monitoring_schedule_exists" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.monitoring_schedule_exists" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">monitoring_schedule_exists</span><span class="p">()</span></code>
 
 </h3>
@@ -5538,7 +5538,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.monitoring_schedule_ex
         <p>Code to figure out if a monitoring schedule already exists for this endpoint</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-432">432</a></span>
 <span class="normal"><a href="#__codelineno-0-433">433</a></span>
 <span class="normal"><a href="#__codelineno-0-434">434</a></span>
@@ -5569,7 +5569,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.monitoring_schedule_ex
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.process_captured_data" class="doc doc-heading">
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             <code class="highlight language-python"><span class="n">process_captured_data</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5638,7 +5638,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.process_captured_data"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-250">250</a></span>
 <span class="normal"><a href="#__codelineno-0-251">251</a></span>
 <span class="normal"><a href="#__codelineno-0-252">252</a></span>
@@ -5733,7 +5733,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.process_captured_data"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.setup_alerts" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.setup_alerts" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">setup_alerts</span><span class="p">()</span></code>
 
 </h3>
@@ -5744,7 +5744,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.setup_alerts" class="d
         <p>Code to set up alerts based on monitoring results</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-428">428</a></span>
 <span class="normal"><a href="#__codelineno-0-429">429</a></span>
 <span class="normal"><a href="#__codelineno-0-430">430</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-428"><a id="__codelineno-0-428" name="__codelineno-0-428"></a><span class="k">def</span> <span class="nf">setup_alerts</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -5759,7 +5759,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.setup_alerts" class="d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.summary" class="doc doc-heading">
+<h3 id="workbench.core.artifacts.monitor_core.MonitorCore.summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">summary</span><span class="p">()</span></code>
 
 </h3>
@@ -5793,7 +5793,7 @@ <h3 id="sageworks.core.artifacts.monitor_core.MonitorCore.summary" class="doc do
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/artifacts/monitor_core.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-53">53</a></span>
 <span class="normal"><a href="#__codelineno-0-54">54</a></span>
 <span class="normal"><a href="#__codelineno-0-55">55</a></span>
diff --git a/core_classes/artifacts/overview/index.html b/core_classes/artifacts/overview/index.html
index b2c961ff6..aa0aa6f23 100644
--- a/core_classes/artifacts/overview/index.html
+++ b/core_classes/artifacts/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
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-        <a href="#sageworks-artifacts" class="md-skip">
+        <a href="#workbench-artifacts" class="md-skip">
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@@ -90,7 +90,7 @@
 
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-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
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@@ -211,13 +211,13 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
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+    Workbench
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+    Workbench REPL
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@@ -1122,9 +1122,9 @@
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         <li class="md-nav__item">
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+  <a href="#welcome-to-the-workbench-core-artifact-classes" class="md-nav__link">
     <span class="md-ellipsis">
-      Welcome to the SageWorks Core Artifact Classes
+      Welcome to the Workbench Core Artifact Classes
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@@ -1880,7 +1880,7 @@
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-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3099,11 +3099,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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@@ -3155,9 +3155,9 @@
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         <li class="md-nav__item">
-  <a href="#welcome-to-the-sageworks-core-artifact-classes" class="md-nav__link">
+  <a href="#welcome-to-the-workbench-core-artifact-classes" class="md-nav__link">
     <span class="md-ellipsis">
-      Welcome to the SageWorks Core Artifact Classes
+      Welcome to the Workbench Core Artifact Classes
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   </a>
   
@@ -3178,20 +3178,20 @@
                   
 
 
-<h1 id="sageworks-artifacts">SageWorks Artifacts</h1>
+<h1 id="workbench-artifacts">Workbench Artifacts</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">API Classes</p>
 <p>For most users the <a href="../../../api_classes/overview/">API Classes</a> will provide all the general functionality to create a full AWS ML Pipeline</p>
 </div>
-<h2 id="welcome-to-the-sageworks-core-artifact-classes">Welcome to the SageWorks Core Artifact Classes</h2>
-<p>These classes provide low-level APIs for the SageWorks package, they interact more directly with AWS Services and are therefore more complex with a fairly large number of methods. </p>
+<h2 id="welcome-to-the-workbench-core-artifact-classes">Welcome to the Workbench Core Artifact Classes</h2>
+<p>These classes provide low-level APIs for the Workbench package, they interact more directly with AWS Services and are therefore more complex with a fairly large number of methods. </p>
 <ul>
 <li><strong><a href="../athena_source/">AthenaSource</a>:</strong> Manages AWS Data Catalog and Athena</li>
 <li><strong><a href="../feature_set_core/">FeatureSetCore</a>:</strong> Manages AWS Feature Store and Feature Groups</li>
 <li><strong><a href="../model_core/">ModelCore</a>:</strong> Manages the training and deployment of AWS Model Groups and Packages</li>
 <li><strong><a href="../endpoint_core/">EndpointCore</a>:</strong> Manages the deployment and invocations/inference on AWS Endpoints</li>
 </ul>
-<p><img alt="ML Pipeline" src="../../../images/sageworks_concepts.png" /></p>
+<p><img alt="ML Pipeline" src="../../../images/workbench_concepts.png" /></p>
 
 
 
diff --git a/core_classes/overview/index.html b/core_classes/overview/index.html
index 27c1c9b1d..b622dd532 100644
--- a/core_classes/overview/index.html
+++ b/core_classes/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
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@@ -211,13 +211,13 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
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   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
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@@ -1877,7 +1877,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
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@@ -1887,7 +1887,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3106,11 +3106,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3196,13 +3196,13 @@
 
 <h1 id="core-classes">Core Classes</h1>
 <div class="admonition warning inline end">
-<p class="admonition-title">SageWorks Core Classes</p>
+<p class="admonition-title">Workbench Core Classes</p>
 <p>These classes interact with many of the Cloud Platform services and are therefore more complex. They provide additional control and refinement over the AWS ML Pipline. For most use cases the <a href="../../api_classes/overview/">API Classes</a> should be used</p>
 </div>
-<p><strong>Welcome to the SageWorks Core Classes</strong></p>
-<p>The Core Classes provide low-level APIs for the SageWorks package, these classes directly interface with the AWS Sagemaker Pipeline interfaces and have a large number of methods with reasonable complexity.</p>
+<p><strong>Welcome to the Workbench Core Classes</strong></p>
+<p>The Core Classes provide low-level APIs for the Workbench package, these classes directly interface with the AWS Sagemaker Pipeline interfaces and have a large number of methods with reasonable complexity.</p>
 <p>The <a href="../../api_classes/overview/">API Classes</a> have method pass-through so just call the method on the API Class and voilà it works the same.</p>
-<p><img alt="ML Pipeline" src="../../images/sageworks_concepts.png" /></p>
+<p><img alt="ML Pipeline" src="../../images/workbench_concepts.png" /></p>
 <h2 id="artifacts">Artifacts</h2>
 <ul>
 <li><strong><a href="../artifacts/athena_source/">AthenaSource</a>:</strong> Manages AWS Data Catalog and Athena</li>
diff --git a/core_classes/transforms/data_loaders_heavy/index.html b/core_classes/transforms/data_loaders_heavy/index.html
index 7f0ce2880..8c8e44d6e 100644
--- a/core_classes/transforms/data_loaders_heavy/index.html
+++ b/core_classes/transforms/data_loaders_heavy/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Data Loaders Heavy - SageWorks</title>
+        <title>Data Loaders Heavy - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
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@@ -211,13 +211,13 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
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-    SageWorks
+    Workbench
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-    SageWorks REPL
+    Workbench REPL
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@@ -1502,7 +1502,7 @@
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-  <a href="#sageworks.core.transforms.data_loaders.heavy" class="md-nav__link">
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@@ -1539,7 +1539,7 @@
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     <span class="md-ellipsis">
       timestamp_conversions
     </span>
@@ -1548,7 +1548,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform" class="md-nav__link">
     <span class="md-ellipsis">
       transform
     </span>
@@ -1921,7 +1921,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1931,7 +1931,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3150,11 +3150,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3206,7 +3206,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.heavy" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.heavy" class="md-nav__link">
     <span class="md-ellipsis">
       heavy
     </span>
@@ -3215,7 +3215,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class="md-nav__link">
     <span class="md-ellipsis">
       S3HeavyToDataSource
     </span>
@@ -3225,7 +3225,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3234,7 +3234,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_periods_from_columns" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_periods_from_columns" class="md-nav__link">
     <span class="md-ellipsis">
       remove_periods_from_columns
     </span>
@@ -3243,7 +3243,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timestamp_conversions" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timestamp_conversions" class="md-nav__link">
     <span class="md-ellipsis">
       timestamp_conversions
     </span>
@@ -3252,7 +3252,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform" class="md-nav__link">
     <span class="md-ellipsis">
       transform
     </span>
@@ -3289,10 +3289,10 @@ <h1 id="dataloaders-heavy">DataLoaders Heavy</h1>
 
 
 
-<a id="sageworks.core.transforms.data_loaders.heavy"></a>
+<a id="workbench.core.transforms.data_loaders.heavy"></a>
     <div class="doc doc-contents first">
 
-        <p>Welcome to the SageWorks DataLoaders Heavy Classes</p>
+        <p>Welcome to the Workbench DataLoaders Heavy Classes</p>
 <p>These classes provide low-level APIs for loading larger data into AWS services</p>
 <ul>
 <li>S3HeavyToDataSource: Loads large data from S3 into a DataSource</li>
@@ -3318,7 +3318,7 @@ <h1 id="dataloaders-heavy">DataLoaders Heavy</h1>
 
 
 
-<h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class="doc doc-heading">
+<h2 id="workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class="doc doc-heading">
             <code>S3HeavyToDataSource</code>
 
 
@@ -3334,7 +3334,7 @@ <h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class=
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
 <span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
 <span class="normal"><a href="#__codelineno-0-19"> 19</a></span>
@@ -3540,21 +3540,21 @@ <h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class=
 <span class="normal"><a href="#__codelineno-0-219">219</a></span>
 <span class="normal"><a href="#__codelineno-0-220">220</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="k">class</span> <span class="nc">S3HeavyToDataSource</span><span class="p">:</span>
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">glue_context</span><span class="p">:</span> <span class="n">GlueContext</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;S3HeavyToDataSource: Class to move HEAVY S3 Files into a SageWorks DataSource</span>
+</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;S3HeavyToDataSource: Class to move HEAVY S3 Files into a Workbench DataSource</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">            glue_context: GlueContext, AWS Glue Specific wrapper around SparkContext</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">            input_uuid (str): The S3 Path to the files to be loaded</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">            output_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">            output_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">glue_context</span><span class="o">.</span><span class="n">get_logger</span><span class="p">()</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="c1"># FIXME: Pull these from Parameter Store or Config</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="n">input_uuid</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="n">output_uuid</span>
-</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>        <span class="n">sageworks_bucket</span> <span class="o">=</span> <span class="s2">&quot;s3://sandbox-sageworks-artifacts&quot;</span>
-</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
+</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>        <span class="n">workbench_bucket</span> <span class="o">=</span> <span class="s2">&quot;s3://sandbox-workbench-artifacts&quot;</span>
+</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>        <span class="c1"># Our Spark Context</span>
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">glue_context</span> <span class="o">=</span> <span class="n">glue_context</span>
@@ -3618,8 +3618,8 @@ <h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class=
 </span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>        <span class="n">timestamp_columns</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>        <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">,</span>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>    <span class="p">):</span>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the CSV or JSON data into Parquet Format in the SageWorks S3 Bucket, and</span>
-</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">        store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the CSV or JSON data into Parquet Format in the Workbench S3 Bucket, and</span>
+</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">        store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="sd">            input_type (str): The type of input files, either &#39;csv&#39; or &#39;json&#39;</span>
 </span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a><span class="sd">            timestamp_columns (list): A list of column names to convert to timestamp</span>
@@ -3684,11 +3684,11 @@ <h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class=
 </span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>            <span class="nb">format</span><span class="o">=</span><span class="n">output_format</span><span class="p">,</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>        <span class="p">)</span>
 </span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>
-</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>        <span class="c1"># Set up our SageWorks metadata (description, tags, etc)</span>
-</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>        <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;SageWorks data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
-</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>        <span class="n">sageworks_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)}</span>
+</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>        <span class="c1"># Set up our Workbench metadata (description, tags, etc)</span>
+</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>        <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Workbench data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
+</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>        <span class="n">workbench_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)}</span>
 </span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a>        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>            <span class="n">sageworks_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>            <span class="n">workbench_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
 </span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a>
 </span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a>        <span class="c1"># Create a new table in the AWS Data Catalog</span>
 </span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating Data Catalog Table: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
@@ -3714,7 +3714,7 @@ <h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class=
 </span><span id="__span-0-190"><a id="__codelineno-0-190" name="__codelineno-0-190"></a>        <span class="n">table_input</span> <span class="o">=</span> <span class="p">{</span>
 </span><span id="__span-0-191"><a id="__codelineno-0-191" name="__codelineno-0-191"></a>            <span class="s2">&quot;Name&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
 </span><span id="__span-0-192"><a id="__codelineno-0-192" name="__codelineno-0-192"></a>            <span class="s2">&quot;Description&quot;</span><span class="p">:</span> <span class="n">description</span><span class="p">,</span>
-</span><span id="__span-0-193"><a id="__codelineno-0-193" name="__codelineno-0-193"></a>            <span class="s2">&quot;Parameters&quot;</span><span class="p">:</span> <span class="n">sageworks_meta</span><span class="p">,</span>
+</span><span id="__span-0-193"><a id="__codelineno-0-193" name="__codelineno-0-193"></a>            <span class="s2">&quot;Parameters&quot;</span><span class="p">:</span> <span class="n">workbench_meta</span><span class="p">,</span>
 </span><span id="__span-0-194"><a id="__codelineno-0-194" name="__codelineno-0-194"></a>            <span class="s2">&quot;TableType&quot;</span><span class="p">:</span> <span class="s2">&quot;EXTERNAL_TABLE&quot;</span><span class="p">,</span>
 </span><span id="__span-0-195"><a id="__codelineno-0-195" name="__codelineno-0-195"></a>            <span class="s2">&quot;StorageDescriptor&quot;</span><span class="p">:</span> <span class="p">{</span>
 </span><span id="__span-0-196"><a id="__codelineno-0-196" name="__codelineno-0-196"></a>                <span class="s2">&quot;Columns&quot;</span><span class="p">:</span> <span class="n">column_name_types</span><span class="p">,</span>
@@ -3731,14 +3731,14 @@ <h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class=
 </span><span id="__span-0-207"><a id="__codelineno-0-207" name="__codelineno-0-207"></a>        <span class="c1"># Delete the Data Catalog Table if it already exists</span>
 </span><span id="__span-0-208"><a id="__codelineno-0-208" name="__codelineno-0-208"></a>        <span class="n">glue_client</span> <span class="o">=</span> <span class="n">boto3</span><span class="o">.</span><span class="n">client</span><span class="p">(</span><span class="s2">&quot;glue&quot;</span><span class="p">)</span>
 </span><span id="__span-0-209"><a id="__codelineno-0-209" name="__codelineno-0-209"></a>        <span class="k">try</span><span class="p">:</span>
-</span><span id="__span-0-210"><a id="__codelineno-0-210" name="__codelineno-0-210"></a>            <span class="n">glue_client</span><span class="o">.</span><span class="n">delete_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="n">Name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
+</span><span id="__span-0-210"><a id="__codelineno-0-210" name="__codelineno-0-210"></a>            <span class="n">glue_client</span><span class="o">.</span><span class="n">delete_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="n">Name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-211"><a id="__codelineno-0-211" name="__codelineno-0-211"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Deleting Data Catalog Table: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-212"><a id="__codelineno-0-212" name="__codelineno-0-212"></a>        <span class="k">except</span> <span class="n">ClientError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
 </span><span id="__span-0-213"><a id="__codelineno-0-213" name="__codelineno-0-213"></a>            <span class="k">if</span> <span class="n">e</span><span class="o">.</span><span class="n">response</span><span class="p">[</span><span class="s2">&quot;Error&quot;</span><span class="p">][</span><span class="s2">&quot;Code&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;EntityNotFoundException&quot;</span><span class="p">:</span>
 </span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a>                <span class="k">raise</span> <span class="n">e</span>
 </span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>
 </span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating Data Catalog Table: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>        <span class="n">glue_client</span><span class="o">.</span><span class="n">create_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="n">TableInput</span><span class="o">=</span><span class="n">table_input</span><span class="p">)</span>
+</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>        <span class="n">glue_client</span><span class="o">.</span><span class="n">create_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="n">TableInput</span><span class="o">=</span><span class="n">table_input</span><span class="p">)</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a>
 </span><span id="__span-0-219"><a id="__codelineno-0-219" name="__codelineno-0-219"></a>        <span class="c1"># All done!</span>
 </span><span id="__span-0-220"><a id="__codelineno-0-220" name="__codelineno-0-220"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="si">}</span><span class="s2"> --&gt; </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2"> complete!&quot;</span><span class="p">)</span>
@@ -3760,7 +3760,7 @@ <h2 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">glue_context</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -3768,7 +3768,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init_
 
     <div class="doc doc-contents ">
 
-        <p>S3HeavyToDataSource: Class to move HEAVY S3 Files into a SageWorks DataSource</p>
+        <p>S3HeavyToDataSource: Class to move HEAVY S3 Files into a Workbench DataSource</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -3823,7 +3823,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init_
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks DataSource to be created</p>
+                <p>The UUID of the Workbench DataSource to be created</p>
               </div>
             </td>
             <td>
@@ -3834,7 +3834,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init_
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-18">18</a></span>
 <span class="normal"><a href="#__codelineno-0-19">19</a></span>
 <span class="normal"><a href="#__codelineno-0-20">20</a></span>
@@ -3854,21 +3854,21 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init_
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span>
 <span class="normal"><a href="#__codelineno-0-36">36</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">glue_context</span><span class="p">:</span> <span class="n">GlueContext</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;S3HeavyToDataSource: Class to move HEAVY S3 Files into a SageWorks DataSource</span>
+</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;S3HeavyToDataSource: Class to move HEAVY S3 Files into a Workbench DataSource</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        glue_context: GlueContext, AWS Glue Specific wrapper around SparkContext</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">        input_uuid (str): The S3 Path to the files to be loaded</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">        output_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">        output_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">glue_context</span><span class="o">.</span><span class="n">get_logger</span><span class="p">()</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="c1"># FIXME: Pull these from Parameter Store or Config</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="n">input_uuid</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="n">output_uuid</span>
-</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>    <span class="n">sageworks_bucket</span> <span class="o">=</span> <span class="s2">&quot;s3://sandbox-sageworks-artifacts&quot;</span>
-</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
+</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a>    <span class="n">workbench_bucket</span> <span class="o">=</span> <span class="s2">&quot;s3://sandbox-workbench-artifacts&quot;</span>
+</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>    <span class="c1"># Our Spark Context</span>
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">glue_context</span> <span class="o">=</span> <span class="n">glue_context</span>
@@ -3881,7 +3881,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_periods_from_columns" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_periods_from_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">remove_periods_from_columns</span><span class="p">(</span><span class="n">dyf</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -3900,7 +3900,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_
     DynamicFrame: The converted DynamicFrame</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-70">70</a></span>
 <span class="normal"><a href="#__codelineno-0-71">71</a></span>
 <span class="normal"><a href="#__codelineno-0-72">72</a></span>
@@ -3949,7 +3949,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timestamp_conversions" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timestamp_conversions" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">timestamp_conversions</span><span class="p">(</span><span class="n">dyf</span><span class="p">,</span> <span class="n">time_columns</span><span class="o">=</span><span class="p">[])</span></code>
 
 </h3>
@@ -3965,7 +3965,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timesta
     DynamicFrame: The converted DynamicFrame</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-53">53</a></span>
 <span class="normal"><a href="#__codelineno-0-54">54</a></span>
 <span class="normal"><a href="#__codelineno-0-55">55</a></span>
@@ -4006,7 +4006,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timesta
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform</span><span class="p">(</span><span class="n">input_type</span><span class="o">=</span><span class="s1">&#39;json&#39;</span><span class="p">,</span> <span class="n">timestamp_columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">output_format</span><span class="o">=</span><span class="s1">&#39;parquet&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -4014,15 +4014,15 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transfo
 
     <div class="doc doc-contents ">
 
-        <p>Convert the CSV or JSON data into Parquet Format in the SageWorks S3 Bucket, and
-store the information about the data to the AWS Data Catalog sageworks database
+        <p>Convert the CSV or JSON data into Parquet Format in the Workbench S3 Bucket, and
+store the information about the data to the AWS Data Catalog workbench database
 Args:
     input_type (str): The type of input files, either 'csv' or 'json'
     timestamp_columns (list): A list of column names to convert to timestamp
     output_format (str): The format of the output files, either 'parquet' or 'orc'</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-91"> 91</a></span>
 <span class="normal"><a href="#__codelineno-0-92"> 92</a></span>
 <span class="normal"><a href="#__codelineno-0-93"> 93</a></span>
@@ -4158,8 +4158,8 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transfo
 </span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="n">timestamp_columns</span><span class="p">:</span> <span class="nb">list</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
 </span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>    <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">,</span>
 </span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a><span class="p">):</span>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the CSV or JSON data into Parquet Format in the SageWorks S3 Bucket, and</span>
-</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">    store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the CSV or JSON data into Parquet Format in the Workbench S3 Bucket, and</span>
+</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">    store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="sd">        input_type (str): The type of input files, either &#39;csv&#39; or &#39;json&#39;</span>
 </span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a><span class="sd">        timestamp_columns (list): A list of column names to convert to timestamp</span>
@@ -4224,11 +4224,11 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transfo
 </span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>        <span class="nb">format</span><span class="o">=</span><span class="n">output_format</span><span class="p">,</span>
 </span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>    <span class="p">)</span>
 </span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>
-</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>    <span class="c1"># Set up our SageWorks metadata (description, tags, etc)</span>
-</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>    <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;SageWorks data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
-</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>    <span class="n">sageworks_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)}</span>
+</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>    <span class="c1"># Set up our Workbench metadata (description, tags, etc)</span>
+</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>    <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Workbench data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
+</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>    <span class="n">workbench_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)}</span>
 </span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a>    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>        <span class="n">sageworks_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>        <span class="n">workbench_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
 </span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a>
 </span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a>    <span class="c1"># Create a new table in the AWS Data Catalog</span>
 </span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating Data Catalog Table: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
@@ -4254,7 +4254,7 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transfo
 </span><span id="__span-0-190"><a id="__codelineno-0-190" name="__codelineno-0-190"></a>    <span class="n">table_input</span> <span class="o">=</span> <span class="p">{</span>
 </span><span id="__span-0-191"><a id="__codelineno-0-191" name="__codelineno-0-191"></a>        <span class="s2">&quot;Name&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
 </span><span id="__span-0-192"><a id="__codelineno-0-192" name="__codelineno-0-192"></a>        <span class="s2">&quot;Description&quot;</span><span class="p">:</span> <span class="n">description</span><span class="p">,</span>
-</span><span id="__span-0-193"><a id="__codelineno-0-193" name="__codelineno-0-193"></a>        <span class="s2">&quot;Parameters&quot;</span><span class="p">:</span> <span class="n">sageworks_meta</span><span class="p">,</span>
+</span><span id="__span-0-193"><a id="__codelineno-0-193" name="__codelineno-0-193"></a>        <span class="s2">&quot;Parameters&quot;</span><span class="p">:</span> <span class="n">workbench_meta</span><span class="p">,</span>
 </span><span id="__span-0-194"><a id="__codelineno-0-194" name="__codelineno-0-194"></a>        <span class="s2">&quot;TableType&quot;</span><span class="p">:</span> <span class="s2">&quot;EXTERNAL_TABLE&quot;</span><span class="p">,</span>
 </span><span id="__span-0-195"><a id="__codelineno-0-195" name="__codelineno-0-195"></a>        <span class="s2">&quot;StorageDescriptor&quot;</span><span class="p">:</span> <span class="p">{</span>
 </span><span id="__span-0-196"><a id="__codelineno-0-196" name="__codelineno-0-196"></a>            <span class="s2">&quot;Columns&quot;</span><span class="p">:</span> <span class="n">column_name_types</span><span class="p">,</span>
@@ -4271,14 +4271,14 @@ <h3 id="sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transfo
 </span><span id="__span-0-207"><a id="__codelineno-0-207" name="__codelineno-0-207"></a>    <span class="c1"># Delete the Data Catalog Table if it already exists</span>
 </span><span id="__span-0-208"><a id="__codelineno-0-208" name="__codelineno-0-208"></a>    <span class="n">glue_client</span> <span class="o">=</span> <span class="n">boto3</span><span class="o">.</span><span class="n">client</span><span class="p">(</span><span class="s2">&quot;glue&quot;</span><span class="p">)</span>
 </span><span id="__span-0-209"><a id="__codelineno-0-209" name="__codelineno-0-209"></a>    <span class="k">try</span><span class="p">:</span>
-</span><span id="__span-0-210"><a id="__codelineno-0-210" name="__codelineno-0-210"></a>        <span class="n">glue_client</span><span class="o">.</span><span class="n">delete_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="n">Name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
+</span><span id="__span-0-210"><a id="__codelineno-0-210" name="__codelineno-0-210"></a>        <span class="n">glue_client</span><span class="o">.</span><span class="n">delete_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="n">Name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-211"><a id="__codelineno-0-211" name="__codelineno-0-211"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Deleting Data Catalog Table: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-212"><a id="__codelineno-0-212" name="__codelineno-0-212"></a>    <span class="k">except</span> <span class="n">ClientError</span> <span class="k">as</span> <span class="n">e</span><span class="p">:</span>
 </span><span id="__span-0-213"><a id="__codelineno-0-213" name="__codelineno-0-213"></a>        <span class="k">if</span> <span class="n">e</span><span class="o">.</span><span class="n">response</span><span class="p">[</span><span class="s2">&quot;Error&quot;</span><span class="p">][</span><span class="s2">&quot;Code&quot;</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;EntityNotFoundException&quot;</span><span class="p">:</span>
 </span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a>            <span class="k">raise</span> <span class="n">e</span>
 </span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>
 </span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating Data Catalog Table: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>    <span class="n">glue_client</span><span class="o">.</span><span class="n">create_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;sageworks&quot;</span><span class="p">,</span> <span class="n">TableInput</span><span class="o">=</span><span class="n">table_input</span><span class="p">)</span>
+</span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>    <span class="n">glue_client</span><span class="o">.</span><span class="n">create_table</span><span class="p">(</span><span class="n">DatabaseName</span><span class="o">=</span><span class="s2">&quot;workbench&quot;</span><span class="p">,</span> <span class="n">TableInput</span><span class="o">=</span><span class="n">table_input</span><span class="p">)</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a>
 </span><span id="__span-0-219"><a id="__codelineno-0-219" name="__codelineno-0-219"></a>    <span class="c1"># All done!</span>
 </span><span id="__span-0-220"><a id="__codelineno-0-220" name="__codelineno-0-220"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="si">}</span><span class="s2"> --&gt; </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2"> complete!&quot;</span><span class="p">)</span>
diff --git a/core_classes/transforms/data_loaders_light/index.html b/core_classes/transforms/data_loaders_light/index.html
index d79894263..05323f114 100644
--- a/core_classes/transforms/data_loaders_light/index.html
+++ b/core_classes/transforms/data_loaders_light/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Data Loaders Light - SageWorks</title>
+        <title>Data Loaders Light - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1481,7 +1481,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light" class="md-nav__link">
     <span class="md-ellipsis">
       light
     </span>
@@ -1490,7 +1490,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource" class="md-nav__link">
     <span class="md-ellipsis">
       CSVToDataSource
     </span>
@@ -1500,7 +1500,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1509,7 +1509,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -1518,7 +1518,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -1532,7 +1532,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource" class="md-nav__link">
     <span class="md-ellipsis">
       JSONToDataSource
     </span>
@@ -1542,7 +1542,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1551,7 +1551,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -1560,7 +1560,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -1574,7 +1574,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight" class="md-nav__link">
     <span class="md-ellipsis">
       S3ToDataSourceLight
     </span>
@@ -1584,7 +1584,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1593,7 +1593,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb" class="md-nav__link">
     <span class="md-ellipsis">
       input_size_mb
     </span>
@@ -1602,7 +1602,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -1611,7 +1611,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -2005,7 +2005,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -2015,7 +2015,7 @@
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           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3234,11 +3234,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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@@ -3290,7 +3290,7 @@
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         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light" class="md-nav__link">
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       light
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@@ -3299,7 +3299,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource" class="md-nav__link">
     <span class="md-ellipsis">
       CSVToDataSource
     </span>
@@ -3309,7 +3309,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3318,7 +3318,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3327,7 +3327,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.CSVToDataSource.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3341,7 +3341,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource" class="md-nav__link">
     <span class="md-ellipsis">
       JSONToDataSource
     </span>
@@ -3351,7 +3351,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3360,7 +3360,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3369,7 +3369,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.JSONToDataSource.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3383,7 +3383,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight" class="md-nav__link">
     <span class="md-ellipsis">
       S3ToDataSourceLight
     </span>
@@ -3393,7 +3393,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3402,7 +3402,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb" class="md-nav__link">
     <span class="md-ellipsis">
       input_size_mb
     </span>
@@ -3411,7 +3411,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3420,7 +3420,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3460,10 +3460,10 @@ <h1 id="dataloaders-light">DataLoaders Light</h1>
 
 
 
-<a id="sageworks.core.transforms.data_loaders.light"></a>
+<a id="workbench.core.transforms.data_loaders.light"></a>
     <div class="doc doc-contents first">
 
-        <p>Welcome to the SageWorks DataLoaders Light Classes</p>
+        <p>Welcome to the Workbench DataLoaders Light Classes</p>
 <p>These classes provide low-level APIs for loading smaller data into AWS services</p>
 <ul>
 <li>CSVToDataSource: Loads local CSV data into a DataSource</li>
@@ -3491,7 +3491,7 @@ <h1 id="dataloaders-light">DataLoaders Light</h1>
 
 
 
-<h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc doc-heading">
+<h2 id="workbench.core.transforms.data_loaders.light.CSVToDataSource" class="doc doc-heading">
             <code>CSVToDataSource</code>
 
 
@@ -3500,10 +3500,10 @@ <h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
-        <p>CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource</p>
+        <p>CSVToDataSource: Class to move local CSV Files into a Workbench DataSource</p>
 
 
 <details class="common-usage" open>
@@ -3519,7 +3519,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
 <span class="normal"><a href="#__codelineno-0-14">14</a></span>
@@ -3572,7 +3572,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc
 <span class="normal"><a href="#__codelineno-0-61">61</a></span>
 <span class="normal"><a href="#__codelineno-0-62">62</a></span>
 <span class="normal"><a href="#__codelineno-0-63">63</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="k">class</span> <span class="nc">CSVToDataSource</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CSVToDataSource: Class to move local CSV Files into a Workbench DataSource</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="sd">        ```python</span>
@@ -3583,11 +3583,11 @@ <h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">csv_file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;CSVToDataSource: Class to move local CSV Files into a Workbench DataSource</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">            csv_file_path (str): The path to the CSV file to be transformed</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">            data_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">            data_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>        <span class="c1"># Call superclass init</span>
@@ -3598,8 +3598,8 @@ <h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="n">TransformOutput</span><span class="o">.</span><span class="n">DATA_SOURCE</span>
 </span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>
 </span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a>    <span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the local CSV file into Parquet Format in the SageWorks Data Sources Bucket, and</span>
-</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">        store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the local CSV file into Parquet Format in the Workbench Data Sources Bucket, and</span>
+</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">        store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>
 </span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>        <span class="c1"># Report the transformation initiation</span>
@@ -3610,7 +3610,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="n">df</span> <span class="o">=</span> <span class="n">convert_object_columns</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
-</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="c1"># Use the SageWorks Pandas to Data Source class</span>
+</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="c1"># Use the Workbench Pandas to Data Source class</span>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>        <span class="n">pandas_to_data</span> <span class="o">=</span> <span class="n">PandasToData</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>        <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>        <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">)</span>
@@ -3641,7 +3641,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.CSVToDataSource.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">csv_file_path</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -3649,7 +3649,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" c
 
     <div class="doc doc-contents ">
 
-        <p>CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource</p>
+        <p>CSVToDataSource: Class to move local CSV Files into a Workbench DataSource</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -3688,7 +3688,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" c
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks DataSource to be created</p>
+                <p>The UUID of the Workbench DataSource to be created</p>
               </div>
             </td>
             <td>
@@ -3699,7 +3699,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-23">23</a></span>
 <span class="normal"><a href="#__codelineno-0-24">24</a></span>
 <span class="normal"><a href="#__codelineno-0-25">25</a></span>
@@ -3714,11 +3714,11 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" c
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span>
 <span class="normal"><a href="#__codelineno-0-36">36</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">csv_file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;CSVToDataSource: Class to move local CSV Files into a Workbench DataSource</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">        csv_file_path (str): The path to the CSV file to be transformed</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">        data_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">        data_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>    <span class="c1"># Call superclass init</span>
@@ -3736,7 +3736,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.CSVToDataSource.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3747,7 +3747,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.post_transf
         <p>Post-Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-61">61</a></span>
 <span class="normal"><a href="#__codelineno-0-62">62</a></span>
 <span class="normal"><a href="#__codelineno-0-63">63</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
@@ -3762,7 +3762,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.post_transf
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.CSVToDataSource.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -3770,11 +3770,11 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_i
 
     <div class="doc doc-contents ">
 
-        <p>Convert the local CSV file into Parquet Format in the SageWorks Data Sources Bucket, and
-store the information about the data to the AWS Data Catalog sageworks database</p>
+        <p>Convert the local CSV file into Parquet Format in the Workbench Data Sources Bucket, and
+store the information about the data to the AWS Data Catalog workbench database</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-38">38</a></span>
 <span class="normal"><a href="#__codelineno-0-39">39</a></span>
 <span class="normal"><a href="#__codelineno-0-40">40</a></span>
@@ -3797,8 +3797,8 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_i
 <span class="normal"><a href="#__codelineno-0-57">57</a></span>
 <span class="normal"><a href="#__codelineno-0-58">58</a></span>
 <span class="normal"><a href="#__codelineno-0-59">59</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the local CSV file into Parquet Format in the SageWorks Data Sources Bucket, and</span>
-</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">    store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the local CSV file into Parquet Format in the Workbench Data Sources Bucket, and</span>
+</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">    store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>
 </span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="c1"># Report the transformation initiation</span>
@@ -3809,7 +3809,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_i
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">,</span> <span class="n">low_memory</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="n">df</span> <span class="o">=</span> <span class="n">convert_object_columns</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
-</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="c1"># Use the SageWorks Pandas to Data Source class</span>
+</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="c1"># Use the Workbench Pandas to Data Source class</span>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="n">pandas_to_data</span> <span class="o">=</span> <span class="n">PandasToData</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>    <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>    <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">)</span>
@@ -3836,7 +3836,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_i
 
 
 
-<h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="doc doc-heading">
+<h2 id="workbench.core.transforms.data_loaders.light.JSONToDataSource" class="doc doc-heading">
             <code>JSONToDataSource</code>
 
 
@@ -3845,10 +3845,10 @@ <h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="do
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
-        <p>JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource</p>
+        <p>JSONToDataSource: Class to move local JSON Files into a Workbench DataSource</p>
 
 
 <details class="common-usage" open>
@@ -3864,7 +3864,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="do
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-11">11</a></span>
 <span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
@@ -3916,7 +3916,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="do
 <span class="normal"><a href="#__codelineno-0-59">59</a></span>
 <span class="normal"><a href="#__codelineno-0-60">60</a></span>
 <span class="normal"><a href="#__codelineno-0-61">61</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11"></a><span class="k">class</span> <span class="nc">JSONToDataSource</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource</span>
+</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;JSONToDataSource: Class to move local JSON Files into a Workbench DataSource</span>
 </span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="sd">        ```python</span>
@@ -3927,11 +3927,11 @@ <h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="do
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">json_file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource</span>
+</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;JSONToDataSource: Class to move local JSON Files into a Workbench DataSource</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">            json_file_path (str): The path to the JSON file to be transformed</span>
-</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">            data_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">            data_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>        <span class="c1"># Call superclass init</span>
@@ -3942,8 +3942,8 @@ <h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="do
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="n">TransformOutput</span><span class="o">.</span><span class="n">DATA_SOURCE</span>
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>
 </span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>    <span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the local JSON file into Parquet Format in the SageWorks Data Sources Bucket, and</span>
-</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">        store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the local JSON file into Parquet Format in the Workbench Data Sources Bucket, and</span>
+</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">        store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>        <span class="c1"># Report the transformation initiation</span>
@@ -3953,7 +3953,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="do
 </span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>        <span class="c1"># Read in the Local JSON as a Pandas DataFrame</span>
 </span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">,</span> <span class="n">lines</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>
-</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="c1"># Use the SageWorks Pandas to Data Source class</span>
+</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="c1"># Use the Workbench Pandas to Data Source class</span>
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>        <span class="n">pandas_to_data</span> <span class="o">=</span> <span class="n">PandasToData</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>        <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">)</span>
@@ -3984,7 +3984,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.JSONToDataSource.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">json_file_path</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -3992,7 +3992,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__"
 
     <div class="doc doc-contents ">
 
-        <p>JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource</p>
+        <p>JSONToDataSource: Class to move local JSON Files into a Workbench DataSource</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -4031,7 +4031,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__"
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks DataSource to be created</p>
+                <p>The UUID of the Workbench DataSource to be created</p>
               </div>
             </td>
             <td>
@@ -4042,7 +4042,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-22">22</a></span>
 <span class="normal"><a href="#__codelineno-0-23">23</a></span>
 <span class="normal"><a href="#__codelineno-0-24">24</a></span>
@@ -4057,11 +4057,11 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__"
 <span class="normal"><a href="#__codelineno-0-33">33</a></span>
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">json_file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource</span>
+</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;JSONToDataSource: Class to move local JSON Files into a Workbench DataSource</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">        json_file_path (str): The path to the JSON file to be transformed</span>
-</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">        data_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">        data_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>    <span class="c1"># Call superclass init</span>
@@ -4079,7 +4079,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.JSONToDataSource.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4090,7 +4090,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.post_trans
         <p>Post-Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-59">59</a></span>
 <span class="normal"><a href="#__codelineno-0-60">60</a></span>
 <span class="normal"><a href="#__codelineno-0-61">61</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
@@ -4105,7 +4105,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.post_trans
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.JSONToDataSource.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -4113,11 +4113,11 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_
 
     <div class="doc doc-contents ">
 
-        <p>Convert the local JSON file into Parquet Format in the SageWorks Data Sources Bucket, and
-store the information about the data to the AWS Data Catalog sageworks database</p>
+        <p>Convert the local JSON file into Parquet Format in the Workbench Data Sources Bucket, and
+store the information about the data to the AWS Data Catalog workbench database</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-37">37</a></span>
 <span class="normal"><a href="#__codelineno-0-38">38</a></span>
 <span class="normal"><a href="#__codelineno-0-39">39</a></span>
@@ -4139,8 +4139,8 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_
 <span class="normal"><a href="#__codelineno-0-55">55</a></span>
 <span class="normal"><a href="#__codelineno-0-56">56</a></span>
 <span class="normal"><a href="#__codelineno-0-57">57</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the local JSON file into Parquet Format in the SageWorks Data Sources Bucket, and</span>
-</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">    store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the local JSON file into Parquet Format in the Workbench Data Sources Bucket, and</span>
+</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="sd">    store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>
 </span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="c1"># Report the transformation initiation</span>
@@ -4150,7 +4150,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_
 </span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>    <span class="c1"># Read in the Local JSON as a Pandas DataFrame</span>
 </span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>    <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_json</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">,</span> <span class="n">lines</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>
-</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="c1"># Use the SageWorks Pandas to Data Source class</span>
+</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="c1"># Use the Workbench Pandas to Data Source class</span>
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="n">pandas_to_data</span> <span class="o">=</span> <span class="n">PandasToData</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">)</span>
@@ -4177,7 +4177,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_
 
 
 
-<h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class="doc doc-heading">
+<h2 id="workbench.core.transforms.data_loaders.light.S3ToDataSourceLight" class="doc doc-heading">
             <code>S3ToDataSourceLight</code>
 
 
@@ -4186,10 +4186,10 @@ <h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class=
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
-        <p>S3ToDataSourceLight: Class to move LIGHT S3 Files into a SageWorks DataSource</p>
+        <p>S3ToDataSourceLight: Class to move LIGHT S3 Files into a Workbench DataSource</p>
 
 
 <details class="common-usage" open>
@@ -4205,7 +4205,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class=
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
 <span class="normal"><a href="#__codelineno-0-14">14</a></span>
@@ -4277,7 +4277,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class=
 <span class="normal"><a href="#__codelineno-0-80">80</a></span>
 <span class="normal"><a href="#__codelineno-0-81">81</a></span>
 <span class="normal"><a href="#__codelineno-0-82">82</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="k">class</span> <span class="nc">S3ToDataSourceLight</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;S3ToDataSourceLight: Class to move LIGHT S3 Files into a SageWorks DataSource</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;S3ToDataSourceLight: Class to move LIGHT S3 Files into a Workbench DataSource</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="sd">        ```python</span>
@@ -4292,7 +4292,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class=
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">            s3_path (str): The S3 Path to the file to be transformed</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">            data_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">            data_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">            datatype (str): The datatype of the file to be transformed (defaults to &quot;csv&quot;)</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>
@@ -4311,8 +4311,8 @@ <h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class=
 </span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>        <span class="k">return</span> <span class="n">size_in_mb</span>
 </span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>
 </span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>    <span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the S3 CSV data into Parquet Format in the SageWorks Data Sources Bucket, and</span>
-</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="sd">        store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the S3 CSV data into Parquet Format in the Workbench Data Sources Bucket, and</span>
+</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="sd">        store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="c1"># Sanity Check for S3 Object size</span>
@@ -4329,12 +4329,12 @@ <h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class=
 </span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="c1"># Temporary hack to limit the number of columns in the dataframe</span>
 </span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">40</span><span class="p">:</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="si">}</span><span class="s2"> Too Many Columns! Talk to SageWorks Support...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="si">}</span><span class="s2"> Too Many Columns! Talk to Workbench Support...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>        <span class="c1"># Convert object columns before sending to SageWorks Data Source</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>        <span class="c1"># Convert object columns before sending to Workbench Data Source</span>
 </span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>        <span class="n">df</span> <span class="o">=</span> <span class="n">convert_object_columns</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>
-</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>        <span class="c1"># Use the SageWorks Pandas to Data Source class</span>
+</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>        <span class="c1"># Use the Workbench Pandas to Data Source class</span>
 </span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>        <span class="n">pandas_to_data</span> <span class="o">=</span> <span class="n">PandasToData</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>        <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>        <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">)</span>
@@ -4365,7 +4365,7 @@ <h2 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight" class=
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">s3_path</span><span class="p">,</span> <span class="n">data_uuid</span><span class="p">,</span> <span class="n">datatype</span><span class="o">=</span><span class="s1">&#39;csv&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -4412,7 +4412,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init_
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks DataSource to be created</p>
+                <p>The UUID of the Workbench DataSource to be created</p>
               </div>
             </td>
             <td>
@@ -4439,7 +4439,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init_
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-23">23</a></span>
 <span class="normal"><a href="#__codelineno-0-24">24</a></span>
 <span class="normal"><a href="#__codelineno-0-25">25</a></span>
@@ -4460,7 +4460,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init_
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="sd">        s3_path (str): The S3 Path to the file to be transformed</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">        data_uuid (str): The UUID of the SageWorks DataSource to be created</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="sd">        data_uuid (str): The UUID of the Workbench DataSource to be created</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="sd">        datatype (str): The datatype of the file to be transformed (defaults to &quot;csv&quot;)</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a>
@@ -4480,7 +4480,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">input_size_mb</span><span class="p">()</span></code>
 
 </h3>
@@ -4491,7 +4491,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.input_s
         <p>Get the size of the input S3 object in MBytes</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-40">40</a></span>
 <span class="normal"><a href="#__codelineno-0-41">41</a></span>
 <span class="normal"><a href="#__codelineno-0-42">42</a></span>
@@ -4510,7 +4510,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.input_s
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4521,7 +4521,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.post_tr
         <p>Post-Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-80">80</a></span>
 <span class="normal"><a href="#__codelineno-0-81">81</a></span>
 <span class="normal"><a href="#__codelineno-0-82">82</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
@@ -4536,7 +4536,7 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.post_tr
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -4544,11 +4544,11 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.transfo
 
     <div class="doc doc-contents ">
 
-        <p>Convert the S3 CSV data into Parquet Format in the SageWorks Data Sources Bucket, and
-store the information about the data to the AWS Data Catalog sageworks database</p>
+        <p>Convert the S3 CSV data into Parquet Format in the Workbench Data Sources Bucket, and
+store the information about the data to the AWS Data Catalog workbench database</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-46">46</a></span>
 <span class="normal"><a href="#__codelineno-0-47">47</a></span>
 <span class="normal"><a href="#__codelineno-0-48">48</a></span>
@@ -4582,8 +4582,8 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.transfo
 <span class="normal"><a href="#__codelineno-0-76">76</a></span>
 <span class="normal"><a href="#__codelineno-0-77">77</a></span>
 <span class="normal"><a href="#__codelineno-0-78">78</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
-</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the S3 CSV data into Parquet Format in the SageWorks Data Sources Bucket, and</span>
-</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="sd">    store the information about the data to the AWS Data Catalog sageworks database</span>
+</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the S3 CSV data into Parquet Format in the Workbench Data Sources Bucket, and</span>
+</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="sd">    store the information about the data to the AWS Data Catalog workbench database</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="c1"># Sanity Check for S3 Object size</span>
@@ -4600,12 +4600,12 @@ <h3 id="sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.transfo
 </span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>    <span class="c1"># Temporary hack to limit the number of columns in the dataframe</span>
 </span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">40</span><span class="p">:</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="si">}</span><span class="s2"> Too Many Columns! Talk to SageWorks Support...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="si">}</span><span class="s2"> Too Many Columns! Talk to Workbench Support...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>    <span class="c1"># Convert object columns before sending to SageWorks Data Source</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>    <span class="c1"># Convert object columns before sending to Workbench Data Source</span>
 </span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>    <span class="n">df</span> <span class="o">=</span> <span class="n">convert_object_columns</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>
-</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>    <span class="c1"># Use the SageWorks Pandas to Data Source class</span>
+</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>    <span class="c1"># Use the Workbench Pandas to Data Source class</span>
 </span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>    <span class="n">pandas_to_data</span> <span class="o">=</span> <span class="n">PandasToData</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
 </span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>    <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_input</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
 </span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>    <span class="n">pandas_to_data</span><span class="o">.</span><span class="n">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">)</span>
diff --git a/core_classes/transforms/data_to_features/index.html b/core_classes/transforms/data_to_features/index.html
index aa2c945e7..3c155f56e 100644
--- a/core_classes/transforms/data_to_features/index.html
+++ b/core_classes/transforms/data_to_features/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Data To Features - SageWorks</title>
+        <title>Data To Features - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
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   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1523,7 +1523,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light" class="md-nav__link">
     <span class="md-ellipsis">
       data_to_features_light
     </span>
@@ -1532,7 +1532,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" class="md-nav__link">
     <span class="md-ellipsis">
       DataToFeaturesLight
     </span>
@@ -1542,7 +1542,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1551,7 +1551,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -1560,7 +1560,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform" class="md-nav__link">
     <span class="md-ellipsis">
       pre_transform
     </span>
@@ -1569,7 +1569,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -1583,7 +1583,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors" class="md-nav__link">
     <span class="md-ellipsis">
       molecular_descriptors
     </span>
@@ -1592,7 +1592,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors" class="md-nav__link">
     <span class="md-ellipsis">
       MolecularDescriptors
     </span>
@@ -1602,7 +1602,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1611,7 +1611,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -1963,7 +1963,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1973,7 +1973,7 @@
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           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3192,11 +3192,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3248,7 +3248,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light" class="md-nav__link">
     <span class="md-ellipsis">
       data_to_features_light
     </span>
@@ -3257,7 +3257,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" class="md-nav__link">
     <span class="md-ellipsis">
       DataToFeaturesLight
     </span>
@@ -3267,7 +3267,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3276,7 +3276,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3285,7 +3285,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform" class="md-nav__link">
     <span class="md-ellipsis">
       pre_transform
     </span>
@@ -3294,7 +3294,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3308,7 +3308,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors" class="md-nav__link">
     <span class="md-ellipsis">
       molecular_descriptors
     </span>
@@ -3317,7 +3317,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors" class="md-nav__link">
     <span class="md-ellipsis">
       MolecularDescriptors
     </span>
@@ -3327,7 +3327,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3336,7 +3336,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3375,7 +3375,7 @@ <h1 id="data-to-features">Data To Features</h1>
 
 
 
-<a id="sageworks.core.transforms.data_to_features.light.data_to_features_light"></a>
+<a id="workbench.core.transforms.data_to_features.light.data_to_features_light"></a>
     <div class="doc doc-contents first">
 
         <p>DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas</p>
@@ -3400,7 +3400,7 @@ <h1 id="data-to-features">Data To Features</h1>
 
 
 
-<h2 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" class="doc doc-heading">
+<h2 id="workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" class="doc doc-heading">
             <code>DataToFeaturesLight</code>
 
 
@@ -3409,7 +3409,7 @@ <h2 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas</p>
@@ -3428,7 +3428,7 @@ <h2 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-9"> 9</a></span>
 <span class="normal"><a href="#__codelineno-0-10">10</a></span>
 <span class="normal"><a href="#__codelineno-0-11">11</a></span>
@@ -3509,8 +3509,8 @@ <h2 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;DataToFeaturesLight Initialization</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">            data_uuid (str): The UUID of the SageWorks DataSource to be transformed</span>
-</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">            feature_uuid (str): The UUID of the SageWorks FeatureSet to be created</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">            data_uuid (str): The UUID of the Workbench DataSource to be transformed</span>
+</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">            feature_uuid (str): The UUID of the Workbench FeatureSet to be created</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>        <span class="c1"># Call superclass init</span>
@@ -3578,7 +3578,7 @@ <h2 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">feature_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -3609,7 +3609,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks DataSource to be transformed</p>
+                <p>The UUID of the Workbench DataSource to be transformed</p>
               </div>
             </td>
             <td>
@@ -3625,7 +3625,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks FeatureSet to be created</p>
+                <p>The UUID of the Workbench FeatureSet to be created</p>
               </div>
             </td>
             <td>
@@ -3636,7 +3636,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-20">20</a></span>
 <span class="normal"><a href="#__codelineno-0-21">21</a></span>
 <span class="normal"><a href="#__codelineno-0-22">22</a></span>
@@ -3656,8 +3656,8 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;DataToFeaturesLight Initialization</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">        data_uuid (str): The UUID of the SageWorks DataSource to be transformed</span>
-</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">        feature_uuid (str): The UUID of the SageWorks FeatureSet to be created</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">        data_uuid (str): The UUID of the Workbench DataSource to be transformed</span>
+</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">        feature_uuid (str): The UUID of the Workbench FeatureSet to be created</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="c1"># Call superclass init</span>
@@ -3677,7 +3677,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="n">id_column</span><span class="p">,</span> <span class="n">event_time_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">one_hot_columns</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3751,7 +3751,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-59">59</a></span>
 <span class="normal"><a href="#__codelineno-0-60">60</a></span>
 <span class="normal"><a href="#__codelineno-0-61">61</a></span>
@@ -3792,7 +3792,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pre_transform</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3805,7 +3805,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
     query(str): Optional query to filter the input DataFrame</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-37">37</a></span>
 <span class="normal"><a href="#__codelineno-0-38">38</a></span>
 <span class="normal"><a href="#__codelineno-0-39">39</a></span>
@@ -3844,7 +3844,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3855,7 +3855,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
         <p>Transform the input DataFrame into a Feature Set</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-53">53</a></span>
 <span class="normal"><a href="#__codelineno-0-54">54</a></span>
 <span class="normal"><a href="#__codelineno-0-55">55</a></span>
@@ -3892,7 +3892,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 
 
 
-<a id="sageworks.core.transforms.data_to_features.light.molecular_descriptors"></a>
+<a id="workbench.core.transforms.data_to_features.light.molecular_descriptors"></a>
     <div class="doc doc-contents first">
 
         <p>MolecularDescriptors: Compute a Feature Set based on RDKit Descriptors</p>
@@ -3926,7 +3926,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.data_to_features_light.
 
 
 
-<h2 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors" class="doc doc-heading">
+<h2 id="workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors" class="doc doc-heading">
             <code>MolecularDescriptors</code>
 
 
@@ -3935,7 +3935,7 @@ <h2 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" href="#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight">DataToFeaturesLight</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight" href="#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight">DataToFeaturesLight</a></code></p>
 
 
         <p>MolecularDescriptors: Create a FeatureSet (RDKit Descriptors) from a DataSource</p>
@@ -3954,7 +3954,7 @@ <h2 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/molecular_descriptors.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/molecular_descriptors.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-16">16</a></span>
 <span class="normal"><a href="#__codelineno-0-17">17</a></span>
 <span class="normal"><a href="#__codelineno-0-18">18</a></span>
@@ -3996,8 +3996,8 @@ <h2 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;MolecularDescriptors Initialization</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">            data_uuid (str): The UUID of the SageWorks DataSource to be transformed</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">            feature_uuid (str): The UUID of the SageWorks FeatureSet to be created</span>
+</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">            data_uuid (str): The UUID of the Workbench DataSource to be transformed</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">            feature_uuid (str): The UUID of the Workbench FeatureSet to be created</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>        <span class="c1"># Call superclass init</span>
@@ -4026,7 +4026,7 @@ <h2 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">data_uuid</span><span class="p">,</span> <span class="n">feature_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -4057,7 +4057,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks DataSource to be transformed</p>
+                <p>The UUID of the Workbench DataSource to be transformed</p>
               </div>
             </td>
             <td>
@@ -4073,7 +4073,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The UUID of the SageWorks FeatureSet to be created</p>
+                <p>The UUID of the Workbench FeatureSet to be created</p>
               </div>
             </td>
             <td>
@@ -4084,7 +4084,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/molecular_descriptors.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/molecular_descriptors.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-27">27</a></span>
 <span class="normal"><a href="#__codelineno-0-28">28</a></span>
 <span class="normal"><a href="#__codelineno-0-29">29</a></span>
@@ -4098,8 +4098,8 @@ <h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;MolecularDescriptors Initialization</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">        data_uuid (str): The UUID of the SageWorks DataSource to be transformed</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">        feature_uuid (str): The UUID of the SageWorks FeatureSet to be created</span>
+</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">        data_uuid (str): The UUID of the Workbench DataSource to be transformed</span>
+</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">        feature_uuid (str): The UUID of the Workbench FeatureSet to be created</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>
 </span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>    <span class="c1"># Call superclass init</span>
@@ -4113,7 +4113,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4124,7 +4124,7 @@ <h3 id="sageworks.core.transforms.data_to_features.light.molecular_descriptors.M
         <p>Compute a Feature Set based on RDKit Descriptors</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/data_to_features/light/molecular_descriptors.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/data_to_features/light/molecular_descriptors.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-38">38</a></span>
 <span class="normal"><a href="#__codelineno-0-39">39</a></span>
 <span class="normal"><a href="#__codelineno-0-40">40</a></span>
diff --git a/core_classes/transforms/features_to_model/index.html b/core_classes/transforms/features_to_model/index.html
index e5ccf05d8..2e5908fa5 100644
--- a/core_classes/transforms/features_to_model/index.html
+++ b/core_classes/transforms/features_to_model/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Features To Model - SageWorks</title>
+        <title>Features To Model - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
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         </div>
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@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
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-    SageWorks
+    Workbench
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@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
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@@ -1544,7 +1544,7 @@
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         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model" class="md-nav__link">
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       features_to_model
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@@ -1553,7 +1553,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel" class="md-nav__link">
     <span class="md-ellipsis">
       FeaturesToModel
     </span>
@@ -1563,7 +1563,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1572,7 +1572,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model" class="md-nav__link">
     <span class="md-ellipsis">
       create_and_register_model
     </span>
@@ -1581,7 +1581,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -1590,7 +1590,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -1996,7 +1996,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -2006,7 +2006,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3225,11 +3225,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3281,7 +3281,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model" class="md-nav__link">
     <span class="md-ellipsis">
       features_to_model
     </span>
@@ -3290,7 +3290,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel" class="md-nav__link">
     <span class="md-ellipsis">
       FeaturesToModel
     </span>
@@ -3300,7 +3300,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3309,7 +3309,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model" class="md-nav__link">
     <span class="md-ellipsis">
       create_and_register_model
     </span>
@@ -3318,7 +3318,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3327,7 +3327,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3441,7 +3441,7 @@ <h1 id="features-to-model">Features To Model</h1>
 
 
 
-<a id="sageworks.core.transforms.features_to_model.features_to_model"></a>
+<a id="workbench.core.transforms.features_to_model.features_to_model"></a>
     <div class="doc doc-contents first">
 
         <p>FeaturesToModel: Train/Create a Model from a Feature Set</p>
@@ -3466,7 +3466,7 @@ <h1 id="features-to-model">Features To Model</h1>
 
 
 
-<h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel" class="doc doc-heading">
+<h2 id="workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel" class="doc doc-heading">
             <code>FeaturesToModel</code>
 
 
@@ -3475,7 +3475,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>FeaturesToModel: Train/Create a Model from a FeatureSet</p>
@@ -3483,7 +3483,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 
 <details class="common-usage" open>
   <summary>Common Usage</summary>
-  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
+  <div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="n">to_model</span> <span class="o">=</span> <span class="n">FeaturesToModel</span><span class="p">(</span><span class="n">feature_uuid</span><span class="p">,</span> <span class="n">model_uuid</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="n">ModelType</span><span class="p">)</span>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="n">to_model</span><span class="o">.</span><span class="n">set_output_tags</span><span class="p">([</span><span class="s2">&quot;abalone&quot;</span><span class="p">,</span> <span class="s2">&quot;public&quot;</span><span class="p">,</span> <span class="s2">&quot;whatever&quot;</span><span class="p">])</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">to_model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">target_column</span><span class="o">=</span><span class="s2">&quot;class_number_of_rings&quot;</span><span class="p">,</span>
@@ -3496,7 +3496,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
 <span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
 <span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
@@ -3771,7 +3771,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">    Common Usage:</span>
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        ```python</span>
-</span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        from sageworks.core.transforms.features_to_model.features_to_model import FeaturesToModel</span>
+</span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        from workbench.core.transforms.features_to_model.features_to_model import FeaturesToModel</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        to_model = FeaturesToModel(feature_uuid, model_uuid, model_type=ModelType)</span>
 </span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">        to_model.set_output_tags([&quot;abalone&quot;, &quot;public&quot;, &quot;whatever&quot;])</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a><span class="sd">        to_model.transform(target_column=&quot;class_number_of_rings&quot;,</span>
@@ -3896,7 +3896,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>            <span class="n">script_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">custom_script</span>
 </span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Custom script path: </span><span class="si">{script_path}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>            <span class="c1"># Fixme: We&#39;ll need to circle back to this later</span>
-</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>            <span class="n">copy_imports_to_script_dir</span><span class="p">(</span><span class="n">script_path</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;sageworks.utils.chem_utils&quot;</span><span class="p">])</span>
+</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>            <span class="n">copy_imports_to_script_dir</span><span class="p">(</span><span class="n">script_path</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;workbench.utils.chem_utils&quot;</span><span class="p">])</span>
 </span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>
 </span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>        <span class="c1"># We&#39;re using one of the built-in model script templates</span>
 </span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>        <span class="k">else</span><span class="p">:</span>
@@ -3966,7 +3966,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-213"><a id="__codelineno-0-213" name="__codelineno-0-213"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">SKLearn</span><span class="p">(</span>
 </span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a>            <span class="n">entry_point</span><span class="o">=</span><span class="n">entry_point</span><span class="p">,</span>
 </span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>            <span class="n">source_dir</span><span class="o">=</span><span class="n">source_dir</span><span class="p">,</span>
-</span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>            <span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">,</span>
+</span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>            <span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">,</span>
 </span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>            <span class="n">instance_type</span><span class="o">=</span><span class="s2">&quot;ml.m5.large&quot;</span><span class="p">,</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a>            <span class="n">sagemaker_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span><span class="p">,</span>
 </span><span id="__span-0-219"><a id="__codelineno-0-219" name="__codelineno-0-219"></a>            <span class="n">framework_version</span><span class="o">=</span><span class="s2">&quot;1.2-1&quot;</span><span class="p">,</span>
@@ -3996,10 +3996,10 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-243"><a id="__codelineno-0-243" name="__codelineno-0-243"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Post-Transform: Calling onboard() on the Model&quot;&quot;&quot;</span>
 </span><span id="__span-0-244"><a id="__codelineno-0-244" name="__codelineno-0-244"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Post-Transform: Calling onboard() on the Model...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-245"><a id="__codelineno-0-245" name="__codelineno-0-245"></a>
-</span><span id="__span-0-246"><a id="__codelineno-0-246" name="__codelineno-0-246"></a>        <span class="c1"># Store the model feature_list and target_column in the sageworks_meta</span>
+</span><span id="__span-0-246"><a id="__codelineno-0-246" name="__codelineno-0-246"></a>        <span class="c1"># Store the model feature_list and target_column in the workbench_meta</span>
 </span><span id="__span-0-247"><a id="__codelineno-0-247" name="__codelineno-0-247"></a>        <span class="n">output_model</span> <span class="o">=</span> <span class="n">ModelCore</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="p">)</span>
-</span><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a>        <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_features&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_feature_list</span><span class="p">})</span>
-</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a>        <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_target&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_column</span><span class="p">})</span>
+</span><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a>        <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_features&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_feature_list</span><span class="p">})</span>
+</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a>        <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_target&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_column</span><span class="p">})</span>
 </span><span id="__span-0-250"><a id="__codelineno-0-250" name="__codelineno-0-250"></a>
 </span><span id="__span-0-251"><a id="__codelineno-0-251" name="__codelineno-0-251"></a>        <span class="c1"># Store the class labels (if they exist)</span>
 </span><span id="__span-0-252"><a id="__codelineno-0-252" name="__codelineno-0-252"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">class_labels</span><span class="p">:</span>
@@ -4024,7 +4024,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-271"><a id="__codelineno-0-271" name="__codelineno-0-271"></a>        <span class="c1"># Register our model</span>
 </span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>        <span class="n">image</span> <span class="o">=</span> <span class="n">InferenceImage</span><span class="o">.</span><span class="n">get_image_uri</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span><span class="o">.</span><span class="n">boto_region_name</span><span class="p">,</span> <span class="s2">&quot;sklearn&quot;</span><span class="p">,</span> <span class="s2">&quot;1.2.1&quot;</span><span class="p">)</span>
 </span><span id="__span-0-273"><a id="__codelineno-0-273" name="__codelineno-0-273"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Registering model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2"> with image </span><span class="si">{</span><span class="n">image</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>        <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">)</span>
+</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>        <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">)</span>
 </span><span id="__span-0-275"><a id="__codelineno-0-275" name="__codelineno-0-275"></a>        <span class="n">model</span><span class="o">.</span><span class="n">register</span><span class="p">(</span>
 </span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>            <span class="n">model_package_group_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
 </span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a>            <span class="n">framework_version</span><span class="o">=</span><span class="s2">&quot;1.2.1&quot;</span><span class="p">,</span>
@@ -4054,7 +4054,7 @@ <h2 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">feature_uuid</span><span class="p">,</span> <span class="n">model_uuid</span><span class="p">,</span> <span class="n">model_type</span><span class="p">,</span> <span class="n">scikit_model_class</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">model_import_str</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">custom_script</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -4072,7 +4072,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
     custom_script (str, optional): Custom script to use for the model (default None)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-29">29</a></span>
 <span class="normal"><a href="#__codelineno-0-30">30</a></span>
 <span class="normal"><a href="#__codelineno-0-31">31</a></span>
@@ -4157,7 +4157,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model" class="doc doc-heading">
+<h3 id="workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create_and_register_model</span><span class="p">()</span></code>
 
 </h3>
@@ -4168,7 +4168,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
         <p>Create and Register the Model</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-258">258</a></span>
 <span class="normal"><a href="#__codelineno-0-259">259</a></span>
 <span class="normal"><a href="#__codelineno-0-260">260</a></span>
@@ -4212,7 +4212,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-271"><a id="__codelineno-0-271" name="__codelineno-0-271"></a>    <span class="c1"># Register our model</span>
 </span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>    <span class="n">image</span> <span class="o">=</span> <span class="n">InferenceImage</span><span class="o">.</span><span class="n">get_image_uri</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span><span class="o">.</span><span class="n">boto_region_name</span><span class="p">,</span> <span class="s2">&quot;sklearn&quot;</span><span class="p">,</span> <span class="s2">&quot;1.2.1&quot;</span><span class="p">)</span>
 </span><span id="__span-0-273"><a id="__codelineno-0-273" name="__codelineno-0-273"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Registering model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2"> with image </span><span class="si">{</span><span class="n">image</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>    <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">)</span>
+</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>    <span class="n">model</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span><span class="o">.</span><span class="n">create_model</span><span class="p">(</span><span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">)</span>
 </span><span id="__span-0-275"><a id="__codelineno-0-275" name="__codelineno-0-275"></a>    <span class="n">model</span><span class="o">.</span><span class="n">register</span><span class="p">(</span>
 </span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>        <span class="n">model_package_group_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
 </span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a>        <span class="n">framework_version</span><span class="o">=</span><span class="s2">&quot;1.2.1&quot;</span><span class="p">,</span>
@@ -4233,7 +4233,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4244,7 +4244,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
         <p>Post-Transform: Calling onboard() on the Model</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-242">242</a></span>
 <span class="normal"><a href="#__codelineno-0-243">243</a></span>
 <span class="normal"><a href="#__codelineno-0-244">244</a></span>
@@ -4263,10 +4263,10 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-243"><a id="__codelineno-0-243" name="__codelineno-0-243"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Post-Transform: Calling onboard() on the Model&quot;&quot;&quot;</span>
 </span><span id="__span-0-244"><a id="__codelineno-0-244" name="__codelineno-0-244"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Post-Transform: Calling onboard() on the Model...&quot;</span><span class="p">)</span>
 </span><span id="__span-0-245"><a id="__codelineno-0-245" name="__codelineno-0-245"></a>
-</span><span id="__span-0-246"><a id="__codelineno-0-246" name="__codelineno-0-246"></a>    <span class="c1"># Store the model feature_list and target_column in the sageworks_meta</span>
+</span><span id="__span-0-246"><a id="__codelineno-0-246" name="__codelineno-0-246"></a>    <span class="c1"># Store the model feature_list and target_column in the workbench_meta</span>
 </span><span id="__span-0-247"><a id="__codelineno-0-247" name="__codelineno-0-247"></a>    <span class="n">output_model</span> <span class="o">=</span> <span class="n">ModelCore</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span> <span class="n">model_type</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">model_type</span><span class="p">)</span>
-</span><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a>    <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_features&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_feature_list</span><span class="p">})</span>
-</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a>    <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_sageworks_meta</span><span class="p">({</span><span class="s2">&quot;sageworks_model_target&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_column</span><span class="p">})</span>
+</span><span id="__span-0-248"><a id="__codelineno-0-248" name="__codelineno-0-248"></a>    <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_features&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">model_feature_list</span><span class="p">})</span>
+</span><span id="__span-0-249"><a id="__codelineno-0-249" name="__codelineno-0-249"></a>    <span class="n">output_model</span><span class="o">.</span><span class="n">upsert_workbench_meta</span><span class="p">({</span><span class="s2">&quot;workbench_model_target&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">target_column</span><span class="p">})</span>
 </span><span id="__span-0-250"><a id="__codelineno-0-250" name="__codelineno-0-250"></a>
 </span><span id="__span-0-251"><a id="__codelineno-0-251" name="__codelineno-0-251"></a>    <span class="c1"># Store the class labels (if they exist)</span>
 </span><span id="__span-0-252"><a id="__codelineno-0-252" name="__codelineno-0-252"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">class_labels</span><span class="p">:</span>
@@ -4283,7 +4283,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">target_column</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">feature_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">train_all_data</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4300,7 +4300,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
     train_all_data (bool): Train on ALL (100%) of the data (default False)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-68"> 68</a></span>
 <span class="normal"><a href="#__codelineno-0-69"> 69</a></span>
 <span class="normal"><a href="#__codelineno-0-70"> 70</a></span>
@@ -4551,7 +4551,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>        <span class="n">script_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">custom_script</span>
 </span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Custom script path: </span><span class="si">{script_path}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>        <span class="c1"># Fixme: We&#39;ll need to circle back to this later</span>
-</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>        <span class="n">copy_imports_to_script_dir</span><span class="p">(</span><span class="n">script_path</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;sageworks.utils.chem_utils&quot;</span><span class="p">])</span>
+</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>        <span class="n">copy_imports_to_script_dir</span><span class="p">(</span><span class="n">script_path</span><span class="p">,</span> <span class="p">[</span><span class="s2">&quot;workbench.utils.chem_utils&quot;</span><span class="p">])</span>
 </span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>
 </span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>    <span class="c1"># We&#39;re using one of the built-in model script templates</span>
 </span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>    <span class="k">else</span><span class="p">:</span>
@@ -4621,7 +4621,7 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
 </span><span id="__span-0-213"><a id="__codelineno-0-213" name="__codelineno-0-213"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">estimator</span> <span class="o">=</span> <span class="n">SKLearn</span><span class="p">(</span>
 </span><span id="__span-0-214"><a id="__codelineno-0-214" name="__codelineno-0-214"></a>        <span class="n">entry_point</span><span class="o">=</span><span class="n">entry_point</span><span class="p">,</span>
 </span><span id="__span-0-215"><a id="__codelineno-0-215" name="__codelineno-0-215"></a>        <span class="n">source_dir</span><span class="o">=</span><span class="n">source_dir</span><span class="p">,</span>
-</span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>        <span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">,</span>
+</span><span id="__span-0-216"><a id="__codelineno-0-216" name="__codelineno-0-216"></a>        <span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">,</span>
 </span><span id="__span-0-217"><a id="__codelineno-0-217" name="__codelineno-0-217"></a>        <span class="n">instance_type</span><span class="o">=</span><span class="s2">&quot;ml.m5.large&quot;</span><span class="p">,</span>
 </span><span id="__span-0-218"><a id="__codelineno-0-218" name="__codelineno-0-218"></a>        <span class="n">sagemaker_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span><span class="p">,</span>
 </span><span id="__span-0-219"><a id="__codelineno-0-219" name="__codelineno-0-219"></a>        <span class="n">framework_version</span><span class="o">=</span><span class="s2">&quot;1.2-1&quot;</span><span class="p">,</span>
@@ -4668,9 +4668,9 @@ <h3 id="sageworks.core.transforms.features_to_model.features_to_model.FeaturesTo
     </div>
 
 </div><h2 id="supported-models">Supported Models</h2>
-<p>Currently SageWorks supports XGBoost (classifier/regressor), and Scikit Learn models. Those models can be created by just specifying different parameters to the <code>FeaturesToModel</code> class. The main issue with the supported models is they are vanilla versions with default parameters, any customization should be done with <a href="#custom-models">Custom Models</a></p>
+<p>Currently Workbench supports XGBoost (classifier/regressor), and Scikit Learn models. Those models can be created by just specifying different parameters to the <code>FeaturesToModel</code> class. The main issue with the supported models is they are vanilla versions with default parameters, any customization should be done with <a href="#custom-models">Custom Models</a></p>
 <h3 id="xgboost">XGBoost</h3>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># XGBoost Regression Model</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">input_uuid</span> <span class="o">=</span> <span class="s2">&quot;abalone_features&quot;</span>
@@ -4694,7 +4694,7 @@ <h3 id="xgboost">XGBoost</h3>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22" href="#__codelineno-0-22"></a><span class="n">to_model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">target_column</span><span class="o">=</span><span class="s2">&quot;class_number_of_rings&quot;</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Abalone Quantile Regression&quot;</span><span class="p">)</span>
 </span></code></pre></div>
 <h3 id="scikit-learn">Scikit-Learn</h3>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="c1"># Scikit-Learn Kmeans Clustering Model</span>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="n">input_uuid</span> <span class="o">=</span> <span class="s2">&quot;wine_features&quot;</span>
@@ -4739,11 +4739,11 @@ <h2 id="custom-models">Custom Models</h2>
 <p>For custom models we recommend the following steps:</p>
 <div class="admonition warning inline end">
 <p class="admonition-title">Experimental</p>
-<p>The SageWorks Custom Models are currently in experimental mode so have fun but expect issues. Requires <code>sageworks &gt;= 0.8.60</code>. Feel free to submit issues to <a href="https://github.com/SuperCowPowers/sageworks">SageWorks Github</a></p>
+<p>The Workbench Custom Models are currently in experimental mode so have fun but expect issues. Requires <code>workbench &gt;= 0.8.60</code>. Feel free to submit issues to <a href="https://github.com/SuperCowPowers/workbench">Workbench Github</a></p>
 </div>
 <ul>
 <li>Copy the example custom model script into your own directory<ul>
-<li>See: <a href="https://github.com/SuperCowPowers/sageworks/tree/main/src/sageworks/model_scripts/custom_script_example">Custom Model Script</a></li>
+<li>See: <a href="https://github.com/SuperCowPowers/workbench/tree/main/src/workbench/model_scripts/custom_script_example">Custom Model Script</a></li>
 </ul>
 </li>
 <li>Make a requirements.txt and put into the same directory</li>
@@ -4756,8 +4756,8 @@ <h2 id="custom-models">Custom Models</h2>
 <li>Train/deploy your custom script</li>
 </ul>
 <h3 id="trainingdeploying-custom-models">Training/Deploying Custom Models</h3>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">ModelType</span>
-</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="kn">from</span> <span class="nn">sageworks.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">ModelType</span>
+</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a><span class="kn">from</span> <span class="nn">workbench.core.transforms.features_to_model.features_to_model</span> <span class="kn">import</span> <span class="n">FeaturesToModel</span>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="c1"># Note this directory should also have a requirements.txt in it</span>
 </span><span id="__span-2-5"><a id="__codelineno-2-5" name="__codelineno-2-5" href="#__codelineno-2-5"></a><span class="n">my_custom_script</span> <span class="o">=</span> <span class="s2">&quot;/full/path/to/my/directory/my_custom_script.py&quot;</span>
@@ -4771,7 +4771,7 @@ <h3 id="trainingdeploying-custom-models">Training/Deploying Custom Models</h3>
 </span><span id="__span-2-13"><a id="__codelineno-2-13" name="__codelineno-2-13" href="#__codelineno-2-13"></a><span class="n">to_model</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">target_column</span><span class="o">=</span><span class="n">target_column</span><span class="p">,</span> <span class="n">description</span><span class="o">=</span><span class="s2">&quot;Custom Model&quot;</span><span class="p">)</span>
 </span></code></pre></div>
 <h3 id="custom-models-create-an-endpointrun-inference">Custom Models: Create an Endpoint/Run Inference</h3>
-<div class="language-python highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">Model</span><span class="p">,</span> <span class="n">Endpoint</span>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">Model</span><span class="p">,</span> <span class="n">Endpoint</span>
 </span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>
 </span><span id="__span-3-3"><a id="__codelineno-3-3" name="__codelineno-3-3" href="#__codelineno-3-3"></a><span class="n">model</span> <span class="o">=</span> <span class="n">Model</span><span class="p">(</span><span class="s2">&quot;my-custom-model&quot;</span><span class="p">)</span>
 </span><span id="__span-3-4"><a id="__codelineno-3-4" name="__codelineno-3-4" href="#__codelineno-3-4"></a><span class="n">end</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">to_endpoint</span><span class="p">()</span>   <span class="c1"># Note: This takes a while</span>
@@ -4785,7 +4785,7 @@ <h3 id="custom-models-create-an-endpointrun-inference">Custom Models: Create an
 </span></code></pre></div>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a></p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a></p>
 
 
 
diff --git a/core_classes/transforms/model_to_endpoint/index.html b/core_classes/transforms/model_to_endpoint/index.html
index c090a6044..957f44ae0 100644
--- a/core_classes/transforms/model_to_endpoint/index.html
+++ b/core_classes/transforms/model_to_endpoint/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Model to Endpoint - SageWorks</title>
+        <title>Model to Endpoint - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1565,7 +1565,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       model_to_endpoint
     </span>
@@ -1574,7 +1574,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint" class="md-nav__link">
     <span class="md-ellipsis">
       ModelToEndpoint
     </span>
@@ -1584,7 +1584,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1593,7 +1593,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -1602,7 +1602,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -1912,7 +1912,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1922,7 +1922,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3141,11 +3141,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3197,7 +3197,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint" class="md-nav__link">
     <span class="md-ellipsis">
       model_to_endpoint
     </span>
@@ -3206,7 +3206,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint" class="md-nav__link">
     <span class="md-ellipsis">
       ModelToEndpoint
     </span>
@@ -3216,7 +3216,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3225,7 +3225,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3234,7 +3234,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3273,7 +3273,7 @@ <h1 id="model-to-endpoint">Model to Endpoint</h1>
 
 
 
-<a id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint"></a>
+<a id="workbench.core.transforms.model_to_endpoint.model_to_endpoint"></a>
     <div class="doc doc-contents first">
 
         <p>ModelToEndpoint: Deploy an Endpoint for a Model</p>
@@ -3298,7 +3298,7 @@ <h1 id="model-to-endpoint">Model to Endpoint</h1>
 
 
 
-<h2 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint" class="doc doc-heading">
+<h2 id="workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint" class="doc doc-heading">
             <code>ModelToEndpoint</code>
 
 
@@ -3307,7 +3307,7 @@ <h2 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>ModelToEndpoint: Deploy an Endpoint for a Model</p>
@@ -3326,7 +3326,7 @@ <h2 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
 <span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
 <span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
@@ -3479,7 +3479,7 @@ <h2 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
 </span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>        <span class="c1"># Grab the specified Model Package</span>
 </span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>        <span class="n">model_package</span> <span class="o">=</span> <span class="n">ModelPackage</span><span class="p">(</span>
-</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>            <span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">,</span>
+</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>            <span class="n">role</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">,</span>
 </span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>            <span class="n">model_package_arn</span><span class="o">=</span><span class="n">model_package_arn</span><span class="p">,</span>
 </span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>            <span class="n">sagemaker_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span><span class="p">,</span>
 </span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>        <span class="p">)</span>
@@ -3532,7 +3532,7 @@ <h2 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">model_uuid</span><span class="p">,</span> <span class="n">endpoint_uuid</span><span class="p">,</span> <span class="n">serverless</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -3547,7 +3547,7 @@ <h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
     serverless(bool): Deploy the Endpoint in serverless mode (default: True)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-27">27</a></span>
 <span class="normal"><a href="#__codelineno-0-28">28</a></span>
 <span class="normal"><a href="#__codelineno-0-29">29</a></span>
@@ -3592,7 +3592,7 @@ <h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -3603,7 +3603,7 @@ <h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
         <p>Post-Transform: Calling onboard() for the Endpoint</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-103">103</a></span>
 <span class="normal"><a href="#__codelineno-0-104">104</a></span>
 <span class="normal"><a href="#__codelineno-0-105">105</a></span>
@@ -3626,7 +3626,7 @@ <h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">()</span></code>
 
 </h3>
@@ -3637,7 +3637,7 @@ <h3 id="sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEnd
         <p>Deploy an Endpoint for a Model</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-46">46</a></span>
 <span class="normal"><a href="#__codelineno-0-47">47</a></span>
 <span class="normal"><a href="#__codelineno-0-48">48</a></span>
diff --git a/core_classes/transforms/overview/index.html b/core_classes/transforms/overview/index.html
index 749488dc8..7230ed738 100644
--- a/core_classes/transforms/overview/index.html
+++ b/core_classes/transforms/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
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-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
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 <h2 id="transform-details">Transform Details</h2>
 <ul>
 <li><strong><a href="../data_loaders_light/">DataLoaders Light</a>:</strong> Loads various light/smaller data into AWS Data Catalog and Athena</li>
@@ -3193,7 +3193,7 @@ <h2 id="transform-details">Transform Details</h2>
 <li><strong><a href="../model_to_endpoint/">ModelToEndpoint</a>:</strong> Manages the provisioning and deployment of a Model Endpoint</li>
 <li><strong><a href="../pandas_transforms/">PandasTransforms</a>:</strong> Pandas DataFrame transforms and helper methods.</li>
 </ul>
-<p><img alt="ML Pipeline" src="../../../images/sageworks_concepts.png" /></p>
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             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3489,11 +3489,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3545,7 +3545,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms" class="md-nav__link">
     <span class="md-ellipsis">
       pandas_transforms
     </span>
@@ -3554,7 +3554,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.DataToPandas" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.DataToPandas" class="md-nav__link">
     <span class="md-ellipsis">
       DataToPandas
     </span>
@@ -3564,7 +3564,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.DataToPandas.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.DataToPandas.__init__" class="md-nav__link">
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@@ -3573,7 +3573,7 @@
 </li>
         
           <li class="md-nav__item">
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+  <a href="#workbench.core.transforms.pandas_transforms.DataToPandas.get_output" class="md-nav__link">
     <span class="md-ellipsis">
       get_output
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@@ -3582,7 +3582,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.DataToPandas.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.DataToPandas.post_transform" class="md-nav__link">
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@@ -3591,7 +3591,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.DataToPandas.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.DataToPandas.transform_impl" class="md-nav__link">
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@@ -3605,7 +3605,7 @@
 </li>
       
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-  <a href="#sageworks.core.transforms.pandas_transforms.FeaturesToPandas" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.FeaturesToPandas" class="md-nav__link">
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           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.FeaturesToPandas.__init__" class="md-nav__link">
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@@ -3624,7 +3624,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.get_output" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.FeaturesToPandas.get_output" class="md-nav__link">
     <span class="md-ellipsis">
       get_output
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@@ -3633,7 +3633,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.FeaturesToPandas.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
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@@ -3642,7 +3642,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.FeaturesToPandas.transform_impl" class="md-nav__link">
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@@ -3656,7 +3656,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData" class="md-nav__link">
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       PandasToData
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@@ -3666,7 +3666,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3675,7 +3675,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.convert_datetime_columns" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.convert_datetime_columns" class="md-nav__link">
     <span class="md-ellipsis">
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     </span>
@@ -3684,7 +3684,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_to_datetime" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.convert_object_to_datetime" class="md-nav__link">
     <span class="md-ellipsis">
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@@ -3693,7 +3693,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_to_string" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.convert_object_to_string" class="md-nav__link">
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@@ -3702,7 +3702,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
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@@ -3711,7 +3711,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.pre_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.pre_transform" class="md-nav__link">
     <span class="md-ellipsis">
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@@ -3720,7 +3720,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.set_input" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.set_input" class="md-nav__link">
     <span class="md-ellipsis">
       set_input
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@@ -3729,7 +3729,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToData.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToData.transform_impl" class="md-nav__link">
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       transform_impl
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@@ -3743,7 +3743,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures" class="md-nav__link">
     <span class="md-ellipsis">
       PandasToFeatures
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@@ -3753,7 +3753,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.__init__" class="md-nav__link">
     <span class="md-ellipsis">
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@@ -3762,7 +3762,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_column_types" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.convert_column_types" class="md-nav__link">
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       convert_column_types
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@@ -3771,7 +3771,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_columns_to_categorical" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.convert_columns_to_categorical" class="md-nav__link">
     <span class="md-ellipsis">
       convert_columns_to_categorical
     </span>
@@ -3780,7 +3780,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.create_feature_group" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.create_feature_group" class="md-nav__link">
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       create_feature_group
     </span>
@@ -3789,7 +3789,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.manual_categorical_converter" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.manual_categorical_converter" class="md-nav__link">
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       manual_categorical_converter
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@@ -3798,7 +3798,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.one_hot_encode" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.one_hot_encode" class="md-nav__link">
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@@ -3807,7 +3807,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
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@@ -3816,7 +3816,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.pre_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.pre_transform" class="md-nav__link">
     <span class="md-ellipsis">
       pre_transform
     </span>
@@ -3825,7 +3825,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.prep_dataframe" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.prep_dataframe" class="md-nav__link">
     <span class="md-ellipsis">
       prep_dataframe
     </span>
@@ -3834,7 +3834,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.process_column_name" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.process_column_name" class="md-nav__link">
     <span class="md-ellipsis">
       process_column_name
     </span>
@@ -3843,7 +3843,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.set_input" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.set_input" class="md-nav__link">
     <span class="md-ellipsis">
       set_input
     </span>
@@ -3852,7 +3852,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3861,7 +3861,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeatures.wait_for_rows" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeatures.wait_for_rows" class="md-nav__link">
     <span class="md-ellipsis">
       wait_for_rows
     </span>
@@ -3875,7 +3875,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked" class="md-nav__link">
     <span class="md-ellipsis">
       PandasToFeaturesChunked
     </span>
@@ -3885,7 +3885,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3894,7 +3894,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.add_chunk" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.add_chunk" class="md-nav__link">
     <span class="md-ellipsis">
       add_chunk
     </span>
@@ -3903,7 +3903,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3912,7 +3912,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_transform" class="md-nav__link">
     <span class="md-ellipsis">
       pre_transform
     </span>
@@ -3921,7 +3921,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_categorical_info" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_categorical_info" class="md-nav__link">
     <span class="md-ellipsis">
       set_categorical_info
     </span>
@@ -3930,7 +3930,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3963,7 +3963,7 @@ <h1 id="pandas-transforms">Pandas Transforms</h1>
 <p class="admonition-title">API Classes</p>
 <p>The <a href="../../../api_classes/overview/">API Classes</a> will often provide helpful methods that give you a DataFrame (data_source.query() for instance), so always check out the <a href="../../../api_classes/overview/">API Classes</a> first.</p>
 </div>
-<p>These Transforms will give you the ultimate in customization and flexibility when creating AWS Machine Learning Pipelines. Grab a Pandas DataFrame from a DataSource or FeatureSet process in whatever way for your use case and simply create another Sageworks DataSource or FeatureSet from the resulting DataFrame.</p>
+<p>These Transforms will give you the ultimate in customization and flexibility when creating AWS Machine Learning Pipelines. Grab a Pandas DataFrame from a DataSource or FeatureSet process in whatever way for your use case and simply create another Workbench DataSource or FeatureSet from the resulting DataFrame.</p>
 <p><strong>Lots of Options:</strong></p>
 <div class="admonition warning inline end">
 <p class="admonition-title">Not for Large Data</p>
@@ -3981,17 +3981,17 @@ <h1 id="pandas-transforms">Pandas Transforms</h1>
 
 
 
-<a id="sageworks.core.transforms.pandas_transforms"></a>
+<a id="workbench.core.transforms.pandas_transforms"></a>
     <div class="doc doc-contents first">
 
-        <p>Welcome to the SageWorks Pandas Transform Classes</p>
+        <p>Welcome to the Workbench Pandas Transform Classes</p>
 <p>These classes provide low-level APIs for using Pandas DataFrames</p>
 <ul>
-<li>DataToPandas: Pull a dataframe from a SageWorks DataSource</li>
-<li>FeaturesToPandas: Pull a dataframe from a SageWorks FeatureSet</li>
-<li>PandasToData: Create a SageWorks DataSource using a Pandas DataFrame as the source</li>
-<li>PandasToFeatures: Create a SageWorks FeatureSet using a Pandas DataFrame as the source</li>
-<li>PandasToFeaturesChunked: Create a SageWorks FeatureSet using a Chunked/Streaming Pandas DataFrame as the source</li>
+<li>DataToPandas: Pull a dataframe from a Workbench DataSource</li>
+<li>FeaturesToPandas: Pull a dataframe from a Workbench FeatureSet</li>
+<li>PandasToData: Create a Workbench DataSource using a Pandas DataFrame as the source</li>
+<li>PandasToFeatures: Create a Workbench FeatureSet using a Pandas DataFrame as the source</li>
+<li>PandasToFeaturesChunked: Create a Workbench FeatureSet using a Chunked/Streaming Pandas DataFrame as the source</li>
 </ul>
 
 
@@ -4014,7 +4014,7 @@ <h1 id="pandas-transforms">Pandas Transforms</h1>
 
 
 
-<h2 id="sageworks.core.transforms.pandas_transforms.DataToPandas" class="doc doc-heading">
+<h2 id="workbench.core.transforms.pandas_transforms.DataToPandas" class="doc doc-heading">
             <code>DataToPandas</code>
 
 
@@ -4023,7 +4023,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.DataToPandas" class="doc doc
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>DataToPandas: Class to transform a Data Source into a Pandas DataFrame</p>
@@ -4045,7 +4045,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.DataToPandas" class="doc doc
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-10">10</a></span>
 <span class="normal"><a href="#__codelineno-0-11">11</a></span>
 <span class="normal"><a href="#__codelineno-0-12">12</a></span>
@@ -4191,7 +4191,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.DataToPandas" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.DataToPandas.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -4202,7 +4202,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.__init__" class
         <p>DataToPandas Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-24">24</a></span>
 <span class="normal"><a href="#__codelineno-0-25">25</a></span>
 <span class="normal"><a href="#__codelineno-0-26">26</a></span>
@@ -4231,7 +4231,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.__init__" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.get_output" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.DataToPandas.get_output" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_output</span><span class="p">()</span></code>
 
 </h3>
@@ -4242,7 +4242,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.get_output" cla
         <p>Get the DataFrame Output from this Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-71">71</a></span>
 <span class="normal"><a href="#__codelineno-0-72">72</a></span>
 <span class="normal"><a href="#__codelineno-0-73">73</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a><span class="k">def</span> <span class="nf">get_output</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
@@ -4257,7 +4257,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.get_output" cla
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.DataToPandas.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4268,7 +4268,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.post_transform"
         <p>Post-Transform: Any checks on the Pandas DataFrame that need to be done</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
 <span class="normal"><a href="#__codelineno-0-68">68</a></span>
@@ -4285,7 +4285,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.post_transform"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.DataToPandas.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">query</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">max_rows</span><span class="o">=</span><span class="mi">100000</span><span class="p">)</span></code>
 
 </h3>
@@ -4299,7 +4299,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.transform_impl"
     max_rows(int): The maximum number of rows to return (default: 100000)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-35">35</a></span>
 <span class="normal"><a href="#__codelineno-0-36">36</a></span>
 <span class="normal"><a href="#__codelineno-0-37">37</a></span>
@@ -4377,7 +4377,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.DataToPandas.transform_impl"
 
 
 
-<h2 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas" class="doc doc-heading">
+<h2 id="workbench.core.transforms.pandas_transforms.FeaturesToPandas" class="doc doc-heading">
             <code>FeaturesToPandas</code>
 
 
@@ -4386,7 +4386,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas" class="doc
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>FeaturesToPandas: Class to transform a FeatureSet into a Pandas DataFrame</p>
@@ -4405,7 +4405,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas" class="doc
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-10">10</a></span>
 <span class="normal"><a href="#__codelineno-0-11">11</a></span>
 <span class="normal"><a href="#__codelineno-0-12">12</a></span>
@@ -4553,7 +4553,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.FeaturesToPandas.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">feature_set_name</span><span class="p">)</span></code>
 
 </h3>
@@ -4564,7 +4564,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.__init__" c
         <p>FeaturesToPandas Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-21">21</a></span>
 <span class="normal"><a href="#__codelineno-0-22">22</a></span>
 <span class="normal"><a href="#__codelineno-0-23">23</a></span>
@@ -4595,7 +4595,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.__init__" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.get_output" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.FeaturesToPandas.get_output" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_output</span><span class="p">()</span></code>
 
 </h3>
@@ -4606,7 +4606,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.get_output"
         <p>Get the DataFrame Output from this Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-70">70</a></span>
 <span class="normal"><a href="#__codelineno-0-71">71</a></span>
 <span class="normal"><a href="#__codelineno-0-72">72</a></span>
@@ -4625,7 +4625,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.get_output"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.FeaturesToPandas.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4636,7 +4636,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.post_transf
         <p>Post-Transform: Any checks on the Pandas DataFrame that need to be done</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-65">65</a></span>
 <span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
@@ -4653,7 +4653,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.post_transf
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.FeaturesToPandas.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">max_rows</span><span class="o">=</span><span class="mi">100000</span><span class="p">)</span></code>
 
 </h3>
@@ -4664,7 +4664,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.transform_i
         <p>Convert the FeatureSet into a Pandas DataFrame</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-33">33</a></span>
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span>
@@ -4744,7 +4744,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.FeaturesToPandas.transform_i
 
 
 
-<h2 id="sageworks.core.transforms.pandas_transforms.PandasToData" class="doc doc-heading">
+<h2 id="workbench.core.transforms.pandas_transforms.PandasToData" class="doc doc-heading">
             <code>PandasToData</code>
 
 
@@ -4753,7 +4753,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToData" class="doc doc
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>PandasToData: Class to publish a Pandas DataFrame as a DataSource</p>
@@ -4773,7 +4773,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToData" class="doc doc
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
 <span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
 <span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
@@ -4935,7 +4935,8 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToData" class="doc doc
 <span class="normal"><a href="#__codelineno-0-174">174</a></span>
 <span class="normal"><a href="#__codelineno-0-175">175</a></span>
 <span class="normal"><a href="#__codelineno-0-176">176</a></span>
-<span class="normal"><a href="#__codelineno-0-177">177</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="k">class</span> <span class="nc">PandasToData</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
+<span class="normal"><a href="#__codelineno-0-177">177</a></span>
+<span class="normal"><a href="#__codelineno-0-178">178</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a><span class="k">class</span> <span class="nc">PandasToData</span><span class="p">(</span><span class="n">Transform</span><span class="p">):</span>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;PandasToData: Class to publish a Pandas DataFrame as a DataSource</span>
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a>
 </span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19"></a><span class="sd">    Common Usage:</span>
@@ -4947,156 +4948,157 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToData" class="doc doc
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a><span class="sd">        ```</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="sd">    &quot;&quot;&quot;</span>
 </span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">):</span>
+</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">,</span> <span class="n">catalog_db</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;PandasToData Initialization</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">        Args:</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">            output_uuid (str): The UUID of the DataSource to create</span>
 </span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">            output_format (str): The file format to store the S3 object data in (default: &quot;parquet&quot;)</span>
-</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>        <span class="c1"># Make sure the output_uuid is a valid name/id</span>
-</span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>        <span class="n">Artifact</span><span class="o">.</span><span class="n">is_name_valid</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>
-</span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>
-</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a>        <span class="c1"># Call superclass init</span>
-</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;DataFrame&quot;</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">)</span>
-</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a>
-</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>        <span class="c1"># Set up all my instance attributes</span>
-</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="n">TransformInput</span><span class="o">.</span><span class="n">PANDAS_DF</span>
-</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="n">TransformOutput</span><span class="o">.</span><span class="n">DATA_SOURCE</span>
-</span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="kc">None</span>
-</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>
-</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>        <span class="c1"># Give a message that Parquet is best in most cases</span>
-</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>        <span class="k">if</span> <span class="n">output_format</span> <span class="o">!=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
-</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Parquet format works the best in most cases please consider using it&quot;</span><span class="p">)</span>
-</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">=</span> <span class="n">output_format</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
-</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the DataFrame Input for this Transform&quot;&quot;&quot;</span>
-</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="n">input_df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
-</span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>
-</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>    <span class="k">def</span> <span class="nf">delete_existing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>        <span class="c1"># Delete the existing FeatureSet if it exists</span>
-</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Deleting the </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2"> DataSource...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>        <span class="n">AthenaSource</span><span class="o">.</span><span class="n">managed_delete</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>
-</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>    <span class="k">def</span> <span class="nf">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to string columns&quot;&quot;&quot;</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>            <span class="k">try</span><span class="p">:</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>                <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;string&quot;</span><span class="p">)</span>
-</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>                <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">,</span> <span class="s1">&#39;&quot;&#39;</span><span class="p">)</span>  <span class="c1"># This is for nested JSON</span>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>            <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
-</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to string...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>        <span class="k">return</span> <span class="n">df</span>
-</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>
-</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>    <span class="k">def</span> <span class="nf">convert_object_to_datetime</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to datetime or string columns&quot;&quot;&quot;</span>
-</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
-</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>            <span class="k">try</span><span class="p">:</span>
-</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>                <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
-</span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>            <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
-</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to datetime...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>        <span class="k">return</span> <span class="n">df</span>
-</span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>
-</span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>    <span class="nd">@staticmethod</span>
-</span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>    <span class="k">def</span> <span class="nf">convert_datetime_columns</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert datetime columns to ISO-8601 string&quot;&quot;&quot;</span>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>        <span class="n">datetime_type</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;datetime&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64[ns]&quot;</span><span class="p">,</span> <span class="s2">&quot;datetimetz&quot;</span><span class="p">]</span>
-</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="n">datetime_type</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">datetime_to_iso8601</span><span class="p">)</span>
-</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">StringDtype</span><span class="p">())</span>
-</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>        <span class="k">return</span> <span class="n">df</span>
-</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>
-</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>    <span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Pre-Transform: Delete the existing DataSource if it exists&quot;&quot;&quot;</span>
-</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">delete_existing</span><span class="p">()</span>
-</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>    <span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the Pandas DataFrame into Parquet Format in the SageWorks S3 Bucket, and</span>
-</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a><span class="sd">        store the information about the data to the AWS Data Catalog sageworks database</span>
-</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">            overwrite (bool): Overwrite the existing data in the SageWorks S3 Bucket</span>
-</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;DataFrame to SageWorks DataSource: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>
-</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a>        <span class="c1"># Set up our metadata storage</span>
-</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>        <span class="n">sageworks_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
-</span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a>        <span class="n">sageworks_meta</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="p">)</span>
-</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>
-</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a>        <span class="c1"># Create the Output Parquet file S3 Storage Path</span>
-</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>        <span class="n">s3_storage_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
-</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a>
-</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>        <span class="c1"># Convert columns names to lowercase, Athena will not work with uppercase column names</span>
-</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()):</span>
-</span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>                <span class="k">if</span> <span class="n">c</span> <span class="o">!=</span> <span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
-</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>                    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column name </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> converted to lowercase: </span><span class="si">{</span><span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>
-</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a>        <span class="c1"># Convert Object Columns to String</span>
-</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">)</span>
-</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a>
-</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a>        <span class="c1"># Note: Both of these conversions may not be necessary, so we&#39;re leaving them commented out</span>
-</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
-</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a><span class="sd">        # Convert Object Columns to Datetime</span>
-</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="sd">        self.output_df = self.convert_object_to_datetime(self.output_df)</span>
-</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a>
-</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a><span class="sd">        # Now convert datetime columns to ISO-8601 string</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="sd">        # self.output_df = self.convert_datetime_columns(self.output_df)</span>
-</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a>
-</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a>        <span class="c1"># Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format</span>
-</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>        <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;SageWorks data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
-</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>        <span class="n">glue_table_settings</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="n">description</span><span class="p">,</span> <span class="s2">&quot;parameters&quot;</span><span class="p">:</span> <span class="n">sageworks_meta</span><span class="p">}</span>
-</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
-</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>            <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span>
-</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
-</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a>                <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
-</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>                <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a>                <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
-</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>                <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
-</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a>                <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
-</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>                <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
-</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a>                <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
-</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>                <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a>                <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
-</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>                <span class="n">sanitize_columns</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-</span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a>            <span class="p">)</span>  <span class="c1"># FIXME: Have some logic around partition columns</span>
-</span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>
-</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>        <span class="c1"># Note: In general Parquet works will for most uses cases. We recommend using Parquet</span>
-</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>        <span class="c1">#       You can use JSON_EXTRACT on Parquet string field, and it works great.</span>
-</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;jsonl&quot;</span><span class="p">:</span>
-</span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We recommend using Parquet format for most use cases&quot;</span><span class="p">)</span>
-</span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;If you have a use case that requires JSONL please contact SageWorks support&quot;</span><span class="p">)</span>
-</span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We&#39;d like to understand what functionality JSONL is providing that isn&#39;t already&quot;</span><span class="p">)</span>
-</span><span id="__span-0-152"><a id="__codelineno-0-152" name="__codelineno-0-152"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;provided with Parquet and JSON_EXTRACT() for your Athena Queries&quot;</span><span class="p">)</span>
-</span><span id="__span-0-153"><a id="__codelineno-0-153" name="__codelineno-0-153"></a>            <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span>
-</span><span id="__span-0-154"><a id="__codelineno-0-154" name="__codelineno-0-154"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
-</span><span id="__span-0-155"><a id="__codelineno-0-155" name="__codelineno-0-155"></a>                <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
-</span><span id="__span-0-156"><a id="__codelineno-0-156" name="__codelineno-0-156"></a>                <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;records&quot;</span><span class="p">,</span>
-</span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a>                <span class="n">lines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-</span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a>                <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;iso&quot;</span><span class="p">,</span>
-</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>                <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>                <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
-</span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>                <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
-</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>                <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
-</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>                <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
-</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>                <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
-</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>                <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-</span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a>                <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
-</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>            <span class="p">)</span>
-</span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a>        <span class="k">else</span><span class="p">:</span>
-</span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a>            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unsupported file format: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_format</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a>
-</span><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a>    <span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-172"><a id="__codelineno-0-172" name="__codelineno-0-172"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Post-Transform: Calling onboard() fnr the DataSource&quot;&quot;&quot;</span>
-</span><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Post-Transform: Calling onboard() for the DataSource...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a>
-</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a>        <span class="c1"># Onboard the DataSource</span>
-</span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a>        <span class="n">output_data_source</span> <span class="o">=</span> <span class="n">DataSourceFactory</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
-</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>        <span class="n">output_data_source</span><span class="o">.</span><span class="n">onboard</span><span class="p">()</span>
+</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">            catalog_db (str): The AWS Data Catalog Database to use (default: &quot;workbench&quot;)</span>
+</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">        &quot;&quot;&quot;</span>
+</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>
+</span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>        <span class="c1"># Make sure the output_uuid is a valid name/id</span>
+</span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>        <span class="n">Artifact</span><span class="o">.</span><span class="n">is_name_valid</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>
+</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a>
+</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a>        <span class="c1"># Call superclass init</span>
+</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a>        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;DataFrame&quot;</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">,</span> <span class="n">catalog_db</span><span class="p">)</span>
+</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>
+</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>        <span class="c1"># Set up all my instance attributes</span>
+</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="n">TransformInput</span><span class="o">.</span><span class="n">PANDAS_DF</span>
+</span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="n">TransformOutput</span><span class="o">.</span><span class="n">DATA_SOURCE</span>
+</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="kc">None</span>
+</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>
+</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>        <span class="c1"># Give a message that Parquet is best in most cases</span>
+</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>        <span class="k">if</span> <span class="n">output_format</span> <span class="o">!=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
+</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Parquet format works the best in most cases please consider using it&quot;</span><span class="p">)</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">=</span> <span class="n">output_format</span>
+</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the DataFrame Input for this Transform&quot;&quot;&quot;</span>
+</span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="n">input_df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>
+</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>    <span class="k">def</span> <span class="nf">delete_existing</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>        <span class="c1"># Delete the existing FeatureSet if it exists</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Deleting the </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2"> DataSource...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>        <span class="n">AthenaSource</span><span class="o">.</span><span class="n">managed_delete</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>        <span class="n">time</span><span class="o">.</span><span class="n">sleep</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>    <span class="k">def</span> <span class="nf">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to string columns&quot;&quot;&quot;</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>            <span class="k">try</span><span class="p">:</span>
+</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>                <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;string&quot;</span><span class="p">)</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>                <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">,</span> <span class="s1">&#39;&quot;&#39;</span><span class="p">)</span>  <span class="c1"># This is for nested JSON</span>
+</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>            <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
+</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to string...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>        <span class="k">return</span> <span class="n">df</span>
+</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>
+</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>    <span class="k">def</span> <span class="nf">convert_object_to_datetime</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to datetime or string columns&quot;&quot;&quot;</span>
+</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
+</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>            <span class="k">try</span><span class="p">:</span>
+</span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>                <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>            <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
+</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to datetime...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>        <span class="k">return</span> <span class="n">df</span>
+</span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>
+</span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>    <span class="nd">@staticmethod</span>
+</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a>    <span class="k">def</span> <span class="nf">convert_datetime_columns</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert datetime columns to ISO-8601 string&quot;&quot;&quot;</span>
+</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>        <span class="n">datetime_type</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;datetime&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64[ns]&quot;</span><span class="p">,</span> <span class="s2">&quot;datetimetz&quot;</span><span class="p">]</span>
+</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="n">datetime_type</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">datetime_to_iso8601</span><span class="p">)</span>
+</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">StringDtype</span><span class="p">())</span>
+</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>        <span class="k">return</span> <span class="n">df</span>
+</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>
+</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a>    <span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Pre-Transform: Delete the existing DataSource if it exists&quot;&quot;&quot;</span>
+</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">delete_existing</span><span class="p">()</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert the Pandas DataFrame into Parquet Format in the Workbench S3 Bucket, and</span>
+</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a><span class="sd">        store the information about the data to the AWS Data Catalog workbench database</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>
+</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">        Args:</span>
+</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">            overwrite (bool): Overwrite the existing data in the Workbench S3 Bucket (default: True)</span>
+</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="sd">        &quot;&quot;&quot;</span>
+</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;DataFrame to Workbench DataSource: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a>
+</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>        <span class="c1"># Set up our metadata storage</span>
+</span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a>        <span class="n">workbench_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
+</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>        <span class="n">workbench_meta</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="p">)</span>
+</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a>
+</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>        <span class="c1"># Create the Output Parquet file S3 Storage Path</span>
+</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a>        <span class="n">s3_storage_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
+</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>
+</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>        <span class="c1"># Convert columns names to lowercase, Athena will not work with uppercase column names</span>
+</span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>        <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()):</span>
+</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>            <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>                <span class="k">if</span> <span class="n">c</span> <span class="o">!=</span> <span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>                    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column name </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> converted to lowercase: </span><span class="si">{</span><span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a>
+</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a>        <span class="c1"># Convert Object Columns to String</span>
+</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">)</span>
+</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a>
+</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a>        <span class="c1"># Note: Both of these conversions may not be necessary, so we&#39;re leaving them commented out</span>
+</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="sd">        # Convert Object Columns to Datetime</span>
+</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a><span class="sd">        self.output_df = self.convert_object_to_datetime(self.output_df)</span>
+</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>
+</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="sd">        # Now convert datetime columns to ISO-8601 string</span>
+</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a><span class="sd">        # self.output_df = self.convert_datetime_columns(self.output_df)</span>
+</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a><span class="sd">        &quot;&quot;&quot;</span>
+</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a>
+</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>        <span class="c1"># Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format</span>
+</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>        <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Workbench data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
+</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>        <span class="n">glue_table_settings</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="n">description</span><span class="p">,</span> <span class="s2">&quot;parameters&quot;</span><span class="p">:</span> <span class="n">workbench_meta</span><span class="p">}</span>
+</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
+</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a>            <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span>
+</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
+</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>                <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
+</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a>                <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>                <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
+</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a>                <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
+</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>                <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
+</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a>                <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
+</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>                <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
+</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a>                <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>                <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
+</span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a>                <span class="n">sanitize_columns</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+</span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>            <span class="p">)</span>  <span class="c1"># FIXME: Have some logic around partition columns</span>
+</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>
+</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>        <span class="c1"># Note: In general Parquet works will for most uses cases. We recommend using Parquet</span>
+</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>        <span class="c1">#       You can use JSON_EXTRACT on Parquet string field, and it works great.</span>
+</span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;jsonl&quot;</span><span class="p">:</span>
+</span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We recommend using Parquet format for most use cases&quot;</span><span class="p">)</span>
+</span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;If you have a use case that requires JSONL please contact Workbench support&quot;</span><span class="p">)</span>
+</span><span id="__span-0-152"><a id="__codelineno-0-152" name="__codelineno-0-152"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We&#39;d like to understand what functionality JSONL is providing that isn&#39;t already&quot;</span><span class="p">)</span>
+</span><span id="__span-0-153"><a id="__codelineno-0-153" name="__codelineno-0-153"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;provided with Parquet and JSON_EXTRACT() for your Athena Queries&quot;</span><span class="p">)</span>
+</span><span id="__span-0-154"><a id="__codelineno-0-154" name="__codelineno-0-154"></a>            <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span>
+</span><span id="__span-0-155"><a id="__codelineno-0-155" name="__codelineno-0-155"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
+</span><span id="__span-0-156"><a id="__codelineno-0-156" name="__codelineno-0-156"></a>                <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
+</span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a>                <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;records&quot;</span><span class="p">,</span>
+</span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a>                <span class="n">lines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>                <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;iso&quot;</span><span class="p">,</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>                <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+</span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>                <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
+</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>                <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
+</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>                <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
+</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>                <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
+</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>                <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
+</span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a>                <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>                <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
+</span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a>            <span class="p">)</span>
+</span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a>        <span class="k">else</span><span class="p">:</span>
+</span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a>            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unsupported file format: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_format</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</span><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a>
+</span><span id="__span-0-172"><a id="__codelineno-0-172" name="__codelineno-0-172"></a>    <span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Post-Transform: Calling onboard() fnr the DataSource&quot;&quot;&quot;</span>
+</span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Post-Transform: Calling onboard() for the DataSource...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a>
+</span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a>        <span class="c1"># Onboard the DataSource</span>
+</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>        <span class="n">output_data_source</span> <span class="o">=</span> <span class="n">DataSourceFactory</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
+</span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a>        <span class="n">output_data_source</span><span class="o">.</span><span class="n">onboard</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
               </details>
 
@@ -5115,8 +5117,8 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToData" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.__init__" class="doc doc-heading">
-            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">,</span> <span class="n">output_format</span><span class="o">=</span><span class="s1">&#39;parquet&#39;</span><span class="p">)</span></code>
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.__init__" class="doc doc-heading">
+            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">,</span> <span class="n">output_format</span><span class="o">=</span><span class="s1">&#39;parquet&#39;</span><span class="p">,</span> <span class="n">catalog_db</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -5126,10 +5128,11 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.__init__" class
         <p>PandasToData Initialization
 Args:
     output_uuid (str): The UUID of the DataSource to create
-    output_format (str): The file format to store the S3 object data in (default: "parquet")</p>
+    output_format (str): The file format to store the S3 object data in (default: "parquet")
+    catalog_db (str): The AWS Data Catalog Database to use (default: "workbench")</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-28">28</a></span>
 <span class="normal"><a href="#__codelineno-0-29">29</a></span>
 <span class="normal"><a href="#__codelineno-0-30">30</a></span>
@@ -5151,28 +5154,30 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.__init__" class
 <span class="normal"><a href="#__codelineno-0-46">46</a></span>
 <span class="normal"><a href="#__codelineno-0-47">47</a></span>
 <span class="normal"><a href="#__codelineno-0-48">48</a></span>
-<span class="normal"><a href="#__codelineno-0-49">49</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">):</span>
+<span class="normal"><a href="#__codelineno-0-49">49</a></span>
+<span class="normal"><a href="#__codelineno-0-50">50</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_format</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">,</span> <span class="n">catalog_db</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;PandasToData Initialization</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a><span class="sd">    Args:</span>
 </span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31"></a><span class="sd">        output_uuid (str): The UUID of the DataSource to create</span>
 </span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">        output_format (str): The file format to store the S3 object data in (default: &quot;parquet&quot;)</span>
-</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>    <span class="c1"># Make sure the output_uuid is a valid name/id</span>
-</span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>    <span class="n">Artifact</span><span class="o">.</span><span class="n">is_name_valid</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>
-</span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>
-</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a>    <span class="c1"># Call superclass init</span>
-</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a>    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;DataFrame&quot;</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">)</span>
-</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a>
-</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>    <span class="c1"># Set up all my instance attributes</span>
-</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="n">TransformInput</span><span class="o">.</span><span class="n">PANDAS_DF</span>
-</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="n">TransformOutput</span><span class="o">.</span><span class="n">DATA_SOURCE</span>
-</span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="kc">None</span>
-</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>
-</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>    <span class="c1"># Give a message that Parquet is best in most cases</span>
-</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>    <span class="k">if</span> <span class="n">output_format</span> <span class="o">!=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
-</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Parquet format works the best in most cases please consider using it&quot;</span><span class="p">)</span>
-</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">=</span> <span class="n">output_format</span>
+</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a><span class="sd">        catalog_db (str): The AWS Data Catalog Database to use (default: &quot;workbench&quot;)</span>
+</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a>
+</span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a>    <span class="c1"># Make sure the output_uuid is a valid name/id</span>
+</span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a>    <span class="n">Artifact</span><span class="o">.</span><span class="n">is_name_valid</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>
+</span><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a>
+</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a>    <span class="c1"># Call superclass init</span>
+</span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a>    <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="s2">&quot;DataFrame&quot;</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">,</span> <span class="n">catalog_db</span><span class="p">)</span>
+</span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>
+</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="c1"># Set up all my instance attributes</span>
+</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="n">TransformInput</span><span class="o">.</span><span class="n">PANDAS_DF</span>
+</span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="n">TransformOutput</span><span class="o">.</span><span class="n">DATA_SOURCE</span>
+</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="kc">None</span>
+</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>
+</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>    <span class="c1"># Give a message that Parquet is best in most cases</span>
+</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>    <span class="k">if</span> <span class="n">output_format</span> <span class="o">!=</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
+</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Parquet format works the best in most cases please consider using it&quot;</span><span class="p">)</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">=</span> <span class="n">output_format</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5182,7 +5187,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.__init__" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_datetime_columns" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.convert_datetime_columns" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">convert_datetime_columns</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -5197,22 +5202,22 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_datetim
         <p>Convert datetime columns to ISO-8601 string</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-80">80</a></span>
-<span class="normal"><a href="#__codelineno-0-81">81</a></span>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-81">81</a></span>
 <span class="normal"><a href="#__codelineno-0-82">82</a></span>
 <span class="normal"><a href="#__codelineno-0-83">83</a></span>
 <span class="normal"><a href="#__codelineno-0-84">84</a></span>
 <span class="normal"><a href="#__codelineno-0-85">85</a></span>
 <span class="normal"><a href="#__codelineno-0-86">86</a></span>
-<span class="normal"><a href="#__codelineno-0-87">87</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a><span class="nd">@staticmethod</span>
-</span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a><span class="k">def</span> <span class="nf">convert_datetime_columns</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert datetime columns to ISO-8601 string&quot;&quot;&quot;</span>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="n">datetime_type</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;datetime&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64[ns]&quot;</span><span class="p">,</span> <span class="s2">&quot;datetimetz&quot;</span><span class="p">]</span>
-</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="n">datetime_type</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>        <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">datetime_to_iso8601</span><span class="p">)</span>
-</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>        <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">StringDtype</span><span class="p">())</span>
-</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>    <span class="k">return</span> <span class="n">df</span>
+<span class="normal"><a href="#__codelineno-0-87">87</a></span>
+<span class="normal"><a href="#__codelineno-0-88">88</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a><span class="nd">@staticmethod</span>
+</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="k">def</span> <span class="nf">convert_datetime_columns</span><span class="p">(</span><span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert datetime columns to ISO-8601 string&quot;&quot;&quot;</span>
+</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>    <span class="n">datetime_type</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;datetime&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64&quot;</span><span class="p">,</span> <span class="s2">&quot;datetime64[ns]&quot;</span><span class="p">,</span> <span class="s2">&quot;datetimetz&quot;</span><span class="p">]</span>
+</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">select_dtypes</span><span class="p">(</span><span class="n">include</span><span class="o">=</span><span class="n">datetime_type</span><span class="p">)</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>        <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="n">datetime_to_iso8601</span><span class="p">)</span>
+</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>        <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">pd</span><span class="o">.</span><span class="n">StringDtype</span><span class="p">())</span>
+</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>    <span class="k">return</span> <span class="n">df</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5222,7 +5227,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_datetim
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_to_datetime" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.convert_object_to_datetime" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">convert_object_to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
 
 </h3>
@@ -5233,22 +5238,22 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_
         <p>Try to automatically convert object columns to datetime or string columns</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-71">71</a></span>
-<span class="normal"><a href="#__codelineno-0-72">72</a></span>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-72">72</a></span>
 <span class="normal"><a href="#__codelineno-0-73">73</a></span>
 <span class="normal"><a href="#__codelineno-0-74">74</a></span>
 <span class="normal"><a href="#__codelineno-0-75">75</a></span>
 <span class="normal"><a href="#__codelineno-0-76">76</a></span>
 <span class="normal"><a href="#__codelineno-0-77">77</a></span>
-<span class="normal"><a href="#__codelineno-0-78">78</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a><span class="k">def</span> <span class="nf">convert_object_to_datetime</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to datetime or string columns&quot;&quot;&quot;</span>
-</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
-</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>        <span class="k">try</span><span class="p">:</span>
-</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
-</span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>        <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
-</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to datetime...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>    <span class="k">return</span> <span class="n">df</span>
+<span class="normal"><a href="#__codelineno-0-78">78</a></span>
+<span class="normal"><a href="#__codelineno-0-79">79</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a><span class="k">def</span> <span class="nf">convert_object_to_datetime</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to datetime or string columns&quot;&quot;&quot;</span>
+</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
+</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>        <span class="k">try</span><span class="p">:</span>
+</span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">])</span>
+</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>        <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
+</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to datetime...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>    <span class="k">return</span> <span class="n">df</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5258,7 +5263,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_to_string" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.convert_object_to_string" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">convert_object_to_string</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
 
 </h3>
@@ -5269,24 +5274,24 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_
         <p>Try to automatically convert object columns to string columns</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-61">61</a></span>
-<span class="normal"><a href="#__codelineno-0-62">62</a></span>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-62">62</a></span>
 <span class="normal"><a href="#__codelineno-0-63">63</a></span>
 <span class="normal"><a href="#__codelineno-0-64">64</a></span>
 <span class="normal"><a href="#__codelineno-0-65">65</a></span>
 <span class="normal"><a href="#__codelineno-0-66">66</a></span>
 <span class="normal"><a href="#__codelineno-0-67">67</a></span>
 <span class="normal"><a href="#__codelineno-0-68">68</a></span>
-<span class="normal"><a href="#__codelineno-0-69">69</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a><span class="k">def</span> <span class="nf">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to string columns&quot;&quot;&quot;</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="k">try</span><span class="p">:</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;string&quot;</span><span class="p">)</span>
-</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">,</span> <span class="s1">&#39;&quot;&#39;</span><span class="p">)</span>  <span class="c1"># This is for nested JSON</span>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>        <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
-</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to string...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>    <span class="k">return</span> <span class="n">df</span>
+<span class="normal"><a href="#__codelineno-0-69">69</a></span>
+<span class="normal"><a href="#__codelineno-0-70">70</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a><span class="k">def</span> <span class="nf">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">:</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Try to automatically convert object columns to string columns&quot;&quot;&quot;</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="n">df</span><span class="o">.</span><span class="n">columns</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">dtypes</span> <span class="o">==</span> <span class="s2">&quot;object&quot;</span><span class="p">]:</span>  <span class="c1"># Look at the object columns</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>        <span class="k">try</span><span class="p">:</span>
+</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s2">&quot;string&quot;</span><span class="p">)</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>            <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s2">&quot;&#39;&quot;</span><span class="p">,</span> <span class="s1">&#39;&quot;&#39;</span><span class="p">)</span>  <span class="c1"># This is for nested JSON</span>
+</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>        <span class="k">except</span> <span class="p">(</span><span class="n">ParserError</span><span class="p">,</span> <span class="ne">ValueError</span><span class="p">,</span> <span class="ne">TypeError</span><span class="p">):</span>
+</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> could not be converted to string...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>    <span class="k">return</span> <span class="n">df</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5296,7 +5301,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -5307,20 +5312,20 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.post_transform"
         <p>Post-Transform: Calling onboard() fnr the DataSource</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-171">171</a></span>
-<span class="normal"><a href="#__codelineno-0-172">172</a></span>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-172">172</a></span>
 <span class="normal"><a href="#__codelineno-0-173">173</a></span>
 <span class="normal"><a href="#__codelineno-0-174">174</a></span>
 <span class="normal"><a href="#__codelineno-0-175">175</a></span>
 <span class="normal"><a href="#__codelineno-0-176">176</a></span>
-<span class="normal"><a href="#__codelineno-0-177">177</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-171"><a id="__codelineno-0-171" name="__codelineno-0-171"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-172"><a id="__codelineno-0-172" name="__codelineno-0-172"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Post-Transform: Calling onboard() fnr the DataSource&quot;&quot;&quot;</span>
-</span><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Post-Transform: Calling onboard() for the DataSource...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a>
-</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a>    <span class="c1"># Onboard the DataSource</span>
-</span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a>    <span class="n">output_data_source</span> <span class="o">=</span> <span class="n">DataSourceFactory</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
-</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>    <span class="n">output_data_source</span><span class="o">.</span><span class="n">onboard</span><span class="p">()</span>
+<span class="normal"><a href="#__codelineno-0-177">177</a></span>
+<span class="normal"><a href="#__codelineno-0-178">178</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-172"><a id="__codelineno-0-172" name="__codelineno-0-172"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-173"><a id="__codelineno-0-173" name="__codelineno-0-173"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Post-Transform: Calling onboard() fnr the DataSource&quot;&quot;&quot;</span>
+</span><span id="__span-0-174"><a id="__codelineno-0-174" name="__codelineno-0-174"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Post-Transform: Calling onboard() for the DataSource...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-175"><a id="__codelineno-0-175" name="__codelineno-0-175"></a>
+</span><span id="__span-0-176"><a id="__codelineno-0-176" name="__codelineno-0-176"></a>    <span class="c1"># Onboard the DataSource</span>
+</span><span id="__span-0-177"><a id="__codelineno-0-177" name="__codelineno-0-177"></a>    <span class="n">output_data_source</span> <span class="o">=</span> <span class="n">DataSourceFactory</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">)</span>
+</span><span id="__span-0-178"><a id="__codelineno-0-178" name="__codelineno-0-178"></a>    <span class="n">output_data_source</span><span class="o">.</span><span class="n">onboard</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5330,7 +5335,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.post_transform"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.pre_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.pre_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pre_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -5341,12 +5346,12 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.pre_transform"
         <p>Pre-Transform: Delete the existing DataSource if it exists</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-89">89</a></span>
-<span class="normal"><a href="#__codelineno-0-90">90</a></span>
-<span class="normal"><a href="#__codelineno-0-91">91</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a><span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Pre-Transform: Delete the existing DataSource if it exists&quot;&quot;&quot;</span>
-</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">delete_existing</span><span class="p">()</span>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-90">90</a></span>
+<span class="normal"><a href="#__codelineno-0-91">91</a></span>
+<span class="normal"><a href="#__codelineno-0-92">92</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Pre-Transform: Delete the existing DataSource if it exists&quot;&quot;&quot;</span>
+</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">delete_existing</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5356,7 +5361,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.pre_transform"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.set_input" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.set_input" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_input</span><span class="p">(</span><span class="n">input_df</span><span class="p">)</span></code>
 
 </h3>
@@ -5367,12 +5372,12 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.set_input" clas
         <p>Set the DataFrame Input for this Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-51">51</a></span>
-<span class="normal"><a href="#__codelineno-0-52">52</a></span>
-<span class="normal"><a href="#__codelineno-0-53">53</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a><span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the DataFrame Input for this Transform&quot;&quot;&quot;</span>
-</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="n">input_df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-52">52</a></span>
+<span class="normal"><a href="#__codelineno-0-53">53</a></span>
+<span class="normal"><a href="#__codelineno-0-54">54</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="k">def</span> <span class="nf">set_input</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_df</span><span class="p">:</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">):</span>
+</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the DataFrame Input for this Transform&quot;&quot;&quot;</span>
+</span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="n">input_df</span><span class="o">.</span><span class="n">copy</span><span class="p">()</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5382,16 +5387,16 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.set_input" clas
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.transform_impl" class="doc doc-heading">
-            <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToData.transform_impl" class="doc doc-heading">
+            <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="n">overwrite</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
 
 
     <div class="doc doc-contents ">
 
-        <p>Convert the Pandas DataFrame into Parquet Format in the SageWorks S3 Bucket, and
-store the information about the data to the AWS Data Catalog sageworks database</p>
+        <p>Convert the Pandas DataFrame into Parquet Format in the Workbench S3 Bucket, and
+store the information about the data to the AWS Data Catalog workbench database</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -5414,7 +5419,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.transform_impl"
             </td>
             <td>
               <div class="doc-md-description">
-                <p>Overwrite the existing data in the SageWorks S3 Bucket</p>
+                <p>Overwrite the existing data in the Workbench S3 Bucket (default: True)</p>
               </div>
             </td>
             <td>
@@ -5425,9 +5430,8 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.transform_impl"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-93"> 93</a></span>
-<span class="normal"><a href="#__codelineno-0-94"> 94</a></span>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-94"> 94</a></span>
 <span class="normal"><a href="#__codelineno-0-95"> 95</a></span>
 <span class="normal"><a href="#__codelineno-0-96"> 96</a></span>
 <span class="normal"><a href="#__codelineno-0-97"> 97</a></span>
@@ -5502,83 +5506,84 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.transform_impl"
 <span class="normal"><a href="#__codelineno-0-166">166</a></span>
 <span class="normal"><a href="#__codelineno-0-167">167</a></span>
 <span class="normal"><a href="#__codelineno-0-168">168</a></span>
-<span class="normal"><a href="#__codelineno-0-169">169</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the Pandas DataFrame into Parquet Format in the SageWorks S3 Bucket, and</span>
-</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a><span class="sd">    store the information about the data to the AWS Data Catalog sageworks database</span>
-</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">        overwrite (bool): Overwrite the existing data in the SageWorks S3 Bucket</span>
-</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;DataFrame to SageWorks DataSource: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>
-</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a>    <span class="c1"># Set up our metadata storage</span>
-</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>    <span class="n">sageworks_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
-</span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a>    <span class="n">sageworks_meta</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="p">)</span>
-</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>
-</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a>    <span class="c1"># Create the Output Parquet file S3 Storage Path</span>
-</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>    <span class="n">s3_storage_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
-</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a>
-</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>    <span class="c1"># Convert columns names to lowercase, Athena will not work with uppercase column names</span>
-</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>    <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()):</span>
-</span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
-</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>            <span class="k">if</span> <span class="n">c</span> <span class="o">!=</span> <span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
-</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column name </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> converted to lowercase: </span><span class="si">{</span><span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
-</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
-</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>
-</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a>    <span class="c1"># Convert Object Columns to String</span>
-</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">)</span>
-</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a>
-</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a>    <span class="c1"># Note: Both of these conversions may not be necessary, so we&#39;re leaving them commented out</span>
-</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
-</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a><span class="sd">    # Convert Object Columns to Datetime</span>
-</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="sd">    self.output_df = self.convert_object_to_datetime(self.output_df)</span>
-</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a>
-</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a><span class="sd">    # Now convert datetime columns to ISO-8601 string</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="sd">    # self.output_df = self.convert_datetime_columns(self.output_df)</span>
-</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a>
-</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a>    <span class="c1"># Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format</span>
-</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>    <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;SageWorks data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
-</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>    <span class="n">glue_table_settings</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="n">description</span><span class="p">,</span> <span class="s2">&quot;parameters&quot;</span><span class="p">:</span> <span class="n">sageworks_meta</span><span class="p">}</span>
-</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
-</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>        <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span>
-</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
-</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a>            <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
-</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>            <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a>            <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
-</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>            <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
-</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a>            <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
-</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>            <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
-</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a>            <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
-</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>            <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a>            <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
-</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>            <span class="n">sanitize_columns</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-</span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a>        <span class="p">)</span>  <span class="c1"># FIXME: Have some logic around partition columns</span>
-</span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>
-</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>    <span class="c1"># Note: In general Parquet works will for most uses cases. We recommend using Parquet</span>
-</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>    <span class="c1">#       You can use JSON_EXTRACT on Parquet string field, and it works great.</span>
-</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>    <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;jsonl&quot;</span><span class="p">:</span>
-</span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We recommend using Parquet format for most use cases&quot;</span><span class="p">)</span>
-</span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;If you have a use case that requires JSONL please contact SageWorks support&quot;</span><span class="p">)</span>
-</span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We&#39;d like to understand what functionality JSONL is providing that isn&#39;t already&quot;</span><span class="p">)</span>
-</span><span id="__span-0-152"><a id="__codelineno-0-152" name="__codelineno-0-152"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;provided with Parquet and JSON_EXTRACT() for your Athena Queries&quot;</span><span class="p">)</span>
-</span><span id="__span-0-153"><a id="__codelineno-0-153" name="__codelineno-0-153"></a>        <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span>
-</span><span id="__span-0-154"><a id="__codelineno-0-154" name="__codelineno-0-154"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
-</span><span id="__span-0-155"><a id="__codelineno-0-155" name="__codelineno-0-155"></a>            <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
-</span><span id="__span-0-156"><a id="__codelineno-0-156" name="__codelineno-0-156"></a>            <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;records&quot;</span><span class="p">,</span>
-</span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a>            <span class="n">lines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-</span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a>            <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;iso&quot;</span><span class="p">,</span>
-</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>            <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>            <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
-</span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>            <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
-</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>            <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
-</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>            <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
-</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>            <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
-</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>            <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
-</span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a>            <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
-</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>        <span class="p">)</span>
-</span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a>    <span class="k">else</span><span class="p">:</span>
-</span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a>        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unsupported file format: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_format</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="normal"><a href="#__codelineno-0-169">169</a></span>
+<span class="normal"><a href="#__codelineno-0-170">170</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">overwrite</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the Pandas DataFrame into Parquet Format in the Workbench S3 Bucket, and</span>
+</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a><span class="sd">    store the information about the data to the AWS Data Catalog workbench database</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>
+</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">    Args:</span>
+</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">        overwrite (bool): Overwrite the existing data in the Workbench S3 Bucket (default: True)</span>
+</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;DataFrame to Workbench DataSource: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a>
+</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>    <span class="c1"># Set up our metadata storage</span>
+</span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a>    <span class="n">workbench_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
+</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>    <span class="n">workbench_meta</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="p">)</span>
+</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a>
+</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a>    <span class="c1"># Create the Output Parquet file S3 Storage Path</span>
+</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a>    <span class="n">s3_storage_path</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
+</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>
+</span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>    <span class="c1"># Convert columns names to lowercase, Athena will not work with uppercase column names</span>
+</span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>    <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()):</span>
+</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
+</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>            <span class="k">if</span> <span class="n">c</span> <span class="o">!=</span> <span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">():</span>
+</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>                <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Column name </span><span class="si">{</span><span class="n">c</span><span class="si">}</span><span class="s2"> converted to lowercase: </span><span class="si">{</span><span class="n">c</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="o">.</span><span class="n">str</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
+</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a>
+</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a>    <span class="c1"># Convert Object Columns to String</span>
+</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_object_to_string</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">)</span>
+</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a>
+</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a>    <span class="c1"># Note: Both of these conversions may not be necessary, so we&#39;re leaving them commented out</span>
+</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="sd">    # Convert Object Columns to Datetime</span>
+</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a><span class="sd">    self.output_df = self.convert_object_to_datetime(self.output_df)</span>
+</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>
+</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a><span class="sd">    # Now convert datetime columns to ISO-8601 string</span>
+</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a><span class="sd">    # self.output_df = self.convert_datetime_columns(self.output_df)</span>
+</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a>
+</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>    <span class="c1"># Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format</span>
+</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>    <span class="n">description</span> <span class="o">=</span> <span class="sa">f</span><span class="s2">&quot;Workbench data source: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">&quot;</span>
+</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>    <span class="n">glue_table_settings</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;description&quot;</span><span class="p">:</span> <span class="n">description</span><span class="p">,</span> <span class="s2">&quot;parameters&quot;</span><span class="p">:</span> <span class="n">workbench_meta</span><span class="p">}</span>
+</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;parquet&quot;</span><span class="p">:</span>
+</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a>        <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_parquet</span><span class="p">(</span>
+</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
+</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>            <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
+</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a>            <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>            <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
+</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a>            <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
+</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>            <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
+</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a>            <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
+</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>            <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
+</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a>            <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>            <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
+</span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a>            <span class="n">sanitize_columns</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+</span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>        <span class="p">)</span>  <span class="c1"># FIXME: Have some logic around partition columns</span>
+</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a>
+</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>    <span class="c1"># Note: In general Parquet works will for most uses cases. We recommend using Parquet</span>
+</span><span id="__span-0-148"><a id="__codelineno-0-148" name="__codelineno-0-148"></a>    <span class="c1">#       You can use JSON_EXTRACT on Parquet string field, and it works great.</span>
+</span><span id="__span-0-149"><a id="__codelineno-0-149" name="__codelineno-0-149"></a>    <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_format</span> <span class="o">==</span> <span class="s2">&quot;jsonl&quot;</span><span class="p">:</span>
+</span><span id="__span-0-150"><a id="__codelineno-0-150" name="__codelineno-0-150"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We recommend using Parquet format for most use cases&quot;</span><span class="p">)</span>
+</span><span id="__span-0-151"><a id="__codelineno-0-151" name="__codelineno-0-151"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;If you have a use case that requires JSONL please contact Workbench support&quot;</span><span class="p">)</span>
+</span><span id="__span-0-152"><a id="__codelineno-0-152" name="__codelineno-0-152"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;We&#39;d like to understand what functionality JSONL is providing that isn&#39;t already&quot;</span><span class="p">)</span>
+</span><span id="__span-0-153"><a id="__codelineno-0-153" name="__codelineno-0-153"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;provided with Parquet and JSON_EXTRACT() for your Athena Queries&quot;</span><span class="p">)</span>
+</span><span id="__span-0-154"><a id="__codelineno-0-154" name="__codelineno-0-154"></a>        <span class="n">wr</span><span class="o">.</span><span class="n">s3</span><span class="o">.</span><span class="n">to_json</span><span class="p">(</span>
+</span><span id="__span-0-155"><a id="__codelineno-0-155" name="__codelineno-0-155"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">,</span>
+</span><span id="__span-0-156"><a id="__codelineno-0-156" name="__codelineno-0-156"></a>            <span class="n">path</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
+</span><span id="__span-0-157"><a id="__codelineno-0-157" name="__codelineno-0-157"></a>            <span class="n">orient</span><span class="o">=</span><span class="s2">&quot;records&quot;</span><span class="p">,</span>
+</span><span id="__span-0-158"><a id="__codelineno-0-158" name="__codelineno-0-158"></a>            <span class="n">lines</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+</span><span id="__span-0-159"><a id="__codelineno-0-159" name="__codelineno-0-159"></a>            <span class="n">date_format</span><span class="o">=</span><span class="s2">&quot;iso&quot;</span><span class="p">,</span>
+</span><span id="__span-0-160"><a id="__codelineno-0-160" name="__codelineno-0-160"></a>            <span class="n">dataset</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+</span><span id="__span-0-161"><a id="__codelineno-0-161" name="__codelineno-0-161"></a>            <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;overwrite&quot;</span><span class="p">,</span>
+</span><span id="__span-0-162"><a id="__codelineno-0-162" name="__codelineno-0-162"></a>            <span class="n">database</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span><span class="p">,</span>
+</span><span id="__span-0-163"><a id="__codelineno-0-163" name="__codelineno-0-163"></a>            <span class="n">table</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="p">,</span>
+</span><span id="__span-0-164"><a id="__codelineno-0-164" name="__codelineno-0-164"></a>            <span class="n">filename_prefix</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span><span class="si">}</span><span class="s2">_&quot;</span><span class="p">,</span>
+</span><span id="__span-0-165"><a id="__codelineno-0-165" name="__codelineno-0-165"></a>            <span class="n">boto3_session</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span><span class="p">,</span>
+</span><span id="__span-0-166"><a id="__codelineno-0-166" name="__codelineno-0-166"></a>            <span class="n">partition_cols</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+</span><span id="__span-0-167"><a id="__codelineno-0-167" name="__codelineno-0-167"></a>            <span class="n">glue_table_settings</span><span class="o">=</span><span class="n">glue_table_settings</span><span class="p">,</span>
+</span><span id="__span-0-168"><a id="__codelineno-0-168" name="__codelineno-0-168"></a>        <span class="p">)</span>
+</span><span id="__span-0-169"><a id="__codelineno-0-169" name="__codelineno-0-169"></a>    <span class="k">else</span><span class="p">:</span>
+</span><span id="__span-0-170"><a id="__codelineno-0-170" name="__codelineno-0-170"></a>        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Unsupported file format: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">output_format</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -5597,7 +5602,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToData.transform_impl"
 
 
 
-<h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures" class="doc doc-heading">
+<h2 id="workbench.core.transforms.pandas_transforms.PandasToFeatures" class="doc doc-heading">
             <code>PandasToFeatures</code>
 
 
@@ -5606,7 +5611,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures" class="doc
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>PandasToFeatures: Class to publish a Pandas DataFrame into a FeatureSet</p>
@@ -5626,7 +5631,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures" class="doc
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
 <span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
 <span class="normal"><a href="#__codelineno-0-18"> 18</a></span>
@@ -6274,10 +6279,10 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures" class="doc
 </span><span id="__span-0-271"><a id="__codelineno-0-271" name="__codelineno-0-271"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">_ensure_id_column</span><span class="p">()</span>
 </span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">_ensure_event_time</span><span class="p">()</span>
 </span><span id="__span-0-273"><a id="__codelineno-0-273" name="__codelineno-0-273"></a>
-</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>        <span class="c1"># Check for a training column (SageWorks uses dynamic training columns)</span>
+</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>        <span class="c1"># Check for a training column (Workbench uses dynamic training columns)</span>
 </span><span id="__span-0-275"><a id="__codelineno-0-275" name="__codelineno-0-275"></a>        <span class="k">if</span> <span class="s2">&quot;training&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
 </span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span>
-</span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a><span class="w">                </span><span class="sd">&quot;&quot;&quot;Training column detected: Since FeatureSets are read-only, SageWorks creates a training view</span>
+</span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a><span class="w">                </span><span class="sd">&quot;&quot;&quot;Training column detected: Since FeatureSets are read-only, Workbench creates a training view</span>
 </span><span id="__span-0-278"><a id="__codelineno-0-278" name="__codelineno-0-278"></a><span class="sd">                that can be dynamically changed. We&#39;ll use this training column to create a training view.&quot;&quot;&quot;</span>
 </span><span id="__span-0-279"><a id="__codelineno-0-279" name="__codelineno-0-279"></a>            <span class="p">)</span>
 </span><span id="__span-0-280"><a id="__codelineno-0-280" name="__codelineno-0-280"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">incoming_hold_out_ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">[</span><span class="o">~</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">[</span><span class="s2">&quot;training&quot;</span><span class="p">]][</span><span class="bp">self</span><span class="o">.</span><span class="n">id_column</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
@@ -6308,7 +6313,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures" class="doc
 </span><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a>            <span class="n">s3_uri</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
 </span><span id="__span-0-306"><a id="__codelineno-0-306" name="__codelineno-0-306"></a>            <span class="n">record_identifier_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_column</span><span class="p">,</span>
 </span><span id="__span-0-307"><a id="__codelineno-0-307" name="__codelineno-0-307"></a>            <span class="n">event_time_feature_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">event_time_column</span><span class="p">,</span>
-</span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a>            <span class="n">role_arn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">,</span>
+</span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a>            <span class="n">role_arn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">,</span>
 </span><span id="__span-0-309"><a id="__codelineno-0-309" name="__codelineno-0-309"></a>            <span class="n">enable_online_store</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 </span><span id="__span-0-310"><a id="__codelineno-0-310" name="__codelineno-0-310"></a>            <span class="n">table_format</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">table_format</span><span class="p">,</span>
 </span><span id="__span-0-311"><a id="__codelineno-0-311" name="__codelineno-0-311"></a>            <span class="n">tags</span><span class="o">=</span><span class="n">aws_tags</span><span class="p">,</span>
@@ -6424,7 +6429,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -6466,7 +6471,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.__init__" c
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-28">28</a></span>
 <span class="normal"><a href="#__codelineno-0-29">29</a></span>
 <span class="normal"><a href="#__codelineno-0-30">30</a></span>
@@ -6531,7 +6536,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.__init__" c
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_column_types" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.convert_column_types" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">convert_column_types</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -6546,7 +6551,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_col
         <p>Convert the types of the DataFrame to the correct types for the Feature Store</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-229">229</a></span>
 <span class="normal"><a href="#__codelineno-0-230">230</a></span>
 <span class="normal"><a href="#__codelineno-0-231">231</a></span>
@@ -6597,7 +6602,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_col
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_columns_to_categorical" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.convert_columns_to_categorical" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">convert_columns_to_categorical</span><span class="p">(</span><span class="n">columns</span><span class="p">)</span></code>
 
 </h3>
@@ -6608,7 +6613,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_col
         <p>Convert column to Categorical type</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-207">207</a></span>
 <span class="normal"><a href="#__codelineno-0-208">208</a></span>
 <span class="normal"><a href="#__codelineno-0-209">209</a></span>
@@ -6637,7 +6642,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_col
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.create_feature_group" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.create_feature_group" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create_feature_group</span><span class="p">()</span></code>
 
 </h3>
@@ -6648,7 +6653,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.create_feat
         <p>Create a Feature Group, load our Feature Definitions, and wait for it to be ready</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-290">290</a></span>
 <span class="normal"><a href="#__codelineno-0-291">291</a></span>
 <span class="normal"><a href="#__codelineno-0-292">292</a></span>
@@ -6693,7 +6698,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.create_feat
 </span><span id="__span-0-305"><a id="__codelineno-0-305" name="__codelineno-0-305"></a>        <span class="n">s3_uri</span><span class="o">=</span><span class="n">s3_storage_path</span><span class="p">,</span>
 </span><span id="__span-0-306"><a id="__codelineno-0-306" name="__codelineno-0-306"></a>        <span class="n">record_identifier_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">id_column</span><span class="p">,</span>
 </span><span id="__span-0-307"><a id="__codelineno-0-307" name="__codelineno-0-307"></a>        <span class="n">event_time_feature_name</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">event_time_column</span><span class="p">,</span>
-</span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a>        <span class="n">role_arn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span><span class="p">,</span>
+</span><span id="__span-0-308"><a id="__codelineno-0-308" name="__codelineno-0-308"></a>        <span class="n">role_arn</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span><span class="p">,</span>
 </span><span id="__span-0-309"><a id="__codelineno-0-309" name="__codelineno-0-309"></a>        <span class="n">enable_online_store</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 </span><span id="__span-0-310"><a id="__codelineno-0-310" name="__codelineno-0-310"></a>        <span class="n">table_format</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">table_format</span><span class="p">,</span>
 </span><span id="__span-0-311"><a id="__codelineno-0-311" name="__codelineno-0-311"></a>        <span class="n">tags</span><span class="o">=</span><span class="n">aws_tags</span><span class="p">,</span>
@@ -6711,7 +6716,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.create_feat
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.manual_categorical_converter" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.manual_categorical_converter" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">manual_categorical_converter</span><span class="p">()</span></code>
 
 </h3>
@@ -6729,7 +6734,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.manual_cate
 their respective categorical types.</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-218">218</a></span>
 <span class="normal"><a href="#__codelineno-0-219">219</a></span>
 <span class="normal"><a href="#__codelineno-0-220">220</a></span>
@@ -6758,7 +6763,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.manual_cate
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.one_hot_encode" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.one_hot_encode" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">one_hot_encode</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">one_hot_columns</span><span class="p">)</span></code>
 
 </h3>
@@ -6839,7 +6844,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.one_hot_enc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-172">172</a></span>
 <span class="normal"><a href="#__codelineno-0-173">173</a></span>
 <span class="normal"><a href="#__codelineno-0-174">174</a></span>
@@ -6914,7 +6919,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.one_hot_enc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -6925,7 +6930,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.post_transf
         <p>Post-Transform: Populating Offline Storage and onboard()</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-357">357</a></span>
 <span class="normal"><a href="#__codelineno-0-358">358</a></span>
 <span class="normal"><a href="#__codelineno-0-359">359</a></span>
@@ -6968,7 +6973,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.post_transf
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.pre_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.pre_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pre_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -6979,7 +6984,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.pre_transfo
         <p>Pre-Transform: Delete any existing FeatureSet and Create the Feature Group</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-318">318</a></span>
 <span class="normal"><a href="#__codelineno-0-319">319</a></span>
 <span class="normal"><a href="#__codelineno-0-320">320</a></span>
@@ -6996,7 +7001,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.pre_transfo
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.prep_dataframe" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.prep_dataframe" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">prep_dataframe</span><span class="p">()</span></code>
 
 </h3>
@@ -7007,7 +7012,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.prep_datafr
         <p>Prep the DataFrame for Feature Store Creation</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-251">251</a></span>
 <span class="normal"><a href="#__codelineno-0-252">252</a></span>
 <span class="normal"><a href="#__codelineno-0-253">253</a></span>
@@ -7068,10 +7073,10 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.prep_datafr
 </span><span id="__span-0-271"><a id="__codelineno-0-271" name="__codelineno-0-271"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">_ensure_id_column</span><span class="p">()</span>
 </span><span id="__span-0-272"><a id="__codelineno-0-272" name="__codelineno-0-272"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">_ensure_event_time</span><span class="p">()</span>
 </span><span id="__span-0-273"><a id="__codelineno-0-273" name="__codelineno-0-273"></a>
-</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>    <span class="c1"># Check for a training column (SageWorks uses dynamic training columns)</span>
+</span><span id="__span-0-274"><a id="__codelineno-0-274" name="__codelineno-0-274"></a>    <span class="c1"># Check for a training column (Workbench uses dynamic training columns)</span>
 </span><span id="__span-0-275"><a id="__codelineno-0-275" name="__codelineno-0-275"></a>    <span class="k">if</span> <span class="s2">&quot;training&quot;</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="o">.</span><span class="n">columns</span><span class="p">:</span>
 </span><span id="__span-0-276"><a id="__codelineno-0-276" name="__codelineno-0-276"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span>
-</span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a><span class="w">            </span><span class="sd">&quot;&quot;&quot;Training column detected: Since FeatureSets are read-only, SageWorks creates a training view</span>
+</span><span id="__span-0-277"><a id="__codelineno-0-277" name="__codelineno-0-277"></a><span class="w">            </span><span class="sd">&quot;&quot;&quot;Training column detected: Since FeatureSets are read-only, Workbench creates a training view</span>
 </span><span id="__span-0-278"><a id="__codelineno-0-278" name="__codelineno-0-278"></a><span class="sd">            that can be dynamically changed. We&#39;ll use this training column to create a training view.&quot;&quot;&quot;</span>
 </span><span id="__span-0-279"><a id="__codelineno-0-279" name="__codelineno-0-279"></a>        <span class="p">)</span>
 </span><span id="__span-0-280"><a id="__codelineno-0-280" name="__codelineno-0-280"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">incoming_hold_out_ids</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">[</span><span class="o">~</span><span class="bp">self</span><span class="o">.</span><span class="n">output_df</span><span class="p">[</span><span class="s2">&quot;training&quot;</span><span class="p">]][</span><span class="bp">self</span><span class="o">.</span><span class="n">id_column</span><span class="p">]</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
@@ -7092,7 +7097,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.prep_datafr
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.process_column_name" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.process_column_name" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">process_column_name</span><span class="p">(</span><span class="n">column</span><span class="p">,</span> <span class="n">shorten</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -7106,7 +7111,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.process_col
     shorten (bool): Should we shorten the column name? (default: False)</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-123">123</a></span>
 <span class="normal"><a href="#__codelineno-0-124">124</a></span>
 <span class="normal"><a href="#__codelineno-0-125">125</a></span>
@@ -7147,7 +7152,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.process_col
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.set_input" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.set_input" class="doc doc-heading">
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 </h3>
@@ -7237,7 +7242,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.set_input"
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-57">57</a></span>
 <span class="normal"><a href="#__codelineno-0-58">58</a></span>
 <span class="normal"><a href="#__codelineno-0-59">59</a></span>
@@ -7278,7 +7283,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.set_input"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">()</span></code>
 
 </h3>
@@ -7289,7 +7294,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.transform_i
         <p>Transform Implementation: Ingest the data into the Feature Group</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-323">323</a></span>
 <span class="normal"><a href="#__codelineno-0-324">324</a></span>
 <span class="normal"><a href="#__codelineno-0-325">325</a></span>
@@ -7364,7 +7369,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.transform_i
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.wait_for_rows" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeatures.wait_for_rows" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">wait_for_rows</span><span class="p">(</span><span class="n">expected_rows</span><span class="p">)</span></code>
 
 </h3>
@@ -7375,7 +7380,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.wait_for_ro
         <p>Wait for AWS Feature Group to fully populate the Offline Storage</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code></summary>
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 <span class="normal"><a href="#__codelineno-0-387">387</a></span>
 <span class="normal"><a href="#__codelineno-0-388">388</a></span>
@@ -7433,7 +7438,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeatures.wait_for_ro
 
 
 
-<h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked" class="doc doc-heading">
+<h2 id="workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked" class="doc doc-heading">
             <code>PandasToFeaturesChunked</code>
 
 
@@ -7442,7 +7447,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked" cla
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><a class="autorefs autorefs-internal" title="sageworks.core.transforms.transform.Transform" href="../transform/#sageworks.core.transforms.transform.Transform">Transform</a></code></p>
+              Bases: <code><a class="autorefs autorefs-internal" title="workbench.core.transforms.transform.Transform" href="../transform/#workbench.core.transforms.transform.Transform">Transform</a></code></p>
 
 
         <p>PandasToFeaturesChunked:  Class to manage a bunch of chunked Pandas DataFrames into a FeatureSet</p>
@@ -7466,7 +7471,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked" cla
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-13">13</a></span>
 <span class="normal"><a href="#__codelineno-0-14">14</a></span>
 <span class="normal"><a href="#__codelineno-0-15">15</a></span>
@@ -7630,7 +7635,7 @@ <h2 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked" cla
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.__init__" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">,</span> <span class="n">id_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">event_time_column</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
 </h3>
@@ -7641,7 +7646,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.__in
         <p>PandasToFeaturesChunked Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
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 <span class="normal"><a href="#__codelineno-0-31">31</a></span>
@@ -7678,7 +7683,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.__in
 <div class="doc doc-object doc-function">
 
 
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 </h3>
@@ -7689,7 +7694,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.add_
         <p>Add a Chunk of Data to the FeatureSet</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-58">58</a></span>
 <span class="normal"><a href="#__codelineno-0-59">59</a></span>
 <span class="normal"><a href="#__codelineno-0-60">60</a></span>
@@ -7726,7 +7731,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.add_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.post_transform" class="doc doc-heading">
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 </h3>
@@ -7737,7 +7742,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.post
         <p>Post-Transform: Any Post Transform Steps</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-83">83</a></span>
 <span class="normal"><a href="#__codelineno-0-84">84</a></span>
 <span class="normal"><a href="#__codelineno-0-85">85</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
@@ -7752,7 +7757,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.post
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_transform" class="doc doc-heading">
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 </h3>
@@ -7763,7 +7768,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_
         <p>Pre-Transform: Create the Feature Group with Chunked Data</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-73">73</a></span>
 <span class="normal"><a href="#__codelineno-0-74">74</a></span>
 <span class="normal"><a href="#__codelineno-0-75">75</a></span>
@@ -7782,7 +7787,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_categorical_info" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_categorical_info" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_categorical_info</span><span class="p">(</span><span class="n">cat_column_info</span><span class="p">)</span></code>
 
 </h3>
@@ -7795,7 +7800,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_
     cat_column_info (dict[list[str]]): Dictionary of categorical columns and their possible values</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-44">44</a></span>
 <span class="normal"><a href="#__codelineno-0-45">45</a></span>
 <span class="normal"><a href="#__codelineno-0-46">46</a></span>
@@ -7830,7 +7835,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">()</span></code>
 
 </h3>
@@ -7841,7 +7846,7 @@ <h3 id="sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.tran
         <p>Required implementation of the Transform interface</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-79">79</a></span>
 <span class="normal"><a href="#__codelineno-0-80">80</a></span>
 <span class="normal"><a href="#__codelineno-0-81">81</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
diff --git a/core_classes/transforms/transform/index.html b/core_classes/transforms/transform/index.html
index 1f5a705aa..9f5374f81 100644
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@@ -20,7 +20,7 @@
     
     
       
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+        <title>Transform - Workbench</title>
       
     
     
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+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+  <a href="#workbench.core.transforms.transform.Transform.transform_impl" class="md-nav__link">
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       transform_impl
     </span>
@@ -1748,7 +1748,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.TransformInput" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.TransformInput" class="md-nav__link">
     <span class="md-ellipsis">
       TransformInput
     </span>
@@ -1757,7 +1757,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.TransformOutput" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.TransformOutput" class="md-nav__link">
     <span class="md-ellipsis">
       TransformOutput
     </span>
@@ -2020,7 +2020,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -2030,7 +2030,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3249,11 +3249,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3305,7 +3305,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform" class="md-nav__link">
     <span class="md-ellipsis">
       transform
     </span>
@@ -3314,7 +3314,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform" class="md-nav__link">
     <span class="md-ellipsis">
       Transform
     </span>
@@ -3324,7 +3324,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.__init__" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3333,7 +3333,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.add_output_meta" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.add_output_meta" class="md-nav__link">
     <span class="md-ellipsis">
       add_output_meta
     </span>
@@ -3342,7 +3342,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.convert_to_aws_tags" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.convert_to_aws_tags" class="md-nav__link">
     <span class="md-ellipsis">
       convert_to_aws_tags
     </span>
@@ -3351,7 +3351,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.get_aws_tags" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.get_aws_tags" class="md-nav__link">
     <span class="md-ellipsis">
       get_aws_tags
     </span>
@@ -3360,7 +3360,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.input_type" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.input_type" class="md-nav__link">
     <span class="md-ellipsis">
       input_type
     </span>
@@ -3369,7 +3369,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.output_type" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.output_type" class="md-nav__link">
     <span class="md-ellipsis">
       output_type
     </span>
@@ -3378,7 +3378,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.post_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.post_transform" class="md-nav__link">
     <span class="md-ellipsis">
       post_transform
     </span>
@@ -3387,7 +3387,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.pre_transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.pre_transform" class="md-nav__link">
     <span class="md-ellipsis">
       pre_transform
     </span>
@@ -3396,7 +3396,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.set_input_uuid" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.set_input_uuid" class="md-nav__link">
     <span class="md-ellipsis">
       set_input_uuid
     </span>
@@ -3405,7 +3405,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.set_output_tags" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.set_output_tags" class="md-nav__link">
     <span class="md-ellipsis">
       set_output_tags
     </span>
@@ -3414,7 +3414,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.set_output_uuid" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.set_output_uuid" class="md-nav__link">
     <span class="md-ellipsis">
       set_output_uuid
     </span>
@@ -3423,7 +3423,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.transform" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.transform" class="md-nav__link">
     <span class="md-ellipsis">
       transform
     </span>
@@ -3432,7 +3432,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.Transform.transform_impl" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.Transform.transform_impl" class="md-nav__link">
     <span class="md-ellipsis">
       transform_impl
     </span>
@@ -3446,7 +3446,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.TransformInput" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.TransformInput" class="md-nav__link">
     <span class="md-ellipsis">
       TransformInput
     </span>
@@ -3455,7 +3455,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.transforms.transform.TransformOutput" class="md-nav__link">
+  <a href="#workbench.core.transforms.transform.TransformOutput" class="md-nav__link">
     <span class="md-ellipsis">
       TransformOutput
     </span>
@@ -3483,17 +3483,17 @@ <h1 id="transform">Transform</h1>
 <p class="admonition-title">API Classes</p>
 <p>The <a href="../../../api_classes/overview/">API Classes</a> will use Transforms internally. So model.to_endpoint() uses the ModelToEndpoint() transform. If you need more control over the Transform you can use the <a href="../../overview/">Core Classes</a> directly.</p>
 </div>
-<p>The SageWorks Transform class is a base/abstract class that defines API implemented by all the child classes (DataLoaders, DataSourceToFeatureSet, ModelToEndpoint, etc).</p>
+<p>The Workbench Transform class is a base/abstract class that defines API implemented by all the child classes (DataLoaders, DataSourceToFeatureSet, ModelToEndpoint, etc).</p>
 
 
 <div class="doc doc-object doc-module">
 
 
 
-<a id="sageworks.core.transforms.transform"></a>
+<a id="workbench.core.transforms.transform"></a>
     <div class="doc doc-contents first">
 
-        <p>Transform: Base Class for all transforms within SageWorks
+        <p>Transform: Base Class for all transforms within Workbench
 Inherited Classes must implement the abstract transform_impl() method</p>
 
 
@@ -3516,7 +3516,7 @@ <h1 id="transform">Transform</h1>
 
 
 
-<h2 id="sageworks.core.transforms.transform.Transform" class="doc doc-heading">
+<h2 id="workbench.core.transforms.transform.Transform" class="doc doc-heading">
             <code>Transform</code>
 
 
@@ -3528,7 +3528,7 @@ <h2 id="sageworks.core.transforms.transform.Transform" class="doc doc-heading">
               Bases: <code><span title="abc.ABC">ABC</span></code></p>
 
 
-        <p>Transform: Abstract Base Class for all transforms within SageWorks. Inherited Classes
+        <p>Transform: Abstract Base Class for all transforms within Workbench. Inherited Classes
 must implement the abstract transform_impl() method</p>
 
 
@@ -3537,7 +3537,7 @@ <h2 id="sageworks.core.transforms.transform.Transform" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-38"> 38</a></span>
 <span class="normal"><a href="#__codelineno-0-39"> 39</a></span>
 <span class="normal"><a href="#__codelineno-0-40"> 40</a></span>
@@ -3641,110 +3641,122 @@ <h2 id="sageworks.core.transforms.transform.Transform" class="doc doc-heading">
 <span class="normal"><a href="#__codelineno-0-138">138</a></span>
 <span class="normal"><a href="#__codelineno-0-139">139</a></span>
 <span class="normal"><a href="#__codelineno-0-140">140</a></span>
-<span class="normal"><a href="#__codelineno-0-141">141</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="k">class</span> <span class="nc">Transform</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
-</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Transform: Abstract Base Class for all transforms within SageWorks. Inherited Classes</span>
+<span class="normal"><a href="#__codelineno-0-141">141</a></span>
+<span class="normal"><a href="#__codelineno-0-142">142</a></span>
+<span class="normal"><a href="#__codelineno-0-143">143</a></span>
+<span class="normal"><a href="#__codelineno-0-144">144</a></span>
+<span class="normal"><a href="#__codelineno-0-145">145</a></span>
+<span class="normal"><a href="#__codelineno-0-146">146</a></span>
+<span class="normal"><a href="#__codelineno-0-147">147</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-38"><a id="__codelineno-0-38" name="__codelineno-0-38"></a><span class="k">class</span> <span class="nc">Transform</span><span class="p">(</span><span class="n">ABC</span><span class="p">):</span>
+</span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Transform: Abstract Base Class for all transforms within Workbench. Inherited Classes</span>
 </span><span id="__span-0-40"><a id="__codelineno-0-40" name="__codelineno-0-40"></a><span class="sd">    must implement the abstract transform_impl() method&quot;&quot;&quot;</span>
 </span><span id="__span-0-41"><a id="__codelineno-0-41" name="__codelineno-0-41"></a>
-</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Transform Initialization&quot;&quot;&quot;</span>
+</span><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">catalog_db</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
+</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Transform Initialization</span>
 </span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>
-</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
-</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="kc">None</span>
-</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="kc">None</span>
-</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
-</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span>
-</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>
-</span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>        <span class="c1"># Grab our SageWorks Bucket</span>
-</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>        <span class="n">cm</span> <span class="o">=</span> <span class="n">ConfigManager</span><span class="p">()</span>
-</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="n">cm</span><span class="o">.</span><span class="n">config_okay</span><span class="p">():</span>
-</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;SageWorks Configuration Incomplete...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Run the &#39;sageworks&#39; command and follow the prompts...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>            <span class="k">raise</span> <span class="n">FatalConfigError</span><span class="p">()</span>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_config</span><span class="p">(</span><span class="s2">&quot;SAGEWORKS_BUCKET&quot;</span><span class="p">)</span>
-</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">feature_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/feature-sets&quot;</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">models_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/models&quot;</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">endpoints_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/endpoints&quot;</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>
-</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>        <span class="c1"># Grab a SageWorks Role ARN, Boto3, SageMaker Session, and SageMaker Client</span>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span>
-</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_sageworks_execution_role_arn</span><span class="p">()</span>
-</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">boto3_session</span>
-</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_session</span><span class="p">()</span>
-</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">sm_client</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_client</span><span class="p">()</span>
-</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>
-</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>        <span class="c1"># Delimiter for storing lists in AWS Tags</span>
-</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span> <span class="o">=</span> <span class="s2">&quot;::&quot;</span>
-</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>
-</span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>    <span class="nd">@abstractmethod</span>
-</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>    <span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Abstract Method: Implement the Transformation from Input to Output&quot;&quot;&quot;</span>
-</span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>        <span class="k">pass</span>
-</span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>
-</span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>    <span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform any Pre-Transform operations&quot;&quot;&quot;</span>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Pre-Transform...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>
-</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>    <span class="nd">@abstractmethod</span>
-</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>    <span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Post-Transform ensures that the output Artifact is ready for use&quot;&quot;&quot;</span>
-</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>        <span class="k">pass</span>
-</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>
-</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a>    <span class="k">def</span> <span class="nf">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tags</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">str</span><span class="p">]):</span>
-</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the tags that will be associated with the output object</span>
-</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">            tags (Union[list, str]): The list of tags or a &#39;::&#39; separated string of tags&quot;&quot;&quot;</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tags</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)</span>
-</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>        <span class="k">else</span><span class="p">:</span>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="n">tags</span>
-</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a>
-</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a>    <span class="k">def</span> <span class="nf">add_output_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Add additional metadata that will be associated with the output artifact</span>
-</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a><span class="sd">            meta (dict): A dictionary of metadata&quot;&quot;&quot;</span>
-</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">|</span> <span class="n">meta</span>
+</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a><span class="sd">        Args:</span>
+</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a><span class="sd">            input_uuid (str): The UUID of the Input Artifact</span>
+</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a><span class="sd">            output_uuid (str): The UUID of the Output Artifact</span>
+</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="sd">            catalog_db (str): The AWS Data Catalog Database to use (default: &quot;workbench&quot;)</span>
+</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a><span class="sd">        &quot;&quot;&quot;</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
+</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="kc">None</span>
+</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="kc">None</span>
+</span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
+</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
+</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span> <span class="o">=</span> <span class="n">catalog_db</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>        <span class="c1"># Grab our Workbench Bucket</span>
+</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>        <span class="n">cm</span> <span class="o">=</span> <span class="n">ConfigManager</span><span class="p">()</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="n">cm</span><span class="o">.</span><span class="n">config_okay</span><span class="p">():</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Workbench Configuration Incomplete...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Run the &#39;workbench&#39; command and follow the prompts...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>            <span class="k">raise</span> <span class="n">FatalConfigError</span><span class="p">()</span>
+</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_config</span><span class="p">(</span><span class="s2">&quot;WORKBENCH_BUCKET&quot;</span><span class="p">)</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
+</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">feature_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/feature-sets&quot;</span>
+</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">models_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/models&quot;</span>
+</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">endpoints_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/endpoints&quot;</span>
+</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>
+</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>        <span class="c1"># Grab a Workbench Role ARN, Boto3, SageMaker Session, and SageMaker Client</span>
+</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span>
+</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_workbench_execution_role_arn</span><span class="p">()</span>
+</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">boto3_session</span>
+</span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_session</span><span class="p">()</span>
+</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">sm_client</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_client</span><span class="p">()</span>
+</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>
+</span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>        <span class="c1"># Delimiter for storing lists in AWS Tags</span>
+</span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span> <span class="o">=</span> <span class="s2">&quot;::&quot;</span>
+</span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>
+</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a>    <span class="nd">@abstractmethod</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Abstract Method: Implement the Transformation from Input to Output&quot;&quot;&quot;</span>
+</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>        <span class="k">pass</span>
+</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a>
+</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a>    <span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform any Pre-Transform operations&quot;&quot;&quot;</span>
+</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Pre-Transform...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a>
+</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a>    <span class="nd">@abstractmethod</span>
+</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a>    <span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Post-Transform ensures that the output Artifact is ready for use&quot;&quot;&quot;</span>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>        <span class="k">pass</span>
+</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>
+</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>    <span class="k">def</span> <span class="nf">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tags</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">str</span><span class="p">]):</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the tags that will be associated with the output object</span>
+</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">        Args:</span>
+</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">            tags (Union[list, str]): The list of tags or a &#39;::&#39; separated string of tags&quot;&quot;&quot;</span>
+</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a>        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tags</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)</span>
+</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a>        <span class="k">else</span><span class="p">:</span>
+</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>            <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="n">tags</span>
 </span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a>
-</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>    <span class="nd">@staticmethod</span>
-</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a>    <span class="k">def</span> <span class="nf">convert_to_aws_tags</span><span class="p">(</span><span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert a dictionary to the AWS tag format (list of dicts)</span>
-</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a><span class="sd">        [ {Key: key_name, Value: value}, {..}, ...]&quot;&quot;&quot;</span>
-</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>        <span class="k">return</span> <span class="p">[{</span><span class="s2">&quot;Key&quot;</span><span class="p">:</span> <span class="n">key</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">value</span><span class="p">}</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">metadata</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
+</span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>    <span class="k">def</span> <span class="nf">add_output_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Add additional metadata that will be associated with the output artifact</span>
+</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a><span class="sd">        Args:</span>
+</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a><span class="sd">            meta (dict): A dictionary of metadata&quot;&quot;&quot;</span>
+</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">|</span> <span class="n">meta</span>
 </span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>
-</span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>    <span class="k">def</span> <span class="nf">get_aws_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the metadata/tags and convert them into AWS Tag Format&quot;&quot;&quot;</span>
-</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>        <span class="c1"># Set up our metadata storage</span>
-</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>        <span class="n">sageworks_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
-</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a>            <span class="n">sageworks_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a>        <span class="n">aws_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_aws_tags</span><span class="p">(</span><span class="n">sageworks_meta</span><span class="p">)</span>
-</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a>        <span class="k">return</span> <span class="n">aws_tags</span>
-</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a>
-</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a>    <span class="nd">@final</span>
-</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a>    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations&quot;&quot;&quot;</span>
-</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">pre_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">transform_impl</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a>
-</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a>    <span class="k">def</span> <span class="nf">input_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformInput</span><span class="p">:</span>
-</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;What Input Type does this Transform Consume&quot;&quot;&quot;</span>
-</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span>
-</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>
-</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>    <span class="k">def</span> <span class="nf">output_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformOutput</span><span class="p">:</span>
-</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;What Output Type does this Transform Produce&quot;&quot;&quot;</span>
-</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span>
-</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a>
-</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>    <span class="k">def</span> <span class="nf">set_input_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the Input UUID (Name) for this Transform&quot;&quot;&quot;</span>
-</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="n">input_uuid</span>
-</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a>
-</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>    <span class="k">def</span> <span class="nf">set_output_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the Output UUID (Name) for this Transform&quot;&quot;&quot;</span>
-</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="n">output_uuid</span>
+</span><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a>    <span class="nd">@staticmethod</span>
+</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a>    <span class="k">def</span> <span class="nf">convert_to_aws_tags</span><span class="p">(</span><span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Convert a dictionary to the AWS tag format (list of dicts)</span>
+</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a><span class="sd">        [ {Key: key_name, Value: value}, {..}, ...]&quot;&quot;&quot;</span>
+</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>        <span class="k">return</span> <span class="p">[{</span><span class="s2">&quot;Key&quot;</span><span class="p">:</span> <span class="n">key</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">value</span><span class="p">}</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">metadata</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
+</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a>
+</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a>    <span class="k">def</span> <span class="nf">get_aws_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Get the metadata/tags and convert them into AWS Tag Format&quot;&quot;&quot;</span>
+</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a>        <span class="c1"># Set up our metadata storage</span>
+</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a>        <span class="n">workbench_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
+</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a>        <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a>            <span class="n">workbench_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a>        <span class="n">aws_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_aws_tags</span><span class="p">(</span><span class="n">workbench_meta</span><span class="p">)</span>
+</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>        <span class="k">return</span> <span class="n">aws_tags</span>
+</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a>
+</span><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a>    <span class="nd">@final</span>
+</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a>    <span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations&quot;&quot;&quot;</span>
+</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">pre_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">transform_impl</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a>
+</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a>    <span class="k">def</span> <span class="nf">input_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformInput</span><span class="p">:</span>
+</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;What Input Type does this Transform Consume&quot;&quot;&quot;</span>
+</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span>
+</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a>
+</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>    <span class="k">def</span> <span class="nf">output_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformOutput</span><span class="p">:</span>
+</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;What Output Type does this Transform Produce&quot;&quot;&quot;</span>
+</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span>
+</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a>
+</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>    <span class="k">def</span> <span class="nf">set_input_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the Input UUID (Name) for this Transform&quot;&quot;&quot;</span>
+</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="n">input_uuid</span>
+</span><span id="__span-0-144"><a id="__codelineno-0-144" name="__codelineno-0-144"></a>
+</span><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a>    <span class="k">def</span> <span class="nf">set_output_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Set the Output UUID (Name) for this Transform&quot;&quot;&quot;</span>
+</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="n">output_uuid</span>
 </span></code></pre></div></td></tr></table></div>
               </details>
 
@@ -3763,8 +3775,8 @@ <h2 id="sageworks.core.transforms.transform.Transform" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.__init__" class="doc doc-heading">
-            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">)</span></code>
+<h3 id="workbench.core.transforms.transform.Transform.__init__" class="doc doc-heading">
+            <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">,</span> <span class="n">catalog_db</span><span class="o">=</span><span class="s1">&#39;workbench&#39;</span><span class="p">)</span></code>
 
 </h3>
 
@@ -3773,8 +3785,71 @@ <h3 id="sageworks.core.transforms.transform.Transform.__init__" class="doc doc-h
 
         <p>Transform Initialization</p>
 
+
+<p><span class="doc-section-title">Parameters:</span></p>
+    <table>
+      <thead>
+        <tr>
+          <th>Name</th>
+          <th>Type</th>
+          <th>Description</th>
+          <th>Default</th>
+        </tr>
+      </thead>
+      <tbody>
+          <tr class="doc-section-item">
+            <td>
+                <code>input_uuid</code>
+            </td>
+            <td>
+                  <code>str</code>
+            </td>
+            <td>
+              <div class="doc-md-description">
+                <p>The UUID of the Input Artifact</p>
+              </div>
+            </td>
+            <td>
+                <em>required</em>
+            </td>
+          </tr>
+          <tr class="doc-section-item">
+            <td>
+                <code>output_uuid</code>
+            </td>
+            <td>
+                  <code>str</code>
+            </td>
+            <td>
+              <div class="doc-md-description">
+                <p>The UUID of the Output Artifact</p>
+              </div>
+            </td>
+            <td>
+                <em>required</em>
+            </td>
+          </tr>
+          <tr class="doc-section-item">
+            <td>
+                <code>catalog_db</code>
+            </td>
+            <td>
+                  <code>str</code>
+            </td>
+            <td>
+              <div class="doc-md-description">
+                <p>The AWS Data Catalog Database to use (default: "workbench")</p>
+              </div>
+            </td>
+            <td>
+                  <code>&#39;workbench&#39;</code>
+            </td>
+          </tr>
+      </tbody>
+    </table>
+
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-42">42</a></span>
 <span class="normal"><a href="#__codelineno-0-43">43</a></span>
 <span class="normal"><a href="#__codelineno-0-44">44</a></span>
@@ -3807,39 +3882,51 @@ <h3 id="sageworks.core.transforms.transform.Transform.__init__" class="doc doc-h
 <span class="normal"><a href="#__codelineno-0-71">71</a></span>
 <span class="normal"><a href="#__codelineno-0-72">72</a></span>
 <span class="normal"><a href="#__codelineno-0-73">73</a></span>
-<span class="normal"><a href="#__codelineno-0-74">74</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Transform Initialization&quot;&quot;&quot;</span>
+<span class="normal"><a href="#__codelineno-0-74">74</a></span>
+<span class="normal"><a href="#__codelineno-0-75">75</a></span>
+<span class="normal"><a href="#__codelineno-0-76">76</a></span>
+<span class="normal"><a href="#__codelineno-0-77">77</a></span>
+<span class="normal"><a href="#__codelineno-0-78">78</a></span>
+<span class="normal"><a href="#__codelineno-0-79">79</a></span>
+<span class="normal"><a href="#__codelineno-0-80">80</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-42"><a id="__codelineno-0-42" name="__codelineno-0-42"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">catalog_db</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;workbench&quot;</span><span class="p">):</span>
+</span><span id="__span-0-43"><a id="__codelineno-0-43" name="__codelineno-0-43"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Transform Initialization</span>
 </span><span id="__span-0-44"><a id="__codelineno-0-44" name="__codelineno-0-44"></a>
-</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
-</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="kc">None</span>
-</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="kc">None</span>
-</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
-</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
-</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
-</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span> <span class="o">=</span> <span class="s2">&quot;sageworks&quot;</span>
-</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>
-</span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>    <span class="c1"># Grab our SageWorks Bucket</span>
-</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>    <span class="n">cm</span> <span class="o">=</span> <span class="n">ConfigManager</span><span class="p">()</span>
-</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>    <span class="k">if</span> <span class="ow">not</span> <span class="n">cm</span><span class="o">.</span><span class="n">config_okay</span><span class="p">():</span>
-</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;SageWorks Configuration Incomplete...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Run the &#39;sageworks&#39; command and follow the prompts...&quot;</span><span class="p">)</span>
-</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>        <span class="k">raise</span> <span class="n">FatalConfigError</span><span class="p">()</span>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_config</span><span class="p">(</span><span class="s2">&quot;SAGEWORKS_BUCKET&quot;</span><span class="p">)</span>
-</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">feature_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/feature-sets&quot;</span>
-</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">models_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/models&quot;</span>
-</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">endpoints_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_bucket</span> <span class="o">+</span> <span class="s2">&quot;/endpoints&quot;</span>
-</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>
-</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>    <span class="c1"># Grab a SageWorks Role ARN, Boto3, SageMaker Session, and SageMaker Client</span>
-</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span>
-</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">sageworks_role_arn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_sageworks_execution_role_arn</span><span class="p">()</span>
-</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">boto3_session</span>
-</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_session</span><span class="p">()</span>
-</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">sm_client</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_client</span><span class="p">()</span>
-</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>
-</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>    <span class="c1"># Delimiter for storing lists in AWS Tags</span>
-</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span> <span class="o">=</span> <span class="s2">&quot;::&quot;</span>
+</span><span id="__span-0-45"><a id="__codelineno-0-45" name="__codelineno-0-45"></a><span class="sd">    Args:</span>
+</span><span id="__span-0-46"><a id="__codelineno-0-46" name="__codelineno-0-46"></a><span class="sd">        input_uuid (str): The UUID of the Input Artifact</span>
+</span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a><span class="sd">        output_uuid (str): The UUID of the Output Artifact</span>
+</span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="sd">        catalog_db (str): The AWS Data Catalog Database to use (default: &quot;workbench&quot;)</span>
+</span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a><span class="sd">    &quot;&quot;&quot;</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a>
+</span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
+</span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span> <span class="o">=</span> <span class="kc">None</span>
+</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span> <span class="o">=</span> <span class="kc">None</span>
+</span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span>
+</span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
+</span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span>  <span class="c1"># Occasionally we get a pathlib.Path object</span>
+</span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_input&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span><span class="p">}</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">data_catalog_db</span> <span class="o">=</span> <span class="n">catalog_db</span>
+</span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>    <span class="c1"># Grab our Workbench Bucket</span>
+</span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>    <span class="n">cm</span> <span class="o">=</span> <span class="n">ConfigManager</span><span class="p">()</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>    <span class="k">if</span> <span class="ow">not</span> <span class="n">cm</span><span class="o">.</span><span class="n">config_okay</span><span class="p">():</span>
+</span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Workbench Configuration Incomplete...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">&quot;Run the &#39;workbench&#39; command and follow the prompts...&quot;</span><span class="p">)</span>
+</span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>        <span class="k">raise</span> <span class="n">FatalConfigError</span><span class="p">()</span>
+</span><span id="__span-0-66"><a id="__codelineno-0-66" name="__codelineno-0-66"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">=</span> <span class="n">cm</span><span class="o">.</span><span class="n">get_config</span><span class="p">(</span><span class="s2">&quot;WORKBENCH_BUCKET&quot;</span><span class="p">)</span>
+</span><span id="__span-0-67"><a id="__codelineno-0-67" name="__codelineno-0-67"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">data_sources_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/data-sources&quot;</span>
+</span><span id="__span-0-68"><a id="__codelineno-0-68" name="__codelineno-0-68"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">feature_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/feature-sets&quot;</span>
+</span><span id="__span-0-69"><a id="__codelineno-0-69" name="__codelineno-0-69"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">models_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/models&quot;</span>
+</span><span id="__span-0-70"><a id="__codelineno-0-70" name="__codelineno-0-70"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">endpoints_sets_s3_path</span> <span class="o">=</span> <span class="s2">&quot;s3://&quot;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">workbench_bucket</span> <span class="o">+</span> <span class="s2">&quot;/endpoints&quot;</span>
+</span><span id="__span-0-71"><a id="__codelineno-0-71" name="__codelineno-0-71"></a>
+</span><span id="__span-0-72"><a id="__codelineno-0-72" name="__codelineno-0-72"></a>    <span class="c1"># Grab a Workbench Role ARN, Boto3, SageMaker Session, and SageMaker Client</span>
+</span><span id="__span-0-73"><a id="__codelineno-0-73" name="__codelineno-0-73"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span> <span class="o">=</span> <span class="n">AWSAccountClamp</span><span class="p">()</span>
+</span><span id="__span-0-74"><a id="__codelineno-0-74" name="__codelineno-0-74"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">workbench_role_arn</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">aws_session</span><span class="o">.</span><span class="n">get_workbench_execution_role_arn</span><span class="p">()</span>
+</span><span id="__span-0-75"><a id="__codelineno-0-75" name="__codelineno-0-75"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">boto3_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">boto3_session</span>
+</span><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">sm_session</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_session</span><span class="p">()</span>
+</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">sm_client</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">aws_account_clamp</span><span class="o">.</span><span class="n">sagemaker_client</span><span class="p">()</span>
+</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a>
+</span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>    <span class="c1"># Delimiter for storing lists in AWS Tags</span>
+</span><span id="__span-0-80"><a id="__codelineno-0-80" name="__codelineno-0-80"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span> <span class="o">=</span> <span class="s2">&quot;::&quot;</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -3849,7 +3936,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.__init__" class="doc doc-h
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.add_output_meta" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.add_output_meta" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">add_output_meta</span><span class="p">(</span><span class="n">meta</span><span class="p">)</span></code>
 
 </h3>
@@ -3862,16 +3949,16 @@ <h3 id="sageworks.core.transforms.transform.Transform.add_output_meta" class="do
     meta (dict): A dictionary of metadata</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-99"> 99</a></span>
-<span class="normal"><a href="#__codelineno-0-100">100</a></span>
-<span class="normal"><a href="#__codelineno-0-101">101</a></span>
-<span class="normal"><a href="#__codelineno-0-102">102</a></span>
-<span class="normal"><a href="#__codelineno-0-103">103</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="k">def</span> <span class="nf">add_output_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Add additional metadata that will be associated with the output artifact</span>
-</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a><span class="sd">        meta (dict): A dictionary of metadata&quot;&quot;&quot;</span>
-</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">|</span> <span class="n">meta</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-105">105</a></span>
+<span class="normal"><a href="#__codelineno-0-106">106</a></span>
+<span class="normal"><a href="#__codelineno-0-107">107</a></span>
+<span class="normal"><a href="#__codelineno-0-108">108</a></span>
+<span class="normal"><a href="#__codelineno-0-109">109</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a><span class="k">def</span> <span class="nf">add_output_meta</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">meta</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Add additional metadata that will be associated with the output artifact</span>
+</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a><span class="sd">    Args:</span>
+</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a><span class="sd">        meta (dict): A dictionary of metadata&quot;&quot;&quot;</span>
+</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span> <span class="o">|</span> <span class="n">meta</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -3881,7 +3968,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.add_output_meta" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.convert_to_aws_tags" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.convert_to_aws_tags" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">convert_to_aws_tags</span><span class="p">(</span><span class="n">metadata</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -3897,16 +3984,16 @@ <h3 id="sageworks.core.transforms.transform.Transform.convert_to_aws_tags" class
 [ {Key: key_name, Value: value}, {..}, ...]</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-105">105</a></span>
-<span class="normal"><a href="#__codelineno-0-106">106</a></span>
-<span class="normal"><a href="#__codelineno-0-107">107</a></span>
-<span class="normal"><a href="#__codelineno-0-108">108</a></span>
-<span class="normal"><a href="#__codelineno-0-109">109</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a><span class="nd">@staticmethod</span>
-</span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a><span class="k">def</span> <span class="nf">convert_to_aws_tags</span><span class="p">(</span><span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
-</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a dictionary to the AWS tag format (list of dicts)</span>
-</span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a><span class="sd">    [ {Key: key_name, Value: value}, {..}, ...]&quot;&quot;&quot;</span>
-</span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a>    <span class="k">return</span> <span class="p">[{</span><span class="s2">&quot;Key&quot;</span><span class="p">:</span> <span class="n">key</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">value</span><span class="p">}</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">metadata</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-111">111</a></span>
+<span class="normal"><a href="#__codelineno-0-112">112</a></span>
+<span class="normal"><a href="#__codelineno-0-113">113</a></span>
+<span class="normal"><a href="#__codelineno-0-114">114</a></span>
+<span class="normal"><a href="#__codelineno-0-115">115</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a><span class="nd">@staticmethod</span>
+</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a><span class="k">def</span> <span class="nf">convert_to_aws_tags</span><span class="p">(</span><span class="n">metadata</span><span class="p">:</span> <span class="nb">dict</span><span class="p">):</span>
+</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a dictionary to the AWS tag format (list of dicts)</span>
+</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a><span class="sd">    [ {Key: key_name, Value: value}, {..}, ...]&quot;&quot;&quot;</span>
+</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>    <span class="k">return</span> <span class="p">[{</span><span class="s2">&quot;Key&quot;</span><span class="p">:</span> <span class="n">key</span><span class="p">,</span> <span class="s2">&quot;Value&quot;</span><span class="p">:</span> <span class="n">value</span><span class="p">}</span> <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="n">metadata</span><span class="o">.</span><span class="n">items</span><span class="p">()]</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -3916,7 +4003,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.convert_to_aws_tags" class
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.get_aws_tags" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.get_aws_tags" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_aws_tags</span><span class="p">()</span></code>
 
 </h3>
@@ -3927,22 +4014,22 @@ <h3 id="sageworks.core.transforms.transform.Transform.get_aws_tags" class="doc d
         <p>Get the metadata/tags and convert them into AWS Tag Format</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-111">111</a></span>
-<span class="normal"><a href="#__codelineno-0-112">112</a></span>
-<span class="normal"><a href="#__codelineno-0-113">113</a></span>
-<span class="normal"><a href="#__codelineno-0-114">114</a></span>
-<span class="normal"><a href="#__codelineno-0-115">115</a></span>
-<span class="normal"><a href="#__codelineno-0-116">116</a></span>
-<span class="normal"><a href="#__codelineno-0-117">117</a></span>
-<span class="normal"><a href="#__codelineno-0-118">118</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-111"><a id="__codelineno-0-111" name="__codelineno-0-111"></a><span class="k">def</span> <span class="nf">get_aws_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-</span><span id="__span-0-112"><a id="__codelineno-0-112" name="__codelineno-0-112"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the metadata/tags and convert them into AWS Tag Format&quot;&quot;&quot;</span>
-</span><span id="__span-0-113"><a id="__codelineno-0-113" name="__codelineno-0-113"></a>    <span class="c1"># Set up our metadata storage</span>
-</span><span id="__span-0-114"><a id="__codelineno-0-114" name="__codelineno-0-114"></a>    <span class="n">sageworks_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;sageworks_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
-</span><span id="__span-0-115"><a id="__codelineno-0-115" name="__codelineno-0-115"></a>    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
-</span><span id="__span-0-116"><a id="__codelineno-0-116" name="__codelineno-0-116"></a>        <span class="n">sageworks_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
-</span><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a>    <span class="n">aws_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_aws_tags</span><span class="p">(</span><span class="n">sageworks_meta</span><span class="p">)</span>
-</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a>    <span class="k">return</span> <span class="n">aws_tags</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-117">117</a></span>
+<span class="normal"><a href="#__codelineno-0-118">118</a></span>
+<span class="normal"><a href="#__codelineno-0-119">119</a></span>
+<span class="normal"><a href="#__codelineno-0-120">120</a></span>
+<span class="normal"><a href="#__codelineno-0-121">121</a></span>
+<span class="normal"><a href="#__codelineno-0-122">122</a></span>
+<span class="normal"><a href="#__codelineno-0-123">123</a></span>
+<span class="normal"><a href="#__codelineno-0-124">124</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-117"><a id="__codelineno-0-117" name="__codelineno-0-117"></a><span class="k">def</span> <span class="nf">get_aws_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+</span><span id="__span-0-118"><a id="__codelineno-0-118" name="__codelineno-0-118"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Get the metadata/tags and convert them into AWS Tag Format&quot;&quot;&quot;</span>
+</span><span id="__span-0-119"><a id="__codelineno-0-119" name="__codelineno-0-119"></a>    <span class="c1"># Set up our metadata storage</span>
+</span><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a>    <span class="n">workbench_meta</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;workbench_tags&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span><span class="p">}</span>
+</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a>    <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">value</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_meta</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a>        <span class="n">workbench_meta</span><span class="p">[</span><span class="n">key</span><span class="p">]</span> <span class="o">=</span> <span class="n">value</span>
+</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a>    <span class="n">aws_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">convert_to_aws_tags</span><span class="p">(</span><span class="n">workbench_meta</span><span class="p">)</span>
+</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>    <span class="k">return</span> <span class="n">aws_tags</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -3952,7 +4039,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.get_aws_tags" class="doc d
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.input_type" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.input_type" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">input_type</span><span class="p">()</span></code>
 
 </h3>
@@ -3963,12 +4050,12 @@ <h3 id="sageworks.core.transforms.transform.Transform.input_type" class="doc doc
         <p>What Input Type does this Transform Consume</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-127">127</a></span>
-<span class="normal"><a href="#__codelineno-0-128">128</a></span>
-<span class="normal"><a href="#__codelineno-0-129">129</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a><span class="k">def</span> <span class="nf">input_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformInput</span><span class="p">:</span>
-</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;What Input Type does this Transform Consume&quot;&quot;&quot;</span>
-</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-133">133</a></span>
+<span class="normal"><a href="#__codelineno-0-134">134</a></span>
+<span class="normal"><a href="#__codelineno-0-135">135</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a><span class="k">def</span> <span class="nf">input_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformInput</span><span class="p">:</span>
+</span><span id="__span-0-134"><a id="__codelineno-0-134" name="__codelineno-0-134"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;What Input Type does this Transform Consume&quot;&quot;&quot;</span>
+</span><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">input_type</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -3978,7 +4065,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.input_type" class="doc doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.output_type" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.output_type" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">output_type</span><span class="p">()</span></code>
 
 </h3>
@@ -3989,12 +4076,12 @@ <h3 id="sageworks.core.transforms.transform.Transform.output_type" class="doc do
         <p>What Output Type does this Transform Produce</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-131">131</a></span>
-<span class="normal"><a href="#__codelineno-0-132">132</a></span>
-<span class="normal"><a href="#__codelineno-0-133">133</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a><span class="k">def</span> <span class="nf">output_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformOutput</span><span class="p">:</span>
-</span><span id="__span-0-132"><a id="__codelineno-0-132" name="__codelineno-0-132"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;What Output Type does this Transform Produce&quot;&quot;&quot;</span>
-</span><span id="__span-0-133"><a id="__codelineno-0-133" name="__codelineno-0-133"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-137">137</a></span>
+<span class="normal"><a href="#__codelineno-0-138">138</a></span>
+<span class="normal"><a href="#__codelineno-0-139">139</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a><span class="k">def</span> <span class="nf">output_type</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">TransformOutput</span><span class="p">:</span>
+</span><span id="__span-0-138"><a id="__codelineno-0-138" name="__codelineno-0-138"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;What Output Type does this Transform Produce&quot;&quot;&quot;</span>
+</span><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a>    <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_type</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4004,7 +4091,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.output_type" class="doc do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.post_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.post_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4019,14 +4106,14 @@ <h3 id="sageworks.core.transforms.transform.Transform.post_transform" class="doc
         <p>Post-Transform ensures that the output Artifact is ready for use</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-85">85</a></span>
-<span class="normal"><a href="#__codelineno-0-86">86</a></span>
-<span class="normal"><a href="#__codelineno-0-87">87</a></span>
-<span class="normal"><a href="#__codelineno-0-88">88</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a><span class="nd">@abstractmethod</span>
-</span><span id="__span-0-86"><a id="__codelineno-0-86" name="__codelineno-0-86"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Post-Transform ensures that the output Artifact is ready for use&quot;&quot;&quot;</span>
-</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a>    <span class="k">pass</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-91">91</a></span>
+<span class="normal"><a href="#__codelineno-0-92">92</a></span>
+<span class="normal"><a href="#__codelineno-0-93">93</a></span>
+<span class="normal"><a href="#__codelineno-0-94">94</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a><span class="nd">@abstractmethod</span>
+</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a><span class="k">def</span> <span class="nf">post_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Post-Transform ensures that the output Artifact is ready for use&quot;&quot;&quot;</span>
+</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="k">pass</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4036,7 +4123,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.post_transform" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.pre_transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.pre_transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">pre_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4047,12 +4134,12 @@ <h3 id="sageworks.core.transforms.transform.Transform.pre_transform" class="doc
         <p>Perform any Pre-Transform operations</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-81">81</a></span>
-<span class="normal"><a href="#__codelineno-0-82">82</a></span>
-<span class="normal"><a href="#__codelineno-0-83">83</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a><span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Perform any Pre-Transform operations&quot;&quot;&quot;</span>
-</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Pre-Transform...&quot;</span><span class="p">)</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-87">87</a></span>
+<span class="normal"><a href="#__codelineno-0-88">88</a></span>
+<span class="normal"><a href="#__codelineno-0-89">89</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-87"><a id="__codelineno-0-87" name="__codelineno-0-87"></a><span class="k">def</span> <span class="nf">pre_transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-88"><a id="__codelineno-0-88" name="__codelineno-0-88"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Perform any Pre-Transform operations&quot;&quot;&quot;</span>
+</span><span id="__span-0-89"><a id="__codelineno-0-89" name="__codelineno-0-89"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">debug</span><span class="p">(</span><span class="s2">&quot;Pre-Transform...&quot;</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4062,7 +4149,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.pre_transform" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.set_input_uuid" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.set_input_uuid" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_input_uuid</span><span class="p">(</span><span class="n">input_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -4073,12 +4160,12 @@ <h3 id="sageworks.core.transforms.transform.Transform.set_input_uuid" class="doc
         <p>Set the Input UUID (Name) for this Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-135">135</a></span>
-<span class="normal"><a href="#__codelineno-0-136">136</a></span>
-<span class="normal"><a href="#__codelineno-0-137">137</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-135"><a id="__codelineno-0-135" name="__codelineno-0-135"></a><span class="k">def</span> <span class="nf">set_input_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-136"><a id="__codelineno-0-136" name="__codelineno-0-136"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the Input UUID (Name) for this Transform&quot;&quot;&quot;</span>
-</span><span id="__span-0-137"><a id="__codelineno-0-137" name="__codelineno-0-137"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="n">input_uuid</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-141">141</a></span>
+<span class="normal"><a href="#__codelineno-0-142">142</a></span>
+<span class="normal"><a href="#__codelineno-0-143">143</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a><span class="k">def</span> <span class="nf">set_input_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+</span><span id="__span-0-142"><a id="__codelineno-0-142" name="__codelineno-0-142"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the Input UUID (Name) for this Transform&quot;&quot;&quot;</span>
+</span><span id="__span-0-143"><a id="__codelineno-0-143" name="__codelineno-0-143"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">input_uuid</span> <span class="o">=</span> <span class="n">input_uuid</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4088,7 +4175,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.set_input_uuid" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.set_output_tags" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.set_output_tags" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_output_tags</span><span class="p">(</span><span class="n">tags</span><span class="p">)</span></code>
 
 </h3>
@@ -4101,22 +4188,22 @@ <h3 id="sageworks.core.transforms.transform.Transform.set_output_tags" class="do
     tags (Union[list, str]): The list of tags or a '::' separated string of tags</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-90">90</a></span>
-<span class="normal"><a href="#__codelineno-0-91">91</a></span>
-<span class="normal"><a href="#__codelineno-0-92">92</a></span>
-<span class="normal"><a href="#__codelineno-0-93">93</a></span>
-<span class="normal"><a href="#__codelineno-0-94">94</a></span>
-<span class="normal"><a href="#__codelineno-0-95">95</a></span>
-<span class="normal"><a href="#__codelineno-0-96">96</a></span>
-<span class="normal"><a href="#__codelineno-0-97">97</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-90"><a id="__codelineno-0-90" name="__codelineno-0-90"></a><span class="k">def</span> <span class="nf">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tags</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">str</span><span class="p">]):</span>
-</span><span id="__span-0-91"><a id="__codelineno-0-91" name="__codelineno-0-91"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the tags that will be associated with the output object</span>
-</span><span id="__span-0-92"><a id="__codelineno-0-92" name="__codelineno-0-92"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-93"><a id="__codelineno-0-93" name="__codelineno-0-93"></a><span class="sd">        tags (Union[list, str]): The list of tags or a &#39;::&#39; separated string of tags&quot;&quot;&quot;</span>
-</span><span id="__span-0-94"><a id="__codelineno-0-94" name="__codelineno-0-94"></a>    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tags</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
-</span><span id="__span-0-95"><a id="__codelineno-0-95" name="__codelineno-0-95"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)</span>
-</span><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a>    <span class="k">else</span><span class="p">:</span>
-</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="n">tags</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-96"> 96</a></span>
+<span class="normal"><a href="#__codelineno-0-97"> 97</a></span>
+<span class="normal"><a href="#__codelineno-0-98"> 98</a></span>
+<span class="normal"><a href="#__codelineno-0-99"> 99</a></span>
+<span class="normal"><a href="#__codelineno-0-100">100</a></span>
+<span class="normal"><a href="#__codelineno-0-101">101</a></span>
+<span class="normal"><a href="#__codelineno-0-102">102</a></span>
+<span class="normal"><a href="#__codelineno-0-103">103</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-96"><a id="__codelineno-0-96" name="__codelineno-0-96"></a><span class="k">def</span> <span class="nf">set_output_tags</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tags</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">list</span><span class="p">,</span> <span class="nb">str</span><span class="p">]):</span>
+</span><span id="__span-0-97"><a id="__codelineno-0-97" name="__codelineno-0-97"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the tags that will be associated with the output object</span>
+</span><span id="__span-0-98"><a id="__codelineno-0-98" name="__codelineno-0-98"></a><span class="sd">    Args:</span>
+</span><span id="__span-0-99"><a id="__codelineno-0-99" name="__codelineno-0-99"></a><span class="sd">        tags (Union[list, str]): The list of tags or a &#39;::&#39; separated string of tags&quot;&quot;&quot;</span>
+</span><span id="__span-0-100"><a id="__codelineno-0-100" name="__codelineno-0-100"></a>    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">tags</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+</span><span id="__span-0-101"><a id="__codelineno-0-101" name="__codelineno-0-101"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tag_delimiter</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">tags</span><span class="p">)</span>
+</span><span id="__span-0-102"><a id="__codelineno-0-102" name="__codelineno-0-102"></a>    <span class="k">else</span><span class="p">:</span>
+</span><span id="__span-0-103"><a id="__codelineno-0-103" name="__codelineno-0-103"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">output_tags</span> <span class="o">=</span> <span class="n">tags</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4126,7 +4213,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.set_output_tags" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.set_output_uuid" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.set_output_uuid" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">set_output_uuid</span><span class="p">(</span><span class="n">output_uuid</span><span class="p">)</span></code>
 
 </h3>
@@ -4137,12 +4224,12 @@ <h3 id="sageworks.core.transforms.transform.Transform.set_output_uuid" class="do
         <p>Set the Output UUID (Name) for this Transform</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-139">139</a></span>
-<span class="normal"><a href="#__codelineno-0-140">140</a></span>
-<span class="normal"><a href="#__codelineno-0-141">141</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-139"><a id="__codelineno-0-139" name="__codelineno-0-139"></a><span class="k">def</span> <span class="nf">set_output_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
-</span><span id="__span-0-140"><a id="__codelineno-0-140" name="__codelineno-0-140"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the Output UUID (Name) for this Transform&quot;&quot;&quot;</span>
-</span><span id="__span-0-141"><a id="__codelineno-0-141" name="__codelineno-0-141"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="n">output_uuid</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-145">145</a></span>
+<span class="normal"><a href="#__codelineno-0-146">146</a></span>
+<span class="normal"><a href="#__codelineno-0-147">147</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-145"><a id="__codelineno-0-145" name="__codelineno-0-145"></a><span class="k">def</span> <span class="nf">set_output_uuid</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_uuid</span><span class="p">:</span> <span class="nb">str</span><span class="p">):</span>
+</span><span id="__span-0-146"><a id="__codelineno-0-146" name="__codelineno-0-146"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Set the Output UUID (Name) for this Transform&quot;&quot;&quot;</span>
+</span><span id="__span-0-147"><a id="__codelineno-0-147" name="__codelineno-0-147"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">output_uuid</span> <span class="o">=</span> <span class="n">output_uuid</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4152,7 +4239,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.set_output_uuid" class="do
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.transform" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
 </h3>
@@ -4163,18 +4250,18 @@ <h3 id="sageworks.core.transforms.transform.Transform.transform" class="doc doc-
         <p>Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-120">120</a></span>
-<span class="normal"><a href="#__codelineno-0-121">121</a></span>
-<span class="normal"><a href="#__codelineno-0-122">122</a></span>
-<span class="normal"><a href="#__codelineno-0-123">123</a></span>
-<span class="normal"><a href="#__codelineno-0-124">124</a></span>
-<span class="normal"><a href="#__codelineno-0-125">125</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-120"><a id="__codelineno-0-120" name="__codelineno-0-120"></a><span class="nd">@final</span>
-</span><span id="__span-0-121"><a id="__codelineno-0-121" name="__codelineno-0-121"></a><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-122"><a id="__codelineno-0-122" name="__codelineno-0-122"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations&quot;&quot;&quot;</span>
-</span><span id="__span-0-123"><a id="__codelineno-0-123" name="__codelineno-0-123"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">pre_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-</span><span id="__span-0-124"><a id="__codelineno-0-124" name="__codelineno-0-124"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">transform_impl</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-</span><span id="__span-0-125"><a id="__codelineno-0-125" name="__codelineno-0-125"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-126">126</a></span>
+<span class="normal"><a href="#__codelineno-0-127">127</a></span>
+<span class="normal"><a href="#__codelineno-0-128">128</a></span>
+<span class="normal"><a href="#__codelineno-0-129">129</a></span>
+<span class="normal"><a href="#__codelineno-0-130">130</a></span>
+<span class="normal"><a href="#__codelineno-0-131">131</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-126"><a id="__codelineno-0-126" name="__codelineno-0-126"></a><span class="nd">@final</span>
+</span><span id="__span-0-127"><a id="__codelineno-0-127" name="__codelineno-0-127"></a><span class="k">def</span> <span class="nf">transform</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-128"><a id="__codelineno-0-128" name="__codelineno-0-128"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations&quot;&quot;&quot;</span>
+</span><span id="__span-0-129"><a id="__codelineno-0-129" name="__codelineno-0-129"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">pre_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</span><span id="__span-0-130"><a id="__codelineno-0-130" name="__codelineno-0-130"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">transform_impl</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+</span><span id="__span-0-131"><a id="__codelineno-0-131" name="__codelineno-0-131"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">post_transform</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4184,7 +4271,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.transform" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.transforms.transform.Transform.transform_impl" class="doc doc-heading">
+<h3 id="workbench.core.transforms.transform.Transform.transform_impl" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform_impl</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4199,14 +4286,14 @@ <h3 id="sageworks.core.transforms.transform.Transform.transform_impl" class="doc
         <p>Abstract Method: Implement the Transformation from Input to Output</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
-              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-76">76</a></span>
-<span class="normal"><a href="#__codelineno-0-77">77</a></span>
-<span class="normal"><a href="#__codelineno-0-78">78</a></span>
-<span class="normal"><a href="#__codelineno-0-79">79</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-76"><a id="__codelineno-0-76" name="__codelineno-0-76"></a><span class="nd">@abstractmethod</span>
-</span><span id="__span-0-77"><a id="__codelineno-0-77" name="__codelineno-0-77"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
-</span><span id="__span-0-78"><a id="__codelineno-0-78" name="__codelineno-0-78"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Abstract Method: Implement the Transformation from Input to Output&quot;&quot;&quot;</span>
-</span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>    <span class="k">pass</span>
+              <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
+              <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-82">82</a></span>
+<span class="normal"><a href="#__codelineno-0-83">83</a></span>
+<span class="normal"><a href="#__codelineno-0-84">84</a></span>
+<span class="normal"><a href="#__codelineno-0-85">85</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a><span class="nd">@abstractmethod</span>
+</span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a><span class="k">def</span> <span class="nf">transform_impl</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+</span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Abstract Method: Implement the Transformation from Input to Output&quot;&quot;&quot;</span>
+</span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>    <span class="k">pass</span>
 </span></code></pre></div></td></tr></table></div>
             </details>
     </div>
@@ -4225,7 +4312,7 @@ <h3 id="sageworks.core.transforms.transform.Transform.transform_impl" class="doc
 
 
 
-<h2 id="sageworks.core.transforms.transform.TransformInput" class="doc doc-heading">
+<h2 id="workbench.core.transforms.transform.TransformInput" class="doc doc-heading">
             <code>TransformInput</code>
 
 
@@ -4237,7 +4324,7 @@ <h2 id="sageworks.core.transforms.transform.TransformInput" class="doc doc-headi
               Bases: <code><span title="enum.Enum">Enum</span></code></p>
 
 
-        <p>Enumerated Types for SageWorks Transform Inputs</p>
+        <p>Enumerated Types for Workbench Transform Inputs</p>
 
 
 
@@ -4245,7 +4332,7 @@ <h2 id="sageworks.core.transforms.transform.TransformInput" class="doc doc-headi
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-14">14</a></span>
 <span class="normal"><a href="#__codelineno-0-15">15</a></span>
 <span class="normal"><a href="#__codelineno-0-16">16</a></span>
@@ -4256,7 +4343,7 @@ <h2 id="sageworks.core.transforms.transform.TransformInput" class="doc doc-headi
 <span class="normal"><a href="#__codelineno-0-21">21</a></span>
 <span class="normal"><a href="#__codelineno-0-22">22</a></span>
 <span class="normal"><a href="#__codelineno-0-23">23</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="k">class</span> <span class="nc">TransformInput</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
-</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Enumerated Types for SageWorks Transform Inputs&quot;&quot;&quot;</span>
+</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Enumerated Types for Workbench Transform Inputs&quot;&quot;&quot;</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a>    <span class="n">LOCAL_FILE</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
 </span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a>    <span class="n">PANDAS_DF</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
@@ -4292,7 +4379,7 @@ <h2 id="sageworks.core.transforms.transform.TransformInput" class="doc doc-headi
 
 
 
-<h2 id="sageworks.core.transforms.transform.TransformOutput" class="doc doc-heading">
+<h2 id="workbench.core.transforms.transform.TransformOutput" class="doc doc-heading">
             <code>TransformOutput</code>
 
 
@@ -4304,7 +4391,7 @@ <h2 id="sageworks.core.transforms.transform.TransformOutput" class="doc doc-head
               Bases: <code><span title="enum.Enum">Enum</span></code></p>
 
 
-        <p>Enumerated Types for SageWorks Transform Outputs</p>
+        <p>Enumerated Types for Workbench Transform Outputs</p>
 
 
 
@@ -4312,7 +4399,7 @@ <h2 id="sageworks.core.transforms.transform.TransformOutput" class="doc doc-head
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/transforms/transform.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/transforms/transform.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-26">26</a></span>
 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
 <span class="normal"><a href="#__codelineno-0-28">28</a></span>
@@ -4323,7 +4410,7 @@ <h2 id="sageworks.core.transforms.transform.TransformOutput" class="doc doc-head
 <span class="normal"><a href="#__codelineno-0-33">33</a></span>
 <span class="normal"><a href="#__codelineno-0-34">34</a></span>
 <span class="normal"><a href="#__codelineno-0-35">35</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a><span class="k">class</span> <span class="nc">TransformOutput</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
-</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Enumerated Types for SageWorks Transform Outputs&quot;&quot;&quot;</span>
+</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Enumerated Types for Workbench Transform Outputs&quot;&quot;&quot;</span>
 </span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28"></a>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29"></a>    <span class="n">PANDAS_DF</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
 </span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30"></a>    <span class="n">SPARK_DF</span> <span class="o">=</span> <span class="n">auto</span><span class="p">()</span>
diff --git a/core_classes/views/computation_view/index.html b/core_classes/views/computation_view/index.html
index cfedf9cf5..6a5e71f0c 100644
--- a/core_classes/views/computation_view/index.html
+++ b/core_classes/views/computation_view/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>ComputationView - SageWorks</title>
+        <title>ComputationView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1333,7 +1333,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.computation_view" class="md-nav__link">
+  <a href="#workbench.core.views.computation_view" class="md-nav__link">
     <span class="md-ellipsis">
       computation_view
     </span>
@@ -1342,7 +1342,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.computation_view.ComputationView" class="md-nav__link">
+  <a href="#workbench.core.views.computation_view.ComputationView" class="md-nav__link">
     <span class="md-ellipsis">
       ComputationView
     </span>
@@ -1352,7 +1352,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.computation_view.ComputationView.create" class="md-nav__link">
+  <a href="#workbench.core.views.computation_view.ComputationView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -1903,7 +1903,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1913,7 +1913,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3132,11 +3132,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3188,7 +3188,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.computation_view" class="md-nav__link">
+  <a href="#workbench.core.views.computation_view" class="md-nav__link">
     <span class="md-ellipsis">
       computation_view
     </span>
@@ -3197,7 +3197,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.computation_view.ComputationView" class="md-nav__link">
+  <a href="#workbench.core.views.computation_view.ComputationView" class="md-nav__link">
     <span class="md-ellipsis">
       ComputationView
     </span>
@@ -3207,7 +3207,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.computation_view.ComputationView.create" class="md-nav__link">
+  <a href="#workbench.core.views.computation_view.ComputationView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -3247,7 +3247,7 @@
 <h1 id="computation-view">Computation View</h1>
 <div class="admonition warning inline end">
 <p class="admonition-title">Experimental</p>
-<p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
+<p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
 </div>
 <p><strong>Note:</strong> This class can be automatically invoked from DataSource/FeatureSet <code>set_computation_columns()</code> <a href="../../artifacts/athena_source/">DataSource</a> or <a href="../../artifacts/feature_set_core/">FeatureSet</a>. If you need more control then you can use this class directly.</p>
 
@@ -3256,7 +3256,7 @@ <h1 id="computation-view">Computation View</h1>
 
 
 
-<a id="sageworks.core.views.computation_view"></a>
+<a id="workbench.core.views.computation_view"></a>
     <div class="doc doc-contents first">
 
         <p>ComputationView Class: Create a View with a subset of columns for display purposes</p>
@@ -3281,7 +3281,7 @@ <h1 id="computation-view">Computation View</h1>
 
 
 
-<h2 id="sageworks.core.views.computation_view.ComputationView" class="doc doc-heading">
+<h2 id="workbench.core.views.computation_view.ComputationView" class="doc doc-heading">
             <code>ComputationView</code>
 
 
@@ -3290,7 +3290,7 @@ <h2 id="sageworks.core.views.computation_view.ComputationView" class="doc doc-he
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.views.column_subset_view.ColumnSubsetView">ColumnSubsetView</span></code></p>
+              Bases: <code><span title="workbench.core.views.column_subset_view.ColumnSubsetView">ColumnSubsetView</span></code></p>
 
 
         <p>ComputationView Class: Create a View with a subset of columns for computation purposes</p>
@@ -3316,7 +3316,7 @@ <h2 id="sageworks.core.views.computation_view.ComputationView" class="doc doc-he
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/views/computation_view.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/views/computation_view.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-11">11</a></span>
 <span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
@@ -3412,7 +3412,7 @@ <h2 id="sageworks.core.views.computation_view.ComputationView" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc doc-heading">
+<h3 id="workbench.core.views.computation_view.ComputationView.create" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create</span><span class="p">(</span><span class="n">artifact</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">column_limit</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -3443,7 +3443,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
                 <code>artifact</code>
             </td>
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.api.DataSource" href="../../../api_classes/data_source/#sageworks.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="sageworks.api.FeatureSet" href="../../../api_classes/feature_set/#sageworks.api.feature_set.FeatureSet">FeatureSet</a>]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.api.DataSource" href="../../../api_classes/data_source/#workbench.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="workbench.api.FeatureSet" href="../../../api_classes/feature_set/#workbench.api.feature_set.FeatureSet">FeatureSet</a>]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3517,7 +3517,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
       <tbody>
           <tr class="doc-section-item">
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.core.views.view.View" href="../../../api_classes/views/#sageworks.core.views.view.View">View</a>, None]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.core.views.view.View" href="../../../api_classes/views/#workbench.core.views.view.View">View</a>, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3529,7 +3529,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/computation_view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/computation_view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-29">29</a></span>
 <span class="normal"><a href="#__codelineno-0-30">30</a></span>
 <span class="normal"><a href="#__codelineno-0-31">31</a></span>
@@ -3594,7 +3594,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
 
 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/core_classes/views/display_view/index.html b/core_classes/views/display_view/index.html
index 5ef34cd09..3b9e9da3f 100644
--- a/core_classes/views/display_view/index.html
+++ b/core_classes/views/display_view/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>DisplayView - SageWorks</title>
+        <title>DisplayView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1354,7 +1354,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.display_view" class="md-nav__link">
+  <a href="#workbench.core.views.display_view" class="md-nav__link">
     <span class="md-ellipsis">
       display_view
     </span>
@@ -1363,7 +1363,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.display_view.DisplayView" class="md-nav__link">
+  <a href="#workbench.core.views.display_view.DisplayView" class="md-nav__link">
     <span class="md-ellipsis">
       DisplayView
     </span>
@@ -1373,7 +1373,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.display_view.DisplayView.create" class="md-nav__link">
+  <a href="#workbench.core.views.display_view.DisplayView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -1903,7 +1903,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1913,7 +1913,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3132,11 +3132,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3188,7 +3188,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.display_view" class="md-nav__link">
+  <a href="#workbench.core.views.display_view" class="md-nav__link">
     <span class="md-ellipsis">
       display_view
     </span>
@@ -3197,7 +3197,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.display_view.DisplayView" class="md-nav__link">
+  <a href="#workbench.core.views.display_view.DisplayView" class="md-nav__link">
     <span class="md-ellipsis">
       DisplayView
     </span>
@@ -3207,7 +3207,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.display_view.DisplayView.create" class="md-nav__link">
+  <a href="#workbench.core.views.display_view.DisplayView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -3247,7 +3247,7 @@
 <h1 id="display-view">Display View</h1>
 <div class="admonition warning inline end">
 <p class="admonition-title">Experimental</p>
-<p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
+<p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
 </div>
 <p><strong>Note:</strong> This class will be used in the future to fine tune what columns get displayed. For now just use the DataSource/FeatureSet <code>set_computation_columns()</code> <a href="../../artifacts/athena_source/">DataSource</a> or <a href="../../artifacts/feature_set_core/">FeatureSet</a></p>
 
@@ -3256,7 +3256,7 @@ <h1 id="display-view">Display View</h1>
 
 
 
-<a id="sageworks.core.views.display_view"></a>
+<a id="workbench.core.views.display_view"></a>
     <div class="doc doc-contents first">
 
         <p>DisplayView Class: Create a View with a subset of columns for display purposes</p>
@@ -3281,7 +3281,7 @@ <h1 id="display-view">Display View</h1>
 
 
 
-<h2 id="sageworks.core.views.display_view.DisplayView" class="doc doc-heading">
+<h2 id="workbench.core.views.display_view.DisplayView" class="doc doc-heading">
             <code>DisplayView</code>
 
 
@@ -3290,7 +3290,7 @@ <h2 id="sageworks.core.views.display_view.DisplayView" class="doc doc-heading">
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.views.column_subset_view.ColumnSubsetView">ColumnSubsetView</span></code></p>
+              Bases: <code><span title="workbench.core.views.column_subset_view.ColumnSubsetView">ColumnSubsetView</span></code></p>
 
 
         <p>DisplayView Class: Create a View with a subset of columns for display purposes</p>
@@ -3316,7 +3316,7 @@ <h2 id="sageworks.core.views.display_view.DisplayView" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/views/display_view.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/views/display_view.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-11">11</a></span>
 <span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
@@ -3412,7 +3412,7 @@ <h2 id="sageworks.core.views.display_view.DisplayView" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.display_view.DisplayView.create" class="doc doc-heading">
+<h3 id="workbench.core.views.display_view.DisplayView.create" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create</span><span class="p">(</span><span class="n">artifact</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">column_limit</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -3443,7 +3443,7 @@ <h3 id="sageworks.core.views.display_view.DisplayView.create" class="doc doc-hea
                 <code>artifact</code>
             </td>
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.api.DataSource" href="../../../api_classes/data_source/#sageworks.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="sageworks.api.FeatureSet" href="../../../api_classes/feature_set/#sageworks.api.feature_set.FeatureSet">FeatureSet</a>]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.api.DataSource" href="../../../api_classes/data_source/#workbench.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="workbench.api.FeatureSet" href="../../../api_classes/feature_set/#workbench.api.feature_set.FeatureSet">FeatureSet</a>]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3517,7 +3517,7 @@ <h3 id="sageworks.core.views.display_view.DisplayView.create" class="doc doc-hea
       <tbody>
           <tr class="doc-section-item">
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.core.views.view.View" href="../../../api_classes/views/#sageworks.core.views.view.View">View</a>, None]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.core.views.view.View" href="../../../api_classes/views/#workbench.core.views.view.View">View</a>, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3529,7 +3529,7 @@ <h3 id="sageworks.core.views.display_view.DisplayView.create" class="doc doc-hea
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/display_view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/display_view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-29">29</a></span>
 <span class="normal"><a href="#__codelineno-0-30">30</a></span>
 <span class="normal"><a href="#__codelineno-0-31">31</a></span>
@@ -3594,7 +3594,7 @@ <h3 id="sageworks.core.views.display_view.DisplayView.create" class="doc doc-hea
 
 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/core_classes/views/mdq_view/index.html b/core_classes/views/mdq_view/index.html
index a3de2a8e0..0eb667575 100644
--- a/core_classes/views/mdq_view/index.html
+++ b/core_classes/views/mdq_view/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>ModelDataQualityView - SageWorks</title>
+        <title>ModelDataQualityView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1396,7 +1396,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.mdq_view" class="md-nav__link">
+  <a href="#workbench.core.views.mdq_view" class="md-nav__link">
     <span class="md-ellipsis">
       mdq_view
     </span>
@@ -1405,7 +1405,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.mdq_view.MDQView" class="md-nav__link">
+  <a href="#workbench.core.views.mdq_view.MDQView" class="md-nav__link">
     <span class="md-ellipsis">
       MDQView
     </span>
@@ -1415,7 +1415,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.mdq_view.MDQView.create" class="md-nav__link">
+  <a href="#workbench.core.views.mdq_view.MDQView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -1903,7 +1903,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1913,7 +1913,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3132,11 +3132,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3188,7 +3188,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.mdq_view" class="md-nav__link">
+  <a href="#workbench.core.views.mdq_view" class="md-nav__link">
     <span class="md-ellipsis">
       mdq_view
     </span>
@@ -3197,7 +3197,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.mdq_view.MDQView" class="md-nav__link">
+  <a href="#workbench.core.views.mdq_view.MDQView" class="md-nav__link">
     <span class="md-ellipsis">
       MDQView
     </span>
@@ -3207,7 +3207,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.mdq_view.MDQView.create" class="md-nav__link">
+  <a href="#workbench.core.views.mdq_view.MDQView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -3247,7 +3247,7 @@
 <h1 id="modeldataquality-view">ModelDataQuality View</h1>
 <div class="admonition warning inline end">
 <p class="admonition-title">Experimental</p>
-<p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
+<p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
 </div>
 
 
@@ -3255,7 +3255,7 @@ <h1 id="modeldataquality-view">ModelDataQuality View</h1>
 
 
 
-<a id="sageworks.core.views.mdq_view"></a>
+<a id="workbench.core.views.mdq_view"></a>
     <div class="doc doc-contents first">
 
         <p>MDQView Class: A View that computes various endpoint data quality metrics</p>
@@ -3280,7 +3280,7 @@ <h1 id="modeldataquality-view">ModelDataQuality View</h1>
 
 
 
-<h2 id="sageworks.core.views.mdq_view.MDQView" class="doc doc-heading">
+<h2 id="workbench.core.views.mdq_view.MDQView" class="doc doc-heading">
             <code>MDQView</code>
 
 
@@ -3313,7 +3313,7 @@ <h2 id="sageworks.core.views.mdq_view.MDQView" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/views/mdq_view.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/views/mdq_view.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
 <span class="normal"><a href="#__codelineno-0-15"> 15</a></span>
 <span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
@@ -3547,7 +3547,7 @@ <h2 id="sageworks.core.views.mdq_view.MDQView" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.mdq_view.MDQView.create" class="doc doc-heading">
+<h3 id="workbench.core.views.mdq_view.MDQView.create" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create</span><span class="p">(</span><span class="n">fs</span><span class="p">,</span> <span class="n">endpoint</span><span class="p">,</span> <span class="n">id_column</span><span class="p">,</span> <span class="n">use_reference_model</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -3578,7 +3578,7 @@ <h3 id="sageworks.core.views.mdq_view.MDQView.create" class="doc doc-heading">
                 <code>fs</code>
             </td>
             <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.api.FeatureSet" href="../../../api_classes/feature_set/#sageworks.api.feature_set.FeatureSet">FeatureSet</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.api.FeatureSet" href="../../../api_classes/feature_set/#workbench.api.feature_set.FeatureSet">FeatureSet</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3594,7 +3594,7 @@ <h3 id="sageworks.core.views.mdq_view.MDQView.create" class="doc doc-heading">
                 <code>endpoint</code>
             </td>
             <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.api.Endpoint" href="../../../api_classes/endpoint/#sageworks.api.endpoint.Endpoint">Endpoint</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.api.Endpoint" href="../../../api_classes/endpoint/#workbench.api.endpoint.Endpoint">Endpoint</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3652,7 +3652,7 @@ <h3 id="sageworks.core.views.mdq_view.MDQView.create" class="doc doc-heading">
       <tbody>
           <tr class="doc-section-item">
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.core.views.view.View" href="../../../api_classes/views/#sageworks.core.views.view.View">View</a>, None]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.core.views.view.View" href="../../../api_classes/views/#workbench.core.views.view.View">View</a>, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3664,7 +3664,7 @@ <h3 id="sageworks.core.views.mdq_view.MDQView.create" class="doc doc-heading">
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/mdq_view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/mdq_view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-32"> 32</a></span>
 <span class="normal"><a href="#__codelineno-0-33"> 33</a></span>
 <span class="normal"><a href="#__codelineno-0-34"> 34</a></span>
@@ -3843,7 +3843,7 @@ <h3 id="sageworks.core.views.mdq_view.MDQView.create" class="doc doc-heading">
 
 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/core_classes/views/overview/index.html b/core_classes/views/overview/index.html
index 871d2ce9c..7f176864b 100644
--- a/core_classes/views/overview/index.html
+++ b/core_classes/views/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1879,7 +1879,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1889,7 +1889,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3108,11 +3108,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3199,22 +3199,22 @@
 <h1 id="views">Views</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">View Examples</p>
-<p>Examples of using the Views classes to extend the functionality of SageWorks Artifacts are in the <a href="#examples">Examples</a> section at the bottom of this page. </p>
+<p>Examples of using the Views classes to extend the functionality of Workbench Artifacts are in the <a href="#examples">Examples</a> section at the bottom of this page. </p>
 </div>
-<p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing SageWorks Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p>
+<p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing Workbench Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p>
 <p>If you're looking to read and pull data from a view please see the <a href="../../../api_classes/views/">Views</a> documentation.</p>
 <h2 id="view-constructor-classes">View Constructor Classes</h2>
 <p>These classes provide APIs for creating Views for DataSources and FeatureSets.</p>
 <ul>
-<li><strong><a href="../display_view/">DisplayView</a>:</strong> The Display View is leveraged by the web views/components and allows fine tuning of the UI for the SageWorks Dashboard.</li>
+<li><strong><a href="../display_view/">DisplayView</a>:</strong> The Display View is leveraged by the web views/components and allows fine tuning of the UI for the Workbench Dashboard.</li>
 <li><strong><a href="../computation_view/">ComputationView</a>:</strong> The Computation View controls which columns have descriptive stats, outliers, and correlation calculations. Typically the computation view is a superset of the display view.</li>
 <li><strong><a href="../training_view/">TrainingView</a>:</strong> The Training View will add a 'training' column to the data for model training, validation, and testing. Each row will have a 1 or 0 indicated whether is was used in the model training.</li>
 <li><strong><a href="../mdq_view/">MDQView</a>:</strong> The Model Data Quality View computes various data quality metrics that include modeling inference results.</li>
 </ul>
 <h2 id="examples">Examples</h2>
-<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <p><strong>Listing Views</strong></p>
-<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Convert the Data Source to a Feature Set</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="n">test_data</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="s1">&#39;test_data&#39;</span><span class="p">)</span>
@@ -3222,7 +3222,7 @@ <h2 id="examples">Examples</h2>
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="p">[</span><span class="s2">&quot;display&quot;</span><span class="p">,</span> <span class="s2">&quot;training&quot;</span><span class="p">,</span> <span class="s2">&quot;computation&quot;</span><span class="p">]</span>
 </span></code></pre></div>
 <p><strong>Getting a Particular View</strong></p>
-<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<div class="language-py highlight"><span class="filename">views.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="n">fs</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="s1">&#39;test_features&#39;</span><span class="p">)</span>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a>
@@ -3237,8 +3237,8 @@ <h2 id="examples">Examples</h2>
 </span><span id="__span-1-13"><a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a><span class="mi">0</span>   <span class="mi">58</span>  <span class="n">Person</span> <span class="mi">58</span>  <span class="mf">71.781227</span>  <span class="mf">275.088196</span>  <span class="mf">162053.140625</span>  
 </span></code></pre></div>
 <p><strong>View Queries</strong></p>
-<p>All SageWorks Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p>
-<div class="language-py highlight"><span class="filename">view_query.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">sageworks.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+<p>All Workbench Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p>
+<div class="language-py highlight"><span class="filename">view_query.py</span><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">workbench.api.feature_set</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="c1"># Grab a FeatureSet View</span>
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a><span class="n">fs</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="s2">&quot;abalone_features&quot;</span><span class="p">)</span>
@@ -3260,7 +3260,7 @@ <h2 id="examples">Examples</h2>
 </span><span id="__span-3-6"><a id="__codelineno-3-6" name="__codelineno-3-6" href="#__codelineno-3-6"></a><span class="mi">0</span>   <span class="n">I</span>   <span class="mf">0.075</span>     <span class="mf">0.055</span>   <span class="mf">0.010</span>         <span class="mf">0.002</span>          <span class="mf">0.0010</span>          <span class="mf">0.0005</span>        <span class="mf">0.0015</span>                      <span class="mi">1</span>
 </span><span id="__span-3-7"><a id="__codelineno-3-7" name="__codelineno-3-7" href="#__codelineno-3-7"></a><span class="mi">1</span>   <span class="n">I</span>   <span class="mf">0.150</span>     <span class="mf">0.100</span>   <span class="mf">0.025</span>         <span class="mf">0.015</span>          <span class="mf">0.0045</span>          <span class="mf">0.0040</span>         <span class="mf">0.0050</span>                      <span class="mi">2</span>
 </span></code></pre></div>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/core_classes/views/training_view/index.html b/core_classes/views/training_view/index.html
index 90ea95140..b7b08ca2c 100644
--- a/core_classes/views/training_view/index.html
+++ b/core_classes/views/training_view/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>TrainingView - SageWorks</title>
+        <title>TrainingView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1375,7 +1375,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.training_view" class="md-nav__link">
+  <a href="#workbench.core.views.training_view" class="md-nav__link">
     <span class="md-ellipsis">
       training_view
     </span>
@@ -1384,7 +1384,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.training_view.TrainingView" class="md-nav__link">
+  <a href="#workbench.core.views.training_view.TrainingView" class="md-nav__link">
     <span class="md-ellipsis">
       TrainingView
     </span>
@@ -1394,7 +1394,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.training_view.TrainingView.create" class="md-nav__link">
+  <a href="#workbench.core.views.training_view.TrainingView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -1903,7 +1903,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1913,7 +1913,7 @@
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           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3132,11 +3132,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3188,7 +3188,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.training_view" class="md-nav__link">
+  <a href="#workbench.core.views.training_view" class="md-nav__link">
     <span class="md-ellipsis">
       training_view
     </span>
@@ -3197,7 +3197,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.core.views.training_view.TrainingView" class="md-nav__link">
+  <a href="#workbench.core.views.training_view.TrainingView" class="md-nav__link">
     <span class="md-ellipsis">
       TrainingView
     </span>
@@ -3207,7 +3207,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.core.views.training_view.TrainingView.create" class="md-nav__link">
+  <a href="#workbench.core.views.training_view.TrainingView.create" class="md-nav__link">
     <span class="md-ellipsis">
       create
     </span>
@@ -3247,7 +3247,7 @@
 <h1 id="training-view">Training View</h1>
 <div class="admonition warning inline end">
 <p class="admonition-title">Experimental</p>
-<p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
+<p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p>
 </div>
 
 
@@ -3255,7 +3255,7 @@ <h1 id="training-view">Training View</h1>
 
 
 
-<a id="sageworks.core.views.training_view"></a>
+<a id="workbench.core.views.training_view"></a>
     <div class="doc doc-contents first">
 
         <p>TrainingView Class: A View with an additional training column that marks holdout ids</p>
@@ -3280,7 +3280,7 @@ <h1 id="training-view">Training View</h1>
 
 
 
-<h2 id="sageworks.core.views.training_view.TrainingView" class="doc doc-heading">
+<h2 id="workbench.core.views.training_view.TrainingView" class="doc doc-heading">
             <code>TrainingView</code>
 
 
@@ -3289,7 +3289,7 @@ <h2 id="sageworks.core.views.training_view.TrainingView" class="doc doc-heading"
 
     <div class="doc doc-contents ">
             <p class="doc doc-class-bases">
-              Bases: <code><span title="sageworks.core.views.create_view.CreateView">CreateView</span></code></p>
+              Bases: <code><span title="workbench.core.views.create_view.CreateView">CreateView</span></code></p>
 
 
         <p>TrainingView Class: A View with an additional training column that marks holdout ids</p>
@@ -3315,7 +3315,7 @@ <h2 id="sageworks.core.views.training_view.TrainingView" class="doc doc-heading"
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/views/training_view.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/views/training_view.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12"> 12</a></span>
 <span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
 <span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
@@ -3529,7 +3529,7 @@ <h2 id="sageworks.core.views.training_view.TrainingView" class="doc doc-heading"
 </span><span id="__span-0-104"><a id="__codelineno-0-104" name="__codelineno-0-104"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a default view in Athena that assigns roughly 80% of the data to training</span>
 </span><span id="__span-0-105"><a id="__codelineno-0-105" name="__codelineno-0-105"></a>
 </span><span id="__span-0-106"><a id="__codelineno-0-106" name="__codelineno-0-106"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a><span class="sd">            data_source (DataSource): The SageWorks DataSource object</span>
+</span><span id="__span-0-107"><a id="__codelineno-0-107" name="__codelineno-0-107"></a><span class="sd">            data_source (DataSource): The Workbench DataSource object</span>
 </span><span id="__span-0-108"><a id="__codelineno-0-108" name="__codelineno-0-108"></a><span class="sd">            id_column (str): The name of the id column</span>
 </span><span id="__span-0-109"><a id="__codelineno-0-109" name="__codelineno-0-109"></a><span class="sd">        &quot;&quot;&quot;</span>
 </span><span id="__span-0-110"><a id="__codelineno-0-110" name="__codelineno-0-110"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span><span class="o">.</span><span class="n">important</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating default Training View </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">table</span><span class="si">}</span><span class="s2">...&quot;</span><span class="p">)</span>
@@ -3571,7 +3571,7 @@ <h2 id="sageworks.core.views.training_view.TrainingView" class="doc doc-heading"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.training_view.TrainingView.create" class="doc doc-heading">
+<h3 id="workbench.core.views.training_view.TrainingView.create" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create</span><span class="p">(</span><span class="n">feature_set</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">id_column</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">holdout_ids</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -3602,7 +3602,7 @@ <h3 id="sageworks.core.views.training_view.TrainingView.create" class="doc doc-h
                 <code>feature_set</code>
             </td>
             <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.api.FeatureSet" href="../../../api_classes/feature_set/#sageworks.api.feature_set.FeatureSet">FeatureSet</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.api.FeatureSet" href="../../../api_classes/feature_set/#workbench.api.feature_set.FeatureSet">FeatureSet</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3676,7 +3676,7 @@ <h3 id="sageworks.core.views.training_view.TrainingView.create" class="doc doc-h
       <tbody>
           <tr class="doc-section-item">
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.core.views.view.View" href="../../../api_classes/views/#sageworks.core.views.view.View">View</a>, None]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.core.views.view.View" href="../../../api_classes/views/#workbench.core.views.view.View">View</a>, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3688,7 +3688,7 @@ <h3 id="sageworks.core.views.training_view.TrainingView.create" class="doc doc-h
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/training_view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/training_view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-30"> 30</a></span>
 <span class="normal"><a href="#__codelineno-0-31"> 31</a></span>
 <span class="normal"><a href="#__codelineno-0-32"> 32</a></span>
@@ -3853,7 +3853,7 @@ <h3 id="sageworks.core.views.training_view.TrainingView.create" class="doc doc-h
 
 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a></p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a></p>
 
 
 
diff --git a/data_algorithms/dataframes/overview/index.html b/data_algorithms/dataframes/overview/index.html
index b38c3b14a..d3762b257 100644
--- a/data_algorithms/dataframes/overview/index.html
+++ b/data_algorithms/dataframes/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>DataFrames - SageWorks</title>
+        <title>DataFrames - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
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         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+    Workbench
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   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
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@@ -1760,7 +1760,7 @@
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         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity" class="md-nav__link">
     <span class="md-ellipsis">
       feature_space_proximity
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@@ -1769,7 +1769,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" class="md-nav__link">
     <span class="md-ellipsis">
       FeatureSpaceProximity
     </span>
@@ -1779,7 +1779,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -1788,7 +1788,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model" class="md-nav__link">
     <span class="md-ellipsis">
       from_model
     </span>
@@ -1797,7 +1797,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances" class="md-nav__link">
     <span class="md-ellipsis">
       get_neighbor_indices_and_distances
     </span>
@@ -1806,7 +1806,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors" class="md-nav__link">
     <span class="md-ellipsis">
       neighbors
     </span>
@@ -1815,7 +1815,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk" class="md-nav__link">
     <span class="md-ellipsis">
       neighbors_bulk
     </span>
@@ -1824,7 +1824,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers" class="md-nav__link">
     <span class="md-ellipsis">
       outliers
     </span>
@@ -1833,7 +1833,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency" class="md-nav__link">
     <span class="md-ellipsis">
       target_consistency
     </span>
@@ -1842,7 +1842,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary" class="md-nav__link">
     <span class="md-ellipsis">
       target_summary
     </span>
@@ -1851,7 +1851,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores" class="md-nav__link">
     <span class="md-ellipsis">
       target_z_scores
     </span>
@@ -1865,7 +1865,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.residuals_calculator" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator" class="md-nav__link">
     <span class="md-ellipsis">
       residuals_calculator
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@@ -1874,7 +1874,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator" class="md-nav__link">
     <span class="md-ellipsis">
       ResidualsCalculator
     </span>
@@ -1884,7 +1884,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__" class="md-nav__link">
     <span class="md-ellipsis">
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     </span>
@@ -1893,7 +1893,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.fit" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.fit" class="md-nav__link">
     <span class="md-ellipsis">
       fit
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@@ -1902,7 +1902,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.transform" class="md-nav__link">
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@@ -1916,7 +1916,7 @@
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     <span class="md-ellipsis">
       dimensionality_reduction
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@@ -1925,7 +1925,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction" class="md-nav__link">
     <span class="md-ellipsis">
       DimensionalityReduction
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@@ -1935,7 +1935,7 @@
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+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.__init__" class="md-nav__link">
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@@ -1944,7 +1944,7 @@
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+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.fit_transform" class="md-nav__link">
     <span class="md-ellipsis">
       fit_transform
     </span>
@@ -1953,7 +1953,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points" class="md-nav__link">
     <span class="md-ellipsis">
       resolve_coincident_points
     </span>
@@ -1967,7 +1967,7 @@
 </li>
       
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+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.test" class="md-nav__link">
     <span class="md-ellipsis">
       test
     </span>
@@ -2084,7 +2084,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
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@@ -2094,7 +2094,7 @@
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-            SageWorks Enterprise
+            Workbench Enterprise
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@@ -3313,11 +3313,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3369,7 +3369,7 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity" class="md-nav__link">
     <span class="md-ellipsis">
       feature_space_proximity
     </span>
@@ -3378,7 +3378,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" class="md-nav__link">
     <span class="md-ellipsis">
       FeatureSpaceProximity
     </span>
@@ -3388,7 +3388,7 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3397,7 +3397,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model" class="md-nav__link">
     <span class="md-ellipsis">
       from_model
     </span>
@@ -3406,7 +3406,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances" class="md-nav__link">
     <span class="md-ellipsis">
       get_neighbor_indices_and_distances
     </span>
@@ -3415,7 +3415,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors" class="md-nav__link">
     <span class="md-ellipsis">
       neighbors
     </span>
@@ -3424,7 +3424,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk" class="md-nav__link">
     <span class="md-ellipsis">
       neighbors_bulk
     </span>
@@ -3433,7 +3433,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers" class="md-nav__link">
     <span class="md-ellipsis">
       outliers
     </span>
@@ -3442,7 +3442,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency" class="md-nav__link">
     <span class="md-ellipsis">
       target_consistency
     </span>
@@ -3451,7 +3451,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary" class="md-nav__link">
     <span class="md-ellipsis">
       target_summary
     </span>
@@ -3460,7 +3460,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores" class="md-nav__link">
     <span class="md-ellipsis">
       target_z_scores
     </span>
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 </li>
       
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-  <a href="#sageworks.algorithms.dataframe.residuals_calculator" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator" class="md-nav__link">
     <span class="md-ellipsis">
       residuals_calculator
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@@ -3483,7 +3483,7 @@
 </li>
       
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-  <a href="#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator" class="md-nav__link">
     <span class="md-ellipsis">
       ResidualsCalculator
     </span>
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
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 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.fit" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.fit" class="md-nav__link">
     <span class="md-ellipsis">
       fit
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@@ -3511,7 +3511,7 @@
 </li>
         
           <li class="md-nav__item">
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+  <a href="#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.transform" class="md-nav__link">
     <span class="md-ellipsis">
       transform
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@@ -3525,7 +3525,7 @@
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         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction" class="md-nav__link">
     <span class="md-ellipsis">
       dimensionality_reduction
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@@ -3534,7 +3534,7 @@
 </li>
       
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-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction" class="md-nav__link">
     <span class="md-ellipsis">
       DimensionalityReduction
     </span>
@@ -3544,7 +3544,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.__init__" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
     </span>
@@ -3553,7 +3553,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.fit_transform" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.fit_transform" class="md-nav__link">
     <span class="md-ellipsis">
       fit_transform
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@@ -3562,7 +3562,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points" class="md-nav__link">
     <span class="md-ellipsis">
       resolve_coincident_points
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@@ -3576,7 +3576,7 @@
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         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.dataframe.dimensionality_reduction.test" class="md-nav__link">
+  <a href="#workbench.algorithms.dataframe.dimensionality_reduction.test" class="md-nav__link">
     <span class="md-ellipsis">
       test
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@@ -3614,14 +3614,14 @@ <h1 id="pandas-dataframe-algorithms">Pandas Dataframe Algorithms</h1>
 <p>Pandas dataframes are obviously not going to scale as well as our Spark and SQL Algorithms, but for 'moderate' sized data these algorithms provide some nice functionality.</p>
 </div>
 <p><strong>Pandas Dataframe Algorithms</strong></p>
-<p>SageWorks has a growing set of algorithms and data processing tools for Pandas Dataframes. In general these algorithm will take a dataframe as input and give you back a dataframe with additional columns.</p>
+<p>Workbench has a growing set of algorithms and data processing tools for Pandas Dataframes. In general these algorithm will take a dataframe as input and give you back a dataframe with additional columns.</p>
 
 
 <div class="doc doc-object doc-module">
 
 
 
-<a id="sageworks.algorithms.dataframe.feature_space_proximity"></a>
+<a id="workbench.algorithms.dataframe.feature_space_proximity"></a>
     <div class="doc doc-contents first">
 
         <p>FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.</p>
@@ -3646,7 +3646,7 @@ <h1 id="pandas-dataframe-algorithms">Pandas Dataframe Algorithms</h1>
 
 
 
-<h2 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" class="doc doc-heading">
+<h2 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" class="doc doc-heading">
             <code>FeatureSpaceProximity</code>
 
 
@@ -3662,7 +3662,7 @@ <h2 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+                <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12"> 12</a></span>
 <span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
 <span class="normal"><a href="#__codelineno-0-14"> 14</a></span>
@@ -3906,7 +3906,7 @@ <h2 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">            target: Optional name of the target column to include target-based functionality (default: None)</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">            neighbors: Number of neighbors to use in the KNN model (default: 10)</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="n">features</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">id_column</span> <span class="o">=</span> <span class="n">id_column</span>
@@ -3933,17 +3933,17 @@ <h2 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 </span><span id="__span-0-47"><a id="__codelineno-0-47" name="__codelineno-0-47"></a>
 </span><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a>    <span class="nd">@classmethod</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a>    <span class="k">def</span> <span class="nf">from_model</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;FeatureSpaceProximity&quot;</span><span class="p">:</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a FeatureSpaceProximity instance from a SageWorks model object.</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a FeatureSpaceProximity instance from a Workbench model object.</span>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="sd">            model (Model): A SageWorks model object.</span>
+</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="sd">            model (Model): A Workbench model object.</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="sd">        Returns:</span>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="sd">            FeatureSpaceProximity: A new instance of the FeatureSpaceProximity class.</span>
 </span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="sd">        &quot;&quot;&quot;</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>        <span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>        <span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>        <span class="c1"># Extract necessary attributes from the SageWorks model</span>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>        <span class="c1"># Extract necessary attributes from the Workbench model</span>
 </span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>        <span class="n">fs</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">get_input</span><span class="p">())</span>
 </span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="n">features</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">features</span><span class="p">()</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="n">target</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">target</span><span class="p">()</span>
@@ -4146,7 +4146,7 @@ <h2 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">features</span><span class="p">,</span> <span class="n">id_column</span><span class="p">,</span> <span class="n">target</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">neighbors</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span></code>
 
 </h3>
@@ -4252,7 +4252,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-13">13</a></span>
 <span class="normal"><a href="#__codelineno-0-14">14</a></span>
 <span class="normal"><a href="#__codelineno-0-15">15</a></span>
@@ -4296,7 +4296,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        target: Optional name of the target column to include target-based functionality (default: None)</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        neighbors: Number of neighbors to use in the KNN model (default: 10)</span>
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">df</span> <span class="o">=</span> <span class="n">df</span>
 </span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="n">features</span>
 </span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">id_column</span> <span class="o">=</span> <span class="n">id_column</span>
@@ -4329,7 +4329,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">from_model</span><span class="p">(</span><span class="n">model</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4341,7 +4341,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 
     <div class="doc doc-contents ">
 
-        <p>Create a FeatureSpaceProximity instance from a SageWorks model object.</p>
+        <p>Create a FeatureSpaceProximity instance from a Workbench model object.</p>
 
 
 <p><span class="doc-section-title">Parameters:</span></p>
@@ -4360,11 +4360,11 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
                 <code>model</code>
             </td>
             <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.api.Model" href="../../../api_classes/model/#sageworks.api.model.Model">Model</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.api.Model" href="../../../api_classes/model/#workbench.api.model.Model">Model</a></code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>A SageWorks model object.</p>
+                <p>A Workbench model object.</p>
               </div>
             </td>
             <td>
@@ -4386,7 +4386,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
       <tbody>
           <tr class="doc-section-item">
 <td><code>FeatureSpaceProximity</code></td>            <td>
-                  <code><a class="autorefs autorefs-internal" title="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" href="#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity">FeatureSpaceProximity</a></code>
+                  <code><a class="autorefs autorefs-internal" title="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity" href="#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity">FeatureSpaceProximity</a></code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -4398,7 +4398,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-48">48</a></span>
 <span class="normal"><a href="#__codelineno-0-49">49</a></span>
 <span class="normal"><a href="#__codelineno-0-50">50</a></span>
@@ -4422,17 +4422,17 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <span class="normal"><a href="#__codelineno-0-68">68</a></span>
 <span class="normal"><a href="#__codelineno-0-69">69</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-48"><a id="__codelineno-0-48" name="__codelineno-0-48"></a><span class="nd">@classmethod</span>
 </span><span id="__span-0-49"><a id="__codelineno-0-49" name="__codelineno-0-49"></a><span class="k">def</span> <span class="nf">from_model</span><span class="p">(</span><span class="bp">cls</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="s2">&quot;FeatureSpaceProximity&quot;</span><span class="p">:</span>
-</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a FeatureSpaceProximity instance from a SageWorks model object.</span>
+</span><span id="__span-0-50"><a id="__codelineno-0-50" name="__codelineno-0-50"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;Create a FeatureSpaceProximity instance from a Workbench model object.</span>
 </span><span id="__span-0-51"><a id="__codelineno-0-51" name="__codelineno-0-51"></a>
 </span><span id="__span-0-52"><a id="__codelineno-0-52" name="__codelineno-0-52"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="sd">        model (Model): A SageWorks model object.</span>
+</span><span id="__span-0-53"><a id="__codelineno-0-53" name="__codelineno-0-53"></a><span class="sd">        model (Model): A Workbench model object.</span>
 </span><span id="__span-0-54"><a id="__codelineno-0-54" name="__codelineno-0-54"></a>
 </span><span id="__span-0-55"><a id="__codelineno-0-55" name="__codelineno-0-55"></a><span class="sd">    Returns:</span>
 </span><span id="__span-0-56"><a id="__codelineno-0-56" name="__codelineno-0-56"></a><span class="sd">        FeatureSpaceProximity: A new instance of the FeatureSpaceProximity class.</span>
 </span><span id="__span-0-57"><a id="__codelineno-0-57" name="__codelineno-0-57"></a><span class="sd">    &quot;&quot;&quot;</span>
-</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>    <span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
+</span><span id="__span-0-58"><a id="__codelineno-0-58" name="__codelineno-0-58"></a>    <span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">FeatureSet</span>
 </span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>
-</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>    <span class="c1"># Extract necessary attributes from the SageWorks model</span>
+</span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>    <span class="c1"># Extract necessary attributes from the Workbench model</span>
 </span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>    <span class="n">fs</span> <span class="o">=</span> <span class="n">FeatureSet</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">get_input</span><span class="p">())</span>
 </span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>    <span class="n">features</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">features</span><span class="p">()</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>    <span class="n">target</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">target</span><span class="p">()</span>
@@ -4451,7 +4451,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_neighbor_indices_and_distances</span><span class="p">()</span></code>
 
 </h3>
@@ -4462,7 +4462,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
         <p>Retrieve neighbor indices and distances for all points in the dataset.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-231">231</a></span>
 <span class="normal"><a href="#__codelineno-0-232">232</a></span>
 <span class="normal"><a href="#__codelineno-0-233">233</a></span>
@@ -4479,7 +4479,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">neighbors</span><span class="p">(</span><span class="n">query_id</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include_self</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></code>
 
 </h3>
@@ -4576,7 +4576,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-71"> 71</a></span>
 <span class="normal"><a href="#__codelineno-0-72"> 72</a></span>
 <span class="normal"><a href="#__codelineno-0-73"> 73</a></span>
@@ -4653,7 +4653,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">neighbors_bulk</span><span class="p">(</span><span class="n">query_df</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">include_self</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -4750,7 +4750,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-106">106</a></span>
 <span class="normal"><a href="#__codelineno-0-107">107</a></span>
 <span class="normal"><a href="#__codelineno-0-108">108</a></span>
@@ -4887,7 +4887,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">outliers</span><span class="p">()</span></code>
 
 </h3>
@@ -4898,7 +4898,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
         <p>Compute a unified 'outlier' score based on either 'target_z' or 'target_consistency'.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-171">171</a></span>
 <span class="normal"><a href="#__codelineno-0-172">172</a></span>
 <span class="normal"><a href="#__codelineno-0-173">173</a></span>
@@ -4931,7 +4931,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">target_consistency</span><span class="p">()</span></code>
 
 </h3>
@@ -4942,7 +4942,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
         <p>Compute a Neighborhood Consistency Score for CATEGORICAL targets.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-207">207</a></span>
 <span class="normal"><a href="#__codelineno-0-208">208</a></span>
 <span class="normal"><a href="#__codelineno-0-209">209</a></span>
@@ -4997,7 +4997,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">target_summary</span><span class="p">(</span><span class="n">query_id</span><span class="p">)</span></code>
 
 </h3>
@@ -5008,7 +5008,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
         <p>WIP: Provide a summary of target values in the neighborhood of the given query ID</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-236">236</a></span>
 <span class="normal"><a href="#__codelineno-0-237">237</a></span>
 <span class="normal"><a href="#__codelineno-0-238">238</a></span>
@@ -5035,7 +5035,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">target_z_scores</span><span class="p">()</span></code>
 
 </h3>
@@ -5046,7 +5046,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
         <p>Compute Z-Scores for NUMERIC target values.</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-184">184</a></span>
 <span class="normal"><a href="#__codelineno-0-185">185</a></span>
 <span class="normal"><a href="#__codelineno-0-186">186</a></span>
@@ -5117,7 +5117,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 
 
 
-<a id="sageworks.algorithms.dataframe.residuals_calculator"></a>
+<a id="workbench.algorithms.dataframe.residuals_calculator"></a>
     <div class="doc doc-contents first">
 
 
@@ -5140,7 +5140,7 @@ <h3 id="sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProxi
 
 
 
-<h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator" class="doc doc-heading">
+<h2 id="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator" class="doc doc-heading">
             <code>ResidualsCalculator</code>
 
 
@@ -5169,7 +5169,7 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
       </thead>
       <tbody>
           <tr class="doc-section-item">
-            <td><code><span title="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.model_class">model_class</span></code></td>
+            <td><code><span title="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.model_class">model_class</span></code></td>
             <td>
                   <code><span title="typing.Union">Union</span>[<span title="sklearn.base.RegressorMixin">RegressorMixin</span>, <span title="xgboost.XGBRegressor">XGBRegressor</span>]</code>
             </td>
@@ -5180,7 +5180,7 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td><code><span title="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.n_splits">n_splits</span></code></td>
+            <td><code><span title="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.n_splits">n_splits</span></code></td>
             <td>
                   <code>int</code>
             </td>
@@ -5191,7 +5191,7 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td><code><span title="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.random_state">random_state</span></code></td>
+            <td><code><span title="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.random_state">random_state</span></code></td>
             <td>
                   <code>int</code>
             </td>
@@ -5202,13 +5202,13 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
             </td>
           </tr>
           <tr class="doc-section-item">
-            <td><code><span title="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.endpoint">endpoint</span></code></td>
+            <td><code><span title="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.endpoint">endpoint</span></code></td>
             <td>
                   <code><span title="typing.Optional">Optional</span></code>
             </td>
             <td>
               <div class="doc-md-description">
-                <p>The SageWorks endpoint object for running inference, if provided.</p>
+                <p>The Workbench endpoint object for running inference, if provided.</p>
               </div>
             </td>
           </tr>
@@ -5221,7 +5221,7 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code></summary>
+                <summary>Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-11"> 11</a></span>
 <span class="normal"><a href="#__codelineno-0-12"> 12</a></span>
 <span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
@@ -5396,7 +5396,7 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
 </span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20"></a><span class="sd">        model_class (Union[RegressorMixin, XGBRegressor]): The machine learning model class used for predictions.</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21"></a><span class="sd">        n_splits (int): Number of splits for cross-validation.</span>
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+</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23"></a><span class="sd">        endpoint (Optional): The Workbench endpoint object for running inference, if provided.</span>
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@@ -5408,7 +5408,7 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
 </span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32"></a><span class="sd">        Initializes the ResidualsCalculator with the specified parameters.</span>
 </span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33"></a>
 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">        Args:</span>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="sd">            endpoint (Optional): A SageWorks endpoint object to run inference, if available.</span>
+</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="sd">            endpoint (Optional): A Workbench endpoint object to run inference, if available.</span>
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a><span class="sd">            reference_model_class (Union[RegressorMixin, XGBRegressor]): The reference model class for predictions.</span>
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@@ -5565,7 +5565,7 @@ <h2 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator"
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">(</span><span class="n">endpoint</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">reference_model_class</span><span class="o">=</span><span class="n">XGBRegressor</span><span class="p">)</span></code>
 
 </h3>
@@ -5596,7 +5596,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
             </td>
             <td>
               <div class="doc-md-description">
-                <p>A SageWorks endpoint object to run inference, if available.</p>
+                <p>A Workbench endpoint object to run inference, if available.</p>
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@@ -5623,7 +5623,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code></summary>
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 <span class="normal"><a href="#__codelineno-0-27">27</a></span>
 <span class="normal"><a href="#__codelineno-0-28">28</a></span>
@@ -5652,7 +5652,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
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 </span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34"></a><span class="sd">    Args:</span>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="sd">        endpoint (Optional): A SageWorks endpoint object to run inference, if available.</span>
+</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35"></a><span class="sd">        endpoint (Optional): A Workbench endpoint object to run inference, if available.</span>
 </span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36"></a><span class="sd">        reference_model_class (Union[RegressorMixin, XGBRegressor]): The reference model class for predictions.</span>
 </span><span id="__span-0-37"><a id="__codelineno-0-37" name="__codelineno-0-37"></a><span class="sd">    &quot;&quot;&quot;</span>
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@@ -5672,7 +5672,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
 <div class="doc doc-object doc-function">
 
 
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+<h3 id="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.fit" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span></code>
 
 </h3>
@@ -5754,7 +5754,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code></summary>
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@@ -5801,7 +5801,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
 <div class="doc doc-object doc-function">
 
 
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+<h3 id="workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.transform" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">transform</span><span class="p">(</span><span class="n">X</span><span class="p">)</span></code>
 
 </h3>
@@ -5867,7 +5867,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code></summary>
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@@ -5936,7 +5936,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
 
 
 
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+<a id="workbench.algorithms.dataframe.dimensionality_reduction"></a>
     <div class="doc doc-contents first">
 
         <p>DimensionalityReduction: Perform Dimensionality Reduction on a DataFrame</p>
@@ -5961,7 +5961,7 @@ <h3 id="sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.
 
 
 
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+<h2 id="workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction" class="doc doc-heading">
             <code>DimensionalityReduction</code>
 
 
@@ -5977,7 +5977,7 @@ <h2 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code></summary>
+                <summary>Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-12"> 12</a></span>
 <span class="normal"><a href="#__codelineno-0-13"> 13</a></span>
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@@ -6070,7 +6070,7 @@ <h2 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
 <span class="normal"><a href="#__codelineno-0-101">101</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12"></a><span class="k">class</span> <span class="nc">DimensionalityReduction</span><span class="p">:</span>
 </span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13"></a>    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="w">        </span><span class="sd">&quot;&quot;&quot;DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame&quot;&quot;&quot;</span>
-</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">projection_model</span> <span class="o">=</span> <span class="kc">None</span>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a>        <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="kc">None</span>
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@@ -6175,7 +6175,7 @@ <h2 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.__init__" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">()</span></code>
 
 </h3>
@@ -6186,14 +6186,14 @@ <h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
         <p>DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-13">13</a></span>
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 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14"></a><span class="w">    </span><span class="sd">&quot;&quot;&quot;DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame&quot;&quot;&quot;</span>
-</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;sageworks&quot;</span><span class="p">)</span>
+</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">log</span> <span class="o">=</span> <span class="n">logging</span><span class="o">.</span><span class="n">getLogger</span><span class="p">(</span><span class="s2">&quot;workbench&quot;</span><span class="p">)</span>
 </span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">projection_model</span> <span class="o">=</span> <span class="kc">None</span>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17"></a>    <span class="bp">self</span><span class="o">.</span><span class="n">features</span> <span class="o">=</span> <span class="kc">None</span>
 </span></code></pre></div></td></tr></table></div>
@@ -6205,7 +6205,7 @@ <h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.fit_transform" class="doc doc-heading">
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             <code class="highlight language-python"><span class="n">fit_transform</span><span class="p">(</span><span class="n">df</span><span class="p">,</span> <span class="n">features</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">&#39;TSNE&#39;</span><span class="p">)</span></code>
 
 </h3>
@@ -6222,7 +6222,7 @@ <h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
     Pandas DataFrame with new columns x and y</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code></summary>
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@@ -6367,7 +6367,7 @@ <h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points" class="doc doc-heading">
+<h3 id="workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">resolve_coincident_points</span><span class="p">(</span><span class="n">df</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -6386,7 +6386,7 @@ <h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
     pd.DataFrame: The DataFrame with resolved coincident points</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-88"> 88</a></span>
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@@ -6432,7 +6432,7 @@ <h3 id="sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityRe
 <div class="doc doc-object doc-function">
 
 
-<h2 id="sageworks.algorithms.dataframe.dimensionality_reduction.test" class="doc doc-heading">
+<h2 id="workbench.algorithms.dataframe.dimensionality_reduction.test" class="doc doc-heading">
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 </h2>
@@ -6443,7 +6443,7 @@ <h2 id="sageworks.algorithms.dataframe.dimensionality_reduction.test" class="doc
         <p>Test for the Dimensionality Reduction Class</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-104">104</a></span>
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@@ -6533,7 +6533,7 @@ <h2 id="sageworks.algorithms.dataframe.dimensionality_reduction.test" class="doc
 
 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
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index 8e107bb4b..e9b621e73 100644
--- a/data_algorithms/graphs/overview/index.html
+++ b/data_algorithms/graphs/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
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+        <title>Graphs - Workbench</title>
       
     
     
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-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+<h2 id="workbench.algorithms.graph.light.proximity_graph.ProximityGraph" class="doc doc-heading">
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@@ -3335,7 +3335,7 @@ <h2 id="sageworks.algorithms.graph.light.proximity_graph.ProximityGraph" class="
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code></summary>
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@@ -3603,7 +3603,7 @@ <h3 id="sageworks.algorithms.graph.light.proximity_graph.ProximityGraph.__init__
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code></summary>
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             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code></summary>
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             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code></summary>
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 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/data_algorithms/overview/index.html b/data_algorithms/overview/index.html
index ed12cb7a4..c1b6cbabf 100644
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-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
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-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
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-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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 </ul>
 </li>
 </ul>
-<p><strong>Welcome to the SageWorks Data Algorithms</strong></p>
+<p><strong>Welcome to the Workbench Data Algorithms</strong></p>
 <p>Docs TBD</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
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@@ -3318,7 +3318,7 @@ <h2 id="sageworks.core.views.computation_view.ComputationView" class="doc doc-he
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/core/views/computation_view.py</code></summary>
+                <summary>Source code in <code>src/workbench/core/views/computation_view.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-11">11</a></span>
 <span class="normal"><a href="#__codelineno-0-12">12</a></span>
 <span class="normal"><a href="#__codelineno-0-13">13</a></span>
@@ -3414,7 +3414,7 @@ <h2 id="sageworks.core.views.computation_view.ComputationView" class="doc doc-he
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc doc-heading">
+<h3 id="workbench.core.views.computation_view.ComputationView.create" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">create</span><span class="p">(</span><span class="n">artifact</span><span class="p">,</span> <span class="n">source_table</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">column_list</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">column_limit</span><span class="o">=</span><span class="mi">30</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -3445,7 +3445,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
                 <code>artifact</code>
             </td>
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.api.DataSource" href="../../../api_classes/data_source/#sageworks.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="sageworks.api.FeatureSet" href="../../../api_classes/feature_set/#sageworks.api.feature_set.FeatureSet">FeatureSet</a>]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.api.DataSource" href="../../../api_classes/data_source/#workbench.api.data_source.DataSource">DataSource</a>, <a class="autorefs autorefs-internal" title="workbench.api.FeatureSet" href="../../../api_classes/feature_set/#workbench.api.feature_set.FeatureSet">FeatureSet</a>]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3519,7 +3519,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
       <tbody>
           <tr class="doc-section-item">
             <td>
-                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="sageworks.core.views.view.View" href="../../../api_classes/views/#sageworks.core.views.view.View">View</a>, None]</code>
+                  <code><span title="typing.Union">Union</span>[<a class="autorefs autorefs-internal" title="workbench.core.views.view.View" href="../../../api_classes/views/#workbench.core.views.view.View">View</a>, None]</code>
             </td>
             <td>
               <div class="doc-md-description">
@@ -3531,7 +3531,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/core/views/computation_view.py</code></summary>
+              <summary>Source code in <code>src/workbench/core/views/computation_view.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-29">29</a></span>
 <span class="normal"><a href="#__codelineno-0-30">30</a></span>
 <span class="normal"><a href="#__codelineno-0-31">31</a></span>
@@ -3596,7 +3596,7 @@ <h3 id="sageworks.core.views.computation_view.ComputationView.create" class="doc
 
 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/data_algorithms/sql/overview/index.html b/data_algorithms/sql/overview/index.html
index bcb7783ac..f81cc7a4d 100644
--- a/data_algorithms/sql/overview/index.html
+++ b/data_algorithms/sql/overview/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>SQL - SageWorks</title>
+        <title>SQL - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
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-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+            Workbench
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@@ -211,13 +211,13 @@
 
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   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
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+    Workbench
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-    SageWorks REPL
+    Workbench REPL
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@@ -1823,7 +1823,7 @@
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-  <a href="#sageworks.algorithms.sql.outliers" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.outliers" class="md-nav__link">
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@@ -1832,7 +1832,7 @@
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+  <a href="#workbench.algorithms.sql.outliers.Outliers" class="md-nav__link">
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       Outliers
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@@ -1842,7 +1842,7 @@
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           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.sql.outliers.Outliers.__init__" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.outliers.Outliers.__init__" class="md-nav__link">
     <span class="md-ellipsis">
       __init__
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@@ -1851,7 +1851,7 @@
 </li>
         
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-  <a href="#sageworks.algorithms.sql.outliers.Outliers.compute_outliers" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.outliers.Outliers.compute_outliers" class="md-nav__link">
     <span class="md-ellipsis">
       compute_outliers
     </span>
@@ -1860,7 +1860,7 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks.algorithms.sql.outliers.Outliers.get_top_n_outliers" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.outliers.Outliers.get_top_n_outliers" class="md-nav__link">
     <span class="md-ellipsis">
       get_top_n_outliers
     </span>
@@ -1874,7 +1874,7 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks.algorithms.sql.descriptive_stats" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.descriptive_stats" class="md-nav__link">
     <span class="md-ellipsis">
       descriptive_stats
     </span>
@@ -1883,7 +1883,7 @@
 </li>
       
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-  <a href="#sageworks.algorithms.sql.descriptive_stats.descriptive_stats" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.descriptive_stats.descriptive_stats" class="md-nav__link">
     <span class="md-ellipsis">
       descriptive_stats
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@@ -1892,7 +1892,7 @@
 </li>
       
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+  <a href="#workbench.algorithms.sql.descriptive_stats.descriptive_stats_query" class="md-nav__link">
     <span class="md-ellipsis">
       descriptive_stats_query
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@@ -1901,7 +1901,7 @@
 </li>
       
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-  <a href="#sageworks.algorithms.sql.correlations" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.correlations" class="md-nav__link">
     <span class="md-ellipsis">
       correlations
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@@ -1910,7 +1910,7 @@
 </li>
       
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+  <a href="#workbench.algorithms.sql.correlations.correlation_query" class="md-nav__link">
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       correlation_query
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+  <a href="#workbench.algorithms.sql.correlations.correlations" class="md-nav__link">
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       correlations
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+    Workbench Enterprise
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+            Workbench Enterprise
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+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
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+  <a href="#workbench.algorithms.sql.outliers" class="md-nav__link">
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       outliers
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-  <a href="#sageworks.algorithms.sql.outliers.Outliers" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.outliers.Outliers" class="md-nav__link">
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+  <a href="#workbench.algorithms.sql.outliers.Outliers.__init__" class="md-nav__link">
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+  <a href="#workbench.algorithms.sql.outliers.Outliers.compute_outliers" class="md-nav__link">
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+  <a href="#workbench.algorithms.sql.descriptive_stats.descriptive_stats_query" class="md-nav__link">
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       descriptive_stats_query
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@@ -3336,7 +3336,7 @@
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+  <a href="#workbench.algorithms.sql.correlations" class="md-nav__link">
     <span class="md-ellipsis">
       correlations
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@@ -3345,7 +3345,7 @@
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         <li class="md-nav__item">
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+  <a href="#workbench.algorithms.sql.correlations.correlation_query" class="md-nav__link">
     <span class="md-ellipsis">
       correlation_query
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@@ -3354,7 +3354,7 @@
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-  <a href="#sageworks.algorithms.sql.correlations.correlations" class="md-nav__link">
+  <a href="#workbench.algorithms.sql.correlations.correlations" class="md-nav__link">
     <span class="md-ellipsis">
       correlations
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@@ -3409,7 +3409,7 @@ <h1 id="sql-algorithms">SQL Algorithms</h1>
 
 
 
-<a id="sageworks.algorithms.sql.outliers"></a>
+<a id="workbench.algorithms.sql.outliers"></a>
     <div class="doc doc-contents first">
 
         <p>SQL based Outliers: Compute outliers for all the columns in a DataSource using SQL</p>
@@ -3434,7 +3434,7 @@ <h1 id="sql-algorithms">SQL Algorithms</h1>
 
 
 
-<h2 id="sageworks.algorithms.sql.outliers.Outliers" class="doc doc-heading">
+<h2 id="workbench.algorithms.sql.outliers.Outliers" class="doc doc-heading">
             <code>Outliers</code>
 
 
@@ -3452,7 +3452,7 @@ <h2 id="sageworks.algorithms.sql.outliers.Outliers" class="doc doc-heading">
 
 
               <details class="quote">
-                <summary>Source code in <code>src/sageworks/algorithms/sql/outliers.py</code></summary>
+                <summary>Source code in <code>src/workbench/algorithms/sql/outliers.py</code></summary>
                 <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-15"> 15</a></span>
 <span class="normal"><a href="#__codelineno-0-16"> 16</a></span>
 <span class="normal"><a href="#__codelineno-0-17"> 17</a></span>
@@ -3726,7 +3726,7 @@ <h2 id="sageworks.algorithms.sql.outliers.Outliers" class="doc doc-heading">
 </span><span id="__span-0-79"><a id="__codelineno-0-79" name="__codelineno-0-79"></a>        <span class="k">if</span> <span class="ow">not</span> <span class="n">descriptive_stats</span><span class="p">:</span>
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 </span><span id="__span-0-81"><a id="__codelineno-0-81" name="__codelineno-0-81"></a>            <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;If the data source was created from a DataFrame, ensure that the DataFrame was properly typed&quot;</span><span class="p">)</span>
-</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a>            <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Recommendation: Properly type the DataFrame and recreate the SageWorks artifact&quot;</span><span class="p">)</span>
+</span><span id="__span-0-82"><a id="__codelineno-0-82" name="__codelineno-0-82"></a>            <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Recommendation: Properly type the DataFrame and recreate the Workbench artifact&quot;</span><span class="p">)</span>
 </span><span id="__span-0-83"><a id="__codelineno-0-83" name="__codelineno-0-83"></a>            <span class="k">return</span> <span class="kc">None</span>
 </span><span id="__span-0-84"><a id="__codelineno-0-84" name="__codelineno-0-84"></a>
 </span><span id="__span-0-85"><a id="__codelineno-0-85" name="__codelineno-0-85"></a>        <span class="c1"># Get the column names and types from the DataSource</span>
@@ -3884,7 +3884,7 @@ <h2 id="sageworks.algorithms.sql.outliers.Outliers" class="doc doc-heading">
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.sql.outliers.Outliers.__init__" class="doc doc-heading">
+<h3 id="workbench.algorithms.sql.outliers.Outliers.__init__" class="doc doc-heading">
             <code class="highlight language-python"><span class="fm">__init__</span><span class="p">()</span></code>
 
 </h3>
@@ -3895,7 +3895,7 @@ <h3 id="sageworks.algorithms.sql.outliers.Outliers.__init__" class="doc doc-head
         <p>SQLOutliers Initialization</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/sql/outliers.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/sql/outliers.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-18">18</a></span>
 <span class="normal"><a href="#__codelineno-0-19">19</a></span>
 <span class="normal"><a href="#__codelineno-0-20">20</a></span></pre></div></td><td class="code"><div><pre><span></span><code><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18"></a><span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
@@ -3910,7 +3910,7 @@ <h3 id="sageworks.algorithms.sql.outliers.Outliers.__init__" class="doc doc-head
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.sql.outliers.Outliers.compute_outliers" class="doc doc-heading">
+<h3 id="workbench.algorithms.sql.outliers.Outliers.compute_outliers" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">compute_outliers</span><span class="p">(</span><span class="n">data_source</span><span class="p">,</span> <span class="n">scale</span><span class="o">=</span><span class="mf">1.5</span><span class="p">,</span> <span class="n">use_stddev</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span></code>
 
 </h3>
@@ -3930,7 +3930,7 @@ <h3 id="sageworks.algorithms.sql.outliers.Outliers.compute_outliers" class="doc
     The scale parameter can be adjusted to change the IQR multiplier</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/sql/outliers.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/sql/outliers.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-22">22</a></span>
 <span class="normal"><a href="#__codelineno-0-23">23</a></span>
 <span class="normal"><a href="#__codelineno-0-24">24</a></span>
@@ -4021,7 +4021,7 @@ <h3 id="sageworks.algorithms.sql.outliers.Outliers.compute_outliers" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h3 id="sageworks.algorithms.sql.outliers.Outliers.get_top_n_outliers" class="doc doc-heading">
+<h3 id="workbench.algorithms.sql.outliers.Outliers.get_top_n_outliers" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">get_top_n_outliers</span><span class="p">(</span><span class="n">outlier_df</span><span class="p">,</span> <span class="n">n</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span></code>
 
   <span class="doc doc-labels">
@@ -4106,7 +4106,7 @@ <h3 id="sageworks.algorithms.sql.outliers.Outliers.get_top_n_outliers" class="do
     </table>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/sql/outliers.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/sql/outliers.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-197">197</a></span>
 <span class="normal"><a href="#__codelineno-0-198">198</a></span>
 <span class="normal"><a href="#__codelineno-0-199">199</a></span>
@@ -4183,7 +4183,7 @@ <h3 id="sageworks.algorithms.sql.outliers.Outliers.get_top_n_outliers" class="do
 
 
 
-<a id="sageworks.algorithms.sql.descriptive_stats"></a>
+<a id="workbench.algorithms.sql.descriptive_stats"></a>
     <div class="doc doc-contents first">
 
         <p>SQL based Descriptive Stats: Compute Descriptive Stats for all the numeric columns in a DataSource using SQL</p>
@@ -4208,7 +4208,7 @@ <h3 id="sageworks.algorithms.sql.outliers.Outliers.get_top_n_outliers" class="do
 <div class="doc doc-object doc-function">
 
 
-<h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats" class="doc doc-heading">
+<h2 id="workbench.algorithms.sql.descriptive_stats.descriptive_stats" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">descriptive_stats</span><span class="p">(</span><span class="n">data_source</span><span class="p">)</span></code>
 
 </h2>
@@ -4225,7 +4225,7 @@ <h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats" class="doc
           'col2': ...}</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/sql/descriptive_stats.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/sql/descriptive_stats.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-41">41</a></span>
 <span class="normal"><a href="#__codelineno-0-42">42</a></span>
 <span class="normal"><a href="#__codelineno-0-43">43</a></span>
@@ -4290,7 +4290,7 @@ <h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats" class="doc
 </span><span id="__span-0-59"><a id="__codelineno-0-59" name="__codelineno-0-59"></a>    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">numeric</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
 </span><span id="__span-0-60"><a id="__codelineno-0-60" name="__codelineno-0-60"></a>        <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;No numeric columns found in the current computation view of the DataSource&quot;</span><span class="p">)</span>
 </span><span id="__span-0-61"><a id="__codelineno-0-61" name="__codelineno-0-61"></a>        <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;If the data source was created from a DataFrame, ensure that the DataFrame was properly typed&quot;</span><span class="p">)</span>
-</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Recommendation: Properly type the DataFrame and recreate the SageWorks artifact&quot;</span><span class="p">)</span>
+</span><span id="__span-0-62"><a id="__codelineno-0-62" name="__codelineno-0-62"></a>        <span class="n">log</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">&quot;Recommendation: Properly type the DataFrame and recreate the Workbench artifact&quot;</span><span class="p">)</span>
 </span><span id="__span-0-63"><a id="__codelineno-0-63" name="__codelineno-0-63"></a>        <span class="k">return</span> <span class="p">{}</span>
 </span><span id="__span-0-64"><a id="__codelineno-0-64" name="__codelineno-0-64"></a>
 </span><span id="__span-0-65"><a id="__codelineno-0-65" name="__codelineno-0-65"></a>    <span class="c1"># Build the query</span>
@@ -4322,7 +4322,7 @@ <h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats" class="doc
 <div class="doc doc-object doc-function">
 
 
-<h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats_query" class="doc doc-heading">
+<h2 id="workbench.algorithms.sql.descriptive_stats.descriptive_stats_query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">descriptive_stats_query</span><span class="p">(</span><span class="n">columns</span><span class="p">,</span> <span class="n">table_name</span><span class="p">)</span></code>
 
 </h2>
@@ -4338,7 +4338,7 @@ <h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats_query" clas
     str: The SQL query to compute descriptive stats</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/sql/descriptive_stats.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/sql/descriptive_stats.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-15">15</a></span>
 <span class="normal"><a href="#__codelineno-0-16">16</a></span>
 <span class="normal"><a href="#__codelineno-0-17">17</a></span>
@@ -4404,7 +4404,7 @@ <h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats_query" clas
 
 
 
-<a id="sageworks.algorithms.sql.correlations"></a>
+<a id="workbench.algorithms.sql.correlations"></a>
     <div class="doc doc-contents first">
 
         <p>SQL based Correlations: Compute Correlations for all the numeric columns in a DataSource using SQL</p>
@@ -4429,7 +4429,7 @@ <h2 id="sageworks.algorithms.sql.descriptive_stats.descriptive_stats_query" clas
 <div class="doc doc-object doc-function">
 
 
-<h2 id="sageworks.algorithms.sql.correlations.correlation_query" class="doc doc-heading">
+<h2 id="workbench.algorithms.sql.correlations.correlation_query" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">correlation_query</span><span class="p">(</span><span class="n">columns</span><span class="p">,</span> <span class="n">table_name</span><span class="p">)</span></code>
 
 </h2>
@@ -4517,7 +4517,7 @@ <h2 id="sageworks.algorithms.sql.correlations.correlation_query" class="doc doc-
  0 indicates no linear relationship.</p>
 </details>
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/sql/correlations.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/sql/correlations.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-15">15</a></span>
 <span class="normal"><a href="#__codelineno-0-16">16</a></span>
 <span class="normal"><a href="#__codelineno-0-17">17</a></span>
@@ -4576,7 +4576,7 @@ <h2 id="sageworks.algorithms.sql.correlations.correlation_query" class="doc doc-
 <div class="doc doc-object doc-function">
 
 
-<h2 id="sageworks.algorithms.sql.correlations.correlations" class="doc doc-heading">
+<h2 id="workbench.algorithms.sql.correlations.correlations" class="doc doc-heading">
             <code class="highlight language-python"><span class="n">correlations</span><span class="p">(</span><span class="n">data_source</span><span class="p">)</span></code>
 
 </h2>
@@ -4593,7 +4593,7 @@ <h2 id="sageworks.algorithms.sql.correlations.correlations" class="doc doc-headi
           'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}</p>
 
             <details class="quote">
-              <summary>Source code in <code>src/sageworks/algorithms/sql/correlations.py</code></summary>
+              <summary>Source code in <code>src/workbench/algorithms/sql/correlations.py</code></summary>
               <div class="language-python highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span></span><span class="normal"><a href="#__codelineno-0-42">42</a></span>
 <span class="normal"><a href="#__codelineno-0-43">43</a></span>
 <span class="normal"><a href="#__codelineno-0-44">44</a></span>
@@ -4709,7 +4709,7 @@ <h2 id="sageworks.algorithms.sql.correlations.correlations" class="doc doc-headi
 
 </div><h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/enterprise/index.html b/enterprise/index.html
index c01134a02..fb3e62437 100644
--- a/enterprise/index.html
+++ b/enterprise/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-enterprise" class="md-skip">
+        <a href="#workbench-enterprise" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1819,7 +1819,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1829,7 +1829,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="true">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -1886,9 +1886,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#try-sageworks" class="md-nav__link">
+  <a href="#try-workbench" class="md-nav__link">
     <span class="md-ellipsis">
-      Try SageWorks
+      Try Workbench
     </span>
   </a>
   
@@ -3115,11 +3115,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3171,9 +3171,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#try-sageworks" class="md-nav__link">
+  <a href="#try-workbench" class="md-nav__link">
     <span class="md-ellipsis">
-      Try SageWorks
+      Try Workbench
     </span>
   </a>
   
@@ -3212,10 +3212,10 @@
                   
 
 
-<h1 id="sageworks-enterprise">SageWorks Enterprise</h1>
-<p>The SageWorks API and User Interfaces cover a broad set of AWS Machine Learning services and provide easy to use abstractions and visualizations of your AWS ML data. We offer a wide range of options to best fit your companies needs.</p>
+<h1 id="workbench-enterprise">Workbench Enterprise</h1>
+<p>The Workbench API and User Interfaces cover a broad set of AWS Machine Learning services and provide easy to use abstractions and visualizations of your AWS ML data. We offer a wide range of options to best fit your companies needs.</p>
 <figure style="width: 550px;">
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4">
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4">
 <figcaption>Accelerate ML Pipeline development with an Enterprise License!</figcaption>
 </figure>
 
@@ -3231,63 +3231,63 @@ <h1 id="sageworks-enterprise">SageWorks Enterprise</h1>
 </thead>
 <tbody>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/api_classes/overview/" target="_blank">Python API</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/api_classes/overview/" target="_blank">Python API</a></strong></td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/repl/" target="_blank">SageWorks REPL</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/repl/" target="_blank">Workbench REPL</a></strong></td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/" target="_blank">Dashboard</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/" target="_blank">Dashboard</a></strong></td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/aws_setup/core_stack/" target="_blank">AWS Onboarding</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/aws_setup/core_stack/" target="_blank">AWS Onboarding</a></strong></td>
 <td>➖</td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/plugins/" target="_blank">Dashboard Plugins</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/plugins/" target="_blank">Dashboard Plugins</a></strong></td>
 <td>➖</td>
 <td>🟢</td>
 <td>🟢</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/plugins/" target="_blank">Custom Pages</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/plugins/" target="_blank">Custom Pages</a></strong></td>
 <td>➖</td>
 <td>➖</td>
 <td>🟢</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/enterprise/themes/" target="_blank">Themes</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/enterprise/themes/" target="_blank">Themes</a></strong></td>
 <td>➖</td>
 <td>➖</td>
 <td>🟢</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/api_classes/pipelines/" target="_blank">ML Pipelines</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/api_classes/pipelines/" target="_blank">ML Pipelines</a></strong></td>
 <td>➖</td>
 <td>➖</td>
 <td>➖</td>
 <td>🟢</td>
 </tr>
 <tr>
-<td><strong><a href="https://supercowpowers.github.io/sageworks/enterprise/project_branding/" target="_blank">Project Branding</a></strong></td>
+<td><strong><a href="https://supercowpowers.github.io/workbench/enterprise/project_branding/" target="_blank">Project Branding</a></strong></td>
 <td>➖</td>
 <td>➖</td>
 <td>➖</td>
@@ -3309,14 +3309,14 @@ <h1 id="sageworks-enterprise">SageWorks Enterprise</h1>
 </tr>
 </tbody>
 </table>
-<p>*<small><em>USD per month, includes AWS setup, support, and training: Everything needed to accelerate your AWS ML Development team. Interested in Data Science/Engineering consulting? We have top notch <a href="https://supercowpowers.github.io/sageworks/enterprise/#data-engineeringscience-consulting">Consultants</a> with a depth and breadth of AWS ML/DS/Engineering expertise.</em></small></p>
-<h2 id="try-sageworks">Try SageWorks</h2>
-<p>We encourage new users to try out the <strong>free</strong> version, first. We offer support in our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel and our <a href="https://supercowpowers.github.io/sageworks/">Documentation</a> has instructions for how to get started with SageWorks. So try it out and when you're ready to accelerate your AWS ML Adventure with an <strong>Enterprise</strong> licence contact us at <a href="mailto:sales@supercowpowers.com">SageWorks Sales</a></p>
+<p>*<small><em>USD per month, includes AWS setup, support, and training: Everything needed to accelerate your AWS ML Development team. Interested in Data Science/Engineering consulting? We have top notch <a href="https://supercowpowers.github.io/workbench/enterprise/#data-engineeringscience-consulting">Consultants</a> with a depth and breadth of AWS ML/DS/Engineering expertise.</em></small></p>
+<h2 id="try-workbench">Try Workbench</h2>
+<p>We encourage new users to try out the <strong>free</strong> version, first. We offer support in our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel and our <a href="https://supercowpowers.github.io/workbench/">Documentation</a> has instructions for how to get started with Workbench. So try it out and when you're ready to accelerate your AWS ML Adventure with an <strong>Enterprise</strong> licence contact us at <a href="mailto:sales@supercowpowers.com">Workbench Sales</a></p>
 <h2 id="data-engineeringscience-consulting">Data Engineering/Science Consulting</h2>
-<p>Alongside our SageWorks Enterprise offerings, we provide comprehensive consulting services and domain expertise through our <a href="https://www.supercowpowers.com/home#h.qau620rju99x">Partnerships</a>. We specialize in <strong>AWS Machine Learning Systems</strong> and our extended team of Data Scientists and Engineers, have Masters and Ph.D. degrees in Computer Science, Chemistry, and Pharmacology. We also have a parntership with <a href="https://nomicnetworks.com">Nomic Networks</a> to support our Network Security Clients.</p>
-<p>Using <strong>AWS</strong> and <strong>SageWorks</strong>, our experts are equipped to deliver tailored solutions that are focused on your project needs and deliverables. For more information please touch base and we'll set up a free initial consultation <a href="mailto:consulting@supercowpowers.com">SageWorks Consulting</a></p>
+<p>Alongside our Workbench Enterprise offerings, we provide comprehensive consulting services and domain expertise through our <a href="https://www.supercowpowers.com/home#h.qau620rju99x">Partnerships</a>. We specialize in <strong>AWS Machine Learning Systems</strong> and our extended team of Data Scientists and Engineers, have Masters and Ph.D. degrees in Computer Science, Chemistry, and Pharmacology. We also have a parntership with <a href="https://nomicnetworks.com">Nomic Networks</a> to support our Network Security Clients.</p>
+<p>Using <strong>AWS</strong> and <strong>Workbench</strong>, our experts are equipped to deliver tailored solutions that are focused on your project needs and deliverables. For more information please touch base and we'll set up a free initial consultation <a href="mailto:consulting@supercowpowers.com">Workbench Consulting</a></p>
 <h2 id="contact-us">Contact Us</h2>
-<p>Contact us on our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel, we're happy to answer any questions that you might have about <strong>SageWorks</strong> and accelerating your AWS ML Pipelines. You can also send us email at <a href="mailto:sageworks@supercowpowers.com">SageWorks Info</a> or  <a href="mailto:sales@supercowpowers.com">SageWorks Sales</a></p>
+<p>Contact us on our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel, we're happy to answer any questions that you might have about <strong>Workbench</strong> and accelerating your AWS ML Pipelines. You can also send us email at <a href="mailto:workbench@supercowpowers.com">Workbench Info</a> or  <a href="mailto:sales@supercowpowers.com">Workbench Sales</a></p>
 
 
 
diff --git a/enterprise/private_saas/index.html b/enterprise/private_saas/index.html
index 7a7fee327..956d3d822 100644
--- a/enterprise/private_saas/index.html
+++ b/enterprise/private_saas/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Private SaaS - SageWorks</title>
+        <title>Private SaaS - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1819,7 +1819,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1829,7 +1829,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="true">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3106,11 +3106,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3209,10 +3209,10 @@ <h3 id="self-hosted-vs-private-saas-vs-public-saas">Self Hosted vs Private SaaS
 <li><strong>Private SaaS</strong></li>
 <li><strong>Public SaaS</strong></li>
 </ul>
-<p>The other choice, that we're not going to cover here, is whether you use AWS, Azure, GCP, or something else. SageWorks is architected and powered by a broad and rich set of AWS ML Pipeline services. We believe that AWS provides the best set of functionality and APIs for flexible, real world ML architectures.</p>
+<p>The other choice, that we're not going to cover here, is whether you use AWS, Azure, GCP, or something else. Workbench is architected and powered by a broad and rich set of AWS ML Pipeline services. We believe that AWS provides the best set of functionality and APIs for flexible, real world ML architectures.</p>
 <p><img alt="private_saas_compare" src="https://github.com/user-attachments/assets/2f6d3724-e340-4a70-bb97-d05383917cfe"></p>
 <h3 id="resources">Resources</h3>
-<p>See our full presentation on the SageWorks <a href="https://docs.google.com/presentation/d/1f_1gmE4-UAeUDDsoNdzK_d_MxALFXIkxORZwbJBjPq4/edit?usp=sharing``">Private SaaS Architecture</a></p>
+<p>See our full presentation on the Workbench <a href="https://docs.google.com/presentation/d/1f_1gmE4-UAeUDDsoNdzK_d_MxALFXIkxORZwbJBjPq4/edit?usp=sharing``">Private SaaS Architecture</a></p>
 
 
 
diff --git a/enterprise/project_branding/index.html b/enterprise/project_branding/index.html
index 9c9ce4be0..0772aec5c 100644
--- a/enterprise/project_branding/index.html
+++ b/enterprise/project_branding/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Project Branding - SageWorks</title>
+        <title>Project Branding - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1819,7 +1819,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1829,7 +1829,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="true">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3097,11 +3097,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3177,10 +3177,10 @@
 
 
 <h1 id="project-branding">Project Branding</h1>
-<p><img src="https://github.com/SuperCowPowers/sageworks/assets/4806709/4cdd3700-a9d8-441f-a27d-3da1038c7c4d" style="float: right; width: 500px; height: auto; padding: 10px 0px 0px 20px"></p>
-<p>The SageWorks Dashboard can be customized extensively. Using SageWorks <strong>Project Branding</strong> allows you to change page headers, titles, and logos to match your project. All user interfaces will reflect your project name and company logos. </p>
+<p><img src="https://github.com/SuperCowPowers/workbench/assets/4806709/4cdd3700-a9d8-441f-a27d-3da1038c7c4d" style="float: right; width: 500px; height: auto; padding: 10px 0px 0px 20px"></p>
+<p>The Workbench Dashboard can be customized extensively. Using Workbench <strong>Project Branding</strong> allows you to change page headers, titles, and logos to match your project. All user interfaces will reflect your project name and company logos. </p>
 <h2 id="contact-us">Contact Us</h2>
-<p>Contact us on our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel, we're happy to answer any questions that you might have about <strong>SageWorks</strong> and accelerating your AWS ML Pipelines. You can also send us email at <a href="mailto:sageworks@supercowpowers.com">SageWorks Info</a> or  <a href="mailto:sales@supercowpowers.com">SageWorks Sales</a>.</p>
+<p>Contact us on our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel, we're happy to answer any questions that you might have about <strong>Workbench</strong> and accelerating your AWS ML Pipelines. You can also send us email at <a href="mailto:workbench@supercowpowers.com">Workbench Info</a> or  <a href="mailto:sales@supercowpowers.com">Workbench Sales</a>.</p>
 
 
 
diff --git a/enterprise/themes/index.html b/enterprise/themes/index.html
index 82e0bc927..a9b775882 100644
--- a/enterprise/themes/index.html
+++ b/enterprise/themes/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Themes - SageWorks</title>
+        <title>Themes - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-themes" class="md-skip">
+        <a href="#workbench-themes" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1819,7 +1819,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1829,7 +1829,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="true">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3097,11 +3097,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3176,11 +3176,11 @@
                   
 
 
-<h1 id="sageworks-themes">SageWorks Themes</h1>
-<p><img src="https://github.com/SuperCowPowers/sageworks/assets/4806709/4cdd3700-a9d8-441f-a27d-3da1038c7c4d" style="float: right; width: 500px; height: auto; padding: 10px 0px 0px 20px"></p>
-<p>The SageWorks Dashboard can be customized extensively. Using SageWorks <strong>Themes</strong> allows you to customize the User Interfaces to suit your preferences, including completely customized color palettes and fonts. We offer a set of default 'dark' and 'light' themes, but we'll also customize the theme to match your company's color palette and logos.</p>
+<h1 id="workbench-themes">Workbench Themes</h1>
+<p><img src="https://github.com/SuperCowPowers/workbench/assets/4806709/4cdd3700-a9d8-441f-a27d-3da1038c7c4d" style="float: right; width: 500px; height: auto; padding: 10px 0px 0px 20px"></p>
+<p>The Workbench Dashboard can be customized extensively. Using Workbench <strong>Themes</strong> allows you to customize the User Interfaces to suit your preferences, including completely customized color palettes and fonts. We offer a set of default 'dark' and 'light' themes, but we'll also customize the theme to match your company's color palette and logos.</p>
 <h2 id="contact-us">Contact Us</h2>
-<p>Contact us on our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel, we're happy to answer any questions that you might have about <strong>SageWorks</strong> and accelerating your AWS ML Pipelines. You can also send us email at <a href="mailto:sageworks@supercowpowers.com">SageWorks Info</a> or  <a href="mailto:sales@supercowpowers.com">SageWorks Sales</a>.</p>
+<p>Contact us on our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> channel, we're happy to answer any questions that you might have about <strong>Workbench</strong> and accelerating your AWS ML Pipelines. You can also send us email at <a href="mailto:workbench@supercowpowers.com">Workbench Info</a> or  <a href="mailto:sales@supercowpowers.com">Workbench Sales</a>.</p>
 
 
 
diff --git a/getting_started/index.html b/getting_started/index.html
index 4e8d587f3..9f240fa55 100644
--- a/getting_started/index.html
+++ b/getting_started/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Getting Started - SageWorks</title>
+        <title>Getting Started - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -375,7 +375,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1899,7 +1899,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1909,7 +1909,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3128,11 +3128,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3241,40 +3241,40 @@
 
 
 <h1 id="getting-started">Getting Started</h1>
-<p>For the initial setup of SageWorks we'll be using the SageWorks REPL. When you start <code>sageworks</code> it will recognize that it needs to complete the initial configuration and will guide you through that process.</p>
+<p>For the initial setup of Workbench we'll be using the Workbench REPL. When you start <code>workbench</code> it will recognize that it needs to complete the initial configuration and will guide you through that process.</p>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
 <h2 id="initial-setupconfig">Initial Setup/Config</h2>
-<p><strong>Notes:</strong> SageWorks uses your existing AWS account/profile/SSO. So if you don't already have an AWS Profile or SSO Setup you'll need to do that first <a href="../aws_setup/aws_setup/">AWS Setup</a></p>
-<p>Okay so <strong>after</strong> you've completed your <a href="../aws_setup/aws_setup/">AWS Setup</a> you can now install SageWorks.</p>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>&gt; pip install sageworks
-</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>&gt; sageworks &lt;-- This starts the REPL
+<p><strong>Notes:</strong> Workbench uses your existing AWS account/profile/SSO. So if you don't already have an AWS Profile or SSO Setup you'll need to do that first <a href="../aws_setup/aws_setup/">AWS Setup</a></p>
+<p>Okay so <strong>after</strong> you've completed your <a href="../aws_setup/aws_setup/">AWS Setup</a> you can now install Workbench.</p>
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>&gt; pip install workbench
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>&gt; workbench &lt;-- This starts the REPL
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>Welcome to SageWorks!
-</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>Looks like this is your first time using SageWorks...
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>Welcome to Workbench!
+</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>Looks like this is your first time using Workbench...
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a>Let&#39;s get you set up...
 </span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a>AWS_PROFILE: my_aws_profile
-</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a>SAGEWORKS_BUCKET: my-company-sageworks
+</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a>WORKBENCH_BUCKET: my-company-workbench
 </span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a>[optional] REDIS_HOST(localhost): my-redis.cache.amazon (or leave blank)
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>[optional] REDIS_PORT(6379):
 </span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a>[optional] REDIS_PASSWORD():
-</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a>[optional] SAGEWORKS_API_KEY(open_source): my_api_key (or leave blank)
+</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a>[optional] WORKBENCH_API_KEY(open_source): my_api_key (or leave blank)
 </span></code></pre></div>
 <strong>That's It:</strong> You're now all set. This configuration only needs to be <strong>ONCE</strong> :)</p>
 <h3 id="data-scientistsengineers">Data Scientists/Engineers</h3>
 <ul>
-<li>SageWorks REPL: <a href="../repl/">SageWorks REPL</a></li>
-<li>Using SageWorks for ML Pipelines: <a href="../api_classes/overview/">SageWorks API Classes</a></li>
-<li>SCP SageWorks Github: <a href="https://github.com/SuperCowPowers/sageworks">Github Repo</a></li>
+<li>Workbench REPL: <a href="../repl/">Workbench REPL</a></li>
+<li>Using Workbench for ML Pipelines: <a href="../api_classes/overview/">Workbench API Classes</a></li>
+<li>SCP Workbench Github: <a href="https://github.com/SuperCowPowers/workbench">Github Repo</a></li>
 </ul>
 <h3 id="aws-administrators">AWS Administrators</h3>
-<p>For companies that are setting up SageWorks on an internal AWS Account: <a href="../aws_setup/core_stack/">Company AWS Setup</a></p>
+<p>For companies that are setting up Workbench on an internal AWS Account: <a href="../aws_setup/core_stack/">Company AWS Setup</a></p>
 <h2 id="additional-resources">Additional Resources</h2>
 <p><img align="right" src="../images/scp.png" width="180"></p>
 <ul>
-<li>SageWorks Core Classes: <a href="../core_classes/overview/">Core Classes</a></li>
+<li>Workbench Core Classes: <a href="../core_classes/overview/">Core Classes</a></li>
 <li>Consulting Available: <a href="https://www.supercowpowers.com">SuperCowPowers LLC</a></li>
 </ul>
 
diff --git a/glue/index.html b/glue/index.html
index 9c2d23538..22ed35a34 100644
--- a/glue/index.html
+++ b/glue/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS Glue Jobs - SageWorks</title>
+        <title>AWS Glue Jobs - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -2000,9 +2000,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-glue-example" class="md-nav__link">
+  <a href="#workbench-glue-example" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Glue Example
+      Workbench Glue Example
     </span>
   </a>
   
@@ -3136,11 +3136,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3199,9 +3199,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-glue-example" class="md-nav__link">
+  <a href="#workbench-glue-example" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Glue Example
+      Workbench Glue Example
     </span>
   </a>
   
@@ -3262,72 +3262,72 @@ <h1>AWS Glue Jobs</h1>
 
 <div class="admonition tip inline end">
 <p class="admonition-title">AWS Glue Simplified</p>
-<p>AWS Glue Jobs are a great way to automate ETL and data processing. SageWorks takes all the hassle out of creating and debugging Glue Jobs. Follow this guide and empower your Glue Jobs with SageWorks!</p>
+<p>AWS Glue Jobs are a great way to automate ETL and data processing. Workbench takes all the hassle out of creating and debugging Glue Jobs. Follow this guide and empower your Glue Jobs with Workbench!</p>
 </div>
-<p>SageWorks make creating, testing, and debugging of AWS Glue Jobs easy. The exact same <a href="../api_classes/overview/">SageWorks API Classes</a> are used in your Glue Jobs. Also since SageWorks manages the roles for both API and Glue Jobs you'll be able to test new Glue Jobs locally and minimizes surprises when deploying your Glue Job.</p>
+<p>Workbench make creating, testing, and debugging of AWS Glue Jobs easy. The exact same <a href="../api_classes/overview/">Workbench API Classes</a> are used in your Glue Jobs. Also since Workbench manages the roles for both API and Glue Jobs you'll be able to test new Glue Jobs locally and minimizes surprises when deploying your Glue Job.</p>
 <h2 id="glue-job-setup">Glue Job Setup</h2>
-<p>Setting up a AWS Glue Job that uses SageWorks is straight forward. SageWorks can be 'installed' on AWS Glue via the <code>--additional-python-modules</code> parameter and then you can use the Sageworks API just like normal. </p>
-<p><img alt="sageworks_repl" style="float: right; width: 348px; padding-left: 12px; border: 1px solid grey;""
-src="https://github.com/SuperCowPowers/sageworks/assets/4806709/64b72d12-e5d6-411a-9ce5-a64926afceea"></p>
-<p>Here are the settings and a screen shot to guide you. There are several ways to set up and run Glue Jobs, with either the SageWorks-ExecutionRole or using the SageWorksAPIPolicy. Please feel free to contact SageWorks support if you need any help with setting up Glue Jobs.</p>
+<p>Setting up a AWS Glue Job that uses Workbench is straight forward. Workbench can be 'installed' on AWS Glue via the <code>--additional-python-modules</code> parameter and then you can use the Workbench API just like normal. </p>
+<p><img alt="workbench_repl" style="float: right; width: 348px; padding-left: 12px; border: 1px solid grey;""
+src="https://github.com/SuperCowPowers/workbench/assets/4806709/64b72d12-e5d6-411a-9ce5-a64926afceea"></p>
+<p>Here are the settings and a screen shot to guide you. There are several ways to set up and run Glue Jobs, with either the Workbench-ExecutionRole or using the WorkbenchAPIPolicy. Please feel free to contact Workbench support if you need any help with setting up Glue Jobs.</p>
 <ul>
-<li>IAM Role: SageWorks-ExecutionRole</li>
+<li>IAM Role: Workbench-ExecutionRole</li>
 <li>Type: Spark</li>
 <li>Glue Version: Glue 4.0</li>
 <li>Worker Type: G.1X</li>
 <li>Number of Workers: 2</li>
 <li>Job Parameters</li>
-<li>--additional-python-modules: sageworks&gt;=0.4.6</li>
-<li>--sageworks-bucket: &lt;your sageworks bucket&gt;</li>
+<li>--additional-python-modules: workbench&gt;=0.4.6</li>
+<li>--workbench-bucket: &lt;your workbench bucket&gt;</li>
 </ul>
 <div class="admonition tip">
 <p class="admonition-title">Glue IAM Role Details</p>
-<p>If your Glue Jobs already use an existing IAM Role then you can add the <code>SageWorksAPIPolicy</code> to that Role to enable the Glue Job to perform SageWorks API Tasks.</p>
+<p>If your Glue Jobs already use an existing IAM Role then you can add the <code>WorkbenchAPIPolicy</code> to that Role to enable the Glue Job to perform Workbench API Tasks.</p>
 </div>
-<h2 id="sageworks-glue-example">SageWorks Glue Example</h2>
-<p>Anyone familiar with a typical Glue Job should be pleasantly surpised by how simple the example below is. Also SageWorks allows you to test Glue Jobs locally using the same code that you use for script and Notebooks (see <a href="#glue-job-local-testing">Glue Testing</a>)</p>
+<h2 id="workbench-glue-example">Workbench Glue Example</h2>
+<p>Anyone familiar with a typical Glue Job should be pleasantly surpised by how simple the example below is. Also Workbench allows you to test Glue Jobs locally using the same code that you use for script and Notebooks (see <a href="#glue-job-local-testing">Glue Testing</a>)</p>
 <div class="admonition tip">
 <p class="admonition-title">Glue Job Arguments</p>
-<p>AWS Glue Jobs take arguments in the form of <strong>Job Parameters</strong> (see screenshot above). There's a SageWorks utility function <code>get_resolved_options</code> that turns these Job Parameters into a nice dictionary for ease of use.</p>
+<p>AWS Glue Jobs take arguments in the form of <strong>Job Parameters</strong> (see screenshot above). There's a Workbench utility function <code>get_resolved_options</code> that turns these Job Parameters into a nice dictionary for ease of use.</p>
 </div>
 <div class="language-py highlight"><span class="filename">examples/glue_hello_world.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">import</span> <span class="nn">sys</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
-</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># SageWorks Imports</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
-</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="kn">from</span> <span class="nn">sageworks.utils.config_manager</span> <span class="kn">import</span> <span class="n">ConfigManager</span>
-</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="kn">from</span> <span class="nn">sageworks.utils.glue_utils</span> <span class="kn">import</span> <span class="n">get_resolved_options</span>
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># Workbench Imports</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="kn">from</span> <span class="nn">workbench.utils.config_manager</span> <span class="kn">import</span> <span class="n">ConfigManager</span>
+</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="kn">from</span> <span class="nn">workbench.utils.glue_utils</span> <span class="kn">import</span> <span class="n">get_resolved_options</span>
 </span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a>
 </span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="c1"># Convert Glue Job Args to a Dictionary</span>
 </span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="n">glue_args</span> <span class="o">=</span> <span class="n">get_resolved_options</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span>
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>
-</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="c1"># Set the SAGEWORKS_BUCKET for the ConfigManager</span>
+</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="c1"># Set the WORKBENCH_BUCKET for the ConfigManager</span>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="n">cm</span> <span class="o">=</span> <span class="n">ConfigManager</span><span class="p">()</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a><span class="n">cm</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="s2">&quot;SAGEWORKS_BUCKET&quot;</span><span class="p">,</span> <span class="n">glue_args</span><span class="p">[</span><span class="s2">&quot;sageworks-bucket&quot;</span><span class="p">])</span>
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a><span class="n">cm</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="s2">&quot;WORKBENCH_BUCKET&quot;</span><span class="p">,</span> <span class="n">glue_args</span><span class="p">[</span><span class="s2">&quot;workbench-bucket&quot;</span><span class="p">])</span>
 </span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a>
 </span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15" href="#__codelineno-0-15"></a><span class="c1"># Create a new Data Source from an S3 Path</span>
-</span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a><span class="n">source_path</span> <span class="o">=</span> <span class="s2">&quot;s3://sageworks-public-data/common/abalone.csv&quot;</span>
+</span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a><span class="n">source_path</span> <span class="o">=</span> <span class="s2">&quot;s3://workbench-public-data/common/abalone.csv&quot;</span>
 </span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a><span class="n">my_data</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="n">source_path</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">&quot;abalone_glue_test&quot;</span><span class="p">)</span>
 </span></code></pre></div>
 <h2 id="glue-example-2">Glue Example 2</h2>
 <p>This example takes two 'Job Parameters'</p>
 <ul>
-<li>--sageworks-bucket : &lt;your sageworks bucket&gt;</li>
+<li>--workbench-bucket : &lt;your workbench bucket&gt;</li>
 <li>--input-s3-path : &lt;your S3 input path&gt;</li>
 </ul>
-<p>The example will convert all CSV files in an S3 bucket/prefix and load them up as DataSources in SageWorks.</p>
+<p>The example will convert all CSV files in an S3 bucket/prefix and load them up as DataSources in Workbench.</p>
 <div class="language-py highlight"><span class="filename">examples/glue_load_s3_bucket.py</span><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">import</span> <span class="nn">sys</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
-</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="c1"># SageWorks Imports</span>
-</span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="kn">from</span> <span class="nn">sageworks.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
-</span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a><span class="kn">from</span> <span class="nn">sageworks.utils.config_manager</span> <span class="kn">import</span> <span class="n">ConfigManager</span>
-</span><span id="__span-1-6"><a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a><span class="kn">from</span> <span class="nn">sageworks.utils.glue_utils</span> <span class="kn">import</span> <span class="n">get_resolved_options</span><span class="p">,</span> <span class="n">list_s3_files</span>
+</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="c1"># Workbench Imports</span>
+</span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a><span class="kn">from</span> <span class="nn">workbench.api.data_source</span> <span class="kn">import</span> <span class="n">DataSource</span>
+</span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a><span class="kn">from</span> <span class="nn">workbench.utils.config_manager</span> <span class="kn">import</span> <span class="n">ConfigManager</span>
+</span><span id="__span-1-6"><a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a><span class="kn">from</span> <span class="nn">workbench.utils.glue_utils</span> <span class="kn">import</span> <span class="n">get_resolved_options</span><span class="p">,</span> <span class="n">list_s3_files</span>
 </span><span id="__span-1-7"><a id="__codelineno-1-7" name="__codelineno-1-7" href="#__codelineno-1-7"></a>
 </span><span id="__span-1-8"><a id="__codelineno-1-8" name="__codelineno-1-8" href="#__codelineno-1-8"></a><span class="c1"># Convert Glue Job Args to a Dictionary</span>
 </span><span id="__span-1-9"><a id="__codelineno-1-9" name="__codelineno-1-9" href="#__codelineno-1-9"></a><span class="n">glue_args</span> <span class="o">=</span> <span class="n">get_resolved_options</span><span class="p">(</span><span class="n">sys</span><span class="o">.</span><span class="n">argv</span><span class="p">)</span>
 </span><span id="__span-1-10"><a id="__codelineno-1-10" name="__codelineno-1-10" href="#__codelineno-1-10"></a>
-</span><span id="__span-1-11"><a id="__codelineno-1-11" name="__codelineno-1-11" href="#__codelineno-1-11"></a><span class="c1"># Set the SAGEWORKS_BUCKET for the ConfigManager</span>
+</span><span id="__span-1-11"><a id="__codelineno-1-11" name="__codelineno-1-11" href="#__codelineno-1-11"></a><span class="c1"># Set the WORKBENCH_BUCKET for the ConfigManager</span>
 </span><span id="__span-1-12"><a id="__codelineno-1-12" name="__codelineno-1-12" href="#__codelineno-1-12"></a><span class="n">cm</span> <span class="o">=</span> <span class="n">ConfigManager</span><span class="p">()</span>
-</span><span id="__span-1-13"><a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a><span class="n">cm</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="s2">&quot;SAGEWORKS_BUCKET&quot;</span><span class="p">,</span> <span class="n">glue_args</span><span class="p">[</span><span class="s2">&quot;sageworks-bucket&quot;</span><span class="p">])</span>
+</span><span id="__span-1-13"><a id="__codelineno-1-13" name="__codelineno-1-13" href="#__codelineno-1-13"></a><span class="n">cm</span><span class="o">.</span><span class="n">set_config</span><span class="p">(</span><span class="s2">&quot;WORKBENCH_BUCKET&quot;</span><span class="p">,</span> <span class="n">glue_args</span><span class="p">[</span><span class="s2">&quot;workbench-bucket&quot;</span><span class="p">])</span>
 </span><span id="__span-1-14"><a id="__codelineno-1-14" name="__codelineno-1-14" href="#__codelineno-1-14"></a>
 </span><span id="__span-1-15"><a id="__codelineno-1-15" name="__codelineno-1-15" href="#__codelineno-1-15"></a><span class="c1"># List all the CSV files in the given S3 Path</span>
 </span><span id="__span-1-16"><a id="__codelineno-1-16" name="__codelineno-1-16" href="#__codelineno-1-16"></a><span class="n">input_s3_path</span> <span class="o">=</span> <span class="n">glue_args</span><span class="p">[</span><span class="s2">&quot;input-s3-path&quot;</span><span class="p">]</span>
@@ -3337,8 +3337,8 @@ <h2 id="glue-example-2">Glue Example 2</h2>
 </span><span id="__span-1-20"><a id="__codelineno-1-20" name="__codelineno-1-20" href="#__codelineno-1-20"></a>    <span class="n">my_data</span> <span class="o">=</span> <span class="n">DataSource</span><span class="p">(</span><span class="n">input_file</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
 </span></code></pre></div>
 <h2 id="exception-log-forwarding">Exception Log Forwarding</h2>
-<p>When a Glue Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use SageWorks log forwarding the exception/stack will be forwarded to CloudWatch.</p>
-<p><div class="language-py highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">sageworks.utils.sageworks_logging</span> <span class="kn">import</span> <span class="n">exception_log_forward</span>
+<p>When a Glue Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use Workbench log forwarding the exception/stack will be forwarded to CloudWatch.</p>
+<p><div class="language-py highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a><span class="kn">from</span> <span class="nn">workbench.utils.workbench_logging</span> <span class="kn">import</span> <span class="n">exception_log_forward</span>
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a><span class="k">with</span> <span class="n">exception_log_forward</span><span class="p">():</span>
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a>   <span class="o">&lt;</span><span class="n">my</span> <span class="n">glue</span> <span class="n">code</span><span class="o">&gt;</span>
@@ -3348,19 +3348,19 @@ <h2 id="exception-log-forwarding">Exception Log Forwarding</h2>
 </span></code></pre></div>
 The <code>exception_log_forward</code> sets up a <strong>context manager</strong> that will trap exceptions and forward the exception/stack to CloudWatch for diagnosis. </p>
 <h2 id="glue-job-local-testing">Glue Job Local Testing</h2>
-<p>Glue Power without the Pain. SageWorks manages the AWS Execution Role, so local API and Glue Jobs will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Glue Jobs a breeze.</p>
-<div class="language-shell highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="nb">export</span><span class="w"> </span><span class="nv">SAGEWORKS_CONFIG</span><span class="o">=</span>&lt;your<span class="w"> </span>config&gt;<span class="w">  </span><span class="c1"># Only if not already set up</span>
-</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>python<span class="w"> </span>my_glue_job.py<span class="w"> </span>--sageworks-bucket<span class="w"> </span>&lt;your<span class="w"> </span>bucket&gt;
+<p>Glue Power without the Pain. Workbench manages the AWS Execution Role, so local API and Glue Jobs will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Glue Jobs a breeze.</p>
+<div class="language-shell highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a><span class="nb">export</span><span class="w"> </span><span class="nv">WORKBENCH_CONFIG</span><span class="o">=</span>&lt;your<span class="w"> </span>config&gt;<span class="w">  </span><span class="c1"># Only if not already set up</span>
+</span><span id="__span-3-2"><a id="__codelineno-3-2" name="__codelineno-3-2" href="#__codelineno-3-2"></a>python<span class="w"> </span>my_glue_job.py<span class="w"> </span>--workbench-bucket<span class="w"> </span>&lt;your<span class="w"> </span>bucket&gt;
 </span></code></pre></div>
 <h2 id="additional-resources">Additional Resources</h2>
 <ul>
-<li>SageWorks Glue Jobs: <a href="https://docs.google.com/presentation/d/1Bdcve27BDLbUkslZJAc1OrG6VkDopEtnL8Wh8HaLrco/edit?usp=sharing">SageWorks Glue</a></li>
-<li>Setting up SageWorks on your AWS Account: <a href="../aws_setup/core_stack/">AWS Setup</a></li>
-<li>Using SageWorks for ML Pipelines: <a href="../api_classes/overview/">SageWorks API Classes</a></li>
+<li>Workbench Glue Jobs: <a href="https://docs.google.com/presentation/d/1Bdcve27BDLbUkslZJAc1OrG6VkDopEtnL8Wh8HaLrco/edit?usp=sharing">Workbench Glue</a></li>
+<li>Setting up Workbench on your AWS Account: <a href="../aws_setup/core_stack/">AWS Setup</a></li>
+<li>Using Workbench for ML Pipelines: <a href="../api_classes/overview/">Workbench API Classes</a></li>
 </ul>
 <p><img align="right" src="../images/scp.png" width="180"></p>
 <ul>
-<li>SageWorks Core Classes: <a href="../core_classes/overview/">Core Classes</a></li>
+<li>Workbench Core Classes: <a href="../core_classes/overview/">Core Classes</a></li>
 <li>Consulting Available: <a href="https://www.supercowpowers.com">SuperCowPowers LLC</a></li>
 </ul>
 
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diff --git a/images/sageworks_concepts.png b/images/workbench_concepts.png
similarity index 100%
rename from images/sageworks_concepts.png
rename to images/workbench_concepts.png
diff --git a/index.html b/index.html
index b26ec6eb5..4d7622191 100644
--- a/index.html
+++ b/index.html
@@ -18,7 +18,7 @@
     
     
       
-        <title>SageWorks</title>
+        <title>Workbench</title>
       
     
     
@@ -72,7 +72,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#welcome-to-sageworks" class="md-skip">
+        <a href="#welcome-to-workbench" class="md-skip">
           Skip to content
         </a>
       
@@ -88,7 +88,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -102,7 +102,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -209,13 +209,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -409,7 +409,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1933,7 +1933,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1943,7 +1943,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3162,11 +3162,11 @@
   
   
     <li class="md-nav__item">
-      <a href="admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3310,11 +3310,11 @@
                   
 
 
-<h1 id="welcome-to-sageworks">Welcome to SageWorks</h1>
-<p>The SageWorks framework makes AWS® both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Jobs, Athena, Feature Store, Models, and Endpoints. SageWorks makes it easy to build production ready, AWS powered, machine learning pipelines.</p>
+<h1 id="welcome-to-workbench">Welcome to Workbench</h1>
+<p>The Workbench framework makes AWS® both easier to use and more powerful. Workbench handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, Workbench makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Jobs, Athena, Feature Store, Models, and Endpoints. Workbench makes it easy to build production ready, AWS powered, machine learning pipelines.</p>
 <figure style="float: right; width: 500px;">
-<img alt="sageworks_new_light" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4">
-<figcaption>SageWorks Dashboard: AWS Pipelines in a Whole New Light!</figcaption>
+<img alt="workbench_new_light" src="https://github.com/SuperCowPowers/workbench/assets/4806709/5f8b32a2-ed72-45f2-bd96-91b7bbbccff4">
+<figcaption>Workbench Dashboard: AWS Pipelines in a Whole New Light!</figcaption>
 </figure>
 
 <h3 id="full-aws-overview">Full AWS OverView</h3>
@@ -3333,11 +3333,11 @@ <h3 id="drill-down-views">Drill-Down Views</h3>
 </ul>
 <h2 id="private-saas-architecture">Private SaaS Architecture</h2>
 <p><strong>Secure your Data, Empower your ML Pipelines</strong></p>
-<p>SageWorks is architected as a Private SaaS. This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. SageWorks deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support, both components and full web interfaces. By using SageWorks, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. <a href="https://docs.google.com/presentation/d/1f_1gmE4-UAeUDDsoNdzK_d_MxALFXIkxORZwbJBjPq4/edit?usp=sharing">SageWorks Private SaaS Architecture</a></p>
+<p>Workbench is architected as a Private SaaS. This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. Workbench deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support, both components and full web interfaces. By using Workbench, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. <a href="https://docs.google.com/presentation/d/1f_1gmE4-UAeUDDsoNdzK_d_MxALFXIkxORZwbJBjPq4/edit?usp=sharing">Workbench Private SaaS Architecture</a></p>
 <h2 id="dashboard-and-api">Dashboard and API</h2>
-<p>The SageWorks package has two main components, a Web Interface that provides visibility into AWS ML PIpelines and a Python API that makes creation and usage of the AWS ML Services easier than using/learning the services directly.</p>
+<p>The Workbench package has two main components, a Web Interface that provides visibility into AWS ML PIpelines and a Python API that makes creation and usage of the AWS ML Services easier than using/learning the services directly.</p>
 <h3 id="web-interfaces">Web Interfaces</h3>
-<p>The SageWorks Dashboard has a set of web interfaces that give visibility into the AWS Glue and SageMaker Services. There are currently 5 web interfaces available:</p>
+<p>The Workbench Dashboard has a set of web interfaces that give visibility into the AWS Glue and SageMaker Services. There are currently 5 web interfaces available:</p>
 <ul>
 <li><strong>Top Level Dashboard:</strong> Shows all AWS ML Artifacts (Glue and SageMaker)</li>
 <li><strong>DataSources:</strong> DataSource Column Details, Distributions and Correlations</li>
@@ -3346,15 +3346,15 @@ <h3 id="web-interfaces">Web Interfaces</h3>
 <li><strong>Endpoints:</strong> Endpoint details, realtime charts of endpoint performance and latency</li>
 </ul>
 <h3 id="python-api">Python API</h3>
-<p>SageWorks API Documentation: <a href="api_classes/overview/">SageWorks API Classes</a> </p>
+<p>Workbench API Documentation: <a href="api_classes/overview/">Workbench API Classes</a> </p>
 <p>The main functionality of the Python API is to encapsulate and manage a set of AWS services underneath a Python Object interface. The Python Classes are used to create and interact with Machine Learning Pipeline Artifacts.</p>
 <h2 id="getting-started">Getting Started</h2>
-<p>SageWorks will need some initial setup when you first start using it. See our <a href="getting_started/">Getting Started</a> guide on how to connect SageWorks to your AWS Account.</p>
+<p>Workbench will need some initial setup when you first start using it. See our <a href="getting_started/">Getting Started</a> guide on how to connect Workbench to your AWS Account.</p>
 <h2 id="additional-resources">Additional Resources</h2>
 <p><img align="right" src="images/scp.png" width="180"></p>
 <ul>
 <li>Getting Started: <a href="getting_started/">Getting Started</a> </li>
-<li>SageWorks API Classes: <a href="api_classes/overview/">API Classes</a></li>
+<li>Workbench API Classes: <a href="api_classes/overview/">API Classes</a></li>
 <li>Consulting Available: <a href="https://www.supercowpowers.com">SuperCowPowers LLC</a></li>
 </ul>
 
diff --git a/lambda_layer/index.html b/lambda_layer/index.html
index ff289edbd..2f7204152 100644
--- a/lambda_layer/index.html
+++ b/lambda_layer/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>AWS Lambda Layer - SageWorks</title>
+        <title>AWS Lambda Layer - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -2035,9 +2035,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-lambda-example" class="md-nav__link">
+  <a href="#workbench-lambda-example" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Lambda Example
+      Workbench Lambda Example
     </span>
   </a>
   
@@ -3142,11 +3142,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3220,9 +3220,9 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-lambda-example" class="md-nav__link">
+  <a href="#workbench-lambda-example" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Lambda Example
+      Workbench Lambda Example
     </span>
   </a>
   
@@ -3273,25 +3273,25 @@
   <h1>AWS Lambda Layer</h1>
 
 <div class="admonition tip inline end">
-<p class="admonition-title">SageWorks Lambda Layers</p>
-<p>AWS Lambda Jobs are a great way to spin up data processing jobs. Follow this guide and empower AWS Lambda with SageWorks!</p>
+<p class="admonition-title">Workbench Lambda Layers</p>
+<p>AWS Lambda Jobs are a great way to spin up data processing jobs. Follow this guide and empower AWS Lambda with Workbench!</p>
 </div>
-<p>SageWorks makes creating, testing, and debugging of AWS Lambda Functions easy. The exact same <a href="../api_classes/overview/">SageWorks API Classes</a> are used in your AWS Lambda Functions. Also since SageWorks manages the access policies you'll be able to test new Lambda Jobs locally and minimizes surprises when deploying.</p>
+<p>Workbench makes creating, testing, and debugging of AWS Lambda Functions easy. The exact same <a href="../api_classes/overview/">Workbench API Classes</a> are used in your AWS Lambda Functions. Also since Workbench manages the access policies you'll be able to test new Lambda Jobs locally and minimizes surprises when deploying.</p>
 <div class="admonition warning inline end">
 <p class="admonition-title">Work In Progress</p>
-<p>The SageWorks Lambda Layers are a great way to use SageWorks but they are still in 'beta' mode so please let us know if you have any issues.</p>
+<p>The Workbench Lambda Layers are a great way to use Workbench but they are still in 'beta' mode so please let us know if you have any issues.</p>
 </div>
 <h2 id="lambda-job-setup">Lambda Job Setup</h2>
-<p>Setting up a AWS Lambda Job that uses SageWorks is straight forward. SageWorks can be 'installed' using a Lambda Layer and then you can use the Sageworks API just like normal.</p>
-<p>Here are the ARNs for the current SageWorks Lambda Layers, please note they are specified with region and Python version in the name, so if your lambda is us-east-1, python 3.12, pick this ARN with those values in it.</p>
+<p>Setting up a AWS Lambda Job that uses Workbench is straight forward. Workbench can be 'installed' using a Lambda Layer and then you can use the Workbench API just like normal.</p>
+<p>Here are the ARNs for the current Workbench Lambda Layers, please note they are specified with region and Python version in the name, so if your lambda is us-east-1, python 3.12, pick this ARN with those values in it.</p>
 <h3 id="python-312-if-you-need-another-versionregion-let-us-know">Python 3.12 (if you need another version/region let us know)</h3>
 <p><strong>us-east-1</strong></p>
 <ul>
-<li>arn:aws:lambda:us-east-1:507740646243:layer:sageworks_lambda_layer-us-east-1-python312:7</li>
+<li>arn:aws:lambda:us-east-1:507740646243:layer:workbench_lambda_layer-us-east-1-python312:7</li>
 </ul>
 <p><strong>us-west-2</strong></p>
 <ul>
-<li>arn:aws:lambda:us-west-2:507740646243:layer:sageworks_lambda_layer-us-west-2-python312:8</li>
+<li>arn:aws:lambda:us-west-2:507740646243:layer:workbench_lambda_layer-us-west-2-python312:8</li>
 </ul>
 <p><strong>Note:</strong> If you're using lambdas on a different region or with a different Python version, just let us know and we'll publish some additional layers.</p>
 <p><img alt="lambda_layer"  padding: 20px; border: 1px solid grey;""
@@ -3302,30 +3302,30 @@ <h3 id="python-312-if-you-need-another-versionregion-let-us-know">Python 3.12 (i
 <li>Memory: 4096</li>
 <li>Ephemeral storage: 2048</li>
 </ul>
-<p><strong>Set the SAGEWORKS_BUCKET ENV</strong>
-SageWorks will need to know what bucket to work out of, so go into the Configuration...Environment Variables... and add one for the SageWorks bucket that your are using for AWS Account (dev, prod, etc).
+<p><strong>Set the WORKBENCH_BUCKET ENV</strong>
+Workbench will need to know what bucket to work out of, so go into the Configuration...Environment Variables... and add one for the Workbench bucket that your are using for AWS Account (dev, prod, etc).
 <img alt="lambda_layer"  padding: 20px; border: 1px solid grey;""
 src="https://github.com/user-attachments/assets/a5afdaff-188f-45ca-bd66-1ab62d7b0b2a"></p>
 <div class="admonition tip">
 <p class="admonition-title">Lambda Role Details</p>
-<p>If your Lambda Function already use an existing IAM Role then you can add the SageWorks policies to that Role to enable the Lambda Job to perform SageWorks API Tasks. See <a href="https://docs.google.com/presentation/d/1_KwbaBsyBoiWW_8SEallHg8RMsi9FdK10dr2wwzo3CA/edit?usp=sharing">SageWorks Access Controls</a></p>
+<p>If your Lambda Function already use an existing IAM Role then you can add the Workbench policies to that Role to enable the Lambda Job to perform Workbench API Tasks. See <a href="https://docs.google.com/presentation/d/1_KwbaBsyBoiWW_8SEallHg8RMsi9FdK10dr2wwzo3CA/edit?usp=sharing">Workbench Access Controls</a></p>
 </div>
-<h2 id="sageworks-lambda-example">SageWorks Lambda Example</h2>
-<p>Here's a simple example of using SageWorks in your Lambda Function. </p>
+<h2 id="workbench-lambda-example">Workbench Lambda Example</h2>
+<p>Here's a simple example of using Workbench in your Lambda Function. </p>
 <div class="admonition warning">
-<p class="admonition-title">SageWorks Layer is Compressed</p>
-<p>The SageWorks Lambda Layer is compressed (<em>to fit all the awesome</em>). This means that the <code>load_lambda_layer()</code> method must be called before using any other SageWorks imports, see the example below. If you do not do this you'll probably get a <code>No module named 'numpy'</code> error or something like that.</p>
+<p class="admonition-title">Workbench Layer is Compressed</p>
+<p>The Workbench Lambda Layer is compressed (<em>to fit all the awesome</em>). This means that the <code>load_lambda_layer()</code> method must be called before using any other Workbench imports, see the example below. If you do not do this you'll probably get a <code>No module named 'numpy'</code> error or something like that.</p>
 </div>
 <div class="language-py highlight"><span class="filename">examples/lambda_hello_world.py</span><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="kn">import</span> <span class="nn">json</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a><span class="kn">from</span> <span class="nn">pprint</span> <span class="kn">import</span> <span class="n">pprint</span>
-</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">sageworks.utils.lambda_utils</span> <span class="kn">import</span> <span class="n">load_lambda_layer</span>
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="kn">from</span> <span class="nn">workbench.utils.lambda_utils</span> <span class="kn">import</span> <span class="n">load_lambda_layer</span>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>
-</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="c1"># Load/Decompress the SageWorks Lambda Layer</span>
+</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a><span class="c1"># Load/Decompress the Workbench Lambda Layer</span>
 </span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="n">load_lambda_layer</span><span class="p">()</span>
 </span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a>
-</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="c1"># After &#39;load_lambda_layer()&#39; we can use other SageWorks imports</span>
-</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">Meta</span>
-</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="kn">from</span> <span class="nn">sageworks.api</span> <span class="kn">import</span> <span class="n">Model</span> 
+</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="c1"># After &#39;load_lambda_layer()&#39; we can use other Workbench imports</span>
+</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">Meta</span>
+</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="kn">from</span> <span class="nn">workbench.api</span> <span class="kn">import</span> <span class="n">Model</span> 
 </span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a>
 </span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="k">def</span> <span class="nf">lambda_handler</span><span class="p">(</span><span class="n">event</span><span class="p">,</span> <span class="n">context</span><span class="p">):</span>
 </span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a>
@@ -3347,8 +3347,8 @@ <h2 id="sageworks-lambda-example">SageWorks Lambda Example</h2>
 </span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29" href="#__codelineno-0-29"></a>    <span class="p">}</span>
 </span></code></pre></div>
 <h2 id="exception-log-forwarding">Exception Log Forwarding</h2>
-<p>When a Lambda Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use SageWorks log forwarding the exception/stack will be forwarded to CloudWatch.</p>
-<p><div class="language-py highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">sageworks.utils.sageworks_logging</span> <span class="kn">import</span> <span class="n">exception_log_forward</span>
+<p>When a Lambda Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use Workbench log forwarding the exception/stack will be forwarded to CloudWatch.</p>
+<p><div class="language-py highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a><span class="kn">from</span> <span class="nn">workbench.utils.workbench_logging</span> <span class="kn">import</span> <span class="n">exception_log_forward</span>
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
 </span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="k">with</span> <span class="n">exception_log_forward</span><span class="p">():</span>
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a>   <span class="o">&lt;</span><span class="n">my</span> <span class="k">lambda</span> <span class="n">code</span><span class="o">&gt;</span>
@@ -3358,18 +3358,18 @@ <h2 id="exception-log-forwarding">Exception Log Forwarding</h2>
 </span></code></pre></div>
 The <code>exception_log_forward</code> sets up a <strong>context manager</strong> that will trap exceptions and forward the exception/stack to CloudWatch for diagnosis. </p>
 <h2 id="lambda-function-local-testing">Lambda Function Local Testing</h2>
-<p>Lambda Power without the Pain. SageWorks manages the AWS Execution Role/Policies, so local API and Lambda Functions will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Lambda Functions a breeze.</p>
-<div class="language-shell highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>python<span class="w"> </span>my_lambda_function.py<span class="w"> </span>--sageworks-bucket<span class="w"> </span>&lt;your<span class="w"> </span>bucket&gt;
+<p>Lambda Power without the Pain. Workbench manages the AWS Execution Role/Policies, so local API and Lambda Functions will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Lambda Functions a breeze.</p>
+<div class="language-shell highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>python<span class="w"> </span>my_lambda_function.py<span class="w"> </span>--workbench-bucket<span class="w"> </span>&lt;your<span class="w"> </span>bucket&gt;
 </span></code></pre></div>
 <h2 id="additional-resources">Additional Resources</h2>
 <ul>
-<li>SageWorks Access Management: <a href="https://docs.google.com/presentation/d/1_KwbaBsyBoiWW_8SEallHg8RMsi9FdK10dr2wwzo3CA/edit?usp=sharing">SageWorks Access Management</a></li>
-<li>Setting up SageWorks on your AWS Account: <a href="../aws_setup/core_stack/">AWS Setup</a></li>
+<li>Workbench Access Management: <a href="https://docs.google.com/presentation/d/1_KwbaBsyBoiWW_8SEallHg8RMsi9FdK10dr2wwzo3CA/edit?usp=sharing">Workbench Access Management</a></li>
+<li>Setting up Workbench on your AWS Account: <a href="../aws_setup/core_stack/">AWS Setup</a></li>
 </ul>
 <p><img align="right" src="../images/scp.png" width="180"></p>
 <ul>
 <li>
-<p>Using SageWorks for ML Pipelines: <a href="../api_classes/overview/">SageWorks API Classes</a></p>
+<p>Using Workbench for ML Pipelines: <a href="../api_classes/overview/">Workbench API Classes</a></p>
 </li>
 <li>
 <p>Consulting Available: <a href="https://www.supercowpowers.com">SuperCowPowers LLC</a></p>
diff --git a/misc/faq/index.html b/misc/faq/index.html
index 0d743fcd3..f1346bb2d 100644
--- a/misc/faq/index.html
+++ b/misc/faq/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>FAQ - SageWorks</title>
+        <title>FAQ - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-faq" class="md-skip">
+        <a href="#workbench-faq" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3113,11 +3113,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3210,10 +3210,10 @@
                   
 
 
-<h1 id="sageworks-faq">SageWorks: FAQ</h1>
+<h1 id="workbench-faq">Workbench: FAQ</h1>
 <div class="admonition tip question">
 <p class="admonition-title">Artifact and Column Naming?</p>
-<p>You might have noticed that SageWorks has some unintuitive constraints when naming Artifacts and restrictions on column names. All of these restrictions come from AWS. SageWorks uses Glue, Athena, Feature Store, Models and Endpoints, each of these services have their own constraints, SageWorks simply 'reflects' those contraints.</p>
+<p>You might have noticed that Workbench has some unintuitive constraints when naming Artifacts and restrictions on column names. All of these restrictions come from AWS. Workbench uses Glue, Athena, Feature Store, Models and Endpoints, each of these services have their own constraints, Workbench simply 'reflects' those contraints.</p>
 </div>
 <h2 id="naming-underscores-dashes-and-lower-case">Naming: Underscores, Dashes, and Lower Case</h2>
 <p>Data Sources and Feature Sets must adhere to AWS restrictions on table names and columns names (here is a snippet from the AWS documentation)</p>
@@ -3227,7 +3227,7 @@ <h2 id="naming-underscores-dashes-and-lower-case">Naming: Underscores, Dashes, a
 </blockquote>
 <p>For more info see: <a href="https://docs.aws.amazon.com/athena/latest/ug/glue-best-practices.html#schema-names">Glue Best Practices</a></p>
 <h2 id="datasourcefeatureset-use-_-and-modelendpoint-use-">DataSource/FeatureSet use '_'  and Model/Endpoint use '-'</h2>
-<p>You may notice that DataSource and FeatureSet uuid/name examples have underscores but the model and endpoints have dashes. Yes, it’s super annoying to have one convention for DataSources and FeatureSets and another for Models and Endpoints but this is an AWS restriction and not something that SageWorks can control.</p>
+<p>You may notice that DataSource and FeatureSet uuid/name examples have underscores but the model and endpoints have dashes. Yes, it’s super annoying to have one convention for DataSources and FeatureSets and another for Models and Endpoints but this is an AWS restriction and not something that Workbench can control.</p>
 <p><strong>DataSources and FeatureSet:</strong> Underscores. You cannot use a dash because both classes use Athena for Storage and Athena tables names cannot have a dash.</p>
 <p><strong>Models and Endpoints:</strong> Dashes. You cannot use an underscores because AWS imposes a restriction on the naming.</p>
 <h2 id="additional-information-on-the-lower-case-issue">Additional information on the lower case issue</h2>
@@ -3239,8 +3239,8 @@ <h2 id="additional-information-on-the-lower-case-issue">Additional information o
 </ul>
 <p><strong>Scripts to Reproduce</strong></p>
 <ul>
-<li><a href="https://github.com/SuperCowPowers/sageworks/blob/main/scripts/athena_ddl_mixed_case.py">scripts/athena_ddl_mixed_case.py</a></li>
-<li><a href="https://github.com/SuperCowPowers/sageworks/blob/main/scripts/glue_mixed_case.py">scripts/glue_mixed_case.py</a></li>
+<li><a href="https://github.com/SuperCowPowers/workbench/blob/main/scripts/athena_ddl_mixed_case.py">scripts/athena_ddl_mixed_case.py</a></li>
+<li><a href="https://github.com/SuperCowPowers/workbench/blob/main/scripts/glue_mixed_case.py">scripts/glue_mixed_case.py</a></li>
 </ul>
 
 
diff --git a/misc/general_info/index.html b/misc/general_info/index.html
index f76f6f444..3d9f4dec1 100644
--- a/misc/general_info/index.html
+++ b/misc/general_info/index.html
@@ -16,7 +16,7 @@
     
     
       
-        <title>General info - SageWorks</title>
+        <title>General info - Workbench</title>
       
     
     
@@ -86,7 +86,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -100,7 +100,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -207,13 +207,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -289,7 +289,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1813,7 +1813,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1823,7 +1823,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3042,11 +3042,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3106,27 +3106,27 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks-the-scientists-workbench-powered-by-aws-for-scalability-flexibility-and-security" class="md-nav__link">
+  <a href="#workbench-the-scientists-workbench-powered-by-aws-for-scalability-flexibility-and-security" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks: The scientist's workbench powered by AWS® for scalability, flexibility, and security.
+      Workbench: The scientist's workbench powered by AWS® for scalability, flexibility, and security.
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks-documentation" class="md-nav__link">
+  <a href="#workbench-documentation" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Documentation
+      Workbench Documentation
     </span>
   </a>
   
 </li>
         
           <li class="md-nav__item">
-  <a href="#full-sageworks-overview" class="md-nav__link">
+  <a href="#full-workbench-overview" class="md-nav__link">
     <span class="md-ellipsis">
-      Full SageWorks OverView
+      Full Workbench OverView
     </span>
   </a>
   
@@ -3138,13 +3138,13 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#why-sageworks" class="md-nav__link">
+  <a href="#why-workbench" class="md-nav__link">
     <span class="md-ellipsis">
-      Why SageWorks?
+      Why Workbench?
     </span>
   </a>
   
-    <nav class="md-nav" aria-label="Why SageWorks?">
+    <nav class="md-nav" aria-label="Why Workbench?">
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
@@ -3172,9 +3172,9 @@
       <ul class="md-nav__list">
         
           <li class="md-nav__item">
-  <a href="#sageworks-zen" class="md-nav__link">
+  <a href="#workbench-zen" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Zen
+      Workbench Zen
     </span>
   </a>
   
@@ -3199,9 +3199,9 @@
 </li>
         
           <li class="md-nav__item">
-  <a href="#sageworks-alpha-testers-wanted" class="md-nav__link">
+  <a href="#workbench-alpha-testers-wanted" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Alpha Testers Wanted
+      Workbench Alpha Testers Wanted
     </span>
   </a>
   
@@ -3231,20 +3231,20 @@ <h1>General info</h1>
 
 <h2 id="general-info">General Info</h2>
 <p align="center">
-<img width="720" alt="DataSource_EDA" aligh="right" src="https://github.com/SuperCowPowers/sageworks/assets/4806709/a5b063f7-a291-4dba-a766-b2e906920066">
+<img width="720" alt="DataSource_EDA" aligh="right" src="https://github.com/SuperCowPowers/workbench/assets/4806709/a5b063f7-a291-4dba-a766-b2e906920066">
 </p>
 
-<h4 id="sageworks-the-scientists-workbench-powered-by-aws-for-scalability-flexibility-and-security">SageWorks: The scientist's workbench powered by AWS® for scalability, flexibility, and security.</h4>
-<p>SageWorks is a medium granularity framework that manages and aggregates AWS® Services into classes and concepts. When you use SageWorks you think about <strong>DataSources</strong>, <strong>FeatureSets</strong>, <strong>Models</strong>, and <strong>Endpoints</strong>. Underneath the hood those classes handle all the details around updating and managing a <strong>complex set of AWS Services</strong>. All the power and none of the pain so that your team can <strong>Do Science Faster!</strong></p>
-<h3 id="sageworks-documentation">SageWorks Documentation</h3>
-<p>See our Python API and AWS documentation here: <a href="https://supercowpowers.github.io/sageworks/">SageWorks Documentation</a></p>
-<h3 id="full-sageworks-overview">Full SageWorks OverView</h3>
-<p><a href="https://docs.google.com/presentation/d/1ZiSy4ulEx5gfNQS76yRv8vgkehJ9gXRJ1PulutLKzis/edit?usp=sharing">SageWorks Architected FrameWork</a></p>
-<h2 id="why-sageworks">Why SageWorks?</h2>
+<h4 id="workbench-the-scientists-workbench-powered-by-aws-for-scalability-flexibility-and-security">Workbench: The scientist's workbench powered by AWS® for scalability, flexibility, and security.</h4>
+<p>Workbench is a medium granularity framework that manages and aggregates AWS® Services into classes and concepts. When you use Workbench you think about <strong>DataSources</strong>, <strong>FeatureSets</strong>, <strong>Models</strong>, and <strong>Endpoints</strong>. Underneath the hood those classes handle all the details around updating and managing a <strong>complex set of AWS Services</strong>. All the power and none of the pain so that your team can <strong>Do Science Faster!</strong></p>
+<h3 id="workbench-documentation">Workbench Documentation</h3>
+<p>See our Python API and AWS documentation here: <a href="https://supercowpowers.github.io/workbench/">Workbench Documentation</a></p>
+<h3 id="full-workbench-overview">Full Workbench OverView</h3>
+<p><a href="https://docs.google.com/presentation/d/1ZiSy4ulEx5gfNQS76yRv8vgkehJ9gXRJ1PulutLKzis/edit?usp=sharing">Workbench Architected FrameWork</a></p>
+<h2 id="why-workbench">Why Workbench?</h2>
 <ul>
 <li>The AWS SageMaker® ecosystem is <strong>awesome</strong> but has a large number of services with significant complexity</li>
-<li>SageWorks provides <strong>rapid prototyping</strong> through easy to use <strong>classes</strong> and <strong>transforms</strong></li>
-<li>SageWorks provides <strong>visibility</strong> and <strong>transparency</strong> into AWS SageMaker® Pipelines<ul>
+<li>Workbench provides <strong>rapid prototyping</strong> through easy to use <strong>classes</strong> and <strong>transforms</strong></li>
+<li>Workbench provides <strong>visibility</strong> and <strong>transparency</strong> into AWS SageMaker® Pipelines<ul>
 <li>What S3 data sources are getting pulled?</li>
 <li>What Features Store/Group is the Model Using?</li>
 <li>What's the <strong><em>Provenance</em></strong> of a Model in Model Registry?</li>
@@ -3253,35 +3253,35 @@ <h2 id="why-sageworks">Why SageWorks?</h2>
 </li>
 </ul>
 <h3 id="single-pane-of-glass">Single Pane of Glass</h3>
-<p>Visibility into the AWS Services that underpin the SageWorks Classes. We can see that SageWorks automatically tags and tracks the inputs of all artifacts providing 'data provenance' for all steps in the AWS modeling pipeline.</p>
+<p>Visibility into the AWS Services that underpin the Workbench Classes. We can see that Workbench automatically tags and tracks the inputs of all artifacts providing 'data provenance' for all steps in the AWS modeling pipeline.</p>
 <p>Image TBD</p>
-<p><i><b> Clearly illustrated:</b> SageWorks provides intuitive and transparent visibility into the full pipeline of your AWS Sagemaker Deployments.</i></p>
+<p><i><b> Clearly illustrated:</b> Workbench provides intuitive and transparent visibility into the full pipeline of your AWS Sagemaker Deployments.</i></p>
 <h2 id="getting-started">Getting Started</h2>
 <ul>
-<li><a href="https://docs.google.com/presentation/d/1ZiSy4ulEx5gfNQS76yRv8vgkehJ9gXRJ1PulutLKzis/edit?usp=sharing">SageWorks Overview</a> Slides that cover and illustrate the SageWorks Modeling Pipeline.</li>
-<li><a href="https://github.com/SuperCowPowers/sageworks/wiki">SageWorks Docs/Wiki</a> Our general documentation for getting started with SageWorks.</li>
-<li><a href="https://github.com/SuperCowPowers/sageworks/wiki/Onboarding-SageWorks-to-AWS">SageWorks AWS Onboarding</a> Deploy the SageWorks Stack to your AWS Account. </li>
-<li><a href="https://nbviewer.org/github/SuperCowPowers/sageworks/blob/main/notebooks/ML_Pipeline_with_SageWorks.ipynb">Notebook: Start to Finish AWS ML Pipeline</a> Building an AWS® ML Pipeline from start to finish.</li>
-<li><a href="https://drive.google.com/file/d/1iO7IuQtTYdx4BtQjxv9lI1aVJ2ZcAo43/view?usp=sharing">Video: Coding with SageWorks</a> Informal coding + chatting while building a full ML pipeline.</li>
-<li>Join our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> for questions and advice on using SageWorks within your organization.</li>
+<li><a href="https://docs.google.com/presentation/d/1ZiSy4ulEx5gfNQS76yRv8vgkehJ9gXRJ1PulutLKzis/edit?usp=sharing">Workbench Overview</a> Slides that cover and illustrate the Workbench Modeling Pipeline.</li>
+<li><a href="https://github.com/SuperCowPowers/workbench/wiki">Workbench Docs/Wiki</a> Our general documentation for getting started with Workbench.</li>
+<li><a href="https://github.com/SuperCowPowers/workbench/wiki/Onboarding-Workbench-to-AWS">Workbench AWS Onboarding</a> Deploy the Workbench Stack to your AWS Account. </li>
+<li><a href="https://nbviewer.org/github/SuperCowPowers/workbench/blob/main/notebooks/ML_Pipeline_with_Workbench.ipynb">Notebook: Start to Finish AWS ML Pipeline</a> Building an AWS® ML Pipeline from start to finish.</li>
+<li><a href="https://drive.google.com/file/d/1iO7IuQtTYdx4BtQjxv9lI1aVJ2ZcAo43/view?usp=sharing">Video: Coding with Workbench</a> Informal coding + chatting while building a full ML pipeline.</li>
+<li>Join our <a href="https://discord.gg/WHAJuz8sw8">Discord</a> for questions and advice on using Workbench within your organization.</li>
 </ul>
-<h3 id="sageworks-zen">SageWorks Zen</h3>
+<h3 id="workbench-zen">Workbench Zen</h3>
 <ul>
 <li>The AWS SageMaker® set of services is vast and <strong>complex</strong>.</li>
-<li>SageWorks Classes encapsulate, organize, and manage sets of AWS® Services.</li>
+<li>Workbench Classes encapsulate, organize, and manage sets of AWS® Services.</li>
 <li><strong>Heavy</strong> transforms typically use <strong><a href="https://aws.amazon.com/athena/">AWS Athena</a></strong> or <strong><a href="https://spark.apache.org/">Apache Spark</a></strong> (AWS Glue/EMR Serverless).</li>
 <li><strong>Light</strong> transforms will typically use <strong><a href="https://pandas.pydata.org/">Pandas</a></strong>.</li>
 <li>Heavy and Light transforms both update <strong>AWS Artifacts</strong> (collections of AWS Services).</li>
 <li><strong>Quick prototypes</strong> are typically built with the <strong>light path</strong> and then flipped to the <strong>heavy path</strong> as the system matures and usage grows.</li>
 </ul>
 <h3 id="classes-and-concepts">Classes and Concepts</h3>
-<p>The SageWorks Classes are organized to work in concert with AWS Services. For more details on the current classes and class hierarchies see <a href="../sageworks_classes_concepts/">SageWorks Classes and Concepts</a>.</p>
+<p>The Workbench Classes are organized to work in concert with AWS Services. For more details on the current classes and class hierarchies see <a href="../workbench_classes_concepts/">Workbench Classes and Concepts</a>.</p>
 <h3 id="contributions">Contributions</h3>
-<p>If you'd like to contribute to the SageWorks project, you're more than welcome. All contributions will fall under the existing project <a href="https://github.com/SuperCowPowers/sageworks/blob/main/LICENSE">license</a>. If you are interested in contributing or have questions please feel free to contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a>.</p>
-<h3 id="sageworks-alpha-testers-wanted">SageWorks Alpha Testers Wanted</h3>
+<p>If you'd like to contribute to the Workbench project, you're more than welcome. All contributions will fall under the existing project <a href="https://github.com/SuperCowPowers/workbench/blob/main/LICENSE">license</a>. If you are interested in contributing or have questions please feel free to contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a>.</p>
+<h3 id="workbench-alpha-testers-wanted">Workbench Alpha Testers Wanted</h3>
 <p>Our experienced team can provide development and consulting services to help you effectively use Amazon’s Machine Learning services within your organization.</p>
 <p>The popularity of cloud based Machine Learning services is booming. The problem many companies face is how that capability gets effectively used and harnessed to drive real business decisions and provide concrete value for their organization.</p>
-<p>Using SageWorks will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Alpha Tester, contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a>.</p>
+<p>Using Workbench will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a Workbench Alpha Tester, contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a>.</p>
 <p>® Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates.</p>
 
 
diff --git a/misc/scp_consulting/index.html b/misc/scp_consulting/index.html
index 0001117f9..6e03a00c2 100644
--- a/misc/scp_consulting/index.html
+++ b/misc/scp_consulting/index.html
@@ -16,7 +16,7 @@
     
     
       
-        <title>Scp consulting - SageWorks</title>
+        <title>Scp consulting - Workbench</title>
       
     
     
@@ -86,7 +86,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -100,7 +100,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -207,13 +207,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -289,7 +289,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1813,7 +1813,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1823,7 +1823,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3042,11 +3042,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3098,9 +3098,9 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks-scp-consulting-awesome" class="md-nav__link">
+  <a href="#workbench-scp-consulting-awesome" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks + SCP Consulting = Awesome
+      Workbench + SCP Consulting = Awesome
     </span>
   </a>
   
@@ -3132,15 +3132,15 @@
 
 <p><img align="left" src="images/scp.png" width="180"></p>
 <h1 id="consulting">Consulting</h1>
-<h3 id="sageworks-scp-consulting-awesome">SageWorks + SCP Consulting = Awesome</h3>
+<h3 id="workbench-scp-consulting-awesome">Workbench + SCP Consulting = Awesome</h3>
 <p>Our experienced team can provide development and consulting services to help you effectively use Amazon’s Machine Learning services within your organization.</p>
 <p>The popularity of cloud based Machine Learning services is booming. The problem many companies face is how that capability gets effectively used and harnessed to drive real business decisions and provide concrete value for their organization.</p>
-<p>Using SageWorks will minimizize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Alpha Tester, contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a>.</p>
+<p>Using Workbench will minimizize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a Workbench Alpha Tester, contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a>.</p>
 <h3 id="typical-engagements">Typical Engagements</h3>
-<p>SageWorks clients typically want a tailored web_interface that helps to drive business decisions and provides value for their organization.</p>
+<p>Workbench clients typically want a tailored web_interface that helps to drive business decisions and provides value for their organization.</p>
 <ul>
-<li>SageWorks components provide a set of classes and transforms the will dramatically <strong>reduce time</strong> and <strong>increase productivity</strong> when building AWS ML Systems.</li>
-<li>SageWorks enables rapid prototyping via it's <strong>light paths</strong> and provides AWS production workflows on <strong>large scale data</strong> through it's <strong>heavy paths</strong>.</li>
+<li>Workbench components provide a set of classes and transforms the will dramatically <strong>reduce time</strong> and <strong>increase productivity</strong> when building AWS ML Systems.</li>
+<li>Workbench enables rapid prototyping via it's <strong>light paths</strong> and provides AWS production workflows on <strong>large scale data</strong> through it's <strong>heavy paths</strong>.</li>
 <li>
 <p>Rapid Prototyping is typically done via these steps.</p>
 </li>
@@ -3157,7 +3157,7 @@ <h3 id="typical-engagements">Typical Engagements</h3>
 <p>When the client is happy/excited about the ProtoType we then <strong>bolt down</strong> the system, test the heavy paths, review AWS access, security and ensure 'least privileged' roles and policies.</p>
 </li>
 </ul>
-<p>Contact us for a <strong>free</strong> initial consultation on how we can accelerate the use of AWS ML at your company <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a>.</p>
+<p>Contact us for a <strong>free</strong> initial consultation on how we can accelerate the use of AWS ML at your company <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a>.</p>
 
 
 
diff --git a/misc/sageworks_classes_concepts/index.html b/misc/workbench_classes_concepts/index.html
similarity index 98%
rename from misc/sageworks_classes_concepts/index.html
rename to misc/workbench_classes_concepts/index.html
index 220687ecb..ed2a02073 100644
--- a/misc/sageworks_classes_concepts/index.html
+++ b/misc/workbench_classes_concepts/index.html
@@ -16,7 +16,7 @@
     
     
       
-        <title>SageWorks Classes and Concepts - SageWorks</title>
+        <title>Workbench Classes and Concepts - Workbench</title>
       
     
     
@@ -70,7 +70,7 @@
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-        <a href="#sageworks-classes-and-concepts" class="md-skip">
+        <a href="#workbench-classes-and-concepts" class="md-skip">
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         </a>
       
@@ -86,7 +86,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
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         <div class="md-header__topic">
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-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
           <span class="md-ellipsis">
             
-              SageWorks Classes and Concepts
+              Workbench Classes and Concepts
             
           </span>
         </div>
@@ -207,13 +207,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
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-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
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-    SageWorks REPL
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-    SageWorks Enterprise
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-            SageWorks Enterprise
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           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3042,11 +3042,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3104,9 +3104,9 @@
                   
 
 
-<h1 id="sageworks-classes-and-concepts">SageWorks Classes and Concepts</h1>
-<p>A flexible, rapid, and customizable AWS® ML Sandbox. Here's some of the classes and concepts we use in the SageWorks system:</p>
-<p><img src="images/sageworks_concepts.png"></p>
+<h1 id="workbench-classes-and-concepts">Workbench Classes and Concepts</h1>
+<p>A flexible, rapid, and customizable AWS® ML Sandbox. Here's some of the classes and concepts we use in the Workbench system:</p>
+<p><img src="images/workbench_concepts.png"></p>
 <ul>
 <li>Artifacts</li>
 <li>DataLoader</li>
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diff --git a/plugins/index.html b/plugins/index.html
index 7d9741b19..be7083a85 100644
--- a/plugins/index.html
+++ b/plugins/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>OverView - SageWorks</title>
+        <title>OverView - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1906,7 +1906,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1916,7 +1916,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3257,12 +3257,12 @@
   <h1>OverView</h1>
 
 <div class="admonition tip inline end">
-<p class="admonition-title">SageWorks Plugins</p>
-<p>The SageWorks toolkit provides a flexible plugin architecture to expand, enhance, or even replace the <a href="../aws_setup/dashboard_stack/">Dashboard</a>. Make custom UI components, views, and entire pages with the plugin classes described here.</p>
+<p class="admonition-title">Workbench Plugins</p>
+<p>The Workbench toolkit provides a flexible plugin architecture to expand, enhance, or even replace the <a href="../aws_setup/dashboard_stack/">Dashboard</a>. Make custom UI components, views, and entire pages with the plugin classes described here.</p>
 </div>
-<p>The SageWorks Plugin system allows clients to customize how their AWS Machine Learning Pipeline is displayed, analyzed, and visualized. Our easy to use Python API enables developers to make new <a href="https://plotly.com/">Dash/Plotly</a> components, data views, and entirely new web pages focused on business use cases.</p>
+<p>The Workbench Plugin system allows clients to customize how their AWS Machine Learning Pipeline is displayed, analyzed, and visualized. Our easy to use Python API enables developers to make new <a href="https://plotly.com/">Dash/Plotly</a> components, data views, and entirely new web pages focused on business use cases.</p>
 <h3 id="concept-docs">Concept Docs</h3>
-<p>Many classes in SageWorks need additional high-level material that covers class design and illustrates class usage. Here's the Concept Docs for Plugins:</p>
+<p>Many classes in Workbench need additional high-level material that covers class design and illustrates class usage. Here's the Concept Docs for Plugins:</p>
 <ul>
 <li><a href="https://docs.google.com/presentation/d/1RjpMmJW1i9auPztn2xXYmYKXsZjsnG7vVaCQQ4FLIMM/edit?usp=sharing">Plugin Concepts: Read First!</a></li>
 <li><a href="https://docs.google.com/presentation/d/1S_-XapmyTsXIkO6od9AVkTbEU2nqS-mEZwFrtUucUME/edit?usp=sharing">How to Write a Plugin</a> </li>
@@ -3270,8 +3270,8 @@ <h3 id="concept-docs">Concept Docs</h3>
 <li><a href="https://docs.google.com/presentation/d/1sByTnZa24lY6d4INRMm7OHmQndIZmLbTxOyTeAJol20/edit?usp=sharing">Plugins Advanced</a></li>
 </ul>
 <h2 id="make-a-plugin">Make a plugin</h2>
-<p>Each plugin class inherits from the SageWorks PluginInterface class and needs to set two attributes and implement two methods. These requirements are set so that each Plugin will conform to the Sageworks infrastructure; if the required attributes and methods aren’t included in the class definition, errors will be raised during tests and at runtime.</p>
-<p><strong>Note:</strong> For full code see <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples/plugins/components/model_plugin.py">Model Plugin Example</a></p>
+<p>Each plugin class inherits from the Workbench PluginInterface class and needs to set two attributes and implement two methods. These requirements are set so that each Plugin will conform to the Workbench infrastructure; if the required attributes and methods aren’t included in the class definition, errors will be raised during tests and at runtime.</p>
+<p><strong>Note:</strong> For full code see <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples/plugins/components/model_plugin.py">Model Plugin Example</a></p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>class ModelPlugin(PluginInterface):
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>    &quot;&quot;&quot;MyModelPlugin Component&quot;&quot;&quot;
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
@@ -3313,13 +3313,13 @@ <h2 id="make-a-plugin">Make a plugin</h2>
 </span><span id="__span-0-39"><a id="__codelineno-0-39" name="__codelineno-0-39" href="#__codelineno-0-39"></a>        return [pie_figure]
 </span></code></pre></div>
 <h3 id="required-attributes">Required Attributes</h3>
-<p>The class variable plugin_page determines what type of plugin the MyPlugin class is. This variable is inspected during plugin loading at runtime in order to load the plugin to the correct artifact page in the Sageworks dashboard. The PluginPage class can be DATA_SOURCE, FEATURE_SET, MODEL, or ENDPOINT.</p>
+<p>The class variable plugin_page determines what type of plugin the MyPlugin class is. This variable is inspected during plugin loading at runtime in order to load the plugin to the correct artifact page in the Workbench dashboard. The PluginPage class can be DATA_SOURCE, FEATURE_SET, MODEL, or ENDPOINT.</p>
 <h2 id="s3-bucket-plugins-work-in-progress">S3 Bucket Plugins (Work in Progress)</h2>
 <p>Note: This functionality is coming soon</p>
-<p>Offers the most flexibility and fast prototyping. Simple set your config/env for  blah to an S3 Path and SageWorks will load the plugins from S3 directly.</p>
+<p>Offers the most flexibility and fast prototyping. Simple set your config/env for  blah to an S3 Path and Workbench will load the plugins from S3 directly.</p>
 <p><strong>Helpful Tip</strong></p>
 <p>You can copy files from your local system up to S3 with this handy AWS CLI call</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a> aws s3 cp . s3://my-sageworks/sageworks_plugins \
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a> aws s3 cp . s3://my-workbench/workbench_plugins \
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a> --recursive --exclude &quot;*&quot; --include &quot;*.py&quot;
 </span></code></pre></div>
 <h2 id="additional-resources">Additional Resources</h2>
diff --git a/presentations/index.html b/presentations/index.html
index 6d8b30869..dbdcbc35b 100644
--- a/presentations/index.html
+++ b/presentations/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Presentations - SageWorks</title>
+        <title>Presentations - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#sageworks-presentations" class="md-skip">
+        <a href="#workbench-presentations" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -295,18 +295,18 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks-presentations_1" class="md-nav__link">
+  <a href="#workbench-presentations_1" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Presentations
+      Workbench Presentations
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-python-api-docs" class="md-nav__link">
+  <a href="#workbench-python-api-docs" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Python API Docs
+      Workbench Python API Docs
     </span>
   </a>
   
@@ -360,7 +360,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1884,7 +1884,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1894,7 +1894,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3113,11 +3113,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3169,18 +3169,18 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#sageworks-presentations_1" class="md-nav__link">
+  <a href="#workbench-presentations_1" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Presentations
+      Workbench Presentations
     </span>
   </a>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#sageworks-python-api-docs" class="md-nav__link">
+  <a href="#workbench-python-api-docs" class="md-nav__link">
     <span class="md-ellipsis">
-      SageWorks Python API Docs
+      Workbench Python API Docs
     </span>
   </a>
   
@@ -3210,15 +3210,15 @@
                   
 
 
-<h1 id="sageworks-presentations">SageWorks Presentations</h1>
-<p>The SageWorks framework makes AWS® both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap.</p>
+<h1 id="workbench-presentations">Workbench Presentations</h1>
+<p>The Workbench framework makes AWS® both easier to use and more powerful. Workbench handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, Workbench makes creating AWS ML pipelines a snap.</p>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<h3 id="sageworks-presentations_1">SageWorks Presentations</h3>
+<h3 id="workbench-presentations_1">Workbench Presentations</h3>
 <ul>
-<li><a href="https://docs.google.com/presentation/d/1JCUzCtjQLGoQ7HJA_jhEHy3Q2o_j-ibkCCN864CvcVE/edit?usp=sharing">SageWorks Overview</a></li>
+<li><a href="https://docs.google.com/presentation/d/1JCUzCtjQLGoQ7HJA_jhEHy3Q2o_j-ibkCCN864CvcVE/edit?usp=sharing">Workbench Overview</a></li>
 <li><a href="https://docs.google.com/presentation/d/1f_1gmE4-UAeUDDsoNdzK_d_MxALFXIkxORZwbJBjPq4/edit?usp=drive_link">Private SaaS Architecture</a></li>
 <li><a href="https://docs.google.com/presentation/d/1dvk7JoNcssEVEh7eWI-Qrh7lJbVOMM2qvrn60SrAFI8/edit?usp=drive_link">Python API</a></li>
 <li><a href="https://docs.google.com/presentation/d/1RjpMmJW1i9auPztn2xXYmYKXsZjsnG7vVaCQQ4FLIMM/edit?usp=drive_link">Plugins Overview</a></li>
@@ -3228,14 +3228,14 @@ <h3 id="sageworks-presentations_1">SageWorks Presentations</h3>
 <li><a href="https://docs.google.com/presentation/d/1YBOFBMsZM7NdOyakkTEOzT3X8h5iQCACpx3cF897HEU/edit?usp=drive_link">Exploratory Data Analysis</a></li>
 <li><a href="https://docs.google.com/presentation/d/1ZiSy4ulEx5gfNQS76yRv8vgkehJ9gXRJ1PulutLKzis/edit?usp=drive_link">Architected ML Framework</a></li>
 <li><a href="https://docs.google.com/presentation/d/1_KwbaBsyBoiWW_8SEallHg8RMsi9FdK10dr2wwzo3CA/edit?usp=drive_link">AWS Access Management</a></li>
-<li><a href="https://docs.google.com/presentation/d/1u9UU2-0ZhJ2WpDI7FxyJj6lHAdhpm3_hyMe1KsdZI6A/edit?usp=drive_link">Sageworks Config</a></li>
-<li><a href="https://docs.google.com/presentation/d/1H9OY94kLwB6dgXjWoKBf3fmE7b6fZMm5-Iex8LDr4xk/edit?usp=drive_link">SageWorks REPL</a></li>
+<li><a href="https://docs.google.com/presentation/d/1u9UU2-0ZhJ2WpDI7FxyJj6lHAdhpm3_hyMe1KsdZI6A/edit?usp=drive_link">Workbench Config</a></li>
+<li><a href="https://docs.google.com/presentation/d/1H9OY94kLwB6dgXjWoKBf3fmE7b6fZMm5-Iex8LDr4xk/edit?usp=drive_link">Workbench REPL</a></li>
 </ul>
-<h3 id="sageworks-python-api-docs">SageWorks Python API Docs</h3>
-<p><img align="right" width="300" alt="sageworks_api" style="padding-left: 10px;"  src="https://github.com/SuperCowPowers/sageworks/assets/4806709/bf0e8591-75d4-44c1-be05-4bfdee4b7186"></p>
-<p>The SageWorks API documentation <a href="https://supercowpowers.github.io/sageworks/">SageWorks API</a> covers our in-depth Python API and contains code examples. The code examples are provided in the Github repo <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/sageworks/blob/main/examples">SageWorks Examples</a></p>
+<h3 id="workbench-python-api-docs">Workbench Python API Docs</h3>
+<p><img align="right" width="300" alt="workbench_api" style="padding-left: 10px;"  src="https://github.com/SuperCowPowers/workbench/assets/4806709/bf0e8591-75d4-44c1-be05-4bfdee4b7186"></p>
+<p>The Workbench API documentation <a href="https://supercowpowers.github.io/workbench/">Workbench API</a> covers our in-depth Python API and contains code examples. The code examples are provided in the Github repo <code>examples/</code> directory. For a full code listing of any example please visit our <a href="https://github.com/SuperCowPowers/workbench/blob/main/examples">Workbench Examples</a></p>
 <h2 id="questions">Questions?</h2>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 <p><img align="right" src="../images/scp.png" width="180"></p>
 <p>® Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates</p>
 
diff --git a/release_notes/0_8_50/index.html b/release_notes/0_8_50/index.html
index 8efbb6c7d..c7fd63773 100644
--- a/release_notes/0_8_50/index.html
+++ b/release_notes/0_8_50/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.50 - SageWorks</title>
+        <title>0.8.50 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3190,11 +3190,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3365,9 +3365,9 @@
 <h1 id="release-0850">Release 0.8.50</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.46</code> to <code>0.8.50</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
@@ -3375,7 +3375,7 @@ <h3 id="featureset-id_column-lock-in">FeatureSet: id_column lock in</h3>
 <p>We're going to lock in id_columns when FeatureSets are created, AWS FeatureGroup requires an id column, so this is the best place to do it, see API Changes below.</p>
 <h3 id="featureset-robust-handling-of-training-column">FeatureSet: Robust handling of training column</h3>
 <p>In the past we haven't supported giving a training column as input data. FeatureSets are read-only, so locking in the training rows is 'suboptimal'. In general you might want to use the FeatureSet for several models with different training/hold_out sets. Now if a FeatureSet detects a training column it will give the follow message:</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>Training column detected: Since FeatureSets are read only, SageWorks 
+<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>Training column detected: Since FeatureSets are read only, Workbench 
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>creates training views that can be dynamically changed. We&#39;ll use 
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>this training column to create a training view.
 </span></code></pre></div>
@@ -3410,7 +3410,7 @@ <h2 id="improvements">Improvements</h2>
 </code></pre></div>
 <h2 id="internal-changes">Internal Changes</h2>
 <p>There's a whole new directory structure that helps isolate Cloud Platform specific funcitonality.</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>- sageworks/src
+<div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>- workbench/src
 </span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>     - core/cloud_platform
 </span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>        - aws
 </span><span id="__span-5-4"><a id="__codelineno-5-4" name="__codelineno-5-4" href="#__codelineno-5-4"></a>        - azure
@@ -3423,7 +3423,7 @@ <h2 id="internal-changes">Internal Changes</h2>
 <h3 id="internal-caching">Internal Caching</h3>
 <p>So as part of our <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a> we're continuing to revamp caching. We're experimenting with <a href="../../cached/cached_meta/">CachedMeta Class</a> inside the Artifact classes. Caching continues to be challenging for the framework, it's an absolute must for Web Inferface/UI performance and then it needs to get out of the way for batch jobs and the concurrent building of ML pipelines.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.46...v0.8.50" target="_blank">Code Diff v0.8.46 --&gt; v0.8.50</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.46...v0.8.50" target="_blank">Code Diff v0.8.46 --&gt; v0.8.50</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>That feeling like you’ve done this before?
@@ -3431,7 +3431,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/0_8_55/index.html b/release_notes/0_8_55/index.html
index 4fcace8b5..a74ed6fa7 100644
--- a/release_notes/0_8_55/index.html
+++ b/release_notes/0_8_55/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.55 - SageWorks</title>
+        <title>0.8.55 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3157,11 +3157,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3299,9 +3299,9 @@
 <h1 id="release-0855">Release 0.8.55</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.50</code> to <code>0.8.55</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
@@ -3340,12 +3340,12 @@ <h2 id="api-changes">API Changes</h2>
 </span></code></pre></div>
 For more details see: <a href="../../cached/cached_model/">CachedModel</a></p>
 <h2 id="improvements">Improvements</h2>
-<p><strong>SageWorks REPL Onboarding</strong></p>
-<p>At some point the onboarding with SageWorks REPL got broken and wasn't properly responding when the user didn't have a complete AWS/SageWorks setup.</p>
+<p><strong>Workbench REPL Onboarding</strong></p>
+<p>At some point the onboarding with Workbench REPL got broken and wasn't properly responding when the user didn't have a complete AWS/Workbench setup.</p>
 <h2 id="internal-changes">Internal Changes</h2>
 <p>The decorator for the CachedMeta class did not work properly in Python 3.9 so had to be slightly refactored.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.50...v0.8.55" target="_blank">Code Diff v0.8.50 --&gt; v0.8.55</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.50...v0.8.55" target="_blank">Code Diff v0.8.50 --&gt; v0.8.55</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>That feeling like you’ve done this before?
@@ -3353,7 +3353,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/0_8_58/index.html b/release_notes/0_8_58/index.html
index 1c037e745..3b8585d7b 100644
--- a/release_notes/0_8_58/index.html
+++ b/release_notes/0_8_58/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.58 - SageWorks</title>
+        <title>0.8.58 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3148,11 +3148,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3281,9 +3281,9 @@
 <h1 id="release-0858">Release 0.8.58</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.55</code> to <code>0.8.58</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
@@ -3295,18 +3295,18 @@ <h3 id="caching-refactoring">Caching Refactoring</h3>
 <li>CachedModel</li>
 <li>CacheEndpoint</li>
 </ul>
-<p>As part of this there's now a <code>sageworks/cached</code> directory that housed these classes and the <code>CachedMeta</code> class.</p>
+<p>As part of this there's now a <code>workbench/cached</code> directory that housed these classes and the <code>CachedMeta</code> class.</p>
 <h2 id="api-changes">API Changes</h2>
 <p><strong>Meta Imports</strong>
 Yes, this changed AGAIN :)</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>from sageworks.meta import Meta
+<div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>from workbench.meta import Meta
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>&lt;change to&gt;
-</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>from sageworks.api import Meta
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>from workbench.api import Meta
 </span></code></pre></div>
 <p><strong>CachedModel Import</strong>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>from sageworks.api import CachedModel
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>from workbench.api import CachedModel
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>&lt;change to&gt;
-</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>from sageworks.cached.cached_model import CachedModel
+</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>from workbench.cached.cached_model import CachedModel
 </span></code></pre></div>
 For more details see: <a href="../../cached/cached_model/">CachedModel</a></p>
 <h2 id="improvements">Improvements</h2>
@@ -3317,7 +3317,7 @@ <h2 id="improvements">Improvements</h2>
 <p><strong>Internal</strong>
 The Dashboard now refreshes all data every 90 seconds, so if you don't see you're new model on the dashboard... just wait longer. :)</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.55...v0.8.58" target="_blank">Code Diff v0.8.55 --&gt; v0.8.58</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.55...v0.8.58" target="_blank">Code Diff v0.8.55 --&gt; v0.8.58</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>What do you call a nonsense meeting?
@@ -3325,7 +3325,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/0_8_60/index.html b/release_notes/0_8_60/index.html
index 5450ba0ee..2bfdeb852 100644
--- a/release_notes/0_8_60/index.html
+++ b/release_notes/0_8_60/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.60 - SageWorks</title>
+        <title>0.8.60 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3148,11 +3148,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3281,9 +3281,9 @@
 <h1 id="release-0860">Release 0.8.60</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.58</code> to <code>0.8.60</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
@@ -3294,7 +3294,7 @@ <h2 id="api-changes">API Changes</h2>
 <h2 id="notes">Notes</h2>
 <p>Custom models introduce models that don't have model metrics or inference runs, so you'll see a lot of log messages complaining about not finding metrics or inference results, please just ignore those, we'll put in additional logic to address those cases.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.58...v0.8.60" target="_blank">Code Diff v0.8.58 --&gt; v0.8.60</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.58...v0.8.60" target="_blank">Code Diff v0.8.58 --&gt; v0.8.60</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>What do you call a nonsense meeting?
@@ -3302,7 +3302,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/0_8_71/index.html b/release_notes/0_8_71/index.html
index 12cb5b13c..7bcb43f4b 100644
--- a/release_notes/0_8_71/index.html
+++ b/release_notes/0_8_71/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.71 - SageWorks</title>
+        <title>0.8.71 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3148,11 +3148,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3281,9 +3281,9 @@
 <h1 id="release-0871">Release 0.8.71</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.60</code> to <code>0.8.71</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
@@ -3300,7 +3300,7 @@ <h2 id="internal-changes">Internal Changes</h2>
 </span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>df_to_features.transform()      &lt;--- Overwrite happens here
 </span></code></pre></div>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.60...v0.8.71" target="_blank">Code Diff v0.8.60 --&gt; v0.8.71</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.60...v0.8.71" target="_blank">Code Diff v0.8.60 --&gt; v0.8.71</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>What do you call that feeling like you’ve done this before?
@@ -3308,7 +3308,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/0_8_74/index.html b/release_notes/0_8_74/index.html
index 63538abd2..5b0e88ba8 100644
--- a/release_notes/0_8_74/index.html
+++ b/release_notes/0_8_74/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.74 - SageWorks</title>
+        <title>0.8.74 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3166,11 +3166,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3317,16 +3317,16 @@
 <h1 id="release-0874">Release 0.8.74</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.71</code> to <code>0.8.74</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
 <h3 id="s3-based-plugins">S3 based Plugins</h3>
 <p>We've added the ability to grab Dashboard plugins directly from an S3 bucket. Please see <a href="../../admin/dashboard_s3_plugins/">Dashboard S3 Plugin</a></p>
 <h3 id="artifact-hashes">Artifact Hashes</h3>
-<p>For content verification purposes we've added a <code>hash()</code> method to all of the SageWorks Artifact classes (DataSource, FeatureSet, Model, Endpoint, Graph, etc). Also for DataSources and FeatureSets there is a <code>table_hash()</code> method that will compute a total hash of all data in the Athena table.</p>
+<p>For content verification purposes we've added a <code>hash()</code> method to all of the Workbench Artifact classes (DataSource, FeatureSet, Model, Endpoint, Graph, etc). Also for DataSources and FeatureSets there is a <code>table_hash()</code> method that will compute a total hash of all data in the Athena table.</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>ds = DataSource(&quot;abalone_data&quot;)
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>ds.modified()
@@ -3392,7 +3392,7 @@ <h2 id="api-changes">API Changes</h2>
 <h2 id="internal-changes">Internal Changes</h2>
 <p>There was a small refactor of the cache decorator. We fixed a case where if we blocked on getting a value we also spun up a background thread to get it. This chance will not affect existing code or APIs.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.71...v0.8.74" target="_blank">Code Diff v0.8.71 --&gt; v0.8.74</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.71...v0.8.74" target="_blank">Code Diff v0.8.71 --&gt; v0.8.74</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>What do you call that feeling like you’ve done this before?
@@ -3400,7 +3400,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/0_8_78/index.html b/release_notes/0_8_78/index.html
index 6818b0224..13a6bc19e 100644
--- a/release_notes/0_8_78/index.html
+++ b/release_notes/0_8_78/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.78 - SageWorks</title>
+        <title>0.8.78 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3148,11 +3148,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3281,9 +3281,9 @@
 <h1 id="release-0878">Release 0.8.78</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.74</code> to <code>0.8.78</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
@@ -3292,7 +3292,7 @@ <h3 id="s3-based-plugins">S3 based Plugins</h3>
 <h3 id="tbd-stuff">TBD Stuff</h3>
 <h2 id="api-changes">API Changes</h2>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.74...v0.8.78" target="_blank">Code Diff v0.8.74 --&gt; v0.8.78</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.74...v0.8.78" target="_blank">Code Diff v0.8.74 --&gt; v0.8.78</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>What do you call that feeling like you’ve done this before?
@@ -3300,7 +3300,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_7_8/index.html b/release_notes/archived/0_7_8/index.html
index e0f2a907e..ac1193c1d 100644
--- a/release_notes/archived/0_7_8/index.html
+++ b/release_notes/archived/0_7_8/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.7.8 - SageWorks</title>
+        <title>0.7.8 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3126,11 +3126,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3235,7 +3235,7 @@
 <h1 id="release-078">Release 0.7.8</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on [Discord](https://discord.gg/WHAJuz8sw8</p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on [Discord](https://discord.gg/WHAJuz8sw8</p>
 </div>
 <p>Since we've recently introduced a <strong>View()</strong> class for DataSources and FeatureSets we needed to rename a few classes/modules.</p>
 <h3 id="featuresets">FeatureSets</h3>
@@ -3249,14 +3249,14 @@ <h3 id="models">Models</h3>
 </span></code></pre></div>
 <h3 id="webplugins">Web/Plugins</h3>
 <p>We've changed the Web/UI View class to 'WebView'. So anywhere where you used to have <strong>view</strong> just replace with <strong>web_view</strong></p>
-<p><div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>from sageworks.views.artifacts_view import ArtifactsView
+<p><div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>from workbench.views.artifacts_view import ArtifactsView
 </span></code></pre></div>
 is now</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>from sageworks.web_interface.page_views.all_artifacts import AllArtifacts
+<div class="language-text highlight"><pre><span></span><code><span id="__span-3-1"><a id="__codelineno-3-1" name="__codelineno-3-1" href="#__codelineno-3-1"></a>from workbench.web_interface.page_views.all_artifacts import AllArtifacts
 </span></code></pre></div>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_11/index.html b/release_notes/archived/0_8_11/index.html
index 202e65a6c..7a4eebea1 100644
--- a/release_notes/archived/0_8_11/index.html
+++ b/release_notes/archived/0_8_11/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.11 - SageWorks</title>
+        <title>0.8.11 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,9 +3253,9 @@
 <h1 id="release-0811">Release 0.8.11</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover all the changes from <code>0.8.8</code> to <code>0.8.11</code></p>
 <h3 id="general">General</h3>
 <p>The AWSAccountClamp had too many responsibilities so that class has been split up into two classes and a set of utilities:</p>
@@ -3305,18 +3305,18 @@ <h3 id="api-changes">API Changes</h3>
 </li>
 </ul>
 <h3 id="glue-job-fixes">Glue Job Fixes</h3>
-<p>For <code>sageworks==0.8.8</code> you needed to be careful about when/where you set your config/ENV vars. With <code>&gt;=0.8.9</code> you can now use the typical setup like this:</p>
+<p>For <code>workbench==0.8.8</code> you needed to be careful about when/where you set your config/ENV vars. With <code>&gt;=0.8.9</code> you can now use the typical setup like this:</p>
 <div class="language-text highlight"><pre><span></span><code>```
-from sageworks.utils.config_manager import ConfigManager
+from workbench.utils.config_manager import ConfigManager
 
-# Set the SageWorks Config
+# Set the Workbench Config
 cm = ConfigManager()
-cm.set_config(&quot;SAGEWORKS_BUCKET&quot;, args_dict[&quot;sageworks-bucket&quot;])
+cm.set_config(&quot;WORKBENCH_BUCKET&quot;, args_dict[&quot;workbench-bucket&quot;])
 cm.set_config(&quot;REDIS_HOST&quot;, args_dict[&quot;redis-host&quot;])
 ```
 </code></pre></div>
 <h3 id="robust-modelnotreadyexception-handling">Robust ModelNotReadyException Handling</h3>
-<p>AWS will 'deep freeze' Serverless Endpoints and if that endpoint hasn't been used for a while it can sometimes take a long time to come up and be ready for inference. SageWorks now properly manages this AWS error condition, it will report the issue, wait 60 seconds, and try again 5 times before raising the exception.</p>
+<p>AWS will 'deep freeze' Serverless Endpoints and if that endpoint hasn't been used for a while it can sometimes take a long time to come up and be ready for inference. Workbench now properly manages this AWS error condition, it will report the issue, wait 60 seconds, and try again 5 times before raising the exception.</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-5-1"><a id="__codelineno-5-1" name="__codelineno-5-1" href="#__codelineno-5-1"></a>(endpoint_core.py:502) ERROR Endpoint model not ready
 </span><span id="__span-5-2"><a id="__codelineno-5-2" name="__codelineno-5-2" href="#__codelineno-5-2"></a>(endpoint_core.py:503) ERROR Waiting and Retrying...
 </span><span id="__span-5-3"><a id="__codelineno-5-3" name="__codelineno-5-3" href="#__codelineno-5-3"></a>...
@@ -3324,7 +3324,7 @@ <h3 id="robust-modelnotreadyexception-handling">Robust ModelNotReadyException Ha
 </span></code></pre></div>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_20/index.html b/release_notes/archived/0_8_20/index.html
index 94a61c93c..0379fa616 100644
--- a/release_notes/archived/0_8_20/index.html
+++ b/release_notes/archived/0_8_20/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.20 - SageWorks</title>
+        <title>0.8.20 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3144,11 +3144,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3271,9 +3271,9 @@
 <h1 id="release-0820">Release 0.8.20</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.11</code> to <code>0.8.20</code></p>
 <h3 id="general">General</h3>
 <p>The <code>cloud_watch</code> AWS log aggregator, is now officially awesome. It provides a fairly sophisticated way of both doing broad scanning and deep dives on individual streams. Please see our <a href="../../../cloudwatch/">Cloud Watch</a> documentation.</p>
@@ -3307,19 +3307,19 @@ <h3 id="bug-fixes">Bug Fixes</h3>
 <h3 id="improvements">Improvements</h3>
 <p>The log messages that you receive on a plugin validation failure should now be more distinguishable and more informative. They will look like this and in some cases even tell you the line to look at.</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>ERROR Plugin &#39;MyPlugin&#39; failed validation:
-</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>ERROR    File: ../sageworks_plugins/components/my_plugin.py
+</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>ERROR    File: ../workbench_plugins/components/my_plugin.py
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>ERROR    Class: MyPlugin
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a>ERROR    Details: my_plugin.py (line 35): Incorrect return type for update_properties (expected list, got Figure)
 </span></code></pre></div>
 <h3 id="internal-api-changes">Internal API Changes</h3>
-<p>In theory these API should not affect end user of the SageWorks API but are documented here for completeness.</p>
+<p>In theory these API should not affect end user of the Workbench API but are documented here for completeness.</p>
 <ul>
 <li><strong>Artifact Name Validation</strong></li>
 </ul>
 <p>The internal method used by Artifact subclasses has changed names from <code>ensure_valid_name</code> to <code>is_name_valid</code>, we've also introduced an optional argument to turn on/off lowercase enforcement, this will be used later when we support uppercase for models, endpoints, and graphs.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_22/index.html b/release_notes/archived/0_8_22/index.html
index cc471be56..238ff1ccf 100644
--- a/release_notes/archived/0_8_22/index.html
+++ b/release_notes/archived/0_8_22/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.22 - SageWorks</title>
+        <title>0.8.22 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,9 +3253,9 @@
 <h1 id="release-0822">Release 0.8.22</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.20</code> to <code>0.8.22</code></p>
 <h3 id="general">General</h3>
 <p>Mostly bug fixes and minor API changes.</p>
@@ -3275,7 +3275,7 @@ <h3 id="improvements">Improvements</h3>
 <p>The optional requirements for <code>[UI]</code> now include matplotlib since it will probably be useful in the future.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_23/index.html b/release_notes/archived/0_8_23/index.html
index 14cd97abf..a4d7497ac 100644
--- a/release_notes/archived/0_8_23/index.html
+++ b/release_notes/archived/0_8_23/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.23 - SageWorks</title>
+        <title>0.8.23 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,9 +3253,9 @@
 <h1 id="release-0823">Release 0.8.23</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.22</code> to <code>0.8.23</code></p>
 <h3 id="general">General</h3>
 <p>Mostly bug fixes and minor API changes.</p>
@@ -3286,7 +3286,7 @@ <h3 id="improvements">Improvements</h3>
 </span></code></pre></div>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_27/index.html b/release_notes/archived/0_8_27/index.html
index 6030049e4..4114bfaa5 100644
--- a/release_notes/archived/0_8_27/index.html
+++ b/release_notes/archived/0_8_27/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.27 - SageWorks</title>
+        <title>0.8.27 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3126,11 +3126,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3235,9 +3235,9 @@
 <h1 id="release-0827">Release 0.8.27</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.23</code> to <code>0.8.27</code></p>
 <h3 id="general">General</h3>
 <ul>
@@ -3258,7 +3258,7 @@ <h3 id="minor-improvements">Minor Improvements</h3>
 <p>CloudWatch default buffers have been increased to 60 seconds as we appears to have been hitting some AWS limits with running 10 concurrent glue jobs.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_29/index.html b/release_notes/archived/0_8_29/index.html
index 7ce1fe228..e0fc43377 100644
--- a/release_notes/archived/0_8_29/index.html
+++ b/release_notes/archived/0_8_29/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.29 - SageWorks</title>
+        <title>0.8.29 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3126,11 +3126,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3235,9 +3235,9 @@
 <h1 id="release-0829">Release 0.8.29</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.27</code> to <code>0.8.29</code></p>
 <h3 id="general">General</h3>
 <p><strong>Locking AWS Model Training Image:</strong> AWS will randomly update the images associated with training and model registration. In particular the SKLearn Estimator has been updated into a non-working state for our use cases. So for both training and registration we're now explicitly specifying the image that we want to use.</p>
@@ -3263,14 +3263,14 @@ <h3 id="api-changes">API Changes</h3>
 <h3 id="minor-improvements">Minor Improvements</h3>
 <p><strong>Bulk Delete:</strong>
 Added a Bulk Delete utility</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>from sageworks.utils.bulk_utils import bulk_delete
+<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>from workbench.utils.bulk_utils import bulk_delete
 </span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>delete_list = [(&quot;DataSource&quot;, &quot;abc&quot;), (&quot;FeatureSet&quot;, &quot;abc_features&quot;)]
 </span><span id="__span-2-4"><a id="__codelineno-2-4" name="__codelineno-2-4" href="#__codelineno-2-4"></a>bulk_delete(delete_list)
 </span></code></pre></div>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_33/index.html b/release_notes/archived/0_8_33/index.html
index dd02b0031..132d3c8d2 100644
--- a/release_notes/archived/0_8_33/index.html
+++ b/release_notes/archived/0_8_33/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.33 - SageWorks</title>
+        <title>0.8.33 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3126,11 +3126,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3235,9 +3235,9 @@
 <h1 id="release-0833">Release 0.8.33</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.29</code> to <code>0.8.33</code></p>
 <h3 id="general">General</h3>
 <p><strong>Replaced WatchTower Code:</strong> Had <strong>lots</strong> of issues with WatchTower on Glue/Lambda, the use of forks/threads was overkill for our logging needs, so simply replaced the code with boto3 <code>put_log_events()</code> calls and some simple token handling and buffering.</p>
@@ -3249,7 +3249,7 @@ <h3 id="improvementsfixes">Improvements/Fixes</h3>
 <p><strong>ROCAUC on subset of classes:</strong> When running inference on input data that only had a subset of the classification labels (e.g. rows only had "low" and "medium" when model was trained on "low", "medium", "high"). The input to ROCAUC needed to be adjusted so that ROCAUC doesn't crash. When this case happens we're returning proper defaults based on scikit learn docs.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_35/index.html b/release_notes/archived/0_8_35/index.html
index df2f17852..4fdc9dfed 100644
--- a/release_notes/archived/0_8_35/index.html
+++ b/release_notes/archived/0_8_35/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.35 - SageWorks</title>
+        <title>0.8.35 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,12 +3253,12 @@
 <h1 id="release-0835">Release 0.8.35</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.33</code> to <code>0.8.35</code></p>
 <h3 id="general">General</h3>
-<p><strong>SageWorks REPL:</strong> The REPL now has a workaround for the current iPython embedded shell namespace scoping issue. See: <a href="https://github.com/ipython/ipython/issues/12199">iPython Embedded Shell Scoping Issue</a>. So this pretty much means the REPL is 110% more <strong>awesome</strong> now!</p>
+<p><strong>Workbench REPL:</strong> The REPL now has a workaround for the current iPython embedded shell namespace scoping issue. See: <a href="https://github.com/ipython/ipython/issues/12199">iPython Embedded Shell Scoping Issue</a>. So this pretty much means the REPL is 110% more <strong>awesome</strong> now!</p>
 <h3 id="api-changes">API Changes</h3>
 <p>None</p>
 <h3 id="improvementsfixes">Improvements/Fixes</h3>
@@ -3266,7 +3266,7 @@ <h3 id="improvementsfixes">Improvements/Fixes</h3>
 The AWS service broker was dramatic when it pulls meta data for something that just got deleted (or partially deleted), it was throwing CRITICAL log messages. We've refined the AWS error handling so that it's more granular about the error_codes for Validation or ResourceNotFound exceptions those are reduced to WARNINGS.</p>
 <p><strong>ROCAUC modifications:</strong> Version <code>0.8.33</code> put in quite a few changes, for <code>0.8.35</code> we've also added logic to both validate and ensure proper order of the probability columns with the class labels.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.33...v0.8.35" target="_blank">Code Diff v0.8.33 --&gt; v0.8.35</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.33...v0.8.35" target="_blank">Code Diff v0.8.33 --&gt; v0.8.35</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>What do you call a cow with no legs?
@@ -3274,7 +3274,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_36/index.html b/release_notes/archived/0_8_36/index.html
index 8d642b170..dd325efc3 100644
--- a/release_notes/archived/0_8_36/index.html
+++ b/release_notes/archived/0_8_36/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.36 - SageWorks</title>
+        <title>0.8.36 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,9 +3253,9 @@
 <h1 id="release-0836">Release 0.8.36</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.35</code> to <code>0.8.36</code></p>
 <h3 id="general">General</h3>
 <p><strong>Fast Inference:</strong> The current inference method for endpoints provides error handling, metrics calculations and capture mechanics. There are use cases where the inference needs to happen as fast as possible without all the additional features. So we've added a <code>fast_inference()</code> method that streamlines the calls to the endpoint.</p>
@@ -3270,10 +3270,10 @@ <h3 id="api-changes">API Changes</h3>
 <p>None</p>
 <h3 id="improvementsfixes">Improvements/Fixes</h3>
 <p><strong>Version Update Check:</strong>
-Added functionality that checks the current SageWorks version against the latest released and gives a log message for update available.</p>
+Added functionality that checks the current Workbench version against the latest released and gives a log message for update available.</p>
 <p><strong>ROCAUC modifications:</strong> Functionality now includes 'per label' rocauc calculation along with label order and alignment from previous versions.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.35...v0.8.36" target="_blank">Code Diff v0.8.35 --&gt; v0.8.36</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.35...v0.8.36" target="_blank">Code Diff v0.8.35 --&gt; v0.8.36</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>What’s a cow’s best subject in school?
@@ -3281,7 +3281,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_39/index.html b/release_notes/archived/0_8_39/index.html
index 750bcf82f..061a3d2c8 100644
--- a/release_notes/archived/0_8_39/index.html
+++ b/release_notes/archived/0_8_39/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.39 - SageWorks</title>
+        <title>0.8.39 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,9 +3253,9 @@
 <h1 id="release-0839">Release 0.8.39</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.36</code> to <code>0.8.39</code></p>
 <h3 id="general">General</h3>
 <p>Just a small set of error handling and bug fixes.</p>
@@ -3269,7 +3269,7 @@ <h3 id="improvementsfixes">Improvements/Fixes</h3>
 <p><strong>FeatureSet Creation:</strong>
 Put in additional error handling and retries when pulling AWS meta data for FeatureSets (and internal DataSources).</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.36...v0.8.39" target="_blank">Code Diff v0.8.36 --&gt; v0.8.39</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.36...v0.8.39" target="_blank">Code Diff v0.8.36 --&gt; v0.8.39</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>That feeling like you’ve done this before?
@@ -3277,7 +3277,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_42/index.html b/release_notes/archived/0_8_42/index.html
index 7cd03ed1a..0281a64c6 100644
--- a/release_notes/archived/0_8_42/index.html
+++ b/release_notes/archived/0_8_42/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.42 - SageWorks</title>
+        <title>0.8.42 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
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           <span class="md-ellipsis">
-            SageWorks
+            Workbench
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         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
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   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,9 +3253,9 @@
 <h1 id="release-0842">Release 0.8.42</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.39</code> to <code>0.8.42</code></p>
 <h2 id="general">General</h2>
 <p>Artifact deletion got a substantial overhaul. The 4 main classes received internal code changes for how they get deleted. Specifically deletion is now handled via a class method that allows an artifact to be delteed without instantiating an object. The API for deletion is actually more flexible now, please see API Changes below.</p>
@@ -3276,7 +3276,7 @@ <h2 id="improvementsfixes">Improvements/Fixes</h2>
 <p><strong>Slightly Better Throttling Logic</strong></p>
 <p>The AWS Throttles have been 'tuned' a bit to back off a bit faster and also not retry the list_tags request when the ARN isn't found.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.39...v0.8.42" target="_blank">Code Diff v0.8.39 --&gt; v0.8.42</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.39...v0.8.42" target="_blank">Code Diff v0.8.39 --&gt; v0.8.42</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>That feeling like you’ve done this before?
@@ -3284,7 +3284,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_46/index.html b/release_notes/archived/0_8_46/index.html
index dcb098c31..1f6386926 100644
--- a/release_notes/archived/0_8_46/index.html
+++ b/release_notes/archived/0_8_46/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.46 - SageWorks</title>
+        <title>0.8.46 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3162,11 +3162,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3307,9 +3307,9 @@
 <h1 id="release-0846">Release 0.8.46</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <p><strong>Note:</strong> These release notes cover the changes from <code>0.8.42</code> to <code>0.8.46</code></p>
 <h2 id="general">General</h2>
 <p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: <a href="../../../road_maps/0_9_0/">v0.9.0 Roadmap</a>. </p>
@@ -3353,7 +3353,7 @@ <h2 id="internal-changes">Internal Changes</h2>
 <p><strong>Artifacts</strong></p>
 <p>We're got rid of most (soon all) caching for individual Artifacts, if you're constructing an artifact object, you probably want detailed information that's 'up to date' and waiting a bit is probably fine. <strong>Note:</strong> We'll still make these instantiations as fast as we can, removing the caching logic will as least simplify the implementations.</p>
 <h2 id="specific-code-changes">Specific Code Changes</h2>
-<p><a href="https://github.com/supercowpowers/sageworks/compare/v0.8.42...v0.8.46" target="_blank">Code Diff v0.8.42 --&gt; v0.8.46</a> </p>
+<p><a href="https://github.com/supercowpowers/workbench/compare/v0.8.42...v0.8.46" target="_blank">Code Diff v0.8.42 --&gt; v0.8.46</a> </p>
 <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p>
 <blockquote>
 <p><strong><em>That feeling like you’ve done this before?
@@ -3361,7 +3361,7 @@ <h2 id="specific-code-changes">Specific Code Changes</h2>
 </blockquote>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_6/index.html b/release_notes/archived/0_8_6/index.html
index 6a1e1e132..cd00fed99 100644
--- a/release_notes/archived/0_8_6/index.html
+++ b/release_notes/archived/0_8_6/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.6 - SageWorks</title>
+        <title>0.8.6 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3117,11 +3117,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3217,15 +3217,15 @@
 <h1 id="release-086">Release 0.8.6</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. We've also fixed various corner cases mostly around 'half constructed' AWS artifacts (models/endpoints).</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. We've also fixed various corner cases mostly around 'half constructed' AWS artifacts (models/endpoints).</p>
 <h3 id="additional-functionality">Additional Functionality</h3>
 <ul>
 <li>View Support (phase 1)</li>
 <li>CloudWatch (phase 1)</li>
 <li>Exception Log Forwarding <a href="../../../glue/#exception-log-forwarding">Exception Handling</a></li>
-<li>Lambda Layers <a href="../../../lambda_layer/">SageWorks Lambda Layers</a></li>
+<li>Lambda Layers <a href="../../../lambda_layer/">Workbench Lambda Layers</a></li>
 <li>Better Docs for Deploying Plugins <a href="../../../admin/dashboard_with_plugins/">Deploying Plugins</a></li>
 </ul>
 <h3 id="issues-addressed">Issues Addressed</h3>
@@ -3236,12 +3236,12 @@ <h3 id="issues-addressed">Issues Addressed</h3>
 </li>
 <li>
 <p><strong>Empty Model Package Groups</strong></p>
-<p>There are cases where customers might construct a Model Package Group (MPG) container and not put any Model Packages in that Group. SageWorks has assumed that all MPGs would have at least one model package. The current 'support' for empty MPGs treats it as an error condition but the API tries to accommodate the condition and will properly display the model group. The group will indicate that it's 'empty' and provides an alert health icons.</p>
+<p>There are cases where customers might construct a Model Package Group (MPG) container and not put any Model Packages in that Group. Workbench has assumed that all MPGs would have at least one model package. The current 'support' for empty MPGs treats it as an error condition but the API tries to accommodate the condition and will properly display the model group. The group will indicate that it's 'empty' and provides an alert health icons.</p>
 </li>
 </ul>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/release_notes/archived/0_8_8/index.html b/release_notes/archived/0_8_8/index.html
index b5302b28c..d296a3876 100644
--- a/release_notes/archived/0_8_8/index.html
+++ b/release_notes/archived/0_8_8/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.8.8 - SageWorks</title>
+        <title>0.8.8 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3135,11 +3135,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3253,9 +3253,9 @@
 <h1 id="release-088">Release 0.8.8</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <h3 id="additional-functionality">Additional Functionality</h3>
 <ul>
 <li>View Support (<a href="../../../api_classes/views/">Views</a>)</li>
@@ -3292,25 +3292,25 @@ <h3 id="api-changes">API Changes</h3>
 </span></code></pre></div>
 <h3 id="glue-job-changes">Glue Job Changes</h3>
 <p>We're spinning up the CloudWatch Handler much earlier now, so if you're setting config like this:</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>from sageworks.utils.config_manager import ConfigManager
+<div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>from workbench.utils.config_manager import ConfigManager
 </span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>
-</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a># Set the SageWorks Config
+</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a># Set the Workbench Config
 </span><span id="__span-1-4"><a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a>cm = ConfigManager()
-</span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a>cm.set_config(&quot;SAGEWORKS_BUCKET&quot;, args_dict[&quot;sageworks-bucket&quot;])
+</span><span id="__span-1-5"><a id="__codelineno-1-5" name="__codelineno-1-5" href="#__codelineno-1-5"></a>cm.set_config(&quot;WORKBENCH_BUCKET&quot;, args_dict[&quot;workbench-bucket&quot;])
 </span><span id="__span-1-6"><a id="__codelineno-1-6" name="__codelineno-1-6" href="#__codelineno-1-6"></a>cm.set_config(&quot;REDIS_HOST&quot;, args_dict[&quot;redis-host&quot;])
 </span></code></pre></div>
-<p>Just switch out that code for this code. <strong>Note:</strong> these need to be set before importing sageworks</p>
-<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a># Set these ENV vars for SageWorks 
-</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>os.environ[&#39;SAGEWORKS_BUCKET&#39;] = args_dict[&quot;sageworks-bucket&quot;]
+<p>Just switch out that code for this code. <strong>Note:</strong> these need to be set before importing workbench</p>
+<div class="language-text highlight"><pre><span></span><code><span id="__span-2-1"><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a># Set these ENV vars for Workbench 
+</span><span id="__span-2-2"><a id="__codelineno-2-2" name="__codelineno-2-2" href="#__codelineno-2-2"></a>os.environ[&#39;WORKBENCH_BUCKET&#39;] = args_dict[&quot;workbench-bucket&quot;]
 </span><span id="__span-2-3"><a id="__codelineno-2-3" name="__codelineno-2-3" href="#__codelineno-2-3"></a>os.environ[&quot;REDIS_HOST&quot;] = args_dict[&quot;redis-host&quot;]
 </span></code></pre></div>
 <h3 id="misc">Misc</h3>
 <p><strong>Confusion Matrix support for 'ordinal' labels</strong></p>
 <p>Pandas has an ‘ordinal’ type, so the confusion matrix method <code>endpoint.confusion_matrix()</code> now checks the label column to see if it’s ordinal and uses that order, if not just it will alphabetically sort. </p>
-<p><strong>Note:</strong> This change may not affect your UI experience. Confusion matricies are saved in the Sageworks/S3 meta data storage, so a bunch of stuff upstream will also need to happen. FeatureSet object/api for setting the label order, recreation of the model/endpoint and confustion matrix, etc. In general this is a forwarding looking change that will be useful later. :)</p>
+<p><strong>Note:</strong> This change may not affect your UI experience. Confusion matricies are saved in the Workbench/S3 meta data storage, so a bunch of stuff upstream will also need to happen. FeatureSet object/api for setting the label order, recreation of the model/endpoint and confustion matrix, etc. In general this is a forwarding looking change that will be useful later. :)</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/repl/index.html b/repl/index.html
index 0eb183542..06098190a 100644
--- a/repl/index.html
+++ b/repl/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>SageWorks REPL - SageWorks</title>
+        <title>Workbench REPL - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,13 +104,13 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
           <span class="md-ellipsis">
             
-              SageWorks REPL
+              Workbench REPL
             
           </span>
         </div>
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -300,7 +300,7 @@
           
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -311,7 +311,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1880,7 +1880,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1890,7 +1890,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3109,11 +3109,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3204,20 +3204,20 @@
                   
 
 
-  <h1>SageWorks REPL</h1>
+  <h1>Workbench REPL</h1>
 
 <div class="admonition tip inline end">
 <p class="admonition-title">Visibility and Control</p>
-<p>The SageWorks REPL provides AWS ML Pipeline visibility just like the <a href="../aws_setup/dashboard_stack/">SageWorks Dashboard</a> but also provides control over the creation, modification, and deletion of artifacts through the Python API.</p>
+<p>The Workbench REPL provides AWS ML Pipeline visibility just like the <a href="../aws_setup/dashboard_stack/">Workbench Dashboard</a> but also provides control over the creation, modification, and deletion of artifacts through the Python API.</p>
 </div>
-<p>The SageWorks REPL is a customized iPython shell. It provides tailored functionality for easy interaction with SageWorks objects and since it's based on iPython developers will feel right at home using autocomplete, history, help, etc. Both easy and powerful, the SageWorks REPL puts control of AWS ML Pipelines at your fingertips.</p>
+<p>The Workbench REPL is a customized iPython shell. It provides tailored functionality for easy interaction with Workbench objects and since it's based on iPython developers will feel right at home using autocomplete, history, help, etc. Both easy and powerful, the Workbench REPL puts control of AWS ML Pipelines at your fingertips.</p>
 <h3 id="installation">Installation</h3>
-<p><code>pip install sageworks</code></p>
+<p><code>pip install workbench</code></p>
 <h3 id="usage">Usage</h3>
-<p>Just type <code>sageworks</code> at the command line and the SageWorks shell will spin up and provide a command view of your AWS Machine Learning Pipelines.</p>
-<p>At startup the SageWorks shell, will connect to your AWS Account and create a summary of the Machine Learning artifacts currently residing on the account.</p>
-<p><img alt="sageworks_repl" style="float: right; width: 450px; padding-left: 5px;"
-src="https://github.com/SuperCowPowers/sageworks/assets/4806709/10a969ed-3415-4d9f-ad0d-ac23706e6202"></p>
+<p>Just type <code>workbench</code> at the command line and the Workbench shell will spin up and provide a command view of your AWS Machine Learning Pipelines.</p>
+<p>At startup the Workbench shell, will connect to your AWS Account and create a summary of the Machine Learning artifacts currently residing on the account.</p>
+<p><img alt="workbench_repl" style="float: right; width: 450px; padding-left: 5px;"
+src="https://github.com/SuperCowPowers/workbench/assets/4806709/10a969ed-3415-4d9f-ad0d-ac23706e6202"></p>
 <p><strong>Available Commands:</strong></p>
 <ul>
 <li>status</li>
@@ -3237,12 +3237,12 @@ <h3 id="usage">Usage</h3>
 </span></code></pre></div>
 <h2 id="additional-resources">Additional Resources</h2>
 <ul>
-<li>Setting up SageWorks on your AWS Account: <a href="../aws_setup/core_stack/">AWS Setup</a></li>
-<li>Using SageWorks for ML Pipelines: <a href="../api_classes/overview/">SageWorks API Classes</a></li>
+<li>Setting up Workbench on your AWS Account: <a href="../aws_setup/core_stack/">AWS Setup</a></li>
+<li>Using Workbench for ML Pipelines: <a href="../api_classes/overview/">Workbench API Classes</a></li>
 </ul>
 <p><img align="right" src="../images/scp.png" width="180"></p>
 <ul>
-<li>SageWorks Core Classes: <a href="../core_classes/overview/">Core Classes</a></li>
+<li>Workbench Core Classes: <a href="../core_classes/overview/">Core Classes</a></li>
 <li>Consulting Available: <a href="https://www.supercowpowers.com">SuperCowPowers LLC</a></li>
 </ul>
 
diff --git a/road_maps/0_9_0/index.html b/road_maps/0_9_0/index.html
index a37202387..d350c1153 100644
--- a/road_maps/0_9_0/index.html
+++ b/road_maps/0_9_0/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.9.0 - SageWorks</title>
+        <title>0.9.0 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3151,11 +3151,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3287,9 +3287,9 @@
 <h1 id="road-map-v090">Road Map v0.9.0</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <h2 id="general">General</h2>
 <p><strong>Streamlining</strong></p>
 <p>We've learned a lot from our beta testers!</p>
@@ -3302,19 +3302,19 @@ <h2 id="general">General</h2>
 <li><a href="https://docs.aws.amazon.com/general/latest/gr/sagemaker.html">Sagemaker Endpoint Quotas</a></li>
 </ul>
 <p><strong>Full Artifact Load or None</strong></p>
-<p>For the SageWorks’ DataSource, FeatureSet, Model, and Endpoint classes the new functionality will ensure that objects are only instantiated when all required data is fully available, returning <strong>None</strong> if the artifact ID is invalid or if the object is only partially constructed in AWS. </p>
+<p>For the Workbench’ DataSource, FeatureSet, Model, and Endpoint classes the new functionality will ensure that objects are only instantiated when all required data is fully available, returning <strong>None</strong> if the artifact ID is invalid or if the object is only partially constructed in AWS. </p>
 <p>By preventing partially constructed objects, this approach reduces runtime errors when accessing incomplete attributes and simplifies error handling for clients, enhancing robustness and reliability. We are looking at Pydantic for formally capturing schema and types (see <a href="https://github.com/pydantic/pydantic">Pydantic</a>).</p>
 <p><strong>Onboarding</strong></p>
-<p>We'll have to balance the 'full artifact or None' with the need to <code>onboard()</code> artifacts created outside of SageWorks. We'll probably have a class method for onboarding, something like:</p>
+<p>We'll have to balance the 'full artifact or None' with the need to <code>onboard()</code> artifacts created outside of Workbench. We'll probably have a class method for onboarding, something like:</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>my_model = Model.onboard(&quot;some_random_model&quot;)
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>if my_model is None:
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>    &lt;handle failure to onboard&gt;
 </span></code></pre></div>
 <p><strong>Caching</strong></p>
-<p>Caching needs a substantial overhaul. Right now SageWorks over uses caching. We baked it into our AWSServiceBroker and that gets used by everything.</p>
+<p>Caching needs a substantial overhaul. Right now Workbench over uses caching. We baked it into our AWSServiceBroker and that gets used by everything.</p>
 <p>Caching only really makes sense when we can't wait for AWS to respond to requests. The Dashboard and Web Interfaces are the only use case where responsiveness is important. Other use cases like nightly batch processing, scripts or notebooks, will work totally fine waiting for AWS responses.</p>
 <p><strong>Class/API Reductions</strong></p>
-<p>The organic growth of SageWorks was based on user feedback and testing, that organic growth has led to an <strong>abundance</strong> of Classes and API calls. We'll be identifying classes and methods that are 'cruft' from some development push and will be deprecating those.</p>
+<p>The organic growth of Workbench was based on user feedback and testing, that organic growth has led to an <strong>abundance</strong> of Classes and API calls. We'll be identifying classes and methods that are 'cruft' from some development push and will be deprecating those.</p>
 <h2 id="deprecation-warnings">Deprecation Warnings</h2>
 <p>We're starting to put in deprecation warning as we streamline classes and APIs. If you're using a class or method that's going to be deprecated you'll see a log message like this:</p>
 <div class="language-text highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>broker = AWSServiceBroker()
@@ -3353,7 +3353,7 @@ <h2 id="internal-changes">Internal Changes</h2>
 <p>We're getting rid of caching for individual Artifacts, if you're constructing an artifact object, you probably want detailed information that's 'up to date' and waiting a bit is probably fine. <strong>Note:</strong> We'll still make these instantiations as fast as we can, removing the caching logic will as least simplify the implementations.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/road_maps/0_9_5/index.html b/road_maps/0_9_5/index.html
index 03e48e744..b45f54534 100644
--- a/road_maps/0_9_5/index.html
+++ b/road_maps/0_9_5/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>0.9.5 - SageWorks</title>
+        <title>0.9.5 - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1817,7 +1817,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1827,7 +1827,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3115,11 +3115,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3215,9 +3215,9 @@
 <h1 id="road-map-v095">Road Map v0.9.5</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
+<p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>
 <h2 id="general">General</h2>
 <p><strong>ML Pipelines</strong></p>
 <p>We've learned a lot from our beta testers!</p>
@@ -3249,7 +3249,7 @@ <h2 id="versioned-artifacts">Versioned Artifacts</h2>
 <p>Our beta customers have requested versioning for artifacts, so we support versioning for both Model and FeatureSets. Endpoints and DataSources typically do not need versioning, so we may wait on the versioning support for those artifact until a later version.</p>
 <h2 id="questions">Questions?</h2>
 <p><img align="right" src="../../images/scp.png" width="180"></p>
-<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 
 
 
diff --git a/search/search_index.json b/search/search_index.json
index da427ca8c..a0e12c008 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to SageWorks","text":"<p>The SageWorks framework makes AWS\u00ae both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Jobs, Athena, Feature Store, Models, and Endpoints. SageWorks makes it easy to build production ready, AWS powered, machine learning pipelines.</p> SageWorks Dashboard: AWS Pipelines in a Whole New Light!"},{"location":"#full-aws-overview","title":"Full AWS OverView","text":"<ul> <li>Health Monitoring \ud83d\udfe2</li> <li>Dynamic Updates</li> <li>High Level Summary</li> </ul>"},{"location":"#drill-down-views","title":"Drill-Down Views","text":"<ul> <li>Glue Jobs</li> <li>DataSources</li> <li>FeatureSets</li> <li>Models</li> <li>Endpoints</li> </ul>"},{"location":"#private-saas-architecture","title":"Private SaaS Architecture","text":"<p>Secure your Data, Empower your ML Pipelines</p> <p>SageWorks is architected as a Private SaaS. This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. SageWorks deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support, both components and full web interfaces. By using SageWorks, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. SageWorks Private SaaS Architecture</p>"},{"location":"#dashboard-and-api","title":"Dashboard and API","text":"<p>The SageWorks package has two main components, a Web Interface that provides visibility into AWS ML PIpelines and a Python API that makes creation and usage of the AWS ML Services easier than using/learning the services directly.</p>"},{"location":"#web-interfaces","title":"Web Interfaces","text":"<p>The SageWorks Dashboard has a set of web interfaces that give visibility into the AWS Glue and SageMaker Services. There are currently 5 web interfaces available:</p> <ul> <li>Top Level Dashboard: Shows all AWS ML Artifacts (Glue and SageMaker)</li> <li>DataSources: DataSource Column Details, Distributions and Correlations</li> <li>FeatureSets: FeatureSet Details, Distributions and Correlations</li> <li>Model: Model details, performance metric, and inference plots</li> <li>Endpoints: Endpoint details, realtime charts of endpoint performance and latency</li> </ul>"},{"location":"#python-api","title":"Python API","text":"<p>SageWorks API Documentation: SageWorks API Classes </p> <p>The main functionality of the Python API is to encapsulate and manage a set of AWS services underneath a Python Object interface. The Python Classes are used to create and interact with Machine Learning Pipeline Artifacts.</p>"},{"location":"#getting-started","title":"Getting Started","text":"<p>SageWorks will need some initial setup when you first start using it. See our Getting Started guide on how to connect SageWorks to your AWS Account.</p>"},{"location":"#additional-resources","title":"Additional Resources","text":"<ul> <li>Getting Started: Getting Started </li> <li>SageWorks API Classes: API Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"admin/aws_service_limits/","title":"AWS Service Limits","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>In general SageWorks works well, out of the box, with the standard set of limits for AWS accounts. SageWorks supports throttling, timeouts, and a broad set of AWS error handling routines for general purpose usage.</p> <p>When using SageWorks for large scale deployments there are a set of AWS Service limits that will need to be increased.</p>"},{"location":"admin/aws_service_limits/#serverless-endpoints","title":"ServerLess Endpoints","text":"<p>There are two serverless endpoint quotas that will need to be adjusted.</p> <ul> <li>Maximum number of serverless endpoints (defaults to 5): Set to 50-100</li> <li>Maximum total concurrency serverless endpoints (defaults to 10): Set to 100-200</li> </ul> <p></p>"},{"location":"admin/aws_service_limits/#parallel-featuregroup-creation","title":"Parallel FeatureGroup Creation","text":"<p>When running a large set of parallel Glue/Batch Jobs that are creating FeatureGroups, some clients have hit this limit. </p> <p>\"ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling the CreateFeatureGroup operation: The account-level service limit 'Maximum number of feature group creation workflows executing in parallel' is 4 FeatureGroups, with current utilization of 4 FeatureGroups and a request delta of 1 FeatureGroups. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota.\"}</p> <p>Unfortunately this one is not adjustable through the AWS Service Quota console and you'll have to initiate an AWS Support ticket.</p>"},{"location":"admin/aws_service_limits/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"admin/base_docker_push/","title":"SageWorks Base Docker Build and Push","text":"<p>Notes and information on how to do the Docker Builds and Push to AWS ECR.</p>"},{"location":"admin/base_docker_push/#update-sageworks-version","title":"Update SageWorks Version","text":"<pre><code>vi Dockerfile\n\n# Install latest Sageworks\nRUN pip install --no-cache-dir 'sageworks[ml-tool,chem]'==0.7.0\n</code></pre>"},{"location":"admin/base_docker_push/#build-the-docker-image","title":"Build the Docker Image","text":"<p>Note: For a client specific config file you'll need to copy it locally so that it's within Dockers 'build context'. If you're building the 'vanilla' open source Docker image, then you can use the <code>open_source_config.json</code> that's in the directory already.</p> <pre><code>docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \\\nsageworks_base:v0_7_0_amd64 --platform linux/amd64 .\n</code></pre>"},{"location":"admin/base_docker_push/#test-the-image-locally","title":"Test the Image Locally","text":"<p>You have a <code>docker_local_base</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/base_docker_push/#login-to-ecr","title":"Login to ECR","text":"<pre><code>aws ecr-public get-login-password --region us-east-1 --profile \\\nscp_sandbox_admin | docker login --username AWS \\\n--password-stdin public.ecr.aws\n</code></pre>"},{"location":"admin/base_docker_push/#tagpush-the-image-to-aws-ecr","title":"Tag/Push the Image to AWS ECR","text":"<p><pre><code>docker tag sageworks_base:v0_7_0_amd64 \\\npublic.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64\n</code></pre></p>"},{"location":"admin/base_docker_push/#update-the-latest-tag","title":"Update the 'latest' tag","text":"<p><pre><code>docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \\\npublic.ecr.aws/m6i5k1r2/sageworks_base:latest\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/sageworks_base:latest\n</code></pre></p>"},{"location":"admin/base_docker_push/#update-the-stable-tag","title":"Update the 'stable' tag","text":"<p>This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :)</p> <p><pre><code>docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \\\npublic.ecr.aws/m6i5k1r2/sageworks_base:stable\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable\n</code></pre></p>"},{"location":"admin/base_docker_push/#test-the-ecr-image","title":"Test the ECR Image","text":"<p>You have a <code>docker_ecr_base</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/dashboard_docker_push/","title":"Dashboard Docker Build and Push","text":"<p>Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR.</p>"},{"location":"admin/dashboard_docker_push/#update-sageworks-version","title":"Update SageWorks Version","text":"<pre><code>cd applications/aws_dashboard\nvi Dockerfile\n\n# Install Sageworks (changes often)\nRUN pip install --no-cache-dir sageworks==0.4.13 &lt;-- change this\n</code></pre>"},{"location":"admin/dashboard_docker_push/#build-the-docker-image","title":"Build the Docker Image","text":"<p>Note: For a client specific config file you'll need to copy it locally so that it's within Dockers 'build context'. If you're building the 'vanilla' open source Docker image, then you can use the <code>open_source_config.json</code> that's in the directory already.</p> <pre><code>docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \\\nsageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 .\n</code></pre> <p>Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page.</p>"},{"location":"admin/dashboard_docker_push/#test-the-image-locally","title":"Test the Image Locally","text":"<p>You have a <code>docker_local_dashboard</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/dashboard_docker_push/#login-to-ecr","title":"Login to ECR","text":"<pre><code>aws ecr-public get-login-password --region us-east-1 --profile \\\nscp_sandbox_admin | docker login --username AWS \\\n--password-stdin public.ecr.aws\n</code></pre>"},{"location":"admin/dashboard_docker_push/#tagpush-the-image-to-aws-ecr","title":"Tag/Push the Image to AWS ECR","text":"<p><pre><code>docker tag sageworks_dashboard:v0_4_13_amd64 \\\npublic.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64\n</code></pre></p>"},{"location":"admin/dashboard_docker_push/#update-the-latest-tag","title":"Update the 'latest' tag","text":"<p><pre><code>docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \\\npublic.ecr.aws/m6i5k1r2/sageworks_dashboard:latest\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest\n</code></pre></p>"},{"location":"admin/dashboard_docker_push/#update-the-stable-tag","title":"Update the 'stable' tag","text":"<p>This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :)</p> <p><pre><code>docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \\\npublic.ecr.aws/m6i5k1r2/sageworks_dashboard:stable\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable\n</code></pre></p>"},{"location":"admin/dashboard_docker_push/#test-the-ecr-image","title":"Test the ECR Image","text":"<p>You have a <code>docker_ecr_dashboard</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/dashboard_s3_plugins/","title":"Deploying S3 Plugins with the Dashboard","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>Notes and information on how to include S3 based plugins with your SageWorks Dashboard. </p> <p>Deploying your Dashboard plugins via an S3 bucket allows plugin developers to modify and improve plugins without a bunch of Docker builds, ECR, and CDK Deploy.</p> <p>On, the other hand when you have a 'final build' you might want to lock it in with a Docker image. If you'd like to bundle up your plugins in a Docker image and deploy please see Dashboard with Plugins.</p>"},{"location":"admin/dashboard_s3_plugins/#check-your-dashboard","title":"Check your Dashboard","text":"<p>Make sure your Dashboard is configured to pull plugin pages, views, and components from an S3 bucket. Go to your main dashboard page and there's a 'secret link' when you click on the main title that brings up the Dashboard Status Page.</p> <p></p> <p>Dashboard Status Page Okay now check your Plugin Path: config and make sure it points to the S3 bucket you're expecting.</p> <p></p>"},{"location":"admin/dashboard_s3_plugins/#develop-your-plugins","title":"Develop your Plugins","text":"<p>During development it's good to both unit testing and local dashboard testing. Please see our main plugin docs for how to do local testing Plugins General.</p> <p>When you're ready to 'deploy' the plugins you can copy them up to the S3 bucket/prefix. You want to copy recursively so if you're plugin directory looks like the listing below you want to copy all the files/directories, so that the dashboard picks up everything.</p> <pre><code>- my_plugins\n   - pages\n      - page_1.py\n   - views\n      - view_1.py\n   - components\n       -component_1.py\n       -component_2.py\n</code></pre> <p>Careful with Plugin Copy</p> <p>In particular, pay attention to additional files in the directory structure. You do not want to copy _pycache_ and *.pyc files. So we recommend using this CLI.</p> <pre><code>cd my_plugins\naws s3 cp . s3://my_bucket/prefix --recursive --exclude \"*\" --include \"*.py\"\n</code></pre>"},{"location":"admin/dashboard_s3_plugins/#restart-the-ecs-service","title":"Restart the ECS Service","text":"<p>Okay, so this is a bit heavy handed, but automatically removing/adding/modifying the plugin pages, views, and components was 'amazingly complicated' and will be a feature request for a later date. :)</p> <p>Getting Cluster and Service Names</p> <p>You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service.</p> <p>The cluster will be something like:</p> <pre><code>SageworksDashboard-SageworksCluster123456\n</code></pre> <p>and the service will be something like:</p> <pre><code>SageworksDashboard-SageworksService789123\n</code></pre> <p>Anyway, find those two things and run this command below (Note: You probably need admin permisions)</p> <pre><code>aws ecs update-service --cluster your-cluster-name \\\n--service your-service-name --force-new-deployment\n</code></pre> <p>Important: Even though this command will finish immediately, the ECS service will slowly flip over to the new instance (like 5-10 minutes), so wait a bit before testing the changes.</p>"},{"location":"admin/dashboard_s3_plugins/#verify-new-plugin-changes","title":"Verify new Plugin changes","text":"<p>Okay now that the ECS service has restarted (which can take a bit) you can now go to the Dashboard and test/verify that the changes you made now show up on the Dashboard.</p>"},{"location":"admin/dashboard_s3_plugins/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"admin/dashboard_with_plugins/","title":"Deploying Plugins with the Dashboard","text":"<p>Notes and information on how to include plugins with your SageWorks Dashboard.</p> <ul> <li>ECR: AWS Elastic Container Registry (stores Docker images)</li> <li>ECS: AWS Elastic Container Service (uses Docker images)</li> </ul>"},{"location":"admin/dashboard_with_plugins/#install-docker","title":"Install Docker","text":"<ul> <li>Linux: https://docs.docker.com/engine/install/ubuntu/</li> <li>Windows: https://docs.docker.com/desktop/install/windows-install/</li> </ul>"},{"location":"admin/dashboard_with_plugins/#build-the-docker-image","title":"Build the Docker Image","text":"<p>If you don't already have a Dockerfile, here's one to get you started, just place this into your repo/directory that has the plugins. </p> <pre><code># Pull base sageworks dashboard image with specific tag (pick latest or stable)\nFROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest\n\n# Copy the plugin files into the Dashboard plugins dir\nCOPY ./sageworks_plugins /app/sageworks_plugins\nENV SAGEWORKS_PLUGINS=/app/sageworks_plugins\n</code></pre> <p>Note: Your plugins directory should looks like this</p> <pre><code>sageworks_plugins/\n   pages/\n      my_plugin_page.py\n      ...\n   views/\n      my_plugin_view.py\n      ...\n   components/\n      my_component.py\n      ...\n</code></pre>"},{"location":"admin/dashboard_with_plugins/#build-it","title":"Build it","text":"<pre><code>docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 .\n</code></pre>"},{"location":"admin/dashboard_with_plugins/#test-the-image-locally","title":"Test the Image Locally","text":"<p>You'll need to use AWS Credentials for this, it's a bit complicated, please contact SageWorks Support sageworks@supercowpowers.com or chat us up on Discord </p>"},{"location":"admin/dashboard_with_plugins/#login-to-your-ecr","title":"Login to your ECR","text":"<p>Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR.</p> <p>Note: This ECR should be private as your plugins are customized for specific business use cases.</p> <p>Your ECR location will have this form <pre><code>&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com\n</code></pre></p> <pre><code>aws ecr get-login-password --region us-east-1 --profile &lt;aws_profile&gt; \\\n| docker login --username AWS --password-stdin \\\n&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com\n</code></pre>"},{"location":"admin/dashboard_with_plugins/#tagpush-the-image-to-aws-ecr","title":"Tag/Push the Image to AWS ECR","text":"<p><pre><code>docker tag my_sageworks_with_plugins:v1_0 \\\n&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0\n</code></pre> <pre><code>docker push \\\n&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0\n</code></pre></p>"},{"location":"admin/dashboard_with_plugins/#deploying-plugin-docker-image-to-aws","title":"Deploying Plugin Docker Image to AWS","text":"<p>Okay now that you have your plugin Docker Image you can deploy to your AWS account:</p> <p>Copy the Dashboard CDK files</p> <p>This is cheesy but just copy all the CDK files into your repo/directory.</p> <pre><code>cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/\n</code></pre> <p>Change the Docker Image to Deploy</p> <p>Now open up the <code>app.py</code> file and change this line to your Docker Image</p> <pre><code># When you want a different docker image change this line\ndashboard_image = \"public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64\"\n</code></pre> <p>Make sure your <code>SAGEWORKS_CONFIG</code> is properly set, and run the following commands:</p> <pre><code>export SAGEWORKS_CONFIG=/Users/&lt;user_name&gt;/.sageworks/sageworks_config.json\ncdk diff\ncdk deploy\n</code></pre> <p>CDK Diff</p> <p>In particular, pay attention to the <code>cdk diff</code> it should ONLY have the image name as a difference. <pre><code>cdk diff\n[-] \"Image\": \"&lt;account&gt;.dkr.ecr.us-east-1/my-plugins:latest_123\",\n[+] \"Image\": \"&lt;account&gt;.dkr.ecr.us-east-1/my-plugins:latest_456\",\n</code></pre></p>"},{"location":"admin/dashboard_with_plugins/#note-on-sageworks-configuration","title":"Note on SageWorks Configuration","text":"<p>All Configuration is managed by the CDK Python Script and the <code>SAGEWORKS_CONFIG</code> ENV var. If you want to change things like <code>REDIS_HOST</code> or <code>SAGEWORKS_BUCKET</code> you should do that with a <code>sageworks.config</code> file and then point the <code>SAGEWORKS_CONFIG</code> ENV var to that file.</p>"},{"location":"admin/pypi_release/","title":"PyPI Release Notes","text":"<p>Notes and information on how to do the PyPI release for the SageMaker project. For full details on packaging you can reference this page Packaging</p> <p>The following instructions should work, but things change :)</p>"},{"location":"admin/pypi_release/#package-requirements","title":"Package Requirements","text":"<ul> <li>pip install tox</li> <li>pip install --upgrade wheel build twine</li> </ul>"},{"location":"admin/pypi_release/#setup-pypirc","title":"Setup pypirc","text":"<p>The easiest thing to do is setup a \\~/.pypirc file with the following contents</p> <pre><code>[distutils]\nindex-servers =\n  pypi\n  testpypi\n\n[pypi]\nusername = __token__\npassword = pypi-AgEIcH...\n\n[testpypi]\nusername = __token__\npassword = pypi-AgENdG...\n</code></pre>"},{"location":"admin/pypi_release/#tox-background","title":"Tox Background","text":"<p>Tox will install the SageMaker Sandbox package into a blank virtualenv and then execute all the tests against the newly installed package. So if everything goes okay, you know the pypi package installed fine and the tests (which puls from the installed <code>sageworks</code> package) also ran okay.</p>"},{"location":"admin/pypi_release/#make-sure-all-tests-pass","title":"Make sure ALL tests pass","text":"<pre><code>$ cd sageworks\n$ tox \n</code></pre> <p>If ALL the test above pass...</p>"},{"location":"admin/pypi_release/#clean-any-previous-distribution-files","title":"Clean any previous distribution files","text":"<pre><code>make clean\n</code></pre>"},{"location":"admin/pypi_release/#tag-the-new-version","title":"Tag the New Version","text":"<pre><code>git tag v0.1.8 (or whatever)\ngit push --tags\n</code></pre>"},{"location":"admin/pypi_release/#create-the-test-pypi-release","title":"Create the TEST PyPI Release","text":"<pre><code>python -m build\ntwine upload dist/* -r testpypi\n</code></pre>"},{"location":"admin/pypi_release/#install-the-test-pypi-release","title":"Install the TEST PyPI Release","text":"<pre><code>pip install --index-url https://test.pypi.org/simple sageworks\n</code></pre>"},{"location":"admin/pypi_release/#create-the-real-pypi-release","title":"Create the REAL PyPI Release","text":"<pre><code>twine upload dist/* -r pypi\n</code></pre>"},{"location":"admin/pypi_release/#push-any-possible-changes-to-github","title":"Push any possible changes to Github","text":"<pre><code>git push\n</code></pre>"},{"location":"admin/sageworks_docker_for_lambdas/","title":"SageWorks Docker Image for Lambdas","text":"<p>Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks.</p> <p>AWS, for some reason, does not allow Public ECRs to be used for Lambda Docker images. So you'll have to copy the Docker image into your private ECR. </p>"},{"location":"admin/sageworks_docker_for_lambdas/#creating-a-private-ecr","title":"Creating a Private ECR","text":"<p>You only need to do this if you don't already have a private ECR.</p>"},{"location":"admin/sageworks_docker_for_lambdas/#aws-console-to-create-private-ecr","title":"AWS Console to create Private ECR","text":"<ol> <li>Open the Amazon ECR console.</li> <li>Choose \"Create repository\".</li> <li>For \"Repository name\", enter <code>sageworks_base</code>.</li> <li>Ensure \"Private\" is selected.</li> <li>Choose \"Create repository\".</li> </ol>"},{"location":"admin/sageworks_docker_for_lambdas/#command-line-to-create-private-ecr","title":"Command Line to create Private ECR","text":"<p>Create the ECR repository using the AWS CLI:</p> <pre><code>aws ecr create-repository --repository-name sageworks_base --region &lt;region&gt;\n</code></pre>"},{"location":"admin/sageworks_docker_for_lambdas/#pulling-docker-image-into-private-ecr","title":"Pulling Docker Image into Private ECR","text":"<p>Note: You'll only need to do this when you want to update the SageWorks Docker image</p> <p>Pull the SageWorks Public ECR Image</p> <pre><code>docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest\n</code></pre> <p>Tag the image for your private ECR</p> <pre><code>docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \\\n&lt;your-account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:latest\n</code></pre> <p>Push the image to your private ECR</p> <pre><code>aws ecr get-login-password --region &lt;region&gt; --profile &lt;profile&gt; | \\\ndocker login --username AWS --password-stdin &lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com\n\ndocker push &lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;\n</code></pre>"},{"location":"admin/sageworks_docker_for_lambdas/#using-the-docker-image-for-your-lambdas","title":"Using the Docker Image for your Lambdas","text":"<p>Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs.</p>"},{"location":"admin/sageworks_docker_for_lambdas/#aws-console","title":"AWS Console","text":"<ol> <li>Open the AWS Lambda console.</li> <li>Create a new function.</li> <li>Select \"Container image\".</li> <li>Use the ECR image URI: <code>&lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;</code>.</li> </ol>"},{"location":"admin/sageworks_docker_for_lambdas/#command-line","title":"Command Line","text":"<p>Create the Lambda function using the AWS CLI:</p> <pre><code>aws lambda create-function \\\n --region &lt;region&gt; \\\n --function-name myLambdaFunction \\\n --package-type Image \\\n --code ImageUri=&lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt; \\\n --role arn:aws:iam::&lt;account-id&gt;:role/&lt;execution-role&gt;\n</code></pre>"},{"location":"admin/sageworks_docker_for_lambdas/#python-cdk","title":"Python CDK","text":"<p>Define the Lambda function in your CDK app:</p> <pre><code>from aws_cdk import (\n   aws_lambda as _lambda,\n   core\n)\n\nclass MyLambdaStack(core.Stack):\n   def __init__(self, scope: core.Construct, id: str, **kwargs) -&gt; None:\n       super().__init__(scope, id, **kwargs)\n\n       _lambda.Function(self, \"MyLambdaFunction\",\n                        code=_lambda.Code.from_ecr_image(\"&lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;\"),\n                        handler=_lambda.Handler.FROM_IMAGE,\n                        runtime=_lambda.Runtime.FROM_IMAGE,\n                        role=iam.Role.from_role_arn(self, \"LambdaRole\", \"arn:aws:iam::&lt;account-id&gt;:role/&lt;execution-role&gt;\"))\n\napp = core.App()\nMyLambdaStack(app, \"MyLambdaStack\")\napp.synth()\n</code></pre>"},{"location":"admin/sageworks_docker_for_lambdas/#cloudformation","title":"Cloudformation","text":"<p>Define the Lambda function in your CloudFormation template.</p> <pre><code>Resources:\n MyLambdaFunction:\n   Type: AWS::Lambda::Function\n   Properties:\n     Code:\n       ImageUri: &lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;\n     Role: arn:aws:iam::&lt;account-id&gt;:role/&lt;execution-role&gt;\n     PackageType: Image\n</code></pre>"},{"location":"api_classes/data_source/","title":"DataSource","text":"<p>DataSource Examples</p> <p>Examples of using the DataSource class are in the Examples section at the bottom of this page. S3 data, local files, and Pandas dataframes, DataSource can read data from many different sources.</p> <p>DataSource: Manages AWS Data Catalog creation and management. DataSources are set up so that can easily be queried with AWS Athena. All DataSources are run through a full set of Exploratory Data Analysis (EDA) techniques (data quality, distributions, stats, outliers, etc.) DataSources can be viewed and explored within the SageWorks Dashboard UI.</p>"},{"location":"api_classes/data_source/#sageworks.api.data_source.DataSource","title":"<code>DataSource</code>","text":"<p>               Bases: <code>AthenaSource</code></p> <p>DataSource: SageWorks DataSource API Class</p> Common Usage <pre><code>my_data = DataSource(name_of_source)\nmy_data.details()\nmy_features = my_data.to_features()\n</code></pre> Source code in <code>src/sageworks/api/data_source.py</code> <pre><code>class DataSource(AthenaSource):\n    \"\"\"DataSource: SageWorks DataSource API Class\n\n    Common Usage:\n        ```python\n        my_data = DataSource(name_of_source)\n        my_data.details()\n        my_features = my_data.to_features()\n        ```\n    \"\"\"\n\n    def __init__(self, source: Union[str, pd.DataFrame], name: str = None, tags: list = None, **kwargs):\n        \"\"\"\n        Initializes a new DataSource object.\n\n        Args:\n            source (Union[str, pd.DataFrame]): Source of data (existing name, filepath, S3 path, or a Pandas DataFrame)\n            name (str): The name of the data source (must be lowercase). If not specified, a name will be generated\n            tags (list[str]): A list of tags associated with the data source. If not specified tags will be generated.\n        \"\"\"\n\n        # Ensure the ds_name is valid\n        if name:\n            Artifact.is_name_valid(name)\n\n        # If the data source name wasn't given, generate it\n        else:\n            name = extract_data_source_basename(source)\n            name = Artifact.generate_valid_name(name)\n\n            # Sanity check for dataframe sources\n            if name == \"dataframe\":\n                msg = \"Set the 'name' argument in the constructor: DataSource(df, name='my_data')\"\n                self.log.critical(msg)\n                raise ValueError(msg)\n\n        # Set the tags and load the source\n        tags = [name] if tags is None else tags\n        self._load_source(source, name, tags)\n\n        # Call superclass init\n        super().__init__(name, **kwargs)\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"DataSource Details\n\n        Returns:\n            dict: A dictionary of details about the DataSource\n        \"\"\"\n        return super().details(**kwargs)\n\n    def query(self, query: str) -&gt; pd.DataFrame:\n        \"\"\"Query the AthenaSource\n\n        Args:\n            query (str): The query to run against the DataSource\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        return super().query(query)\n\n    def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame of ALL the data from this DataSource\n\n        Args:\n            include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n        Returns:\n            pd.DataFrame: A DataFrame of ALL the data from this DataSource\n\n        Note:\n            Obviously this is not recommended for large datasets :)\n        \"\"\"\n\n        # Get the table associated with the data\n        self.log.info(f\"Pulling all data from {self.uuid}...\")\n        table = super().table\n        query = f'SELECT * FROM \"{table}\"'\n        df = self.query(query)\n\n        # Drop any columns generated from AWS\n        if not include_aws_columns:\n            aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n            df = df.drop(columns=aws_cols, errors=\"ignore\")\n        return df\n\n    def to_features(\n        self,\n        name: str,\n        id_column: str = None,\n        tags: list = None,\n        event_time_column: str = None,\n        one_hot_columns: list = None,\n    ) -&gt; Union[FeatureSet, None]:\n        \"\"\"\n        Convert the DataSource to a FeatureSet\n\n        Args:\n            name (str): Set the name for feature set (must be lowercase).\n            id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n            tags (list, optional: Set the tags for the feature set. If not specified tags will be generated\n            event_time_column (str, optional): Set the event time for feature set. If not specified will be generated\n            one_hot_columns (list, optional): Set the columns to be one-hot encoded. (default: None)\n\n        Returns:\n            FeatureSet: The FeatureSet created from the DataSource (or None if the FeatureSet isn't created)\n        \"\"\"\n\n        # Ensure the feature_set_name is valid\n        if not Artifact.is_name_valid(name):\n            self.log.critical(f\"Invalid FeatureSet name: {name}, not creating FeatureSet!\")\n            return None\n\n        # Set the Tags\n        tags = [name] if tags is None else tags\n\n        # Transform the DataSource to a FeatureSet\n        data_to_features = DataToFeaturesLight(self.uuid, name)\n        data_to_features.set_output_tags(tags)\n        data_to_features.transform(\n            id_column=id_column,\n            event_time_column=event_time_column,\n            one_hot_columns=one_hot_columns,\n        )\n\n        # Return the FeatureSet (which will now be up-to-date)\n        return FeatureSet(name)\n\n    def _load_source(self, source: str, name: str, tags: list):\n        \"\"\"Load the source of the data\"\"\"\n        self.log.info(f\"Loading source: {source}...\")\n\n        # Pandas DataFrame Source\n        if isinstance(source, pd.DataFrame):\n            my_loader = PandasToData(name)\n            my_loader.set_input(source)\n            my_loader.set_output_tags(tags)\n            my_loader.transform()\n\n        # S3 Source\n        source = source if isinstance(source, str) else str(source)\n        if source.startswith(\"s3://\"):\n            my_loader = S3ToDataSourceLight(source, name)\n            my_loader.set_output_tags(tags)\n            my_loader.transform()\n\n        # File Source\n        elif os.path.isfile(source):\n            my_loader = CSVToDataSource(source, name)\n            my_loader.set_output_tags(tags)\n            my_loader.transform()\n</code></pre>"},{"location":"api_classes/data_source/#sageworks.api.data_source.DataSource.__init__","title":"<code>__init__(source, name=None, tags=None, **kwargs)</code>","text":"<p>Initializes a new DataSource object.</p> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>Union[str, DataFrame]</code> <p>Source of data (existing name, filepath, S3 path, or a Pandas DataFrame)</p> required <code>name</code> <code>str</code> <p>The name of the data source (must be lowercase). If not specified, a name will be generated</p> <code>None</code> <code>tags</code> <code>list[str]</code> <p>A list of tags associated with the data source. If not specified tags will be generated.</p> <code>None</code> Source code in <code>src/sageworks/api/data_source.py</code> <pre><code>def __init__(self, source: Union[str, pd.DataFrame], name: str = None, tags: list = None, **kwargs):\n    \"\"\"\n    Initializes a new DataSource object.\n\n    Args:\n        source (Union[str, pd.DataFrame]): Source of data (existing name, filepath, S3 path, or a Pandas DataFrame)\n        name (str): The name of the data source (must be lowercase). If not specified, a name will be generated\n        tags (list[str]): A list of tags associated with the data source. If not specified tags will be generated.\n    \"\"\"\n\n    # Ensure the ds_name is valid\n    if name:\n        Artifact.is_name_valid(name)\n\n    # If the data source name wasn't given, generate it\n    else:\n        name = extract_data_source_basename(source)\n        name = Artifact.generate_valid_name(name)\n\n        # Sanity check for dataframe sources\n        if name == \"dataframe\":\n            msg = \"Set the 'name' argument in the constructor: DataSource(df, name='my_data')\"\n            self.log.critical(msg)\n            raise ValueError(msg)\n\n    # Set the tags and load the source\n    tags = [name] if tags is None else tags\n    self._load_source(source, name, tags)\n\n    # Call superclass init\n    super().__init__(name, **kwargs)\n</code></pre>"},{"location":"api_classes/data_source/#sageworks.api.data_source.DataSource.details","title":"<code>details(**kwargs)</code>","text":"<p>DataSource Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the DataSource</p> Source code in <code>src/sageworks/api/data_source.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"DataSource Details\n\n    Returns:\n        dict: A dictionary of details about the DataSource\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/data_source/#sageworks.api.data_source.DataSource.pull_dataframe","title":"<code>pull_dataframe(include_aws_columns=False)</code>","text":"<p>Return a DataFrame of ALL the data from this DataSource</p> <p>Parameters:</p> Name Type Description Default <code>include_aws_columns</code> <code>bool</code> <p>Include the AWS columns in the DataFrame (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of ALL the data from this DataSource</p> Note <p>Obviously this is not recommended for large datasets :)</p> Source code in <code>src/sageworks/api/data_source.py</code> <pre><code>def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame of ALL the data from this DataSource\n\n    Args:\n        include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n    Returns:\n        pd.DataFrame: A DataFrame of ALL the data from this DataSource\n\n    Note:\n        Obviously this is not recommended for large datasets :)\n    \"\"\"\n\n    # Get the table associated with the data\n    self.log.info(f\"Pulling all data from {self.uuid}...\")\n    table = super().table\n    query = f'SELECT * FROM \"{table}\"'\n    df = self.query(query)\n\n    # Drop any columns generated from AWS\n    if not include_aws_columns:\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        df = df.drop(columns=aws_cols, errors=\"ignore\")\n    return df\n</code></pre>"},{"location":"api_classes/data_source/#sageworks.api.data_source.DataSource.query","title":"<code>query(query)</code>","text":"<p>Query the AthenaSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the DataSource</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/sageworks/api/data_source.py</code> <pre><code>def query(self, query: str) -&gt; pd.DataFrame:\n    \"\"\"Query the AthenaSource\n\n    Args:\n        query (str): The query to run against the DataSource\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    return super().query(query)\n</code></pre>"},{"location":"api_classes/data_source/#sageworks.api.data_source.DataSource.to_features","title":"<code>to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None)</code>","text":"<p>Convert the DataSource to a FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>Set the name for feature set (must be lowercase).</p> required <code>id_column</code> <code>str</code> <p>The ID column (if not specified, an 'auto_id' will be generated).</p> <code>None</code> <code>tags</code> <code>list</code> <p>Set the tags for the feature set. If not specified tags will be generated</p> <code>None</code> <code>event_time_column</code> <code>str</code> <p>Set the event time for feature set. If not specified will be generated</p> <code>None</code> <code>one_hot_columns</code> <code>list</code> <p>Set the columns to be one-hot encoded. (default: None)</p> <code>None</code> <p>Returns:</p> Name Type Description <code>FeatureSet</code> <code>Union[FeatureSet, None]</code> <p>The FeatureSet created from the DataSource (or None if the FeatureSet isn't created)</p> Source code in <code>src/sageworks/api/data_source.py</code> <pre><code>def to_features(\n    self,\n    name: str,\n    id_column: str = None,\n    tags: list = None,\n    event_time_column: str = None,\n    one_hot_columns: list = None,\n) -&gt; Union[FeatureSet, None]:\n    \"\"\"\n    Convert the DataSource to a FeatureSet\n\n    Args:\n        name (str): Set the name for feature set (must be lowercase).\n        id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n        tags (list, optional: Set the tags for the feature set. If not specified tags will be generated\n        event_time_column (str, optional): Set the event time for feature set. If not specified will be generated\n        one_hot_columns (list, optional): Set the columns to be one-hot encoded. (default: None)\n\n    Returns:\n        FeatureSet: The FeatureSet created from the DataSource (or None if the FeatureSet isn't created)\n    \"\"\"\n\n    # Ensure the feature_set_name is valid\n    if not Artifact.is_name_valid(name):\n        self.log.critical(f\"Invalid FeatureSet name: {name}, not creating FeatureSet!\")\n        return None\n\n    # Set the Tags\n    tags = [name] if tags is None else tags\n\n    # Transform the DataSource to a FeatureSet\n    data_to_features = DataToFeaturesLight(self.uuid, name)\n    data_to_features.set_output_tags(tags)\n    data_to_features.transform(\n        id_column=id_column,\n        event_time_column=event_time_column,\n        one_hot_columns=one_hot_columns,\n    )\n\n    # Return the FeatureSet (which will now be up-to-date)\n    return FeatureSet(name)\n</code></pre>"},{"location":"api_classes/data_source/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Create a DataSource from an S3 Path or File Path</p> datasource_from_s3.py<pre><code>from sageworks.api.data_source import DataSource\n\n# Create a DataSource from an S3 Path (or a local file)\nsource_path = \"s3://sageworks-public-data/common/abalone.csv\"\n# source_path = \"/full/path/to/local/file.csv\"\n\nmy_data = DataSource(source_path)\nprint(my_data.details())\n</code></pre> <p>Create a DataSource from a Pandas Dataframe</p> datasource_from_df.py<pre><code>from sageworks.utils.test_data_generator import TestDataGenerator\nfrom sageworks.api.data_source import DataSource\n\n# Create a DataSource from a Pandas DataFrame\ngen_data = TestDataGenerator()\ndf = gen_data.person_data()\n\ntest_data = DataSource(df, name=\"test_data\")\nprint(test_data.details())\n</code></pre> <p>Query a DataSource</p> <p>All SageWorks DataSources use AWS Athena, so any query that you can make with Athena is accessible through the DataSource API.</p> datasource_query.py<pre><code>from sageworks.api.data_source import DataSource\n\n# Grab a DataSource\nmy_data = DataSource(\"abalone_data\")\n\n# Make some queries using the Athena backend\ndf = my_data.query(\"select * from abalone_data where height &gt; .3\")\nprint(df.head())\n\ndf = my_data.query(\"select * from abalone_data where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040        0.0050                      2\n</code></pre> <p>Create a FeatureSet from a DataSource</p> datasource_to_featureset.py<pre><code>from sageworks.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\ntest_data = DataSource('test_data')\nmy_features = test_data.to_features(\"test_features\")\nprint(my_features.details())\n</code></pre>"},{"location":"api_classes/data_source/#sageworks-ui","title":"SageWorks UI","text":"<p>Whenever a DataSource is created SageWorks performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p> SageWorks Dashboard: DataSources <p>Not Finding a particular method?</p> <p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: SageWorks Core Classes</p>"},{"location":"api_classes/df_store/","title":"SageWorks DataFrame Storage","text":"<p>Examples</p> <p>Examples of using the Parameter Storage class are listed at the bottom of this page Examples.</p>"},{"location":"api_classes/df_store/#why-dataframe-storage","title":"Why DataFrame Storage?","text":"<p>Great question, there's a couple of reasons. The first is that the Parameter Store in AWS has a 4KB limit, so that won't support any kind of 'real data'. The second reason is that DataFrames are commonly used as part of the data engineering, science, and ML pipeline construction process. Providing storage of named DataFrames in an accessible location that can be inspected and used by your ML Team comes in super handy.</p>"},{"location":"api_classes/df_store/#efficient-storage","title":"Efficient Storage","text":"<p>All DataFrames are stored in the Parquet format using 'snappy' storage. Parquet is a columnar storage format that efficiently handles large datasets, and using Snappy compression reduces file size while maintaining fast read/write speeds.</p> <p>DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy</p>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore","title":"<code>DFStore</code>","text":"<p>               Bases: <code>AWSDFStore</code></p> <p>DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy</p> Common Usage <pre><code>df_store = DFStore()\n\n# List Data\ndf_store.list()\n\n# Add DataFrame\ndf = pd.DataFrame({\"A\": [1, 2], \"B\": [3, 4]})\ndf_store.upsert(\"/test/my_data\", df)\n\n# Retrieve DataFrame\ndf = df_store.get(\"/test/my_data\")\nprint(df)\n\n# Delete Data\ndf_store.delete(\"/test/my_data\")\n</code></pre> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>class DFStore(AWSDFStore):\n    \"\"\"DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy\n\n    Common Usage:\n        ```python\n        df_store = DFStore()\n\n        # List Data\n        df_store.list()\n\n        # Add DataFrame\n        df = pd.DataFrame({\"A\": [1, 2], \"B\": [3, 4]})\n        df_store.upsert(\"/test/my_data\", df)\n\n        # Retrieve DataFrame\n        df = df_store.get(\"/test/my_data\")\n        print(df)\n\n        # Delete Data\n        df_store.delete(\"/test/my_data\")\n        ```\n    \"\"\"\n\n    def __init__(self, path_prefix: Union[str, None] = None):\n        \"\"\"DFStore Init Method\n\n        Args:\n            path_prefix (Union[str, None], optional): Add a path prefix to storage locations (Defaults to None)\n        \"\"\"\n        self.log = logging.getLogger(\"sageworks\")\n\n        # Initialize the SuperClass\n        super().__init__(path_prefix=path_prefix)\n\n    def list(self, include_cache: bool = False) -&gt; list:\n        \"\"\"List all the objects in the data_store prefix.\n\n        Args:\n            include_cache (bool, optional): Include cache objects in the list (Defaults to False).\n\n        Returns:\n            list: A list of all the objects in the data_store prefix.\n        \"\"\"\n        return super().list(include_cache=include_cache)\n\n    def summary(self, include_cache: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Return a nicely formatted summary of object locations, sizes (in MB), and modified dates.\n\n        Args:\n            include_cache (bool, optional): Include cache objects in the summary (Defaults to False).\n\n        Returns:\n            pd.DataFrame: A formatted DataFrame with the summary details.\n        \"\"\"\n        return super().summary(include_cache=include_cache)\n\n    def details(self, include_cache: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame with detailed metadata for all objects in the data_store prefix.\n\n        Args:\n            include_cache (bool, optional): Include cache objects in the details (Defaults to False).\n\n        Returns:\n            pd.DataFrame: A DataFrame with detailed metadata for all objects in the data_store prefix.\n        \"\"\"\n        return super().details(include_cache=include_cache)\n\n    def check(self, location: str) -&gt; bool:\n        \"\"\"Check if a DataFrame exists at the specified location\n\n        Args:\n            location (str): The location of the data to check.\n\n        Returns:\n            bool: True if the data exists, False otherwise.\n        \"\"\"\n        return super().check(location)\n\n    def get(self, location: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve a DataFrame from AWS S3.\n\n        Args:\n            location (str): The location of the data to retrieve.\n\n        Returns:\n            pd.DataFrame: The retrieved DataFrame or None if not found.\n        \"\"\"\n        _df = super().get(location)\n        if _df is None:\n            self.log.error(f\"Dataframe not found at location: {location}\")\n        return _df\n\n    def upsert(self, location: str, data: Union[pd.DataFrame, pd.Series]):\n        \"\"\"Insert or update a DataFrame or Series in the AWS S3.\n\n        Args:\n            location (str): The location of the data.\n            data (Union[pd.DataFrame, pd.Series]): The data to be stored.\n        \"\"\"\n        super().upsert(location, data)\n\n    def last_modified(self, location: str) -&gt; Union[datetime, None]:\n        \"\"\"Get the last modified date of the DataFrame at the specified location.\n\n        Args:\n            location (str): The location of the data to check.\n\n        Returns:\n            Union[datetime, None]: The last modified date of the DataFrame or None if not found.\n        \"\"\"\n        return super().last_modified(location)\n\n    def delete(self, location: str):\n        \"\"\"Delete a DataFrame from the AWS S3.\n\n        Args:\n            location (str): The location of the data to delete.\n        \"\"\"\n        super().delete(location)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.__init__","title":"<code>__init__(path_prefix=None)</code>","text":"<p>DFStore Init Method</p> <p>Parameters:</p> Name Type Description Default <code>path_prefix</code> <code>Union[str, None]</code> <p>Add a path prefix to storage locations (Defaults to None)</p> <code>None</code> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def __init__(self, path_prefix: Union[str, None] = None):\n    \"\"\"DFStore Init Method\n\n    Args:\n        path_prefix (Union[str, None], optional): Add a path prefix to storage locations (Defaults to None)\n    \"\"\"\n    self.log = logging.getLogger(\"sageworks\")\n\n    # Initialize the SuperClass\n    super().__init__(path_prefix=path_prefix)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.check","title":"<code>check(location)</code>","text":"<p>Check if a DataFrame exists at the specified location</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to check.</p> required <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the data exists, False otherwise.</p> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def check(self, location: str) -&gt; bool:\n    \"\"\"Check if a DataFrame exists at the specified location\n\n    Args:\n        location (str): The location of the data to check.\n\n    Returns:\n        bool: True if the data exists, False otherwise.\n    \"\"\"\n    return super().check(location)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.delete","title":"<code>delete(location)</code>","text":"<p>Delete a DataFrame from the AWS S3.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to delete.</p> required Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def delete(self, location: str):\n    \"\"\"Delete a DataFrame from the AWS S3.\n\n    Args:\n        location (str): The location of the data to delete.\n    \"\"\"\n    super().delete(location)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.details","title":"<code>details(include_cache=False)</code>","text":"<p>Return a DataFrame with detailed metadata for all objects in the data_store prefix.</p> <p>Parameters:</p> Name Type Description Default <code>include_cache</code> <code>bool</code> <p>Include cache objects in the details (Defaults to False).</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame with detailed metadata for all objects in the data_store prefix.</p> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def details(self, include_cache: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame with detailed metadata for all objects in the data_store prefix.\n\n    Args:\n        include_cache (bool, optional): Include cache objects in the details (Defaults to False).\n\n    Returns:\n        pd.DataFrame: A DataFrame with detailed metadata for all objects in the data_store prefix.\n    \"\"\"\n    return super().details(include_cache=include_cache)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.get","title":"<code>get(location)</code>","text":"<p>Retrieve a DataFrame from AWS S3.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to retrieve.</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The retrieved DataFrame or None if not found.</p> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def get(self, location: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve a DataFrame from AWS S3.\n\n    Args:\n        location (str): The location of the data to retrieve.\n\n    Returns:\n        pd.DataFrame: The retrieved DataFrame or None if not found.\n    \"\"\"\n    _df = super().get(location)\n    if _df is None:\n        self.log.error(f\"Dataframe not found at location: {location}\")\n    return _df\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.last_modified","title":"<code>last_modified(location)</code>","text":"<p>Get the last modified date of the DataFrame at the specified location.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to check.</p> required <p>Returns:</p> Type Description <code>Union[datetime, None]</code> <p>Union[datetime, None]: The last modified date of the DataFrame or None if not found.</p> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def last_modified(self, location: str) -&gt; Union[datetime, None]:\n    \"\"\"Get the last modified date of the DataFrame at the specified location.\n\n    Args:\n        location (str): The location of the data to check.\n\n    Returns:\n        Union[datetime, None]: The last modified date of the DataFrame or None if not found.\n    \"\"\"\n    return super().last_modified(location)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.list","title":"<code>list(include_cache=False)</code>","text":"<p>List all the objects in the data_store prefix.</p> <p>Parameters:</p> Name Type Description Default <code>include_cache</code> <code>bool</code> <p>Include cache objects in the list (Defaults to False).</p> <code>False</code> <p>Returns:</p> Name Type Description <code>list</code> <code>list</code> <p>A list of all the objects in the data_store prefix.</p> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def list(self, include_cache: bool = False) -&gt; list:\n    \"\"\"List all the objects in the data_store prefix.\n\n    Args:\n        include_cache (bool, optional): Include cache objects in the list (Defaults to False).\n\n    Returns:\n        list: A list of all the objects in the data_store prefix.\n    \"\"\"\n    return super().list(include_cache=include_cache)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.summary","title":"<code>summary(include_cache=False)</code>","text":"<p>Return a nicely formatted summary of object locations, sizes (in MB), and modified dates.</p> <p>Parameters:</p> Name Type Description Default <code>include_cache</code> <code>bool</code> <p>Include cache objects in the summary (Defaults to False).</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A formatted DataFrame with the summary details.</p> Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def summary(self, include_cache: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Return a nicely formatted summary of object locations, sizes (in MB), and modified dates.\n\n    Args:\n        include_cache (bool, optional): Include cache objects in the summary (Defaults to False).\n\n    Returns:\n        pd.DataFrame: A formatted DataFrame with the summary details.\n    \"\"\"\n    return super().summary(include_cache=include_cache)\n</code></pre>"},{"location":"api_classes/df_store/#sageworks.api.df_store.DFStore.upsert","title":"<code>upsert(location, data)</code>","text":"<p>Insert or update a DataFrame or Series in the AWS S3.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data.</p> required <code>data</code> <code>Union[DataFrame, Series]</code> <p>The data to be stored.</p> required Source code in <code>src/sageworks/api/df_store.py</code> <pre><code>def upsert(self, location: str, data: Union[pd.DataFrame, pd.Series]):\n    \"\"\"Insert or update a DataFrame or Series in the AWS S3.\n\n    Args:\n        location (str): The location of the data.\n        data (Union[pd.DataFrame, pd.Series]): The data to be stored.\n    \"\"\"\n    super().upsert(location, data)\n</code></pre>"},{"location":"api_classes/df_store/#examples","title":"Examples","text":"<p>These example show how to use the <code>DFStore()</code> class to list, add, and get dataframes from AWS Storage.</p> <p>SageWorks REPL</p> <p>If you'd like to experiment with listing, adding, and getting dataframe with the <code>DFStore()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! SageWorks REPL</p> Using DataFrame Store<pre><code>from sageworks.api.df_store import DFStore\ndf_store = DFStore()\n\n# List DataFrames\ndf_store().list()\n\nOut[1]:\nml/confustion_matrix  (0.002MB/2024-09-23 16:44:48)\nml/hold_out_ids  (0.094MB/2024-09-23 16:57:01)\nml/my_awesome_df  (0.002MB/2024-09-23 16:43:30)\nml/shap_values  (0.019MB/2024-09-23 16:57:21)\n\n# Add a DataFrame\ndf = pd.DataFrame({\"A\": [1]*1000, \"B\": [3]*1000})\ndf_store.upsert(\"test/test_df\", df)\n\n# List DataFrames (we can just use the REPR)\ndf_store\n\nOut[2]:\nml/confustion_matrix  (0.002MB/2024-09-23 16:44:48)\nml/hold_out_ids  (0.094MB/2024-09-23 16:57:01)\nml/my_awesome_df  (0.002MB/2024-09-23 16:43:30)\nml/shap_values  (0.019MB/2024-09-23 16:57:21)\ntest/test_df  (0.002MB/2024-09-23 16:59:27)\n\n# Retrieve dataframes\nreturn_df = df_store.get(\"test/test_df\")\nreturn_df.head()\n\nOut[3]:\n   A  B\n0  1  3\n1  1  3\n2  1  3\n3  1  3\n4  1  3\n\n# Delete dataframes\ndf_store.delete(\"test/test_df\")\n</code></pre> <p>Compressed Storage is Automatic</p> <p>All DataFrames are stored in the Parquet format using 'snappy' storage. Parquet is a columnar storage format that efficiently handles large datasets, and using Snappy compression reduces file size while maintaining fast read/write speeds.</p>"},{"location":"api_classes/endpoint/","title":"Endpoint","text":"<p>Endpoint Examples</p> <p>Examples of using the Endpoint class are listed at the bottom of this page Examples.</p> <p>Endpoint: Manages AWS Endpoint creation and deployment. Endpoints are automatically set up and provisioned for deployment into AWS. Endpoints can be viewed in the AWS Sagemaker interfaces or in the SageWorks Dashboard UI, which provides additional model details and performance metrics</p>"},{"location":"api_classes/endpoint/#sageworks.api.endpoint.Endpoint","title":"<code>Endpoint</code>","text":"<p>               Bases: <code>EndpointCore</code></p> <p>Endpoint: SageWorks Endpoint API Class</p> Common Usage <pre><code>my_endpoint = Endpoint(name)\nmy_endpoint.details()\nmy_endpoint.inference(eval_df)\n</code></pre> Source code in <code>src/sageworks/api/endpoint.py</code> <pre><code>class Endpoint(EndpointCore):\n    \"\"\"Endpoint: SageWorks Endpoint API Class\n\n    Common Usage:\n        ```python\n        my_endpoint = Endpoint(name)\n        my_endpoint.details()\n        my_endpoint.inference(eval_df)\n        ```\n    \"\"\"\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Endpoint Details\n\n        Returns:\n            dict: A dictionary of details about the Endpoint\n        \"\"\"\n        return super().details(**kwargs)\n\n    def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n        Args:\n            eval_df (pd.DataFrame): The DataFrame to run predictions on\n            capture_uuid (str, optional): The UUID of the capture to use (default: None)\n            id_column (str, optional): The name of the column to use as the ID (default: None)\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n        \"\"\"\n        return super().inference(eval_df, capture_uuid, id_column)\n\n    def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the FeatureSet evaluation data\n\n        Args:\n            capture (bool): Capture the inference results\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n        \"\"\"\n        return super().auto_inference(capture)\n\n    def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n        Args:\n            eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n\n        Note:\n            There's no sanity checks or error handling... just FAST Inference!\n        \"\"\"\n        return super().fast_inference(eval_df)\n</code></pre>"},{"location":"api_classes/endpoint/#sageworks.api.endpoint.Endpoint.auto_inference","title":"<code>auto_inference(capture=False)</code>","text":"<p>Run inference on the Endpoint using the FeatureSet evaluation data</p> <p>Parameters:</p> Name Type Description Default <code>capture</code> <code>bool</code> <p>Capture the inference results</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Source code in <code>src/sageworks/api/endpoint.py</code> <pre><code>def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the FeatureSet evaluation data\n\n    Args:\n        capture (bool): Capture the inference results\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n    \"\"\"\n    return super().auto_inference(capture)\n</code></pre>"},{"location":"api_classes/endpoint/#sageworks.api.endpoint.Endpoint.details","title":"<code>details(**kwargs)</code>","text":"<p>Endpoint Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Endpoint</p> Source code in <code>src/sageworks/api/endpoint.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"Endpoint Details\n\n    Returns:\n        dict: A dictionary of details about the Endpoint\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/endpoint/#sageworks.api.endpoint.Endpoint.fast_inference","title":"<code>fast_inference(eval_df)</code>","text":"<p>Run inference on the Endpoint using the provided DataFrame</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>The DataFrame to run predictions on</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Note <p>There's no sanity checks or error handling... just FAST Inference!</p> Source code in <code>src/sageworks/api/endpoint.py</code> <pre><code>def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n    Args:\n        eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n\n    Note:\n        There's no sanity checks or error handling... just FAST Inference!\n    \"\"\"\n    return super().fast_inference(eval_df)\n</code></pre>"},{"location":"api_classes/endpoint/#sageworks.api.endpoint.Endpoint.inference","title":"<code>inference(eval_df, capture_uuid=None, id_column=None)</code>","text":"<p>Run inference on the Endpoint using the provided DataFrame</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>The DataFrame to run predictions on</p> required <code>capture_uuid</code> <code>str</code> <p>The UUID of the capture to use (default: None)</p> <code>None</code> <code>id_column</code> <code>str</code> <p>The name of the column to use as the ID (default: None)</p> <code>None</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Source code in <code>src/sageworks/api/endpoint.py</code> <pre><code>def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n    Args:\n        eval_df (pd.DataFrame): The DataFrame to run predictions on\n        capture_uuid (str, optional): The UUID of the capture to use (default: None)\n        id_column (str, optional): The name of the column to use as the ID (default: None)\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n    \"\"\"\n    return super().inference(eval_df, capture_uuid, id_column)\n</code></pre>"},{"location":"api_classes/endpoint/#examples","title":"Examples","text":"<p>Run Inference on an Endpoint</p> endpoint_inference.py<pre><code>from sageworks.api.feature_set import FeatureSet\nfrom sageworks.api.model import Model\nfrom sageworks.api.endpoint import Endpoint\n\n# Grab an existing Endpoint\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# SageWorks has full ML Pipeline provenance, so we can backtrack the inputs,\n# get a DataFrame of data (not used for training) and run inference\nmodel = Model(endpoint.get_input())\nfs = FeatureSet(model.get_input())\nathena_table = fs.view(\"training\").table\ndf = fs.query(f\"SELECT * FROM {athena_table} where training = FALSE\")\n\n# Run inference/predictions on the Endpoint\nresults_df = endpoint.inference(df)\n\n# Run inference/predictions and capture the results\nresults_df = endpoint.inference(df, capture=True)\n\n# Run inference/predictions using the FeatureSet evaluation data\nresults_df = endpoint.auto_inference(capture=True)\n</code></pre> <p>Output</p> <p><pre><code>Processing...\n     class_number_of_rings  prediction\n0                       13   11.477922\n1                       12   12.316887\n2                        8    7.612847\n3                        8    9.663341\n4                        9    9.075263\n..                     ...         ...\n839                      8    8.069856\n840                     15   14.915502\n841                     11   10.977605\n842                     10   10.173433\n843                      7    7.297976\n</code></pre> Endpoint Details</p> <p>The details() method</p> <p>The <code>detail()</code> method on the Endpoint class provides a lot of useful information. All of the SageWorks classes have a <code>details()</code> method try it out!</p> endpoint_details.py<pre><code>from sageworks.api.endpoint import Endpoint\nfrom pprint import pprint\n\n# Get Endpoint and print out it's details\nendpoint = Endpoint(\"abalone-regression-end\")\npprint(endpoint.details())\n</code></pre> <p>Output</p> <pre><code>{\n 'input': 'abalone-regression',\n 'instance': 'Serverless (2GB/5)',\n 'model_metrics':   metric_name  value\n            0        RMSE  2.190\n            1         MAE  1.544\n            2          R2  0.504,\n 'model_name': 'abalone-regression',\n 'model_type': 'regressor',\n 'modified': datetime.datetime(2023, 12, 29, 17, 48, 35, 115000, tzinfo=datetime.timezone.utc),\n     class_number_of_rings  prediction\n0                        9    8.648378\n1                       11    9.717787\n2                       11   10.933070\n3                       10    9.899738\n4                        9   10.014504\n..                     ...         ...\n495                     10   10.261657\n496                      9   10.788254\n497                     13    7.779886\n498                     12   14.718514\n499                     13   10.637320\n 'sageworks_tags': ['abalone', 'regression'],\n 'status': 'InService',\n 'uuid': 'abalone-regression-end',\n 'variant': 'AllTraffic'}\n</code></pre> <p>Endpoint Metrics</p> endpoint_metrics.py<pre><code>from sageworks.api.endpoint import Endpoint\n\n# Grab an existing Endpoint\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# SageWorks tracks both Model performance and Endpoint Metrics\nmodel_metrics = endpoint.details()[\"model_metrics\"]\nendpoint_metrics = endpoint.endpoint_metrics()\nprint(model_metrics)\nprint(endpoint_metrics)\n</code></pre> <p>Output</p> <pre><code>  metric_name  value\n0        RMSE  2.190\n1         MAE  1.544\n2          R2  0.504\n\n    Invocations  ModelLatency  OverheadLatency  ModelSetupTime  Invocation5XXErrors\n29          0.0          0.00             0.00            0.00                  0.0\n30          1.0          1.11            23.73           23.34                  0.0\n31          0.0          0.00             0.00            0.00                  0.0\n48          0.0          0.00             0.00            0.00                  0.0\n49          5.0          0.45             9.64           23.57                  0.0\n50          2.0          0.57             0.08            0.00                  0.0\n51          0.0          0.00             0.00            0.00                  0.0\n60          4.0          0.33             5.80           22.65                  0.0\n61          1.0          1.11            23.35           23.10                  0.0\n62          0.0          0.00             0.00            0.00                  0.0\n...\n</code></pre>"},{"location":"api_classes/endpoint/#sageworks-ui","title":"SageWorks UI","text":"<p>Running these few lines of code creates and deploys an AWS Endpoint. The Endpoint artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the SageWorks Dashboard UI. SageWorks will monitor the endpoint, plot invocations, latencies, and tracks error metrics.</p> SageWorks Dashboard: Endpoints <p>Not Finding a particular method?</p> <p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: SageWorks Core Classes</p>"},{"location":"api_classes/feature_set/","title":"FeatureSet","text":"<p>FeatureSet Examples</p> <p>Examples of using the FeatureSet Class are in the Examples section at the bottom of this page. AWS Feature Store and Feature Groups are quite complicated to set up manually but the SageWorks FeatureSet makes it a breeze!</p> <p>FeatureSet: Manages AWS Feature Store/Group creation and management. FeatureSets are set up so they can easily be queried with AWS Athena. All FeatureSets are run through a full set of Exploratory Data Analysis (EDA) techniques (data quality, distributions, stats, outliers, etc.) FeatureSets can be viewed and explored within the SageWorks Dashboard UI.</p>"},{"location":"api_classes/feature_set/#sageworks.api.feature_set.FeatureSet","title":"<code>FeatureSet</code>","text":"<p>               Bases: <code>FeatureSetCore</code></p> <p>FeatureSet: SageWorks FeatureSet API Class</p> Common Usage <pre><code>my_features = FeatureSet(name)\nmy_features.details()\nmy_features.to_model(\n    name=\"abalone-regression\",\n    model_type=ModelType.REGRESSOR,\n    target_column=\"class_number_of_rings\"\n    feature_list=[\"my\", \"best\", \"features\"])\n)\n</code></pre> Source code in <code>src/sageworks/api/feature_set.py</code> <pre><code>class FeatureSet(FeatureSetCore):\n    \"\"\"FeatureSet: SageWorks FeatureSet API Class\n\n    Common Usage:\n        ```python\n        my_features = FeatureSet(name)\n        my_features.details()\n        my_features.to_model(\n            name=\"abalone-regression\",\n            model_type=ModelType.REGRESSOR,\n            target_column=\"class_number_of_rings\"\n            feature_list=[\"my\", \"best\", \"features\"])\n        )\n        ```\n    \"\"\"\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"FeatureSet Details\n\n        Returns:\n            dict: A dictionary of details about the FeatureSet\n        \"\"\"\n        return super().details(**kwargs)\n\n    def query(self, query: str, **kwargs) -&gt; pd.DataFrame:\n        \"\"\"Query the AthenaSource\n\n        Args:\n            query (str): The query to run against the FeatureSet\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        return super().query(query, **kwargs)\n\n    def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame of ALL the data from this FeatureSet\n\n        Args:\n            include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n        Returns:\n            pd.DataFrame: A DataFrame of ALL the data from this FeatureSet\n\n        Note:\n            Obviously this is not recommended for large datasets :)\n        \"\"\"\n\n        # Get the table associated with the data\n        self.log.info(f\"Pulling all data from {self.uuid}...\")\n        query = f\"SELECT * FROM {self.athena_table}\"\n        df = self.query(query)\n\n        # Drop any columns generated from AWS\n        if not include_aws_columns:\n            aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n            df = df.drop(columns=aws_cols, errors=\"ignore\")\n        return df\n\n    def to_model(\n        self,\n        name: str,\n        model_type: ModelType,\n        tags: list = None,\n        description: str = None,\n        feature_list: list = None,\n        target_column: str = None,\n        scikit_model_class: str = None,\n        model_import_str: str = None,\n        **kwargs,\n    ) -&gt; Union[Model, None]:\n        \"\"\"Create a Model from the FeatureSet\n\n        Args:\n\n            name (str): The name of the Model to create\n            model_type (ModelType): The type of model to create (See sageworks.model.ModelType)\n            tags (list, optional): Set the tags for the model.  If not given tags will be generated.\n            description (str, optional): Set the description for the model. If not give a description is generated.\n            feature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.\n            target_column (str, optional): The target column for the model (use None for unsupervised model)\n            scikit_model_class (str, optional): Scikit model class to use (e.g. \"KMeans\", default: None)\n            model_import_str (str, optional): The import for the model (e.g. \"from sklearn.cluster import KMeans\")\n\n        Returns:\n            Model: The Model created from the FeatureSet (or None if the Model could not be created)\n        \"\"\"\n\n        # Ensure the model_name is valid\n        if name:\n            if not Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False):\n                self.log.critical(f\"Invalid Model name: {name}, not creating Model!\")\n                return None\n\n        # If the model_name wasn't given generate it\n        else:\n            name = self.uuid.replace(\"_features\", \"\") + \"-model\"\n            name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n        # Create the Model Tags\n        tags = [name] if tags is None else tags\n\n        # Transform the FeatureSet into a Model\n        features_to_model = FeaturesToModel(\n            feature_uuid=self.uuid,\n            model_uuid=name,\n            model_type=model_type,\n            scikit_model_class=scikit_model_class,\n            model_import_str=model_import_str,\n        )\n        features_to_model.set_output_tags(tags)\n        features_to_model.transform(\n            target_column=target_column, description=description, feature_list=feature_list, **kwargs\n        )\n\n        # Return the Model\n        return Model(name)\n</code></pre>"},{"location":"api_classes/feature_set/#sageworks.api.feature_set.FeatureSet.details","title":"<code>details(**kwargs)</code>","text":"<p>FeatureSet Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the FeatureSet</p> Source code in <code>src/sageworks/api/feature_set.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"FeatureSet Details\n\n    Returns:\n        dict: A dictionary of details about the FeatureSet\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/feature_set/#sageworks.api.feature_set.FeatureSet.pull_dataframe","title":"<code>pull_dataframe(include_aws_columns=False)</code>","text":"<p>Return a DataFrame of ALL the data from this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>include_aws_columns</code> <code>bool</code> <p>Include the AWS columns in the DataFrame (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of ALL the data from this FeatureSet</p> Note <p>Obviously this is not recommended for large datasets :)</p> Source code in <code>src/sageworks/api/feature_set.py</code> <pre><code>def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame of ALL the data from this FeatureSet\n\n    Args:\n        include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n    Returns:\n        pd.DataFrame: A DataFrame of ALL the data from this FeatureSet\n\n    Note:\n        Obviously this is not recommended for large datasets :)\n    \"\"\"\n\n    # Get the table associated with the data\n    self.log.info(f\"Pulling all data from {self.uuid}...\")\n    query = f\"SELECT * FROM {self.athena_table}\"\n    df = self.query(query)\n\n    # Drop any columns generated from AWS\n    if not include_aws_columns:\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        df = df.drop(columns=aws_cols, errors=\"ignore\")\n    return df\n</code></pre>"},{"location":"api_classes/feature_set/#sageworks.api.feature_set.FeatureSet.query","title":"<code>query(query, **kwargs)</code>","text":"<p>Query the AthenaSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the FeatureSet</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/sageworks/api/feature_set.py</code> <pre><code>def query(self, query: str, **kwargs) -&gt; pd.DataFrame:\n    \"\"\"Query the AthenaSource\n\n    Args:\n        query (str): The query to run against the FeatureSet\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    return super().query(query, **kwargs)\n</code></pre>"},{"location":"api_classes/feature_set/#sageworks.api.feature_set.FeatureSet.to_model","title":"<code>to_model(name, model_type, tags=None, description=None, feature_list=None, target_column=None, scikit_model_class=None, model_import_str=None, **kwargs)</code>","text":"<p>Create a Model from the FeatureSet</p> <p>Args:</p> <pre><code>name (str): The name of the Model to create\nmodel_type (ModelType): The type of model to create (See sageworks.model.ModelType)\ntags (list, optional): Set the tags for the model.  If not given tags will be generated.\ndescription (str, optional): Set the description for the model. If not give a description is generated.\nfeature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.\ntarget_column (str, optional): The target column for the model (use None for unsupervised model)\nscikit_model_class (str, optional): Scikit model class to use (e.g. \"KMeans\", default: None)\nmodel_import_str (str, optional): The import for the model (e.g. \"from sklearn.cluster import KMeans\")\n</code></pre> <p>Returns:</p> Name Type Description <code>Model</code> <code>Union[Model, None]</code> <p>The Model created from the FeatureSet (or None if the Model could not be created)</p> Source code in <code>src/sageworks/api/feature_set.py</code> <pre><code>def to_model(\n    self,\n    name: str,\n    model_type: ModelType,\n    tags: list = None,\n    description: str = None,\n    feature_list: list = None,\n    target_column: str = None,\n    scikit_model_class: str = None,\n    model_import_str: str = None,\n    **kwargs,\n) -&gt; Union[Model, None]:\n    \"\"\"Create a Model from the FeatureSet\n\n    Args:\n\n        name (str): The name of the Model to create\n        model_type (ModelType): The type of model to create (See sageworks.model.ModelType)\n        tags (list, optional): Set the tags for the model.  If not given tags will be generated.\n        description (str, optional): Set the description for the model. If not give a description is generated.\n        feature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.\n        target_column (str, optional): The target column for the model (use None for unsupervised model)\n        scikit_model_class (str, optional): Scikit model class to use (e.g. \"KMeans\", default: None)\n        model_import_str (str, optional): The import for the model (e.g. \"from sklearn.cluster import KMeans\")\n\n    Returns:\n        Model: The Model created from the FeatureSet (or None if the Model could not be created)\n    \"\"\"\n\n    # Ensure the model_name is valid\n    if name:\n        if not Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False):\n            self.log.critical(f\"Invalid Model name: {name}, not creating Model!\")\n            return None\n\n    # If the model_name wasn't given generate it\n    else:\n        name = self.uuid.replace(\"_features\", \"\") + \"-model\"\n        name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n    # Create the Model Tags\n    tags = [name] if tags is None else tags\n\n    # Transform the FeatureSet into a Model\n    features_to_model = FeaturesToModel(\n        feature_uuid=self.uuid,\n        model_uuid=name,\n        model_type=model_type,\n        scikit_model_class=scikit_model_class,\n        model_import_str=model_import_str,\n    )\n    features_to_model.set_output_tags(tags)\n    features_to_model.transform(\n        target_column=target_column, description=description, feature_list=feature_list, **kwargs\n    )\n\n    # Return the Model\n    return Model(name)\n</code></pre>"},{"location":"api_classes/feature_set/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Create a FeatureSet from a Datasource</p> datasource_to_featureset.py<pre><code>from sageworks.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\nds = DataSource('test_data')\nfs = ds.to_features(\"test_features\", id_column=\"id\")\nprint(fs.details())\n</code></pre> <p>FeatureSet EDA Statistics</p> <p>featureset_eda.py<pre><code>from sageworks.api.feature_set import FeatureSet\nimport pandas as pd\n\n# Grab a FeatureSet and pull some of the EDA Stats\nmy_features = FeatureSet('test_features')\n\n# Grab some of the EDA Stats\ncorr_data = my_features.correlations()\ncorr_df = pd.DataFrame(corr_data)\nprint(corr_df)\n\n# Get some outliers\noutliers = my_features.outliers()\npprint(outliers.head())\n\n# Full set of EDA Stats\neda_stats = my_features.column_stats()\npprint(eda_stats)\n</code></pre> Output</p> <pre><code>                 age  food_pizza  food_steak  food_sushi  food_tacos    height        id  iq_score\nage              NaN   -0.188645   -0.256356    0.263048    0.054211  0.439678 -0.054948 -0.295513\nfood_pizza -0.188645         NaN   -0.288175   -0.229591   -0.196818 -0.494380  0.137282  0.395378\nfood_steak -0.256356   -0.288175         NaN   -0.374920   -0.321403 -0.002542 -0.005199  0.076477\nfood_sushi  0.263048   -0.229591   -0.374920         NaN   -0.256064  0.536396  0.038279 -0.435033\nfood_tacos  0.054211   -0.196818   -0.321403   -0.256064         NaN -0.091493 -0.051398  0.033364\nheight      0.439678   -0.494380   -0.002542    0.536396   -0.091493       NaN -0.117372 -0.655210\nid         -0.054948    0.137282   -0.005199    0.038279   -0.051398 -0.117372       NaN  0.106020\niq_score   -0.295513    0.395378    0.076477   -0.435033    0.033364 -0.655210  0.106020       NaN\n\n        name     height      weight         salary  age    iq_score  likes_dogs  food_pizza  food_steak  food_sushi  food_tacos outlier_group\n0  Person 96  57.582840  148.461349   80000.000000   43  150.000000           1           0           0           0           0    height_low\n1  Person 68  73.918663  189.527313  219994.000000   80  100.000000           0           0           0           1           0  iq_score_low\n2  Person 49  70.381790  261.237000  175633.703125   49  107.933998           0           0           0           1           0  iq_score_low\n3  Person 90  73.488739  193.840698  227760.000000   72  110.821541           1           0           0           0           0   salary_high\n\n&lt;lots of EDA data and statistics&gt;\n</code></pre> <p>Query a FeatureSet</p> <p>All SageWorks FeatureSet have an 'offline' store that uses AWS Athena, so any query that you can make with Athena is accessible through the FeatureSet API.</p> featureset_query.py<pre><code>from sageworks.api.feature_set import FeatureSet\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"abalone_features\")\n\n# Make some queries using the Athena backend\ndf = my_features.query(\"select * from abalone_features where height &gt; .3\")\nprint(df.head())\n\ndf = my_features.query(\"select * from abalone_features where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040         0.0050                      2\n</code></pre> <p>Create a Model from a FeatureSet</p> featureset_to_model.py<pre><code>from sageworks.api.feature_set import FeatureSet\nfrom sageworks.api.model import ModelType\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet('test_features')\n\n# Create a Model from the FeatureSet\n# Note: ModelTypes can be CLASSIFIER, REGRESSOR, \n#       UNSUPERVISED, or TRANSFORMER\nmy_model = my_features.to_model(name=\"test-model\", \n                                model_type=ModelType.REGRESSOR, \n                                target_column=\"iq_score\")\npprint(my_model.details())\n</code></pre> <p>Output</p> <pre><code>{'approval_status': 'Approved',\n 'content_types': ['text/csv'],\n ...\n 'inference_types': ['ml.t2.medium'],\n 'input': 'test_features',\n 'model_metrics':   metric_name  value\n                0        RMSE  7.924\n                1         MAE  6.554,\n                2          R2  0.604,\n 'regression_predictions':       iq_score  prediction\n                            0   136.519012  139.964460\n                            1   133.616974  130.819950\n                            2   122.495415  124.967834\n                            3   133.279510  121.010284\n                            4   127.881073  113.825005\n    ...\n 'response_types': ['text/csv'],\n 'sageworks_tags': ['test-model'],\n 'shapley_values': None,\n 'size': 0.0,\n 'status': 'Completed',\n 'transform_types': ['ml.m5.large'],\n 'uuid': 'test-model',\n 'version': 1}\n</code></pre>"},{"location":"api_classes/feature_set/#sageworks-ui","title":"SageWorks UI","text":"<p>Whenever a FeatureSet is created SageWorks performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p> SageWorks Dashboard: FeatureSets <p>Not Finding a particular method?</p> <p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: SageWorks Core Classes</p>"},{"location":"api_classes/meta/","title":"Meta","text":"<p>Meta Examples</p> <p>Examples of using the Meta class are listed at the bottom of this page Examples.</p> <p>Meta: A class that provides high level information and summaries of Cloud Platform Artifacts. The Meta class provides 'account' information, configuration, etc. It also provides metadata for Artifacts, such as Data Sources, Feature Sets, Models, and Endpoints.</p>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta","title":"<code>Meta</code>","text":"<p>               Bases: <code>CloudMeta</code></p> <p>Meta: A class that provides metadata functionality for Cloud Platform Artifacts.</p> Common Usage <pre><code>from sageworks.api import Meta\nmeta = Meta()\n\n# Get the AWS Account Info\nmeta.account()\nmeta.config()\n\n# These are 'list' methods\nmeta.etl_jobs()\nmeta.data_sources()\nmeta.feature_sets(details=True/False)\nmeta.models(details=True/False)\nmeta.endpoints()\nmeta.views()\nmeta.pipelines()\n\n# These are 'describe' methods\nmeta.data_source(\"abalone_data\")\nmeta.feature_set(\"abalone_features\")\nmeta.model(\"abalone-regression\")\nmeta.endpoint(\"abalone-endpoint\")\n</code></pre> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>class Meta(CloudMeta):\n    \"\"\"Meta: A class that provides metadata functionality for Cloud Platform Artifacts.\n\n    Common Usage:\n       ```python\n       from sageworks.api import Meta\n       meta = Meta()\n\n       # Get the AWS Account Info\n       meta.account()\n       meta.config()\n\n       # These are 'list' methods\n       meta.etl_jobs()\n       meta.data_sources()\n       meta.feature_sets(details=True/False)\n       meta.models(details=True/False)\n       meta.endpoints()\n       meta.views()\n       meta.pipelines()\n\n       # These are 'describe' methods\n       meta.data_source(\"abalone_data\")\n       meta.feature_set(\"abalone_features\")\n       meta.model(\"abalone-regression\")\n       meta.endpoint(\"abalone-endpoint\")\n       ```\n    \"\"\"\n\n    def account(self) -&gt; dict:\n        \"\"\"Cloud Platform Account Info\n\n        Returns:\n            dict: Cloud Platform Account Info\n        \"\"\"\n        return super().account()\n\n    def config(self) -&gt; dict:\n        \"\"\"Return the current SageWorks Configuration\n\n        Returns:\n            dict: The current SageWorks Configuration\n        \"\"\"\n        return super().config()\n\n    def incoming_data(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about data in the incoming raw data\n\n        Returns:\n            pd.DataFrame: A summary of the incoming raw data\n        \"\"\"\n        return super().incoming_data()\n\n    def etl_jobs(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n        Returns:\n            pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n        \"\"\"\n        return super().etl_jobs()\n\n    def data_sources(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n        \"\"\"\n        return super().data_sources()\n\n    def views(self, database: str = \"sageworks\") -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n        Args:\n            database (str, optional): Glue database. Defaults to 'sageworks'.\n\n        Returns:\n            pd.DataFrame: A summary of all the Views, for the given database, in AWS\n        \"\"\"\n        return super().views(database=database)\n\n    def feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n        \"\"\"\n        return super().feature_sets(details=details)\n\n    def models(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n        \"\"\"\n        return super().models(details=details)\n\n    def endpoints(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n        \"\"\"\n        return super().endpoints()\n\n    def pipelines(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the ML Pipelines deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Pipelines in the Cloud Platform\n        \"\"\"\n        return super().pipelines()\n\n    def glue_job(self, job_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Glue Job\n\n        Args:\n            job_name (str): The name of the Glue Job\n\n        Returns:\n            dict: The details of the Glue Job (None if not found)\n        \"\"\"\n        return super().glue_job(job_name=job_name)\n\n    def data_source(self, data_source_name: str, database: str = \"sageworks\") -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Data Source\n\n        Args:\n            data_source_name (str): The name of the Data Source\n            database (str, optional): The Glue database. Defaults to 'sageworks'.\n\n        Returns:\n            dict: The details of the Data Source (None if not found)\n        \"\"\"\n        return super().data_source(table_name=data_source_name, database=database)\n\n    def feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Feature Set\n\n        Args:\n            feature_set_name (str): The name of the Feature Set\n\n        Returns:\n            dict: The details of the Feature Set (None if not found)\n        \"\"\"\n        return super().feature_set(feature_group_name=feature_set_name)\n\n    def model(self, model_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Model\n\n        Args:\n            model_name (str): The name of the Model\n\n        Returns:\n            dict: The details of the Model (None if not found)\n        \"\"\"\n        return super().model(model_group_name=model_name)\n\n    def endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Endpoint\n\n        Args:\n            endpoint_name (str): The name of the Endpoint\n\n        Returns:\n            dict: The details of the Endpoint (None if not found)\n        \"\"\"\n        return super().endpoint(endpoint_name=endpoint_name)\n\n    def __repr__(self):\n        return f\"Meta()\\n\\t{super().__repr__()}\"\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.account","title":"<code>account()</code>","text":"<p>Cloud Platform Account Info</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>Cloud Platform Account Info</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def account(self) -&gt; dict:\n    \"\"\"Cloud Platform Account Info\n\n    Returns:\n        dict: Cloud Platform Account Info\n    \"\"\"\n    return super().account()\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.config","title":"<code>config()</code>","text":"<p>Return the current SageWorks Configuration</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The current SageWorks Configuration</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def config(self) -&gt; dict:\n    \"\"\"Return the current SageWorks Configuration\n\n    Returns:\n        dict: The current SageWorks Configuration\n    \"\"\"\n    return super().config()\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.data_source","title":"<code>data_source(data_source_name, database='sageworks')</code>","text":"<p>Get the details of a specific Data Source</p> <p>Parameters:</p> Name Type Description Default <code>data_source_name</code> <code>str</code> <p>The name of the Data Source</p> required <code>database</code> <code>str</code> <p>The Glue database. Defaults to 'sageworks'.</p> <code>'sageworks'</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Data Source (None if not found)</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def data_source(self, data_source_name: str, database: str = \"sageworks\") -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Data Source\n\n    Args:\n        data_source_name (str): The name of the Data Source\n        database (str, optional): The Glue database. Defaults to 'sageworks'.\n\n    Returns:\n        dict: The details of the Data Source (None if not found)\n    \"\"\"\n    return super().data_source(table_name=data_source_name, database=database)\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.data_sources","title":"<code>data_sources()</code>","text":"<p>Get a summary of the Data Sources deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def data_sources(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n    \"\"\"\n    return super().data_sources()\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.endpoint","title":"<code>endpoint(endpoint_name)</code>","text":"<p>Get the details of a specific Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>The name of the Endpoint</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Endpoint (None if not found)</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Endpoint\n\n    Args:\n        endpoint_name (str): The name of the Endpoint\n\n    Returns:\n        dict: The details of the Endpoint (None if not found)\n    \"\"\"\n    return super().endpoint(endpoint_name=endpoint_name)\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.endpoints","title":"<code>endpoints()</code>","text":"<p>Get a summary of the Endpoints deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Endpoints in the Cloud Platform</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def endpoints(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n    \"\"\"\n    return super().endpoints()\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.etl_jobs","title":"<code>etl_jobs()</code>","text":"<p>Get summary data about Extract, Transform, Load (ETL) Jobs</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def etl_jobs(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n    Returns:\n        pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n    \"\"\"\n    return super().etl_jobs()\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.feature_set","title":"<code>feature_set(feature_set_name)</code>","text":"<p>Get the details of a specific Feature Set</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_name</code> <code>str</code> <p>The name of the Feature Set</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Feature Set (None if not found)</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Feature Set\n\n    Args:\n        feature_set_name (str): The name of the Feature Set\n\n    Returns:\n        dict: The details of the Feature Set (None if not found)\n    \"\"\"\n    return super().feature_set(feature_group_name=feature_set_name)\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.feature_sets","title":"<code>feature_sets(details=False)</code>","text":"<p>Get a summary of the Feature Sets deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n    \"\"\"\n    return super().feature_sets(details=details)\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.glue_job","title":"<code>glue_job(job_name)</code>","text":"<p>Get the details of a specific Glue Job</p> <p>Parameters:</p> Name Type Description Default <code>job_name</code> <code>str</code> <p>The name of the Glue Job</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Glue Job (None if not found)</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def glue_job(self, job_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Glue Job\n\n    Args:\n        job_name (str): The name of the Glue Job\n\n    Returns:\n        dict: The details of the Glue Job (None if not found)\n    \"\"\"\n    return super().glue_job(job_name=job_name)\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.incoming_data","title":"<code>incoming_data()</code>","text":"<p>Get summary data about data in the incoming raw data</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the incoming raw data</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def incoming_data(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about data in the incoming raw data\n\n    Returns:\n        pd.DataFrame: A summary of the incoming raw data\n    \"\"\"\n    return super().incoming_data()\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.model","title":"<code>model(model_name)</code>","text":"<p>Get the details of a specific Model</p> <p>Parameters:</p> Name Type Description Default <code>model_name</code> <code>str</code> <p>The name of the Model</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Model (None if not found)</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def model(self, model_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Model\n\n    Args:\n        model_name (str): The name of the Model\n\n    Returns:\n        dict: The details of the Model (None if not found)\n    \"\"\"\n    return super().model(model_group_name=model_name)\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.models","title":"<code>models(details=False)</code>","text":"<p>Get a summary of the Models deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Models deployed in the Cloud Platform</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def models(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n    \"\"\"\n    return super().models(details=details)\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.pipelines","title":"<code>pipelines()</code>","text":"<p>Get a summary of the ML Pipelines deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Pipelines in the Cloud Platform</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def pipelines(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the ML Pipelines deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Pipelines in the Cloud Platform\n    \"\"\"\n    return super().pipelines()\n</code></pre>"},{"location":"api_classes/meta/#sageworks.api.meta.Meta.views","title":"<code>views(database='sageworks')</code>","text":"<p>Get a summary of the all the Views, for the given database, in AWS</p> <p>Parameters:</p> Name Type Description Default <code>database</code> <code>str</code> <p>Glue database. Defaults to 'sageworks'.</p> <code>'sageworks'</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of all the Views, for the given database, in AWS</p> Source code in <code>src/sageworks/api/meta.py</code> <pre><code>def views(self, database: str = \"sageworks\") -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n    Args:\n        database (str, optional): Glue database. Defaults to 'sageworks'.\n\n    Returns:\n        pd.DataFrame: A summary of all the Views, for the given database, in AWS\n    \"\"\"\n    return super().views(database=database)\n</code></pre>"},{"location":"api_classes/meta/#examples","title":"Examples","text":"<p>These example show how to use the <code>Meta()</code> class to pull lists of artifacts from AWS. DataSources, FeatureSets, Models, Endpoints and more. If you're building a web interface plugin, the Meta class is a great place to start.</p> <p>SageWorks REPL</p> <p>If you'd like to see exactly what data/details you get back from the <code>Meta()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! SageWorks REPL</p> Using SageWorks REPL<pre><code>meta = Meta()\nmodel_df = meta.models()\nmodel_df\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\n</code></pre> <p>List the Models in AWS</p> meta_list_models.py<pre><code>from sageworks.api import Meta\n\n# Create our Meta Class and get a list of our Models\nmeta = Meta()\nmodel_df = meta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names:\n    pprint(meta.model(name))\n</code></pre> <p>Output</p> <pre><code>Number of Models: 3\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\nwine-classification\nabalone-regression-full\nabalone-regression\n</code></pre> <p>Getting Model Performance Metrics</p> meta_model_metrics.py<pre><code>from sageworks.api import Meta\n\n# Create our Meta Class and get a list of our Models\nmeta = Meta()\nmodel_df = meta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names[:5]:\n    model_details = meta.model(name)\n    print(f\"\\n\\nModel: {name}\")\n    performance_metrics = model_details[\"sageworks_meta\"][\"sageworks_inference_metrics\"]\n    print(f\"\\tPerformance Metrics: {performance_metrics}\")\n</code></pre> <p>Output</p> <pre><code>wine-classification\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/wine-classification\n    Description: Wine Classification Model\n    Tags: wine::classification\n    Performance Metrics:\n        [{'wine_class': 'TypeA', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 12}, {'wine_class': 'TypeB', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 14}, {'wine_class': 'TypeC', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 9}]\n\nabalone-regression\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/abalone-regression\n    Description: Abalone Regression Model\n    Tags: abalone::regression\n    Performance Metrics:\n        [{'MAE': 1.64, 'RMSE': 2.246, 'R2': 0.502, 'MAPE': 16.393, 'MedAE': 1.209, 'NumRows': 834}]\n</code></pre> <p>List the Endpoints in AWS</p> meta_list_endpoints.py<pre><code>from pprint import pprint\nfrom sageworks.api import Meta\n\n# Create our Meta Class and get a list of our Endpoints\nmeta = Meta()\nendpoint_df = meta.endpoints()\nprint(f\"Number of Endpoints: {len(endpoint_df)}\")\nprint(endpoint_df)\n\n# Get more details data on the Endpoints\nendpoint_names = endpoint_df[\"Name\"].tolist()\nfor name in endpoint_names:\n    pprint(meta.endpoint(name))\n</code></pre> <p>Output</p> <pre><code>Number of Endpoints: 2\n                      Name   Health            Instance           Created  ...     Status     Variant Capture Samp(%)\n0  wine-classification-end  healthy  Serverless (2GB/5)  2024-03-23 23:09  ...  InService  AllTraffic   False       -\n1   abalone-regression-end  healthy  Serverless (2GB/5)  2024-03-23 21:11  ...  InService  AllTraffic   False       -\n\n[2 rows x 10 columns]\nwine-classification-end\n&lt;lots of details about endpoints&gt;\n</code></pre> <p>Not Finding some particular AWS Data?</p> <p>The SageWorks Meta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>"},{"location":"api_classes/model/","title":"Model","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p> <p>Model: Manages AWS Model Package/Group creation and management.</p> <p>Models are automatically set up and provisioned for deployment into AWS. Models can be viewed in the AWS Sagemaker interfaces or in the SageWorks Dashboard UI, which provides additional model details and performance metrics</p>"},{"location":"api_classes/model/#sageworks.api.model.Model","title":"<code>Model</code>","text":"<p>               Bases: <code>ModelCore</code></p> <p>Model: SageWorks Model API Class.</p> Common Usage <pre><code>my_model = Model(name)\nmy_model.details()\nmy_model.to_endpoint()\n</code></pre> Source code in <code>src/sageworks/api/model.py</code> <pre><code>class Model(ModelCore):\n    \"\"\"Model: SageWorks Model API Class.\n\n    Common Usage:\n        ```python\n        my_model = Model(name)\n        my_model.details()\n        my_model.to_endpoint()\n        ```\n    \"\"\"\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Model Details.\n\n        Returns:\n            dict: A dictionary of details about the Model\n        \"\"\"\n        return super().details(**kwargs)\n\n    def to_endpoint(self, name: str = None, tags: list = None, serverless: bool = True) -&gt; Endpoint:\n        \"\"\"Create an Endpoint from the Model.\n\n        Args:\n            name (str): Set the name for the endpoint. If not specified, an automatic name will be generated\n            tags (list): Set the tags for the endpoint. If not specified automatic tags will be generated.\n            serverless (bool): Set the endpoint to be serverless (default: True)\n\n        Returns:\n            Endpoint: The Endpoint created from the Model\n        \"\"\"\n\n        # Ensure the endpoint_name is valid\n        if name:\n            Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False)\n\n        # If the endpoint_name wasn't given generate it\n        else:\n            name = self.uuid.replace(\"_features\", \"\") + \"-end\"\n            name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n        # Create the Endpoint Tags\n        tags = [name] if tags is None else tags\n\n        # Create an Endpoint from the Model\n        model_to_endpoint = ModelToEndpoint(self.uuid, name, serverless=serverless)\n        model_to_endpoint.set_output_tags(tags)\n        model_to_endpoint.transform()\n\n        # Return the Endpoint\n        return Endpoint(name)\n</code></pre>"},{"location":"api_classes/model/#sageworks.api.model.Model.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the Model Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Model</p> Source code in <code>src/sageworks/api/model.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Model Details.\n\n    Returns:\n        dict: A dictionary of details about the Model\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/model/#sageworks.api.model.Model.to_endpoint","title":"<code>to_endpoint(name=None, tags=None, serverless=True)</code>","text":"<p>Create an Endpoint from the Model.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>Set the name for the endpoint. If not specified, an automatic name will be generated</p> <code>None</code> <code>tags</code> <code>list</code> <p>Set the tags for the endpoint. If not specified automatic tags will be generated.</p> <code>None</code> <code>serverless</code> <code>bool</code> <p>Set the endpoint to be serverless (default: True)</p> <code>True</code> <p>Returns:</p> Name Type Description <code>Endpoint</code> <code>Endpoint</code> <p>The Endpoint created from the Model</p> Source code in <code>src/sageworks/api/model.py</code> <pre><code>def to_endpoint(self, name: str = None, tags: list = None, serverless: bool = True) -&gt; Endpoint:\n    \"\"\"Create an Endpoint from the Model.\n\n    Args:\n        name (str): Set the name for the endpoint. If not specified, an automatic name will be generated\n        tags (list): Set the tags for the endpoint. If not specified automatic tags will be generated.\n        serverless (bool): Set the endpoint to be serverless (default: True)\n\n    Returns:\n        Endpoint: The Endpoint created from the Model\n    \"\"\"\n\n    # Ensure the endpoint_name is valid\n    if name:\n        Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False)\n\n    # If the endpoint_name wasn't given generate it\n    else:\n        name = self.uuid.replace(\"_features\", \"\") + \"-end\"\n        name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n    # Create the Endpoint Tags\n    tags = [name] if tags is None else tags\n\n    # Create an Endpoint from the Model\n    model_to_endpoint = ModelToEndpoint(self.uuid, name, serverless=serverless)\n    model_to_endpoint.set_output_tags(tags)\n    model_to_endpoint.transform()\n\n    # Return the Endpoint\n    return Endpoint(name)\n</code></pre>"},{"location":"api_classes/model/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Create a Model from a FeatureSet</p> featureset_to_model.py<pre><code>from sageworks.api.feature_set import FeatureSet\nfrom sageworks.api.model import ModelType\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"test_features\")\n\n# Create a Model from the FeatureSet\n# Note: ModelTypes can be CLASSIFIER, REGRESSOR (XGBoost is default)\nmy_model = my_features.to_model(name=\"test-model\",\n                                model_type=ModelType.REGRESSOR, \n                                target_column=\"iq_score\")\npprint(my_model.details())\n</code></pre> <p>Output</p> <pre><code>{'approval_status': 'Approved',\n 'content_types': ['text/csv'],\n ...\n 'inference_types': ['ml.t2.medium'],\n 'input': 'test_features',\n 'model_metrics':   metric_name  value\n                0        RMSE  7.924\n                1         MAE  6.554,\n                2          R2  0.604,\n 'regression_predictions':       iq_score  prediction\n                            0   136.519012  139.964460\n                            1   133.616974  130.819950\n                            2   122.495415  124.967834\n                            3   133.279510  121.010284\n                            4   127.881073  113.825005\n    ...\n 'response_types': ['text/csv'],\n 'sageworks_tags': ['test-model'],\n 'shapley_values': None,\n 'size': 0.0,\n 'status': 'Completed',\n 'transform_types': ['ml.m5.large'],\n 'uuid': 'test-model',\n 'version': 1}\n</code></pre> <p>Use a specific Scikit-Learn Model</p> <p>featureset_to_knn.py<pre><code>from sageworks.api.feature_set import FeatureSet\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"abalone_features\")\n\n# Transform FeatureSet into KNN Regression Model\n# Note: model_class can be any sckit-learn model \n#  \"KNeighborsRegressor\", \"BayesianRidge\",\n#  \"GaussianNB\", \"AdaBoostClassifier\", etc\nmy_model = my_features.to_model(\n    scikit_model_class=\"KNeighborsRegressor\",\n    model_import_str=\"from sklearn.neighbors import KNeighborsRegressor\",\n    target_column=\"class_number_of_rings\",\n    name=\"abalone-knn-reg\",\n    description=\"Abalone KNN Regression\",\n    tags=[\"abalone\", \"knn\"],\n    train_all_data=True,\n)\npprint(my_model.details())\n</code></pre> Another Scikit-Learn Example</p> featureset_to_rfc.py<pre><code>from sageworks.api.feature_set import FeatureSet\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"wine_features\")\n\n# Using a Scikit-Learn Model\n# Note: model_class can be any sckit-learn model (\"KMeans\", \"BayesianRidge\",\n#       \"GaussianNB\", \"AdaBoostClassifier\", \"Ridge, \"Lasso\", \"SVC\", \"SVR\", etc...)\nmy_model = my_features.to_model(\n    scikit_model_class=\"RandomForestClassifier\",\n    model_import_str=\"from sklearn.ensemble import RandomForestClassifier\",\n    target_column=\"wine_class\",\n    name=\"wine-rfc-class\",\n    description=\"Wine RandomForest Classification\",\n    tags=[\"wine\", \"rfc\"]\n)\npprint(my_model.details())\n</code></pre> <p>Create an Endpoint from a Model</p> <p>Endpoint Costs</p> <p>Serverless endpoints are a great option, they have no AWS charges when not running. A realtime endpoint has less latency (no cold start) but AWS charges an hourly fee which can add up quickly!</p> model_to_endpoint.py<pre><code>from sageworks.api.model import Model\n\n# Grab the abalone regression Model\nmodel = Model(\"abalone-regression\")\n\n# By default, an Endpoint is serverless, you can\n# make a realtime endpoint with serverless=False\nmodel.to_endpoint(name=\"abalone-regression-end\",\n                  tags=[\"abalone\", \"regression\"],\n                  serverless=True)\n</code></pre> <p>Model Health Check and Metrics</p> model_metrics.py<pre><code>from sageworks.api.model import Model\n\n# Grab the abalone-regression Model\nmodel = Model(\"abalone-regression\")\n\n# Perform a health check on the model\n# Note: The health_check() method returns 'issues' if there are any\n#       problems, so if there are no issues, the model is healthy\nhealth_issues = model.health_check()\nif not health_issues:\n    print(\"Model is Healthy\")\nelse:\n    print(\"Model has issues\")\n    print(health_issues)\n\n# Get the model metrics and regression predictions\nprint(model.model_metrics())\nprint(model.regression_predictions())\n</code></pre> <p>Output</p> <pre><code>Model is Healthy\n  metric_name  value\n0        RMSE  2.190\n1         MAE  1.544\n2          R2  0.504\n\n     class_number_of_rings  prediction\n0                        9    8.648378\n1                       11    9.717787\n2                       11   10.933070\n3                       10    9.899738\n4                        9   10.014504\n..                     ...         ...\n495                     10   10.261657\n496                      9   10.788254\n497                     13    7.779886\n498                     12   14.718514\n499                     13   10.637320\n</code></pre>"},{"location":"api_classes/model/#sageworks-ui","title":"SageWorks UI","text":"<p>Running these few lines of code creates an AWS Model Package Group and an AWS Model Package. These model artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the SageWorks Dashboard UI.</p> SageWorks Dashboard: Models <p>Not Finding a particular method?</p> <p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: SageWorks Core Classes</p>"},{"location":"api_classes/monitor/","title":"Monitor","text":"<p>Monitor Examples</p> <p>Examples of using the Monitor class are listed at the bottom of this page Examples.</p> <p>Monitor: Manages AWS Endpoint Monitor creation and deployment. Endpoints Monitors are set up and provisioned for deployment into AWS. Monitors can be viewed in the AWS Sagemaker interfaces or in the SageWorks Dashboard UI, which provides additional monitor details and performance metrics</p>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor","title":"<code>Monitor</code>","text":"<p>               Bases: <code>MonitorCore</code></p> <p>Monitor: SageWorks Monitor API Class</p> Common Usage <pre><code>mon = Endpoint(name).get_monitor()  # Pull from endpoint OR\nmon = Monitor(name)                 # Create using Endpoint Name\nmon.summary()\nmon.details()\n\n# One time setup methods\nmon.add_data_capture()\nmon.create_baseline()\nmon.create_monitoring_schedule()\n\n# Pull information from the monitor\nbaseline_df = mon.get_baseline()\nconstraints_df = mon.get_constraints()\nstats_df = mon.get_statistics()\ninput_df, output_df = mon.get_latest_data_capture()\n</code></pre> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>class Monitor(MonitorCore):\n    \"\"\"Monitor: SageWorks Monitor API Class\n\n    Common Usage:\n       ```\n       mon = Endpoint(name).get_monitor()  # Pull from endpoint OR\n       mon = Monitor(name)                 # Create using Endpoint Name\n       mon.summary()\n       mon.details()\n\n       # One time setup methods\n       mon.add_data_capture()\n       mon.create_baseline()\n       mon.create_monitoring_schedule()\n\n       # Pull information from the monitor\n       baseline_df = mon.get_baseline()\n       constraints_df = mon.get_constraints()\n       stats_df = mon.get_statistics()\n       input_df, output_df = mon.get_latest_data_capture()\n       ```\n    \"\"\"\n\n    def summary(self) -&gt; dict:\n        \"\"\"Monitor Summary\n\n        Returns:\n            dict: A dictionary of summary information about the Monitor\n        \"\"\"\n        return super().summary()\n\n    def details(self) -&gt; dict:\n        \"\"\"Monitor Details\n\n        Returns:\n            dict: A dictionary of details about the Monitor\n        \"\"\"\n        return super().details()\n\n    def add_data_capture(self, capture_percentage=100):\n        \"\"\"\n        Add data capture configuration for this Monitor/endpoint.\n\n        Args:\n            capture_percentage (int): Percentage of data to capture. Defaults to 100.\n        \"\"\"\n        super().add_data_capture(capture_percentage)\n\n    def create_baseline(self, recreate: bool = False):\n        \"\"\"Code to create a baseline for monitoring\n\n        Args:\n            recreate (bool): If True, recreate the baseline even if it already exists\n\n        Notes:\n            This will create/write three files to the baseline_dir:\n            - baseline.csv\n            - constraints.json\n            - statistics.json\n        \"\"\"\n        super().create_baseline(recreate)\n\n    def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n        \"\"\"\n        Sets up the monitoring schedule for the model endpoint.\n\n        Args:\n            schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n            recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n        \"\"\"\n        super().create_monitoring_schedule(schedule, recreate)\n\n    def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"\n        Get the latest data capture input and output from S3.\n\n        Returns:\n            DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n        \"\"\"\n        return super().get_latest_data_capture()\n\n    def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n        Returns:\n            pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n        \"\"\"\n        return super().get_baseline()\n\n    def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the constraints from the baseline\n\n        Returns:\n           pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n        \"\"\"\n        return super().get_constraints()\n\n    def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the statistics from the baseline\n\n        Returns:\n            pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n        \"\"\"\n        return super().get_statistics()\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.add_data_capture","title":"<code>add_data_capture(capture_percentage=100)</code>","text":"<p>Add data capture configuration for this Monitor/endpoint.</p> <p>Parameters:</p> Name Type Description Default <code>capture_percentage</code> <code>int</code> <p>Percentage of data to capture. Defaults to 100.</p> <code>100</code> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def add_data_capture(self, capture_percentage=100):\n    \"\"\"\n    Add data capture configuration for this Monitor/endpoint.\n\n    Args:\n        capture_percentage (int): Percentage of data to capture. Defaults to 100.\n    \"\"\"\n    super().add_data_capture(capture_percentage)\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.create_baseline","title":"<code>create_baseline(recreate=False)</code>","text":"<p>Code to create a baseline for monitoring</p> <p>Parameters:</p> Name Type Description Default <code>recreate</code> <code>bool</code> <p>If True, recreate the baseline even if it already exists</p> <code>False</code> Notes <p>This will create/write three files to the baseline_dir: - baseline.csv - constraints.json - statistics.json</p> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def create_baseline(self, recreate: bool = False):\n    \"\"\"Code to create a baseline for monitoring\n\n    Args:\n        recreate (bool): If True, recreate the baseline even if it already exists\n\n    Notes:\n        This will create/write three files to the baseline_dir:\n        - baseline.csv\n        - constraints.json\n        - statistics.json\n    \"\"\"\n    super().create_baseline(recreate)\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.create_monitoring_schedule","title":"<code>create_monitoring_schedule(schedule='hourly', recreate=False)</code>","text":"<p>Sets up the monitoring schedule for the model endpoint.</p> <p>Parameters:</p> Name Type Description Default <code>schedule</code> <code>str</code> <p>The schedule for the monitoring job (hourly or daily, defaults to hourly).</p> <code>'hourly'</code> <code>recreate</code> <code>bool</code> <p>If True, recreate the monitoring schedule even if it already exists.</p> <code>False</code> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n    \"\"\"\n    Sets up the monitoring schedule for the model endpoint.\n\n    Args:\n        schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n        recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n    \"\"\"\n    super().create_monitoring_schedule(schedule, recreate)\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.details","title":"<code>details()</code>","text":"<p>Monitor Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Monitor</p> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def details(self) -&gt; dict:\n    \"\"\"Monitor Details\n\n    Returns:\n        dict: A dictionary of details about the Monitor\n    \"\"\"\n    return super().details()\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.get_baseline","title":"<code>get_baseline()</code>","text":"<p>Code to get the baseline CSV from the S3 baseline directory</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)</p> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n    Returns:\n        pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n    \"\"\"\n    return super().get_baseline()\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.get_constraints","title":"<code>get_constraints()</code>","text":"<p>Code to get the constraints from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)</p> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the constraints from the baseline\n\n    Returns:\n       pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n    \"\"\"\n    return super().get_constraints()\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.get_latest_data_capture","title":"<code>get_latest_data_capture()</code>","text":"<p>Get the latest data capture input and output from S3.</p> <p>Returns:</p> Name Type Description <code>DataFrame</code> <code>input), DataFrame(output</code> <p>Flattened and processed DataFrames for input and output data.</p> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n    \"\"\"\n    Get the latest data capture input and output from S3.\n\n    Returns:\n        DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n    \"\"\"\n    return super().get_latest_data_capture()\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.get_statistics","title":"<code>get_statistics()</code>","text":"<p>Code to get the statistics from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)</p> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the statistics from the baseline\n\n    Returns:\n        pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n    \"\"\"\n    return super().get_statistics()\n</code></pre>"},{"location":"api_classes/monitor/#sageworks.api.monitor.Monitor.summary","title":"<code>summary()</code>","text":"<p>Monitor Summary</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of summary information about the Monitor</p> Source code in <code>src/sageworks/api/monitor.py</code> <pre><code>def summary(self) -&gt; dict:\n    \"\"\"Monitor Summary\n\n    Returns:\n        dict: A dictionary of summary information about the Monitor\n    \"\"\"\n    return super().summary()\n</code></pre>"},{"location":"api_classes/monitor/#examples","title":"Examples","text":"<p>Initial Setup of the Endpoint Monitor</p> monitor_setup.py<pre><code>from sageworks.api.monitor import Monitor\n\n# Create an Endpoint Monitor Class and perform initial Setup\nendpoint_name = \"abalone-regression-end-rt\"\nmon = Monitor(endpoint_name)\n\n# Add data capture to the endpoint\nmon.add_data_capture(capture_percentage=100)\n\n# Create a baseline for monitoring\nmon.create_baseline()\n\n# Set up the monitoring schedule\nmon.create_monitoring_schedule(schedule=\"hourly\")\n</code></pre> <p>Pulling Information from an Existing Monitor</p> monitor_usage.py<pre><code>from sageworks.api.monitor import Monitor\nfrom sageworks.api.endpoint import Endpoint\n\n# Construct a Monitor Class in one of Two Ways\nmon = Endpoint(\"abalone-regression-end-rt\").get_monitor()\nmon = Monitor(\"abalone-regression-end-rt\")\n\n# Check the summary and details of the monitoring class\nmon.summary()\nmon.details()\n\n# Check the baseline outputs (baseline, constraints, statistics)\nbase_df = mon.get_baseline()\nbase_df.head()\n\nconstraints_df = mon.get_constraints()\nconstraints_df.head()\n\nstatistics_df = mon.get_statistics()\nstatistics_df.head()\n\n# Get the latest data capture (inputs and outputs)\ninput_df, output_df = mon.get_latest_data_capture()\ninput_df.head()\noutput_df.head()\n</code></pre>"},{"location":"api_classes/monitor/#sageworks-ui","title":"SageWorks UI","text":"<p>Running these few lines of code creates and deploys an AWS Endpoint Monitor. The Monitor status and outputs can be viewed in the Sagemaker Console interfaces or in the SageWorks Dashboard UI. SageWorks will use the monitor to track various metrics including Data Quality, Model Bias, etc...</p> SageWorks Dashboard: Endpoints <p>Not Finding a particular method?</p> <p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: SageWorks Core Classes</p>"},{"location":"api_classes/overview/","title":"Overview","text":"<p>Just Getting Started?</p> <p>You're in the right place, the SageWorks API Classes are the best way to get started with SageWorks!</p>"},{"location":"api_classes/overview/#welcome-to-the-sageworks-api-classes","title":"Welcome to the SageWorks API Classes","text":"<p>These classes provide high-level APIs for the SageWorks package, they enable your team to build full AWS Machine Learning Pipelines. They handle all the details around updating and managing a complex set of AWS Services. Each class provides an essential component of the overall ML Pipline. Simply combine the classes to build production ready, AWS powered, machine learning pipelines. </p> <ul> <li>DataSource: Manages AWS Data Catalog and Athena</li> <li>FeatureSet: Manages AWS Feature Store and Feature Groups</li> <li>Model: Manages the training and deployment of AWS Model Groups and Packages</li> <li>Endpoint: Manages the deployment and invocations/inference on AWS Endpoints</li> <li>Monitor: Manages the setup and deployment of AWS Endpoint Monitors</li> </ul> <p></p>"},{"location":"api_classes/overview/#example-ml-pipline","title":"Example ML Pipline","text":"full_ml_pipeline.py<pre><code>from sageworks.api.data_source import DataSource\nfrom sageworks.api.feature_set import FeatureSet\nfrom sageworks.api.model import Model, ModelType\nfrom sageworks.api.endpoint import Endpoint\n\n# Create the abalone_data DataSource\nds = DataSource(\"s3://sageworks-public-data/common/abalone.csv\")\n\n# Now create a FeatureSet\nds.to_features(\"abalone_features\")\n\n# Create the abalone_regression Model\nfs = FeatureSet(\"abalone_features\")\nfs.to_model(\n    name=\"abalone-regression\",\n    model_type=ModelType.REGRESSOR,\n    target_column=\"class_number_of_rings\",\n    tags=[\"abalone\", \"regression\"],\n    description=\"Abalone Regression Model\",\n)\n\n# Create the abalone_regression Endpoint\nmodel = Model(\"abalone-regression\")\nmodel.to_endpoint(name=\"abalone-regression-end\", tags=[\"abalone\", \"regression\"])\n\n# Now we'll run inference on the endpoint\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# Get a DataFrame of data (not used to train) and run predictions\nathena_table = fs.view(\"training\").table\ndf = fs.query(f\"SELECT * FROM {athena_table} where training = FALSE\")\nresults = endpoint.predict(df)\nprint(results[[\"class_number_of_rings\", \"prediction\"]])\n</code></pre> <p>Output</p> <pre><code>Processing...\n     class_number_of_rings  prediction\n0                       12   10.477794\n1                       11    11.11835\n2                       14   13.605763\n3                       12   11.744759\n4                       17    15.55189\n..                     ...         ...\n826                      7    7.981503\n827                     11   11.246113\n828                      9    9.592911\n829                      6    6.129388\n830                      8    7.628252\n</code></pre> <p>Full AWS ML Pipeline Achievement Unlocked!</p> <p>Bing! You just built and deployed a full AWS Machine Learning Pipeline. You can now use the SageWorks Dashboard web interface to inspect your AWS artifacts. A comprehensive set of Exploratory Data Analysis techniques and Model Performance Metrics are available for your entire team to review, inspect and interact with.</p> <p></p> <p>Examples</p> <p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p>"},{"location":"api_classes/parameter_store/","title":"SageWorks Parameter Storage","text":"<p>Examples</p> <p>Examples of using the Parameter Storage class are listed at the bottom of this page Examples.</p> <p>ParameterStore: Manages SageWorks parameters in AWS Systems Manager Parameter Store.</p>"},{"location":"api_classes/parameter_store/#sageworks.api.parameter_store.ParameterStore","title":"<code>ParameterStore</code>","text":"<p>ParameterStore: Manages SageWorks parameters in AWS Systems Manager Parameter Store.</p> Common Usage <pre><code>params = ParameterStore()\n\n# List Parameters\nparams.list()\n\n['/sageworks/abalone_info',\n '/sageworks/my_data',\n '/sageworks/test',\n '/sageworks/pipelines/my_pipeline']\n\n# Add Key\nparams.upsert(\"key\", \"value\")\nvalue = params.get(\"key\")\n\n# Add any data (lists, dictionaries, etc..)\nmy_data = {\"key\": \"value\", \"number\": 4.2, \"list\": [1,2,3]}\nparams.upsert(\"my_data\", my_data)\n\n# Retrieve data\nreturn_value = params.get(\"my_data\")\npprint(return_value)\n\n{'key': 'value', 'list': [1, 2, 3], 'number': 4.2}\n\n# Delete parameters\nparam_store.delete(\"my_data\")\n</code></pre> Source code in <code>src/sageworks/api/parameter_store.py</code> <pre><code>class ParameterStore:\n    \"\"\"ParameterStore: Manages SageWorks parameters in AWS Systems Manager Parameter Store.\n\n    Common Usage:\n        ```python\n        params = ParameterStore()\n\n        # List Parameters\n        params.list()\n\n        ['/sageworks/abalone_info',\n         '/sageworks/my_data',\n         '/sageworks/test',\n         '/sageworks/pipelines/my_pipeline']\n\n        # Add Key\n        params.upsert(\"key\", \"value\")\n        value = params.get(\"key\")\n\n        # Add any data (lists, dictionaries, etc..)\n        my_data = {\"key\": \"value\", \"number\": 4.2, \"list\": [1,2,3]}\n        params.upsert(\"my_data\", my_data)\n\n        # Retrieve data\n        return_value = params.get(\"my_data\")\n        pprint(return_value)\n\n        {'key': 'value', 'list': [1, 2, 3], 'number': 4.2}\n\n        # Delete parameters\n        param_store.delete(\"my_data\")\n        ```\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"ParameterStore Init Method\"\"\"\n        self.log = logging.getLogger(\"sageworks\")\n\n        # Initialize a SageWorks Session (to assume the SageWorks ExecutionRole)\n        self.boto3_session = AWSSession().boto3_session\n\n        # Create a Systems Manager (SSM) client for Parameter Store operations\n        self.ssm_client = self.boto3_session.client(\"ssm\")\n\n    def list(self, prefix: str = None) -&gt; list:\n        \"\"\"List all parameters in the AWS Parameter Store, optionally filtering by a prefix.\n\n        Args:\n            prefix (str, optional): A prefix to filter the parameters by. Defaults to None.\n\n        Returns:\n            list: A list of parameter names and details.\n        \"\"\"\n        try:\n            # Set up parameters for the query\n            params = {\"MaxResults\": 50}\n\n            # If a prefix is provided, add the 'ParameterFilters' for optimization\n            if prefix:\n                params[\"ParameterFilters\"] = [{\"Key\": \"Name\", \"Option\": \"BeginsWith\", \"Values\": [prefix]}]\n\n            # Initialize the list to collect parameter names\n            all_parameters = []\n\n            # Make the initial call to describe parameters\n            response = self.ssm_client.describe_parameters(**params)\n\n            # Aggregate the names from the initial response\n            all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n            # Continue to paginate if there's a NextToken\n            while \"NextToken\" in response:\n                # Update the parameters with the NextToken for subsequent calls\n                params[\"NextToken\"] = response[\"NextToken\"]\n                response = self.ssm_client.describe_parameters(**params)\n\n                # Aggregate the names from the subsequent responses\n                all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n        except Exception as e:\n            self.log.error(f\"Failed to list parameters: {e}\")\n            return []\n\n        # Return the aggregated list of parameter names\n        return all_parameters\n\n    def get(self, name: str, warn: bool = True, decrypt: bool = True) -&gt; Union[str, list, dict, None]:\n        \"\"\"Retrieve a parameter value from the AWS Parameter Store.\n\n        Args:\n            name (str): The name of the parameter to retrieve.\n            warn (bool): Whether to log a warning if the parameter is not found.\n            decrypt (bool): Whether to decrypt secure string parameters.\n\n        Returns:\n            Union[str, list, dict, None]: The value of the parameter or None if not found.\n        \"\"\"\n        try:\n            # Retrieve the parameter from Parameter Store\n            response = self.ssm_client.get_parameter(Name=name, WithDecryption=decrypt)\n            value = response[\"Parameter\"][\"Value\"]\n\n            # Auto-detect and decompress if needed\n            if value.startswith(\"COMPRESSED:\"):\n                # Base64 decode and decompress\n                self.log.important(f\"Decompressing parameter '{name}'...\")\n                compressed_value = base64.b64decode(value[len(\"COMPRESSED:\") :])\n                value = zlib.decompress(compressed_value).decode(\"utf-8\")\n\n            # Attempt to parse the value back to its original type\n            try:\n                parsed_value = json.loads(value)\n                return parsed_value\n            except (json.JSONDecodeError, TypeError):\n                # If parsing fails, return the value as is (assumed to be a simple string)\n                return value\n\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] == \"ParameterNotFound\":\n                if warn:\n                    self.log.warning(f\"Parameter '{name}' not found\")\n            else:\n                self.log.error(f\"Failed to get parameter '{name}': {e}\")\n            return None\n\n    def upsert(self, name: str, value, overwrite: bool = True):\n        \"\"\"Insert or update a parameter in the AWS Parameter Store.\n\n        Args:\n            name (str): The name of the parameter.\n            value (str | list | dict): The value of the parameter.\n            overwrite (bool): Whether to overwrite an existing parameter (default: True)\n        \"\"\"\n        try:\n\n            # Anything that's not a string gets converted to JSON\n            if not isinstance(value, str):\n                value = json.dumps(value)\n\n            # Check size and compress if necessary\n            if len(value) &gt; 4096:\n                self.log.warning(f\"Parameter {name} exceeds 4KB ({len(value)} Bytes)  Compressing...\")\n                compressed_value = zlib.compress(value.encode(\"utf-8\"), level=9)\n                encoded_value = \"COMPRESSED:\" + base64.b64encode(compressed_value).decode(\"utf-8\")\n\n                # Report on the size of the compressed value\n                compressed_size = len(compressed_value)\n                if compressed_size &gt; 4096:\n                    doc_link = \"https://supercowpowers.github.io/sageworks/api_classes/df_store\"\n                    self.log.error(f\"Compressed size {compressed_size} bytes, cannot store &gt; 4KB\")\n                    self.log.error(f\"For larger data use the DFStore() class ({doc_link})\")\n                    return\n\n                # Insert or update the compressed parameter in Parameter Store\n                try:\n                    # Insert or update the compressed parameter in Parameter Store\n                    self.ssm_client.put_parameter(Name=name, Value=encoded_value, Type=\"String\", Overwrite=overwrite)\n                    self.log.info(f\"Parameter '{name}' added/updated successfully with compression.\")\n                    return\n                except Exception as e:\n                    self.log.critical(f\"Failed to add/update compressed parameter '{name}': {e}\")\n                    raise\n\n            # Insert or update the parameter normally if under 4KB\n            self.ssm_client.put_parameter(Name=name, Value=value, Type=\"String\", Overwrite=overwrite)\n            self.log.info(f\"Parameter '{name}' added/updated successfully.\")\n\n        except Exception as e:\n            self.log.critical(f\"Failed to add/update parameter '{name}': {e}\")\n            raise\n\n    def delete(self, name: str):\n        \"\"\"Delete a parameter from the AWS Parameter Store.\n\n        Args:\n            name (str): The name of the parameter to delete.\n        \"\"\"\n        try:\n            # Delete the parameter from Parameter Store\n            self.ssm_client.delete_parameter(Name=name)\n            self.log.info(f\"Parameter '{name}' deleted successfully.\")\n        except Exception as e:\n            self.log.error(f\"Failed to delete parameter '{name}': {e}\")\n\n    def __repr__(self):\n        \"\"\"Return a string representation of the ParameterStore object.\"\"\"\n        return \"\\n\".join(self.list())\n</code></pre>"},{"location":"api_classes/parameter_store/#sageworks.api.parameter_store.ParameterStore.__init__","title":"<code>__init__()</code>","text":"<p>ParameterStore Init Method</p> Source code in <code>src/sageworks/api/parameter_store.py</code> <pre><code>def __init__(self):\n    \"\"\"ParameterStore Init Method\"\"\"\n    self.log = logging.getLogger(\"sageworks\")\n\n    # Initialize a SageWorks Session (to assume the SageWorks ExecutionRole)\n    self.boto3_session = AWSSession().boto3_session\n\n    # Create a Systems Manager (SSM) client for Parameter Store operations\n    self.ssm_client = self.boto3_session.client(\"ssm\")\n</code></pre>"},{"location":"api_classes/parameter_store/#sageworks.api.parameter_store.ParameterStore.__repr__","title":"<code>__repr__()</code>","text":"<p>Return a string representation of the ParameterStore object.</p> Source code in <code>src/sageworks/api/parameter_store.py</code> <pre><code>def __repr__(self):\n    \"\"\"Return a string representation of the ParameterStore object.\"\"\"\n    return \"\\n\".join(self.list())\n</code></pre>"},{"location":"api_classes/parameter_store/#sageworks.api.parameter_store.ParameterStore.delete","title":"<code>delete(name)</code>","text":"<p>Delete a parameter from the AWS Parameter Store.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name of the parameter to delete.</p> required Source code in <code>src/sageworks/api/parameter_store.py</code> <pre><code>def delete(self, name: str):\n    \"\"\"Delete a parameter from the AWS Parameter Store.\n\n    Args:\n        name (str): The name of the parameter to delete.\n    \"\"\"\n    try:\n        # Delete the parameter from Parameter Store\n        self.ssm_client.delete_parameter(Name=name)\n        self.log.info(f\"Parameter '{name}' deleted successfully.\")\n    except Exception as e:\n        self.log.error(f\"Failed to delete parameter '{name}': {e}\")\n</code></pre>"},{"location":"api_classes/parameter_store/#sageworks.api.parameter_store.ParameterStore.get","title":"<code>get(name, warn=True, decrypt=True)</code>","text":"<p>Retrieve a parameter value from the AWS Parameter Store.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name of the parameter to retrieve.</p> required <code>warn</code> <code>bool</code> <p>Whether to log a warning if the parameter is not found.</p> <code>True</code> <code>decrypt</code> <code>bool</code> <p>Whether to decrypt secure string parameters.</p> <code>True</code> <p>Returns:</p> Type Description <code>Union[str, list, dict, None]</code> <p>Union[str, list, dict, None]: The value of the parameter or None if not found.</p> Source code in <code>src/sageworks/api/parameter_store.py</code> <pre><code>def get(self, name: str, warn: bool = True, decrypt: bool = True) -&gt; Union[str, list, dict, None]:\n    \"\"\"Retrieve a parameter value from the AWS Parameter Store.\n\n    Args:\n        name (str): The name of the parameter to retrieve.\n        warn (bool): Whether to log a warning if the parameter is not found.\n        decrypt (bool): Whether to decrypt secure string parameters.\n\n    Returns:\n        Union[str, list, dict, None]: The value of the parameter or None if not found.\n    \"\"\"\n    try:\n        # Retrieve the parameter from Parameter Store\n        response = self.ssm_client.get_parameter(Name=name, WithDecryption=decrypt)\n        value = response[\"Parameter\"][\"Value\"]\n\n        # Auto-detect and decompress if needed\n        if value.startswith(\"COMPRESSED:\"):\n            # Base64 decode and decompress\n            self.log.important(f\"Decompressing parameter '{name}'...\")\n            compressed_value = base64.b64decode(value[len(\"COMPRESSED:\") :])\n            value = zlib.decompress(compressed_value).decode(\"utf-8\")\n\n        # Attempt to parse the value back to its original type\n        try:\n            parsed_value = json.loads(value)\n            return parsed_value\n        except (json.JSONDecodeError, TypeError):\n            # If parsing fails, return the value as is (assumed to be a simple string)\n            return value\n\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] == \"ParameterNotFound\":\n            if warn:\n                self.log.warning(f\"Parameter '{name}' not found\")\n        else:\n            self.log.error(f\"Failed to get parameter '{name}': {e}\")\n        return None\n</code></pre>"},{"location":"api_classes/parameter_store/#sageworks.api.parameter_store.ParameterStore.list","title":"<code>list(prefix=None)</code>","text":"<p>List all parameters in the AWS Parameter Store, optionally filtering by a prefix.</p> <p>Parameters:</p> Name Type Description Default <code>prefix</code> <code>str</code> <p>A prefix to filter the parameters by. Defaults to None.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>list</code> <code>list</code> <p>A list of parameter names and details.</p> Source code in <code>src/sageworks/api/parameter_store.py</code> <pre><code>def list(self, prefix: str = None) -&gt; list:\n    \"\"\"List all parameters in the AWS Parameter Store, optionally filtering by a prefix.\n\n    Args:\n        prefix (str, optional): A prefix to filter the parameters by. Defaults to None.\n\n    Returns:\n        list: A list of parameter names and details.\n    \"\"\"\n    try:\n        # Set up parameters for the query\n        params = {\"MaxResults\": 50}\n\n        # If a prefix is provided, add the 'ParameterFilters' for optimization\n        if prefix:\n            params[\"ParameterFilters\"] = [{\"Key\": \"Name\", \"Option\": \"BeginsWith\", \"Values\": [prefix]}]\n\n        # Initialize the list to collect parameter names\n        all_parameters = []\n\n        # Make the initial call to describe parameters\n        response = self.ssm_client.describe_parameters(**params)\n\n        # Aggregate the names from the initial response\n        all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n        # Continue to paginate if there's a NextToken\n        while \"NextToken\" in response:\n            # Update the parameters with the NextToken for subsequent calls\n            params[\"NextToken\"] = response[\"NextToken\"]\n            response = self.ssm_client.describe_parameters(**params)\n\n            # Aggregate the names from the subsequent responses\n            all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n    except Exception as e:\n        self.log.error(f\"Failed to list parameters: {e}\")\n        return []\n\n    # Return the aggregated list of parameter names\n    return all_parameters\n</code></pre>"},{"location":"api_classes/parameter_store/#sageworks.api.parameter_store.ParameterStore.upsert","title":"<code>upsert(name, value, overwrite=True)</code>","text":"<p>Insert or update a parameter in the AWS Parameter Store.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name of the parameter.</p> required <code>value</code> <code>str | list | dict</code> <p>The value of the parameter.</p> required <code>overwrite</code> <code>bool</code> <p>Whether to overwrite an existing parameter (default: True)</p> <code>True</code> Source code in <code>src/sageworks/api/parameter_store.py</code> <pre><code>def upsert(self, name: str, value, overwrite: bool = True):\n    \"\"\"Insert or update a parameter in the AWS Parameter Store.\n\n    Args:\n        name (str): The name of the parameter.\n        value (str | list | dict): The value of the parameter.\n        overwrite (bool): Whether to overwrite an existing parameter (default: True)\n    \"\"\"\n    try:\n\n        # Anything that's not a string gets converted to JSON\n        if not isinstance(value, str):\n            value = json.dumps(value)\n\n        # Check size and compress if necessary\n        if len(value) &gt; 4096:\n            self.log.warning(f\"Parameter {name} exceeds 4KB ({len(value)} Bytes)  Compressing...\")\n            compressed_value = zlib.compress(value.encode(\"utf-8\"), level=9)\n            encoded_value = \"COMPRESSED:\" + base64.b64encode(compressed_value).decode(\"utf-8\")\n\n            # Report on the size of the compressed value\n            compressed_size = len(compressed_value)\n            if compressed_size &gt; 4096:\n                doc_link = \"https://supercowpowers.github.io/sageworks/api_classes/df_store\"\n                self.log.error(f\"Compressed size {compressed_size} bytes, cannot store &gt; 4KB\")\n                self.log.error(f\"For larger data use the DFStore() class ({doc_link})\")\n                return\n\n            # Insert or update the compressed parameter in Parameter Store\n            try:\n                # Insert or update the compressed parameter in Parameter Store\n                self.ssm_client.put_parameter(Name=name, Value=encoded_value, Type=\"String\", Overwrite=overwrite)\n                self.log.info(f\"Parameter '{name}' added/updated successfully with compression.\")\n                return\n            except Exception as e:\n                self.log.critical(f\"Failed to add/update compressed parameter '{name}': {e}\")\n                raise\n\n        # Insert or update the parameter normally if under 4KB\n        self.ssm_client.put_parameter(Name=name, Value=value, Type=\"String\", Overwrite=overwrite)\n        self.log.info(f\"Parameter '{name}' added/updated successfully.\")\n\n    except Exception as e:\n        self.log.critical(f\"Failed to add/update parameter '{name}': {e}\")\n        raise\n</code></pre>"},{"location":"api_classes/parameter_store/#bypassing-the-4k-limit","title":"Bypassing the 4k Limit","text":"<p>AWS Parameter Storage has a 4k limit on values, the SageWorks class bypasses this limit by detecting large values (strings, data, whatever) and compressing those on the fly. The decompressing is also handled automatically, so for larger data simply use the <code>add()</code> and <code>get()</code> methods and it will all just work.</p>"},{"location":"api_classes/parameter_store/#examples","title":"Examples","text":"<p>These example show how to use the <code>ParameterStore()</code> class to list, add, and get parameters from the AWS Parameter Store Service.</p> <p>SageWorks REPL</p> <p>If you'd like to experiment with listing, adding, and getting data with the <code>ParameterStore()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! SageWorks REPL</p> Using SageWorks REPL<pre><code>params = ParameterStore()\n\n# List Parameters\nparams.list()\n\n['/sageworks/abalone_info',\n '/sageworks/my_data',\n '/sageworks/test',\n '/sageworks/pipelines/my_pipeline']\n\n# Add Key\nparams.upsert(\"key\", \"value\")\nvalue = params.get(\"key\")\n\n# Add any data (lists, dictionaries, etc..)\nmy_data = {\"key\": \"value\", \"number\": 4.2, \"list\": [1,2,3]}\nparams.upsert(\"my_data\", my_data)\n\n# Retrieve data\nreturn_value = params.get(\"my_data\")\npprint(return_value)\n\n{'key': 'value', 'list': [1, 2, 3], 'number': 4.2}\n\n# Delete parameters\nparam_store.delete(\"my_data\")\n</code></pre> <p><code>list()</code> not showing ALL parameters?</p> <p>If you want access to ALL the parameters in the parameter store set <code>prefix=None</code> and everything will show up.</p> <pre><code>params = ParameterStore(prefix=None)\nparams.list()\n&lt;all the keys&gt;\n</code></pre>"},{"location":"api_classes/pipelines/","title":"Pipelines","text":"<p>Pipeline Examples</p> <p>Examples of using the Pipeline classes are listed at the bottom of this page Examples.</p> <p>Pipelines store sequences of SageWorks transforms. So if you have a nightly ML workflow you can capture that as a Pipeline. Here's an example pipeline:</p> nightly_sol_pipeline_v1.json<pre><code>{\n    \"data_source\": {\n         \"name\": \"nightly_data\",\n         \"tags\": [\"solubility\", \"foo\"],\n         \"s3_input\": \"s3://blah/blah.csv\"\n    },\n    \"feature_set\": {\n          \"name\": \"nightly_features\",\n          \"tags\": [\"blah\", \"blah\"],\n          \"input\": \"nightly_data\"\n          \"schema\": \"mol_descriptors_v1\"\n    },\n    \"model\": {\n          \"name\": \u201cnightly_model\u201d,\n          \"tags\": [\"blah\", \"blah\"],\n          \"features\": [\"col1\", \"col2\"],\n          \"target\": \u201csol\u201d,\n          \"input\": \u201cnightly_features\u201d\n    \"endpoint\": {\n          ...\n}    \n</code></pre> <p>Pipeline: Manages the details around a SageWorks Pipeline, including Execution</p>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.log","title":"<code>log = logging.getLogger('sageworks')</code>  <code>module-attribute</code>","text":"<p>my_pipeline = Pipeline(\"aqsol_pipeline_v1\") my_pipeline.set_input(\"s3://sageworks-public-data/comp_chem/aqsol_public_data.csv\") my_pipeline.execute_partial([\"model\", \"endpoint\"]) exit(0)</p>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline","title":"<code>Pipeline</code>","text":"<p>Pipeline: SageWorks Pipeline API Class</p> Common Usage <pre><code>my_pipeline = Pipeline(\"name\")\nmy_pipeline.details()\nmy_pipeline.execute()  # Execute entire pipeline\nmy_pipeline.execute_partial([\"data_source\", \"feature_set\"])\nmy_pipeline.execute_partial([\"model\", \"endpoint\"])\n</code></pre> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>class Pipeline:\n    \"\"\"Pipeline: SageWorks Pipeline API Class\n\n    Common Usage:\n        ```python\n        my_pipeline = Pipeline(\"name\")\n        my_pipeline.details()\n        my_pipeline.execute()  # Execute entire pipeline\n        my_pipeline.execute_partial([\"data_source\", \"feature_set\"])\n        my_pipeline.execute_partial([\"model\", \"endpoint\"])\n        ```\n    \"\"\"\n\n    def __init__(self, name: str):\n        \"\"\"Pipeline Init Method\"\"\"\n        self.log = logging.getLogger(\"sageworks\")\n        self.uuid = name\n\n        # Spin up a Parameter Store for Pipelines\n        self.prefix = \"/sageworks/pipelines\"\n        self.params = ParameterStore()\n        self.pipeline = self.params.get(f\"{self.prefix}/{self.uuid}\")\n\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Pipeline Summary.\n\n        Returns:\n            dict: A dictionary of details about the Pipeline\n        \"\"\"\n        return self.pipeline\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Pipeline Details.\n\n        Returns:\n            dict: A dictionary of details about the Pipeline\n        \"\"\"\n        return self.pipeline\n\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Pipeline Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the Pipeline\n        \"\"\"\n        return {}\n\n    def set_input(self, input: Union[str, pd.DataFrame], artifact: str = \"data_source\"):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n            input (Union[str, pd.DataFrame]): The input for the Pipeline\n            artifact (str): The artifact to set the input for (default: \"data_source\")\n        \"\"\"\n        self.pipeline[artifact][\"input\"] = input\n\n    def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n            id_column (str): The column name of the unique identifier\n            holdout_ids (list[str]): The list of unique identifiers to hold out\n        \"\"\"\n        self.pipeline[\"feature_set\"][\"id_column\"] = id_column\n        self.pipeline[\"feature_set\"][\"holdout_ids\"] = holdout_ids\n\n    def execute(self):\n        \"\"\"Execute the entire Pipeline\n\n        Raises:\n            RunTimeException: If the pipeline execution fails in any way\n        \"\"\"\n        pipeline_executor = PipelineExecutor(self)\n        pipeline_executor.execute()\n\n    def execute_partial(self, subset: list):\n        \"\"\"Execute a partial Pipeline\n\n        Args:\n            subset (list): A subset of the pipeline to execute\n\n        Raises:\n            RunTimeException: If the pipeline execution fails in any way\n        \"\"\"\n        pipeline_executor = PipelineExecutor(self)\n        pipeline_executor.execute_partial(subset)\n\n    def report_settable_fields(self, pipeline: dict = {}, path: str = \"\") -&gt; None:\n        \"\"\"\n        Recursively finds and prints keys with settable fields in a JSON-like dictionary.\n\n        Args:\n        pipeline (dict): pipeline (or sub pipeline) to process.\n        path (str): Current path to the key, used for nested dictionaries.\n        \"\"\"\n        # Grab the entire pipeline if not provided (first call)\n        if not pipeline:\n            self.log.important(f\"Checking Pipeline: {self.uuid}...\")\n            pipeline = self.pipeline\n        for key, value in pipeline.items():\n            if isinstance(value, dict):\n                # Recurse into sub-dictionary\n                self.report_settable_fields(value, path + key + \" -&gt; \")\n            elif isinstance(value, str) and value.startswith(\"&lt;&lt;\") and value.endswith(\"&gt;&gt;\"):\n                # Check if required or optional\n                required = \"[Required]\" if \"required\" in value else \"[Optional]\"\n                self.log.important(f\"{required} Path: {path + key}\")\n\n    def delete(self):\n        \"\"\"Pipeline Deletion\"\"\"\n        self.log.info(f\"Deleting Pipeline: {self.uuid}...\")\n        self.params.delete(f\"{self.prefix}/{self.uuid}\")\n\n    def __repr__(self) -&gt; str:\n        \"\"\"String representation of this pipeline\n\n        Returns:\n            str: String representation of this pipeline\n        \"\"\"\n        # Class name and details\n        class_name = self.__class__.__name__\n        pipeline_details = json.dumps(self.pipeline, indent=4)\n        return f\"{class_name}({pipeline_details})\"\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.__init__","title":"<code>__init__(name)</code>","text":"<p>Pipeline Init Method</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def __init__(self, name: str):\n    \"\"\"Pipeline Init Method\"\"\"\n    self.log = logging.getLogger(\"sageworks\")\n    self.uuid = name\n\n    # Spin up a Parameter Store for Pipelines\n    self.prefix = \"/sageworks/pipelines\"\n    self.params = ParameterStore()\n    self.pipeline = self.params.get(f\"{self.prefix}/{self.uuid}\")\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.__repr__","title":"<code>__repr__()</code>","text":"<p>String representation of this pipeline</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>String representation of this pipeline</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def __repr__(self) -&gt; str:\n    \"\"\"String representation of this pipeline\n\n    Returns:\n        str: String representation of this pipeline\n    \"\"\"\n    # Class name and details\n    class_name = self.__class__.__name__\n    pipeline_details = json.dumps(self.pipeline, indent=4)\n    return f\"{class_name}({pipeline_details})\"\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.delete","title":"<code>delete()</code>","text":"<p>Pipeline Deletion</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def delete(self):\n    \"\"\"Pipeline Deletion\"\"\"\n    self.log.info(f\"Deleting Pipeline: {self.uuid}...\")\n    self.params.delete(f\"{self.prefix}/{self.uuid}\")\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the Pipeline Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Pipeline</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Pipeline Details.\n\n    Returns:\n        dict: A dictionary of details about the Pipeline\n    \"\"\"\n    return self.pipeline\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.execute","title":"<code>execute()</code>","text":"<p>Execute the entire Pipeline</p> <p>Raises:</p> Type Description <code>RunTimeException</code> <p>If the pipeline execution fails in any way</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def execute(self):\n    \"\"\"Execute the entire Pipeline\n\n    Raises:\n        RunTimeException: If the pipeline execution fails in any way\n    \"\"\"\n    pipeline_executor = PipelineExecutor(self)\n    pipeline_executor.execute()\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.execute_partial","title":"<code>execute_partial(subset)</code>","text":"<p>Execute a partial Pipeline</p> <p>Parameters:</p> Name Type Description Default <code>subset</code> <code>list</code> <p>A subset of the pipeline to execute</p> required <p>Raises:</p> Type Description <code>RunTimeException</code> <p>If the pipeline execution fails in any way</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def execute_partial(self, subset: list):\n    \"\"\"Execute a partial Pipeline\n\n    Args:\n        subset (list): A subset of the pipeline to execute\n\n    Raises:\n        RunTimeException: If the pipeline execution fails in any way\n    \"\"\"\n    pipeline_executor = PipelineExecutor(self)\n    pipeline_executor.execute_partial(subset)\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the Pipeline Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the Pipeline</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Pipeline Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the Pipeline\n    \"\"\"\n    return {}\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.report_settable_fields","title":"<code>report_settable_fields(pipeline={}, path='')</code>","text":"<p>Recursively finds and prints keys with settable fields in a JSON-like dictionary.</p> <p>Args: pipeline (dict): pipeline (or sub pipeline) to process. path (str): Current path to the key, used for nested dictionaries.</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def report_settable_fields(self, pipeline: dict = {}, path: str = \"\") -&gt; None:\n    \"\"\"\n    Recursively finds and prints keys with settable fields in a JSON-like dictionary.\n\n    Args:\n    pipeline (dict): pipeline (or sub pipeline) to process.\n    path (str): Current path to the key, used for nested dictionaries.\n    \"\"\"\n    # Grab the entire pipeline if not provided (first call)\n    if not pipeline:\n        self.log.important(f\"Checking Pipeline: {self.uuid}...\")\n        pipeline = self.pipeline\n    for key, value in pipeline.items():\n        if isinstance(value, dict):\n            # Recurse into sub-dictionary\n            self.report_settable_fields(value, path + key + \" -&gt; \")\n        elif isinstance(value, str) and value.startswith(\"&lt;&lt;\") and value.endswith(\"&gt;&gt;\"):\n            # Check if required or optional\n            required = \"[Required]\" if \"required\" in value else \"[Optional]\"\n            self.log.important(f\"{required} Path: {path + key}\")\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.set_input","title":"<code>set_input(input, artifact='data_source')</code>","text":"<p>Set the input for the Pipeline</p> <p>Parameters:</p> Name Type Description Default <code>input</code> <code>Union[str, DataFrame]</code> <p>The input for the Pipeline</p> required <code>artifact</code> <code>str</code> <p>The artifact to set the input for (default: \"data_source\")</p> <code>'data_source'</code> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def set_input(self, input: Union[str, pd.DataFrame], artifact: str = \"data_source\"):\n    \"\"\"Set the input for the Pipeline\n\n    Args:\n        input (Union[str, pd.DataFrame]): The input for the Pipeline\n        artifact (str): The artifact to set the input for (default: \"data_source\")\n    \"\"\"\n    self.pipeline[artifact][\"input\"] = input\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.set_training_holdouts","title":"<code>set_training_holdouts(id_column, holdout_ids)</code>","text":"<p>Set the input for the Pipeline</p> <p>Parameters:</p> Name Type Description Default <code>id_column</code> <code>str</code> <p>The column name of the unique identifier</p> required <code>holdout_ids</code> <code>list[str]</code> <p>The list of unique identifiers to hold out</p> required Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n    \"\"\"Set the input for the Pipeline\n\n    Args:\n        id_column (str): The column name of the unique identifier\n        holdout_ids (list[str]): The list of unique identifiers to hold out\n    \"\"\"\n    self.pipeline[\"feature_set\"][\"id_column\"] = id_column\n    self.pipeline[\"feature_set\"][\"holdout_ids\"] = holdout_ids\n</code></pre>"},{"location":"api_classes/pipelines/#sageworks.api.pipeline.Pipeline.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the Pipeline Summary.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Pipeline</p> Source code in <code>src/sageworks/api/pipeline.py</code> <pre><code>def summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Pipeline Summary.\n\n    Returns:\n        dict: A dictionary of details about the Pipeline\n    \"\"\"\n    return self.pipeline\n</code></pre>"},{"location":"api_classes/pipelines/#examples","title":"Examples","text":"<p>Make a Pipeline</p> <p>Pipelines are just JSON files (see <code>sageworks/examples/pipelines/</code>). You can copy one and make changes to fit your objects/use case, or if you have a set of SageWorks artifacts created you can 'backtrack' from the Endpoint and have it create the Pipeline for you.</p> pipeline_manager.py<pre><code>from sageworks.api.pipeline_manager import PipelineManager\n\n # Create a PipelineManager\nmy_manager = PipelineManager()\n\n# List the Pipelines\npprint(my_manager.list_pipelines())\n\n# Create a Pipeline from an Endpoint\nabalone_pipeline = my_manager.create_from_endpoint(\"abalone-regression-end\")\n\n# Publish the Pipeline\nmy_manager.publish_pipeline(\"abalone_pipeline_v1\", abalone_pipeline)\n</code></pre> <p>Output</p> <p><pre><code>Listing Pipelines...\n[{'last_modified': datetime.datetime(2024, 4, 16, 21, 10, 6, tzinfo=tzutc()),\n  'name': 'abalone_pipeline_v1',\n  'size': 445}]\n</code></pre> Pipeline Details</p> pipeline_details.py<pre><code>from sageworks.api.pipeline import Pipeline\n\n# Retrieve an existing Pipeline\nmy_pipeline = Pipeline(\"abalone_pipeline_v1\")\npprint(my_pipeline.details())\n</code></pre> <p>Output</p> <pre><code>{\n    \"name\": \"abalone_pipeline_v1\",\n    \"s3_path\": \"s3://sandbox/pipelines/abalone_pipeline_v1.json\",\n    \"pipeline\": {\n        \"data_source\": {\n            \"name\": \"abalone_data\",\n            \"tags\": [\n                \"abalone_data\"\n            ],\n            \"input\": \"/Users/briford/work/sageworks/data/abalone.csv\"\n        },\n        \"feature_set\": {\n            \"name\": \"abalone_features\",\n            \"tags\": [\n                \"abalone_features\"\n            ],\n            \"input\": \"abalone_data\"\n        },\n        \"model\": {\n            \"name\": \"abalone-regression\",\n            \"tags\": [\n                \"abalone\",\n                \"regression\"\n            ],\n            \"input\": \"abalone_features\"\n        },\n        ...\n    }\n}\n</code></pre> <p>Pipeline Execution</p> <p>Pipeline Execution</p> <p>Executing the Pipeline is obviously the most important reason for creating one. If gives you a reproducible way to capture, inspect, and run the same ML pipeline on different data (nightly).</p> pipeline_execution.py<pre><code>from sageworks.api.pipeline import Pipeline\n\n# Retrieve an existing Pipeline\nmy_pipeline = Pipeline(\"abalone_pipeline_v1\")\n\n# Execute the Pipeline\nmy_pipeline.execute()  # Full execution\n\n# Partial executions\nmy_pipeline.execute_partial([\"data_source\", \"feature_set\"])\nmy_pipeline.execute_partial([\"model\", \"endpoint\"])\n</code></pre>"},{"location":"api_classes/pipelines/#pipelines-advanced","title":"Pipelines Advanced","text":"<p>As part of the flexible architecture sometimes DataSources or FeatureSets can be created with a Pandas DataFrame. To support a DataFrame as input to a pipeline we can call the <code>set_input()</code> method to the pipeline object. If you'd like to specify the <code>set_hold_out_ids()</code> you can also provide a list of ids.</p> <pre><code>    def set_input(self, input: Union[str, pd.DataFrame], artifact: str = \"data_source\"):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n            input (Union[str, pd.DataFrame]): The input for the Pipeline\n            artifact (str): The artifact to set the input for (default: \"data_source\")\n        \"\"\"\n        self.pipeline[artifact][\"input\"] = input\n\n    def set_hold_out_ids(self, id_list: list):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n           id_list (list): The list of hold out ids\n        \"\"\"\n        self.pipeline[\"feature_set\"][\"hold_out_ids\"] = id_list\n</code></pre> <p>Running a pipeline creates and deploys a set of SageWorks Artifacts, DataSource, FeatureSet, Model and Endpoint. These artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the SageWorks Dashboard UI.</p> <p>Not Finding a particular method?</p> <p>The SageWorks API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: SageWorks Core Classes</p>"},{"location":"api_classes/views/","title":"Views","text":"<p>View Examples</p> <p>Examples of using the Views classes to extend the functionality of SageWorks Artifacts are in the Examples section at the bottom of this page. </p> <p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing SageWorks Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p> <p>View: Read from a view (training, display, etc) for DataSources and FeatureSets.</p>"},{"location":"api_classes/views/#sageworks.core.views.view.View","title":"<code>View</code>","text":"<p>View: Read from a view (training, display, etc) for DataSources and FeatureSets.</p> Common Usage <pre><code># Grab the Display View for a DataSource\ndisplay_view = ds.view(\"display\")\nprint(display_view.columns)\n\n# Pull a DataFrame for the view\ndf = display_view.pull_dataframe()\n\n# Views also work with FeatureSets\ncomp_view = fs.view(\"computation\")\ncomp_df = comp_view.pull_dataframe()\n\n# Query the view with a custom SQL query\nquery = f\"SELECT * FROM {comp_view.table} WHERE age &gt; 30\"\ndf = comp_view.query(query)\n\n# Delete the view\ncomp_view.delete()\n</code></pre> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>class View:\n    \"\"\"View: Read from a view (training, display, etc) for DataSources and FeatureSets.\n\n    Common Usage:\n        ```python\n\n        # Grab the Display View for a DataSource\n        display_view = ds.view(\"display\")\n        print(display_view.columns)\n\n        # Pull a DataFrame for the view\n        df = display_view.pull_dataframe()\n\n        # Views also work with FeatureSets\n        comp_view = fs.view(\"computation\")\n        comp_df = comp_view.pull_dataframe()\n\n        # Query the view with a custom SQL query\n        query = f\"SELECT * FROM {comp_view.table} WHERE age &gt; 30\"\n        df = comp_view.query(query)\n\n        # Delete the view\n        comp_view.delete()\n        ```\n    \"\"\"\n\n    # Class attributes\n    log = logging.getLogger(\"sageworks\")\n    meta = Meta()\n\n    def __init__(self, artifact: Union[DataSource, FeatureSet], view_name: str, **kwargs):\n        \"\"\"View Constructor: Retrieve a View for the given artifact\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): A DataSource or FeatureSet object\n            view_name (str): The name of the view to retrieve (e.g. \"training\")\n        \"\"\"\n\n        # Set the view name\n        self.view_name = view_name\n\n        # Is this a DataSource or a FeatureSet?\n        self.is_feature_set = isinstance(artifact, FeatureSetCore)\n        self.auto_id_column = artifact.id_column if self.is_feature_set else None\n\n        # Get the data_source from the artifact\n        self.artifact_name = artifact.uuid\n        self.data_source = artifact.data_source if self.is_feature_set else artifact\n        self.database = self.data_source.database\n\n        # Construct our base_table_name\n        self.base_table_name = self.data_source.table\n\n        # Check if the view should be auto created\n        self.auto_created = False\n        if kwargs.get(\"auto_create_view\", True) and not self.exists():\n\n            # A direct double check before we auto-create\n            if not self.exists(skip_cache=True):\n                self.log.important(\n                    f\"View {self.view_name} for {self.artifact_name} doesn't exist, attempting to auto-create...\"\n                )\n                self.auto_created = self._auto_create_view()\n\n                # Check for failure of the auto-creation\n                if not self.auto_created:\n                    self.log.error(\n                        f\"View {self.view_name} for {self.artifact_name} doesn't exist and cannot be auto-created...\"\n                    )\n                    self.view_name = self.columns = self.column_types = self.source_table = self.base_table_name = None\n                    return\n\n        # Now fill some details about the view\n        self.columns, self.column_types, self.source_table, self.join_view = view_details(\n            self.table, self.data_source.database, self.data_source.boto3_session\n        )\n\n    def pull_dataframe(self, limit: int = 50000, head: bool = False) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Pull a DataFrame based on the view type\n\n        Args:\n            limit (int): The maximum number of rows to pull (default: 50000)\n            head (bool): Return just the head of the DataFrame (default: False)\n\n        Returns:\n            Union[pd.DataFrame, None]: The DataFrame for the view or None if it doesn't exist\n        \"\"\"\n\n        # Pull the DataFrame\n        if head:\n            limit = 5\n        pull_query = f'SELECT * FROM \"{self.table}\" LIMIT {limit}'\n        df = self.data_source.query(pull_query)\n        return df\n\n    def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Query the view with a custom SQL query\n\n        Args:\n            query (str): The SQL query to execute\n\n        Returns:\n            Union[pd.DataFrame, None]: The DataFrame for the query or None if it doesn't exist\n        \"\"\"\n        return self.data_source.query(query)\n\n    def column_details(self) -&gt; dict:\n        \"\"\"Return a dictionary of the column names and types for this view\n\n        Returns:\n            dict: A dictionary of the column names and types\n        \"\"\"\n        return dict(zip(self.columns, self.column_types))\n\n    @property\n    def table(self) -&gt; str:\n        \"\"\"Construct the view table name for the given view type\n\n        Returns:\n            str: The view table name\n        \"\"\"\n        if self.view_name is None:\n            return None\n        if self.view_name == \"base\":\n            return self.base_table_name\n        return f\"{self.base_table_name}_{self.view_name}\"\n\n    def delete(self):\n        \"\"\"Delete the database view (and supplemental data) if it exists.\"\"\"\n\n        # List any supplemental tables for this data source\n        supplemental_tables = list_supplemental_data_tables(self.base_table_name, self.database)\n        for table in supplemental_tables:\n            if self.view_name in table:\n                self.log.important(f\"Deleting Supplemental Table {table}...\")\n                delete_table(table, self.database, self.data_source.boto3_session)\n\n        # Now drop the view\n        self.log.important(f\"Dropping View {self.table}...\")\n        drop_view_query = f'DROP VIEW \"{self.table}\"'\n\n        # Execute the DROP VIEW query\n        try:\n            self.data_source.execute_statement(drop_view_query, silence_errors=True)\n        except wr.exceptions.QueryFailed as e:\n            if \"View not found\" in str(e):\n                self.log.info(f\"View {self.table} not found, this is fine...\")\n            else:\n                raise\n\n        # We want to do a small sleep so that AWS has time to catch up\n        self.log.info(\"Sleeping for 3 seconds after dropping view to allow AWS to catch up...\")\n        time.sleep(3)\n\n    def exists(self, skip_cache: bool = False) -&gt; bool:\n        \"\"\"Check if the view exists in the database\n\n        Args:\n            skip_cache (bool): Skip the cache and check the database directly (default: False)\n        Returns:\n            bool: True if the view exists, False otherwise.\n        \"\"\"\n        # The BaseView always exists\n        if self.view_name == \"base\":\n            return True\n\n        # If we're skipping the cache, we need to check the database directly\n        if skip_cache:\n            return self._check_database()\n\n        # Use the meta class to see if the view exists\n        views_df = self.meta.views(self.database)\n\n        # Check if we have ANY views\n        if views_df.empty:\n            return False\n\n        # Check if the view exists\n        return self.table in views_df[\"Name\"].values\n\n    def ensure_exists(self):\n        \"\"\"Ensure if the view exists by making a query directly to the database. If it doesn't exist, create it\"\"\"\n\n        # The BaseView always exists\n        if self.view_name == \"base\":\n            return True\n\n        # Check the database directly\n        if not self._check_database():\n            self._auto_create_view()\n\n    def _check_database(self) -&gt; bool:\n        \"\"\"Internal: Check if the view exists in the database\n\n        Returns:\n            bool: True if the view exists, False otherwise\n        \"\"\"\n        # Query to check if the table/view exists\n        check_table_query = f\"\"\"\n        SELECT table_name\n        FROM information_schema.tables\n        WHERE table_schema = '{self.database}' AND table_name = '{self.table}'\n        \"\"\"\n        _df = self.data_source.query(check_table_query)\n        return not _df.empty\n\n    def _auto_create_view(self) -&gt; bool:\n        \"\"\"Internal: Automatically create a view training, display, and computation views\n\n        Returns:\n            bool: True if the view was created, False otherwise\n\n        Raises:\n            ValueError: If the view type is not supported\n        \"\"\"\n        from sageworks.core.views import DisplayView, ComputationView, TrainingView\n\n        # First if we're going to auto-create, we need to make sure the data source exists\n        if not self.data_source.exists():\n            self.log.error(f\"Data Source {self.data_source.uuid} does not exist...\")\n            return False\n\n        # DisplayView\n        if self.view_name == \"display\":\n            self.log.important(f\"Auto creating View {self.view_name} for {self.data_source.uuid}...\")\n            DisplayView.create(self.data_source)\n            return True\n\n        # ComputationView\n        if self.view_name == \"computation\":\n            self.log.important(f\"Auto creating View {self.view_name} for {self.data_source.uuid}...\")\n            ComputationView.create(self.data_source)\n            return True\n\n        # TrainingView\n        if self.view_name == \"training\":\n            # We're only going to create training views for FeatureSets\n            if self.is_feature_set:\n                self.log.important(f\"Auto creating View {self.view_name} for {self.data_source.uuid}...\")\n                TrainingView.create(self.data_source, id_column=self.auto_id_column)\n                return True\n            else:\n                self.log.warning(\"Training Views are only supported for FeatureSets...\")\n                return False\n\n        # If we get here, we don't support auto-creating this view\n        self.log.warning(f\"Auto-Create for {self.view_name} not implemented yet...\")\n        return False\n\n    def __repr__(self):\n        \"\"\"Return a string representation of this object\"\"\"\n\n        # Set up various details that we want to print out\n        auto = \"(Auto-Created)\" if self.auto_created else \"\"\n        artifact = \"FeatureSet\" if self.is_feature_set else \"DataSource\"\n\n        info = f'View: \"{self.view_name}\" for {artifact}(\"{self.artifact_name}\")\\n'\n        info += f\"      Database: {self.database}\\n\"\n        info += f\"      Table: {self.table}{auto}\\n\"\n        info += f\"      Source Table: {self.source_table}\\n\"\n        info += f\"      Join View: {self.join_view}\"\n        return info\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.table","title":"<code>table: str</code>  <code>property</code>","text":"<p>Construct the view table name for the given view type</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>The view table name</p>"},{"location":"api_classes/views/#sageworks.core.views.view.View.__init__","title":"<code>__init__(artifact, view_name, **kwargs)</code>","text":"<p>View Constructor: Retrieve a View for the given artifact</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>A DataSource or FeatureSet object</p> required <code>view_name</code> <code>str</code> <p>The name of the view to retrieve (e.g. \"training\")</p> required Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def __init__(self, artifact: Union[DataSource, FeatureSet], view_name: str, **kwargs):\n    \"\"\"View Constructor: Retrieve a View for the given artifact\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): A DataSource or FeatureSet object\n        view_name (str): The name of the view to retrieve (e.g. \"training\")\n    \"\"\"\n\n    # Set the view name\n    self.view_name = view_name\n\n    # Is this a DataSource or a FeatureSet?\n    self.is_feature_set = isinstance(artifact, FeatureSetCore)\n    self.auto_id_column = artifact.id_column if self.is_feature_set else None\n\n    # Get the data_source from the artifact\n    self.artifact_name = artifact.uuid\n    self.data_source = artifact.data_source if self.is_feature_set else artifact\n    self.database = self.data_source.database\n\n    # Construct our base_table_name\n    self.base_table_name = self.data_source.table\n\n    # Check if the view should be auto created\n    self.auto_created = False\n    if kwargs.get(\"auto_create_view\", True) and not self.exists():\n\n        # A direct double check before we auto-create\n        if not self.exists(skip_cache=True):\n            self.log.important(\n                f\"View {self.view_name} for {self.artifact_name} doesn't exist, attempting to auto-create...\"\n            )\n            self.auto_created = self._auto_create_view()\n\n            # Check for failure of the auto-creation\n            if not self.auto_created:\n                self.log.error(\n                    f\"View {self.view_name} for {self.artifact_name} doesn't exist and cannot be auto-created...\"\n                )\n                self.view_name = self.columns = self.column_types = self.source_table = self.base_table_name = None\n                return\n\n    # Now fill some details about the view\n    self.columns, self.column_types, self.source_table, self.join_view = view_details(\n        self.table, self.data_source.database, self.data_source.boto3_session\n    )\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.__repr__","title":"<code>__repr__()</code>","text":"<p>Return a string representation of this object</p> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def __repr__(self):\n    \"\"\"Return a string representation of this object\"\"\"\n\n    # Set up various details that we want to print out\n    auto = \"(Auto-Created)\" if self.auto_created else \"\"\n    artifact = \"FeatureSet\" if self.is_feature_set else \"DataSource\"\n\n    info = f'View: \"{self.view_name}\" for {artifact}(\"{self.artifact_name}\")\\n'\n    info += f\"      Database: {self.database}\\n\"\n    info += f\"      Table: {self.table}{auto}\\n\"\n    info += f\"      Source Table: {self.source_table}\\n\"\n    info += f\"      Join View: {self.join_view}\"\n    return info\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.column_details","title":"<code>column_details()</code>","text":"<p>Return a dictionary of the column names and types for this view</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of the column names and types</p> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def column_details(self) -&gt; dict:\n    \"\"\"Return a dictionary of the column names and types for this view\n\n    Returns:\n        dict: A dictionary of the column names and types\n    \"\"\"\n    return dict(zip(self.columns, self.column_types))\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.delete","title":"<code>delete()</code>","text":"<p>Delete the database view (and supplemental data) if it exists.</p> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def delete(self):\n    \"\"\"Delete the database view (and supplemental data) if it exists.\"\"\"\n\n    # List any supplemental tables for this data source\n    supplemental_tables = list_supplemental_data_tables(self.base_table_name, self.database)\n    for table in supplemental_tables:\n        if self.view_name in table:\n            self.log.important(f\"Deleting Supplemental Table {table}...\")\n            delete_table(table, self.database, self.data_source.boto3_session)\n\n    # Now drop the view\n    self.log.important(f\"Dropping View {self.table}...\")\n    drop_view_query = f'DROP VIEW \"{self.table}\"'\n\n    # Execute the DROP VIEW query\n    try:\n        self.data_source.execute_statement(drop_view_query, silence_errors=True)\n    except wr.exceptions.QueryFailed as e:\n        if \"View not found\" in str(e):\n            self.log.info(f\"View {self.table} not found, this is fine...\")\n        else:\n            raise\n\n    # We want to do a small sleep so that AWS has time to catch up\n    self.log.info(\"Sleeping for 3 seconds after dropping view to allow AWS to catch up...\")\n    time.sleep(3)\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.ensure_exists","title":"<code>ensure_exists()</code>","text":"<p>Ensure if the view exists by making a query directly to the database. If it doesn't exist, create it</p> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def ensure_exists(self):\n    \"\"\"Ensure if the view exists by making a query directly to the database. If it doesn't exist, create it\"\"\"\n\n    # The BaseView always exists\n    if self.view_name == \"base\":\n        return True\n\n    # Check the database directly\n    if not self._check_database():\n        self._auto_create_view()\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.exists","title":"<code>exists(skip_cache=False)</code>","text":"<p>Check if the view exists in the database</p> <p>Parameters:</p> Name Type Description Default <code>skip_cache</code> <code>bool</code> <p>Skip the cache and check the database directly (default: False)</p> <code>False</code> <p>Returns:     bool: True if the view exists, False otherwise.</p> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def exists(self, skip_cache: bool = False) -&gt; bool:\n    \"\"\"Check if the view exists in the database\n\n    Args:\n        skip_cache (bool): Skip the cache and check the database directly (default: False)\n    Returns:\n        bool: True if the view exists, False otherwise.\n    \"\"\"\n    # The BaseView always exists\n    if self.view_name == \"base\":\n        return True\n\n    # If we're skipping the cache, we need to check the database directly\n    if skip_cache:\n        return self._check_database()\n\n    # Use the meta class to see if the view exists\n    views_df = self.meta.views(self.database)\n\n    # Check if we have ANY views\n    if views_df.empty:\n        return False\n\n    # Check if the view exists\n    return self.table in views_df[\"Name\"].values\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.pull_dataframe","title":"<code>pull_dataframe(limit=50000, head=False)</code>","text":"<p>Pull a DataFrame based on the view type</p> <p>Parameters:</p> Name Type Description Default <code>limit</code> <code>int</code> <p>The maximum number of rows to pull (default: 50000)</p> <code>50000</code> <code>head</code> <code>bool</code> <p>Return just the head of the DataFrame (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>Union[pd.DataFrame, None]: The DataFrame for the view or None if it doesn't exist</p> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def pull_dataframe(self, limit: int = 50000, head: bool = False) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Pull a DataFrame based on the view type\n\n    Args:\n        limit (int): The maximum number of rows to pull (default: 50000)\n        head (bool): Return just the head of the DataFrame (default: False)\n\n    Returns:\n        Union[pd.DataFrame, None]: The DataFrame for the view or None if it doesn't exist\n    \"\"\"\n\n    # Pull the DataFrame\n    if head:\n        limit = 5\n    pull_query = f'SELECT * FROM \"{self.table}\" LIMIT {limit}'\n    df = self.data_source.query(pull_query)\n    return df\n</code></pre>"},{"location":"api_classes/views/#sageworks.core.views.view.View.query","title":"<code>query(query)</code>","text":"<p>Query the view with a custom SQL query</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The SQL query to execute</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>Union[pd.DataFrame, None]: The DataFrame for the query or None if it doesn't exist</p> Source code in <code>src/sageworks/core/views/view.py</code> <pre><code>def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Query the view with a custom SQL query\n\n    Args:\n        query (str): The SQL query to execute\n\n    Returns:\n        Union[pd.DataFrame, None]: The DataFrame for the query or None if it doesn't exist\n    \"\"\"\n    return self.data_source.query(query)\n</code></pre>"},{"location":"api_classes/views/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Listing Views</p> views.py<pre><code>from sageworks.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\ntest_data = DataSource('test_data')\ntest_data.views()\n[\"display\", \"training\", \"computation\"]\n</code></pre> <p>Getting a Particular View</p> views.py<pre><code>from sageworks.api.feature_set import FeatureSet\n\nfs = FeatureSet('test_features')\n\n# Grab the columns for the display view\ndisplay_view = fs.view(\"display\")\ndisplay_view.columns\n['id', 'name', 'height', 'weight', 'salary', ...]\n\n# Pull the dataframe for this view\ndf = display_view.pull_dataframe()\n    id       name     height      weight         salary ...\n0   58  Person 58  71.781227  275.088196  162053.140625  \n</code></pre> <p>View Queries</p> <p>All SageWorks Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p> view_query.py<pre><code>from sageworks.api.feature_set import FeatureSet\n\n# Grab a FeatureSet View\nfs = FeatureSet(\"abalone_features\")\nt_view = fs.view(\"training\")\n\n# Make some queries using the Athena backend\ndf = t_view(f\"select * from {t_view.table} where height &gt; .3\")\nprint(df.head())\n\ndf = t_view.query(\"select * from abalone_features where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040         0.0050                      2\n</code></pre> <p>Classes to construct View</p> <p>The SageWorks Classes used to construct viewss are currently in 'Core'. So you can check out the documentation for those classes here: SageWorks View Creators</p> <p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"aws_setup/aws_access_management/","title":"AWS Acesss Management","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>This page gives an overview of how SageWorks sets up roles and policies in a granular way that provides 'least priviledge' and also provides a unified framework for AWS access management.</p>"},{"location":"aws_setup/aws_access_management/#conceptual-slide-deck","title":"Conceptual Slide Deck","text":"<p>SageWorks AWS Acesss Management</p>"},{"location":"aws_setup/aws_access_management/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Identity Center</li> <li>Users and Groups</li> <li>Permission Sets</li> <li>SSO Command Line/Python Configure</li> </ul>"},{"location":"aws_setup/aws_client_vpn/","title":"Setting Up AWS Client VPN","text":"<p>Follow the steps below to set up and connect using AWS Client VPN.</p>"},{"location":"aws_setup/aws_client_vpn/#step-1-create-a-client-vpn-endpoint-in-aws","title":"Step 1: Create a Client VPN Endpoint in AWS","text":"<ol> <li>Go to the VPC Dashboard in the AWS Management Console.</li> <li>Select Client VPN Endpoints and click Create Client VPN Endpoint.</li> <li>Fill in the required details:</li> <li>Client IPv4 CIDR: Choose an IP range (e.g., <code>10.0.0.0/22</code>) that doesn\u2019t overlap with your VPC CIDR.</li> <li>Server Certificate ARN: Use or create an SSL certificate using AWS Certificate Manager (ACM).</li> <li>Authentication Options: Use either Mutual Authentication (client certificates) or Active Directory (for user-based authentication).</li> <li>Connection Log Options: Optional; you can set up CloudWatch logs.</li> <li>Click Create Client VPN Endpoint.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-2-associate-the-client-vpn-endpoint-with-a-vpc-subnet","title":"Step 2: Associate the Client VPN Endpoint with a VPC Subnet","text":"<ol> <li>Once the endpoint is created, select it and click on Associations.</li> <li>Choose a Target Network Association (a subnet in the VPC where you want VPN clients to access).</li> <li>This allows traffic from the VPN clients to route through your chosen VPC subnet.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-3-authorize-vpn-clients-to-access-the-vpc","title":"Step 3: Authorize VPN Clients to Access the VPC","text":"<ol> <li>Under the Authorization Rules tab, click Add Authorization Rule.</li> <li>For Destination network, specify <code>0.0.0.0/0</code> to allow access to all resources in the VPC.</li> <li>Set Grant access to to <code>Allow access</code> and specify the group you created or allow all users.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-4-download-and-install-aws-vpn-client","title":"Step 4: Download and Install AWS VPN Client","text":"<ol> <li>Download the AWS Client VPN for macOS.</li> <li>Install the client on your Mac.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-5-download-the-client-configuration-file","title":"Step 5: Download the Client Configuration File","text":"<ol> <li>In the AWS Console, go to your Client VPN Endpoint.</li> <li>Click on Download Client Configuration. This file contains the connection details required by the VPN client.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-6-import-the-configuration-file-and-connect","title":"Step 6: Import the Configuration File and Connect","text":"<ol> <li>Open the AWS Client VPN app on your Mac.</li> <li>Click File -&gt; Manage Profiles -&gt; Add Profile.</li> <li>Import the configuration file you downloaded.</li> <li>Enter your credentials if required (depending on the authentication method you chose).</li> <li>Click Connect.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#benefits-of-using-aws-client-vpn","title":"Benefits of Using AWS Client VPN","text":"<ul> <li>Simple Setup: Minimal steps and no need for additional infrastructure.</li> <li>Secure: Uses TLS to secure connections, and you control who has access.</li> <li>Direct Access: Provides direct access to your AWS resources, including the Redis cluster.</li> </ul>"},{"location":"aws_setup/aws_client_vpn/#conclusion","title":"Conclusion","text":"<p>AWS Client VPN is a straightforward, secure, and effective solution for connecting your laptop to an AWS VPC. It requires minimal setup and provides all the security controls you need, making it ideal for a single laptop and user.</p>"},{"location":"aws_setup/aws_setup/","title":"AWS Setup","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"aws_setup/aws_setup/#get-some-information","title":"Get some information","text":"<ul> <li>Go to your AWS Identity Center in the AWS Console</li> <li>On the right side there will be two important pieces of information<ul> <li>Start URL</li> <li>Region </li> </ul> </li> </ul> <p>Write these values down, you'll need them as part of this AWS setup.</p>"},{"location":"aws_setup/aws_setup/#install-aws-cli","title":"Install AWS CLI","text":"<p>AWS CLI Instructions</p>"},{"location":"aws_setup/aws_setup/#running-the-sso-configuration","title":"Running the SSO Configuration","text":"<p>Note: You only need to do this once! Also this will create a NEW profile, so name the profile something like <code>aws_sso</code>.</p> <pre><code>aws configure sso --profile &lt;whatever&gt; (e.g. aws_sso)\nSSO session name (Recommended): sso-session\nSSO start URL []: &lt;the Start URL from info above&gt;\nSSO region []: &lt;the Region from info above&gt;\nSSO registration scopes [sso:account:access]: &lt;just hit return&gt;\n</code></pre> <p>You will get a browser open/redirect at this point and get a list of available accounts.. something like below, just pick the correct account</p> <pre><code>There are 2 AWS accounts available to you.\n&gt; SCP_Sandbox, briford+sandbox@supercowpowers.com (XXXX40646YYY)\n  SCP_Main, briford@supercowpowers.com (XXX576391YYY)\n</code></pre> <p>Now pick the role that you're going to use</p> <pre><code>There are 2 roles available to you.\n&gt; DataScientist\n  AdministratorAccess\n\nCLI default client Region [None]: &lt;same region as above&gt;\nCLI default output format [None]: json\n</code></pre>"},{"location":"aws_setup/aws_setup/#setting-up-some-aliases-for-bashzsh","title":"Setting up some aliases for bash/zsh","text":"<p>Edit your favorite ~/.bashrc ~/.zshrc and add these nice aliases/helper</p> <pre><code># AWS Aliases\nalias aws_sso='export AWS_PROFILE=aws_sso'\n\n# Default AWS Profile\nexport AWS_PROFILE=aws_sso\n</code></pre>"},{"location":"aws_setup/aws_setup/#testing-your-new-aws-profile","title":"Testing your new AWS Profile","text":"<p>Make sure your profile is active/set</p> <p><pre><code>env | grep AWS\nAWS_PROFILE=&lt;aws_sso or whatever&gt;\n</code></pre> Now you can list the S3 buckets in the AWS Account</p> <p><pre><code>aws ls s3\n</code></pre> If you get some message like this...</p> <pre><code>The SSO session associated with this profile has\nexpired or is otherwise invalid. To refresh this SSO\nsession run aws sso login with the corresponding\nprofile.\n</code></pre> <p>This is fine/good, a browser will open up and you can refresh your SSO Token.</p> <p>After that you should get a listing of the S3 buckets without needed to refresh your token.</p> <pre><code>aws s3 ls\n\u276f aws s3 ls\n2023-03-20 20:06:53 aws-athena-query-results-XXXYYY-us-west-2\n2023-03-30 13:22:28 sagemaker-studio-XXXYYY-dbgyvq8ruka\n2023-03-24 22:05:55 sagemaker-us-west-2-XXXYYY\n2023-04-30 13:43:29 scp-sageworks-artifacts\n</code></pre>"},{"location":"aws_setup/aws_setup/#back-to-initial-setup","title":"Back to Initial Setup","text":"<p>If you're doing the initial setup of SageWorks you should now go back and finish that process: Getting Started</p>"},{"location":"aws_setup/aws_setup/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Identity Center</li> <li>Users and Groups</li> <li>Permission Sets</li> <li>SSO Command Line/Python Configure</li> </ul>"},{"location":"aws_setup/aws_tips_and_tricks/","title":"AWS Tips and Tricks","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>This page tries to give helpful guidance when setting up AWS Accounts, Users, and Groups. In general AWS can be a bit tricky to set up the first time. Feel free to use any material in this guide but we're more than happy to help clients get their AWS Setup ready to go for FREE. Below are some guides for setting up a new AWS account for SageWorks and also setting up SSO Users and Groups within AWS.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#new-aws-account-with-aws-organizations-easy","title":"New AWS Account (with AWS Organizations: easy)","text":"<ul> <li>If you already have an AWS Account you can activate the AWS Identity Center/Organization functionality.</li> <li>Now go to AWS Organizations page and hit 'Add an AWS Account' button</li> <li>Add a new User with permissions that allows AWS Stack creation</li> </ul> <p>Email Trick</p> <p>AWS will often not allow the same email to be used for different accounts. If you need a 'new' email just add a plus sign '+' at the end of your existing email (e.g. bob.smith+aws@gmail.com). This email will 'auto forward' to bob.smith@gmail.com.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#new-aws-account-without-aws-organizations-a-bit-harder","title":"New AWS Account (without AWS Organizations: a bit harder)","text":"<ul> <li>Goto: https://aws.amazon.com/free and hit the big button 'Create a Free Account'</li> <li>Enter email and the account name you'd like (anything is fine)</li> <li>You'll get a validation email and go through the rest of the Account setup procedure</li> <li>Add a new User with permissions that allows AWS Stack creation</li> </ul>"},{"location":"aws_setup/aws_tips_and_tricks/#sso-users-and-groups","title":"SSO Users and Groups","text":"<p>AWS SSO (Single Sign-On) is a cloud-based service that allows users to manage access to multiple AWS accounts and business applications using a single set of credentials. It simplifies the authentication process for users and provides centralized management of permissions and access control across various AWS resources. With AWS SSO, users can log in once and access all the applications and accounts they need, streamlining the user experience and increasing productivity. AWS SSO also enables IT administrators to manage access more efficiently by providing a single point of control for managing user access, permissions, and policies, reducing the risk of unauthorized access or security breaches.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#setting-up-sso-users","title":"Setting up SSO Users","text":"<ul> <li>Log in to your AWS account and go to the AWS Identity Center console.</li> <li>Click on the \"Users\" tab and then click on the \"Add user\" button.</li> </ul> <p>The 'Add User' setup is fairly straight forward but here are some screen shots:</p> <p>On the first panel you can fill in the users information.</p> <p></p>"},{"location":"aws_setup/aws_tips_and_tricks/#groups","title":"Groups","text":"<p>On the second panel we suggest that you have at LEAST two groups:</p> <ul> <li>Admin group</li> <li>DataScientists group</li> </ul>"},{"location":"aws_setup/aws_tips_and_tricks/#setting-up-groups","title":"Setting up Groups","text":"<p>This allows you to put most of the users into the DataScientists group that has AWS policies based on their job role. AWS uses 'permission sets' and you assign AWS Policies. This approach makes it easy to give a group of users a set of relevant policies for their tasks. </p> <p>Our standard setup is to have two permission sets with the following policies:</p> <ul> <li>IAM Identity Center --&gt; Permission sets --&gt; DataScientist </li> <li> <p>Add Policy: arn:aws:iam::aws:policy/job-function/DataScientist</p> </li> <li> <p>IAM Identity Center --&gt; Permission sets --&gt; AdministratorAccess </p> </li> <li>Add Policy: arn:aws:iam::aws:policy/job-function/AdministratorAccess</li> </ul> <p>See: Permission Sets for more details and instructions.</p> <p>Another benefit of creating groups is that you can include that group in 'Trust Policy (assume_role)' for the SageWorks-ExecutionRole (this gets deployed as part of the SageWorks AWS Stack). This means that the management of what SageWorks can do/see/read/write is completely done through the SageWorks-ExecutionRole.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#back-to-adding-user","title":"Back to Adding User","text":"<p>Okay now that we have our groups set up we can go back to our original goal of adding a user. So here's the second panel with the groups and now we can hit 'Next'</p> <p></p> <p>On the third panel just review the details and hit the 'Add User' button at the bottom. The user will get an email giving them instructions on how to log on to their AWS account.</p> <p></p>"},{"location":"aws_setup/aws_tips_and_tricks/#aws-console","title":"AWS Console","text":"<p>Now when the user logs onto the AWS Console they should see something like this: </p>"},{"location":"aws_setup/aws_tips_and_tricks/#aws-clisso-setup-for-command-linepython-usage","title":"AWS CLI/SSO Setup for Command Line/Python Usage","text":"<p>Please see our AWS Setup</p>"},{"location":"aws_setup/aws_tips_and_tricks/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Identity Center</li> <li>Users and Groups</li> <li>Permission Sets</li> <li>SSO Command Line/Python Configure</li> </ul>"},{"location":"aws_setup/core_stack/","title":"SageWorks AWS Stack","text":"<p>Welcome to the SageWorks AWS Setup Guide. SageWorks is deployed as an AWS Stack following the well architected system practices of AWS. </p> <p>AWS Setup can be a bit complex</p> <p>Setting up SageWorks with AWS can be a bit complex, but this only needs to be done ONCE for your entire company. The install uses standard CDK --&gt; AWS Stacks and SageWorks tries to make it straight forward. If you have any troubles at all feel free to contact us a sageworks@supercowpowers.com or on Discord and we're happy to help you with AWS for FREE.</p>"},{"location":"aws_setup/core_stack/#two-main-options-when-using-sageworks","title":"Two main options when using SageWorks","text":"<ol> <li>Spin up a new AWS Account for the SageWorks Stacks (Make a New Account)</li> <li>Deploy SageWorks Stacks into your existing AWS Account</li> </ol> <p>Either of these options are fully supported, but we highly suggest a NEW account as it gives the following benefits:</p> <ul> <li>AWS Data Isolation: Data Scientists will feel empowered to play in the sandbox without impacting production services.</li> <li>AWS Cost Accounting: Monitor and Track all those new ML Pipelines that your team creates with SageWorks :)</li> </ul>"},{"location":"aws_setup/core_stack/#setting-up-users-and-groups","title":"Setting up Users and Groups","text":"<p>If your AWS Account already has users and groups set up you can skip this but here's our recommendations on setting up SSO Users and Groups</p>"},{"location":"aws_setup/core_stack/#onboarding-sageworks-to-your-aws-account","title":"Onboarding SageWorks to your AWS Account","text":"<p>Pulling down the SageWorks Repo   <pre><code>git clone https://github.com/SuperCowPowers/sageworks.git\n</code></pre></p>"},{"location":"aws_setup/core_stack/#sageworks-uses-aws-python-cdk-for-deployments","title":"SageWorks uses AWS Python CDK for Deployments","text":"<p>If you don't have AWS CDK already installed you can do these steps:</p> <p>Mac</p> <p><pre><code>brew install node \nnpm install -g aws-cdk\n</code></pre> Linux</p> <p><pre><code>sudo apt install nodejs\nsudo npm install -g aws-cdk\n</code></pre> For more information on Linux installs see Digital Ocean NodeJS</p>"},{"location":"aws_setup/core_stack/#create-an-s3-bucket-for-sageworks","title":"Create an S3 Bucket for SageWorks","text":"<p>SageWorks pushes and pulls data from AWS, it will use this S3 Bucket for storage and processing. You should create a NEW S3 Bucket, we suggest a name like <code>&lt;company_name&gt;-sageworks</code></p>"},{"location":"aws_setup/core_stack/#deploying-the-sageworks-core-stack","title":"Deploying the SageWorks Core Stack","text":"<p>Do the initial setup/config here: Getting Started. After you've done that come back to this section. For Stack Deployment additional things need to be added to your config file. The config file will be located in your home directory <code>~/.sageworks/sageworks_config.json</code>. Edit this file and add addition stuff for the deployment. Specifically there are two additional fields to be added (optional for both)</p> <p><pre><code>\"SAGEWORKS_SSO_GROUP\": DataScientist (or whatever)\n\"SAGEWORKS_ADDITIONAL_BUCKETS\": \"bucket1, bucket2\n</code></pre> These are optional but are set/used by most SageWorks users.</p> <p>AWS Stuff</p> <p>Activate your AWS Account that's used for SageWorks deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p> <pre><code>cd sageworks/aws_setup/sageworks_core\npip install -r requirements.txt\ncdk bootstrap\ncdk deploy\n</code></pre>"},{"location":"aws_setup/core_stack/#aws-account-setup-check","title":"AWS Account Setup Check","text":"<p>After setting up SageWorks config/AWS Account you can run this test/checking script. If the results ends with <code>INFO AWS Account Clamp: AOK!</code> you're in good shape. If not feel free to contact us on Discord and we'll get it straightened out for you :)</p> <pre><code>pip install sageworks (if not already installed)\ncd sageworks/aws_setup\npython aws_account_check.py\n&lt;lot of print outs for various checks&gt;\nINFO AWS Account Clamp: AOK!\n</code></pre> <p>Success</p> <p>Congratulations: SageWorks is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install sageworks</code> and start using the API.</p> <p>If you ran into any issues with this procedure please contact us via Discord or email sageworks@supercowpowers.com and the SCP team will provide free setup and support for new SageWorks users.</p>"},{"location":"aws_setup/dashboard_stack/","title":"Deploy the SageWorks Dashboard Stack","text":"<p>Deploying the Dashboard Stack is reasonably straight forward, it's the same approach as the Core Stack that you've already deployed.</p> <p>Please review the Stack Details section to understand all the AWS components that are included and utilized in the SageWorks Dashboard Stack.</p>"},{"location":"aws_setup/dashboard_stack/#deploying-the-dashboard-stack","title":"Deploying the Dashboard Stack","text":"<p>AWS Stuff</p> <p>Activate your AWS Account that's used for SageWorks deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p> <pre><code>cd sageworks/aws_setup/sageworks_dashboard_full\nexport SAGEWORKS_CONFIG=/full/path/to/config.json\npip install -r requirements.txt\ncdk bootstrap\ncdk deploy\n</code></pre>"},{"location":"aws_setup/dashboard_stack/#stack-details","title":"Stack Details","text":"<p>AWS Questions?</p> <p>There's quite a bit to unpack when deploying an AWS powered Web Service. We're happy to help walk you through the details and options. Contact us anytime for a free consultation.</p> <ul> <li>ECS Fargate</li> <li>Load Balancer</li> <li>2 Availability Zones</li> <li>VPCs / Nat Gateways</li> <li>ElasticCache Cluster (shared Redis Caching)</li> </ul>"},{"location":"aws_setup/dashboard_stack/#aws-stack-benefits","title":"AWS Stack Benefits","text":"<ol> <li>Scalability: Includes an Application Load Balancer and uses ECS with Fargate, and ElasticCache for more robust scaling options.</li> <li>Higher Security: Utilizes security groups for both the ECS tasks, load balancer, plus VPC private subnets for Redis and the utilization of NAT Gateways.</li> </ol> <p>AWS Costs</p> <p>Deploying the SageWorks Dashboard does incur some monthly AWS costs. If you're on a tight budget you can deploy the 'lite' version of the Dashboard Stack.</p> <pre><code>cd sageworks/aws_setup/sageworks_dashboard_lite\nexport SAGEWORKS_CONFIG=/full/path/to/config.json\npip install -r requirements.txt\ncdk bootstrap\ncdk deploy\n</code></pre>"},{"location":"aws_setup/domain_cert_setup/","title":"AWS Domain and Certificate Instructions","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>This page tries to give helpful guidance when setting up a new domain and SSL Certificate in your AWS Account.</p>"},{"location":"aws_setup/domain_cert_setup/#new-domain","title":"New Domain","text":"<p>You'll want the SageWorks Dashboard to have a domain for your companies internal use. Customers will typically use a domain like <code>&lt;company_name&gt;-ml-dashboard.com</code> but you are free to choose any domain you'd like.</p> <p>Domains are tied to AWS Accounts</p> <p>When you create a new domain in AWS Route 53, that domain is tied to that AWS Account. You can do a cross account setup for domains but it's a bit more tricky. We recommend that each account where SageWorks gets deployed owns the domain for that Dashboard.</p>"},{"location":"aws_setup/domain_cert_setup/#multiple-aws-accounts","title":"Multiple AWS Accounts","text":"<p>Many customers will have a dev/stage/prod set of AWS accounts, if that the case then the best practice is to make a domain specific to each account. So for instance:</p> <ul> <li>The AWS Dev Account gets: <code>&lt;company_name&gt;-ml-dashboard-dev.com</code> </li> <li>The AWS Prod Account gets:  <code>&lt;company_name&gt;-ml-dashboard-prod.com</code>.</li> </ul> <p>This means that when you go to that Dashboard it's super obvious which environment your on.</p>"},{"location":"aws_setup/domain_cert_setup/#register-the-domain","title":"Register the Domain","text":"<ul> <li> <p>Open Route 53 Console Route 53 Console</p> </li> <li> <p>Register your New Domain</p> <ul> <li>Click on Registered domains in the left navigation pane.</li> <li>Click on Register Domain.</li> <li>Enter your desired domain name and check for availability.</li> <li>Follow the prompts to complete the domain registration process.</li> <li>After registration, your domain will be listed under Registered domains.</li> </ul> </li> </ul>"},{"location":"aws_setup/domain_cert_setup/#request-a-ssl-certificate-from-acm","title":"Request a SSL Certificate from ACM","text":"<ol> <li> <p>Open ACM Console: AWS Certificate Manager (ACM) Console</p> </li> <li> <p>Request a Certificate:</p> <ul> <li>Click on Request a certificate.</li> <li>Select Request a public certificate and click Next.</li> </ul> </li> <li> <p>Add Domain Names:</p> <ul> <li>Enter the domain name you registered (e.g., <code>yourdomain.com</code>).</li> <li>Add any additional subdomains if needed (e.g., <code>www.yourdomain.com</code>).</li> </ul> </li> <li> <p>Validation Method:</p> <ul> <li>Choose DNS validation (recommended).</li> <li>ACM will provide CNAME records that you need to add to your Route 53 hosted zone.</li> </ul> </li> <li> <p>Add Tags (Optional):</p> <ul> <li>Add any tags if you want to organize your resources.</li> </ul> </li> <li> <p>Review and Request:</p> <ul> <li>Review your request and click Confirm and request.</li> </ul> </li> </ol>"},{"location":"aws_setup/domain_cert_setup/#adding-cname-records-to-route-53","title":"Adding CNAME Records to Route 53","text":"<p>To complete the domain validation process for your SSL/TLS certificate, you need to add the CNAME records provided by AWS Certificate Manager (ACM) to your Route 53 hosted zone. This step ensures that you own the domain and allows ACM to issue the certificate.</p>"},{"location":"aws_setup/domain_cert_setup/#finding-cname-record-names-and-values","title":"Finding CNAME Record Names and Values","text":"<p>You can find the CNAME record names and values in the AWS Certificate Manager (ACM) console:</p> <ol> <li> <p>Open ACM Console: AWS Certificate Manager (ACM) Console</p> </li> <li> <p>Select Your Certificate:</p> <ul> <li>Click on the certificate that is in the Pending Validation state.</li> </ul> </li> <li> <p>View Domains Section:</p> <ul> <li>Under the Domains section, you will see the CNAME record names and values that you need to add to your Route 53 hosted zone.</li> </ul> </li> </ol>"},{"location":"aws_setup/domain_cert_setup/#adding-cname-records-to-domain","title":"Adding CName Records to Domain","text":"<ol> <li> <p>Open Route 53 Console: Route 53 Console</p> </li> <li> <p>Select Your Hosted Zone:</p> <ul> <li>Find and select the hosted zone for your domain (e.g., <code>yourdomain.com</code>).</li> <li>Click on Create record.</li> </ul> </li> <li> <p>Add the First CNAME Record:</p> <ul> <li>For the Record name, enter the name provided by ACM (e.g., <code>_3e8623442477e9eeec.your-domain.com</code>).</li> <li>For the Record type, select <code>CNAME</code>.</li> <li>For the Value, enter the value provided by ACM (e.g., <code>_0908c89646d92.sdgjtdhdhz.acm-validations.aws.</code>) (include the trailing dot).</li> <li>Leave the default settings for TTL.</li> <li>Click on Create records.</li> </ul> </li> <li> <p>Add the Second CNAME Record:</p> <ul> <li>Repeat the process for the second CNAME record.</li> <li>For the Record name, enter the second name provided by ACM (e.g., <code>_75cd9364c643caa.www.your-domain.com</code>).</li> <li>For the Record type, select <code>CNAME</code>.</li> <li>For the Value, enter the second value provided by ACM (e.g., <code>_f72f8cff4fb20f4.sdgjhdhz.acm-validations.aws.</code>)  (include the trailing dot).</li> <li>Leave the default settings for TTL.</li> <li>Click on Create records.</li> </ul> </li> </ol> <p>DNS Propagation and Cert Validation</p> <p>After adding the CNAME records, these DNS records will propagate through the DNS system and ACM will automatically detect the validation records and validate the domain. This process can take a few minutes or up to an hour.</p>"},{"location":"aws_setup/domain_cert_setup/#certificate-states","title":"Certificate States","text":"<p>After requesting a certificate, it will go through the following states:</p> <ul> <li> <p>Pending Validation: The initial state after you request a certificate and before you complete the validation process. ACM is waiting for you to prove domain ownership by adding the CNAME records.</p> </li> <li> <p>Issued: This state indicates that the certificate has been successfully validated and issued. You can now use this certificate with your AWS resources.</p> </li> <li> <p>Validation Timed Out: If you do not complete the validation process within a specified period (usually 72 hours), the certificate request times out and enters this state.</p> </li> <li> <p>Revoked: This state indicates that the certificate has been revoked and is no longer valid.</p> </li> <li> <p>Failed: If the validation process fails for any reason, the certificate enters this state.</p> </li> <li> <p>Inactive: This state indicates that the certificate is not currently in use.</p> </li> </ul> <p>The certificate status should obviously be in the Issued state, if not please contact SageWorks Support Team.</p>"},{"location":"aws_setup/domain_cert_setup/#retrieving-the-certificate-arn","title":"Retrieving the Certificate ARN","text":"<ol> <li> <p>Open ACM Console:</p> <ul> <li>Go back to the AWS Certificate Manager (ACM) Console.</li> </ul> </li> <li> <p>Check the Status:</p> <ul> <li>Once the CNAME records are added, ACM will automatically validate the domain.</li> <li>Refresh the ACM console to see the updated status.</li> <li>The status will change to \"Issued\" once validation is complete.</li> </ul> </li> <li> <p>Copy the Certificate ARN:</p> <ul> <li>Click on your issued certificate.</li> <li>Copy the Amazon Resource Name (ARN) from the certificate details.</li> </ul> </li> </ol> <p>You now have the ARN for your certificate, which you can use in your AWS resources such as API Gateway, CloudFront, etc.</p>"},{"location":"aws_setup/domain_cert_setup/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Adding or Changing DNS Records</li> <li>AWS Certificate Manager (ACM) Documentation</li> <li>Requesting a Public Certificate</li> <li>Validating Domain Ownership</li> <li>AWS Route 53 Documentation</li> <li>AWS API Gateway Documentation</li> </ul>"},{"location":"aws_setup/full_pipeline/","title":"Testing Full ML Pipeline","text":"<p>Now that the core Sageworks AWS Stack has been deployed. Let's test out SageWorks by building a full entire AWS ML Pipeline from start to finish. The script <code>build_ml_pipeline.py</code> uses the SageWorks API to quickly and easily build an AWS Modeling Pipeline.</p> <p>Taste the Awesome</p> <p>The SageWorks \"hello world\" builds a full AWS ML Pipeline. From S3 to deployed model and endpoint. If you have any troubles at all feel free to contact us at sageworks email or on Discord and we're happy to help you for FREE.</p> <ul> <li>DataLoader(abalone.csv) --&gt; DataSource</li> <li>DataToFeatureSet Transform --&gt; FeatureSet</li> <li>FeatureSetToModel Transform --&gt; Model</li> <li>ModelToEndpoint Transform --&gt; Endpoint</li> </ul> <p>This script will take a LONG TiME to run, most of the time is waiting on AWS to finalize FeatureGroups, train Models or deploy Endpoints.</p> <p><pre><code>\u276f python build_ml_pipeline.py\n&lt;lot of building ML pipeline outputs&gt;\n</code></pre> After the script completes you will see that it's built out an AWS ML Pipeline and testing artifacts.</p>"},{"location":"aws_setup/full_pipeline/#run-the-sageworks-dashboard-local","title":"Run the SageWorks Dashboard (Local)","text":"<p>Dashboard AWS Stack</p> <p>Deploying the Dashboard Stack is straight-forward and provides a robust AWS Web Server with Load Balancer, Elastic Container Service, VPC Networks, etc. (see AWS Dashboard Stack)</p> <p>For testing it's nice to run the Dashboard locally, but for longterm use the SageWorks Dashboard should be deployed as an AWS Stack. The deployed Stack allows everyone in the company to use, view, and interact with the AWS Machine Learning Artifacts created with SageWorks.</p> <p><pre><code>cd sageworks/application/aws_dashboard\n./dashboard\n</code></pre> This will open a browser to http://localhost:8000</p> <p> SageWorks Dashboard: AWS Pipelines in a Whole New Light! <p>Success</p> <p>Congratulations: SageWorks is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install sageworks</code> and start using the API.</p> <p>If you ran into any issues with this procedure please contact us via Discord or email sageworks@supercowpowers.com and the SCP team will provide free setup and support for new SageWorks users.</p>"},{"location":"blogs/","title":"SageWorks Blogs","text":"<p>Just Getting Started?</p> <p>The SageWorks Blogs is a great way to see what's possible with SageWorks. Also if you're ready to jump in the API Classes will give you details on the SageWorks ML Pipeline Classes.</p>"},{"location":"blogs/#latest-blogs","title":"Latest Blogs","text":"<ul> <li>AqSol Residual Analysis: In this Blog we'll look at the popular AqSol compound solubility dataset and walk backward through the ML pipeline by starting with model residuals and backtracking to the features and input data.</li> </ul>"},{"location":"blogs/#stupid-yet-interesting","title":"Stupid yet Interesting","text":"<p>These blogs are odd corner cases that we stumbled into and discovered some interesting aspect of Cloud Platforms or ML Pipelines. </p> <ul> <li>AWS Credentials Refresh Lock We discover that the AWS botocore library has a threading lock when refreshing credentials.</li> </ul>"},{"location":"blogs/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"blogs/aws_credentials_lock/","title":"AWS Credentials Lock","text":"<p>AWS Curious?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"blogs/aws_credentials_lock/#tbd","title":"TBD","text":""},{"location":"blogs/aws_credentials_lock/#threading-lock","title":"Threading Lock","text":"<p>A threading lock object in Python is used to ensure that only one thread can access a specific section of code or resource at a time, preventing race conditions.</p> <p>botocore/credentials.py <pre><code>self._refresh_lock = threading.Lock()\n</code></pre></p>"},{"location":"blogs/aws_credentials_lock/#error","title":"Error","text":"<pre><code>elif self.refresh_needed(self._mandatory_refresh_timeout):\n    # If we're within the mandatory refresh window,\n    # we must block until we get refreshed credentials.\n    with self._refresh_lock:\n        if not self.refresh_needed(self._mandatory_refresh_timeout):\n            return\n        self._protected_refresh(is_mandatory=True)\n</code></pre>"},{"location":"blogs/aws_credentials_lock/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"blogs/eda/","title":"Exploratory Data Analysis","text":"<p>SageWorks EDS</p> <p>The SageWorks toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components Dashboard.</p> <p>The SageWorks framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p> <ul> <li>TBD</li> <li>TBD2</li> </ul> SageWorks Exploratory Data Analysis"},{"location":"blogs/eda/#additional-resources","title":"Additional Resources","text":"<ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"blogs/htg/","title":"EDA: High Target Gradients","text":"<p>SageWorks EDS</p> <p>The SageWorks toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components Dashboard.</p> <p>The SageWorks framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p> <ul> <li>TBD</li> <li>TBD2</li> </ul> <p>One of the latest EDA techniques we've added is the addition of a concept called High Target Gradients </p> <ol> <li>Definition: For a given data point (x_i) with target value (y_i), and its neighbor (x_j) with target value (y_j), the target gradient (G_{ij}) can be defined as:</li> </ol> <p>[G_{ij} = \\frac{|y_i - y_j|}{d(x_i, x_j)}]</p> <p>where (d(x_i, x_j)) is the distance between (x_i) and (x_j) in the feature space. This equation gives you the rate of change of the target value with respect to the change in features, similar to a slope in a two-dimensional space.</p> <ol> <li>Max Gradient for Each Point: For each data point (x_i), you can compute the maximum target gradient with respect to all its neighbors:</li> </ol> <p>[G_{i}^{max} = \\max_{j \\neq i} G_{ij}]</p> <p>This gives you a scalar value for each point in your training data that represents the maximum rate of change of the target value in its local neighborhood.</p> <ol> <li> <p>Usage: You can use (G_{i}^{max}) to identify and filter areas in the feature space that have high target gradients, which may indicate potential issues with data quality or feature representation.</p> </li> <li> <p>Visualization: Plotting the distribution of (G_{i}^{max}) values or visualizing them in the context of the feature space can help you identify regions or specific points that warrant further investigation.</p> </li> </ol>"},{"location":"blogs/htg/#additional-resources","title":"Additional Resources","text":"<ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"blogs/model_monitoring/","title":"Model Monitoring","text":"<p>Amazon SageMaker Model Monitor currently provides the following types of monitoring:</p> <ul> <li>Monitor Data Quality: Detect drifts in data quality such as deviations from baseline data types.</li> <li>Monitor Model Quality: Monitor drift in model quality metrics, such as accuracy.</li> <li>Monitor Bias Drift for Models in Production: Monitor bias in your model\u2019s predictions.</li> <li>Monitor Feature Attribution Drift for Models in Production: Monitor drift in feature attribution.</li> </ul>"},{"location":"blogs/residual_analysis/","title":"Residual Analysis","text":""},{"location":"blogs/residual_analysis/#residual-analysis","title":"Residual Analysis","text":"<p>Overview and Definition Residual analysis involves examining the differences between observed and predicted values, known as residuals, to assess the performance of a predictive model. It is a critical step in model evaluation as it helps identify patterns of errors, diagnose potential problems, and improve model performance. By understanding where and why a model's predictions deviate from actual values, we can make informed adjustments to the model or the data to enhance accuracy and robustness.</p> <p>Sparse Data Regions The observation is in a part of feature space with little or no nearby training observations, leading to poor generalization in these regions and resulting in high prediction errors.</p> <p>Noisy/Inconsistent Data and Preprocessing Issues The observation is in a part of feature space where the training data is noisy, incorrect, or has high variance in the target variable. Additionally, missing values or incorrect data transformations can introduce errors, leading to unreliable predictions and high residuals.</p> <p>Feature Resolution The current feature set may not fully resolve the compounds, leading to \u2018collisions\u2019 where different compounds are assigned identical features. Such unresolved features can result in different compounds exhibiting the same features, causing high residuals due to unaccounted structural or chemical nuances.</p> <p>Activity Cliffs Structurally similar compounds exhibit significantly different activities, making accurate prediction challenging due to steep changes in activity with minor structural modifications.</p> <p>Feature Engineering Issues Irrelevant or redundant features and poor feature scaling can negatively impact the model's performance and accuracy, resulting in higher residuals.</p> <p>Model Overfitting or Underfitting Overfitting occurs when the model is too complex and captures noise, while underfitting happens when the model is too simple and misses underlying patterns, both leading to inaccurate predictions.</p>"},{"location":"cached/cached_data_source/","title":"CachedDataSource","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p> <p>CachedDataSource: Caches the method results for SageWorks DataSources</p>"},{"location":"cached/cached_data_source/#sageworks.cached.cached_data_source.CachedDataSource","title":"<code>CachedDataSource</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>AthenaSource</code></p> <p>CachedDataSource: Caches the method results for SageWorks DataSources</p> <p>Note: Cached method values may lag underlying DataSource changes.</p> Common Usage <pre><code>my_data = CachedDataSource(name)\nmy_data.details()\nmy_data.health_check()\nmy_data.sageworks_meta()\n</code></pre> Source code in <code>src/sageworks/cached/cached_data_source.py</code> <pre><code>class CachedDataSource(CachedArtifactMixin, AthenaSource):\n    \"\"\"CachedDataSource: Caches the method results for SageWorks DataSources\n\n    Note: Cached method values may lag underlying DataSource changes.\n\n    Common Usage:\n        ```python\n        my_data = CachedDataSource(name)\n        my_data.details()\n        my_data.health_check()\n        my_data.sageworks_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid: str, database: str = \"sageworks\"):\n        \"\"\"CachedDataSource Initialization\"\"\"\n        AthenaSource.__init__(self, data_uuid=data_uuid, database=database, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the DataSource Details.\n\n        Returns:\n            dict: A dictionary of details about the DataSource\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the DataSource Details.\n\n        Returns:\n            dict: A dictionary of details about the DataSource\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the DataSource Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the DataSource\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def sageworks_meta(self) -&gt; Union[dict, None]:\n        \"\"\"Retrieve the SageWorks Metadata for this DataSource.\n\n        Returns:\n            Union[dict, None]: Dictionary of SageWorks metadata for this Artifact\n        \"\"\"\n        return super().sageworks_meta()\n\n    def smart_sample(self) -&gt; pd.DataFrame:\n        \"\"\"Retrieve the Smart Sample for this DataSource.\n\n        Returns:\n            pd.DataFrame: The Smart Sample DataFrame\n        \"\"\"\n        return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_data_source/#sageworks.cached.cached_data_source.CachedDataSource.__init__","title":"<code>__init__(data_uuid, database='sageworks')</code>","text":"<p>CachedDataSource Initialization</p> Source code in <code>src/sageworks/cached/cached_data_source.py</code> <pre><code>def __init__(self, data_uuid: str, database: str = \"sageworks\"):\n    \"\"\"CachedDataSource Initialization\"\"\"\n    AthenaSource.__init__(self, data_uuid=data_uuid, database=database, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_data_source/#sageworks.cached.cached_data_source.CachedDataSource.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the DataSource Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the DataSource</p> Source code in <code>src/sageworks/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the DataSource Details.\n\n    Returns:\n        dict: A dictionary of details about the DataSource\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_data_source/#sageworks.cached.cached_data_source.CachedDataSource.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the DataSource Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the DataSource</p> Source code in <code>src/sageworks/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the DataSource Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the DataSource\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_data_source/#sageworks.cached.cached_data_source.CachedDataSource.sageworks_meta","title":"<code>sageworks_meta()</code>","text":"<p>Retrieve the SageWorks Metadata for this DataSource.</p> <p>Returns:</p> Type Description <code>Union[dict, None]</code> <p>Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</p> Source code in <code>src/sageworks/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef sageworks_meta(self) -&gt; Union[dict, None]:\n    \"\"\"Retrieve the SageWorks Metadata for this DataSource.\n\n    Returns:\n        Union[dict, None]: Dictionary of SageWorks metadata for this Artifact\n    \"\"\"\n    return super().sageworks_meta()\n</code></pre>"},{"location":"cached/cached_data_source/#sageworks.cached.cached_data_source.CachedDataSource.smart_sample","title":"<code>smart_sample()</code>","text":"<p>Retrieve the Smart Sample for this DataSource.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The Smart Sample DataFrame</p> Source code in <code>src/sageworks/cached/cached_data_source.py</code> <pre><code>def smart_sample(self) -&gt; pd.DataFrame:\n    \"\"\"Retrieve the Smart Sample for this DataSource.\n\n    Returns:\n        pd.DataFrame: The Smart Sample DataFrame\n    \"\"\"\n    return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_data_source/#sageworks.cached.cached_data_source.CachedDataSource.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the DataSource Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the DataSource</p> Source code in <code>src/sageworks/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the DataSource Details.\n\n    Returns:\n        dict: A dictionary of details about the DataSource\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_data_source/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Pull DataSource Details</p> <pre><code>from sageworks.cached.cached_data_source import CachedDataSource\n\n# Grab a DataSource\nds = CachedDataSource(\"abalone_data\")\n\n# Show the details\nds.details()\n\n&gt; ds.details()\n\n{'uuid': 'abalone_data',\n 'health_tags': [],\n 'aws_arn': 'arn:aws:glue:x:table/sageworks/abalone_data',\n 'size': 0.070272,\n 'created': '2024-11-09T20:42:34.000Z',\n 'modified': '2024-11-10T19:57:52.000Z',\n 'input': 's3://sageworks-public-data/common/aBaLone.CSV',\n 'sageworks_health_tags': '',\n 'sageworks_correlations': {'length': {'diameter': 0.9868115846024996,\n</code></pre>"},{"location":"cached/cached_endpoint/","title":"CachedEndpoint","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p> <p>CachedEndpoint: Caches the method results for SageWorks Endpoints</p>"},{"location":"cached/cached_endpoint/#sageworks.cached.cached_endpoint.CachedEndpoint","title":"<code>CachedEndpoint</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>EndpointCore</code></p> <p>CachedEndpoint: Caches the method results for SageWorks Endpoints</p> <p>Note: Cached method values may lag underlying Endpoint changes.</p> Common Usage <pre><code>my_endpoint = CachedEndpoint(name)\nmy_endpoint.details()\nmy_endpoint.health_check()\nmy_endpoint.sageworks_meta()\n</code></pre> Source code in <code>src/sageworks/cached/cached_endpoint.py</code> <pre><code>class CachedEndpoint(CachedArtifactMixin, EndpointCore):\n    \"\"\"CachedEndpoint: Caches the method results for SageWorks Endpoints\n\n    Note: Cached method values may lag underlying Endpoint changes.\n\n    Common Usage:\n        ```python\n        my_endpoint = CachedEndpoint(name)\n        my_endpoint.details()\n        my_endpoint.health_check()\n        my_endpoint.sageworks_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, endpoint_uuid: str):\n        \"\"\"CachedEndpoint Initialization\"\"\"\n        EndpointCore.__init__(self, endpoint_uuid=endpoint_uuid, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedEndpoint Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedEndpoint\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedEndpoint Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedEndpoint\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedEndpoint Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the CachedEndpoint\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def sageworks_meta(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n        Returns:\n            str: The Enumerated Model Type\n        \"\"\"\n        return super().sageworks_meta()\n\n    @CachedArtifactMixin.cache_result\n    def endpoint_metrics(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Endpoint Metrics\n\n        Returns:\n            str: The Endpoint Metrics\n        \"\"\"\n        return super().endpoint_metrics()\n</code></pre>"},{"location":"cached/cached_endpoint/#sageworks.cached.cached_endpoint.CachedEndpoint.__init__","title":"<code>__init__(endpoint_uuid)</code>","text":"<p>CachedEndpoint Initialization</p> Source code in <code>src/sageworks/cached/cached_endpoint.py</code> <pre><code>def __init__(self, endpoint_uuid: str):\n    \"\"\"CachedEndpoint Initialization\"\"\"\n    EndpointCore.__init__(self, endpoint_uuid=endpoint_uuid, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_endpoint/#sageworks.cached.cached_endpoint.CachedEndpoint.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the CachedEndpoint Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedEndpoint</p> Source code in <code>src/sageworks/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedEndpoint Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedEndpoint\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_endpoint/#sageworks.cached.cached_endpoint.CachedEndpoint.endpoint_metrics","title":"<code>endpoint_metrics()</code>","text":"<p>Retrieve the Endpoint Metrics</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Endpoint Metrics</p> Source code in <code>src/sageworks/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef endpoint_metrics(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Endpoint Metrics\n\n    Returns:\n        str: The Endpoint Metrics\n    \"\"\"\n    return super().endpoint_metrics()\n</code></pre>"},{"location":"cached/cached_endpoint/#sageworks.cached.cached_endpoint.CachedEndpoint.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the CachedEndpoint Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the CachedEndpoint</p> Source code in <code>src/sageworks/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedEndpoint Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the CachedEndpoint\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_endpoint/#sageworks.cached.cached_endpoint.CachedEndpoint.sageworks_meta","title":"<code>sageworks_meta()</code>","text":"<p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Enumerated Model Type</p> Source code in <code>src/sageworks/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef sageworks_meta(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n    Returns:\n        str: The Enumerated Model Type\n    \"\"\"\n    return super().sageworks_meta()\n</code></pre>"},{"location":"cached/cached_endpoint/#sageworks.cached.cached_endpoint.CachedEndpoint.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the CachedEndpoint Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedEndpoint</p> Source code in <code>src/sageworks/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedEndpoint Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedEndpoint\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_endpoint/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Get Endpoint Details</p> <pre><code>from sageworks.cached.cached_endpoint import CachedEndpoint\n\n# Grab an Endpoint\nend = CachedEndpoint(\"abalone-regression\")\n\n# Get the Details\n end.details()\n\n{'uuid': 'abalone-regression-end',\n 'health_tags': [],\n 'status': 'InService',\n 'instance': 'Serverless (2GB/5)',\n 'instance_count': '-',\n 'variant': 'AllTraffic',\n 'model_name': 'abalone-regression',\n 'model_type': 'regressor',\n 'model_metrics':        RMSE     R2    MAPE  MedAE  NumRows\n 1.64  2.246  0.502  16.393  1.209      834,\n 'confusion_matrix': None,\n 'predictions':      class_number_of_rings  prediction    id\n 0                       16   10.516158     7\n 1                        9    9.031365     8\n 2                       10    9.264600    17\n 3                        7    8.578638    18\n 4                       12   10.492446    27\n ..                     ...         ...   ...\n 829                     11   11.915862  4148\n 830                      8    8.210898  4157\n 831                      8    7.693689  4158\n 832                      9    7.542521  4167\n 833                      8    9.060015  4168\n</code></pre>"},{"location":"cached/cached_feature_set/","title":"CachedFeatureSet","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p> <p>CachedFeatureSet: Caches the method results for SageWorks FeatureSets</p>"},{"location":"cached/cached_feature_set/#sageworks.cached.cached_feature_set.CachedFeatureSet","title":"<code>CachedFeatureSet</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>FeatureSetCore</code></p> <p>CachedFeatureSet: Caches the method results for SageWorks FeatureSets</p> <p>Note: Cached method values may lag underlying FeatureSet changes.</p> Common Usage <pre><code>my_features = CachedFeatureSet(name)\nmy_features.details()\nmy_features.health_check()\nmy_features.sageworks_meta()\n</code></pre> Source code in <code>src/sageworks/cached/cached_feature_set.py</code> <pre><code>class CachedFeatureSet(CachedArtifactMixin, FeatureSetCore):\n    \"\"\"CachedFeatureSet: Caches the method results for SageWorks FeatureSets\n\n    Note: Cached method values may lag underlying FeatureSet changes.\n\n    Common Usage:\n        ```python\n        my_features = CachedFeatureSet(name)\n        my_features.details()\n        my_features.health_check()\n        my_features.sageworks_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, feature_set_uuid: str, database: str = \"sageworks\"):\n        \"\"\"CachedFeatureSet Initialization\"\"\"\n        FeatureSetCore.__init__(self, feature_set_uuid=feature_set_uuid, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the FeatureSet Details.\n\n        Returns:\n            dict: A dictionary of details about the FeatureSet\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the FeatureSet Details.\n\n        Returns:\n            dict: A dictionary of details about the FeatureSet\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the FeatureSet Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the FeatureSet\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def sageworks_meta(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the SageWorks Metadata for this DataSource.\n\n        Returns:\n            Union[dict, None]: Dictionary of SageWorks metadata for this Artifact\n        \"\"\"\n        return super().sageworks_meta()\n\n    @CachedArtifactMixin.cache_result\n    def smart_sample(self) -&gt; pd.DataFrame:\n        \"\"\"Retrieve the Smart Sample for this FeatureSet.\n\n        Returns:\n            pd.DataFrame: The Smart Sample DataFrame\n        \"\"\"\n        return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_feature_set/#sageworks.cached.cached_feature_set.CachedFeatureSet.__init__","title":"<code>__init__(feature_set_uuid, database='sageworks')</code>","text":"<p>CachedFeatureSet Initialization</p> Source code in <code>src/sageworks/cached/cached_feature_set.py</code> <pre><code>def __init__(self, feature_set_uuid: str, database: str = \"sageworks\"):\n    \"\"\"CachedFeatureSet Initialization\"\"\"\n    FeatureSetCore.__init__(self, feature_set_uuid=feature_set_uuid, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_feature_set/#sageworks.cached.cached_feature_set.CachedFeatureSet.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the FeatureSet Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the FeatureSet</p> Source code in <code>src/sageworks/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the FeatureSet Details.\n\n    Returns:\n        dict: A dictionary of details about the FeatureSet\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_feature_set/#sageworks.cached.cached_feature_set.CachedFeatureSet.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the FeatureSet Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the FeatureSet</p> Source code in <code>src/sageworks/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the FeatureSet Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the FeatureSet\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_feature_set/#sageworks.cached.cached_feature_set.CachedFeatureSet.sageworks_meta","title":"<code>sageworks_meta()</code>","text":"<p>Retrieve the SageWorks Metadata for this DataSource.</p> <p>Returns:</p> Type Description <code>Union[str, None]</code> <p>Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</p> Source code in <code>src/sageworks/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef sageworks_meta(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the SageWorks Metadata for this DataSource.\n\n    Returns:\n        Union[dict, None]: Dictionary of SageWorks metadata for this Artifact\n    \"\"\"\n    return super().sageworks_meta()\n</code></pre>"},{"location":"cached/cached_feature_set/#sageworks.cached.cached_feature_set.CachedFeatureSet.smart_sample","title":"<code>smart_sample()</code>","text":"<p>Retrieve the Smart Sample for this FeatureSet.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The Smart Sample DataFrame</p> Source code in <code>src/sageworks/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef smart_sample(self) -&gt; pd.DataFrame:\n    \"\"\"Retrieve the Smart Sample for this FeatureSet.\n\n    Returns:\n        pd.DataFrame: The Smart Sample DataFrame\n    \"\"\"\n    return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_feature_set/#sageworks.cached.cached_feature_set.CachedFeatureSet.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the FeatureSet Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the FeatureSet</p> Source code in <code>src/sageworks/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the FeatureSet Details.\n\n    Returns:\n        dict: A dictionary of details about the FeatureSet\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_feature_set/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Pull FeatureSet Details</p> <pre><code>from sageworks.cached.cached_feature_set import CachedFeatureSet\n\n# Grab a FeatureSet\nfs = CachedFeatureSet(\"abalone_features\")\n\n# Show the details\nfs.details()\n\n&gt; fs.details()\n\n{'uuid': 'abalone_features',\n 'health_tags': [],\n 'aws_arn': 'arn:aws:glue:x:table/sageworks/abalone_data',\n 'size': 0.070272,\n 'created': '2024-11-09T20:42:34.000Z',\n 'modified': '2024-11-10T19:57:52.000Z',\n 'input': 's3://sageworks-public-data/common/aBaLone.CSV',\n 'sageworks_health_tags': '',\n 'sageworks_correlations': {'length': {'diameter': 0.9868115846024996,\n</code></pre>"},{"location":"cached/cached_meta/","title":"CachedMeta","text":"<p>CachedMeta Examples</p> <p>Examples of using the CachedMeta class are listed at the bottom of this page Examples.</p> <p>CachedMeta: A class that provides caching for the Meta() class</p>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta","title":"<code>CachedMeta</code>","text":"<p>               Bases: <code>CloudMeta</code></p> <p>CachedMeta: Singleton class for caching metadata functionality.</p> Common Usage <pre><code>from sageworks.cached.cached_meta import CachedMeta\nmeta = CachedMeta()\n\n# Get the AWS Account Info\nmeta.account()\nmeta.config()\n\n# These are 'list' methods\nmeta.etl_jobs()\nmeta.data_sources()\nmeta.feature_sets(details=True/False)\nmeta.models(details=True/False)\nmeta.endpoints()\nmeta.views()\n\n# These are 'describe' methods\nmeta.data_source(\"abalone_data\")\nmeta.feature_set(\"abalone_features\")\nmeta.model(\"abalone-regression\")\nmeta.endpoint(\"abalone-endpoint\")\n</code></pre> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>class CachedMeta(CloudMeta):\n    \"\"\"CachedMeta: Singleton class for caching metadata functionality.\n\n    Common Usage:\n       ```python\n       from sageworks.cached.cached_meta import CachedMeta\n       meta = CachedMeta()\n\n       # Get the AWS Account Info\n       meta.account()\n       meta.config()\n\n       # These are 'list' methods\n       meta.etl_jobs()\n       meta.data_sources()\n       meta.feature_sets(details=True/False)\n       meta.models(details=True/False)\n       meta.endpoints()\n       meta.views()\n\n       # These are 'describe' methods\n       meta.data_source(\"abalone_data\")\n       meta.feature_set(\"abalone_features\")\n       meta.model(\"abalone-regression\")\n       meta.endpoint(\"abalone-endpoint\")\n       ```\n    \"\"\"\n\n    _instance = None  # Class attribute to hold the singleton instance\n\n    def __new__(cls, *args, **kwargs):\n        if cls._instance is None:\n            cls._instance = super(CachedMeta, cls).__new__(cls)\n        return cls._instance\n\n    def __init__(self):\n        \"\"\"CachedMeta Initialization\"\"\"\n        if hasattr(self, \"_initialized\") and self._initialized:\n            return  # Prevent reinitialization\n\n        self.log = logging.getLogger(\"sageworks\")\n        self.log.important(\"Initializing CachedMeta...\")\n        super().__init__()\n\n        # Create both our Meta Cache and Fresh Cache (tracks if data is stale)\n        self.meta_cache = SageWorksCache(prefix=\"meta\")\n        self.fresh_cache = SageWorksCache(prefix=\"meta_fresh\", expire=90)  # 90-second expiration\n\n        # Create a ThreadPoolExecutor for refreshing stale data\n        self.thread_pool = ThreadPoolExecutor(max_workers=5)\n\n        # Mark the instance as initialized\n        self._initialized = True\n\n    def check(self):\n        \"\"\"Check if our underlying caches are working\"\"\"\n        return self.meta_cache.check()\n\n    def list_meta_cache(self):\n        \"\"\"List the current Meta Cache\"\"\"\n        return self.meta_cache.list_keys()\n\n    def clear_meta_cache(self):\n        \"\"\"Clear the current Meta Cache\"\"\"\n        self.meta_cache.clear()\n\n    @cache_result\n    def account(self) -&gt; dict:\n        \"\"\"Cloud Platform Account Info\n\n        Returns:\n            dict: Cloud Platform Account Info\n        \"\"\"\n        return super().account()\n\n    @cache_result\n    def config(self) -&gt; dict:\n        \"\"\"Return the current SageWorks Configuration\n\n        Returns:\n            dict: The current SageWorks Configuration\n        \"\"\"\n        return super().config()\n\n    @cache_result\n    def incoming_data(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about data in the incoming raw data\n\n        Returns:\n            pd.DataFrame: A summary of the incoming raw data\n        \"\"\"\n        return super().incoming_data()\n\n    @cache_result\n    def etl_jobs(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n        Returns:\n            pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n        \"\"\"\n        return super().etl_jobs()\n\n    @cache_result\n    def data_sources(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n        \"\"\"\n        return super().data_sources()\n\n    @cache_result\n    def views(self, database: str = \"sageworks\") -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n        Args:\n            database (str, optional): Glue database. Defaults to 'sageworks'.\n\n        Returns:\n            pd.DataFrame: A summary of all the Views, for the given database, in AWS\n        \"\"\"\n        return super().views(database=database)\n\n    @cache_result\n    def feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n        \"\"\"\n        return super().feature_sets(details=details)\n\n    @cache_result\n    def models(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n        \"\"\"\n        return super().models(details=details)\n\n    @cache_result\n    def endpoints(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n        \"\"\"\n        return super().endpoints()\n\n    @cache_result\n    def glue_job(self, job_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Glue Job\n\n        Args:\n            job_name (str): The name of the Glue Job\n\n        Returns:\n            dict: The details of the Glue Job (None if not found)\n        \"\"\"\n        return super().glue_job(job_name=job_name)\n\n    @cache_result\n    def data_source(self, data_source_name: str, database: str = \"sageworks\") -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Data Source\n\n        Args:\n            data_source_name (str): The name of the Data Source\n            database (str, optional): The Glue database. Defaults to 'sageworks'.\n\n        Returns:\n            dict: The details of the Data Source (None if not found)\n        \"\"\"\n        return super().data_source(data_source_name=data_source_name, database=database)\n\n    @cache_result\n    def feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Feature Set\n\n        Args:\n            feature_set_name (str): The name of the Feature Set\n\n        Returns:\n            dict: The details of the Feature Set (None if not found)\n        \"\"\"\n        return super().feature_set(feature_set_name=feature_set_name)\n\n    @cache_result\n    def model(self, model_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Model\n\n        Args:\n            model_name (str): The name of the Model\n\n        Returns:\n            dict: The details of the Model (None if not found)\n        \"\"\"\n        return super().model(model_name=model_name)\n\n    @cache_result\n    def endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Endpoint\n\n        Args:\n            endpoint_name (str): The name of the Endpoint\n\n        Returns:\n            dict: The details of the Endpoint (None if not found)\n        \"\"\"\n        return super().endpoint(endpoint_name=endpoint_name)\n\n    def _refresh_data_in_background(self, cache_key, method, *args, **kwargs):\n        \"\"\"Background task to refresh AWS metadata.\"\"\"\n        result = method(self, *args, **kwargs)\n        self.meta_cache.set(cache_key, result)\n        self.log.debug(f\"Updated Metadata for {cache_key}\")\n\n    @staticmethod\n    def _flatten_redis_key(method, *args, **kwargs):\n        \"\"\"Flatten the args and kwargs into a single string\"\"\"\n        arg_str = \"_\".join(str(arg) for arg in args)\n        kwarg_str = \"_\".join(f\"{k}_{v}\" for k, v in sorted(kwargs.items()))\n        return f\"{method.__name__}_{arg_str}_{kwarg_str}\".replace(\" \", \"\").replace(\"'\", \"\")\n\n    def __del__(self):\n        \"\"\"Destructor to shut down the thread pool gracefully.\"\"\"\n        self.close()\n\n    def close(self):\n        \"\"\"Explicitly close the thread pool, if needed.\"\"\"\n        if self.thread_pool:\n            self.log.important(\"Shutting down the ThreadPoolExecutor...\")\n            try:\n                self.thread_pool.shutdown(wait=True)  # Gracefully shutdown\n            except RuntimeError as e:\n                self.log.error(f\"Error during thread pool shutdown: {e}\")\n            finally:\n                self.thread_pool = None\n\n    def __repr__(self):\n        return f\"CachedMeta()\\n\\t{repr(self.meta_cache)}\\n\\t{super().__repr__()}\"\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.__del__","title":"<code>__del__()</code>","text":"<p>Destructor to shut down the thread pool gracefully.</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>def __del__(self):\n    \"\"\"Destructor to shut down the thread pool gracefully.\"\"\"\n    self.close()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.__init__","title":"<code>__init__()</code>","text":"<p>CachedMeta Initialization</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>def __init__(self):\n    \"\"\"CachedMeta Initialization\"\"\"\n    if hasattr(self, \"_initialized\") and self._initialized:\n        return  # Prevent reinitialization\n\n    self.log = logging.getLogger(\"sageworks\")\n    self.log.important(\"Initializing CachedMeta...\")\n    super().__init__()\n\n    # Create both our Meta Cache and Fresh Cache (tracks if data is stale)\n    self.meta_cache = SageWorksCache(prefix=\"meta\")\n    self.fresh_cache = SageWorksCache(prefix=\"meta_fresh\", expire=90)  # 90-second expiration\n\n    # Create a ThreadPoolExecutor for refreshing stale data\n    self.thread_pool = ThreadPoolExecutor(max_workers=5)\n\n    # Mark the instance as initialized\n    self._initialized = True\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.account","title":"<code>account()</code>","text":"<p>Cloud Platform Account Info</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>Cloud Platform Account Info</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef account(self) -&gt; dict:\n    \"\"\"Cloud Platform Account Info\n\n    Returns:\n        dict: Cloud Platform Account Info\n    \"\"\"\n    return super().account()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.check","title":"<code>check()</code>","text":"<p>Check if our underlying caches are working</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>def check(self):\n    \"\"\"Check if our underlying caches are working\"\"\"\n    return self.meta_cache.check()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.clear_meta_cache","title":"<code>clear_meta_cache()</code>","text":"<p>Clear the current Meta Cache</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>def clear_meta_cache(self):\n    \"\"\"Clear the current Meta Cache\"\"\"\n    self.meta_cache.clear()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.close","title":"<code>close()</code>","text":"<p>Explicitly close the thread pool, if needed.</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>def close(self):\n    \"\"\"Explicitly close the thread pool, if needed.\"\"\"\n    if self.thread_pool:\n        self.log.important(\"Shutting down the ThreadPoolExecutor...\")\n        try:\n            self.thread_pool.shutdown(wait=True)  # Gracefully shutdown\n        except RuntimeError as e:\n            self.log.error(f\"Error during thread pool shutdown: {e}\")\n        finally:\n            self.thread_pool = None\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.config","title":"<code>config()</code>","text":"<p>Return the current SageWorks Configuration</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The current SageWorks Configuration</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef config(self) -&gt; dict:\n    \"\"\"Return the current SageWorks Configuration\n\n    Returns:\n        dict: The current SageWorks Configuration\n    \"\"\"\n    return super().config()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.data_source","title":"<code>data_source(data_source_name, database='sageworks')</code>","text":"<p>Get the details of a specific Data Source</p> <p>Parameters:</p> Name Type Description Default <code>data_source_name</code> <code>str</code> <p>The name of the Data Source</p> required <code>database</code> <code>str</code> <p>The Glue database. Defaults to 'sageworks'.</p> <code>'sageworks'</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Data Source (None if not found)</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef data_source(self, data_source_name: str, database: str = \"sageworks\") -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Data Source\n\n    Args:\n        data_source_name (str): The name of the Data Source\n        database (str, optional): The Glue database. Defaults to 'sageworks'.\n\n    Returns:\n        dict: The details of the Data Source (None if not found)\n    \"\"\"\n    return super().data_source(data_source_name=data_source_name, database=database)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.data_sources","title":"<code>data_sources()</code>","text":"<p>Get a summary of the Data Sources deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef data_sources(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n    \"\"\"\n    return super().data_sources()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.endpoint","title":"<code>endpoint(endpoint_name)</code>","text":"<p>Get the details of a specific Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>The name of the Endpoint</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Endpoint (None if not found)</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Endpoint\n\n    Args:\n        endpoint_name (str): The name of the Endpoint\n\n    Returns:\n        dict: The details of the Endpoint (None if not found)\n    \"\"\"\n    return super().endpoint(endpoint_name=endpoint_name)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.endpoints","title":"<code>endpoints()</code>","text":"<p>Get a summary of the Endpoints deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Endpoints in the Cloud Platform</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef endpoints(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n    \"\"\"\n    return super().endpoints()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.etl_jobs","title":"<code>etl_jobs()</code>","text":"<p>Get summary data about Extract, Transform, Load (ETL) Jobs</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef etl_jobs(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n    Returns:\n        pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n    \"\"\"\n    return super().etl_jobs()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.feature_set","title":"<code>feature_set(feature_set_name)</code>","text":"<p>Get the details of a specific Feature Set</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_name</code> <code>str</code> <p>The name of the Feature Set</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Feature Set (None if not found)</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Feature Set\n\n    Args:\n        feature_set_name (str): The name of the Feature Set\n\n    Returns:\n        dict: The details of the Feature Set (None if not found)\n    \"\"\"\n    return super().feature_set(feature_set_name=feature_set_name)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.feature_sets","title":"<code>feature_sets(details=False)</code>","text":"<p>Get a summary of the Feature Sets deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n    \"\"\"\n    return super().feature_sets(details=details)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.glue_job","title":"<code>glue_job(job_name)</code>","text":"<p>Get the details of a specific Glue Job</p> <p>Parameters:</p> Name Type Description Default <code>job_name</code> <code>str</code> <p>The name of the Glue Job</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Glue Job (None if not found)</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef glue_job(self, job_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Glue Job\n\n    Args:\n        job_name (str): The name of the Glue Job\n\n    Returns:\n        dict: The details of the Glue Job (None if not found)\n    \"\"\"\n    return super().glue_job(job_name=job_name)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.incoming_data","title":"<code>incoming_data()</code>","text":"<p>Get summary data about data in the incoming raw data</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the incoming raw data</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef incoming_data(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about data in the incoming raw data\n\n    Returns:\n        pd.DataFrame: A summary of the incoming raw data\n    \"\"\"\n    return super().incoming_data()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.list_meta_cache","title":"<code>list_meta_cache()</code>","text":"<p>List the current Meta Cache</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>def list_meta_cache(self):\n    \"\"\"List the current Meta Cache\"\"\"\n    return self.meta_cache.list_keys()\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.model","title":"<code>model(model_name)</code>","text":"<p>Get the details of a specific Model</p> <p>Parameters:</p> Name Type Description Default <code>model_name</code> <code>str</code> <p>The name of the Model</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Model (None if not found)</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef model(self, model_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Model\n\n    Args:\n        model_name (str): The name of the Model\n\n    Returns:\n        dict: The details of the Model (None if not found)\n    \"\"\"\n    return super().model(model_name=model_name)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.models","title":"<code>models(details=False)</code>","text":"<p>Get a summary of the Models deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Models deployed in the Cloud Platform</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef models(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n    \"\"\"\n    return super().models(details=details)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.CachedMeta.views","title":"<code>views(database='sageworks')</code>","text":"<p>Get a summary of the all the Views, for the given database, in AWS</p> <p>Parameters:</p> Name Type Description Default <code>database</code> <code>str</code> <p>Glue database. Defaults to 'sageworks'.</p> <code>'sageworks'</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of all the Views, for the given database, in AWS</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>@cache_result\ndef views(self, database: str = \"sageworks\") -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n    Args:\n        database (str, optional): Glue database. Defaults to 'sageworks'.\n\n    Returns:\n        pd.DataFrame: A summary of all the Views, for the given database, in AWS\n    \"\"\"\n    return super().views(database=database)\n</code></pre>"},{"location":"cached/cached_meta/#sageworks.cached.cached_meta.cache_result","title":"<code>cache_result(method)</code>","text":"<p>Decorator to cache method results in meta_cache</p> Source code in <code>src/sageworks/cached/cached_meta.py</code> <pre><code>def cache_result(method):\n    \"\"\"Decorator to cache method results in meta_cache\"\"\"\n\n    @wraps(method)\n    def wrapper(self, *args, **kwargs):\n        # Create a unique cache key based on the method name and arguments\n        cache_key = CachedMeta._flatten_redis_key(method, *args, **kwargs)\n\n        # Check for fresh data, spawn thread to refresh if stale\n        if SageWorksCache.refresh_enabled and self.fresh_cache.get(cache_key) is None:\n            self.log.debug(f\"Async: Metadata for {cache_key} refresh thread started...\")\n            self.fresh_cache.set(cache_key, True)  # Mark as refreshed\n\n            # Spawn a thread to refresh data without blocking\n            self.thread_pool.submit(self._refresh_data_in_background, cache_key, method, *args, **kwargs)\n\n        # Return data (fresh or stale) if available\n        cached_value = self.meta_cache.get(cache_key)\n        if cached_value is not None:\n            return cached_value\n\n        # Fall back to calling the method if no cached data found\n        self.log.important(f\"Blocking: Getting Metadata for {cache_key}\")\n        result = method(self, *args, **kwargs)\n        self.meta_cache.set(cache_key, result)\n        return result\n\n    return wrapper\n</code></pre>"},{"location":"cached/cached_meta/#examples","title":"Examples","text":"<p>These example show how to use the <code>CachedMeta()</code> class to pull lists of artifacts from AWS. DataSources, FeatureSets, Models, Endpoints and more. If you're building a web interface plugin, the CachedMeta class is a great place to start.</p> <p>SageWorks REPL</p> <p>If you'd like to see exactly what data/details you get back from the <code>CachedMeta()</code> class, you can spin up the SageWorks REPL, use the class and test out all the methods. Try it out! SageWorks REPL</p> Using SageWorks REPL<pre><code>CachedMeta = CachedMeta()\nmodel_df = CachedMeta.models()\nmodel_df\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\n</code></pre> <p>List the Models in AWS</p> <pre><code>from sageworks.cached.cached_meta import CachedMeta\n\n# Create our CachedMeta Class and get a list of our Models\nCachedMeta = CachedMeta()\nmodel_df = CachedMeta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names:\n    pprint(CachedMeta.model(name))\n</code></pre> <p>Output</p> <pre><code>Number of Models: 3\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\nwine-classification\nabalone-regression-full\nabalone-regression\n</code></pre> <p>Getting Model Performance Metrics</p> <pre><code>from sageworks.cached.cached_meta import CachedMeta\n\n# Create our CachedMeta Class and get a list of our Models\nCachedMeta = CachedMeta()\nmodel_df = CachedMeta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names[:5]:\n    model_details = CachedMeta.model(name)\n    print(f\"\\n\\nModel: {name}\")\n    performance_metrics = model_details[\"sageworks_CachedMeta\"][\"sageworks_inference_metrics\"]\n    print(f\"\\tPerformance Metrics: {performance_metrics}\")\n</code></pre> <p>Output</p> <pre><code>wine-classification\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/wine-classification\n    Description: Wine Classification Model\n    Tags: wine::classification\n    Performance Metrics:\n        [{'wine_class': 'TypeA', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 12}, {'wine_class': 'TypeB', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 14}, {'wine_class': 'TypeC', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 9}]\n\nabalone-regression\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/abalone-regression\n    Description: Abalone Regression Model\n    Tags: abalone::regression\n    Performance Metrics:\n        [{'MAE': 1.64, 'RMSE': 2.246, 'R2': 0.502, 'MAPE': 16.393, 'MedAE': 1.209, 'NumRows': 834}]\n</code></pre> <p>List the Endpoints in AWS</p> <pre><code>from pprint import pprint\nfrom sageworks.cached.cached_meta import CachedMeta\n\n# Create our CachedMeta Class and get a list of our Endpoints\nCachedMeta = CachedMeta()\nendpoint_df = CachedMeta.endpoints()\nprint(f\"Number of Endpoints: {len(endpoint_df)}\")\nprint(endpoint_df)\n\n# Get more details data on the Endpoints\nendpoint_names = endpoint_df[\"Name\"].tolist()\nfor name in endpoint_names:\n    pprint(CachedMeta.endpoint(name))\n</code></pre> <p>Output</p> <pre><code>Number of Endpoints: 2\n                      Name   Health            Instance           Created  ...     Status     Variant Capture Samp(%)\n0  wine-classification-end  healthy  Serverless (2GB/5)  2024-03-23 23:09  ...  InService  AllTraffic   False       -\n1   abalone-regression-end  healthy  Serverless (2GB/5)  2024-03-23 21:11  ...  InService  AllTraffic   False       -\n\n[2 rows x 10 columns]\nwine-classification-end\n&lt;lots of details about endpoints&gt;\n</code></pre> <p>Not Finding some particular AWS Data?</p> <p>The SageWorks CachedMeta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>"},{"location":"cached/cached_model/","title":"CachedModel","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the SageWorks Model Class makes it a breeze!</p> <p>CachedModel: Caches the method results for SageWorks Models</p>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel","title":"<code>CachedModel</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>ModelCore</code></p> <p>CachedModel: Caches the method results for SageWorks Models</p> <p>Note: Cached method values may lag underlying Model changes.</p> Common Usage <pre><code>my_model = CachedModel(name)\nmy_model.details()\nmy_model.health_check()\nmy_model.sageworks_meta()\n</code></pre> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>class CachedModel(CachedArtifactMixin, ModelCore):\n    \"\"\"CachedModel: Caches the method results for SageWorks Models\n\n    Note: Cached method values may lag underlying Model changes.\n\n    Common Usage:\n        ```python\n        my_model = CachedModel(name)\n        my_model.details()\n        my_model.health_check()\n        my_model.sageworks_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, uuid: str):\n        \"\"\"CachedModel Initialization\"\"\"\n        ModelCore.__init__(self, model_uuid=uuid, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedModel Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedModel\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedModel Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedModel\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedModel Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the CachedModel\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def sageworks_meta(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n        Returns:\n            str: The Enumerated Model Type\n        \"\"\"\n        return super().sageworks_meta()\n\n    @CachedArtifactMixin.cache_result\n    def get_endpoint_inference_path(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Endpoint Inference Path.\n\n        Returns:\n            str: The Endpoint Inference Path\n        \"\"\"\n        return super().get_endpoint_inference_path()\n\n    @CachedArtifactMixin.cache_result\n    def list_inference_runs(self) -&gt; list[str]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Returns:\n            list[str]: List of Inference Runs\n        \"\"\"\n        return super().list_inference_runs()\n\n    @CachedArtifactMixin.cache_result\n    def get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Metrics (might be None)\n        \"\"\"\n        return super().get_inference_metrics(capture_uuid=capture_uuid)\n\n    @CachedArtifactMixin.cache_result\n    def get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n        \"\"\"\n        # Note: This method can generate larger dataframes, so we'll sample if needed\n        df = super().get_inference_predictions(capture_uuid=capture_uuid)\n        if df is not None and len(df) &gt; 5000:\n            self.log.warning(f\"{self.uuid}:{capture_uuid} Sampling Inference Predictions to 5000 rows\")\n            return df.sample(5000)\n        return df\n\n    @CachedArtifactMixin.cache_result\n    def confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the confusion matrix for the model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n        \"\"\"\n        return super().confusion_matrix(capture_uuid=capture_uuid)\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.__init__","title":"<code>__init__(uuid)</code>","text":"<p>CachedModel Initialization</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>def __init__(self, uuid: str):\n    \"\"\"CachedModel Initialization\"\"\"\n    ModelCore.__init__(self, model_uuid=uuid, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.confusion_matrix","title":"<code>confusion_matrix(capture_uuid='latest')</code>","text":"<p>Retrieve the confusion matrix for the model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: latest)</p> <code>'latest'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Confusion Matrix (might be None)</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the confusion matrix for the model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n    \"\"\"\n    return super().confusion_matrix(capture_uuid=capture_uuid)\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the CachedModel Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedModel</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedModel Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedModel\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.get_endpoint_inference_path","title":"<code>get_endpoint_inference_path()</code>","text":"<p>Retrieve the Endpoint Inference Path.</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Endpoint Inference Path</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef get_endpoint_inference_path(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Endpoint Inference Path.\n\n    Returns:\n        str: The Endpoint Inference Path\n    \"\"\"\n    return super().get_endpoint_inference_path()\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.get_inference_metrics","title":"<code>get_inference_metrics(capture_uuid='latest')</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: latest)</p> <code>'latest'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Captured Metrics (might be None)</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Captured Metrics (might be None)\n    \"\"\"\n    return super().get_inference_metrics(capture_uuid=capture_uuid)\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.get_inference_predictions","title":"<code>get_inference_predictions(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: training_holdout)</p> <code>'auto_inference'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Captured Predictions (might be None)</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n    \"\"\"\n    # Note: This method can generate larger dataframes, so we'll sample if needed\n    df = super().get_inference_predictions(capture_uuid=capture_uuid)\n    if df is not None and len(df) &gt; 5000:\n        self.log.warning(f\"{self.uuid}:{capture_uuid} Sampling Inference Predictions to 5000 rows\")\n        return df.sample(5000)\n    return df\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the CachedModel Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the CachedModel</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedModel Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the CachedModel\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.list_inference_runs","title":"<code>list_inference_runs()</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of Inference Runs</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef list_inference_runs(self) -&gt; list[str]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Returns:\n        list[str]: List of Inference Runs\n    \"\"\"\n    return super().list_inference_runs()\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.sageworks_meta","title":"<code>sageworks_meta()</code>","text":"<p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Enumerated Model Type</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef sageworks_meta(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n    Returns:\n        str: The Enumerated Model Type\n    \"\"\"\n    return super().sageworks_meta()\n</code></pre>"},{"location":"cached/cached_model/#sageworks.cached.cached_model.CachedModel.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the CachedModel Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedModel</p> Source code in <code>src/sageworks/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedModel Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedModel\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_model/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Pull Inference Run</p> <pre><code>from sageworks.cached.cached_model import CachedModel\n\n# Grab a Model\nmodel = CachedModel(\"abalone-regression\")\n\n# List the inference runs\nmodel.list_inference_runs()\n['auto_inference', 'model_training']\n\n# Grab specific inference results\nmodel.get_inference_predictions(\"auto_inference\")\n     class_number_of_rings  prediction    id\n0                       16   10.516158     7\n1                        9    9.031365     8\n..                     ...         ...   ...\n831                      8    7.693689  4158\n832                      9    7.542521  4167\n</code></pre>"},{"location":"cached/overview/","title":"Caching Overview","text":"<p>Caching is Crazy</p> <p>Yes, but it's a necessary evil for Web Interfaces. AWS APIs (boto3, Sagemaker) often takes multiple seconds to respond and will often throttle requests if spammed. So for quicker response and less spamming we're using Cached Classes for any Web Interface work.</p>"},{"location":"cached/overview/#welcome-to-the-sageworks-cached-classes","title":"Welcome to the SageWorks Cached Classes","text":"<p>These classes provide caching for the for the most used SageWorks classes. They transparently handle all the details around retrieving and caching results from the underlying classes.</p> <ul> <li>CachedMeta: Manages lists of Artifacts (get all models, endpoints, etc).</li> <li>CachedDataSource: Caches the method results for SageWorks DataSource.</li> <li>CachedFeatureSet: Caches the method results for SageWorks FeatureSets.</li> <li>CachedModel: Caches the method results for SageWorks Models.</li> <li>CachedEndpoint: Caches the method results for SageWorks Endpoints.</li> </ul> <p>Examples</p> <p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p>"},{"location":"cloudwatch/","title":"SageWorks CloudWatch","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. As part of this we're including CloudWatch log forwarding/aggregation for any service using the SageWorks API (Dashboard, Glue, Lambda, Notebook, Laptop, etc).</p>"},{"location":"cloudwatch/#log-groups-and-streams","title":"Log Groups and Streams","text":"<p>The SageWorks logging setup includes the addition of a CloudWatch 'Handler' that forwards all log messages to the <code>SageWorksLogGroup</code></p> <p>Individual Streams</p> <p>Each process running SageWorks will get a unique individual stream.</p> <ul> <li>ecs/DashBoard* (any logs from SageWorks Dashboard)</li> <li>glue/* (logs from Glue jobs)</li> <li>lambda/* (logs from Lambda jobs)</li> <li>docker/* (logs from Docker containers)</li> <li>laptop/* (logs from laptop/notebooks)</li> </ul> <p>Since many jobs are run nightly/often, the stream will also have a date on the end... <code>glue/my_job/2024_08_01_17_15</code></p>"},{"location":"cloudwatch/#aws-cloudwatch-made-easy","title":"AWS CloudWatch made Easy","text":"<p>Logs in Easy Mode</p> <p>The SageWorks <code>cloud_watch</code> command line tool gives you access to important logs without the hassle. Automatic display of important event and the context around those events.</p> <pre><code>pip install sageworks\ncloud_watch\n</code></pre> <p>The <code>cloud_watch</code> script will automatically show the interesting (WARNING and CRITICAL) messages from any source within the last hour. There are lots of options to the script, just use <code>--help</code> to see options and descriptions.</p> <pre><code>cloud_watch --help\n</code></pre> <p>Here are some example options:</p> <pre><code># Show important logs in last 12 hours\ncloud_watch --start-time 720 \n\n# Show a particular stream\ncloud_watch --stream glue/my_job \n\n# Show/search for a message substring\ncloud_watch --search SHAP\n\n# Show a log levels (matching and above)\ncloud_watch --log-level WARNING\ncloud_watch --log-level ERROR\ncloud_watch --log-level CRITICAL\nOR\ncloud_watch --log-level ALL  (for all events)\n\n# Combine flags \ncloud_watch --log-level ERROR --search SHAP\ncloud_watch --log-level ERROR --stream Dashboard\n</code></pre> <p>These options can be used in combination and try out the other options to make the perfect log search :)</p> <p></p>"},{"location":"cloudwatch/#more-information","title":"More Information","text":"<p>Check out our presentation on SageWorks CloudWatch</p>"},{"location":"cloudwatch/#access-through-aws-console","title":"Access through AWS Console","text":"<p>Since we're leveraging AWS functionality you can always use the AWS console to look/investigate the logs. In the AWS console go to CloudWatch... Log Groups... SageWorksLogGroup</p> <p></p>"},{"location":"cloudwatch/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/overview/","title":"Core Classes","text":"<p>SageWorks Core Classes</p> <p>These classes interact with many of the Cloud Platform services and are therefore more complex. They provide additional control and refinement over the AWS ML Pipline. For most use cases the API Classes should be used</p> <p>Welcome to the SageWorks Core Classes</p> <p>The Core Classes provide low-level APIs for the SageWorks package, these classes directly interface with the AWS Sagemaker Pipeline interfaces and have a large number of methods with reasonable complexity.</p> <p>The API Classes have method pass-through so just call the method on the API Class and voil\u00e0 it works the same.</p> <p></p>"},{"location":"core_classes/overview/#artifacts","title":"Artifacts","text":"<ul> <li>AthenaSource: Manages AWS Data Catalog and Athena</li> <li>FeatureSetCore: Manages AWS Feature Store and Feature Groups</li> <li>ModelCore: Manages the training and deployment of AWS Model Groups and Packages</li> <li>EndpointCore: Manages the deployment and invocations/inference on AWS Endpoints</li> </ul>"},{"location":"core_classes/overview/#transforms","title":"Transforms","text":"<p>Transforms are a set of classes that transform one type of <code>Artifact</code> to another type. For instance <code>DataToFeatureSet</code> takes a <code>DataSource</code> artifact and creates a <code>FeatureSet</code> artifact.</p> <ul> <li>DataLoaders Light: Loads various light/smaller data into AWS Data Catalog and Athena</li> <li>DataLoaders Heavy: Loads heavy/larger data (via Glue) into AWS Data Catalog and Athena</li> <li>DataToFeatures: Transforms a DataSource into a FeatureSet (AWS Feature Store/Group)</li> <li>FeaturesToModel: Trains and deploys an AWS Model Package/Group from a FeatureSet</li> <li>ModelToEndpoint: Manages the provisioning and deployment of a Model Endpoint</li> <li>PandasTransforms:Pandas DataFrame transforms and helper methods.</li> </ul>"},{"location":"core_classes/artifacts/artifact/","title":"Artifact","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the any class that inherits from the Artifact Class and voil\u00e0 it works the same.</p> <p>The SageWorks Artifact class is a base/abstract class that defines API implemented by all the child classes (DataSource, FeatureSet, Model, Endpoint).</p> <p>Artifact: Abstract Base Class for all Artifact classes in SageWorks. Artifacts simply reflect and aggregate one or more AWS Services</p>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact","title":"<code>Artifact</code>","text":"<p>               Bases: <code>ABC</code></p> <p>Artifact: Abstract Base Class for all Artifact classes in SageWorks</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>class Artifact(ABC):\n    \"\"\"Artifact: Abstract Base Class for all Artifact classes in SageWorks\"\"\"\n\n    # Class-level shared resources\n    log = logging.getLogger(\"sageworks\")\n\n    # Config Manager\n    cm = ConfigManager()\n    if not cm.config_okay():\n        log = logging.getLogger(\"sageworks\")\n        log.critical(\"SageWorks Configuration Incomplete...\")\n        log.critical(\"Run the 'sageworks' command and follow the prompts...\")\n        raise FatalConfigError()\n\n    # AWS Account Clamp\n    aws_account_clamp = AWSAccountClamp()\n    boto3_session = aws_account_clamp.boto3_session\n    sm_session = aws_account_clamp.sagemaker_session()\n    sm_client = aws_account_clamp.sagemaker_client()\n    aws_region = aws_account_clamp.region\n\n    # Setup Bucket Paths\n    sageworks_bucket = cm.get_config(\"SAGEWORKS_BUCKET\")\n    data_sources_s3_path = f\"s3://{sageworks_bucket}/data-sources\"\n    feature_sets_s3_path = f\"s3://{sageworks_bucket}/feature-sets\"\n    models_s3_path = f\"s3://{sageworks_bucket}/models\"\n    endpoints_s3_path = f\"s3://{sageworks_bucket}/endpoints\"\n\n    # Delimiter for storing lists in AWS Tags\n    tag_delimiter = \"::\"\n\n    # Grab our Dataframe Storage\n    df_cache = DFStore(path_prefix=\"/sageworks/dataframe_cache\")\n\n    def __init__(self, uuid: str, use_cached_meta: bool = False):\n        \"\"\"Initialize the Artifact Base Class\n\n        Args:\n            uuid (str): The UUID of this artifact\n            use_cached_meta (bool): Should we use cached metadata? (default: False)\n        \"\"\"\n        self.uuid = uuid\n        if use_cached_meta:\n            self.log.info(f\"Using Cached Metadata for {self.uuid}\")\n            self.meta = CachedMeta()\n        else:\n            self.meta = CloudMeta()\n\n    def __post_init__(self):\n        \"\"\"Artifact Post Initialization\"\"\"\n\n        # Do I exist? (very metaphysical)\n        if not self.exists():\n            self.log.debug(f\"Artifact {self.uuid} does not exist\")\n            return\n\n        # Conduct a Health Check on this Artifact\n        health_issues = self.health_check()\n        if health_issues:\n            if \"needs_onboard\" in health_issues:\n                self.log.important(f\"Artifact {self.uuid} needs to be onboarded\")\n            elif health_issues == [\"no_activity\"]:\n                self.log.debug(f\"Artifact {self.uuid} has no activity, which is fine\")\n            else:\n                self.log.warning(f\"Health Check Failed {self.uuid}: {health_issues}\")\n            for issue in health_issues:\n                self.add_health_tag(issue)\n        else:\n            self.log.info(f\"Health Check Passed {self.uuid}\")\n\n    @classmethod\n    def is_name_valid(cls, name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; bool:\n        \"\"\"Check if the name adheres to the naming conventions for this Artifact.\n\n        Args:\n            name (str): The name/id to check.\n            delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n            lower_case (bool): Should the name be lowercased? (default: True)\n\n        Returns:\n            bool: True if the name is valid, False otherwise.\n        \"\"\"\n        valid_name = cls.generate_valid_name(name, delimiter=delimiter, lower_case=lower_case)\n        if name != valid_name:\n            cls.log.warning(f\"Artifact name: '{name}' is not valid. Convert it to something like: '{valid_name}'\")\n            return False\n        return True\n\n    @staticmethod\n    def generate_valid_name(name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; str:\n        \"\"\"Only allow letters and the specified delimiter, also lowercase the string.\n\n        Args:\n            name (str): The name/id string to check.\n            delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n            lower_case (bool): Should the name be lowercased? (default: True)\n\n        Returns:\n            str: A generated valid name/id.\n        \"\"\"\n        valid_name = \"\".join(c for c in name if c.isalnum() or c in [\"_\", \"-\"])\n        if lower_case:\n            valid_name = valid_name.lower()\n\n        # Replace with the chosen delimiter\n        return valid_name.replace(\"_\", delimiter).replace(\"-\", delimiter)\n\n    @abstractmethod\n    def exists(self) -&gt; bool:\n        \"\"\"Does the Artifact exist? Can we connect to it?\"\"\"\n        pass\n\n    def sageworks_meta(self) -&gt; Union[dict, None]:\n        \"\"\"Get the SageWorks specific metadata for this Artifact\n\n        Returns:\n            Union[dict, None]: Dictionary of SageWorks metadata for this Artifact\n\n        Note: This functionality will work for FeatureSets, Models, and Endpoints\n              but not for DataSources and Graphs, those classes need to override this method.\n        \"\"\"\n        return self.meta.get_aws_tags(self.arn())\n\n    def expected_meta(self) -&gt; list[str]:\n        \"\"\"Metadata we expect to see for this Artifact when it's ready\n        Returns:\n            list[str]: List of expected metadata keys\n        \"\"\"\n\n        # If an artifact has additional expected metadata override this method\n        return [\"sageworks_status\"]\n\n    @abstractmethod\n    def refresh_meta(self):\n        \"\"\"Refresh the Artifact's metadata\"\"\"\n        pass\n\n    def ready(self) -&gt; bool:\n        \"\"\"Is the Artifact ready? Is initial setup complete and expected metadata populated?\"\"\"\n\n        # If anything goes wrong, assume the artifact is not ready\n        try:\n            # Check for the expected metadata\n            expected_meta = self.expected_meta()\n            existing_meta = self.sageworks_meta()\n            ready = set(existing_meta.keys()).issuperset(expected_meta)\n            if ready:\n                return True\n            else:\n                self.log.info(\"Artifact is not ready!\")\n                return False\n        except Exception as e:\n            self.log.error(f\"Artifact malformed: {e}\")\n            return False\n\n    @abstractmethod\n    def onboard(self) -&gt; bool:\n        \"\"\"Onboard this Artifact into SageWorks\n        Returns:\n            bool: True if the Artifact was successfully onboarded, False otherwise\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def details(self) -&gt; dict:\n        \"\"\"Additional Details about this Artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this artifact in MegaBytes\"\"\"\n        pass\n\n    @abstractmethod\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        pass\n\n    @abstractmethod\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        pass\n\n    @abstractmethod\n    def hash(self) -&gt; str:\n        \"\"\"Return the hash of this artifact, useful for content validation\"\"\"\n        pass\n\n    @abstractmethod\n    def arn(self):\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def aws_url(self):\n        \"\"\"AWS console/web interface for this artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get the full AWS metadata for this artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def delete(self):\n        \"\"\"Delete this artifact including all related AWS objects\"\"\"\n        pass\n\n    def upsert_sageworks_meta(self, new_meta: dict):\n        \"\"\"Add SageWorks specific metadata to this Artifact\n        Args:\n            new_meta (dict): Dictionary of NEW metadata to add\n        Note:\n            This functionality will work for FeatureSets, Models, and Endpoints\n            but not for DataSources. The DataSource class overrides this method.\n        \"\"\"\n        # Sanity check\n        aws_arn = self.arn()\n        if aws_arn is None:\n            self.log.error(f\"ARN is None for {self.uuid}!\")\n            return\n\n        # Add the new metadata to the existing metadata\n        self.log.info(f\"Adding Tags to {self.uuid}:{str(new_meta)[:50]}...\")\n        aws_tags = dict_to_aws_tags(new_meta)\n        try:\n            self.sm_client.add_tags(ResourceArn=aws_arn, Tags=aws_tags)\n        except Exception as e:\n            self.log.error(f\"Error adding metadata to {aws_arn}: {e}\")\n\n    def remove_sageworks_meta(self, key_to_remove: str):\n        \"\"\"Remove SageWorks specific metadata from this Artifact\n        Args:\n            key_to_remove (str): The metadata key to remove\n        Note:\n            This functionality will work for FeatureSets, Models, and Endpoints\n            but not for DataSources. The DataSource class overrides this method.\n        \"\"\"\n        aws_arn = self.arn()\n        # Sanity check\n        if aws_arn is None:\n            self.log.error(f\"ARN is None for {self.uuid}!\")\n            return\n        self.log.info(f\"Removing SageWorks Metadata {key_to_remove} for Artifact: {aws_arn}...\")\n        sagemaker_delete_tag(aws_arn, self.sm_session, key_to_remove)\n\n    def get_tags(self, tag_type=\"user\") -&gt; list:\n        \"\"\"Get the tags for this artifact\n        Args:\n            tag_type (str): Type of tags to return (user or health)\n        Returns:\n            list[str]: List of tags for this artifact\n        \"\"\"\n        if tag_type == \"user\":\n            user_tags = self.sageworks_meta().get(\"sageworks_tags\")\n            return user_tags.split(self.tag_delimiter) if user_tags else []\n\n        # Grab our health tags\n        health_tags = self.sageworks_meta().get(\"sageworks_health_tags\")\n\n        # If we don't have health tags, create the storage and return an empty list\n        if health_tags is None:\n            self.log.important(f\"{self.uuid} creating sageworks_health_tags storage...\")\n            self.upsert_sageworks_meta({\"sageworks_health_tags\": \"\"})\n            return []\n\n        # Otherwise, return the health tags\n        return health_tags.split(self.tag_delimiter) if health_tags else []\n\n    def set_tags(self, tags):\n        self.upsert_sageworks_meta({\"sageworks_tags\": self.tag_delimiter.join(tags)})\n\n    def add_tag(self, tag, tag_type=\"user\"):\n        \"\"\"Add a tag for this artifact, ensuring no duplicates and maintaining order.\n        Args:\n            tag (str): Tag to add for this artifact\n            tag_type (str): Type of tag to add (user or health)\n        \"\"\"\n        current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n        if tag not in current_tags:\n            current_tags.append(tag)\n            combined_tags = self.tag_delimiter.join(current_tags)\n            if tag_type == \"user\":\n                self.upsert_sageworks_meta({\"sageworks_tags\": combined_tags})\n            else:\n                self.upsert_sageworks_meta({\"sageworks_health_tags\": combined_tags})\n\n    def remove_sageworks_tag(self, tag, tag_type=\"user\"):\n        \"\"\"Remove a tag from this artifact if it exists.\n        Args:\n            tag (str): Tag to remove from this artifact\n            tag_type (str): Type of tag to remove (user or health)\n        \"\"\"\n        current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n        if tag in current_tags:\n            current_tags.remove(tag)\n            combined_tags = self.tag_delimiter.join(current_tags)\n            if tag_type == \"user\":\n                self.upsert_sageworks_meta({\"sageworks_tags\": combined_tags})\n            elif tag_type == \"health\":\n                self.upsert_sageworks_meta({\"sageworks_health_tags\": combined_tags})\n\n    # Syntactic sugar for health tags\n    def get_health_tags(self):\n        return self.get_tags(tag_type=\"health\")\n\n    def set_health_tags(self, tags):\n        self.upsert_sageworks_meta({\"sageworks_health_tags\": self.tag_delimiter.join(tags)})\n\n    def add_health_tag(self, tag):\n        self.add_tag(tag, tag_type=\"health\")\n\n    def remove_health_tag(self, tag):\n        self.remove_sageworks_tag(tag, tag_type=\"health\")\n\n    # Owner of this artifact\n    def get_owner(self) -&gt; str:\n        \"\"\"Get the owner of this artifact\"\"\"\n        return self.sageworks_meta().get(\"sageworks_owner\", \"unknown\")\n\n    def set_owner(self, owner: str):\n        \"\"\"Set the owner of this artifact\n\n        Args:\n            owner (str): Owner to set for this artifact\n        \"\"\"\n        self.upsert_sageworks_meta({\"sageworks_owner\": owner})\n\n    def get_input(self) -&gt; str:\n        \"\"\"Get the input data for this artifact\"\"\"\n        return self.sageworks_meta().get(\"sageworks_input\", \"unknown\")\n\n    def set_input(self, input_data: str):\n        \"\"\"Set the input data for this artifact\n\n        Args:\n            input_data (str): Name of input data for this artifact\n        Note:\n            This breaks the official provenance of the artifact, so use with caution.\n        \"\"\"\n        self.log.important(f\"{self.uuid}: Setting input to {input_data}...\")\n        self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n        self.upsert_sageworks_meta({\"sageworks_input\": input_data})\n\n    def get_status(self) -&gt; str:\n        \"\"\"Get the status for this artifact\"\"\"\n        return self.sageworks_meta().get(\"sageworks_status\", \"unknown\")\n\n    def set_status(self, status: str):\n        \"\"\"Set the status for this artifact\n        Args:\n            status (str): Status to set for this artifact\n        \"\"\"\n        self.upsert_sageworks_meta({\"sageworks_status\": status})\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this artifact\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        health_issues = []\n        if not self.ready():\n            return [\"needs_onboard\"]\n        # FIXME: Revisit AWS URL check\n        # if \"unknown\" in self.aws_url():\n        #    health_issues.append(\"aws_url_unknown\")\n        return health_issues\n\n    def summary(self) -&gt; dict:\n        \"\"\"This is generic summary information for all Artifacts. If you\n        want to get more detailed information, call the details() method\n        which is implemented by the specific Artifact class\"\"\"\n        basic = {\n            \"uuid\": self.uuid,\n            \"health_tags\": self.get_health_tags(),\n            \"aws_arn\": self.arn(),\n            \"size\": self.size(),\n            \"created\": self.created(),\n            \"modified\": self.modified(),\n            \"input\": self.get_input(),\n        }\n        # Combine the sageworks metadata with the basic metadata\n        return {**basic, **self.sageworks_meta()}\n\n    def __repr__(self) -&gt; str:\n        \"\"\"String representation of this artifact\n\n        Returns:\n            str: String representation of this artifact\n        \"\"\"\n\n        # If the artifact does not exist, return a message\n        if not self.exists():\n            return f\"{self.__class__.__name__}: {self.uuid} does not exist\"\n\n        summary_dict = self.summary()\n        display_keys = [\n            \"aws_arn\",\n            \"health_tags\",\n            \"size\",\n            \"created\",\n            \"modified\",\n            \"input\",\n            \"sageworks_status\",\n            \"sageworks_tags\",\n        ]\n        summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items() if key in display_keys]\n        summary_str = f\"{self.__class__.__name__}: {self.uuid}\\n\" + \",\\n\".join(summary_items)\n        return summary_str\n\n    def delete_metadata(self, key_to_delete: str):\n        \"\"\"Delete specific metadata from this artifact\n        Args:\n            key_to_delete (str): Metadata key to delete\n        \"\"\"\n\n        aws_arn = self.arn()\n        self.log.important(f\"Deleting Metadata {key_to_delete} for Artifact: {aws_arn}...\")\n\n        # First, fetch all the existing tags\n        response = self.sm_session.list_tags(aws_arn)\n        existing_tags = response.get(\"Tags\", [])\n\n        # Convert existing AWS tags to a dictionary for easy manipulation\n        existing_tags_dict = {item[\"Key\"]: item[\"Value\"] for item in existing_tags}\n\n        # Identify tags to delete\n        tag_list_to_delete = []\n        for key in existing_tags_dict.keys():\n            if key == key_to_delete or key.startswith(f\"{key_to_delete}_chunk_\"):\n                tag_list_to_delete.append(key)\n\n        # Delete the identified tags\n        if tag_list_to_delete:\n            self.sm_client.delete_tags(ResourceArn=aws_arn, TagKeys=tag_list_to_delete)\n        else:\n            self.log.info(f\"No Metadata found: {key_to_delete}...\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.__init__","title":"<code>__init__(uuid, use_cached_meta=False)</code>","text":"<p>Initialize the Artifact Base Class</p> <p>Parameters:</p> Name Type Description Default <code>uuid</code> <code>str</code> <p>The UUID of this artifact</p> required <code>use_cached_meta</code> <code>bool</code> <p>Should we use cached metadata? (default: False)</p> <code>False</code> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def __init__(self, uuid: str, use_cached_meta: bool = False):\n    \"\"\"Initialize the Artifact Base Class\n\n    Args:\n        uuid (str): The UUID of this artifact\n        use_cached_meta (bool): Should we use cached metadata? (default: False)\n    \"\"\"\n    self.uuid = uuid\n    if use_cached_meta:\n        self.log.info(f\"Using Cached Metadata for {self.uuid}\")\n        self.meta = CachedMeta()\n    else:\n        self.meta = CloudMeta()\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.__post_init__","title":"<code>__post_init__()</code>","text":"<p>Artifact Post Initialization</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def __post_init__(self):\n    \"\"\"Artifact Post Initialization\"\"\"\n\n    # Do I exist? (very metaphysical)\n    if not self.exists():\n        self.log.debug(f\"Artifact {self.uuid} does not exist\")\n        return\n\n    # Conduct a Health Check on this Artifact\n    health_issues = self.health_check()\n    if health_issues:\n        if \"needs_onboard\" in health_issues:\n            self.log.important(f\"Artifact {self.uuid} needs to be onboarded\")\n        elif health_issues == [\"no_activity\"]:\n            self.log.debug(f\"Artifact {self.uuid} has no activity, which is fine\")\n        else:\n            self.log.warning(f\"Health Check Failed {self.uuid}: {health_issues}\")\n        for issue in health_issues:\n            self.add_health_tag(issue)\n    else:\n        self.log.info(f\"Health Check Passed {self.uuid}\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.__repr__","title":"<code>__repr__()</code>","text":"<p>String representation of this artifact</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>String representation of this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def __repr__(self) -&gt; str:\n    \"\"\"String representation of this artifact\n\n    Returns:\n        str: String representation of this artifact\n    \"\"\"\n\n    # If the artifact does not exist, return a message\n    if not self.exists():\n        return f\"{self.__class__.__name__}: {self.uuid} does not exist\"\n\n    summary_dict = self.summary()\n    display_keys = [\n        \"aws_arn\",\n        \"health_tags\",\n        \"size\",\n        \"created\",\n        \"modified\",\n        \"input\",\n        \"sageworks_status\",\n        \"sageworks_tags\",\n    ]\n    summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items() if key in display_keys]\n    summary_str = f\"{self.__class__.__name__}: {self.uuid}\\n\" + \",\\n\".join(summary_items)\n    return summary_str\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.add_tag","title":"<code>add_tag(tag, tag_type='user')</code>","text":"<p>Add a tag for this artifact, ensuring no duplicates and maintaining order. Args:     tag (str): Tag to add for this artifact     tag_type (str): Type of tag to add (user or health)</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def add_tag(self, tag, tag_type=\"user\"):\n    \"\"\"Add a tag for this artifact, ensuring no duplicates and maintaining order.\n    Args:\n        tag (str): Tag to add for this artifact\n        tag_type (str): Type of tag to add (user or health)\n    \"\"\"\n    current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n    if tag not in current_tags:\n        current_tags.append(tag)\n        combined_tags = self.tag_delimiter.join(current_tags)\n        if tag_type == \"user\":\n            self.upsert_sageworks_meta({\"sageworks_tags\": combined_tags})\n        else:\n            self.upsert_sageworks_meta({\"sageworks_health_tags\": combined_tags})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.arn","title":"<code>arn()</code>  <code>abstractmethod</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef arn(self):\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.aws_meta","title":"<code>aws_meta()</code>  <code>abstractmethod</code>","text":"<p>Get the full AWS metadata for this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef aws_meta(self) -&gt; dict:\n    \"\"\"Get the full AWS metadata for this artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.aws_url","title":"<code>aws_url()</code>  <code>abstractmethod</code>","text":"<p>AWS console/web interface for this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef aws_url(self):\n    \"\"\"AWS console/web interface for this artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.created","title":"<code>created()</code>  <code>abstractmethod</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.delete","title":"<code>delete()</code>  <code>abstractmethod</code>","text":"<p>Delete this artifact including all related AWS objects</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef delete(self):\n    \"\"\"Delete this artifact including all related AWS objects\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.delete_metadata","title":"<code>delete_metadata(key_to_delete)</code>","text":"<p>Delete specific metadata from this artifact Args:     key_to_delete (str): Metadata key to delete</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def delete_metadata(self, key_to_delete: str):\n    \"\"\"Delete specific metadata from this artifact\n    Args:\n        key_to_delete (str): Metadata key to delete\n    \"\"\"\n\n    aws_arn = self.arn()\n    self.log.important(f\"Deleting Metadata {key_to_delete} for Artifact: {aws_arn}...\")\n\n    # First, fetch all the existing tags\n    response = self.sm_session.list_tags(aws_arn)\n    existing_tags = response.get(\"Tags\", [])\n\n    # Convert existing AWS tags to a dictionary for easy manipulation\n    existing_tags_dict = {item[\"Key\"]: item[\"Value\"] for item in existing_tags}\n\n    # Identify tags to delete\n    tag_list_to_delete = []\n    for key in existing_tags_dict.keys():\n        if key == key_to_delete or key.startswith(f\"{key_to_delete}_chunk_\"):\n            tag_list_to_delete.append(key)\n\n    # Delete the identified tags\n    if tag_list_to_delete:\n        self.sm_client.delete_tags(ResourceArn=aws_arn, TagKeys=tag_list_to_delete)\n    else:\n        self.log.info(f\"No Metadata found: {key_to_delete}...\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.details","title":"<code>details()</code>  <code>abstractmethod</code>","text":"<p>Additional Details about this Artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef details(self) -&gt; dict:\n    \"\"\"Additional Details about this Artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.exists","title":"<code>exists()</code>  <code>abstractmethod</code>","text":"<p>Does the Artifact exist? Can we connect to it?</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef exists(self) -&gt; bool:\n    \"\"\"Does the Artifact exist? Can we connect to it?\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.expected_meta","title":"<code>expected_meta()</code>","text":"<p>Metadata we expect to see for this Artifact when it's ready Returns:     list[str]: List of expected metadata keys</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def expected_meta(self) -&gt; list[str]:\n    \"\"\"Metadata we expect to see for this Artifact when it's ready\n    Returns:\n        list[str]: List of expected metadata keys\n    \"\"\"\n\n    # If an artifact has additional expected metadata override this method\n    return [\"sageworks_status\"]\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.generate_valid_name","title":"<code>generate_valid_name(name, delimiter='_', lower_case=True)</code>  <code>staticmethod</code>","text":"<p>Only allow letters and the specified delimiter, also lowercase the string.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name/id string to check.</p> required <code>delimiter</code> <code>str</code> <p>The delimiter to use in the name/id string (default: \"_\")</p> <code>'_'</code> <code>lower_case</code> <code>bool</code> <p>Should the name be lowercased? (default: True)</p> <code>True</code> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>A generated valid name/id.</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@staticmethod\ndef generate_valid_name(name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; str:\n    \"\"\"Only allow letters and the specified delimiter, also lowercase the string.\n\n    Args:\n        name (str): The name/id string to check.\n        delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n        lower_case (bool): Should the name be lowercased? (default: True)\n\n    Returns:\n        str: A generated valid name/id.\n    \"\"\"\n    valid_name = \"\".join(c for c in name if c.isalnum() or c in [\"_\", \"-\"])\n    if lower_case:\n        valid_name = valid_name.lower()\n\n    # Replace with the chosen delimiter\n    return valid_name.replace(\"_\", delimiter).replace(\"-\", delimiter)\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.get_input","title":"<code>get_input()</code>","text":"<p>Get the input data for this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def get_input(self) -&gt; str:\n    \"\"\"Get the input data for this artifact\"\"\"\n    return self.sageworks_meta().get(\"sageworks_input\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.get_owner","title":"<code>get_owner()</code>","text":"<p>Get the owner of this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def get_owner(self) -&gt; str:\n    \"\"\"Get the owner of this artifact\"\"\"\n    return self.sageworks_meta().get(\"sageworks_owner\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.get_status","title":"<code>get_status()</code>","text":"<p>Get the status for this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def get_status(self) -&gt; str:\n    \"\"\"Get the status for this artifact\"\"\"\n    return self.sageworks_meta().get(\"sageworks_status\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.get_tags","title":"<code>get_tags(tag_type='user')</code>","text":"<p>Get the tags for this artifact Args:     tag_type (str): Type of tags to return (user or health) Returns:     list[str]: List of tags for this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def get_tags(self, tag_type=\"user\") -&gt; list:\n    \"\"\"Get the tags for this artifact\n    Args:\n        tag_type (str): Type of tags to return (user or health)\n    Returns:\n        list[str]: List of tags for this artifact\n    \"\"\"\n    if tag_type == \"user\":\n        user_tags = self.sageworks_meta().get(\"sageworks_tags\")\n        return user_tags.split(self.tag_delimiter) if user_tags else []\n\n    # Grab our health tags\n    health_tags = self.sageworks_meta().get(\"sageworks_health_tags\")\n\n    # If we don't have health tags, create the storage and return an empty list\n    if health_tags is None:\n        self.log.important(f\"{self.uuid} creating sageworks_health_tags storage...\")\n        self.upsert_sageworks_meta({\"sageworks_health_tags\": \"\"})\n        return []\n\n    # Otherwise, return the health tags\n    return health_tags.split(self.tag_delimiter) if health_tags else []\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.hash","title":"<code>hash()</code>  <code>abstractmethod</code>","text":"<p>Return the hash of this artifact, useful for content validation</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef hash(self) -&gt; str:\n    \"\"\"Return the hash of this artifact, useful for content validation\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this artifact Returns:     list[str]: List of health issues</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this artifact\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    health_issues = []\n    if not self.ready():\n        return [\"needs_onboard\"]\n    # FIXME: Revisit AWS URL check\n    # if \"unknown\" in self.aws_url():\n    #    health_issues.append(\"aws_url_unknown\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.is_name_valid","title":"<code>is_name_valid(name, delimiter='_', lower_case=True)</code>  <code>classmethod</code>","text":"<p>Check if the name adheres to the naming conventions for this Artifact.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name/id to check.</p> required <code>delimiter</code> <code>str</code> <p>The delimiter to use in the name/id string (default: \"_\")</p> <code>'_'</code> <code>lower_case</code> <code>bool</code> <p>Should the name be lowercased? (default: True)</p> <code>True</code> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the name is valid, False otherwise.</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@classmethod\ndef is_name_valid(cls, name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; bool:\n    \"\"\"Check if the name adheres to the naming conventions for this Artifact.\n\n    Args:\n        name (str): The name/id to check.\n        delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n        lower_case (bool): Should the name be lowercased? (default: True)\n\n    Returns:\n        bool: True if the name is valid, False otherwise.\n    \"\"\"\n    valid_name = cls.generate_valid_name(name, delimiter=delimiter, lower_case=lower_case)\n    if name != valid_name:\n        cls.log.warning(f\"Artifact name: '{name}' is not valid. Convert it to something like: '{valid_name}'\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.modified","title":"<code>modified()</code>  <code>abstractmethod</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.onboard","title":"<code>onboard()</code>  <code>abstractmethod</code>","text":"<p>Onboard this Artifact into SageWorks Returns:     bool: True if the Artifact was successfully onboarded, False otherwise</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef onboard(self) -&gt; bool:\n    \"\"\"Onboard this Artifact into SageWorks\n    Returns:\n        bool: True if the Artifact was successfully onboarded, False otherwise\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.ready","title":"<code>ready()</code>","text":"<p>Is the Artifact ready? Is initial setup complete and expected metadata populated?</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def ready(self) -&gt; bool:\n    \"\"\"Is the Artifact ready? Is initial setup complete and expected metadata populated?\"\"\"\n\n    # If anything goes wrong, assume the artifact is not ready\n    try:\n        # Check for the expected metadata\n        expected_meta = self.expected_meta()\n        existing_meta = self.sageworks_meta()\n        ready = set(existing_meta.keys()).issuperset(expected_meta)\n        if ready:\n            return True\n        else:\n            self.log.info(\"Artifact is not ready!\")\n            return False\n    except Exception as e:\n        self.log.error(f\"Artifact malformed: {e}\")\n        return False\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.refresh_meta","title":"<code>refresh_meta()</code>  <code>abstractmethod</code>","text":"<p>Refresh the Artifact's metadata</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef refresh_meta(self):\n    \"\"\"Refresh the Artifact's metadata\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.remove_sageworks_meta","title":"<code>remove_sageworks_meta(key_to_remove)</code>","text":"<p>Remove SageWorks specific metadata from this Artifact Args:     key_to_remove (str): The metadata key to remove Note:     This functionality will work for FeatureSets, Models, and Endpoints     but not for DataSources. The DataSource class overrides this method.</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def remove_sageworks_meta(self, key_to_remove: str):\n    \"\"\"Remove SageWorks specific metadata from this Artifact\n    Args:\n        key_to_remove (str): The metadata key to remove\n    Note:\n        This functionality will work for FeatureSets, Models, and Endpoints\n        but not for DataSources. The DataSource class overrides this method.\n    \"\"\"\n    aws_arn = self.arn()\n    # Sanity check\n    if aws_arn is None:\n        self.log.error(f\"ARN is None for {self.uuid}!\")\n        return\n    self.log.info(f\"Removing SageWorks Metadata {key_to_remove} for Artifact: {aws_arn}...\")\n    sagemaker_delete_tag(aws_arn, self.sm_session, key_to_remove)\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.remove_sageworks_tag","title":"<code>remove_sageworks_tag(tag, tag_type='user')</code>","text":"<p>Remove a tag from this artifact if it exists. Args:     tag (str): Tag to remove from this artifact     tag_type (str): Type of tag to remove (user or health)</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def remove_sageworks_tag(self, tag, tag_type=\"user\"):\n    \"\"\"Remove a tag from this artifact if it exists.\n    Args:\n        tag (str): Tag to remove from this artifact\n        tag_type (str): Type of tag to remove (user or health)\n    \"\"\"\n    current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n    if tag in current_tags:\n        current_tags.remove(tag)\n        combined_tags = self.tag_delimiter.join(current_tags)\n        if tag_type == \"user\":\n            self.upsert_sageworks_meta({\"sageworks_tags\": combined_tags})\n        elif tag_type == \"health\":\n            self.upsert_sageworks_meta({\"sageworks_health_tags\": combined_tags})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.sageworks_meta","title":"<code>sageworks_meta()</code>","text":"<p>Get the SageWorks specific metadata for this Artifact</p> <p>Returns:</p> Type Description <code>Union[dict, None]</code> <p>Union[dict, None]: Dictionary of SageWorks metadata for this Artifact</p> This functionality will work for FeatureSets, Models, and Endpoints <p>but not for DataSources and Graphs, those classes need to override this method.</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def sageworks_meta(self) -&gt; Union[dict, None]:\n    \"\"\"Get the SageWorks specific metadata for this Artifact\n\n    Returns:\n        Union[dict, None]: Dictionary of SageWorks metadata for this Artifact\n\n    Note: This functionality will work for FeatureSets, Models, and Endpoints\n          but not for DataSources and Graphs, those classes need to override this method.\n    \"\"\"\n    return self.meta.get_aws_tags(self.arn())\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.set_input","title":"<code>set_input(input_data)</code>","text":"<p>Set the input data for this artifact</p> <p>Parameters:</p> Name Type Description Default <code>input_data</code> <code>str</code> <p>Name of input data for this artifact</p> required <p>Note:     This breaks the official provenance of the artifact, so use with caution.</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def set_input(self, input_data: str):\n    \"\"\"Set the input data for this artifact\n\n    Args:\n        input_data (str): Name of input data for this artifact\n    Note:\n        This breaks the official provenance of the artifact, so use with caution.\n    \"\"\"\n    self.log.important(f\"{self.uuid}: Setting input to {input_data}...\")\n    self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n    self.upsert_sageworks_meta({\"sageworks_input\": input_data})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.set_owner","title":"<code>set_owner(owner)</code>","text":"<p>Set the owner of this artifact</p> <p>Parameters:</p> Name Type Description Default <code>owner</code> <code>str</code> <p>Owner to set for this artifact</p> required Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def set_owner(self, owner: str):\n    \"\"\"Set the owner of this artifact\n\n    Args:\n        owner (str): Owner to set for this artifact\n    \"\"\"\n    self.upsert_sageworks_meta({\"sageworks_owner\": owner})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.set_status","title":"<code>set_status(status)</code>","text":"<p>Set the status for this artifact Args:     status (str): Status to set for this artifact</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def set_status(self, status: str):\n    \"\"\"Set the status for this artifact\n    Args:\n        status (str): Status to set for this artifact\n    \"\"\"\n    self.upsert_sageworks_meta({\"sageworks_status\": status})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.size","title":"<code>size()</code>  <code>abstractmethod</code>","text":"<p>Return the size of this artifact in MegaBytes</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef size(self) -&gt; float:\n    \"\"\"Return the size of this artifact in MegaBytes\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.summary","title":"<code>summary()</code>","text":"<p>This is generic summary information for all Artifacts. If you want to get more detailed information, call the details() method which is implemented by the specific Artifact class</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def summary(self) -&gt; dict:\n    \"\"\"This is generic summary information for all Artifacts. If you\n    want to get more detailed information, call the details() method\n    which is implemented by the specific Artifact class\"\"\"\n    basic = {\n        \"uuid\": self.uuid,\n        \"health_tags\": self.get_health_tags(),\n        \"aws_arn\": self.arn(),\n        \"size\": self.size(),\n        \"created\": self.created(),\n        \"modified\": self.modified(),\n        \"input\": self.get_input(),\n    }\n    # Combine the sageworks metadata with the basic metadata\n    return {**basic, **self.sageworks_meta()}\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#sageworks.core.artifacts.artifact.Artifact.upsert_sageworks_meta","title":"<code>upsert_sageworks_meta(new_meta)</code>","text":"<p>Add SageWorks specific metadata to this Artifact Args:     new_meta (dict): Dictionary of NEW metadata to add Note:     This functionality will work for FeatureSets, Models, and Endpoints     but not for DataSources. The DataSource class overrides this method.</p> Source code in <code>src/sageworks/core/artifacts/artifact.py</code> <pre><code>def upsert_sageworks_meta(self, new_meta: dict):\n    \"\"\"Add SageWorks specific metadata to this Artifact\n    Args:\n        new_meta (dict): Dictionary of NEW metadata to add\n    Note:\n        This functionality will work for FeatureSets, Models, and Endpoints\n        but not for DataSources. The DataSource class overrides this method.\n    \"\"\"\n    # Sanity check\n    aws_arn = self.arn()\n    if aws_arn is None:\n        self.log.error(f\"ARN is None for {self.uuid}!\")\n        return\n\n    # Add the new metadata to the existing metadata\n    self.log.info(f\"Adding Tags to {self.uuid}:{str(new_meta)[:50]}...\")\n    aws_tags = dict_to_aws_tags(new_meta)\n    try:\n        self.sm_client.add_tags(ResourceArn=aws_arn, Tags=aws_tags)\n    except Exception as e:\n        self.log.error(f\"Error adding metadata to {aws_arn}: {e}\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/","title":"AthenaSource","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the DataSource API Class and voil\u00e0 it works the same.</p> <p>AthenaSource: SageWorks Data Source accessible through Athena</p>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource","title":"<code>AthenaSource</code>","text":"<p>               Bases: <code>DataSourceAbstract</code></p> <p>AthenaSource: SageWorks Data Source accessible through Athena</p> Common Usage <pre><code>my_data = AthenaSource(data_uuid, database=\"sageworks\")\nmy_data.summary()\nmy_data.details()\ndf = my_data.query(f\"select * from {data_uuid} limit 5\")\n</code></pre> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>class AthenaSource(DataSourceAbstract):\n    \"\"\"AthenaSource: SageWorks Data Source accessible through Athena\n\n    Common Usage:\n        ```python\n        my_data = AthenaSource(data_uuid, database=\"sageworks\")\n        my_data.summary()\n        my_data.details()\n        df = my_data.query(f\"select * from {data_uuid} limit 5\")\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid, database=\"sageworks\", **kwargs):\n        \"\"\"AthenaSource Initialization\n\n        Args:\n            data_uuid (str): Name of Athena Table\n            database (str): Athena Database Name (default: sageworks)\n        \"\"\"\n        # Ensure the data_uuid is a valid name/id\n        self.is_name_valid(data_uuid)\n\n        # Call superclass init\n        super().__init__(data_uuid, database, **kwargs)\n\n        # Grab our metadata from the Meta class\n        self.log.info(f\"Retrieving metadata for: {self.uuid}...\")\n        self.data_source_meta = self.meta.data_source(data_uuid, database=database)\n        if self.data_source_meta is None:\n            self.log.error(f\"Unable to find {database}:{self.table} in Glue Catalogs...\")\n            return\n\n        # Call superclass post init\n        super().__post_init__()\n\n        # All done\n        self.log.debug(f\"AthenaSource Initialized: {database}.{self.table}\")\n\n    def refresh_meta(self):\n        \"\"\"Refresh our internal AWS Broker catalog metadata\"\"\"\n        self.data_source_meta = self.meta.data_source(self.uuid, database=self.database)\n\n    def exists(self) -&gt; bool:\n        \"\"\"Validation Checks for this Data Source\"\"\"\n\n        # Are we able to pull AWS Metadata for this table_name?\"\"\"\n        # Do we have a valid data_source_meta?\n        if getattr(self, \"data_source_meta\", None) is None:\n            self.log.debug(f\"AthenaSource {self.table} not found in SageWorks Metadata...\")\n            return False\n        return True\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        # Grab our SageWorks Role Manager, get our AWS account id, and region for ARN creation\n        account_id = self.aws_account_clamp.account_id\n        region = self.aws_account_clamp.region\n        arn = f\"arn:aws:glue:{region}:{account_id}:table/{self.database}/{self.table}\"\n        return arn\n\n    def sageworks_meta(self) -&gt; dict:\n        \"\"\"Get the SageWorks specific metadata for this Artifact\"\"\"\n\n        # Sanity Check if we have invalid AWS Metadata\n        if self.data_source_meta is None:\n            if not self.exists():\n                self.log.error(f\"DataSource {self.uuid} doesn't appear to exist...\")\n            else:\n                self.log.critical(f\"Unable to get AWS Metadata for {self.table}\")\n                self.log.critical(\"Malformed Artifact! Delete this Artifact and recreate it!\")\n            return {}\n\n        # Get the SageWorks Metadata from the 'Parameters' section of the DataSource Metadata\n        params = self.data_source_meta.get(\"Parameters\", {})\n        return {key: decode_value(value) for key, value in params.items() if \"sageworks\" in key}\n\n    def upsert_sageworks_meta(self, new_meta: dict):\n        \"\"\"Add SageWorks specific metadata to this Artifact\n\n        Args:\n            new_meta (dict): Dictionary of new metadata to add\n        \"\"\"\n        self.log.important(f\"Upserting SageWorks Metadata {self.uuid}:{str(new_meta)[:50]}...\")\n\n        # Give a warning message for keys that don't start with sageworks_\n        for key in new_meta.keys():\n            if not key.startswith(\"sageworks_\"):\n                self.log.warning(\"Append 'sageworks_' to key names to avoid overwriting AWS meta data\")\n\n        # Now convert any non-string values to JSON strings\n        for key, value in new_meta.items():\n            if not isinstance(value, str):\n                new_meta[key] = json.dumps(value, cls=CustomEncoder)\n\n        # Store our updated metadata\n        try:\n            wr.catalog.upsert_table_parameters(\n                parameters=new_meta,\n                database=self.database,\n                table=self.table,\n                boto3_session=self.boto3_session,\n            )\n        except botocore.exceptions.ClientError as e:\n            error_code = e.response[\"Error\"][\"Code\"]\n            if error_code == \"InvalidInputException\":\n                self.log.error(f\"Unable to upsert metadata for {self.table}\")\n                self.log.error(\"Probably because the metadata is too large\")\n                self.log.error(new_meta)\n            elif error_code == \"ConcurrentModificationException\":\n                self.log.warning(\"ConcurrentModificationException... trying again...\")\n                time.sleep(5)\n                wr.catalog.upsert_table_parameters(\n                    parameters=new_meta,\n                    database=self.database,\n                    table=self.table,\n                    boto3_session=self.boto3_session,\n                )\n            else:\n                self.log.critical(f\"Failed to upsert metadata: {e}\")\n                self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n        except Exception as e:\n            self.log.critical(f\"Failed to upsert metadata: {e}\")\n            self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this data in MegaBytes\"\"\"\n        size_in_bytes = sum(wr.s3.size_objects(self.s3_storage_location(), boto3_session=self.boto3_session).values())\n        size_in_mb = size_in_bytes / 1_000_000\n        return size_in_mb\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get the FULL AWS metadata for this artifact\"\"\"\n        return self.data_source_meta\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n        sageworks_details = self.sageworks_meta().get(\"sageworks_details\", {})\n        return sageworks_details.get(\"aws_url\", \"unknown\")\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        return self.data_source_meta[\"CreateTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        return self.data_source_meta[\"UpdateTime\"]\n\n    def hash(self) -&gt; str:\n        \"\"\"Get the hash for the set of Parquet files used for this Artifact\"\"\"\n        s3_uri = self.s3_storage_location()\n        return compute_parquet_hash(s3_uri, self.boto3_session)\n\n    def table_hash(self) -&gt; str:\n        \"\"\"Get the table hash for this AthenaSource\"\"\"\n        s3_scratch = f\"s3://{self.sageworks_bucket}/temp/athena_output\"\n        return compute_athena_table_hash(self.database, self.table, self.boto3_session, s3_scratch)\n\n    def num_rows(self) -&gt; int:\n        \"\"\"Return the number of rows for this Data Source\"\"\"\n        count_df = self.query(f'select count(*) AS sageworks_count from \"{self.database}\".\"{self.table}\"')\n        return count_df[\"sageworks_count\"][0] if count_df is not None else 0\n\n    def num_columns(self) -&gt; int:\n        \"\"\"Return the number of columns for this Data Source\"\"\"\n        return len(self.columns)\n\n    @property\n    def columns(self) -&gt; list[str]:\n        \"\"\"Return the column names for this Athena Table\"\"\"\n        return [item[\"Name\"] for item in self.data_source_meta[\"StorageDescriptor\"][\"Columns\"]]\n\n    @property\n    def column_types(self) -&gt; list[str]:\n        \"\"\"Return the column types of the internal AthenaSource\"\"\"\n        return [item[\"Type\"] for item in self.data_source_meta[\"StorageDescriptor\"][\"Columns\"]]\n\n    def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Query the AthenaSource\n\n        Args:\n            query (str): The query to run against the AthenaSource\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n\n        # Call internal class _query method\n        return self.database_query(self.database, query)\n\n    @classmethod\n    def database_query(cls, database: str, query: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Specify the Database and Query the Athena Service\n\n        Args:\n            database (str): The Athena Database to query\n            query (str): The query to run against the AthenaSource\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        cls.log.debug(f\"Executing Query: {query}...\")\n        try:\n            df = wr.athena.read_sql_query(\n                sql=query,\n                database=database,\n                ctas_approach=False,\n                boto3_session=cls.boto3_session,\n            )\n            scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n            if scanned_bytes &gt; 0:\n                cls.log.debug(f\"Athena Query successful (scanned bytes: {scanned_bytes})\")\n            return df\n        except wr.exceptions.QueryFailed as e:\n            cls.log.critical(f\"Failed to execute query: {e}\")\n            return None\n\n    def execute_statement(self, query: str, silence_errors: bool = False):\n        \"\"\"Execute a non-returning SQL statement in Athena with retries.\n\n        Args:\n            query (str): The query to run against the AthenaSource\n            silence_errors (bool): Silence errors (default: False)\n        \"\"\"\n        attempt = 0\n        max_retries = 3\n        retry_delay = 10\n        while attempt &lt; max_retries:\n            try:\n                # Start the query execution\n                query_execution_id = wr.athena.start_query_execution(\n                    sql=query,\n                    database=self.database,\n                    boto3_session=self.boto3_session,\n                )\n                self.log.debug(f\"QueryExecutionId: {query_execution_id}\")\n\n                # Wait for the query to complete\n                wr.athena.wait_query(query_execution_id=query_execution_id, boto3_session=self.boto3_session)\n                self.log.debug(f\"Statement executed successfully: {query_execution_id}\")\n                break  # If successful, exit the retry loop\n            except wr.exceptions.QueryFailed as e:\n                if \"AlreadyExistsException\" in str(e):\n                    self.log.warning(f\"Table already exists: {e} \\nIgnoring...\")\n                    break  # No need to retry for this error\n                elif \"ConcurrentModificationException\" in str(e):\n                    self.log.warning(f\"Concurrent modification detected: {e}\\nRetrying...\")\n                    attempt += 1\n                    if attempt &lt; max_retries:\n                        time.sleep(retry_delay)\n                    else:\n                        if not silence_errors:\n                            self.log.critical(f\"Failed to execute statement after {max_retries} attempts: {e}\")\n                        raise\n                else:\n                    if not silence_errors:\n                        self.log.critical(f\"Failed to execute statement: {e}\")\n                    raise\n\n    def s3_storage_location(self) -&gt; str:\n        \"\"\"Get the S3 Storage Location for this Data Source\"\"\"\n        return self.data_source_meta[\"StorageDescriptor\"][\"Location\"]\n\n    def athena_test_query(self):\n        \"\"\"Validate that Athena Queries are working\"\"\"\n        query = f'select count(*) as sageworks_count from \"{self.table}\"'\n        df = wr.athena.read_sql_query(\n            sql=query,\n            database=self.database,\n            ctas_approach=False,\n            boto3_session=self.boto3_session,\n        )\n        scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n        self.log.info(f\"Athena TEST Query successful (scanned bytes: {scanned_bytes})\")\n\n    def descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the descriptive stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of descriptive stats for each column in the form\n                 {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n                  'col2': ...}\n        \"\"\"\n\n        # First check if we have already computed the descriptive stats\n        stat_dict = self.sageworks_meta().get(\"sageworks_descriptive_stats\")\n        if stat_dict and not recompute:\n            return stat_dict\n\n        # Call the SQL function to compute descriptive stats\n        stat_dict = sql.descriptive_stats(self)\n\n        # Push the descriptive stat data into our DataSource Metadata\n        self.upsert_sageworks_meta({\"sageworks_descriptive_stats\": stat_dict})\n\n        # Return the descriptive stats\n        return stat_dict\n\n    @cache_dataframe(\"sample\")\n    def sample(self) -&gt; pd.DataFrame:\n        \"\"\"Pull a sample of rows from the DataSource\n\n        Returns:\n            pd.DataFrame: A sample DataFrame for an Athena DataSource\n        \"\"\"\n\n        # Call the SQL function to pull a sample of the rows\n        return sql.sample_rows(self)\n\n    @cache_dataframe(\"outliers\")\n    def outliers(self, scale: float = 1.5, use_stddev=False) -&gt; pd.DataFrame:\n        \"\"\"Compute outliers for all the numeric columns in a DataSource\n\n        Args:\n            scale (float): The scale to use for the IQR (default: 1.5)\n            use_stddev (bool): Use Standard Deviation instead of IQR (default: False)\n\n        Returns:\n            pd.DataFrame: A DataFrame of outliers from this DataSource\n\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n\n        # Compute outliers using the SQL Outliers class\n        sql_outliers = sql.outliers.Outliers()\n        return sql_outliers.compute_outliers(self, scale=scale, use_stddev=use_stddev)\n\n    @cache_dataframe(\"smart_sample\")\n    def smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a smart sample dataframe for this DataSource\n\n        Args:\n            recompute (bool): Recompute the smart sample (default: False)\n\n        Returns:\n            pd.DataFrame: A combined DataFrame of sample data + outliers\n        \"\"\"\n\n        # Compute/recompute the smart sample\n        self.log.important(f\"Computing Smart Sample {self.uuid}...\")\n\n        # Outliers DataFrame\n        outlier_rows = self.outliers()\n\n        # Sample DataFrame\n        sample_rows = self.sample()\n        sample_rows[\"outlier_group\"] = \"sample\"\n\n        # Combine the sample rows with the outlier rows\n        all_rows = pd.concat([outlier_rows, sample_rows]).reset_index(drop=True)\n\n        # Drop duplicates\n        all_except_outlier_group = [col for col in all_rows.columns if col != \"outlier_group\"]\n        all_rows = all_rows.drop_duplicates(subset=all_except_outlier_group, ignore_index=True)\n\n        # Return the smart_sample data\n        return all_rows\n\n    def correlations(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of correlations for each column in this format\n                 {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n                  'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n        \"\"\"\n\n        # First check if we have already computed the correlations\n        correlations_dict = self.sageworks_meta().get(\"sageworks_correlations\")\n        if correlations_dict and not recompute:\n            return correlations_dict\n\n        # Call the SQL function to compute correlations\n        correlations_dict = sql.correlations(self)\n\n        # Push the correlation data into our DataSource Metadata\n        self.upsert_sageworks_meta({\"sageworks_correlations\": correlations_dict})\n\n        # Return the correlation data\n        return correlations_dict\n\n    def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Column Stats for all the columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of stats for each column this format\n            NB: String columns will NOT have num_zeros, descriptive_stats or correlation data\n                {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n                 'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100,\n                          'descriptive_stats': {...}, 'correlations': {...}},\n                 ...}\n        \"\"\"\n\n        # First check if we have already computed the column stats\n        columns_stats_dict = self.sageworks_meta().get(\"sageworks_column_stats\")\n        if columns_stats_dict and not recompute:\n            return columns_stats_dict\n\n        # Call the SQL function to compute column stats\n        column_stats_dict = sql.column_stats(self, recompute=recompute)\n\n        # Push the column stats data into our DataSource Metadata\n        self.upsert_sageworks_meta({\"sageworks_column_stats\": column_stats_dict})\n\n        # Return the column stats data\n        return column_stats_dict\n\n    def value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the value counts (default: False)\n\n        Returns:\n            dict(dict): A dictionary of value counts for each column in the form\n                 {'col1': {'value_1': 42, 'value_2': 16, 'value_3': 9,...},\n                  'col2': ...}\n        \"\"\"\n\n        # First check if we have already computed the value counts\n        value_counts_dict = self.sageworks_meta().get(\"sageworks_value_counts\")\n        if value_counts_dict and not recompute:\n            return value_counts_dict\n\n        # Call the SQL function to compute value_counts\n        value_count_dict = sql.value_counts(self)\n\n        # Push the value_count data into our DataSource Metadata\n        self.upsert_sageworks_meta({\"sageworks_value_counts\": value_count_dict})\n\n        # Return the value_count data\n        return value_count_dict\n\n    def details(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Additional Details about this AthenaSource Artifact\n\n        Args:\n            recompute (bool): Recompute the details (default: False)\n\n        Returns:\n            dict(dict): A dictionary of details about this AthenaSource\n        \"\"\"\n        self.log.info(f\"Computing DataSource Details ({self.uuid})...\")\n\n        # Get the details from the base class\n        details = super().details()\n\n        # Compute additional details\n        details[\"s3_storage_location\"] = self.s3_storage_location()\n        details[\"storage_type\"] = \"athena\"\n\n        # Compute our AWS URL\n        query = f'select * from \"{self.database}.{self.table}\" limit 10'\n        query_exec_id = wr.athena.start_query_execution(\n            sql=query, database=self.database, boto3_session=self.boto3_session\n        )\n        base_url = \"https://console.aws.amazon.com/athena/home\"\n        details[\"aws_url\"] = f\"{base_url}?region={self.aws_region}#query/history/{query_exec_id}\"\n\n        # Push the aws_url data into our DataSource Metadata\n        # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n        # self.upsert_sageworks_meta({\"sageworks_details\": {\"aws_url\": details[\"aws_url\"]}})\n\n        # Convert any datetime fields to ISO-8601 strings\n        details = convert_all_to_iso8601(details)\n\n        # Add the column stats\n        details[\"column_stats\"] = self.column_stats()\n\n        # Return the details data\n        return details\n\n    def delete(self):\n        \"\"\"Instance Method: Delete the AWS Data Catalog Table and S3 Storage Objects\"\"\"\n\n        # Make sure the AthenaSource exists\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an AthenaSource that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the AthenaSource\n        AthenaSource.managed_delete(self.uuid, database=self.database)\n\n    @classmethod\n    def managed_delete(cls, data_source_name: str, database: str = \"sageworks\"):\n        \"\"\"Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects\n\n        Args:\n            data_source_name (str): Name of DataSource (AthenaSource)\n            database (str): Athena Database Name (default: sageworks)\n        \"\"\"\n        table = data_source_name  # The table name is the same as the data_source_name\n\n        # Check if the Glue Catalog Table exists\n        if not wr.catalog.does_table_exist(database, table, boto3_session=cls.boto3_session):\n            cls.log.info(f\"DataSource {table} not found in database {database}.\")\n            return\n\n        # Delete any views associated with this AthenaSource\n        cls.delete_views(table, database)\n\n        # Delete S3 Storage Objects (if they exist)\n        try:\n            # Make an AWS Query to get the S3 storage location\n            s3_path = wr.catalog.get_table_location(database, table, boto3_session=cls.boto3_session)\n\n            # Delete Data Catalog Table\n            cls.log.info(f\"Deleting DataCatalog Table: {database}.{table}...\")\n            wr.catalog.delete_table_if_exists(database, table, boto3_session=cls.boto3_session)\n\n            # Make sure we add the trailing slash\n            s3_path = s3_path if s3_path.endswith(\"/\") else f\"{s3_path}/\"\n            cls.log.info(f\"Deleting S3 Storage Objects: {s3_path}...\")\n            wr.s3.delete_objects(s3_path, boto3_session=cls.boto3_session)\n        except Exception as e:\n            cls.log.error(f\"Failure when trying to delete {data_source_name}: {e}\")\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(data_source_name)\n\n    @classmethod\n    def delete_views(cls, table: str, database: str):\n        \"\"\"Delete any views associated with this FeatureSet\n\n        Args:\n            table (str): Name of Athena Table\n            database (str): Athena Database Name\n        \"\"\"\n        from sageworks.core.views.view_utils import delete_views_and_supplemental_data\n\n        delete_views_and_supplemental_data(table, database, cls.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.column_types","title":"<code>column_types: list[str]</code>  <code>property</code>","text":"<p>Return the column types of the internal AthenaSource</p>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.columns","title":"<code>columns: list[str]</code>  <code>property</code>","text":"<p>Return the column names for this Athena Table</p>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.__init__","title":"<code>__init__(data_uuid, database='sageworks', **kwargs)</code>","text":"<p>AthenaSource Initialization</p> <p>Parameters:</p> Name Type Description Default <code>data_uuid</code> <code>str</code> <p>Name of Athena Table</p> required <code>database</code> <code>str</code> <p>Athena Database Name (default: sageworks)</p> <code>'sageworks'</code> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def __init__(self, data_uuid, database=\"sageworks\", **kwargs):\n    \"\"\"AthenaSource Initialization\n\n    Args:\n        data_uuid (str): Name of Athena Table\n        database (str): Athena Database Name (default: sageworks)\n    \"\"\"\n    # Ensure the data_uuid is a valid name/id\n    self.is_name_valid(data_uuid)\n\n    # Call superclass init\n    super().__init__(data_uuid, database, **kwargs)\n\n    # Grab our metadata from the Meta class\n    self.log.info(f\"Retrieving metadata for: {self.uuid}...\")\n    self.data_source_meta = self.meta.data_source(data_uuid, database=database)\n    if self.data_source_meta is None:\n        self.log.error(f\"Unable to find {database}:{self.table} in Glue Catalogs...\")\n        return\n\n    # Call superclass post init\n    super().__post_init__()\n\n    # All done\n    self.log.debug(f\"AthenaSource Initialized: {database}.{self.table}\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    # Grab our SageWorks Role Manager, get our AWS account id, and region for ARN creation\n    account_id = self.aws_account_clamp.account_id\n    region = self.aws_account_clamp.region\n    arn = f\"arn:aws:glue:{region}:{account_id}:table/{self.database}/{self.table}\"\n    return arn\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.athena_test_query","title":"<code>athena_test_query()</code>","text":"<p>Validate that Athena Queries are working</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def athena_test_query(self):\n    \"\"\"Validate that Athena Queries are working\"\"\"\n    query = f'select count(*) as sageworks_count from \"{self.table}\"'\n    df = wr.athena.read_sql_query(\n        sql=query,\n        database=self.database,\n        ctas_approach=False,\n        boto3_session=self.boto3_session,\n    )\n    scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n    self.log.info(f\"Athena TEST Query successful (scanned bytes: {scanned_bytes})\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get the FULL AWS metadata for this artifact</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get the FULL AWS metadata for this artifact\"\"\"\n    return self.data_source_meta\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying this data source</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n    sageworks_details = self.sageworks_meta().get(\"sageworks_details\", {})\n    return sageworks_details.get(\"aws_url\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.column_stats","title":"<code>column_stats(recompute=False)</code>","text":"<p>Compute Column Stats for all the columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the column stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of stats for each column this format</p> <code>NB</code> <code>dict[dict]</code> <p>String columns will NOT have num_zeros, descriptive_stats or correlation data {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},  'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100,           'descriptive_stats': {...}, 'correlations': {...}},  ...}</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Column Stats for all the columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of stats for each column this format\n        NB: String columns will NOT have num_zeros, descriptive_stats or correlation data\n            {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n             'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100,\n                      'descriptive_stats': {...}, 'correlations': {...}},\n             ...}\n    \"\"\"\n\n    # First check if we have already computed the column stats\n    columns_stats_dict = self.sageworks_meta().get(\"sageworks_column_stats\")\n    if columns_stats_dict and not recompute:\n        return columns_stats_dict\n\n    # Call the SQL function to compute column stats\n    column_stats_dict = sql.column_stats(self, recompute=recompute)\n\n    # Push the column stats data into our DataSource Metadata\n    self.upsert_sageworks_meta({\"sageworks_column_stats\": column_stats_dict})\n\n    # Return the column stats data\n    return column_stats_dict\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.correlations","title":"<code>correlations(recompute=False)</code>","text":"<p>Compute Correlations for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the column stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of correlations for each column in this format  {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},   'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def correlations(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of correlations for each column in this format\n             {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n              'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n    \"\"\"\n\n    # First check if we have already computed the correlations\n    correlations_dict = self.sageworks_meta().get(\"sageworks_correlations\")\n    if correlations_dict and not recompute:\n        return correlations_dict\n\n    # Call the SQL function to compute correlations\n    correlations_dict = sql.correlations(self)\n\n    # Push the correlation data into our DataSource Metadata\n    self.upsert_sageworks_meta({\"sageworks_correlations\": correlations_dict})\n\n    # Return the correlation data\n    return correlations_dict\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    return self.data_source_meta[\"CreateTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.database_query","title":"<code>database_query(database, query)</code>  <code>classmethod</code>","text":"<p>Specify the Database and Query the Athena Service</p> <p>Parameters:</p> Name Type Description Default <code>database</code> <code>str</code> <p>The Athena Database to query</p> required <code>query</code> <code>str</code> <p>The query to run against the AthenaSource</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>@classmethod\ndef database_query(cls, database: str, query: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Specify the Database and Query the Athena Service\n\n    Args:\n        database (str): The Athena Database to query\n        query (str): The query to run against the AthenaSource\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    cls.log.debug(f\"Executing Query: {query}...\")\n    try:\n        df = wr.athena.read_sql_query(\n            sql=query,\n            database=database,\n            ctas_approach=False,\n            boto3_session=cls.boto3_session,\n        )\n        scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n        if scanned_bytes &gt; 0:\n            cls.log.debug(f\"Athena Query successful (scanned bytes: {scanned_bytes})\")\n        return df\n    except wr.exceptions.QueryFailed as e:\n        cls.log.critical(f\"Failed to execute query: {e}\")\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.delete","title":"<code>delete()</code>","text":"<p>Instance Method: Delete the AWS Data Catalog Table and S3 Storage Objects</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def delete(self):\n    \"\"\"Instance Method: Delete the AWS Data Catalog Table and S3 Storage Objects\"\"\"\n\n    # Make sure the AthenaSource exists\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an AthenaSource that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the AthenaSource\n    AthenaSource.managed_delete(self.uuid, database=self.database)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.delete_views","title":"<code>delete_views(table, database)</code>  <code>classmethod</code>","text":"<p>Delete any views associated with this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>table</code> <code>str</code> <p>Name of Athena Table</p> required <code>database</code> <code>str</code> <p>Athena Database Name</p> required Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>@classmethod\ndef delete_views(cls, table: str, database: str):\n    \"\"\"Delete any views associated with this FeatureSet\n\n    Args:\n        table (str): Name of Athena Table\n        database (str): Athena Database Name\n    \"\"\"\n    from sageworks.core.views.view_utils import delete_views_and_supplemental_data\n\n    delete_views_and_supplemental_data(table, database, cls.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.descriptive_stats","title":"<code>descriptive_stats(recompute=False)</code>","text":"<p>Compute Descriptive Stats for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the descriptive stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of descriptive stats for each column in the form  {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},   'col2': ...}</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the descriptive stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of descriptive stats for each column in the form\n             {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n              'col2': ...}\n    \"\"\"\n\n    # First check if we have already computed the descriptive stats\n    stat_dict = self.sageworks_meta().get(\"sageworks_descriptive_stats\")\n    if stat_dict and not recompute:\n        return stat_dict\n\n    # Call the SQL function to compute descriptive stats\n    stat_dict = sql.descriptive_stats(self)\n\n    # Push the descriptive stat data into our DataSource Metadata\n    self.upsert_sageworks_meta({\"sageworks_descriptive_stats\": stat_dict})\n\n    # Return the descriptive stats\n    return stat_dict\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this AthenaSource Artifact</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the details (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about this AthenaSource</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def details(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Additional Details about this AthenaSource Artifact\n\n    Args:\n        recompute (bool): Recompute the details (default: False)\n\n    Returns:\n        dict(dict): A dictionary of details about this AthenaSource\n    \"\"\"\n    self.log.info(f\"Computing DataSource Details ({self.uuid})...\")\n\n    # Get the details from the base class\n    details = super().details()\n\n    # Compute additional details\n    details[\"s3_storage_location\"] = self.s3_storage_location()\n    details[\"storage_type\"] = \"athena\"\n\n    # Compute our AWS URL\n    query = f'select * from \"{self.database}.{self.table}\" limit 10'\n    query_exec_id = wr.athena.start_query_execution(\n        sql=query, database=self.database, boto3_session=self.boto3_session\n    )\n    base_url = \"https://console.aws.amazon.com/athena/home\"\n    details[\"aws_url\"] = f\"{base_url}?region={self.aws_region}#query/history/{query_exec_id}\"\n\n    # Push the aws_url data into our DataSource Metadata\n    # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n    # self.upsert_sageworks_meta({\"sageworks_details\": {\"aws_url\": details[\"aws_url\"]}})\n\n    # Convert any datetime fields to ISO-8601 strings\n    details = convert_all_to_iso8601(details)\n\n    # Add the column stats\n    details[\"column_stats\"] = self.column_stats()\n\n    # Return the details data\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.execute_statement","title":"<code>execute_statement(query, silence_errors=False)</code>","text":"<p>Execute a non-returning SQL statement in Athena with retries.</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the AthenaSource</p> required <code>silence_errors</code> <code>bool</code> <p>Silence errors (default: False)</p> <code>False</code> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def execute_statement(self, query: str, silence_errors: bool = False):\n    \"\"\"Execute a non-returning SQL statement in Athena with retries.\n\n    Args:\n        query (str): The query to run against the AthenaSource\n        silence_errors (bool): Silence errors (default: False)\n    \"\"\"\n    attempt = 0\n    max_retries = 3\n    retry_delay = 10\n    while attempt &lt; max_retries:\n        try:\n            # Start the query execution\n            query_execution_id = wr.athena.start_query_execution(\n                sql=query,\n                database=self.database,\n                boto3_session=self.boto3_session,\n            )\n            self.log.debug(f\"QueryExecutionId: {query_execution_id}\")\n\n            # Wait for the query to complete\n            wr.athena.wait_query(query_execution_id=query_execution_id, boto3_session=self.boto3_session)\n            self.log.debug(f\"Statement executed successfully: {query_execution_id}\")\n            break  # If successful, exit the retry loop\n        except wr.exceptions.QueryFailed as e:\n            if \"AlreadyExistsException\" in str(e):\n                self.log.warning(f\"Table already exists: {e} \\nIgnoring...\")\n                break  # No need to retry for this error\n            elif \"ConcurrentModificationException\" in str(e):\n                self.log.warning(f\"Concurrent modification detected: {e}\\nRetrying...\")\n                attempt += 1\n                if attempt &lt; max_retries:\n                    time.sleep(retry_delay)\n                else:\n                    if not silence_errors:\n                        self.log.critical(f\"Failed to execute statement after {max_retries} attempts: {e}\")\n                    raise\n            else:\n                if not silence_errors:\n                    self.log.critical(f\"Failed to execute statement: {e}\")\n                raise\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.exists","title":"<code>exists()</code>","text":"<p>Validation Checks for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Validation Checks for this Data Source\"\"\"\n\n    # Are we able to pull AWS Metadata for this table_name?\"\"\"\n    # Do we have a valid data_source_meta?\n    if getattr(self, \"data_source_meta\", None) is None:\n        self.log.debug(f\"AthenaSource {self.table} not found in SageWorks Metadata...\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.hash","title":"<code>hash()</code>","text":"<p>Get the hash for the set of Parquet files used for this Artifact</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def hash(self) -&gt; str:\n    \"\"\"Get the hash for the set of Parquet files used for this Artifact\"\"\"\n    s3_uri = self.s3_storage_location()\n    return compute_parquet_hash(s3_uri, self.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.managed_delete","title":"<code>managed_delete(data_source_name, database='sageworks')</code>  <code>classmethod</code>","text":"<p>Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects</p> <p>Parameters:</p> Name Type Description Default <code>data_source_name</code> <code>str</code> <p>Name of DataSource (AthenaSource)</p> required <code>database</code> <code>str</code> <p>Athena Database Name (default: sageworks)</p> <code>'sageworks'</code> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>@classmethod\ndef managed_delete(cls, data_source_name: str, database: str = \"sageworks\"):\n    \"\"\"Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects\n\n    Args:\n        data_source_name (str): Name of DataSource (AthenaSource)\n        database (str): Athena Database Name (default: sageworks)\n    \"\"\"\n    table = data_source_name  # The table name is the same as the data_source_name\n\n    # Check if the Glue Catalog Table exists\n    if not wr.catalog.does_table_exist(database, table, boto3_session=cls.boto3_session):\n        cls.log.info(f\"DataSource {table} not found in database {database}.\")\n        return\n\n    # Delete any views associated with this AthenaSource\n    cls.delete_views(table, database)\n\n    # Delete S3 Storage Objects (if they exist)\n    try:\n        # Make an AWS Query to get the S3 storage location\n        s3_path = wr.catalog.get_table_location(database, table, boto3_session=cls.boto3_session)\n\n        # Delete Data Catalog Table\n        cls.log.info(f\"Deleting DataCatalog Table: {database}.{table}...\")\n        wr.catalog.delete_table_if_exists(database, table, boto3_session=cls.boto3_session)\n\n        # Make sure we add the trailing slash\n        s3_path = s3_path if s3_path.endswith(\"/\") else f\"{s3_path}/\"\n        cls.log.info(f\"Deleting S3 Storage Objects: {s3_path}...\")\n        wr.s3.delete_objects(s3_path, boto3_session=cls.boto3_session)\n    except Exception as e:\n        cls.log.error(f\"Failure when trying to delete {data_source_name}: {e}\")\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(data_source_name)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    return self.data_source_meta[\"UpdateTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.num_columns","title":"<code>num_columns()</code>","text":"<p>Return the number of columns for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def num_columns(self) -&gt; int:\n    \"\"\"Return the number of columns for this Data Source\"\"\"\n    return len(self.columns)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.num_rows","title":"<code>num_rows()</code>","text":"<p>Return the number of rows for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def num_rows(self) -&gt; int:\n    \"\"\"Return the number of rows for this Data Source\"\"\"\n    count_df = self.query(f'select count(*) AS sageworks_count from \"{self.database}\".\"{self.table}\"')\n    return count_df[\"sageworks_count\"][0] if count_df is not None else 0\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.outliers","title":"<code>outliers(scale=1.5, use_stddev=False)</code>","text":"<p>Compute outliers for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>scale</code> <code>float</code> <p>The scale to use for the IQR (default: 1.5)</p> <code>1.5</code> <code>use_stddev</code> <code>bool</code> <p>Use Standard Deviation instead of IQR (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of outliers from this DataSource</p> Notes <p>Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma) The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>@cache_dataframe(\"outliers\")\ndef outliers(self, scale: float = 1.5, use_stddev=False) -&gt; pd.DataFrame:\n    \"\"\"Compute outliers for all the numeric columns in a DataSource\n\n    Args:\n        scale (float): The scale to use for the IQR (default: 1.5)\n        use_stddev (bool): Use Standard Deviation instead of IQR (default: False)\n\n    Returns:\n        pd.DataFrame: A DataFrame of outliers from this DataSource\n\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n\n    # Compute outliers using the SQL Outliers class\n    sql_outliers = sql.outliers.Outliers()\n    return sql_outliers.compute_outliers(self, scale=scale, use_stddev=use_stddev)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.query","title":"<code>query(query)</code>","text":"<p>Query the AthenaSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the AthenaSource</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Query the AthenaSource\n\n    Args:\n        query (str): The query to run against the AthenaSource\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n\n    # Call internal class _query method\n    return self.database_query(self.database, query)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Refresh our internal AWS Broker catalog metadata</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Refresh our internal AWS Broker catalog metadata\"\"\"\n    self.data_source_meta = self.meta.data_source(self.uuid, database=self.database)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.s3_storage_location","title":"<code>s3_storage_location()</code>","text":"<p>Get the S3 Storage Location for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def s3_storage_location(self) -&gt; str:\n    \"\"\"Get the S3 Storage Location for this Data Source\"\"\"\n    return self.data_source_meta[\"StorageDescriptor\"][\"Location\"]\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.sageworks_meta","title":"<code>sageworks_meta()</code>","text":"<p>Get the SageWorks specific metadata for this Artifact</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def sageworks_meta(self) -&gt; dict:\n    \"\"\"Get the SageWorks specific metadata for this Artifact\"\"\"\n\n    # Sanity Check if we have invalid AWS Metadata\n    if self.data_source_meta is None:\n        if not self.exists():\n            self.log.error(f\"DataSource {self.uuid} doesn't appear to exist...\")\n        else:\n            self.log.critical(f\"Unable to get AWS Metadata for {self.table}\")\n            self.log.critical(\"Malformed Artifact! Delete this Artifact and recreate it!\")\n        return {}\n\n    # Get the SageWorks Metadata from the 'Parameters' section of the DataSource Metadata\n    params = self.data_source_meta.get(\"Parameters\", {})\n    return {key: decode_value(value) for key, value in params.items() if \"sageworks\" in key}\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.sample","title":"<code>sample()</code>","text":"<p>Pull a sample of rows from the DataSource</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A sample DataFrame for an Athena DataSource</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>@cache_dataframe(\"sample\")\ndef sample(self) -&gt; pd.DataFrame:\n    \"\"\"Pull a sample of rows from the DataSource\n\n    Returns:\n        pd.DataFrame: A sample DataFrame for an Athena DataSource\n    \"\"\"\n\n    # Call the SQL function to pull a sample of the rows\n    return sql.sample_rows(self)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.size","title":"<code>size()</code>","text":"<p>Return the size of this data in MegaBytes</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of this data in MegaBytes\"\"\"\n    size_in_bytes = sum(wr.s3.size_objects(self.s3_storage_location(), boto3_session=self.boto3_session).values())\n    size_in_mb = size_in_bytes / 1_000_000\n    return size_in_mb\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.smart_sample","title":"<code>smart_sample(recompute=False)</code>","text":"<p>Get a smart sample dataframe for this DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the smart sample (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A combined DataFrame of sample data + outliers</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>@cache_dataframe(\"smart_sample\")\ndef smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a smart sample dataframe for this DataSource\n\n    Args:\n        recompute (bool): Recompute the smart sample (default: False)\n\n    Returns:\n        pd.DataFrame: A combined DataFrame of sample data + outliers\n    \"\"\"\n\n    # Compute/recompute the smart sample\n    self.log.important(f\"Computing Smart Sample {self.uuid}...\")\n\n    # Outliers DataFrame\n    outlier_rows = self.outliers()\n\n    # Sample DataFrame\n    sample_rows = self.sample()\n    sample_rows[\"outlier_group\"] = \"sample\"\n\n    # Combine the sample rows with the outlier rows\n    all_rows = pd.concat([outlier_rows, sample_rows]).reset_index(drop=True)\n\n    # Drop duplicates\n    all_except_outlier_group = [col for col in all_rows.columns if col != \"outlier_group\"]\n    all_rows = all_rows.drop_duplicates(subset=all_except_outlier_group, ignore_index=True)\n\n    # Return the smart_sample data\n    return all_rows\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.table_hash","title":"<code>table_hash()</code>","text":"<p>Get the table hash for this AthenaSource</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def table_hash(self) -&gt; str:\n    \"\"\"Get the table hash for this AthenaSource\"\"\"\n    s3_scratch = f\"s3://{self.sageworks_bucket}/temp/athena_output\"\n    return compute_athena_table_hash(self.database, self.table, self.boto3_session, s3_scratch)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.upsert_sageworks_meta","title":"<code>upsert_sageworks_meta(new_meta)</code>","text":"<p>Add SageWorks specific metadata to this Artifact</p> <p>Parameters:</p> Name Type Description Default <code>new_meta</code> <code>dict</code> <p>Dictionary of new metadata to add</p> required Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def upsert_sageworks_meta(self, new_meta: dict):\n    \"\"\"Add SageWorks specific metadata to this Artifact\n\n    Args:\n        new_meta (dict): Dictionary of new metadata to add\n    \"\"\"\n    self.log.important(f\"Upserting SageWorks Metadata {self.uuid}:{str(new_meta)[:50]}...\")\n\n    # Give a warning message for keys that don't start with sageworks_\n    for key in new_meta.keys():\n        if not key.startswith(\"sageworks_\"):\n            self.log.warning(\"Append 'sageworks_' to key names to avoid overwriting AWS meta data\")\n\n    # Now convert any non-string values to JSON strings\n    for key, value in new_meta.items():\n        if not isinstance(value, str):\n            new_meta[key] = json.dumps(value, cls=CustomEncoder)\n\n    # Store our updated metadata\n    try:\n        wr.catalog.upsert_table_parameters(\n            parameters=new_meta,\n            database=self.database,\n            table=self.table,\n            boto3_session=self.boto3_session,\n        )\n    except botocore.exceptions.ClientError as e:\n        error_code = e.response[\"Error\"][\"Code\"]\n        if error_code == \"InvalidInputException\":\n            self.log.error(f\"Unable to upsert metadata for {self.table}\")\n            self.log.error(\"Probably because the metadata is too large\")\n            self.log.error(new_meta)\n        elif error_code == \"ConcurrentModificationException\":\n            self.log.warning(\"ConcurrentModificationException... trying again...\")\n            time.sleep(5)\n            wr.catalog.upsert_table_parameters(\n                parameters=new_meta,\n                database=self.database,\n                table=self.table,\n                boto3_session=self.boto3_session,\n            )\n        else:\n            self.log.critical(f\"Failed to upsert metadata: {e}\")\n            self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n    except Exception as e:\n        self.log.critical(f\"Failed to upsert metadata: {e}\")\n        self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#sageworks.core.artifacts.athena_source.AthenaSource.value_counts","title":"<code>value_counts(recompute=False)</code>","text":"<p>Compute 'value_counts' for all the string columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the value counts (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of value counts for each column in the form  {'col1': {'value_1': 42, 'value_2': 16, 'value_3': 9,...},   'col2': ...}</p> Source code in <code>src/sageworks/core/artifacts/athena_source.py</code> <pre><code>def value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the value counts (default: False)\n\n    Returns:\n        dict(dict): A dictionary of value counts for each column in the form\n             {'col1': {'value_1': 42, 'value_2': 16, 'value_3': 9,...},\n              'col2': ...}\n    \"\"\"\n\n    # First check if we have already computed the value counts\n    value_counts_dict = self.sageworks_meta().get(\"sageworks_value_counts\")\n    if value_counts_dict and not recompute:\n        return value_counts_dict\n\n    # Call the SQL function to compute value_counts\n    value_count_dict = sql.value_counts(self)\n\n    # Push the value_count data into our DataSource Metadata\n    self.upsert_sageworks_meta({\"sageworks_value_counts\": value_count_dict})\n\n    # Return the value_count data\n    return value_count_dict\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/","title":"DataSource Abstract","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the DataSource API Class and voil\u00e0 it works the same.</p> <p>The DataSource Abstract class is a base/abstract class that defines API implemented by all the child classes (currently just AthenaSource but later RDSSource, FutureThing ).</p> <p>DataSourceAbstract: Abstract Base Class for all data sources (S3: CSV, JSONL, Parquet, RDS, etc)</p>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract","title":"<code>DataSourceAbstract</code>","text":"<p>               Bases: <code>Artifact</code></p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>class DataSourceAbstract(Artifact):\n    def __init__(self, data_uuid: str, database: str = \"sageworks\", **kwargs):\n        \"\"\"DataSourceAbstract: Abstract Base Class for all data sources\n        Args:\n            data_uuid(str): The UUID for this Data Source\n            database(str): The database to use for this Data Source (default: sageworks)\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(data_uuid, **kwargs)\n\n        # Set up our instance attributes\n        self._database = database\n        self._table_name = data_uuid\n\n    def __post_init__(self):\n        # Call superclass post_init\n        super().__post_init__()\n\n    @deprecated(version=\"0.9\")\n    def get_database(self) -&gt; str:\n        \"\"\"Get the database for this Data Source\"\"\"\n        return self._database\n\n    @property\n    def database(self) -&gt; str:\n        \"\"\"Get the database for this Data Source\"\"\"\n        return self._database\n\n    @property\n    def table(self) -&gt; str:\n        \"\"\"Get the base table name for this Data Source\"\"\"\n        return self._table_name\n\n    @abstractmethod\n    def num_rows(self) -&gt; int:\n        \"\"\"Return the number of rows for this Data Source\"\"\"\n        pass\n\n    @abstractmethod\n    def num_columns(self) -&gt; int:\n        \"\"\"Return the number of columns for this Data Source\"\"\"\n        pass\n\n    @property\n    @abstractmethod\n    def columns(self) -&gt; list[str]:\n        \"\"\"Return the column names for this Data Source\"\"\"\n        pass\n\n    @property\n    @abstractmethod\n    def column_types(self) -&gt; list[str]:\n        \"\"\"Return the column types for this Data Source\"\"\"\n        pass\n\n    def column_details(self) -&gt; dict:\n        \"\"\"Return the column details for this Data Source\n\n        Returns:\n            dict: The column details for this Data Source\n        \"\"\"\n        return dict(zip(self.columns, self.column_types))\n\n    def views(self) -&gt; list[str]:\n        \"\"\"Return the views for this Data Source\"\"\"\n        from sageworks.core.views.view_utils import list_views\n\n        return list_views(self)\n\n    def view(self, view_name: str) -&gt; \"View\":\n        \"\"\"Return a DataFrame for a specific view\n        Args:\n            view_name (str): The name of the view to return\n        Returns:\n            pd.DataFrame: A DataFrame for the specified view\n        \"\"\"\n        from sageworks.core.views import View\n\n        return View(self, view_name)\n\n    def set_display_columns(self, diplay_columns: list[str]):\n        \"\"\"Set the display columns for this Data Source\n\n        Args:\n            diplay_columns (list[str]): The display columns for this Data Source\n        \"\"\"\n        # Check mismatch of display columns to computation columns\n        c_view = self.view(\"computation\")\n        computation_columns = c_view.columns\n        mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n        if mismatch_columns:\n            self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n        self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n        from sageworks.core.views import DisplayView\n\n        # Create a NEW display view\n        DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n\n    def set_computation_columns(self, computation_columns: list[str], recompute_stats: bool = True):\n        \"\"\"Set the computation columns for this Data Source\n\n        Args:\n            computation_columns (list[str]): The computation columns for this Data Source\n            recompute_stats (bool): Recomputes all the stats for this Data Source (default: True)\n        \"\"\"\n        self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n        from sageworks.core.views import ComputationView\n\n        # Create a NEW computation view\n        ComputationView.create(self, column_list=computation_columns)\n        if recompute_stats:\n            self.recompute_stats()\n\n    def _create_display_view(self):\n        \"\"\"Internal: Create the Display View for this DataSource\"\"\"\n        from sageworks.core.views import View\n\n        View(self, \"display\")\n\n    @abstractmethod\n    def query(self, query: str) -&gt; pd.DataFrame:\n        \"\"\"Query the DataSourceAbstract\n        Args:\n            query(str): The SQL query to execute\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def execute_statement(self, query: str):\n        \"\"\"Execute an SQL statement that doesn't return a result\n        Args:\n            query(str): The SQL statement to execute\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def sample(self) -&gt; pd.DataFrame:\n        \"\"\"Return a sample DataFrame from this DataSourceAbstract\n\n        Returns:\n            pd.DataFrame: A sample DataFrame from this DataSource\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n        Args:\n            recompute (bool): Recompute the descriptive stats (default: False)\n        Returns:\n            dict(dict): A dictionary of descriptive stats for each column in the form\n                 {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n                  'col2': ...}\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def outliers(self, scale: float = 1.5) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame of outliers from this DataSource\n\n        Args:\n            scale (float): The scale to use for the IQR (default: 1.5)\n\n        Returns:\n            pd.DataFrame: A DataFrame of outliers from this DataSource\n\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def smart_sample(self) -&gt; pd.DataFrame:\n        \"\"\"Get a SMART sample dataframe from this DataSource\n        Returns:\n            pd.DataFrame: A combined DataFrame of sample data + outliers\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n        Args:\n            recompute (bool): Recompute the value counts (default: False)\n        Returns:\n            dict(dict): A dictionary of value counts for each column in the form\n                 {'col1': {'value_1': X, 'value_2': Y, 'value_3': Z,...},\n                  'col2': ...}\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Column Stats for all the columns in a DataSource\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n        Returns:\n            dict(dict): A dictionary of stats for each column this format\n            NB: String columns will NOT have num_zeros and descriptive stats\n             {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n              'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n              ...}\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def correlations(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of correlations for each column in this format\n                 {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n                  'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n        \"\"\"\n        pass\n\n    def details(self) -&gt; dict:\n        \"\"\"Additional Details about this DataSourceAbstract Artifact\"\"\"\n        details = self.summary()\n        details[\"num_rows\"] = self.num_rows()\n        details[\"num_columns\"] = self.num_columns()\n        details[\"column_details\"] = self.column_details()\n        return details\n\n    def expected_meta(self) -&gt; list[str]:\n        \"\"\"DataSources have quite a bit of expected Metadata for EDA displays\"\"\"\n\n        # For DataSources, we expect to see the following metadata\n        expected_meta = [\n            # FIXME: Revisit this\n            # \"sageworks_details\",\n            \"sageworks_descriptive_stats\",\n            \"sageworks_value_counts\",\n            \"sageworks_correlations\",\n            \"sageworks_column_stats\",\n        ]\n        return expected_meta\n\n    def ready(self) -&gt; bool:\n        \"\"\"Is the DataSource ready?\"\"\"\n\n        # Check if the Artifact is ready\n        if not super().ready():\n            return False\n\n        # If we don't have a smart_sample we're probably not ready\n        if not self.df_cache.check(f\"{self.uuid}/smart_sample\"):\n            self.log.warning(f\"DataSource {self.uuid} not ready...\")\n            return False\n\n        # Okay so we have sample, outliers, and smart_sample so we are ready\n        return True\n\n    def onboard(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will onboard the data source (make it ready)\n\n        Returns:\n            bool: True if the DataSource was onboarded successfully\n        \"\"\"\n        self.log.important(f\"Onboarding {self.uuid}...\")\n        self.set_status(\"onboarding\")\n        self.remove_health_tag(\"needs_onboard\")\n\n        # Make sure our display view actually exists\n        self.view(\"display\").ensure_exists()\n\n        # Recompute the stats\n        self.recompute_stats()\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        self.set_status(\"ready\")\n        return True\n\n    def recompute_stats(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will recompute the stats for the data source\n\n        Returns:\n            bool: True if the DataSource stats were recomputed successfully\n        \"\"\"\n        self.log.important(f\"Recomputing Stats {self.uuid}...\")\n\n        # Make sure our computation view actually exists\n        self.view(\"computation\").ensure_exists()\n\n        # Compute the sample, column stats, outliers, and smart_sample\n        self.df_cache.delete(f\"{self.uuid}/sample\")\n        self.sample()\n        self.column_stats(recompute=True)\n        self.refresh_meta()  # Refresh the meta since outliers needs descriptive_stats and value_counts\n        self.df_cache.delete(f\"{self.uuid}/outliers\")\n        self.outliers()\n        self.df_cache.delete(f\"{self.uuid}/smart_sample\")\n        self.smart_sample()\n        return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_types","title":"<code>column_types: list[str]</code>  <code>abstractmethod</code> <code>property</code>","text":"<p>Return the column types for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.columns","title":"<code>columns: list[str]</code>  <code>abstractmethod</code> <code>property</code>","text":"<p>Return the column names for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.database","title":"<code>database: str</code>  <code>property</code>","text":"<p>Get the database for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.table","title":"<code>table: str</code>  <code>property</code>","text":"<p>Get the base table name for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.__init__","title":"<code>__init__(data_uuid, database='sageworks', **kwargs)</code>","text":"<p>DataSourceAbstract: Abstract Base Class for all data sources Args:     data_uuid(str): The UUID for this Data Source     database(str): The database to use for this Data Source (default: sageworks)</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def __init__(self, data_uuid: str, database: str = \"sageworks\", **kwargs):\n    \"\"\"DataSourceAbstract: Abstract Base Class for all data sources\n    Args:\n        data_uuid(str): The UUID for this Data Source\n        database(str): The database to use for this Data Source (default: sageworks)\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(data_uuid, **kwargs)\n\n    # Set up our instance attributes\n    self._database = database\n    self._table_name = data_uuid\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_details","title":"<code>column_details()</code>","text":"<p>Return the column details for this Data Source</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The column details for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def column_details(self) -&gt; dict:\n    \"\"\"Return the column details for this Data Source\n\n    Returns:\n        dict: The column details for this Data Source\n    \"\"\"\n    return dict(zip(self.columns, self.column_types))\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.column_stats","title":"<code>column_stats(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute Column Stats for all the columns in a DataSource Args:     recompute (bool): Recompute the column stats (default: False) Returns:     dict(dict): A dictionary of stats for each column this format     NB: String columns will NOT have num_zeros and descriptive stats      {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},       'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},       ...}</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Column Stats for all the columns in a DataSource\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n    Returns:\n        dict(dict): A dictionary of stats for each column this format\n        NB: String columns will NOT have num_zeros and descriptive stats\n         {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n          'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n          ...}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.correlations","title":"<code>correlations(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute Correlations for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the column stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of correlations for each column in this format  {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},   'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef correlations(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of correlations for each column in this format\n             {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n              'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.descriptive_stats","title":"<code>descriptive_stats(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute Descriptive Stats for all the numeric columns in a DataSource Args:     recompute (bool): Recompute the descriptive stats (default: False) Returns:     dict(dict): A dictionary of descriptive stats for each column in the form          {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},           'col2': ...}</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n    Args:\n        recompute (bool): Recompute the descriptive stats (default: False)\n    Returns:\n        dict(dict): A dictionary of descriptive stats for each column in the form\n             {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n              'col2': ...}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.details","title":"<code>details()</code>","text":"<p>Additional Details about this DataSourceAbstract Artifact</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def details(self) -&gt; dict:\n    \"\"\"Additional Details about this DataSourceAbstract Artifact\"\"\"\n    details = self.summary()\n    details[\"num_rows\"] = self.num_rows()\n    details[\"num_columns\"] = self.num_columns()\n    details[\"column_details\"] = self.column_details()\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.execute_statement","title":"<code>execute_statement(query)</code>  <code>abstractmethod</code>","text":"<p>Execute an SQL statement that doesn't return a result Args:     query(str): The SQL statement to execute</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef execute_statement(self, query: str):\n    \"\"\"Execute an SQL statement that doesn't return a result\n    Args:\n        query(str): The SQL statement to execute\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.expected_meta","title":"<code>expected_meta()</code>","text":"<p>DataSources have quite a bit of expected Metadata for EDA displays</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def expected_meta(self) -&gt; list[str]:\n    \"\"\"DataSources have quite a bit of expected Metadata for EDA displays\"\"\"\n\n    # For DataSources, we expect to see the following metadata\n    expected_meta = [\n        # FIXME: Revisit this\n        # \"sageworks_details\",\n        \"sageworks_descriptive_stats\",\n        \"sageworks_value_counts\",\n        \"sageworks_correlations\",\n        \"sageworks_column_stats\",\n    ]\n    return expected_meta\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.get_database","title":"<code>get_database()</code>","text":"<p>Get the database for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@deprecated(version=\"0.9\")\ndef get_database(self) -&gt; str:\n    \"\"\"Get the database for this Data Source\"\"\"\n    return self._database\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_columns","title":"<code>num_columns()</code>  <code>abstractmethod</code>","text":"<p>Return the number of columns for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef num_columns(self) -&gt; int:\n    \"\"\"Return the number of columns for this Data Source\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.num_rows","title":"<code>num_rows()</code>  <code>abstractmethod</code>","text":"<p>Return the number of rows for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef num_rows(self) -&gt; int:\n    \"\"\"Return the number of rows for this Data Source\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.onboard","title":"<code>onboard()</code>","text":"<p>This is a BLOCKING method that will onboard the data source (make it ready)</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the DataSource was onboarded successfully</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def onboard(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will onboard the data source (make it ready)\n\n    Returns:\n        bool: True if the DataSource was onboarded successfully\n    \"\"\"\n    self.log.important(f\"Onboarding {self.uuid}...\")\n    self.set_status(\"onboarding\")\n    self.remove_health_tag(\"needs_onboard\")\n\n    # Make sure our display view actually exists\n    self.view(\"display\").ensure_exists()\n\n    # Recompute the stats\n    self.recompute_stats()\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    self.set_status(\"ready\")\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.outliers","title":"<code>outliers(scale=1.5)</code>  <code>abstractmethod</code>","text":"<p>Return a DataFrame of outliers from this DataSource</p> <p>Parameters:</p> Name Type Description Default <code>scale</code> <code>float</code> <p>The scale to use for the IQR (default: 1.5)</p> <code>1.5</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of outliers from this DataSource</p> Notes <p>Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef outliers(self, scale: float = 1.5) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame of outliers from this DataSource\n\n    Args:\n        scale (float): The scale to use for the IQR (default: 1.5)\n\n    Returns:\n        pd.DataFrame: A DataFrame of outliers from this DataSource\n\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.query","title":"<code>query(query)</code>  <code>abstractmethod</code>","text":"<p>Query the DataSourceAbstract Args:     query(str): The SQL query to execute</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef query(self, query: str) -&gt; pd.DataFrame:\n    \"\"\"Query the DataSourceAbstract\n    Args:\n        query(str): The SQL query to execute\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.ready","title":"<code>ready()</code>","text":"<p>Is the DataSource ready?</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def ready(self) -&gt; bool:\n    \"\"\"Is the DataSource ready?\"\"\"\n\n    # Check if the Artifact is ready\n    if not super().ready():\n        return False\n\n    # If we don't have a smart_sample we're probably not ready\n    if not self.df_cache.check(f\"{self.uuid}/smart_sample\"):\n        self.log.warning(f\"DataSource {self.uuid} not ready...\")\n        return False\n\n    # Okay so we have sample, outliers, and smart_sample so we are ready\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.recompute_stats","title":"<code>recompute_stats()</code>","text":"<p>This is a BLOCKING method that will recompute the stats for the data source</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the DataSource stats were recomputed successfully</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def recompute_stats(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will recompute the stats for the data source\n\n    Returns:\n        bool: True if the DataSource stats were recomputed successfully\n    \"\"\"\n    self.log.important(f\"Recomputing Stats {self.uuid}...\")\n\n    # Make sure our computation view actually exists\n    self.view(\"computation\").ensure_exists()\n\n    # Compute the sample, column stats, outliers, and smart_sample\n    self.df_cache.delete(f\"{self.uuid}/sample\")\n    self.sample()\n    self.column_stats(recompute=True)\n    self.refresh_meta()  # Refresh the meta since outliers needs descriptive_stats and value_counts\n    self.df_cache.delete(f\"{self.uuid}/outliers\")\n    self.outliers()\n    self.df_cache.delete(f\"{self.uuid}/smart_sample\")\n    self.smart_sample()\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.sample","title":"<code>sample()</code>  <code>abstractmethod</code>","text":"<p>Return a sample DataFrame from this DataSourceAbstract</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A sample DataFrame from this DataSource</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef sample(self) -&gt; pd.DataFrame:\n    \"\"\"Return a sample DataFrame from this DataSourceAbstract\n\n    Returns:\n        pd.DataFrame: A sample DataFrame from this DataSource\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_computation_columns","title":"<code>set_computation_columns(computation_columns, recompute_stats=True)</code>","text":"<p>Set the computation columns for this Data Source</p> <p>Parameters:</p> Name Type Description Default <code>computation_columns</code> <code>list[str]</code> <p>The computation columns for this Data Source</p> required <code>recompute_stats</code> <code>bool</code> <p>Recomputes all the stats for this Data Source (default: True)</p> <code>True</code> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def set_computation_columns(self, computation_columns: list[str], recompute_stats: bool = True):\n    \"\"\"Set the computation columns for this Data Source\n\n    Args:\n        computation_columns (list[str]): The computation columns for this Data Source\n        recompute_stats (bool): Recomputes all the stats for this Data Source (default: True)\n    \"\"\"\n    self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n    from sageworks.core.views import ComputationView\n\n    # Create a NEW computation view\n    ComputationView.create(self, column_list=computation_columns)\n    if recompute_stats:\n        self.recompute_stats()\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.set_display_columns","title":"<code>set_display_columns(diplay_columns)</code>","text":"<p>Set the display columns for this Data Source</p> <p>Parameters:</p> Name Type Description Default <code>diplay_columns</code> <code>list[str]</code> <p>The display columns for this Data Source</p> required Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def set_display_columns(self, diplay_columns: list[str]):\n    \"\"\"Set the display columns for this Data Source\n\n    Args:\n        diplay_columns (list[str]): The display columns for this Data Source\n    \"\"\"\n    # Check mismatch of display columns to computation columns\n    c_view = self.view(\"computation\")\n    computation_columns = c_view.columns\n    mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n    if mismatch_columns:\n        self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n    self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n    from sageworks.core.views import DisplayView\n\n    # Create a NEW display view\n    DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.smart_sample","title":"<code>smart_sample()</code>  <code>abstractmethod</code>","text":"<p>Get a SMART sample dataframe from this DataSource Returns:     pd.DataFrame: A combined DataFrame of sample data + outliers</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef smart_sample(self) -&gt; pd.DataFrame:\n    \"\"\"Get a SMART sample dataframe from this DataSource\n    Returns:\n        pd.DataFrame: A combined DataFrame of sample data + outliers\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.value_counts","title":"<code>value_counts(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute 'value_counts' for all the string columns in a DataSource Args:     recompute (bool): Recompute the value counts (default: False) Returns:     dict(dict): A dictionary of value counts for each column in the form          {'col1': {'value_1': X, 'value_2': Y, 'value_3': Z,...},           'col2': ...}</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n    Args:\n        recompute (bool): Recompute the value counts (default: False)\n    Returns:\n        dict(dict): A dictionary of value counts for each column in the form\n             {'col1': {'value_1': X, 'value_2': Y, 'value_3': Z,...},\n              'col2': ...}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.view","title":"<code>view(view_name)</code>","text":"<p>Return a DataFrame for a specific view Args:     view_name (str): The name of the view to return Returns:     pd.DataFrame: A DataFrame for the specified view</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def view(self, view_name: str) -&gt; \"View\":\n    \"\"\"Return a DataFrame for a specific view\n    Args:\n        view_name (str): The name of the view to return\n    Returns:\n        pd.DataFrame: A DataFrame for the specified view\n    \"\"\"\n    from sageworks.core.views import View\n\n    return View(self, view_name)\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#sageworks.core.artifacts.data_source_abstract.DataSourceAbstract.views","title":"<code>views()</code>","text":"<p>Return the views for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/data_source_abstract.py</code> <pre><code>def views(self) -&gt; list[str]:\n    \"\"\"Return the views for this Data Source\"\"\"\n    from sageworks.core.views.view_utils import list_views\n\n    return list_views(self)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/","title":"EndpointCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the Endpoint API Class and voil\u00e0 it works the same.</p> <p>EndpointCore: SageWorks EndpointCore Class</p>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore","title":"<code>EndpointCore</code>","text":"<p>               Bases: <code>Artifact</code></p> <p>EndpointCore: SageWorks EndpointCore Class</p> Common Usage <pre><code>my_endpoint = EndpointCore(endpoint_uuid)\nprediction_df = my_endpoint.predict(test_df)\nmetrics = my_endpoint.regression_metrics(target_column, prediction_df)\nfor metric, value in metrics.items():\n    print(f\"{metric}: {value:0.3f}\")\n</code></pre> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>class EndpointCore(Artifact):\n    \"\"\"EndpointCore: SageWorks EndpointCore Class\n\n    Common Usage:\n        ```python\n        my_endpoint = EndpointCore(endpoint_uuid)\n        prediction_df = my_endpoint.predict(test_df)\n        metrics = my_endpoint.regression_metrics(target_column, prediction_df)\n        for metric, value in metrics.items():\n            print(f\"{metric}: {value:0.3f}\")\n        ```\n    \"\"\"\n\n    def __init__(self, endpoint_uuid, **kwargs):\n        \"\"\"EndpointCore Initialization\n\n        Args:\n            endpoint_uuid (str): Name of Endpoint in SageWorks\n        \"\"\"\n\n        # Make sure the endpoint_uuid is a valid name\n        self.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call SuperClass Initialization\n        super().__init__(endpoint_uuid, **kwargs)\n\n        # Grab an Cloud Metadata object and pull information for Endpoints\n        self.endpoint_name = endpoint_uuid\n        self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n\n        # Sanity check that we found the endpoint\n        if self.endpoint_meta is None:\n            self.log.important(f\"Could not find endpoint {self.uuid} within current visibility scope\")\n            return\n\n        # Sanity check the Endpoint state\n        if self.endpoint_meta[\"EndpointStatus\"] == \"Failed\":\n            self.log.critical(f\"Endpoint {self.uuid} is in a failed state\")\n            reason = self.endpoint_meta[\"FailureReason\"]\n            self.log.critical(f\"Failure Reason: {reason}\")\n            self.log.critical(\"Please delete this endpoint and re-deploy...\")\n\n        # Set the Inference, Capture, and Monitoring S3 Paths\n        self.endpoint_inference_path = self.endpoints_s3_path + \"/inference/\" + self.uuid\n        self.endpoint_data_capture_path = self.endpoints_s3_path + \"/data_capture/\" + self.uuid\n        self.endpoint_monitoring_path = self.endpoints_s3_path + \"/monitoring/\" + self.uuid\n\n        # Set the Model Name\n        self.model_name = self.get_input()\n\n        # This is for endpoint error handling later\n        self.endpoint_return_columns = None\n\n        # We temporary cache the endpoint metrics\n        self.temp_storage = Cache(prefix=\"temp_storage\", expire=300)  # 5 minutes\n\n        # Call SuperClass Post Initialization\n        super().__post_init__()\n\n        # All done\n        self.log.info(f\"EndpointCore Initialized: {self.endpoint_name}\")\n\n    def refresh_meta(self):\n        \"\"\"Refresh the Artifact's metadata\"\"\"\n        self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n\n    def exists(self) -&gt; bool:\n        \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n        if self.endpoint_meta is None:\n            self.log.debug(f\"Endpoint {self.endpoint_name} not found in AWS Metadata\")\n            return False\n        return True\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this model\n\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        if not self.ready():\n            return [\"needs_onboard\"]\n\n        # Call the base class health check\n        health_issues = super().health_check()\n\n        # Does this endpoint have a config?\n        # Note: This is not an authoritative check, so improve later\n        if self.endpoint_meta.get(\"ProductionVariants\") is None:\n            health_issues.append(\"no_config\")\n\n        # We're going to check for 5xx errors and no activity\n        endpoint_metrics = self.endpoint_metrics()\n\n        # Check if we have metrics\n        if endpoint_metrics is None:\n            health_issues.append(\"unknown_error\")\n            return health_issues\n\n        # Check for 5xx errors\n        num_errors = endpoint_metrics[\"Invocation5XXErrors\"].sum()\n        if num_errors &gt; 5:\n            health_issues.append(\"5xx_errors\")\n        elif num_errors &gt; 0:\n            health_issues.append(\"5xx_errors_min\")\n        else:\n            self.remove_health_tag(\"5xx_errors\")\n            self.remove_health_tag(\"5xx_errors_min\")\n\n        # Check for Endpoint activity\n        num_invocations = endpoint_metrics[\"Invocations\"].sum()\n        if num_invocations == 0:\n            health_issues.append(\"no_activity\")\n        else:\n            self.remove_health_tag(\"no_activity\")\n        return health_issues\n\n    def is_serverless(self) -&gt; bool:\n        \"\"\"Check if the current endpoint is serverless.\n\n        Returns:\n            bool: True if the endpoint is serverless, False otherwise.\n        \"\"\"\n        return \"Serverless\" in self.endpoint_meta[\"InstanceType\"]\n\n    def add_data_capture(self):\n        \"\"\"Add data capture to the endpoint\"\"\"\n        self.get_monitor().add_data_capture()\n\n    def get_monitor(self):\n        \"\"\"Get the MonitorCore class for this endpoint\"\"\"\n        from sageworks.core.artifacts.monitor_core import MonitorCore\n\n        return MonitorCore(self.endpoint_name)\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this data in MegaBytes\"\"\"\n        return 0.0\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n        return self.endpoint_meta\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        return self.endpoint_meta[\"EndpointArn\"]\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n        return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        return self.endpoint_meta[\"CreationTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        return self.endpoint_meta[\"LastModifiedTime\"]\n\n    def hash(self) -&gt; Optional[str]:\n        \"\"\"Return the hash for the internal model used by this endpoint\n\n        Returns:\n            Optional[str]: The hash for the internal model used by this endpoint\n        \"\"\"\n        from sageworks.utils.endpoint_utils import get_model_data_url  # Avoid circular import\n\n        model_url = get_model_data_url(self.endpoint_config_name(), self.boto3_session)\n        return compute_s3_object_hash(model_url, self.boto3_session)\n\n    def endpoint_metrics(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Return the metrics for this endpoint\n\n        Returns:\n            pd.DataFrame: DataFrame with the metrics for this endpoint (or None if no metrics)\n        \"\"\"\n\n        # Do we have it cached?\n        metrics_key = f\"endpoint:{self.uuid}:endpoint_metrics\"\n        endpoint_metrics = self.temp_storage.get(metrics_key)\n        if endpoint_metrics is not None:\n            return endpoint_metrics\n\n        # We don't have it cached so let's get it from CloudWatch\n        if \"ProductionVariants\" not in self.endpoint_meta:\n            return None\n        self.log.important(\"Updating endpoint metrics...\")\n        variant = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n        endpoint_metrics = EndpointMetrics().get_metrics(self.uuid, variant=variant)\n        self.temp_storage.set(metrics_key, endpoint_metrics)\n        return endpoint_metrics\n\n    def details(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Additional Details about this Endpoint\n        Args:\n            recompute (bool): Recompute the details (default: False)\n        Returns:\n            dict(dict): A dictionary of details about this Endpoint\n        \"\"\"\n\n        # Fill in all the details about this Endpoint\n        details = self.summary()\n\n        # Get details from our AWS Metadata\n        details[\"status\"] = self.endpoint_meta[\"EndpointStatus\"]\n        details[\"instance\"] = self.endpoint_meta[\"InstanceType\"]\n        try:\n            details[\"instance_count\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"CurrentInstanceCount\"] or \"-\"\n        except KeyError:\n            details[\"instance_count\"] = \"-\"\n        if \"ProductionVariants\" in self.endpoint_meta:\n            details[\"variant\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n        else:\n            details[\"variant\"] = \"-\"\n\n        # Add endpoint metrics from CloudWatch\n        details[\"endpoint_metrics\"] = self.endpoint_metrics()\n\n        # Return the details\n        return details\n\n    def onboard(self, interactive: bool = False) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will onboard the Endpoint (make it ready)\n        Args:\n            interactive (bool, optional): If True, will prompt the user for information. (default: False)\n        Returns:\n            bool: True if the Endpoint is successfully onboarded, False otherwise\n        \"\"\"\n\n        # Make sure our input is defined\n        if self.get_input() == \"unknown\":\n            if interactive:\n                input_model = input(\"Input Model?: \")\n            else:\n                self.log.critical(\"Input Model is not defined!\")\n                return False\n        else:\n            input_model = self.get_input()\n\n        # Now that we have the details, let's onboard the Endpoint with args\n        return self.onboard_with_args(input_model)\n\n    def onboard_with_args(self, input_model: str) -&gt; bool:\n        \"\"\"Onboard the Endpoint with the given arguments\n\n        Args:\n            input_model (str): The input model for this endpoint\n        Returns:\n            bool: True if the Endpoint is successfully onboarded, False otherwise\n        \"\"\"\n        # Set the status to onboarding\n        self.set_status(\"onboarding\")\n\n        self.upsert_sageworks_meta({\"sageworks_input\": input_model})\n        self.model_name = input_model\n\n        # Remove the needs_onboard tag\n        self.remove_health_tag(\"needs_onboard\")\n        self.set_status(\"ready\")\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        return True\n\n    def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the endpoint using FeatureSet data\n\n        Args:\n            capture (bool, optional): Capture the inference results and metrics (default=False)\n        \"\"\"\n\n        # Sanity Check that we have a model\n        model = ModelCore(self.get_input())\n        if not model.exists():\n            self.log.error(\"No model found for this endpoint. Returning empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Now get the FeatureSet and make sure it exists\n        fs = FeatureSetCore(model.get_input())\n        if not fs.exists():\n            self.log.error(\"No FeatureSet found for this endpoint. Returning empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Grab the evaluation data from the FeatureSet\n        table = fs.view(\"training\").table\n        eval_df = fs.query(f'SELECT * FROM \"{table}\" where training = FALSE')\n        capture_uuid = \"auto_inference\" if capture else None\n        return self.inference(eval_df, capture_uuid, id_column=fs.id_column)\n\n    def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n        \"\"\"Run inference and compute performance metrics with optional capture\n\n        Args:\n            eval_df (pd.DataFrame): DataFrame to run predictions on (must have superset of features)\n            capture_uuid (str, optional): UUID of the inference capture (default=None)\n            id_column (str, optional): Name of the ID column (default=None)\n\n        Returns:\n            pd.DataFrame: DataFrame with the inference results\n\n        Note:\n            If capture=True inference/performance metrics are written to S3 Endpoint Inference Folder\n        \"\"\"\n\n        # Run predictions on the evaluation data\n        prediction_df = self._predict(eval_df)\n        if prediction_df.empty:\n            self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n            return prediction_df\n\n        # Get the target column\n        model = ModelCore(self.model_name)\n        target_column = model.target()\n\n        # Sanity Check that the target column is present\n        if target_column and (target_column not in prediction_df.columns):\n            self.log.important(f\"Target Column {target_column} not found in prediction_df!\")\n            self.log.important(\"In order to compute metrics, the target column must be present!\")\n            return prediction_df\n\n        # Compute the standard performance metrics for this model\n        model_type = model.model_type\n        if model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n            prediction_df = self.residuals(target_column, prediction_df)\n            metrics = self.regression_metrics(target_column, prediction_df)\n        elif model_type == ModelType.CLASSIFIER:\n            metrics = self.classification_metrics(target_column, prediction_df)\n        else:\n            # For other model types, we don't compute metrics\n            self.log.important(f\"Model Type: {model_type} doesn't have metrics...\")\n            metrics = pd.DataFrame()\n\n        # Print out the metrics\n        if not metrics.empty:\n            print(f\"Performance Metrics for {self.model_name} on {self.uuid}\")\n            print(metrics.head())\n\n            # Capture the inference results and metrics\n            if capture_uuid is not None:\n                description = capture_uuid.replace(\"_\", \" \").title()\n                self._capture_inference_results(\n                    capture_uuid, prediction_df, target_column, model_type, metrics, description, id_column\n                )\n\n        # Return the prediction DataFrame\n        return prediction_df\n\n    def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n        Args:\n            eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n\n        Note:\n            There's no sanity checks or error handling... just FAST Inference!\n        \"\"\"\n        return fast_inference(self.uuid, eval_df, self.sm_session)\n\n    def _predict(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Internal: Run prediction on the given observations in the given DataFrame\n        Args:\n            eval_df (pd.DataFrame): DataFrame to run predictions on (must have superset of features)\n        Returns:\n            pd.DataFrame: Return the DataFrame with additional columns, prediction and any _proba columns\n        \"\"\"\n\n        # Sanity check: Does the DataFrame have 0 rows?\n        if eval_df.empty:\n            self.log.warning(\"Evaluation DataFrame has 0 rows. No predictions to run.\")\n            return pd.DataFrame(columns=eval_df.columns)  # Return empty DataFrame with same structure\n\n        # Sanity check: Does the Model have Features?\n        features = ModelCore(self.model_name).features()\n        if not features:\n            self.log.warning(\"Model does not have features defined, using all columns in the DataFrame\")\n        else:\n            # Sanity check: Does the DataFrame have the required features?\n            df_columns_lower = set(col.lower() for col in eval_df.columns)\n            features_lower = set(feature.lower() for feature in features)\n\n            # Check if the features are a subset of the DataFrame columns (case-insensitive)\n            if not features_lower.issubset(df_columns_lower):\n                missing_features = features_lower - df_columns_lower\n                raise ValueError(f\"DataFrame does not contain required features: {missing_features}\")\n\n        # Create our Endpoint Predictor Class\n        predictor = Predictor(\n            self.endpoint_name,\n            sagemaker_session=self.sm_session,\n            serializer=CSVSerializer(),\n            deserializer=CSVDeserializer(),\n        )\n\n        # Now split up the dataframe into 500 row chunks, send those chunks to our\n        # endpoint (with error handling) and stitch all the chunks back together\n        df_list = []\n        for index in range(0, len(eval_df), 500):\n            self.log.info(\"Processing...\")\n\n            # Compute partial DataFrames, add them to a list, and concatenate at the end\n            partial_df = self._endpoint_error_handling(predictor, eval_df[index : index + 500])\n            df_list.append(partial_df)\n\n        # Concatenate the dataframes\n        combined_df = pd.concat(df_list, ignore_index=True)\n\n        # Convert data to numeric\n        # Note: Since we're using CSV serializers numeric columns often get changed to generic 'object' types\n\n        # Hard Conversion\n        # Note: We explicitly catch exceptions for columns that cannot be converted to numeric\n        converted_df = combined_df.copy()\n        for column in combined_df.columns:\n            try:\n                converted_df[column] = pd.to_numeric(combined_df[column])\n            except ValueError:\n                # If a ValueError is raised, the column cannot be converted to numeric, so we keep it as is\n                pass\n\n        # Soft Conversion\n        # Convert columns to the best possible dtype that supports the pd.NA missing value.\n        converted_df = converted_df.convert_dtypes()\n\n        # Report on any rows that failed\n        failed_rows = converted_df[converted_df[\"prediction\"].isna()]\n        if not failed_rows.empty:\n            self.log.warning(f\"Rows that failed:\\n{failed_rows}\")\n\n        # Convert pd.NA placeholders to pd.NA\n        # Note: CSV serialization converts pd.NA to blank strings, so we have to put in placeholders\n        converted_df.replace(\"__NA__\", pd.NA, inplace=True)\n\n        # Return the Dataframe\n        return converted_df\n\n    def _endpoint_error_handling(self, predictor, feature_df):\n        \"\"\"Internal: Handles errors, retries, and binary search for problematic rows.\"\"\"\n\n        # Convert DataFrame into a CSV buffer\n        csv_buffer = StringIO()\n        feature_df.to_csv(csv_buffer, index=False)\n\n        try:\n            # Send CSV buffer to the predictor and process results\n            results = predictor.predict(csv_buffer.getvalue())\n            results_df = pd.DataFrame.from_records(results[1:], columns=results[0])\n            self.endpoint_return_columns = results_df.columns.tolist()\n            return results_df\n\n        except botocore.exceptions.ClientError as err:\n            error_code = err.response[\"Error\"][\"Code\"]\n\n            if error_code == \"ModelNotReadyException\":\n                self.log.error(f\"Error {error_code}: {err.response.get('Message', 'No message')}\")\n                self.log.error(\"Model not ready. Sleeping and retrying...\")\n                time.sleep(60)\n                return self._endpoint_error_handling(predictor, feature_df)\n\n            elif error_code == \"ModelError\":\n                self.log.warning(\"Model error. Bisecting the DataFrame and retrying...\")\n\n                # Base case: If there is only one row, we can't binary search further\n                if len(feature_df) == 1:\n                    if not self.endpoint_return_columns:\n                        raise\n                    return self._error_df(feature_df, self.endpoint_return_columns)\n\n                # Binary search to find the problematic row(s)\n                mid_point = len(feature_df) // 2\n                first_half = self._endpoint_error_handling(predictor, feature_df.iloc[:mid_point])\n                second_half = self._endpoint_error_handling(predictor, feature_df.iloc[mid_point:])\n                return pd.concat([first_half, second_half], ignore_index=True)\n\n            else:\n                # Unknown ClientError, raise the exception\n                self.log.critical(f\"Unexpected ClientError: {err}\")\n                raise\n\n        except Exception as err:\n            self.log.critical(f\"Unexpected general error: {err}\")\n            raise\n\n    def _error_df(self, df, all_columns):\n        \"\"\"Internal: Method to construct an Error DataFrame (a Pandas DataFrame with one row of NaNs)\"\"\"\n        # Create a new dataframe with all NaNs\n        error_df = pd.DataFrame(dict(zip(all_columns, [[np.NaN]] * len(self.endpoint_return_columns))))\n        # Now set the original values for the incoming dataframe\n        for column in df.columns:\n            error_df[column] = df[column].values\n        return error_df\n\n    def _capture_inference_results(\n        self,\n        capture_uuid: str,\n        pred_results_df: pd.DataFrame,\n        target_column: str,\n        model_type: ModelType,\n        metrics: pd.DataFrame,\n        description: str,\n        id_column: str = None,\n    ):\n        \"\"\"Internal: Capture the inference results and metrics to S3\n\n        Args:\n            capture_uuid (str): UUID of the inference capture\n            pred_results_df (pd.DataFrame): DataFrame with the prediction results\n            target_column (str): Name of the target column\n            model_type (ModelType): Type of the model (e.g. REGRESSOR, CLASSIFIER)\n            metrics (pd.DataFrame): DataFrame with the performance metrics\n            description (str): Description of the inference results\n            id_column (str, optional): Name of the ID column (default=None)\n        \"\"\"\n\n        # Compute a dataframe hash (just use the last 8)\n        data_hash = joblib.hash(pred_results_df)[:8]\n\n        # Metadata for the model inference\n        inference_meta = {\n            \"name\": capture_uuid,\n            \"data_hash\": data_hash,\n            \"num_rows\": len(pred_results_df),\n            \"description\": description,\n        }\n\n        # Create the S3 Path for the Inference Capture\n        inference_capture_path = f\"{self.endpoint_inference_path}/{capture_uuid}\"\n\n        # Write the metadata dictionary and metrics to our S3 Model Inference Folder\n        wr.s3.to_json(\n            pd.DataFrame([inference_meta]),\n            f\"{inference_capture_path}/inference_meta.json\",\n            index=False,\n        )\n        self.log.info(f\"Writing metrics to {inference_capture_path}/inference_metrics.csv\")\n        wr.s3.to_csv(metrics, f\"{inference_capture_path}/inference_metrics.csv\", index=False)\n\n        # Grab the target column, prediction column, any _proba columns, and the ID column (if present)\n        prediction_col = \"prediction\" if \"prediction\" in pred_results_df.columns else \"predictions\"\n        output_columns = [target_column, prediction_col]\n\n        # Add any _proba columns to the output columns\n        output_columns += [col for col in pred_results_df.columns if col.endswith(\"_proba\")]\n\n        # Add any quantile columns to the output columns\n        output_columns += [col for col in pred_results_df.columns if col.startswith(\"q_\") or col.startswith(\"qr_\")]\n\n        # Add the ID column\n        if id_column and id_column in pred_results_df.columns:\n            output_columns.append(id_column)\n\n        # Write the predictions to our S3 Model Inference Folder\n        self.log.info(f\"Writing predictions to {inference_capture_path}/inference_predictions.csv\")\n        subset_df = pred_results_df[output_columns]\n        wr.s3.to_csv(subset_df, f\"{inference_capture_path}/inference_predictions.csv\", index=False)\n\n        # CLASSIFIER: Write the confusion matrix to our S3 Model Inference Folder\n        if model_type == ModelType.CLASSIFIER:\n            conf_mtx = self.generate_confusion_matrix(target_column, pred_results_df)\n            self.log.info(f\"Writing confusion matrix to {inference_capture_path}/inference_cm.csv\")\n            # Note: Unlike other dataframes here, we want to write the index (labels) to the CSV\n            wr.s3.to_csv(conf_mtx, f\"{inference_capture_path}/inference_cm.csv\", index=True)\n\n        # Generate SHAP values for our Prediction Dataframe\n        generate_shap_values(self.endpoint_name, model_type.value, pred_results_df, inference_capture_path)\n\n        # Now recompute the details for our Model\n        self.log.important(f\"Recomputing Details for {self.model_name} to show latest Inference Results...\")\n        model = ModelCore(self.model_name)\n        model._load_inference_metrics(capture_uuid)\n        model.details(recompute=True)\n\n        # Recompute the details so that inference model metrics are updated\n        self.log.important(f\"Recomputing Details for {self.uuid} to show latest Inference Results...\")\n        self.details(recompute=True)\n\n    def regression_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Compute the performance metrics for this Endpoint\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n        Returns:\n            pd.DataFrame: DataFrame with the performance metrics\n        \"\"\"\n\n        # Sanity Check the prediction DataFrame\n        if prediction_df.empty:\n            self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Compute the metrics\n        y_true = prediction_df[target_column]\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        y_pred = prediction_df[prediction_col]\n\n        mae = mean_absolute_error(y_true, y_pred)\n        rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n        r2 = r2_score(y_true, y_pred)\n        # Mean Absolute Percentage Error\n        mape = np.mean(np.where(y_true != 0, np.abs((y_true - y_pred) / y_true), np.abs(y_true - y_pred))) * 100\n        # Median Absolute Error\n        medae = median_absolute_error(y_true, y_pred)\n\n        # Organize and return the metrics\n        metrics = {\n            \"MAE\": round(mae, 3),\n            \"RMSE\": round(rmse, 3),\n            \"R2\": round(r2, 3),\n            \"MAPE\": round(mape, 3),\n            \"MedAE\": round(medae, 3),\n            \"NumRows\": len(prediction_df),\n        }\n        return pd.DataFrame.from_records([metrics])\n\n    def residuals(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Add the residuals to the prediction DataFrame\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n        Returns:\n            pd.DataFrame: DataFrame with two new columns called 'residuals' and 'residuals_abs'\n        \"\"\"\n\n        # Compute the residuals\n        y_true = prediction_df[target_column]\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        y_pred = prediction_df[prediction_col]\n\n        # Check for classification scenario\n        if not pd.api.types.is_numeric_dtype(y_true) or not pd.api.types.is_numeric_dtype(y_pred):\n            self.log.warning(\"Target and Prediction columns are not numeric. Computing 'diffs'...\")\n            prediction_df[\"residuals\"] = (y_true != y_pred).astype(int)\n            prediction_df[\"residuals_abs\"] = prediction_df[\"residuals\"]\n        else:\n            # Compute numeric residuals for regression\n            prediction_df[\"residuals\"] = y_true - y_pred\n            prediction_df[\"residuals_abs\"] = np.abs(prediction_df[\"residuals\"])\n\n        return prediction_df\n\n    @staticmethod\n    def validate_proba_columns(prediction_df: pd.DataFrame, class_labels: list, guessing: bool = False):\n        \"\"\"Ensure probability columns are correctly aligned with class labels\n\n        Args:\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n            class_labels (list): List of class labels\n            guessing (bool, optional): Whether we're guessing the class labels. Defaults to False.\n        \"\"\"\n        proba_columns = [col.replace(\"_proba\", \"\") for col in prediction_df.columns if col.endswith(\"_proba\")]\n\n        if sorted(class_labels) != sorted(proba_columns):\n            if guessing:\n                raise ValueError(f\"_proba columns {proba_columns} != GUESSED class_labels {class_labels}!\")\n            else:\n                raise ValueError(f\"_proba columns {proba_columns} != class_labels {class_labels}!\")\n\n    def classification_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Compute the performance metrics for this Endpoint\n\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n\n        Returns:\n            pd.DataFrame: DataFrame with the performance metrics\n        \"\"\"\n        # Get the class labels from the model\n        class_labels = ModelCore(self.model_name).class_labels()\n        if class_labels is None:\n            self.log.warning(\n                \"Class labels not found in the model. Guessing class labels from the prediction DataFrame.\"\n            )\n            class_labels = prediction_df[target_column].unique().tolist()\n            self.validate_proba_columns(prediction_df, class_labels, guessing=True)\n        else:\n            self.validate_proba_columns(prediction_df, class_labels)\n\n        # Calculate precision, recall, fscore, and support, handling zero division\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        scores = precision_recall_fscore_support(\n            prediction_df[target_column],\n            prediction_df[prediction_col],\n            average=None,\n            labels=class_labels,\n            zero_division=0,\n        )\n\n        # Identify the probability columns and keep them as a Pandas DataFrame\n        proba_columns = [f\"{label}_proba\" for label in class_labels]\n        y_score = prediction_df[proba_columns]\n\n        # One-hot encode the true labels using all class labels (fit with class_labels)\n        encoder = OneHotEncoder(categories=[class_labels], sparse_output=False)\n        y_true = encoder.fit_transform(prediction_df[[target_column]])\n\n        # Calculate ROC AUC per label and handle exceptions for missing classes\n        roc_auc_per_label = []\n        for i, label in enumerate(class_labels):\n            try:\n                roc_auc = roc_auc_score(y_true[:, i], y_score.iloc[:, i])\n            except ValueError as e:\n                self.log.warning(f\"ROC AUC calculation failed for label {label}.\")\n                self.log.warning(f\"{str(e)}\")\n                roc_auc = 0.0\n            roc_auc_per_label.append(roc_auc)\n\n        # Put the scores into a DataFrame\n        score_df = pd.DataFrame(\n            {\n                target_column: class_labels,\n                \"precision\": scores[0],\n                \"recall\": scores[1],\n                \"fscore\": scores[2],\n                \"roc_auc\": roc_auc_per_label,\n                \"support\": scores[3],\n            }\n        )\n\n        # Sort the target labels\n        score_df = score_df.sort_values(by=[target_column], ascending=True)\n        return score_df\n\n    def generate_confusion_matrix(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Compute the confusion matrix for this Endpoint\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n        Returns:\n            pd.DataFrame: DataFrame with the confusion matrix\n        \"\"\"\n\n        y_true = prediction_df[target_column]\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        y_pred = prediction_df[prediction_col]\n\n        # Check if our model has class labels, if not we'll use the unique labels in the prediction\n        class_labels = ModelCore(self.model_name).class_labels()\n        if class_labels is None:\n            class_labels = sorted(list(set(y_true) | set(y_pred)))\n\n        # Compute the confusion matrix (sklearn confusion_matrix)\n        conf_mtx = confusion_matrix(y_true, y_pred, labels=class_labels)\n\n        # Create a DataFrame\n        conf_mtx_df = pd.DataFrame(conf_mtx, index=class_labels, columns=class_labels)\n        conf_mtx_df.index.name = \"labels\"\n\n        # Check if our model has class labels. If so make the index and columns ordered\n        model_class_labels = ModelCore(self.model_name).class_labels()\n        if model_class_labels:\n            self.log.important(\"Reordering the confusion matrix based on model class labels...\")\n            conf_mtx_df.index = pd.Categorical(conf_mtx_df.index, categories=model_class_labels, ordered=True)\n            conf_mtx_df.columns = pd.Categorical(conf_mtx_df.columns, categories=model_class_labels, ordered=True)\n            conf_mtx_df = conf_mtx_df.sort_index().sort_index(axis=1)\n        return conf_mtx_df\n\n    def endpoint_config_name(self) -&gt; str:\n        # Grab the Endpoint Config Name from the AWS\n        details = self.sm_client.describe_endpoint(EndpointName=self.endpoint_name)\n        return details[\"EndpointConfigName\"]\n\n    def set_input(self, input: str, force=False):\n        \"\"\"Override: Set the input data for this artifact\n\n        Args:\n            input (str): Name of input for this artifact\n            force (bool, optional): Force the input to be set. Defaults to False.\n        Note:\n            We're going to not allow this to be used for Models\n        \"\"\"\n        if not force:\n            self.log.warning(f\"Endpoint {self.uuid}: Does not allow manual override of the input!\")\n            return\n\n        # Okay we're going to allow this to be set\n        self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n        self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n        self.upsert_sageworks_meta({\"sageworks_input\": input})\n\n    def delete(self):\n        \"\"\" \"Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint\"\"\"\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the FeatureSet\n        EndpointCore.managed_delete(endpoint_name=self.uuid)\n\n    @classmethod\n    def managed_delete(cls, endpoint_name: str):\n        \"\"\"Delete the Endpoint and associated resources if it exists\"\"\"\n\n        # Check if the endpoint exists\n        try:\n            endpoint_info = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n                cls.log.info(f\"Endpoint {endpoint_name} not found!\")\n                return\n            raise  # Re-raise unexpected errors\n\n        # Delete underlying models (Endpoints store/use models internally)\n        cls.delete_endpoint_models(endpoint_name)\n\n        # Get Endpoint Config Name and delete if exists\n        endpoint_config_name = endpoint_info[\"EndpointConfigName\"]\n        try:\n            cls.log.info(f\"Deleting Endpoint Config {endpoint_config_name}...\")\n            cls.sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n        except ClientError:\n            cls.log.info(f\"Endpoint Config {endpoint_config_name} not found...\")\n\n        # Delete any monitoring schedules associated with the endpoint\n        monitoring_schedules = cls.sm_client.list_monitoring_schedules(EndpointName=endpoint_name)[\n            \"MonitoringScheduleSummaries\"\n        ]\n        for schedule in monitoring_schedules:\n            cls.log.info(f\"Deleting Monitoring Schedule {schedule['MonitoringScheduleName']}...\")\n            cls.sm_client.delete_monitoring_schedule(MonitoringScheduleName=schedule[\"MonitoringScheduleName\"])\n\n        # Delete related S3 artifacts (inference, data capture, monitoring)\n        endpoint_inference_path = cls.endpoints_s3_path + \"/inference/\" + endpoint_name\n        endpoint_data_capture_path = cls.endpoints_s3_path + \"/data_capture/\" + endpoint_name\n        endpoint_monitoring_path = cls.endpoints_s3_path + \"/monitoring/\" + endpoint_name\n        for s3_path in [endpoint_inference_path, endpoint_data_capture_path, endpoint_monitoring_path]:\n            s3_path = f\"{s3_path.rstrip('/')}/\"\n            objects = wr.s3.list_objects(s3_path, boto3_session=cls.boto3_session)\n            if objects:\n                cls.log.info(f\"Deleting S3 Objects at {s3_path}...\")\n                wr.s3.delete_objects(objects, boto3_session=cls.boto3_session)\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(endpoint_name)\n\n        # Delete the endpoint\n        time.sleep(2)  # Allow AWS to catch up\n        try:\n            cls.log.info(f\"Deleting Endpoint {endpoint_name}...\")\n            cls.sm_client.delete_endpoint(EndpointName=endpoint_name)\n        except ClientError as e:\n            cls.log.error(\"Error deleting endpoint.\")\n            raise e\n\n        time.sleep(5)  # Final sleep for AWS to fully register deletions\n\n    @classmethod\n    def delete_endpoint_models(cls, endpoint_name: str):\n        \"\"\"Delete the underlying Model for an Endpoint\n\n        Args:\n            endpoint_name (str): The name of the endpoint to delete\n        \"\"\"\n\n        # Grab the Endpoint Config Name from AWS\n        endpoint_config_name = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)[\"EndpointConfigName\"]\n\n        # Retrieve the Model Names from the Endpoint Config\n        try:\n            endpoint_config = cls.sm_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n        except botocore.exceptions.ClientError:\n            cls.log.info(f\"Endpoint Config {endpoint_config_name} doesn't exist...\")\n            return\n        model_names = [variant[\"ModelName\"] for variant in endpoint_config[\"ProductionVariants\"]]\n        for model_name in model_names:\n            cls.log.info(f\"Deleting Internal Model {model_name}...\")\n            try:\n                cls.sm_client.delete_model(ModelName=model_name)\n            except botocore.exceptions.ClientError as error:\n                error_code = error.response[\"Error\"][\"Code\"]\n                error_message = error.response[\"Error\"][\"Message\"]\n                if error_code == \"ResourceInUse\":\n                    cls.log.warning(f\"Model {model_name} is still in use...\")\n                else:\n                    cls.log.warning(f\"Error: {error_code} - {error_message}\")\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.__init__","title":"<code>__init__(endpoint_uuid, **kwargs)</code>","text":"<p>EndpointCore Initialization</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_uuid</code> <code>str</code> <p>Name of Endpoint in SageWorks</p> required Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def __init__(self, endpoint_uuid, **kwargs):\n    \"\"\"EndpointCore Initialization\n\n    Args:\n        endpoint_uuid (str): Name of Endpoint in SageWorks\n    \"\"\"\n\n    # Make sure the endpoint_uuid is a valid name\n    self.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call SuperClass Initialization\n    super().__init__(endpoint_uuid, **kwargs)\n\n    # Grab an Cloud Metadata object and pull information for Endpoints\n    self.endpoint_name = endpoint_uuid\n    self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n\n    # Sanity check that we found the endpoint\n    if self.endpoint_meta is None:\n        self.log.important(f\"Could not find endpoint {self.uuid} within current visibility scope\")\n        return\n\n    # Sanity check the Endpoint state\n    if self.endpoint_meta[\"EndpointStatus\"] == \"Failed\":\n        self.log.critical(f\"Endpoint {self.uuid} is in a failed state\")\n        reason = self.endpoint_meta[\"FailureReason\"]\n        self.log.critical(f\"Failure Reason: {reason}\")\n        self.log.critical(\"Please delete this endpoint and re-deploy...\")\n\n    # Set the Inference, Capture, and Monitoring S3 Paths\n    self.endpoint_inference_path = self.endpoints_s3_path + \"/inference/\" + self.uuid\n    self.endpoint_data_capture_path = self.endpoints_s3_path + \"/data_capture/\" + self.uuid\n    self.endpoint_monitoring_path = self.endpoints_s3_path + \"/monitoring/\" + self.uuid\n\n    # Set the Model Name\n    self.model_name = self.get_input()\n\n    # This is for endpoint error handling later\n    self.endpoint_return_columns = None\n\n    # We temporary cache the endpoint metrics\n    self.temp_storage = Cache(prefix=\"temp_storage\", expire=300)  # 5 minutes\n\n    # Call SuperClass Post Initialization\n    super().__post_init__()\n\n    # All done\n    self.log.info(f\"EndpointCore Initialized: {self.endpoint_name}\")\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.add_data_capture","title":"<code>add_data_capture()</code>","text":"<p>Add data capture to the endpoint</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def add_data_capture(self):\n    \"\"\"Add data capture to the endpoint\"\"\"\n    self.get_monitor().add_data_capture()\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    return self.endpoint_meta[\"EndpointArn\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.auto_inference","title":"<code>auto_inference(capture=False)</code>","text":"<p>Run inference on the endpoint using FeatureSet data</p> <p>Parameters:</p> Name Type Description Default <code>capture</code> <code>bool</code> <p>Capture the inference results and metrics (default=False)</p> <code>False</code> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the endpoint using FeatureSet data\n\n    Args:\n        capture (bool, optional): Capture the inference results and metrics (default=False)\n    \"\"\"\n\n    # Sanity Check that we have a model\n    model = ModelCore(self.get_input())\n    if not model.exists():\n        self.log.error(\"No model found for this endpoint. Returning empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Now get the FeatureSet and make sure it exists\n    fs = FeatureSetCore(model.get_input())\n    if not fs.exists():\n        self.log.error(\"No FeatureSet found for this endpoint. Returning empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Grab the evaluation data from the FeatureSet\n    table = fs.view(\"training\").table\n    eval_df = fs.query(f'SELECT * FROM \"{table}\" where training = FALSE')\n    capture_uuid = \"auto_inference\" if capture else None\n    return self.inference(eval_df, capture_uuid, id_column=fs.id_column)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get ALL the AWS metadata for this artifact</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n    return self.endpoint_meta\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying this data source</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n    return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.classification_metrics","title":"<code>classification_metrics(target_column, prediction_df)</code>","text":"<p>Compute the performance metrics for this Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>target_column</code> <code>str</code> <p>Name of the target column</p> required <code>prediction_df</code> <code>DataFrame</code> <p>DataFrame with the prediction results</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: DataFrame with the performance metrics</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def classification_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Compute the performance metrics for this Endpoint\n\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n\n    Returns:\n        pd.DataFrame: DataFrame with the performance metrics\n    \"\"\"\n    # Get the class labels from the model\n    class_labels = ModelCore(self.model_name).class_labels()\n    if class_labels is None:\n        self.log.warning(\n            \"Class labels not found in the model. Guessing class labels from the prediction DataFrame.\"\n        )\n        class_labels = prediction_df[target_column].unique().tolist()\n        self.validate_proba_columns(prediction_df, class_labels, guessing=True)\n    else:\n        self.validate_proba_columns(prediction_df, class_labels)\n\n    # Calculate precision, recall, fscore, and support, handling zero division\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    scores = precision_recall_fscore_support(\n        prediction_df[target_column],\n        prediction_df[prediction_col],\n        average=None,\n        labels=class_labels,\n        zero_division=0,\n    )\n\n    # Identify the probability columns and keep them as a Pandas DataFrame\n    proba_columns = [f\"{label}_proba\" for label in class_labels]\n    y_score = prediction_df[proba_columns]\n\n    # One-hot encode the true labels using all class labels (fit with class_labels)\n    encoder = OneHotEncoder(categories=[class_labels], sparse_output=False)\n    y_true = encoder.fit_transform(prediction_df[[target_column]])\n\n    # Calculate ROC AUC per label and handle exceptions for missing classes\n    roc_auc_per_label = []\n    for i, label in enumerate(class_labels):\n        try:\n            roc_auc = roc_auc_score(y_true[:, i], y_score.iloc[:, i])\n        except ValueError as e:\n            self.log.warning(f\"ROC AUC calculation failed for label {label}.\")\n            self.log.warning(f\"{str(e)}\")\n            roc_auc = 0.0\n        roc_auc_per_label.append(roc_auc)\n\n    # Put the scores into a DataFrame\n    score_df = pd.DataFrame(\n        {\n            target_column: class_labels,\n            \"precision\": scores[0],\n            \"recall\": scores[1],\n            \"fscore\": scores[2],\n            \"roc_auc\": roc_auc_per_label,\n            \"support\": scores[3],\n        }\n    )\n\n    # Sort the target labels\n    score_df = score_df.sort_values(by=[target_column], ascending=True)\n    return score_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    return self.endpoint_meta[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.delete","title":"<code>delete()</code>","text":"<p>\"Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def delete(self):\n    \"\"\" \"Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint\"\"\"\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the FeatureSet\n    EndpointCore.managed_delete(endpoint_name=self.uuid)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.delete_endpoint_models","title":"<code>delete_endpoint_models(endpoint_name)</code>  <code>classmethod</code>","text":"<p>Delete the underlying Model for an Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>The name of the endpoint to delete</p> required Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>@classmethod\ndef delete_endpoint_models(cls, endpoint_name: str):\n    \"\"\"Delete the underlying Model for an Endpoint\n\n    Args:\n        endpoint_name (str): The name of the endpoint to delete\n    \"\"\"\n\n    # Grab the Endpoint Config Name from AWS\n    endpoint_config_name = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)[\"EndpointConfigName\"]\n\n    # Retrieve the Model Names from the Endpoint Config\n    try:\n        endpoint_config = cls.sm_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n    except botocore.exceptions.ClientError:\n        cls.log.info(f\"Endpoint Config {endpoint_config_name} doesn't exist...\")\n        return\n    model_names = [variant[\"ModelName\"] for variant in endpoint_config[\"ProductionVariants\"]]\n    for model_name in model_names:\n        cls.log.info(f\"Deleting Internal Model {model_name}...\")\n        try:\n            cls.sm_client.delete_model(ModelName=model_name)\n        except botocore.exceptions.ClientError as error:\n            error_code = error.response[\"Error\"][\"Code\"]\n            error_message = error.response[\"Error\"][\"Message\"]\n            if error_code == \"ResourceInUse\":\n                cls.log.warning(f\"Model {model_name} is still in use...\")\n            else:\n                cls.log.warning(f\"Error: {error_code} - {error_message}\")\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this Endpoint Args:     recompute (bool): Recompute the details (default: False) Returns:     dict(dict): A dictionary of details about this Endpoint</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def details(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Additional Details about this Endpoint\n    Args:\n        recompute (bool): Recompute the details (default: False)\n    Returns:\n        dict(dict): A dictionary of details about this Endpoint\n    \"\"\"\n\n    # Fill in all the details about this Endpoint\n    details = self.summary()\n\n    # Get details from our AWS Metadata\n    details[\"status\"] = self.endpoint_meta[\"EndpointStatus\"]\n    details[\"instance\"] = self.endpoint_meta[\"InstanceType\"]\n    try:\n        details[\"instance_count\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"CurrentInstanceCount\"] or \"-\"\n    except KeyError:\n        details[\"instance_count\"] = \"-\"\n    if \"ProductionVariants\" in self.endpoint_meta:\n        details[\"variant\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n    else:\n        details[\"variant\"] = \"-\"\n\n    # Add endpoint metrics from CloudWatch\n    details[\"endpoint_metrics\"] = self.endpoint_metrics()\n\n    # Return the details\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.endpoint_metrics","title":"<code>endpoint_metrics()</code>","text":"<p>Return the metrics for this endpoint</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame with the metrics for this endpoint (or None if no metrics)</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def endpoint_metrics(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Return the metrics for this endpoint\n\n    Returns:\n        pd.DataFrame: DataFrame with the metrics for this endpoint (or None if no metrics)\n    \"\"\"\n\n    # Do we have it cached?\n    metrics_key = f\"endpoint:{self.uuid}:endpoint_metrics\"\n    endpoint_metrics = self.temp_storage.get(metrics_key)\n    if endpoint_metrics is not None:\n        return endpoint_metrics\n\n    # We don't have it cached so let's get it from CloudWatch\n    if \"ProductionVariants\" not in self.endpoint_meta:\n        return None\n    self.log.important(\"Updating endpoint metrics...\")\n    variant = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n    endpoint_metrics = EndpointMetrics().get_metrics(self.uuid, variant=variant)\n    self.temp_storage.set(metrics_key, endpoint_metrics)\n    return endpoint_metrics\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.exists","title":"<code>exists()</code>","text":"<p>Does the feature_set_name exist in the AWS Metadata?</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n    if self.endpoint_meta is None:\n        self.log.debug(f\"Endpoint {self.endpoint_name} not found in AWS Metadata\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.fast_inference","title":"<code>fast_inference(eval_df)</code>","text":"<p>Run inference on the Endpoint using the provided DataFrame</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>The DataFrame to run predictions on</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Note <p>There's no sanity checks or error handling... just FAST Inference!</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n    Args:\n        eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n\n    Note:\n        There's no sanity checks or error handling... just FAST Inference!\n    \"\"\"\n    return fast_inference(self.uuid, eval_df, self.sm_session)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.generate_confusion_matrix","title":"<code>generate_confusion_matrix(target_column, prediction_df)</code>","text":"<p>Compute the confusion matrix for this Endpoint Args:     target_column (str): Name of the target column     prediction_df (pd.DataFrame): DataFrame with the prediction results Returns:     pd.DataFrame: DataFrame with the confusion matrix</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def generate_confusion_matrix(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Compute the confusion matrix for this Endpoint\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n    Returns:\n        pd.DataFrame: DataFrame with the confusion matrix\n    \"\"\"\n\n    y_true = prediction_df[target_column]\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    y_pred = prediction_df[prediction_col]\n\n    # Check if our model has class labels, if not we'll use the unique labels in the prediction\n    class_labels = ModelCore(self.model_name).class_labels()\n    if class_labels is None:\n        class_labels = sorted(list(set(y_true) | set(y_pred)))\n\n    # Compute the confusion matrix (sklearn confusion_matrix)\n    conf_mtx = confusion_matrix(y_true, y_pred, labels=class_labels)\n\n    # Create a DataFrame\n    conf_mtx_df = pd.DataFrame(conf_mtx, index=class_labels, columns=class_labels)\n    conf_mtx_df.index.name = \"labels\"\n\n    # Check if our model has class labels. If so make the index and columns ordered\n    model_class_labels = ModelCore(self.model_name).class_labels()\n    if model_class_labels:\n        self.log.important(\"Reordering the confusion matrix based on model class labels...\")\n        conf_mtx_df.index = pd.Categorical(conf_mtx_df.index, categories=model_class_labels, ordered=True)\n        conf_mtx_df.columns = pd.Categorical(conf_mtx_df.columns, categories=model_class_labels, ordered=True)\n        conf_mtx_df = conf_mtx_df.sort_index().sort_index(axis=1)\n    return conf_mtx_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.get_monitor","title":"<code>get_monitor()</code>","text":"<p>Get the MonitorCore class for this endpoint</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def get_monitor(self):\n    \"\"\"Get the MonitorCore class for this endpoint\"\"\"\n    from sageworks.core.artifacts.monitor_core import MonitorCore\n\n    return MonitorCore(self.endpoint_name)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.hash","title":"<code>hash()</code>","text":"<p>Return the hash for the internal model used by this endpoint</p> <p>Returns:</p> Type Description <code>Optional[str]</code> <p>Optional[str]: The hash for the internal model used by this endpoint</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def hash(self) -&gt; Optional[str]:\n    \"\"\"Return the hash for the internal model used by this endpoint\n\n    Returns:\n        Optional[str]: The hash for the internal model used by this endpoint\n    \"\"\"\n    from sageworks.utils.endpoint_utils import get_model_data_url  # Avoid circular import\n\n    model_url = get_model_data_url(self.endpoint_config_name(), self.boto3_session)\n    return compute_s3_object_hash(model_url, self.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of health issues</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this model\n\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    if not self.ready():\n        return [\"needs_onboard\"]\n\n    # Call the base class health check\n    health_issues = super().health_check()\n\n    # Does this endpoint have a config?\n    # Note: This is not an authoritative check, so improve later\n    if self.endpoint_meta.get(\"ProductionVariants\") is None:\n        health_issues.append(\"no_config\")\n\n    # We're going to check for 5xx errors and no activity\n    endpoint_metrics = self.endpoint_metrics()\n\n    # Check if we have metrics\n    if endpoint_metrics is None:\n        health_issues.append(\"unknown_error\")\n        return health_issues\n\n    # Check for 5xx errors\n    num_errors = endpoint_metrics[\"Invocation5XXErrors\"].sum()\n    if num_errors &gt; 5:\n        health_issues.append(\"5xx_errors\")\n    elif num_errors &gt; 0:\n        health_issues.append(\"5xx_errors_min\")\n    else:\n        self.remove_health_tag(\"5xx_errors\")\n        self.remove_health_tag(\"5xx_errors_min\")\n\n    # Check for Endpoint activity\n    num_invocations = endpoint_metrics[\"Invocations\"].sum()\n    if num_invocations == 0:\n        health_issues.append(\"no_activity\")\n    else:\n        self.remove_health_tag(\"no_activity\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.inference","title":"<code>inference(eval_df, capture_uuid=None, id_column=None)</code>","text":"<p>Run inference and compute performance metrics with optional capture</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>DataFrame to run predictions on (must have superset of features)</p> required <code>capture_uuid</code> <code>str</code> <p>UUID of the inference capture (default=None)</p> <code>None</code> <code>id_column</code> <code>str</code> <p>Name of the ID column (default=None)</p> <code>None</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: DataFrame with the inference results</p> Note <p>If capture=True inference/performance metrics are written to S3 Endpoint Inference Folder</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n    \"\"\"Run inference and compute performance metrics with optional capture\n\n    Args:\n        eval_df (pd.DataFrame): DataFrame to run predictions on (must have superset of features)\n        capture_uuid (str, optional): UUID of the inference capture (default=None)\n        id_column (str, optional): Name of the ID column (default=None)\n\n    Returns:\n        pd.DataFrame: DataFrame with the inference results\n\n    Note:\n        If capture=True inference/performance metrics are written to S3 Endpoint Inference Folder\n    \"\"\"\n\n    # Run predictions on the evaluation data\n    prediction_df = self._predict(eval_df)\n    if prediction_df.empty:\n        self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n        return prediction_df\n\n    # Get the target column\n    model = ModelCore(self.model_name)\n    target_column = model.target()\n\n    # Sanity Check that the target column is present\n    if target_column and (target_column not in prediction_df.columns):\n        self.log.important(f\"Target Column {target_column} not found in prediction_df!\")\n        self.log.important(\"In order to compute metrics, the target column must be present!\")\n        return prediction_df\n\n    # Compute the standard performance metrics for this model\n    model_type = model.model_type\n    if model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n        prediction_df = self.residuals(target_column, prediction_df)\n        metrics = self.regression_metrics(target_column, prediction_df)\n    elif model_type == ModelType.CLASSIFIER:\n        metrics = self.classification_metrics(target_column, prediction_df)\n    else:\n        # For other model types, we don't compute metrics\n        self.log.important(f\"Model Type: {model_type} doesn't have metrics...\")\n        metrics = pd.DataFrame()\n\n    # Print out the metrics\n    if not metrics.empty:\n        print(f\"Performance Metrics for {self.model_name} on {self.uuid}\")\n        print(metrics.head())\n\n        # Capture the inference results and metrics\n        if capture_uuid is not None:\n            description = capture_uuid.replace(\"_\", \" \").title()\n            self._capture_inference_results(\n                capture_uuid, prediction_df, target_column, model_type, metrics, description, id_column\n            )\n\n    # Return the prediction DataFrame\n    return prediction_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.is_serverless","title":"<code>is_serverless()</code>","text":"<p>Check if the current endpoint is serverless.</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the endpoint is serverless, False otherwise.</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def is_serverless(self) -&gt; bool:\n    \"\"\"Check if the current endpoint is serverless.\n\n    Returns:\n        bool: True if the endpoint is serverless, False otherwise.\n    \"\"\"\n    return \"Serverless\" in self.endpoint_meta[\"InstanceType\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.managed_delete","title":"<code>managed_delete(endpoint_name)</code>  <code>classmethod</code>","text":"<p>Delete the Endpoint and associated resources if it exists</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>@classmethod\ndef managed_delete(cls, endpoint_name: str):\n    \"\"\"Delete the Endpoint and associated resources if it exists\"\"\"\n\n    # Check if the endpoint exists\n    try:\n        endpoint_info = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n            cls.log.info(f\"Endpoint {endpoint_name} not found!\")\n            return\n        raise  # Re-raise unexpected errors\n\n    # Delete underlying models (Endpoints store/use models internally)\n    cls.delete_endpoint_models(endpoint_name)\n\n    # Get Endpoint Config Name and delete if exists\n    endpoint_config_name = endpoint_info[\"EndpointConfigName\"]\n    try:\n        cls.log.info(f\"Deleting Endpoint Config {endpoint_config_name}...\")\n        cls.sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n    except ClientError:\n        cls.log.info(f\"Endpoint Config {endpoint_config_name} not found...\")\n\n    # Delete any monitoring schedules associated with the endpoint\n    monitoring_schedules = cls.sm_client.list_monitoring_schedules(EndpointName=endpoint_name)[\n        \"MonitoringScheduleSummaries\"\n    ]\n    for schedule in monitoring_schedules:\n        cls.log.info(f\"Deleting Monitoring Schedule {schedule['MonitoringScheduleName']}...\")\n        cls.sm_client.delete_monitoring_schedule(MonitoringScheduleName=schedule[\"MonitoringScheduleName\"])\n\n    # Delete related S3 artifacts (inference, data capture, monitoring)\n    endpoint_inference_path = cls.endpoints_s3_path + \"/inference/\" + endpoint_name\n    endpoint_data_capture_path = cls.endpoints_s3_path + \"/data_capture/\" + endpoint_name\n    endpoint_monitoring_path = cls.endpoints_s3_path + \"/monitoring/\" + endpoint_name\n    for s3_path in [endpoint_inference_path, endpoint_data_capture_path, endpoint_monitoring_path]:\n        s3_path = f\"{s3_path.rstrip('/')}/\"\n        objects = wr.s3.list_objects(s3_path, boto3_session=cls.boto3_session)\n        if objects:\n            cls.log.info(f\"Deleting S3 Objects at {s3_path}...\")\n            wr.s3.delete_objects(objects, boto3_session=cls.boto3_session)\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(endpoint_name)\n\n    # Delete the endpoint\n    time.sleep(2)  # Allow AWS to catch up\n    try:\n        cls.log.info(f\"Deleting Endpoint {endpoint_name}...\")\n        cls.sm_client.delete_endpoint(EndpointName=endpoint_name)\n    except ClientError as e:\n        cls.log.error(\"Error deleting endpoint.\")\n        raise e\n\n    time.sleep(5)  # Final sleep for AWS to fully register deletions\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    return self.endpoint_meta[\"LastModifiedTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.onboard","title":"<code>onboard(interactive=False)</code>","text":"<p>This is a BLOCKING method that will onboard the Endpoint (make it ready) Args:     interactive (bool, optional): If True, will prompt the user for information. (default: False) Returns:     bool: True if the Endpoint is successfully onboarded, False otherwise</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def onboard(self, interactive: bool = False) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will onboard the Endpoint (make it ready)\n    Args:\n        interactive (bool, optional): If True, will prompt the user for information. (default: False)\n    Returns:\n        bool: True if the Endpoint is successfully onboarded, False otherwise\n    \"\"\"\n\n    # Make sure our input is defined\n    if self.get_input() == \"unknown\":\n        if interactive:\n            input_model = input(\"Input Model?: \")\n        else:\n            self.log.critical(\"Input Model is not defined!\")\n            return False\n    else:\n        input_model = self.get_input()\n\n    # Now that we have the details, let's onboard the Endpoint with args\n    return self.onboard_with_args(input_model)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.onboard_with_args","title":"<code>onboard_with_args(input_model)</code>","text":"<p>Onboard the Endpoint with the given arguments</p> <p>Parameters:</p> Name Type Description Default <code>input_model</code> <code>str</code> <p>The input model for this endpoint</p> required <p>Returns:     bool: True if the Endpoint is successfully onboarded, False otherwise</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def onboard_with_args(self, input_model: str) -&gt; bool:\n    \"\"\"Onboard the Endpoint with the given arguments\n\n    Args:\n        input_model (str): The input model for this endpoint\n    Returns:\n        bool: True if the Endpoint is successfully onboarded, False otherwise\n    \"\"\"\n    # Set the status to onboarding\n    self.set_status(\"onboarding\")\n\n    self.upsert_sageworks_meta({\"sageworks_input\": input_model})\n    self.model_name = input_model\n\n    # Remove the needs_onboard tag\n    self.remove_health_tag(\"needs_onboard\")\n    self.set_status(\"ready\")\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Refresh the Artifact's metadata</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Refresh the Artifact's metadata\"\"\"\n    self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.regression_metrics","title":"<code>regression_metrics(target_column, prediction_df)</code>","text":"<p>Compute the performance metrics for this Endpoint Args:     target_column (str): Name of the target column     prediction_df (pd.DataFrame): DataFrame with the prediction results Returns:     pd.DataFrame: DataFrame with the performance metrics</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def regression_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Compute the performance metrics for this Endpoint\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n    Returns:\n        pd.DataFrame: DataFrame with the performance metrics\n    \"\"\"\n\n    # Sanity Check the prediction DataFrame\n    if prediction_df.empty:\n        self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Compute the metrics\n    y_true = prediction_df[target_column]\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    y_pred = prediction_df[prediction_col]\n\n    mae = mean_absolute_error(y_true, y_pred)\n    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n    r2 = r2_score(y_true, y_pred)\n    # Mean Absolute Percentage Error\n    mape = np.mean(np.where(y_true != 0, np.abs((y_true - y_pred) / y_true), np.abs(y_true - y_pred))) * 100\n    # Median Absolute Error\n    medae = median_absolute_error(y_true, y_pred)\n\n    # Organize and return the metrics\n    metrics = {\n        \"MAE\": round(mae, 3),\n        \"RMSE\": round(rmse, 3),\n        \"R2\": round(r2, 3),\n        \"MAPE\": round(mape, 3),\n        \"MedAE\": round(medae, 3),\n        \"NumRows\": len(prediction_df),\n    }\n    return pd.DataFrame.from_records([metrics])\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.residuals","title":"<code>residuals(target_column, prediction_df)</code>","text":"<p>Add the residuals to the prediction DataFrame Args:     target_column (str): Name of the target column     prediction_df (pd.DataFrame): DataFrame with the prediction results Returns:     pd.DataFrame: DataFrame with two new columns called 'residuals' and 'residuals_abs'</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def residuals(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Add the residuals to the prediction DataFrame\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n    Returns:\n        pd.DataFrame: DataFrame with two new columns called 'residuals' and 'residuals_abs'\n    \"\"\"\n\n    # Compute the residuals\n    y_true = prediction_df[target_column]\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    y_pred = prediction_df[prediction_col]\n\n    # Check for classification scenario\n    if not pd.api.types.is_numeric_dtype(y_true) or not pd.api.types.is_numeric_dtype(y_pred):\n        self.log.warning(\"Target and Prediction columns are not numeric. Computing 'diffs'...\")\n        prediction_df[\"residuals\"] = (y_true != y_pred).astype(int)\n        prediction_df[\"residuals_abs\"] = prediction_df[\"residuals\"]\n    else:\n        # Compute numeric residuals for regression\n        prediction_df[\"residuals\"] = y_true - y_pred\n        prediction_df[\"residuals_abs\"] = np.abs(prediction_df[\"residuals\"])\n\n    return prediction_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.set_input","title":"<code>set_input(input, force=False)</code>","text":"<p>Override: Set the input data for this artifact</p> <p>Parameters:</p> Name Type Description Default <code>input</code> <code>str</code> <p>Name of input for this artifact</p> required <code>force</code> <code>bool</code> <p>Force the input to be set. Defaults to False.</p> <code>False</code> <p>Note:     We're going to not allow this to be used for Models</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def set_input(self, input: str, force=False):\n    \"\"\"Override: Set the input data for this artifact\n\n    Args:\n        input (str): Name of input for this artifact\n        force (bool, optional): Force the input to be set. Defaults to False.\n    Note:\n        We're going to not allow this to be used for Models\n    \"\"\"\n    if not force:\n        self.log.warning(f\"Endpoint {self.uuid}: Does not allow manual override of the input!\")\n        return\n\n    # Okay we're going to allow this to be set\n    self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n    self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n    self.upsert_sageworks_meta({\"sageworks_input\": input})\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.size","title":"<code>size()</code>","text":"<p>Return the size of this data in MegaBytes</p> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of this data in MegaBytes\"\"\"\n    return 0.0\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#sageworks.core.artifacts.endpoint_core.EndpointCore.validate_proba_columns","title":"<code>validate_proba_columns(prediction_df, class_labels, guessing=False)</code>  <code>staticmethod</code>","text":"<p>Ensure probability columns are correctly aligned with class labels</p> <p>Parameters:</p> Name Type Description Default <code>prediction_df</code> <code>DataFrame</code> <p>DataFrame with the prediction results</p> required <code>class_labels</code> <code>list</code> <p>List of class labels</p> required <code>guessing</code> <code>bool</code> <p>Whether we're guessing the class labels. Defaults to False.</p> <code>False</code> Source code in <code>src/sageworks/core/artifacts/endpoint_core.py</code> <pre><code>@staticmethod\ndef validate_proba_columns(prediction_df: pd.DataFrame, class_labels: list, guessing: bool = False):\n    \"\"\"Ensure probability columns are correctly aligned with class labels\n\n    Args:\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n        class_labels (list): List of class labels\n        guessing (bool, optional): Whether we're guessing the class labels. Defaults to False.\n    \"\"\"\n    proba_columns = [col.replace(\"_proba\", \"\") for col in prediction_df.columns if col.endswith(\"_proba\")]\n\n    if sorted(class_labels) != sorted(proba_columns):\n        if guessing:\n            raise ValueError(f\"_proba columns {proba_columns} != GUESSED class_labels {class_labels}!\")\n        else:\n            raise ValueError(f\"_proba columns {proba_columns} != class_labels {class_labels}!\")\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/","title":"FeatureSetCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the FeatureSet API Class and voil\u00e0 it works the same.</p> <p>FeatureSet: SageWorks Feature Set accessible through Athena</p>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore","title":"<code>FeatureSetCore</code>","text":"<p>               Bases: <code>Artifact</code></p> <p>FeatureSetCore: SageWorks FeatureSetCore Class</p> Common Usage <pre><code>my_features = FeatureSetCore(feature_uuid)\nmy_features.summary()\nmy_features.details()\n</code></pre> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>class FeatureSetCore(Artifact):\n    \"\"\"FeatureSetCore: SageWorks FeatureSetCore Class\n\n    Common Usage:\n        ```python\n        my_features = FeatureSetCore(feature_uuid)\n        my_features.summary()\n        my_features.details()\n        ```\n    \"\"\"\n\n    def __init__(self, feature_set_uuid: str, **kwargs):\n        \"\"\"FeatureSetCore Initialization\n\n        Args:\n            feature_set_uuid (str): Name of Feature Set\n        \"\"\"\n\n        # Make sure the feature_set name is valid\n        self.is_name_valid(feature_set_uuid)\n\n        # Call superclass init\n        super().__init__(feature_set_uuid, **kwargs)\n\n        # Get our FeatureSet metadata\n        self.feature_meta = self.meta.feature_set(self.uuid)\n\n        # Sanity check and then set up our FeatureSet attributes\n        if self.feature_meta is None:\n            self.log.warning(f\"Could not find feature set {self.uuid} within current visibility scope\")\n            self.data_source = None\n            return\n        else:\n            self.id_column = self.feature_meta[\"RecordIdentifierFeatureName\"]\n            self.event_time = self.feature_meta[\"EventTimeFeatureName\"]\n\n            # Pull Athena and S3 Storage information from metadata\n            self.athena_table = self.feature_meta[\"sageworks_meta\"][\"athena_table\"]\n            self.athena_database = self.feature_meta[\"sageworks_meta\"][\"athena_database\"]\n            self.s3_storage = self.feature_meta[\"sageworks_meta\"].get(\"s3_storage\")\n\n            # Create our internal DataSource (hardcoded to Athena for now)\n            self.data_source = AthenaSource(self.athena_table, self.athena_database)\n\n        # Spin up our Feature Store\n        self.feature_store = FeatureStore(self.sm_session)\n\n        # Call superclass post_init\n        super().__post_init__()\n\n        # All done\n        self.log.info(f\"FeatureSet Initialized: {self.uuid}...\")\n\n    @property\n    def table(self) -&gt; str:\n        \"\"\"Get the base table name for this FeatureSet\"\"\"\n        return self.data_source.table\n\n    def refresh_meta(self):\n        \"\"\"Internal: Refresh our internal AWS Feature Store metadata\"\"\"\n        self.log.info(\"Calling refresh_meta() on the underlying DataSource\")\n        self.data_source.refresh_meta()\n\n    def exists(self) -&gt; bool:\n        \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n        if self.feature_meta is None:\n            self.log.debug(f\"FeatureSet {self.uuid} not found in AWS Metadata!\")\n            return False\n        return True\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this model\n\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        # Call the base class health check\n        health_issues = super().health_check()\n\n        # If we have a 'needs_onboard' in the health check then just return\n        if \"needs_onboard\" in health_issues:\n            return health_issues\n\n        # Check our DataSource\n        if not self.data_source.exists():\n            self.log.critical(f\"Data Source check failed for {self.uuid}\")\n            self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n            health_issues.append(\"data_source_missing\")\n        return health_issues\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n        return self.feature_meta\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        return self.feature_meta[\"FeatureGroupArn\"]\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of the internal DataSource in MegaBytes\"\"\"\n        return self.data_source.size()\n\n    @property\n    def columns(self) -&gt; list[str]:\n        \"\"\"Return the column names of the Feature Set\"\"\"\n        return list(self.column_details().keys())\n\n    @property\n    def column_types(self) -&gt; list[str]:\n        \"\"\"Return the column types of the Feature Set\"\"\"\n        return list(self.column_details().values())\n\n    def column_details(self) -&gt; dict:\n        \"\"\"Return the column details of the Feature Set\n\n        Returns:\n            dict: The column details of the Feature Set\n\n        Notes:\n            We can't call just call self.data_source.column_details() because FeatureSets have different\n            types, so we need to overlay that type information on top of the DataSource type information\n        \"\"\"\n        fs_details = {item[\"FeatureName\"]: item[\"FeatureType\"] for item in self.feature_meta[\"FeatureDefinitions\"]}\n        ds_details = self.data_source.column_details()\n\n        # Overlay the FeatureSet type information on top of the DataSource type information\n        for col, dtype in ds_details.items():\n            ds_details[col] = fs_details.get(col, dtype)\n        return ds_details\n\n    def views(self) -&gt; list[str]:\n        \"\"\"Return the views for this Data Source\"\"\"\n        from sageworks.core.views.view_utils import list_views\n\n        return list_views(self.data_source)\n\n    def view(self, view_name: str) -&gt; \"View\":\n        \"\"\"Return a DataFrame for a specific view\n        Args:\n            view_name (str): The name of the view to return\n        Returns:\n            pd.DataFrame: A DataFrame for the specified view\n        \"\"\"\n        from sageworks.core.views import View\n\n        return View(self, view_name)\n\n    def set_display_columns(self, diplay_columns: list[str]):\n        \"\"\"Set the display columns for this Data Source\n\n        Args:\n            diplay_columns (list[str]): The display columns for this Data Source\n        \"\"\"\n        # Check mismatch of display columns to computation columns\n        c_view = self.view(\"computation\")\n        computation_columns = c_view.columns\n        mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n        if mismatch_columns:\n            self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n        self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n        from sageworks.core.views import DisplayView\n\n        # Create a NEW display view\n        DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n\n    def set_computation_columns(self, computation_columns: list[str], reset_display: bool = True):\n        \"\"\"Set the computation columns for this FeatureSet\n\n        Args:\n            computation_columns (list[str]): The computation columns for this FeatureSet\n            reset_display (bool): Also reset the display columns to match (default: True)\n        \"\"\"\n        self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n        from sageworks.core.views import ComputationView\n\n        # Create a NEW computation view\n        ComputationView.create(self, column_list=computation_columns)\n        self.recompute_stats()\n\n        # Reset the display columns to match the computation columns\n        if reset_display:\n            self.set_display_columns(computation_columns)\n\n    def num_columns(self) -&gt; int:\n        \"\"\"Return the number of columns of the Feature Set\"\"\"\n        return len(self.columns)\n\n    def num_rows(self) -&gt; int:\n        \"\"\"Return the number of rows of the internal DataSource\"\"\"\n        return self.data_source.num_rows()\n\n    def query(self, query: str, overwrite: bool = True) -&gt; pd.DataFrame:\n        \"\"\"Query the internal DataSource\n\n        Args:\n            query (str): The query to run against the DataSource\n            overwrite (bool): Overwrite the table name in the query (default: True)\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        if overwrite:\n            query = query.replace(\" \" + self.uuid + \" \", \" \" + self.athena_table + \" \")\n        return self.data_source.query(query)\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying the underlying data source\"\"\"\n        sageworks_details = self.data_source.sageworks_meta().get(\"sageworks_details\", {})\n        return sageworks_details.get(\"aws_url\", \"unknown\")\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        return self.feature_meta[\"CreationTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        # Note: We can't currently figure out how to this from AWS Metadata\n        return self.feature_meta[\"CreationTime\"]\n\n    def hash(self) -&gt; str:\n        \"\"\"Return the hash for the set of Parquet files for this artifact\"\"\"\n        return self.data_source.hash()\n\n    def table_hash(self) -&gt; str:\n        \"\"\"Return the hash for the Athena table\"\"\"\n        return self.data_source.table_hash()\n\n    def get_data_source(self) -&gt; DataSourceFactory:\n        \"\"\"Return the underlying DataSource object\"\"\"\n        return self.data_source\n\n    def get_feature_store(self) -&gt; FeatureStore:\n        \"\"\"Return the underlying AWS FeatureStore object. This can be useful for more advanced usage\n        with create_dataset() such as Joins and time ranges and a host of other options\n        See: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n        \"\"\"\n        return self.feature_store\n\n    def create_s3_training_data(self) -&gt; str:\n        \"\"\"Create some Training Data (S3 CSV) from a Feature Set using standard options. If you want\n        additional options/features use the get_feature_store() method and see AWS docs for all\n        the details: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n        Returns:\n            str: The full path/file for the CSV file created by Feature Store create_dataset()\n        \"\"\"\n\n        # Set up the S3 Query results path\n        date_time = datetime.now(timezone.utc).strftime(\"%Y-%m-%d_%H:%M:%S\")\n        s3_output_path = self.feature_sets_s3_path + f\"/{self.uuid}/datasets/all_{date_time}\"\n\n        # Make the query\n        table = self.view(\"training\").table\n        query = f'SELECT * FROM \"{table}\"'\n        athena_query = FeatureGroup(name=self.uuid, sagemaker_session=self.sm_session).athena_query()\n        athena_query.run(query, output_location=s3_output_path)\n        athena_query.wait()\n        query_execution = athena_query.get_query_execution()\n\n        # Get the full path to the S3 files with the results\n        full_s3_path = s3_output_path + f\"/{query_execution['QueryExecution']['QueryExecutionId']}.csv\"\n        return full_s3_path\n\n    def get_training_data(self) -&gt; pd.DataFrame:\n        \"\"\"Get the training data for this FeatureSet\n\n        Returns:\n            pd.DataFrame: The training data for this FeatureSet\n        \"\"\"\n        from sageworks.core.views.view import View\n\n        return View(self, \"training\").pull_dataframe()\n\n    def snapshot_query(self, table_name: str = None) -&gt; str:\n        \"\"\"An Athena query to get the latest snapshot of features\n\n        Args:\n            table_name (str): The name of the table to query (default: None)\n\n        Returns:\n            str: The Athena query to get the latest snapshot of features\n        \"\"\"\n        # Remove FeatureGroup metadata columns that might have gotten added\n        columns = self.columns\n        filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n        columns = \", \".join(['\"' + x + '\"' for x in columns if x not in filter_columns])\n\n        query = (\n            f\"SELECT {columns} \"\n            f\"    FROM (SELECT *, row_number() OVER (PARTITION BY {self.id_column} \"\n            f\"        ORDER BY {self.event_time} desc, api_invocation_time DESC, write_time DESC) AS row_num \"\n            f'        FROM \"{table_name}\") '\n            \"    WHERE row_num = 1 and NOT is_deleted;\"\n        )\n        return query\n\n    def details(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Additional Details about this FeatureSet Artifact\n\n        Args:\n            recompute (bool): Recompute the details (default: False)\n\n        Returns:\n            dict(dict): A dictionary of details about this FeatureSet\n        \"\"\"\n\n        self.log.info(f\"Computing FeatureSet Details ({self.uuid})...\")\n        details = self.summary()\n        details[\"aws_url\"] = self.aws_url()\n\n        # Store the AWS URL in the SageWorks Metadata\n        # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n        # self.upsert_sageworks_meta({\"aws_url\": details[\"aws_url\"]})\n\n        # Now get a summary of the underlying DataSource\n        details[\"storage_summary\"] = self.data_source.summary()\n\n        # Number of Columns\n        details[\"num_columns\"] = self.num_columns()\n\n        # Number of Rows\n        details[\"num_rows\"] = self.num_rows()\n\n        # Additional Details\n        details[\"sageworks_status\"] = self.get_status()\n        details[\"sageworks_input\"] = self.get_input()\n        details[\"sageworks_tags\"] = self.tag_delimiter.join(self.get_tags())\n\n        # Underlying Storage Details\n        details[\"storage_type\"] = \"athena\"  # TODO: Add RDS support\n        details[\"storage_uuid\"] = self.data_source.uuid\n\n        # Add the column details and column stats\n        details[\"column_details\"] = self.column_details()\n        details[\"column_stats\"] = self.column_stats()\n\n        # Return the details data\n        return details\n\n    def delete(self):\n        \"\"\"Instance Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\"\"\"\n        # Make sure the AthenaSource exists\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an FeatureSet that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the FeatureSet\n        FeatureSetCore.managed_delete(self.uuid)\n\n    @classmethod\n    def managed_delete(cls, feature_set_name: str):\n        \"\"\"Class Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\n\n        Args:\n            feature_set_name (str): The Name of the FeatureSet to delete\n        \"\"\"\n\n        # See if the FeatureSet exists\n        try:\n            response = cls.sm_client.describe_feature_group(FeatureGroupName=feature_set_name)\n        except cls.sm_client.exceptions.ResourceNotFound:\n            cls.log.info(f\"FeatureSet {feature_set_name} not found!\")\n            return\n\n        # Extract database and table information from the response\n        offline_config = response.get(\"OfflineStoreConfig\", {})\n        database = offline_config.get(\"DataCatalogConfig\", {}).get(\"Database\")\n        offline_table = offline_config.get(\"DataCatalogConfig\", {}).get(\"TableName\")\n        data_source_uuid = offline_table  # Our offline storage IS a DataSource\n\n        # Delete the Feature Group and ensure that it gets deleted\n        cls.log.important(f\"Deleting FeatureSet {feature_set_name}...\")\n        remove_fg = cls.aws_feature_group_delete(feature_set_name)\n        cls.ensure_feature_group_deleted(remove_fg)\n\n        # Delete our underlying DataSource (Data Catalog Table and S3 Storage Objects)\n        AthenaSource.managed_delete(data_source_uuid, database=database)\n\n        # Delete any views associated with this FeatureSet\n        cls.delete_views(offline_table, database)\n\n        # Feature Sets can often have a lot of cruft so delete the entire bucket/prefix\n        s3_delete_path = cls.feature_sets_s3_path + f\"/{feature_set_name}/\"\n        cls.log.info(f\"Deleting All FeatureSet S3 Storage Objects {s3_delete_path}\")\n        wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(feature_set_name)\n\n    @classmethod\n    @aws_throttle\n    def aws_feature_group_delete(cls, feature_set_name):\n        remove_fg = FeatureGroup(name=feature_set_name, sagemaker_session=cls.sm_session)\n        remove_fg.delete()\n        return remove_fg\n\n    @classmethod\n    def ensure_feature_group_deleted(cls, feature_group):\n        status = \"Deleting\"\n        while status == \"Deleting\":\n            cls.log.debug(\"FeatureSet being Deleted...\")\n            try:\n                status = feature_group.describe().get(\"FeatureGroupStatus\")\n            except botocore.exceptions.ClientError as error:\n                # For ResourceNotFound/ValidationException, this is fine, otherwise raise all other exceptions\n                if error.response[\"Error\"][\"Code\"] in [\"ResourceNotFound\", \"ValidationException\"]:\n                    break\n                else:\n                    raise error\n            time.sleep(1)\n        cls.log.info(f\"FeatureSet {feature_group.name} successfully deleted\")\n\n    def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n        \"\"\"Set the hold out ids for the training view for this FeatureSet\n\n        Args:\n            id_column (str): The name of the id column.\n            holdout_ids (list[str]): The list of holdout ids.\n        \"\"\"\n        from sageworks.core.views import TrainingView\n\n        # Create a NEW training view\n        self.log.important(f\"Setting Training Holdouts: {len(holdout_ids)} ids...\")\n        TrainingView.create(self, id_column=id_column, holdout_ids=holdout_ids)\n\n    @classmethod\n    def delete_views(cls, table: str, database: str):\n        \"\"\"Delete any views associated with this FeatureSet\n\n        Args:\n            table (str): Name of Athena Table\n            database (str): Athena Database Name\n        \"\"\"\n        from sageworks.core.views.view_utils import delete_views_and_supplemental_data\n\n        delete_views_and_supplemental_data(table, database, cls.boto3_session)\n\n    def descriptive_stats(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Get the descriptive stats for the numeric columns of the underlying DataSource\n        Args:\n            recompute (bool): Recompute the descriptive stats (default=False)\n        Returns:\n            dict: A dictionary of descriptive stats for the numeric columns\n        \"\"\"\n        return self.data_source.descriptive_stats(recompute)\n\n    def sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a sample of the data from the underlying DataSource\n        Args:\n            recompute (bool): Recompute the sample (default=False)\n        Returns:\n            pd.DataFrame: A sample of the data from the underlying DataSource\n        \"\"\"\n        return self.data_source.sample(recompute)\n\n    def outliers(self, scale: float = 1.5, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Compute outliers for all the numeric columns in a DataSource\n        Args:\n            scale (float): The scale to use for the IQR (default: 1.5)\n            recompute (bool): Recompute the outliers (default: False)\n        Returns:\n            pd.DataFrame: A DataFrame of outliers from this DataSource\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n        return self.data_source.outliers(scale=scale, recompute=recompute)\n\n    def smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a SMART sample dataframe from this FeatureSet\n\n        Args:\n            recompute (bool): Recompute the smart sample (default=False)\n\n        Returns:\n            pd.DataFrame: A combined DataFrame of sample data + outliers\n        \"\"\"\n        return self.data_source.smart_sample(recompute=recompute)\n\n    def anomalies(self) -&gt; pd.DataFrame:\n        \"\"\"Get a set of anomalous data from the underlying DataSource\n        Returns:\n            pd.DataFrame: A dataframe of anomalies from the underlying DataSource\n        \"\"\"\n\n        # FIXME: Mock this for now\n        anom_df = self.sample().copy()\n        anom_df[\"anomaly_score\"] = np.random.rand(anom_df.shape[0])\n        anom_df[\"cluster\"] = np.random.randint(0, 10, anom_df.shape[0])\n        anom_df[\"x\"] = np.random.rand(anom_df.shape[0])\n        anom_df[\"y\"] = np.random.rand(anom_df.shape[0])\n        return anom_df\n\n    def value_counts(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Get the value counts for the string columns of the underlying DataSource\n        Args:\n            recompute (bool): Recompute the value counts (default=False)\n        Returns:\n            dict: A dictionary of value counts for the string columns\n        \"\"\"\n        return self.data_source.value_counts(recompute)\n\n    def correlations(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Get the correlations for the numeric columns of the underlying DataSource\n        Args:\n            recompute (bool): Recompute the value counts (default=False)\n        Returns:\n            dict: A dictionary of correlations for the numeric columns\n        \"\"\"\n        return self.data_source.correlations(recompute)\n\n    def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Column Stats for all the columns in the FeatureSets underlying DataSource\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n        Returns:\n            dict(dict): A dictionary of stats for each column this format\n            NB: String columns will NOT have num_zeros and descriptive_stats\n             {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n              'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n              ...}\n        \"\"\"\n\n        # Grab the column stats from our DataSource\n        ds_column_stats = self.data_source.column_stats(recompute)\n\n        # Map the types from our DataSource to the FeatureSet types\n        fs_type_mapper = self.column_details()\n        for col, details in ds_column_stats.items():\n            details[\"fs_dtype\"] = fs_type_mapper.get(col, \"unknown\")\n\n        return ds_column_stats\n\n    def ready(self) -&gt; bool:\n        \"\"\"Is the FeatureSet ready? Is initial setup complete and expected metadata populated?\n        Note: Since FeatureSet is a composite of DataSource and FeatureGroup, we need to\n           check both to see if the FeatureSet is ready.\"\"\"\n\n        # Check the expected metadata for the FeatureSet\n        expected_meta = self.expected_meta()\n        existing_meta = self.sageworks_meta()\n        feature_set_ready = set(existing_meta.keys()).issuperset(expected_meta)\n        if not feature_set_ready:\n            self.log.info(f\"FeatureSet {self.uuid} is not ready!\")\n            return False\n\n        # Okay now call/return the DataSource ready() method\n        return self.data_source.ready()\n\n    def onboard(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will onboard the FeatureSet (make it ready)\"\"\"\n\n        # Set our status to onboarding\n        self.log.important(f\"Onboarding {self.uuid}...\")\n        self.set_status(\"onboarding\")\n        self.remove_health_tag(\"needs_onboard\")\n\n        # Call our underlying DataSource onboard method\n        self.data_source.refresh_meta()\n        if not self.data_source.exists():\n            self.log.critical(f\"Data Source check failed for {self.uuid}\")\n            self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n            return False\n        if not self.data_source.ready():\n            self.data_source.onboard()\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        self.set_status(\"ready\")\n        return True\n\n    def recompute_stats(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will recompute the stats for the FeatureSet\"\"\"\n\n        # Call our underlying DataSource recompute stats method\n        self.log.important(f\"Recomputing Stats {self.uuid}...\")\n        self.data_source.recompute_stats()\n        self.details(recompute=True)\n        return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_types","title":"<code>column_types: list[str]</code>  <code>property</code>","text":"<p>Return the column types of the Feature Set</p>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.columns","title":"<code>columns: list[str]</code>  <code>property</code>","text":"<p>Return the column names of the Feature Set</p>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.table","title":"<code>table: str</code>  <code>property</code>","text":"<p>Get the base table name for this FeatureSet</p>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.__init__","title":"<code>__init__(feature_set_uuid, **kwargs)</code>","text":"<p>FeatureSetCore Initialization</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_uuid</code> <code>str</code> <p>Name of Feature Set</p> required Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def __init__(self, feature_set_uuid: str, **kwargs):\n    \"\"\"FeatureSetCore Initialization\n\n    Args:\n        feature_set_uuid (str): Name of Feature Set\n    \"\"\"\n\n    # Make sure the feature_set name is valid\n    self.is_name_valid(feature_set_uuid)\n\n    # Call superclass init\n    super().__init__(feature_set_uuid, **kwargs)\n\n    # Get our FeatureSet metadata\n    self.feature_meta = self.meta.feature_set(self.uuid)\n\n    # Sanity check and then set up our FeatureSet attributes\n    if self.feature_meta is None:\n        self.log.warning(f\"Could not find feature set {self.uuid} within current visibility scope\")\n        self.data_source = None\n        return\n    else:\n        self.id_column = self.feature_meta[\"RecordIdentifierFeatureName\"]\n        self.event_time = self.feature_meta[\"EventTimeFeatureName\"]\n\n        # Pull Athena and S3 Storage information from metadata\n        self.athena_table = self.feature_meta[\"sageworks_meta\"][\"athena_table\"]\n        self.athena_database = self.feature_meta[\"sageworks_meta\"][\"athena_database\"]\n        self.s3_storage = self.feature_meta[\"sageworks_meta\"].get(\"s3_storage\")\n\n        # Create our internal DataSource (hardcoded to Athena for now)\n        self.data_source = AthenaSource(self.athena_table, self.athena_database)\n\n    # Spin up our Feature Store\n    self.feature_store = FeatureStore(self.sm_session)\n\n    # Call superclass post_init\n    super().__post_init__()\n\n    # All done\n    self.log.info(f\"FeatureSet Initialized: {self.uuid}...\")\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.anomalies","title":"<code>anomalies()</code>","text":"<p>Get a set of anomalous data from the underlying DataSource Returns:     pd.DataFrame: A dataframe of anomalies from the underlying DataSource</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def anomalies(self) -&gt; pd.DataFrame:\n    \"\"\"Get a set of anomalous data from the underlying DataSource\n    Returns:\n        pd.DataFrame: A dataframe of anomalies from the underlying DataSource\n    \"\"\"\n\n    # FIXME: Mock this for now\n    anom_df = self.sample().copy()\n    anom_df[\"anomaly_score\"] = np.random.rand(anom_df.shape[0])\n    anom_df[\"cluster\"] = np.random.randint(0, 10, anom_df.shape[0])\n    anom_df[\"x\"] = np.random.rand(anom_df.shape[0])\n    anom_df[\"y\"] = np.random.rand(anom_df.shape[0])\n    return anom_df\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    return self.feature_meta[\"FeatureGroupArn\"]\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get ALL the AWS metadata for this artifact</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n    return self.feature_meta\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying the underlying data source</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying the underlying data source\"\"\"\n    sageworks_details = self.data_source.sageworks_meta().get(\"sageworks_details\", {})\n    return sageworks_details.get(\"aws_url\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_details","title":"<code>column_details()</code>","text":"<p>Return the column details of the Feature Set</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The column details of the Feature Set</p> Notes <p>We can't call just call self.data_source.column_details() because FeatureSets have different types, so we need to overlay that type information on top of the DataSource type information</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def column_details(self) -&gt; dict:\n    \"\"\"Return the column details of the Feature Set\n\n    Returns:\n        dict: The column details of the Feature Set\n\n    Notes:\n        We can't call just call self.data_source.column_details() because FeatureSets have different\n        types, so we need to overlay that type information on top of the DataSource type information\n    \"\"\"\n    fs_details = {item[\"FeatureName\"]: item[\"FeatureType\"] for item in self.feature_meta[\"FeatureDefinitions\"]}\n    ds_details = self.data_source.column_details()\n\n    # Overlay the FeatureSet type information on top of the DataSource type information\n    for col, dtype in ds_details.items():\n        ds_details[col] = fs_details.get(col, dtype)\n    return ds_details\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.column_stats","title":"<code>column_stats(recompute=False)</code>","text":"<p>Compute Column Stats for all the columns in the FeatureSets underlying DataSource Args:     recompute (bool): Recompute the column stats (default: False) Returns:     dict(dict): A dictionary of stats for each column this format     NB: String columns will NOT have num_zeros and descriptive_stats      {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},       'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},       ...}</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Column Stats for all the columns in the FeatureSets underlying DataSource\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n    Returns:\n        dict(dict): A dictionary of stats for each column this format\n        NB: String columns will NOT have num_zeros and descriptive_stats\n         {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n          'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n          ...}\n    \"\"\"\n\n    # Grab the column stats from our DataSource\n    ds_column_stats = self.data_source.column_stats(recompute)\n\n    # Map the types from our DataSource to the FeatureSet types\n    fs_type_mapper = self.column_details()\n    for col, details in ds_column_stats.items():\n        details[\"fs_dtype\"] = fs_type_mapper.get(col, \"unknown\")\n\n    return ds_column_stats\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.correlations","title":"<code>correlations(recompute=False)</code>","text":"<p>Get the correlations for the numeric columns of the underlying DataSource Args:     recompute (bool): Recompute the value counts (default=False) Returns:     dict: A dictionary of correlations for the numeric columns</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def correlations(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Get the correlations for the numeric columns of the underlying DataSource\n    Args:\n        recompute (bool): Recompute the value counts (default=False)\n    Returns:\n        dict: A dictionary of correlations for the numeric columns\n    \"\"\"\n    return self.data_source.correlations(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.create_s3_training_data","title":"<code>create_s3_training_data()</code>","text":"<p>Create some Training Data (S3 CSV) from a Feature Set using standard options. If you want additional options/features use the get_feature_store() method and see AWS docs for all the details: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html Returns:     str: The full path/file for the CSV file created by Feature Store create_dataset()</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def create_s3_training_data(self) -&gt; str:\n    \"\"\"Create some Training Data (S3 CSV) from a Feature Set using standard options. If you want\n    additional options/features use the get_feature_store() method and see AWS docs for all\n    the details: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n    Returns:\n        str: The full path/file for the CSV file created by Feature Store create_dataset()\n    \"\"\"\n\n    # Set up the S3 Query results path\n    date_time = datetime.now(timezone.utc).strftime(\"%Y-%m-%d_%H:%M:%S\")\n    s3_output_path = self.feature_sets_s3_path + f\"/{self.uuid}/datasets/all_{date_time}\"\n\n    # Make the query\n    table = self.view(\"training\").table\n    query = f'SELECT * FROM \"{table}\"'\n    athena_query = FeatureGroup(name=self.uuid, sagemaker_session=self.sm_session).athena_query()\n    athena_query.run(query, output_location=s3_output_path)\n    athena_query.wait()\n    query_execution = athena_query.get_query_execution()\n\n    # Get the full path to the S3 files with the results\n    full_s3_path = s3_output_path + f\"/{query_execution['QueryExecution']['QueryExecutionId']}.csv\"\n    return full_s3_path\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    return self.feature_meta[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete","title":"<code>delete()</code>","text":"<p>Instance Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def delete(self):\n    \"\"\"Instance Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\"\"\"\n    # Make sure the AthenaSource exists\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an FeatureSet that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the FeatureSet\n    FeatureSetCore.managed_delete(self.uuid)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.delete_views","title":"<code>delete_views(table, database)</code>  <code>classmethod</code>","text":"<p>Delete any views associated with this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>table</code> <code>str</code> <p>Name of Athena Table</p> required <code>database</code> <code>str</code> <p>Athena Database Name</p> required Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>@classmethod\ndef delete_views(cls, table: str, database: str):\n    \"\"\"Delete any views associated with this FeatureSet\n\n    Args:\n        table (str): Name of Athena Table\n        database (str): Athena Database Name\n    \"\"\"\n    from sageworks.core.views.view_utils import delete_views_and_supplemental_data\n\n    delete_views_and_supplemental_data(table, database, cls.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.descriptive_stats","title":"<code>descriptive_stats(recompute=False)</code>","text":"<p>Get the descriptive stats for the numeric columns of the underlying DataSource Args:     recompute (bool): Recompute the descriptive stats (default=False) Returns:     dict: A dictionary of descriptive stats for the numeric columns</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def descriptive_stats(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Get the descriptive stats for the numeric columns of the underlying DataSource\n    Args:\n        recompute (bool): Recompute the descriptive stats (default=False)\n    Returns:\n        dict: A dictionary of descriptive stats for the numeric columns\n    \"\"\"\n    return self.data_source.descriptive_stats(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this FeatureSet Artifact</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the details (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about this FeatureSet</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def details(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Additional Details about this FeatureSet Artifact\n\n    Args:\n        recompute (bool): Recompute the details (default: False)\n\n    Returns:\n        dict(dict): A dictionary of details about this FeatureSet\n    \"\"\"\n\n    self.log.info(f\"Computing FeatureSet Details ({self.uuid})...\")\n    details = self.summary()\n    details[\"aws_url\"] = self.aws_url()\n\n    # Store the AWS URL in the SageWorks Metadata\n    # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n    # self.upsert_sageworks_meta({\"aws_url\": details[\"aws_url\"]})\n\n    # Now get a summary of the underlying DataSource\n    details[\"storage_summary\"] = self.data_source.summary()\n\n    # Number of Columns\n    details[\"num_columns\"] = self.num_columns()\n\n    # Number of Rows\n    details[\"num_rows\"] = self.num_rows()\n\n    # Additional Details\n    details[\"sageworks_status\"] = self.get_status()\n    details[\"sageworks_input\"] = self.get_input()\n    details[\"sageworks_tags\"] = self.tag_delimiter.join(self.get_tags())\n\n    # Underlying Storage Details\n    details[\"storage_type\"] = \"athena\"  # TODO: Add RDS support\n    details[\"storage_uuid\"] = self.data_source.uuid\n\n    # Add the column details and column stats\n    details[\"column_details\"] = self.column_details()\n    details[\"column_stats\"] = self.column_stats()\n\n    # Return the details data\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.exists","title":"<code>exists()</code>","text":"<p>Does the feature_set_name exist in the AWS Metadata?</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n    if self.feature_meta is None:\n        self.log.debug(f\"FeatureSet {self.uuid} not found in AWS Metadata!\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_data_source","title":"<code>get_data_source()</code>","text":"<p>Return the underlying DataSource object</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def get_data_source(self) -&gt; DataSourceFactory:\n    \"\"\"Return the underlying DataSource object\"\"\"\n    return self.data_source\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_feature_store","title":"<code>get_feature_store()</code>","text":"<p>Return the underlying AWS FeatureStore object. This can be useful for more advanced usage with create_dataset() such as Joins and time ranges and a host of other options See: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def get_feature_store(self) -&gt; FeatureStore:\n    \"\"\"Return the underlying AWS FeatureStore object. This can be useful for more advanced usage\n    with create_dataset() such as Joins and time ranges and a host of other options\n    See: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n    \"\"\"\n    return self.feature_store\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.get_training_data","title":"<code>get_training_data()</code>","text":"<p>Get the training data for this FeatureSet</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The training data for this FeatureSet</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def get_training_data(self) -&gt; pd.DataFrame:\n    \"\"\"Get the training data for this FeatureSet\n\n    Returns:\n        pd.DataFrame: The training data for this FeatureSet\n    \"\"\"\n    from sageworks.core.views.view import View\n\n    return View(self, \"training\").pull_dataframe()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.hash","title":"<code>hash()</code>","text":"<p>Return the hash for the set of Parquet files for this artifact</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def hash(self) -&gt; str:\n    \"\"\"Return the hash for the set of Parquet files for this artifact\"\"\"\n    return self.data_source.hash()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of health issues</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this model\n\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    # Call the base class health check\n    health_issues = super().health_check()\n\n    # If we have a 'needs_onboard' in the health check then just return\n    if \"needs_onboard\" in health_issues:\n        return health_issues\n\n    # Check our DataSource\n    if not self.data_source.exists():\n        self.log.critical(f\"Data Source check failed for {self.uuid}\")\n        self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n        health_issues.append(\"data_source_missing\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.managed_delete","title":"<code>managed_delete(feature_set_name)</code>  <code>classmethod</code>","text":"<p>Class Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_name</code> <code>str</code> <p>The Name of the FeatureSet to delete</p> required Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>@classmethod\ndef managed_delete(cls, feature_set_name: str):\n    \"\"\"Class Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\n\n    Args:\n        feature_set_name (str): The Name of the FeatureSet to delete\n    \"\"\"\n\n    # See if the FeatureSet exists\n    try:\n        response = cls.sm_client.describe_feature_group(FeatureGroupName=feature_set_name)\n    except cls.sm_client.exceptions.ResourceNotFound:\n        cls.log.info(f\"FeatureSet {feature_set_name} not found!\")\n        return\n\n    # Extract database and table information from the response\n    offline_config = response.get(\"OfflineStoreConfig\", {})\n    database = offline_config.get(\"DataCatalogConfig\", {}).get(\"Database\")\n    offline_table = offline_config.get(\"DataCatalogConfig\", {}).get(\"TableName\")\n    data_source_uuid = offline_table  # Our offline storage IS a DataSource\n\n    # Delete the Feature Group and ensure that it gets deleted\n    cls.log.important(f\"Deleting FeatureSet {feature_set_name}...\")\n    remove_fg = cls.aws_feature_group_delete(feature_set_name)\n    cls.ensure_feature_group_deleted(remove_fg)\n\n    # Delete our underlying DataSource (Data Catalog Table and S3 Storage Objects)\n    AthenaSource.managed_delete(data_source_uuid, database=database)\n\n    # Delete any views associated with this FeatureSet\n    cls.delete_views(offline_table, database)\n\n    # Feature Sets can often have a lot of cruft so delete the entire bucket/prefix\n    s3_delete_path = cls.feature_sets_s3_path + f\"/{feature_set_name}/\"\n    cls.log.info(f\"Deleting All FeatureSet S3 Storage Objects {s3_delete_path}\")\n    wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(feature_set_name)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    # Note: We can't currently figure out how to this from AWS Metadata\n    return self.feature_meta[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_columns","title":"<code>num_columns()</code>","text":"<p>Return the number of columns of the Feature Set</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def num_columns(self) -&gt; int:\n    \"\"\"Return the number of columns of the Feature Set\"\"\"\n    return len(self.columns)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.num_rows","title":"<code>num_rows()</code>","text":"<p>Return the number of rows of the internal DataSource</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def num_rows(self) -&gt; int:\n    \"\"\"Return the number of rows of the internal DataSource\"\"\"\n    return self.data_source.num_rows()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.onboard","title":"<code>onboard()</code>","text":"<p>This is a BLOCKING method that will onboard the FeatureSet (make it ready)</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def onboard(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will onboard the FeatureSet (make it ready)\"\"\"\n\n    # Set our status to onboarding\n    self.log.important(f\"Onboarding {self.uuid}...\")\n    self.set_status(\"onboarding\")\n    self.remove_health_tag(\"needs_onboard\")\n\n    # Call our underlying DataSource onboard method\n    self.data_source.refresh_meta()\n    if not self.data_source.exists():\n        self.log.critical(f\"Data Source check failed for {self.uuid}\")\n        self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n        return False\n    if not self.data_source.ready():\n        self.data_source.onboard()\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    self.set_status(\"ready\")\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.outliers","title":"<code>outliers(scale=1.5, recompute=False)</code>","text":"<p>Compute outliers for all the numeric columns in a DataSource Args:     scale (float): The scale to use for the IQR (default: 1.5)     recompute (bool): Recompute the outliers (default: False) Returns:     pd.DataFrame: A DataFrame of outliers from this DataSource Notes:     Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers     The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def outliers(self, scale: float = 1.5, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Compute outliers for all the numeric columns in a DataSource\n    Args:\n        scale (float): The scale to use for the IQR (default: 1.5)\n        recompute (bool): Recompute the outliers (default: False)\n    Returns:\n        pd.DataFrame: A DataFrame of outliers from this DataSource\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n    return self.data_source.outliers(scale=scale, recompute=recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.query","title":"<code>query(query, overwrite=True)</code>","text":"<p>Query the internal DataSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the DataSource</p> required <code>overwrite</code> <code>bool</code> <p>Overwrite the table name in the query (default: True)</p> <code>True</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def query(self, query: str, overwrite: bool = True) -&gt; pd.DataFrame:\n    \"\"\"Query the internal DataSource\n\n    Args:\n        query (str): The query to run against the DataSource\n        overwrite (bool): Overwrite the table name in the query (default: True)\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    if overwrite:\n        query = query.replace(\" \" + self.uuid + \" \", \" \" + self.athena_table + \" \")\n    return self.data_source.query(query)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.ready","title":"<code>ready()</code>","text":"<p>Is the FeatureSet ready? Is initial setup complete and expected metadata populated? Note: Since FeatureSet is a composite of DataSource and FeatureGroup, we need to    check both to see if the FeatureSet is ready.</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def ready(self) -&gt; bool:\n    \"\"\"Is the FeatureSet ready? Is initial setup complete and expected metadata populated?\n    Note: Since FeatureSet is a composite of DataSource and FeatureGroup, we need to\n       check both to see if the FeatureSet is ready.\"\"\"\n\n    # Check the expected metadata for the FeatureSet\n    expected_meta = self.expected_meta()\n    existing_meta = self.sageworks_meta()\n    feature_set_ready = set(existing_meta.keys()).issuperset(expected_meta)\n    if not feature_set_ready:\n        self.log.info(f\"FeatureSet {self.uuid} is not ready!\")\n        return False\n\n    # Okay now call/return the DataSource ready() method\n    return self.data_source.ready()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats","title":"<code>recompute_stats()</code>","text":"<p>This is a BLOCKING method that will recompute the stats for the FeatureSet</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def recompute_stats(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will recompute the stats for the FeatureSet\"\"\"\n\n    # Call our underlying DataSource recompute stats method\n    self.log.important(f\"Recomputing Stats {self.uuid}...\")\n    self.data_source.recompute_stats()\n    self.details(recompute=True)\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Internal: Refresh our internal AWS Feature Store metadata</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Internal: Refresh our internal AWS Feature Store metadata\"\"\"\n    self.log.info(\"Calling refresh_meta() on the underlying DataSource\")\n    self.data_source.refresh_meta()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.sample","title":"<code>sample(recompute=False)</code>","text":"<p>Get a sample of the data from the underlying DataSource Args:     recompute (bool): Recompute the sample (default=False) Returns:     pd.DataFrame: A sample of the data from the underlying DataSource</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a sample of the data from the underlying DataSource\n    Args:\n        recompute (bool): Recompute the sample (default=False)\n    Returns:\n        pd.DataFrame: A sample of the data from the underlying DataSource\n    \"\"\"\n    return self.data_source.sample(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_computation_columns","title":"<code>set_computation_columns(computation_columns, reset_display=True)</code>","text":"<p>Set the computation columns for this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>computation_columns</code> <code>list[str]</code> <p>The computation columns for this FeatureSet</p> required <code>reset_display</code> <code>bool</code> <p>Also reset the display columns to match (default: True)</p> <code>True</code> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def set_computation_columns(self, computation_columns: list[str], reset_display: bool = True):\n    \"\"\"Set the computation columns for this FeatureSet\n\n    Args:\n        computation_columns (list[str]): The computation columns for this FeatureSet\n        reset_display (bool): Also reset the display columns to match (default: True)\n    \"\"\"\n    self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n    from sageworks.core.views import ComputationView\n\n    # Create a NEW computation view\n    ComputationView.create(self, column_list=computation_columns)\n    self.recompute_stats()\n\n    # Reset the display columns to match the computation columns\n    if reset_display:\n        self.set_display_columns(computation_columns)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_display_columns","title":"<code>set_display_columns(diplay_columns)</code>","text":"<p>Set the display columns for this Data Source</p> <p>Parameters:</p> Name Type Description Default <code>diplay_columns</code> <code>list[str]</code> <p>The display columns for this Data Source</p> required Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def set_display_columns(self, diplay_columns: list[str]):\n    \"\"\"Set the display columns for this Data Source\n\n    Args:\n        diplay_columns (list[str]): The display columns for this Data Source\n    \"\"\"\n    # Check mismatch of display columns to computation columns\n    c_view = self.view(\"computation\")\n    computation_columns = c_view.columns\n    mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n    if mismatch_columns:\n        self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n    self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n    from sageworks.core.views import DisplayView\n\n    # Create a NEW display view\n    DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.set_training_holdouts","title":"<code>set_training_holdouts(id_column, holdout_ids)</code>","text":"<p>Set the hold out ids for the training view for this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>id_column</code> <code>str</code> <p>The name of the id column.</p> required <code>holdout_ids</code> <code>list[str]</code> <p>The list of holdout ids.</p> required Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n    \"\"\"Set the hold out ids for the training view for this FeatureSet\n\n    Args:\n        id_column (str): The name of the id column.\n        holdout_ids (list[str]): The list of holdout ids.\n    \"\"\"\n    from sageworks.core.views import TrainingView\n\n    # Create a NEW training view\n    self.log.important(f\"Setting Training Holdouts: {len(holdout_ids)} ids...\")\n    TrainingView.create(self, id_column=id_column, holdout_ids=holdout_ids)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.size","title":"<code>size()</code>","text":"<p>Return the size of the internal DataSource in MegaBytes</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of the internal DataSource in MegaBytes\"\"\"\n    return self.data_source.size()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.smart_sample","title":"<code>smart_sample(recompute=False)</code>","text":"<p>Get a SMART sample dataframe from this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the smart sample (default=False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A combined DataFrame of sample data + outliers</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a SMART sample dataframe from this FeatureSet\n\n    Args:\n        recompute (bool): Recompute the smart sample (default=False)\n\n    Returns:\n        pd.DataFrame: A combined DataFrame of sample data + outliers\n    \"\"\"\n    return self.data_source.smart_sample(recompute=recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query","title":"<code>snapshot_query(table_name=None)</code>","text":"<p>An Athena query to get the latest snapshot of features</p> <p>Parameters:</p> Name Type Description Default <code>table_name</code> <code>str</code> <p>The name of the table to query (default: None)</p> <code>None</code> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>The Athena query to get the latest snapshot of features</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def snapshot_query(self, table_name: str = None) -&gt; str:\n    \"\"\"An Athena query to get the latest snapshot of features\n\n    Args:\n        table_name (str): The name of the table to query (default: None)\n\n    Returns:\n        str: The Athena query to get the latest snapshot of features\n    \"\"\"\n    # Remove FeatureGroup metadata columns that might have gotten added\n    columns = self.columns\n    filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n    columns = \", \".join(['\"' + x + '\"' for x in columns if x not in filter_columns])\n\n    query = (\n        f\"SELECT {columns} \"\n        f\"    FROM (SELECT *, row_number() OVER (PARTITION BY {self.id_column} \"\n        f\"        ORDER BY {self.event_time} desc, api_invocation_time DESC, write_time DESC) AS row_num \"\n        f'        FROM \"{table_name}\") '\n        \"    WHERE row_num = 1 and NOT is_deleted;\"\n    )\n    return query\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.table_hash","title":"<code>table_hash()</code>","text":"<p>Return the hash for the Athena table</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def table_hash(self) -&gt; str:\n    \"\"\"Return the hash for the Athena table\"\"\"\n    return self.data_source.table_hash()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.value_counts","title":"<code>value_counts(recompute=False)</code>","text":"<p>Get the value counts for the string columns of the underlying DataSource Args:     recompute (bool): Recompute the value counts (default=False) Returns:     dict: A dictionary of value counts for the string columns</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def value_counts(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Get the value counts for the string columns of the underlying DataSource\n    Args:\n        recompute (bool): Recompute the value counts (default=False)\n    Returns:\n        dict: A dictionary of value counts for the string columns\n    \"\"\"\n    return self.data_source.value_counts(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.view","title":"<code>view(view_name)</code>","text":"<p>Return a DataFrame for a specific view Args:     view_name (str): The name of the view to return Returns:     pd.DataFrame: A DataFrame for the specified view</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def view(self, view_name: str) -&gt; \"View\":\n    \"\"\"Return a DataFrame for a specific view\n    Args:\n        view_name (str): The name of the view to return\n    Returns:\n        pd.DataFrame: A DataFrame for the specified view\n    \"\"\"\n    from sageworks.core.views import View\n\n    return View(self, view_name)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#sageworks.core.artifacts.feature_set_core.FeatureSetCore.views","title":"<code>views()</code>","text":"<p>Return the views for this Data Source</p> Source code in <code>src/sageworks/core/artifacts/feature_set_core.py</code> <pre><code>def views(self) -&gt; list[str]:\n    \"\"\"Return the views for this Data Source\"\"\"\n    from sageworks.core.views.view_utils import list_views\n\n    return list_views(self.data_source)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/","title":"ModelCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the Model API Class and voil\u00e0 it works the same.</p> <p>ModelCore: SageWorks ModelCore Class</p>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.InferenceImage","title":"<code>InferenceImage</code>","text":"<p>Class for retrieving locked Scikit-Learn inference images</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>class InferenceImage:\n    \"\"\"Class for retrieving locked Scikit-Learn inference images\"\"\"\n\n    image_uris = {\n        (\"us-east-1\", \"sklearn\", \"1.2.1\"): (\n            \"683313688378.dkr.ecr.us-east-1.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n        (\"us-east-2\", \"sklearn\", \"1.2.1\"): (\n            \"257758044811.dkr.ecr.us-east-2.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n        (\"us-west-1\", \"sklearn\", \"1.2.1\"): (\n            \"746614075791.dkr.ecr.us-west-1.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n        (\"us-west-2\", \"sklearn\", \"1.2.1\"): (\n            \"246618743249.dkr.ecr.us-west-2.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n    }\n\n    @classmethod\n    def get_image_uri(cls, region, framework, version):\n        key = (region, framework, version)\n        if key in cls.image_uris:\n            return cls.image_uris[key]\n        else:\n            raise ValueError(\n                f\"No matching image found for region: {region}, framework: {framework}, version: {version}\"\n            )\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore","title":"<code>ModelCore</code>","text":"<p>               Bases: <code>Artifact</code></p> <p>ModelCore: SageWorks ModelCore Class</p> Common Usage <pre><code>my_model = ModelCore(model_uuid)\nmy_model.summary()\nmy_model.details()\n</code></pre> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>class ModelCore(Artifact):\n    \"\"\"ModelCore: SageWorks ModelCore Class\n\n    Common Usage:\n        ```python\n        my_model = ModelCore(model_uuid)\n        my_model.summary()\n        my_model.details()\n        ```\n    \"\"\"\n\n    def __init__(self, model_uuid: str, model_type: ModelType = None, **kwargs):\n        \"\"\"ModelCore Initialization\n        Args:\n            model_uuid (str): Name of Model in SageWorks.\n            model_type (ModelType, optional): Set this for newly created Models. Defaults to None.\n            **kwargs: Additional keyword arguments\n        \"\"\"\n\n        # Make sure the model name is valid\n        self.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call SuperClass Initialization\n        super().__init__(model_uuid, **kwargs)\n\n        # Initialize our class attributes\n        self.latest_model = None\n        self.model_type = ModelType.UNKNOWN\n        self.model_training_path = None\n        self.endpoint_inference_path = None\n\n        # Grab an Cloud Platform Meta object and pull information for this Model\n        self.model_name = model_uuid\n        self.model_meta = self.meta.model(self.model_name)\n        if self.model_meta is None:\n            self.log.warning(f\"Could not find model {self.model_name} within current visibility scope\")\n            return\n        else:\n            # Is this a model package group without any models?\n            if len(self.model_meta[\"ModelPackageList\"]) == 0:\n                self.log.warning(f\"Model Group {self.model_name} has no Model Packages!\")\n                self.latest_model = None\n                self.add_health_tag(\"model_not_found\")\n                return\n            try:\n                self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n                self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n                self.training_job_name = self._extract_training_job_name()\n                if model_type:\n                    self._set_model_type(model_type)\n                else:\n                    self.model_type = self._get_model_type()\n            except (IndexError, KeyError):\n                self.log.critical(f\"Model {self.model_name} appears to be malformed. Delete and recreate it!\")\n                return\n\n        # Set the Model Training S3 Path\n        self.model_training_path = self.models_s3_path + \"/training/\" + self.model_name\n\n        # Get our Endpoint Inference Path (might be None)\n        self.endpoint_inference_path = self.get_endpoint_inference_path()\n\n        # Call SuperClass Post Initialization\n        super().__post_init__()\n\n        # All done\n        self.log.info(f\"Model Initialized: {self.model_name}\")\n\n    def refresh_meta(self):\n        \"\"\"Refresh the Artifact's metadata\"\"\"\n        self.model_meta = self.meta.model(self.model_name)\n        self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n        self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n        self.training_job_name = self._extract_training_job_name()\n\n    def exists(self) -&gt; bool:\n        \"\"\"Does the model metadata exist in the AWS Metadata?\"\"\"\n        if self.model_meta is None:\n            self.log.info(f\"Model {self.model_name} not found in AWS Metadata!\")\n            return False\n        return True\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this model\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        # Call the base class health check\n        health_issues = super().health_check()\n\n        # Check if the model exists\n        if self.latest_model is None:\n            health_issues.append(\"model_not_found\")\n\n        # Model Type\n        if self._get_model_type() == ModelType.UNKNOWN:\n            health_issues.append(\"model_type_unknown\")\n        else:\n            self.remove_health_tag(\"model_type_unknown\")\n\n        # Model Performance Metrics\n        needs_metrics = self.model_type in {ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR, ModelType.CLASSIFIER}\n        if needs_metrics and self.get_inference_metrics() is None:\n            health_issues.append(\"metrics_needed\")\n        else:\n            self.remove_health_tag(\"metrics_needed\")\n\n        # Endpoint\n        if not self.endpoints():\n            health_issues.append(\"no_endpoint\")\n        else:\n            self.remove_health_tag(\"no_endpoint\")\n        return health_issues\n\n    def latest_model_object(self) -&gt; SagemakerModel:\n        \"\"\"Return the latest AWS Sagemaker Model object for this SageWorks Model\n\n        Returns:\n           sagemaker.model.Model: AWS Sagemaker Model object\n        \"\"\"\n        return SagemakerModel(\n            model_data=self.model_package_arn(), sagemaker_session=self.sm_session, image_uri=self.container_image()\n        )\n\n    def list_inference_runs(self) -&gt; list[str]:\n        \"\"\"List the inference runs for this model\n\n        Returns:\n            list[str]: List of inference runs\n        \"\"\"\n\n        # Check if we have a model (if not return empty list)\n        if self.latest_model is None:\n            return []\n\n        # Check if we have model training metrics in our metadata\n        have_model_training = True if self.sageworks_meta().get(\"sageworks_training_metrics\") else False\n\n        # Now grab the list of directories from our inference path\n        inference_runs = []\n        if self.endpoint_inference_path:\n            directories = wr.s3.list_directories(path=self.endpoint_inference_path + \"/\")\n            inference_runs = [urlparse(directory).path.split(\"/\")[-2] for directory in directories]\n\n        # We're going to add the model training to the end of the list\n        if have_model_training:\n            inference_runs.append(\"model_training\")\n        return inference_runs\n\n    def delete_inference_run(self, inference_run_uuid: str):\n        \"\"\"Delete the inference run for this model\n\n        Args:\n            inference_run_uuid (str): UUID of the inference run\n        \"\"\"\n        if inference_run_uuid == \"model_training\":\n            self.log.warning(\"Cannot delete model training data!\")\n            return\n\n        if self.endpoint_inference_path:\n            full_path = f\"{self.endpoint_inference_path}/{inference_run_uuid}\"\n            # Check if there are any objects at the path\n            if wr.s3.list_objects(full_path):\n                wr.s3.delete_objects(path=full_path)\n                self.log.important(f\"Deleted inference run {inference_run_uuid} for {self.model_name}\")\n            else:\n                self.log.warning(f\"Inference run {inference_run_uuid} not found for {self.model_name}!\")\n        else:\n            self.log.warning(f\"No inference data found for {self.model_name}!\")\n\n    def get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the inference performance metrics for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n        Returns:\n            pd.DataFrame: DataFrame of the Model Metrics\n\n        Note:\n            If a capture_uuid isn't specified this will try to return something reasonable\n        \"\"\"\n        # Try to get the auto_capture 'training_holdout' or the training\n        if capture_uuid == \"latest\":\n            metrics_df = self.get_inference_metrics(\"auto_inference\")\n            return metrics_df if metrics_df is not None else self.get_inference_metrics(\"model_training\")\n\n        # Grab the metrics captured during model training (could return None)\n        if capture_uuid == \"model_training\":\n            # Sanity check the sageworks metadata\n            if self.sageworks_meta() is None:\n                error_msg = f\"Model {self.model_name} has no sageworks_meta(). Either onboard() or delete this model!\"\n                self.log.critical(error_msg)\n                raise ValueError(error_msg)\n\n            metrics = self.sageworks_meta().get(\"sageworks_training_metrics\")\n            return pd.DataFrame.from_dict(metrics) if metrics else None\n\n        else:  # Specific capture_uuid (could return None)\n            s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_metrics.csv\"\n            metrics = pull_s3_data(s3_path, embedded_index=True)\n            if metrics is not None:\n                return metrics\n            else:\n                self.log.warning(f\"Performance metrics {capture_uuid} not found for {self.model_name}!\")\n                return None\n\n    def confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the confusion_matrix for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n        Returns:\n            pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n        \"\"\"\n\n        # Sanity check the sageworks metadata\n        if self.sageworks_meta() is None:\n            error_msg = f\"Model {self.model_name} has no sageworks_meta(). Either onboard() or delete this model!\"\n            self.log.critical(error_msg)\n            raise ValueError(error_msg)\n\n        # Grab the metrics from the SageWorks Metadata (try inference first, then training)\n        if capture_uuid == \"latest\":\n            cm = self.confusion_matrix(\"auto_inference\")\n            return cm if cm is not None else self.confusion_matrix(\"model_training\")\n\n        # Grab the confusion matrix captured during model training (could return None)\n        if capture_uuid == \"model_training\":\n            cm = self.sageworks_meta().get(\"sageworks_training_cm\")\n            return pd.DataFrame.from_dict(cm) if cm else None\n\n        else:  # Specific capture_uuid\n            s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_cm.csv\"\n            cm = pull_s3_data(s3_path, embedded_index=True)\n            if cm is not None:\n                return cm\n            else:\n                self.log.warning(f\"Confusion Matrix {capture_uuid} not found for {self.model_name}!\")\n                return None\n\n    def set_input(self, input: str, force: bool = False):\n        \"\"\"Override: Set the input data for this artifact\n\n        Args:\n            input (str): Name of input for this artifact\n            force (bool, optional): Force the input to be set (default: False)\n        Note:\n            We're going to not allow this to be used for Models\n        \"\"\"\n        if not force:\n            self.log.warning(f\"Model {self.uuid}: Does not allow manual override of the input!\")\n            return\n\n        # Okay we're going to allow this to be set\n        self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n        self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n        self.upsert_sageworks_meta({\"sageworks_input\": input})\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this data in MegaBytes\"\"\"\n        return 0.0\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n        return self.model_meta\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n        return self.group_arn()\n\n    def group_arn(self) -&gt; Union[str, None]:\n        \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n        return self.model_meta[\"ModelPackageGroupArn\"] if self.model_meta else None\n\n    def model_package_arn(self) -&gt; Union[str, None]:\n        \"\"\"AWS ARN (Amazon Resource Name) for the Latest Model Package (within the Group)\"\"\"\n        if self.latest_model is None:\n            return None\n        return self.latest_model[\"ModelPackageArn\"]\n\n    def container_info(self) -&gt; Union[dict, None]:\n        \"\"\"Container Info for the Latest Model Package\"\"\"\n        return self.latest_model[\"InferenceSpecification\"][\"Containers\"][0] if self.latest_model else None\n\n    def container_image(self) -&gt; str:\n        \"\"\"Container Image for the Latest Model Package\"\"\"\n        return self.container_info()[\"Image\"]\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying this model\"\"\"\n        return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        if self.latest_model is None:\n            return \"-\"\n        return self.latest_model[\"CreationTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        if self.latest_model is None:\n            return \"-\"\n        return self.latest_model[\"CreationTime\"]\n\n    def hash(self) -&gt; Optional[str]:\n        \"\"\"Return the hash for this artifact\n\n        Returns:\n            Optional[str]: The hash for this artifact\n        \"\"\"\n        model_url = self.get_model_data_url()\n        return compute_s3_object_hash(model_url, self.boto3_session)\n\n    def register_endpoint(self, endpoint_name: str):\n        \"\"\"Add this endpoint to the set of registered endpoints for the model\n\n        Args:\n            endpoint_name (str): Name of the endpoint\n        \"\"\"\n        self.log.important(f\"Registering Endpoint {endpoint_name} with Model {self.uuid}...\")\n        registered_endpoints = set(self.sageworks_meta().get(\"sageworks_registered_endpoints\", []))\n        registered_endpoints.add(endpoint_name)\n        self.upsert_sageworks_meta({\"sageworks_registered_endpoints\": list(registered_endpoints)})\n\n        # Remove any health tags\n        self.remove_health_tag(\"no_endpoint\")\n\n        # A new endpoint means we need to refresh our inference path\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.endpoint_inference_path = self.get_endpoint_inference_path()\n\n    def remove_endpoint(self, endpoint_name: str):\n        \"\"\"Remove this endpoint from the set of registered endpoints for the model\n\n        Args:\n            endpoint_name (str): Name of the endpoint\n        \"\"\"\n        self.log.important(f\"Removing Endpoint {endpoint_name} from Model {self.uuid}...\")\n        registered_endpoints = set(self.sageworks_meta().get(\"sageworks_registered_endpoints\", []))\n        registered_endpoints.discard(endpoint_name)\n        self.upsert_sageworks_meta({\"sageworks_registered_endpoints\": list(registered_endpoints)})\n\n        # If we have NO endpionts, then set a health tags\n        if not registered_endpoints:\n            self.add_health_tag(\"no_endpoint\")\n            self.details(recompute=True)\n\n        # A new endpoint means we need to refresh our inference path\n        time.sleep(2)\n\n    def endpoints(self) -&gt; list[str]:\n        \"\"\"Get the list of registered endpoints for this Model\n\n        Returns:\n            list[str]: List of registered endpoints\n        \"\"\"\n        return self.sageworks_meta().get(\"sageworks_registered_endpoints\", [])\n\n    def get_endpoint_inference_path(self) -&gt; Union[str, None]:\n        \"\"\"Get the S3 Path for the Inference Data\n\n        Returns:\n            str: S3 Path for the Inference Data (or None if not found)\n        \"\"\"\n\n        # Look for any Registered Endpoints\n        registered_endpoints = self.sageworks_meta().get(\"sageworks_registered_endpoints\")\n\n        # Note: We may have 0 to N endpoints, so we find the one with the most recent artifacts\n        if registered_endpoints:\n            endpoint_inference_base = self.endpoints_s3_path + \"/inference/\"\n            endpoint_inference_paths = [endpoint_inference_base + e for e in registered_endpoints]\n            inference_path = newest_path(endpoint_inference_paths, self.sm_session)\n            if inference_path is None:\n                self.log.important(f\"No inference data found for {self.model_name}!\")\n                self.log.important(f\"Returning default inference path for {registered_endpoints[0]}...\")\n                self.log.important(f\"{endpoint_inference_paths[0]}\")\n                return endpoint_inference_paths[0]\n            else:\n                return inference_path\n        else:\n            self.log.warning(f\"No registered endpoints found for {self.model_name}!\")\n            return None\n\n    def set_target(self, target_column: str):\n        \"\"\"Set the target for this Model\n\n        Args:\n            target_column (str): Target column for this Model\n        \"\"\"\n        self.upsert_sageworks_meta({\"sageworks_model_target\": target_column})\n\n    def set_features(self, feature_columns: list[str]):\n        \"\"\"Set the features for this Model\n\n        Args:\n            feature_columns (list[str]): List of feature columns\n        \"\"\"\n        self.upsert_sageworks_meta({\"sageworks_model_features\": feature_columns})\n\n    def target(self) -&gt; Union[str, None]:\n        \"\"\"Return the target for this Model (if supervised, else None)\n\n        Returns:\n            str: Target column for this Model (if supervised, else None)\n        \"\"\"\n        return self.sageworks_meta().get(\"sageworks_model_target\")  # Returns None if not found\n\n    def features(self) -&gt; Union[list[str], None]:\n        \"\"\"Return a list of features used for this Model\n\n        Returns:\n            list[str]: List of features used for this Model\n        \"\"\"\n        return self.sageworks_meta().get(\"sageworks_model_features\")  # Returns None if not found\n\n    def class_labels(self) -&gt; Union[list[str], None]:\n        \"\"\"Return the class labels for this Model (if it's a classifier)\n\n        Returns:\n            list[str]: List of class labels\n        \"\"\"\n        if self.model_type == ModelType.CLASSIFIER:\n            return self.sageworks_meta().get(\"class_labels\")  # Returns None if not found\n        else:\n            return None\n\n    def set_class_labels(self, labels: list[str]):\n        \"\"\"Return the class labels for this Model (if it's a classifier)\n\n        Args:\n            labels (list[str]): List of class labels\n        \"\"\"\n        if self.model_type == ModelType.CLASSIFIER:\n            self.upsert_sageworks_meta({\"class_labels\": labels})\n        else:\n            self.log.error(f\"Model {self.model_name} is not a classifier!\")\n\n    def details(self, recompute=False) -&gt; dict:\n        \"\"\"Additional Details about this Model\n        Args:\n            recompute (bool, optional): Recompute the details (default: False)\n        Returns:\n            dict: Dictionary of details about this Model\n        \"\"\"\n        self.log.info(\"Computing Model Details...\")\n        details = self.summary()\n        details[\"pipeline\"] = self.get_pipeline()\n        details[\"model_type\"] = self.model_type.value\n        details[\"model_package_group_arn\"] = self.group_arn()\n        details[\"model_package_arn\"] = self.model_package_arn()\n\n        # Sanity check is we have models in the group\n        if self.latest_model is None:\n            self.log.warning(f\"Model Package Group {self.model_name} has no models!\")\n            return details\n\n        # Grab the Model Details\n        details[\"description\"] = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n        details[\"version\"] = self.latest_model[\"ModelPackageVersion\"]\n        details[\"status\"] = self.latest_model[\"ModelPackageStatus\"]\n        details[\"approval_status\"] = self.latest_model.get(\"ModelApprovalStatus\", \"unknown\")\n        details[\"image\"] = self.container_image().split(\"/\")[-1]  # Shorten the image uri\n\n        # Grab the inference and container info\n        inference_spec = self.latest_model[\"InferenceSpecification\"]\n        container_info = self.container_info()\n        details[\"framework\"] = container_info.get(\"Framework\", \"unknown\")\n        details[\"framework_version\"] = container_info.get(\"FrameworkVersion\", \"unknown\")\n        details[\"inference_types\"] = inference_spec[\"SupportedRealtimeInferenceInstanceTypes\"]\n        details[\"transform_types\"] = inference_spec[\"SupportedTransformInstanceTypes\"]\n        details[\"content_types\"] = inference_spec[\"SupportedContentTypes\"]\n        details[\"response_types\"] = inference_spec[\"SupportedResponseMIMETypes\"]\n        details[\"model_metrics\"] = self.get_inference_metrics()\n        if self.model_type == ModelType.CLASSIFIER:\n            details[\"confusion_matrix\"] = self.confusion_matrix()\n            details[\"predictions\"] = None\n        elif self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n            details[\"confusion_matrix\"] = None\n            details[\"predictions\"] = self.get_inference_predictions()\n        else:\n            details[\"confusion_matrix\"] = None\n            details[\"predictions\"] = None\n\n        # Grab the inference metadata\n        details[\"inference_meta\"] = self.get_inference_metadata()\n\n        # Return the details\n        return details\n\n    # Pipeline for this model\n    def get_pipeline(self) -&gt; str:\n        \"\"\"Get the pipeline for this model\"\"\"\n        return self.sageworks_meta().get(\"sageworks_pipeline\")\n\n    def set_pipeline(self, pipeline: str):\n        \"\"\"Set the pipeline for this model\n\n        Args:\n            pipeline (str): Pipeline that was used to create this model\n        \"\"\"\n        self.upsert_sageworks_meta({\"sageworks_pipeline\": pipeline})\n\n    def expected_meta(self) -&gt; list[str]:\n        \"\"\"Metadata we expect to see for this Model when it's ready\n        Returns:\n            list[str]: List of expected metadata keys\n        \"\"\"\n        # Our current list of expected metadata, we can add to this as needed\n        return [\"sageworks_status\", \"sageworks_training_metrics\", \"sageworks_training_cm\"]\n\n    def is_model_unknown(self) -&gt; bool:\n        \"\"\"Is the Model Type unknown?\"\"\"\n        return self.model_type == ModelType.UNKNOWN\n\n    def _determine_model_type(self):\n        \"\"\"Internal: Determine the Model Type\"\"\"\n        model_type = input(\"Model Type? (classifier, regressor, quantile_regressor, unsupervised, transformer): \")\n        if model_type == \"classifier\":\n            self._set_model_type(ModelType.CLASSIFIER)\n        elif model_type == \"regressor\":\n            self._set_model_type(ModelType.REGRESSOR)\n        elif model_type == \"quantile_regressor\":\n            self._set_model_type(ModelType.QUANTILE_REGRESSOR)\n        elif model_type == \"unsupervised\":\n            self._set_model_type(ModelType.UNSUPERVISED)\n        elif model_type == \"transformer\":\n            self._set_model_type(ModelType.TRANSFORMER)\n        else:\n            self.log.warning(f\"Unknown Model Type {model_type}!\")\n            self._set_model_type(ModelType.UNKNOWN)\n\n    def onboard(self, ask_everything=False) -&gt; bool:\n        \"\"\"This is an interactive method that will onboard the Model (make it ready)\n\n        Args:\n            ask_everything (bool, optional): Ask for all the details. Defaults to False.\n\n        Returns:\n            bool: True if the Model is successfully onboarded, False otherwise\n        \"\"\"\n        # Set the status to onboarding\n        self.set_status(\"onboarding\")\n\n        # Determine the Model Type\n        while self.is_model_unknown():\n            self._determine_model_type()\n\n        # Is our input data set?\n        if self.get_input() in [\"\", \"unknown\"] or ask_everything:\n            input_data = input(\"Input Data?: \")\n            if input_data not in [\"None\", \"none\", \"\", \"unknown\"]:\n                self.set_input(input_data)\n\n        # Determine the Target Column (can be None)\n        target_column = self.target()\n        if target_column is None or ask_everything:\n            target_column = input(\"Target Column? (for unsupervised/transformer just type None): \")\n            if target_column in [\"None\", \"none\", \"\"]:\n                target_column = None\n\n        # Determine the Feature Columns\n        feature_columns = self.features()\n        if feature_columns is None or ask_everything:\n            feature_columns = input(\"Feature Columns? (use commas): \")\n            feature_columns = [e.strip() for e in feature_columns.split(\",\")]\n            if feature_columns in [[\"None\"], [\"none\"], [\"\"]]:\n                feature_columns = None\n\n        # Registered Endpoints?\n        endpoints = self.endpoints()\n        if not endpoints or ask_everything:\n            endpoints = input(\"Register Endpoints? (use commas for multiple): \")\n            endpoints = [e.strip() for e in endpoints.split(\",\")]\n            if endpoints in [[\"None\"], [\"none\"], [\"\"]]:\n                endpoints = None\n\n        # Model Owner?\n        owner = self.get_owner()\n        if owner in [None, \"unknown\"] or ask_everything:\n            owner = input(\"Model Owner: \")\n            if owner in [\"None\", \"none\", \"\"]:\n                owner = \"unknown\"\n\n        # Model Class Labels (if it's a classifier)\n        if self.model_type == ModelType.CLASSIFIER:\n            class_labels = self.class_labels()\n            if class_labels is None or ask_everything:\n                class_labels = input(\"Class Labels? (use commas): \")\n                class_labels = [e.strip() for e in class_labels.split(\",\")]\n                if class_labels in [[\"None\"], [\"none\"], [\"\"]]:\n                    class_labels = None\n            self.set_class_labels(class_labels)\n\n        # Now that we have all the details, let's onboard the Model with all the args\n        return self.onboard_with_args(self.model_type, target_column, feature_columns, endpoints, owner)\n\n    def onboard_with_args(\n        self,\n        model_type: ModelType,\n        target_column: str = None,\n        feature_list: list = None,\n        endpoints: list = None,\n        owner: str = None,\n    ) -&gt; bool:\n        \"\"\"Onboard the Model with the given arguments\n\n        Args:\n            model_type (ModelType): Model Type\n            target_column (str): Target Column\n            feature_list (list): List of Feature Columns\n            endpoints (list, optional): List of Endpoints. Defaults to None.\n            owner (str, optional): Model Owner. Defaults to None.\n        Returns:\n            bool: True if the Model is successfully onboarded, False otherwise\n        \"\"\"\n        # Set the status to onboarding\n        self.set_status(\"onboarding\")\n\n        # Set All the Details\n        self._set_model_type(model_type)\n        if target_column:\n            self.set_target(target_column)\n        if feature_list:\n            self.set_features(feature_list)\n        if endpoints:\n            for endpoint in endpoints:\n                self.register_endpoint(endpoint)\n        if owner:\n            self.set_owner(owner)\n\n        # Load the training metrics and inference metrics\n        self._load_training_metrics()\n        self._load_inference_metrics()\n\n        # Remove the needs_onboard tag\n        self.remove_health_tag(\"needs_onboard\")\n        self.set_status(\"ready\")\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        return True\n\n    def get_model_data_url(self) -&gt; Optional[str]:\n        \"\"\"Retrieve the ModelDataUrl from the model's AWS metadata.\n\n        Returns:\n            Optional[str]: The ModelDataUrl if available, otherwise None.\n        \"\"\"\n        meta = self.aws_meta()\n        try:\n            return meta[\"ModelPackageList\"][0][\"InferenceSpecification\"][\"Containers\"][0][\"ModelDataUrl\"]\n        except (KeyError, IndexError, TypeError):\n            return None\n\n    def delete(self):\n        \"\"\"Delete the Model Packages and the Model Group\"\"\"\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the Model Group\n        ModelCore.managed_delete(model_group_name=self.uuid)\n\n    @classmethod\n    def managed_delete(cls, model_group_name: str):\n        \"\"\"Delete the Model Packages, Model Group, and S3 Storage Objects\n\n        Args:\n            model_group_name (str): The name of the Model Group to delete\n        \"\"\"\n        # Check if the model group exists in SageMaker\n        try:\n            cls.sm_client.describe_model_package_group(ModelPackageGroupName=model_group_name)\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n                cls.log.info(f\"Model Group {model_group_name} not found!\")\n                return\n            else:\n                raise  # Re-raise unexpected errors\n\n        # Delete Model Packages within the Model Group\n        try:\n            paginator = cls.sm_client.get_paginator(\"list_model_packages\")\n            for page in paginator.paginate(ModelPackageGroupName=model_group_name):\n                for model_package in page[\"ModelPackageSummaryList\"]:\n                    package_arn = model_package[\"ModelPackageArn\"]\n                    cls.log.info(f\"Deleting Model Package {package_arn}...\")\n                    cls.sm_client.delete_model_package(ModelPackageName=package_arn)\n        except ClientError as e:\n            cls.log.error(f\"Error while deleting model packages: {e}\")\n            raise\n\n        # Delete the Model Package Group\n        cls.log.info(f\"Deleting Model Group {model_group_name}...\")\n        cls.sm_client.delete_model_package_group(ModelPackageGroupName=model_group_name)\n\n        # Delete S3 training artifacts\n        s3_delete_path = f\"{cls.models_s3_path}/training/{model_group_name}/\"\n        cls.log.info(f\"Deleting S3 Objects at {s3_delete_path}...\")\n        wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(model_group_name)\n\n    def _set_model_type(self, model_type: ModelType):\n        \"\"\"Internal: Set the Model Type for this Model\"\"\"\n        self.model_type = model_type\n        self.upsert_sageworks_meta({\"sageworks_model_type\": self.model_type.value})\n        self.remove_health_tag(\"model_type_unknown\")\n\n    def _get_model_type(self) -&gt; ModelType:\n        \"\"\"Internal: Query the SageWorks Metadata to get the model type\n        Returns:\n            ModelType: The ModelType of this Model\n        Notes:\n            This is an internal method that should not be called directly\n            Use the model_type attribute instead\n        \"\"\"\n        model_type = self.sageworks_meta().get(\"sageworks_model_type\")\n        try:\n            return ModelType(model_type)\n        except ValueError:\n            self.log.warning(f\"Could not determine model type for {self.model_name}!\")\n            return ModelType.UNKNOWN\n\n    def _load_training_metrics(self):\n        \"\"\"Internal: Retrieve the training metrics and Confusion Matrix for this model\n                     and load the data into the SageWorks Metadata\n\n        Notes:\n            This may or may not exist based on whether we have access to TrainingJobAnalytics\n        \"\"\"\n        try:\n            df = TrainingJobAnalytics(training_job_name=self.training_job_name).dataframe()\n            if df.empty:\n                self.log.important(f\"No training job metrics found for {self.training_job_name}\")\n                self.upsert_sageworks_meta({\"sageworks_training_metrics\": None, \"sageworks_training_cm\": None})\n                return\n            if self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n                if \"timestamp\" in df.columns:\n                    df = df.drop(columns=[\"timestamp\"])\n\n                # We're going to pivot the DataFrame to get the desired structure\n                reg_metrics_df = df.set_index(\"metric_name\").T\n\n                # Store and return the metrics in the SageWorks Metadata\n                self.upsert_sageworks_meta(\n                    {\"sageworks_training_metrics\": reg_metrics_df.to_dict(), \"sageworks_training_cm\": None}\n                )\n                return\n\n        except (KeyError, botocore.exceptions.ClientError):\n            self.log.important(f\"No training job metrics found for {self.training_job_name}\")\n            # Store and return the metrics in the SageWorks Metadata\n            self.upsert_sageworks_meta({\"sageworks_training_metrics\": None, \"sageworks_training_cm\": None})\n            return\n\n        # We need additional processing for classification metrics\n        if self.model_type == ModelType.CLASSIFIER:\n            metrics_df, cm_df = self._process_classification_metrics(df)\n\n            # Store and return the metrics in the SageWorks Metadata\n            self.upsert_sageworks_meta(\n                {\"sageworks_training_metrics\": metrics_df.to_dict(), \"sageworks_training_cm\": cm_df.to_dict()}\n            )\n\n    def _load_inference_metrics(self, capture_uuid: str = \"auto_inference\"):\n        \"\"\"Internal: Retrieve the inference model metrics for this model\n                     and load the data into the SageWorks Metadata\n\n        Args:\n            capture_uuid (str, optional): A specific capture_uuid (default: \"auto_inference\")\n        Notes:\n            This may or may not exist based on whether an Endpoint ran Inference\n        \"\"\"\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_metrics.csv\"\n        inference_metrics = pull_s3_data(s3_path)\n\n        # Store data into the SageWorks Metadata\n        metrics_storage = None if inference_metrics is None else inference_metrics.to_dict(\"records\")\n        self.upsert_sageworks_meta({\"sageworks_inference_metrics\": metrics_storage})\n\n    def get_inference_metadata(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the inference metadata for this model\n\n        Args:\n            capture_uuid (str, optional): A specific capture_uuid (default: \"auto_inference\")\n\n        Returns:\n            dict: Dictionary of the inference metadata (might be None)\n        Notes:\n            Basically when Endpoint inference was run, name of the dataset, the MD5, etc\n        \"\"\"\n        # Sanity check the inference path (which may or may not exist)\n        if self.endpoint_inference_path is None:\n            return None\n\n        # Check for model_training capture_uuid\n        if capture_uuid == \"model_training\":\n            # Create a DataFrame with the training metadata\n            meta_df = pd.DataFrame(\n                [\n                    {\n                        \"name\": \"AWS Training Capture\",\n                        \"data_hash\": \"N/A\",\n                        \"num_rows\": \"-\",\n                        \"description\": \"-\",\n                    }\n                ]\n            )\n            return meta_df\n\n        # Pull the inference metadata\n        try:\n            s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_meta.json\"\n            return wr.s3.read_json(s3_path)\n        except NoFilesFound:\n            self.log.info(f\"Could not find model inference meta at {s3_path}...\")\n            return None\n\n    def get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n        \"\"\"\n        self.log.important(f\"Grabbing {capture_uuid} predictions for {self.model_name}...\")\n\n        # Sanity check that the model should have predictions\n        has_predictions = self.model_type in [ModelType.CLASSIFIER, ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]\n        if not has_predictions:\n            self.log.warning(f\"No Predictions for {self.model_name}...\")\n            return None\n\n        # Special case for model_training\n        if capture_uuid == \"model_training\":\n            return self._get_validation_predictions()\n\n        # Construct the S3 path for the Inference Predictions\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_predictions.csv\"\n        return pull_s3_data(s3_path)\n\n    def _get_validation_predictions(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Internal: Retrieve the captured prediction results for this model\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Validation Predictions (might be None)\n        \"\"\"\n        # Sanity check the training path (which may or may not exist)\n        if self.model_training_path is None:\n            self.log.warning(f\"No Validation Predictions for {self.model_name}...\")\n            return None\n        self.log.important(f\"Grabbing Validation Predictions for {self.model_name}...\")\n        s3_path = f\"{self.model_training_path}/validation_predictions.csv\"\n        df = pull_s3_data(s3_path)\n        return df\n\n    def _extract_training_job_name(self) -&gt; Union[str, None]:\n        \"\"\"Internal: Extract the training job name from the ModelDataUrl\"\"\"\n        try:\n            model_data_url = self.container_info()[\"ModelDataUrl\"]\n            parsed_url = urllib.parse.urlparse(model_data_url)\n            training_job_name = parsed_url.path.lstrip(\"/\").split(\"/\")[0]\n            return training_job_name\n        except KeyError:\n            self.log.warning(f\"Could not extract training job name from {model_data_url}\")\n            return None\n\n    @staticmethod\n    def _process_classification_metrics(df: pd.DataFrame) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"Internal: Process classification metrics into a more reasonable format\n        Args:\n            df (pd.DataFrame): DataFrame of training metrics\n        Returns:\n            (pd.DataFrame, pd.DataFrame): Tuple of DataFrames. Metrics and confusion matrix\n        \"\"\"\n        # Split into two DataFrames based on 'metric_name'\n        metrics_df = df[df[\"metric_name\"].str.startswith(\"Metrics:\")].copy()\n        cm_df = df[df[\"metric_name\"].str.startswith(\"ConfusionMatrix:\")].copy()\n\n        # Split the 'metric_name' into different parts\n        metrics_df[\"class\"] = metrics_df[\"metric_name\"].str.split(\":\").str[1]\n        metrics_df[\"metric_type\"] = metrics_df[\"metric_name\"].str.split(\":\").str[2]\n\n        # Pivot the DataFrame to get the desired structure\n        metrics_df = metrics_df.pivot(index=\"class\", columns=\"metric_type\", values=\"value\").reset_index()\n        metrics_df = metrics_df.rename_axis(None, axis=1)\n\n        # Now process the confusion matrix\n        cm_df[\"row_class\"] = cm_df[\"metric_name\"].str.split(\":\").str[1]\n        cm_df[\"col_class\"] = cm_df[\"metric_name\"].str.split(\":\").str[2]\n\n        # Pivot the DataFrame to create a form suitable for the heatmap\n        cm_df = cm_df.pivot(index=\"row_class\", columns=\"col_class\", values=\"value\")\n\n        # Convert the values in cm_df to integers\n        cm_df = cm_df.astype(int)\n\n        return metrics_df, cm_df\n\n    def shapley_values(self, capture_uuid: str = \"auto_inference\") -&gt; Union[list[pd.DataFrame], pd.DataFrame, None]:\n        \"\"\"Retrieve the Shapely values for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n        Returns:\n            pd.DataFrame: Dataframe(s) of the shapley values or None if not found\n\n        Notes:\n            This may or may not exist based on whether an Endpoint ran Shapley\n        \"\"\"\n\n        # Sanity check the inference path (which may or may not exist)\n        if self.endpoint_inference_path is None:\n            return None\n\n        # Construct the S3 path for the Shapley values\n        shapley_s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}\"\n\n        # Multiple CSV if classifier\n        if self.model_type == ModelType.CLASSIFIER:\n            # CSVs for shap values are indexed by prediction class\n            # Because we don't know how many classes there are, we need to search through\n            # a list of S3 objects in the parent folder\n            s3_paths = wr.s3.list_objects(shapley_s3_path)\n            return [pull_s3_data(f) for f in s3_paths if \"inference_shap_values\" in f]\n\n        # One CSV if regressor\n        if self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n            s3_path = f\"{shapley_s3_path}/inference_shap_values.csv\"\n            return pull_s3_data(s3_path)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.__init__","title":"<code>__init__(model_uuid, model_type=None, **kwargs)</code>","text":"<p>ModelCore Initialization Args:     model_uuid (str): Name of Model in SageWorks.     model_type (ModelType, optional): Set this for newly created Models. Defaults to None.     **kwargs: Additional keyword arguments</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def __init__(self, model_uuid: str, model_type: ModelType = None, **kwargs):\n    \"\"\"ModelCore Initialization\n    Args:\n        model_uuid (str): Name of Model in SageWorks.\n        model_type (ModelType, optional): Set this for newly created Models. Defaults to None.\n        **kwargs: Additional keyword arguments\n    \"\"\"\n\n    # Make sure the model name is valid\n    self.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call SuperClass Initialization\n    super().__init__(model_uuid, **kwargs)\n\n    # Initialize our class attributes\n    self.latest_model = None\n    self.model_type = ModelType.UNKNOWN\n    self.model_training_path = None\n    self.endpoint_inference_path = None\n\n    # Grab an Cloud Platform Meta object and pull information for this Model\n    self.model_name = model_uuid\n    self.model_meta = self.meta.model(self.model_name)\n    if self.model_meta is None:\n        self.log.warning(f\"Could not find model {self.model_name} within current visibility scope\")\n        return\n    else:\n        # Is this a model package group without any models?\n        if len(self.model_meta[\"ModelPackageList\"]) == 0:\n            self.log.warning(f\"Model Group {self.model_name} has no Model Packages!\")\n            self.latest_model = None\n            self.add_health_tag(\"model_not_found\")\n            return\n        try:\n            self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n            self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n            self.training_job_name = self._extract_training_job_name()\n            if model_type:\n                self._set_model_type(model_type)\n            else:\n                self.model_type = self._get_model_type()\n        except (IndexError, KeyError):\n            self.log.critical(f\"Model {self.model_name} appears to be malformed. Delete and recreate it!\")\n            return\n\n    # Set the Model Training S3 Path\n    self.model_training_path = self.models_s3_path + \"/training/\" + self.model_name\n\n    # Get our Endpoint Inference Path (might be None)\n    self.endpoint_inference_path = self.get_endpoint_inference_path()\n\n    # Call SuperClass Post Initialization\n    super().__post_init__()\n\n    # All done\n    self.log.info(f\"Model Initialized: {self.model_name}\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for the Model Package Group</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n    return self.group_arn()\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get ALL the AWS metadata for this artifact</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n    return self.model_meta\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying this model</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying this model\"\"\"\n    return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.class_labels","title":"<code>class_labels()</code>","text":"<p>Return the class labels for this Model (if it's a classifier)</p> <p>Returns:</p> Type Description <code>Union[list[str], None]</code> <p>list[str]: List of class labels</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def class_labels(self) -&gt; Union[list[str], None]:\n    \"\"\"Return the class labels for this Model (if it's a classifier)\n\n    Returns:\n        list[str]: List of class labels\n    \"\"\"\n    if self.model_type == ModelType.CLASSIFIER:\n        return self.sageworks_meta().get(\"class_labels\")  # Returns None if not found\n    else:\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.confusion_matrix","title":"<code>confusion_matrix(capture_uuid='latest')</code>","text":"<p>Retrieve the confusion_matrix for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid or \"training\" (default: \"latest\")</p> <code>'latest'</code> <p>Returns:     pd.DataFrame: DataFrame of the Confusion Matrix (might be None)</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the confusion_matrix for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n    Returns:\n        pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n    \"\"\"\n\n    # Sanity check the sageworks metadata\n    if self.sageworks_meta() is None:\n        error_msg = f\"Model {self.model_name} has no sageworks_meta(). Either onboard() or delete this model!\"\n        self.log.critical(error_msg)\n        raise ValueError(error_msg)\n\n    # Grab the metrics from the SageWorks Metadata (try inference first, then training)\n    if capture_uuid == \"latest\":\n        cm = self.confusion_matrix(\"auto_inference\")\n        return cm if cm is not None else self.confusion_matrix(\"model_training\")\n\n    # Grab the confusion matrix captured during model training (could return None)\n    if capture_uuid == \"model_training\":\n        cm = self.sageworks_meta().get(\"sageworks_training_cm\")\n        return pd.DataFrame.from_dict(cm) if cm else None\n\n    else:  # Specific capture_uuid\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_cm.csv\"\n        cm = pull_s3_data(s3_path, embedded_index=True)\n        if cm is not None:\n            return cm\n        else:\n            self.log.warning(f\"Confusion Matrix {capture_uuid} not found for {self.model_name}!\")\n            return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.container_image","title":"<code>container_image()</code>","text":"<p>Container Image for the Latest Model Package</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def container_image(self) -&gt; str:\n    \"\"\"Container Image for the Latest Model Package\"\"\"\n    return self.container_info()[\"Image\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.container_info","title":"<code>container_info()</code>","text":"<p>Container Info for the Latest Model Package</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def container_info(self) -&gt; Union[dict, None]:\n    \"\"\"Container Info for the Latest Model Package\"\"\"\n    return self.latest_model[\"InferenceSpecification\"][\"Containers\"][0] if self.latest_model else None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    if self.latest_model is None:\n        return \"-\"\n    return self.latest_model[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.delete","title":"<code>delete()</code>","text":"<p>Delete the Model Packages and the Model Group</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def delete(self):\n    \"\"\"Delete the Model Packages and the Model Group\"\"\"\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the Model Group\n    ModelCore.managed_delete(model_group_name=self.uuid)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.delete_inference_run","title":"<code>delete_inference_run(inference_run_uuid)</code>","text":"<p>Delete the inference run for this model</p> <p>Parameters:</p> Name Type Description Default <code>inference_run_uuid</code> <code>str</code> <p>UUID of the inference run</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def delete_inference_run(self, inference_run_uuid: str):\n    \"\"\"Delete the inference run for this model\n\n    Args:\n        inference_run_uuid (str): UUID of the inference run\n    \"\"\"\n    if inference_run_uuid == \"model_training\":\n        self.log.warning(\"Cannot delete model training data!\")\n        return\n\n    if self.endpoint_inference_path:\n        full_path = f\"{self.endpoint_inference_path}/{inference_run_uuid}\"\n        # Check if there are any objects at the path\n        if wr.s3.list_objects(full_path):\n            wr.s3.delete_objects(path=full_path)\n            self.log.important(f\"Deleted inference run {inference_run_uuid} for {self.model_name}\")\n        else:\n            self.log.warning(f\"Inference run {inference_run_uuid} not found for {self.model_name}!\")\n    else:\n        self.log.warning(f\"No inference data found for {self.model_name}!\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this Model Args:     recompute (bool, optional): Recompute the details (default: False) Returns:     dict: Dictionary of details about this Model</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def details(self, recompute=False) -&gt; dict:\n    \"\"\"Additional Details about this Model\n    Args:\n        recompute (bool, optional): Recompute the details (default: False)\n    Returns:\n        dict: Dictionary of details about this Model\n    \"\"\"\n    self.log.info(\"Computing Model Details...\")\n    details = self.summary()\n    details[\"pipeline\"] = self.get_pipeline()\n    details[\"model_type\"] = self.model_type.value\n    details[\"model_package_group_arn\"] = self.group_arn()\n    details[\"model_package_arn\"] = self.model_package_arn()\n\n    # Sanity check is we have models in the group\n    if self.latest_model is None:\n        self.log.warning(f\"Model Package Group {self.model_name} has no models!\")\n        return details\n\n    # Grab the Model Details\n    details[\"description\"] = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n    details[\"version\"] = self.latest_model[\"ModelPackageVersion\"]\n    details[\"status\"] = self.latest_model[\"ModelPackageStatus\"]\n    details[\"approval_status\"] = self.latest_model.get(\"ModelApprovalStatus\", \"unknown\")\n    details[\"image\"] = self.container_image().split(\"/\")[-1]  # Shorten the image uri\n\n    # Grab the inference and container info\n    inference_spec = self.latest_model[\"InferenceSpecification\"]\n    container_info = self.container_info()\n    details[\"framework\"] = container_info.get(\"Framework\", \"unknown\")\n    details[\"framework_version\"] = container_info.get(\"FrameworkVersion\", \"unknown\")\n    details[\"inference_types\"] = inference_spec[\"SupportedRealtimeInferenceInstanceTypes\"]\n    details[\"transform_types\"] = inference_spec[\"SupportedTransformInstanceTypes\"]\n    details[\"content_types\"] = inference_spec[\"SupportedContentTypes\"]\n    details[\"response_types\"] = inference_spec[\"SupportedResponseMIMETypes\"]\n    details[\"model_metrics\"] = self.get_inference_metrics()\n    if self.model_type == ModelType.CLASSIFIER:\n        details[\"confusion_matrix\"] = self.confusion_matrix()\n        details[\"predictions\"] = None\n    elif self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n        details[\"confusion_matrix\"] = None\n        details[\"predictions\"] = self.get_inference_predictions()\n    else:\n        details[\"confusion_matrix\"] = None\n        details[\"predictions\"] = None\n\n    # Grab the inference metadata\n    details[\"inference_meta\"] = self.get_inference_metadata()\n\n    # Return the details\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.endpoints","title":"<code>endpoints()</code>","text":"<p>Get the list of registered endpoints for this Model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of registered endpoints</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def endpoints(self) -&gt; list[str]:\n    \"\"\"Get the list of registered endpoints for this Model\n\n    Returns:\n        list[str]: List of registered endpoints\n    \"\"\"\n    return self.sageworks_meta().get(\"sageworks_registered_endpoints\", [])\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.exists","title":"<code>exists()</code>","text":"<p>Does the model metadata exist in the AWS Metadata?</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Does the model metadata exist in the AWS Metadata?\"\"\"\n    if self.model_meta is None:\n        self.log.info(f\"Model {self.model_name} not found in AWS Metadata!\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.expected_meta","title":"<code>expected_meta()</code>","text":"<p>Metadata we expect to see for this Model when it's ready Returns:     list[str]: List of expected metadata keys</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def expected_meta(self) -&gt; list[str]:\n    \"\"\"Metadata we expect to see for this Model when it's ready\n    Returns:\n        list[str]: List of expected metadata keys\n    \"\"\"\n    # Our current list of expected metadata, we can add to this as needed\n    return [\"sageworks_status\", \"sageworks_training_metrics\", \"sageworks_training_cm\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.features","title":"<code>features()</code>","text":"<p>Return a list of features used for this Model</p> <p>Returns:</p> Type Description <code>Union[list[str], None]</code> <p>list[str]: List of features used for this Model</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def features(self) -&gt; Union[list[str], None]:\n    \"\"\"Return a list of features used for this Model\n\n    Returns:\n        list[str]: List of features used for this Model\n    \"\"\"\n    return self.sageworks_meta().get(\"sageworks_model_features\")  # Returns None if not found\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.get_endpoint_inference_path","title":"<code>get_endpoint_inference_path()</code>","text":"<p>Get the S3 Path for the Inference Data</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>S3 Path for the Inference Data (or None if not found)</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def get_endpoint_inference_path(self) -&gt; Union[str, None]:\n    \"\"\"Get the S3 Path for the Inference Data\n\n    Returns:\n        str: S3 Path for the Inference Data (or None if not found)\n    \"\"\"\n\n    # Look for any Registered Endpoints\n    registered_endpoints = self.sageworks_meta().get(\"sageworks_registered_endpoints\")\n\n    # Note: We may have 0 to N endpoints, so we find the one with the most recent artifacts\n    if registered_endpoints:\n        endpoint_inference_base = self.endpoints_s3_path + \"/inference/\"\n        endpoint_inference_paths = [endpoint_inference_base + e for e in registered_endpoints]\n        inference_path = newest_path(endpoint_inference_paths, self.sm_session)\n        if inference_path is None:\n            self.log.important(f\"No inference data found for {self.model_name}!\")\n            self.log.important(f\"Returning default inference path for {registered_endpoints[0]}...\")\n            self.log.important(f\"{endpoint_inference_paths[0]}\")\n            return endpoint_inference_paths[0]\n        else:\n            return inference_path\n    else:\n        self.log.warning(f\"No registered endpoints found for {self.model_name}!\")\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.get_inference_metadata","title":"<code>get_inference_metadata(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the inference metadata for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>A specific capture_uuid (default: \"auto_inference\")</p> <code>'auto_inference'</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[DataFrame, None]</code> <p>Dictionary of the inference metadata (might be None)</p> <p>Notes:     Basically when Endpoint inference was run, name of the dataset, the MD5, etc</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def get_inference_metadata(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the inference metadata for this model\n\n    Args:\n        capture_uuid (str, optional): A specific capture_uuid (default: \"auto_inference\")\n\n    Returns:\n        dict: Dictionary of the inference metadata (might be None)\n    Notes:\n        Basically when Endpoint inference was run, name of the dataset, the MD5, etc\n    \"\"\"\n    # Sanity check the inference path (which may or may not exist)\n    if self.endpoint_inference_path is None:\n        return None\n\n    # Check for model_training capture_uuid\n    if capture_uuid == \"model_training\":\n        # Create a DataFrame with the training metadata\n        meta_df = pd.DataFrame(\n            [\n                {\n                    \"name\": \"AWS Training Capture\",\n                    \"data_hash\": \"N/A\",\n                    \"num_rows\": \"-\",\n                    \"description\": \"-\",\n                }\n            ]\n        )\n        return meta_df\n\n    # Pull the inference metadata\n    try:\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_meta.json\"\n        return wr.s3.read_json(s3_path)\n    except NoFilesFound:\n        self.log.info(f\"Could not find model inference meta at {s3_path}...\")\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.get_inference_metrics","title":"<code>get_inference_metrics(capture_uuid='latest')</code>","text":"<p>Retrieve the inference performance metrics for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid or \"training\" (default: \"latest\")</p> <code>'latest'</code> <p>Returns:     pd.DataFrame: DataFrame of the Model Metrics</p> Note <p>If a capture_uuid isn't specified this will try to return something reasonable</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the inference performance metrics for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n    Returns:\n        pd.DataFrame: DataFrame of the Model Metrics\n\n    Note:\n        If a capture_uuid isn't specified this will try to return something reasonable\n    \"\"\"\n    # Try to get the auto_capture 'training_holdout' or the training\n    if capture_uuid == \"latest\":\n        metrics_df = self.get_inference_metrics(\"auto_inference\")\n        return metrics_df if metrics_df is not None else self.get_inference_metrics(\"model_training\")\n\n    # Grab the metrics captured during model training (could return None)\n    if capture_uuid == \"model_training\":\n        # Sanity check the sageworks metadata\n        if self.sageworks_meta() is None:\n            error_msg = f\"Model {self.model_name} has no sageworks_meta(). Either onboard() or delete this model!\"\n            self.log.critical(error_msg)\n            raise ValueError(error_msg)\n\n        metrics = self.sageworks_meta().get(\"sageworks_training_metrics\")\n        return pd.DataFrame.from_dict(metrics) if metrics else None\n\n    else:  # Specific capture_uuid (could return None)\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_metrics.csv\"\n        metrics = pull_s3_data(s3_path, embedded_index=True)\n        if metrics is not None:\n            return metrics\n        else:\n            self.log.warning(f\"Performance metrics {capture_uuid} not found for {self.model_name}!\")\n            return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.get_inference_predictions","title":"<code>get_inference_predictions(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: training_holdout)</p> <code>'auto_inference'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Captured Predictions (might be None)</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n    \"\"\"\n    self.log.important(f\"Grabbing {capture_uuid} predictions for {self.model_name}...\")\n\n    # Sanity check that the model should have predictions\n    has_predictions = self.model_type in [ModelType.CLASSIFIER, ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]\n    if not has_predictions:\n        self.log.warning(f\"No Predictions for {self.model_name}...\")\n        return None\n\n    # Special case for model_training\n    if capture_uuid == \"model_training\":\n        return self._get_validation_predictions()\n\n    # Construct the S3 path for the Inference Predictions\n    s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_predictions.csv\"\n    return pull_s3_data(s3_path)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.get_model_data_url","title":"<code>get_model_data_url()</code>","text":"<p>Retrieve the ModelDataUrl from the model's AWS metadata.</p> <p>Returns:</p> Type Description <code>Optional[str]</code> <p>Optional[str]: The ModelDataUrl if available, otherwise None.</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def get_model_data_url(self) -&gt; Optional[str]:\n    \"\"\"Retrieve the ModelDataUrl from the model's AWS metadata.\n\n    Returns:\n        Optional[str]: The ModelDataUrl if available, otherwise None.\n    \"\"\"\n    meta = self.aws_meta()\n    try:\n        return meta[\"ModelPackageList\"][0][\"InferenceSpecification\"][\"Containers\"][0][\"ModelDataUrl\"]\n    except (KeyError, IndexError, TypeError):\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.get_pipeline","title":"<code>get_pipeline()</code>","text":"<p>Get the pipeline for this model</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def get_pipeline(self) -&gt; str:\n    \"\"\"Get the pipeline for this model\"\"\"\n    return self.sageworks_meta().get(\"sageworks_pipeline\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.group_arn","title":"<code>group_arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for the Model Package Group</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def group_arn(self) -&gt; Union[str, None]:\n    \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n    return self.model_meta[\"ModelPackageGroupArn\"] if self.model_meta else None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.hash","title":"<code>hash()</code>","text":"<p>Return the hash for this artifact</p> <p>Returns:</p> Type Description <code>Optional[str]</code> <p>Optional[str]: The hash for this artifact</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def hash(self) -&gt; Optional[str]:\n    \"\"\"Return the hash for this artifact\n\n    Returns:\n        Optional[str]: The hash for this artifact\n    \"\"\"\n    model_url = self.get_model_data_url()\n    return compute_s3_object_hash(model_url, self.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this model Returns:     list[str]: List of health issues</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this model\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    # Call the base class health check\n    health_issues = super().health_check()\n\n    # Check if the model exists\n    if self.latest_model is None:\n        health_issues.append(\"model_not_found\")\n\n    # Model Type\n    if self._get_model_type() == ModelType.UNKNOWN:\n        health_issues.append(\"model_type_unknown\")\n    else:\n        self.remove_health_tag(\"model_type_unknown\")\n\n    # Model Performance Metrics\n    needs_metrics = self.model_type in {ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR, ModelType.CLASSIFIER}\n    if needs_metrics and self.get_inference_metrics() is None:\n        health_issues.append(\"metrics_needed\")\n    else:\n        self.remove_health_tag(\"metrics_needed\")\n\n    # Endpoint\n    if not self.endpoints():\n        health_issues.append(\"no_endpoint\")\n    else:\n        self.remove_health_tag(\"no_endpoint\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.is_model_unknown","title":"<code>is_model_unknown()</code>","text":"<p>Is the Model Type unknown?</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def is_model_unknown(self) -&gt; bool:\n    \"\"\"Is the Model Type unknown?\"\"\"\n    return self.model_type == ModelType.UNKNOWN\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.latest_model_object","title":"<code>latest_model_object()</code>","text":"<p>Return the latest AWS Sagemaker Model object for this SageWorks Model</p> <p>Returns:</p> Type Description <code>Model</code> <p>sagemaker.model.Model: AWS Sagemaker Model object</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def latest_model_object(self) -&gt; SagemakerModel:\n    \"\"\"Return the latest AWS Sagemaker Model object for this SageWorks Model\n\n    Returns:\n       sagemaker.model.Model: AWS Sagemaker Model object\n    \"\"\"\n    return SagemakerModel(\n        model_data=self.model_package_arn(), sagemaker_session=self.sm_session, image_uri=self.container_image()\n    )\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.list_inference_runs","title":"<code>list_inference_runs()</code>","text":"<p>List the inference runs for this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of inference runs</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def list_inference_runs(self) -&gt; list[str]:\n    \"\"\"List the inference runs for this model\n\n    Returns:\n        list[str]: List of inference runs\n    \"\"\"\n\n    # Check if we have a model (if not return empty list)\n    if self.latest_model is None:\n        return []\n\n    # Check if we have model training metrics in our metadata\n    have_model_training = True if self.sageworks_meta().get(\"sageworks_training_metrics\") else False\n\n    # Now grab the list of directories from our inference path\n    inference_runs = []\n    if self.endpoint_inference_path:\n        directories = wr.s3.list_directories(path=self.endpoint_inference_path + \"/\")\n        inference_runs = [urlparse(directory).path.split(\"/\")[-2] for directory in directories]\n\n    # We're going to add the model training to the end of the list\n    if have_model_training:\n        inference_runs.append(\"model_training\")\n    return inference_runs\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.managed_delete","title":"<code>managed_delete(model_group_name)</code>  <code>classmethod</code>","text":"<p>Delete the Model Packages, Model Group, and S3 Storage Objects</p> <p>Parameters:</p> Name Type Description Default <code>model_group_name</code> <code>str</code> <p>The name of the Model Group to delete</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>@classmethod\ndef managed_delete(cls, model_group_name: str):\n    \"\"\"Delete the Model Packages, Model Group, and S3 Storage Objects\n\n    Args:\n        model_group_name (str): The name of the Model Group to delete\n    \"\"\"\n    # Check if the model group exists in SageMaker\n    try:\n        cls.sm_client.describe_model_package_group(ModelPackageGroupName=model_group_name)\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n            cls.log.info(f\"Model Group {model_group_name} not found!\")\n            return\n        else:\n            raise  # Re-raise unexpected errors\n\n    # Delete Model Packages within the Model Group\n    try:\n        paginator = cls.sm_client.get_paginator(\"list_model_packages\")\n        for page in paginator.paginate(ModelPackageGroupName=model_group_name):\n            for model_package in page[\"ModelPackageSummaryList\"]:\n                package_arn = model_package[\"ModelPackageArn\"]\n                cls.log.info(f\"Deleting Model Package {package_arn}...\")\n                cls.sm_client.delete_model_package(ModelPackageName=package_arn)\n    except ClientError as e:\n        cls.log.error(f\"Error while deleting model packages: {e}\")\n        raise\n\n    # Delete the Model Package Group\n    cls.log.info(f\"Deleting Model Group {model_group_name}...\")\n    cls.sm_client.delete_model_package_group(ModelPackageGroupName=model_group_name)\n\n    # Delete S3 training artifacts\n    s3_delete_path = f\"{cls.models_s3_path}/training/{model_group_name}/\"\n    cls.log.info(f\"Deleting S3 Objects at {s3_delete_path}...\")\n    wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(model_group_name)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.model_package_arn","title":"<code>model_package_arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for the Latest Model Package (within the Group)</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def model_package_arn(self) -&gt; Union[str, None]:\n    \"\"\"AWS ARN (Amazon Resource Name) for the Latest Model Package (within the Group)\"\"\"\n    if self.latest_model is None:\n        return None\n    return self.latest_model[\"ModelPackageArn\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    if self.latest_model is None:\n        return \"-\"\n    return self.latest_model[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.onboard","title":"<code>onboard(ask_everything=False)</code>","text":"<p>This is an interactive method that will onboard the Model (make it ready)</p> <p>Parameters:</p> Name Type Description Default <code>ask_everything</code> <code>bool</code> <p>Ask for all the details. Defaults to False.</p> <code>False</code> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the Model is successfully onboarded, False otherwise</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def onboard(self, ask_everything=False) -&gt; bool:\n    \"\"\"This is an interactive method that will onboard the Model (make it ready)\n\n    Args:\n        ask_everything (bool, optional): Ask for all the details. Defaults to False.\n\n    Returns:\n        bool: True if the Model is successfully onboarded, False otherwise\n    \"\"\"\n    # Set the status to onboarding\n    self.set_status(\"onboarding\")\n\n    # Determine the Model Type\n    while self.is_model_unknown():\n        self._determine_model_type()\n\n    # Is our input data set?\n    if self.get_input() in [\"\", \"unknown\"] or ask_everything:\n        input_data = input(\"Input Data?: \")\n        if input_data not in [\"None\", \"none\", \"\", \"unknown\"]:\n            self.set_input(input_data)\n\n    # Determine the Target Column (can be None)\n    target_column = self.target()\n    if target_column is None or ask_everything:\n        target_column = input(\"Target Column? (for unsupervised/transformer just type None): \")\n        if target_column in [\"None\", \"none\", \"\"]:\n            target_column = None\n\n    # Determine the Feature Columns\n    feature_columns = self.features()\n    if feature_columns is None or ask_everything:\n        feature_columns = input(\"Feature Columns? (use commas): \")\n        feature_columns = [e.strip() for e in feature_columns.split(\",\")]\n        if feature_columns in [[\"None\"], [\"none\"], [\"\"]]:\n            feature_columns = None\n\n    # Registered Endpoints?\n    endpoints = self.endpoints()\n    if not endpoints or ask_everything:\n        endpoints = input(\"Register Endpoints? (use commas for multiple): \")\n        endpoints = [e.strip() for e in endpoints.split(\",\")]\n        if endpoints in [[\"None\"], [\"none\"], [\"\"]]:\n            endpoints = None\n\n    # Model Owner?\n    owner = self.get_owner()\n    if owner in [None, \"unknown\"] or ask_everything:\n        owner = input(\"Model Owner: \")\n        if owner in [\"None\", \"none\", \"\"]:\n            owner = \"unknown\"\n\n    # Model Class Labels (if it's a classifier)\n    if self.model_type == ModelType.CLASSIFIER:\n        class_labels = self.class_labels()\n        if class_labels is None or ask_everything:\n            class_labels = input(\"Class Labels? (use commas): \")\n            class_labels = [e.strip() for e in class_labels.split(\",\")]\n            if class_labels in [[\"None\"], [\"none\"], [\"\"]]:\n                class_labels = None\n        self.set_class_labels(class_labels)\n\n    # Now that we have all the details, let's onboard the Model with all the args\n    return self.onboard_with_args(self.model_type, target_column, feature_columns, endpoints, owner)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.onboard_with_args","title":"<code>onboard_with_args(model_type, target_column=None, feature_list=None, endpoints=None, owner=None)</code>","text":"<p>Onboard the Model with the given arguments</p> <p>Parameters:</p> Name Type Description Default <code>model_type</code> <code>ModelType</code> <p>Model Type</p> required <code>target_column</code> <code>str</code> <p>Target Column</p> <code>None</code> <code>feature_list</code> <code>list</code> <p>List of Feature Columns</p> <code>None</code> <code>endpoints</code> <code>list</code> <p>List of Endpoints. Defaults to None.</p> <code>None</code> <code>owner</code> <code>str</code> <p>Model Owner. Defaults to None.</p> <code>None</code> <p>Returns:     bool: True if the Model is successfully onboarded, False otherwise</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def onboard_with_args(\n    self,\n    model_type: ModelType,\n    target_column: str = None,\n    feature_list: list = None,\n    endpoints: list = None,\n    owner: str = None,\n) -&gt; bool:\n    \"\"\"Onboard the Model with the given arguments\n\n    Args:\n        model_type (ModelType): Model Type\n        target_column (str): Target Column\n        feature_list (list): List of Feature Columns\n        endpoints (list, optional): List of Endpoints. Defaults to None.\n        owner (str, optional): Model Owner. Defaults to None.\n    Returns:\n        bool: True if the Model is successfully onboarded, False otherwise\n    \"\"\"\n    # Set the status to onboarding\n    self.set_status(\"onboarding\")\n\n    # Set All the Details\n    self._set_model_type(model_type)\n    if target_column:\n        self.set_target(target_column)\n    if feature_list:\n        self.set_features(feature_list)\n    if endpoints:\n        for endpoint in endpoints:\n            self.register_endpoint(endpoint)\n    if owner:\n        self.set_owner(owner)\n\n    # Load the training metrics and inference metrics\n    self._load_training_metrics()\n    self._load_inference_metrics()\n\n    # Remove the needs_onboard tag\n    self.remove_health_tag(\"needs_onboard\")\n    self.set_status(\"ready\")\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Refresh the Artifact's metadata</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Refresh the Artifact's metadata\"\"\"\n    self.model_meta = self.meta.model(self.model_name)\n    self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n    self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n    self.training_job_name = self._extract_training_job_name()\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.register_endpoint","title":"<code>register_endpoint(endpoint_name)</code>","text":"<p>Add this endpoint to the set of registered endpoints for the model</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>Name of the endpoint</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def register_endpoint(self, endpoint_name: str):\n    \"\"\"Add this endpoint to the set of registered endpoints for the model\n\n    Args:\n        endpoint_name (str): Name of the endpoint\n    \"\"\"\n    self.log.important(f\"Registering Endpoint {endpoint_name} with Model {self.uuid}...\")\n    registered_endpoints = set(self.sageworks_meta().get(\"sageworks_registered_endpoints\", []))\n    registered_endpoints.add(endpoint_name)\n    self.upsert_sageworks_meta({\"sageworks_registered_endpoints\": list(registered_endpoints)})\n\n    # Remove any health tags\n    self.remove_health_tag(\"no_endpoint\")\n\n    # A new endpoint means we need to refresh our inference path\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.endpoint_inference_path = self.get_endpoint_inference_path()\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.remove_endpoint","title":"<code>remove_endpoint(endpoint_name)</code>","text":"<p>Remove this endpoint from the set of registered endpoints for the model</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>Name of the endpoint</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def remove_endpoint(self, endpoint_name: str):\n    \"\"\"Remove this endpoint from the set of registered endpoints for the model\n\n    Args:\n        endpoint_name (str): Name of the endpoint\n    \"\"\"\n    self.log.important(f\"Removing Endpoint {endpoint_name} from Model {self.uuid}...\")\n    registered_endpoints = set(self.sageworks_meta().get(\"sageworks_registered_endpoints\", []))\n    registered_endpoints.discard(endpoint_name)\n    self.upsert_sageworks_meta({\"sageworks_registered_endpoints\": list(registered_endpoints)})\n\n    # If we have NO endpionts, then set a health tags\n    if not registered_endpoints:\n        self.add_health_tag(\"no_endpoint\")\n        self.details(recompute=True)\n\n    # A new endpoint means we need to refresh our inference path\n    time.sleep(2)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.set_class_labels","title":"<code>set_class_labels(labels)</code>","text":"<p>Return the class labels for this Model (if it's a classifier)</p> <p>Parameters:</p> Name Type Description Default <code>labels</code> <code>list[str]</code> <p>List of class labels</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def set_class_labels(self, labels: list[str]):\n    \"\"\"Return the class labels for this Model (if it's a classifier)\n\n    Args:\n        labels (list[str]): List of class labels\n    \"\"\"\n    if self.model_type == ModelType.CLASSIFIER:\n        self.upsert_sageworks_meta({\"class_labels\": labels})\n    else:\n        self.log.error(f\"Model {self.model_name} is not a classifier!\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.set_features","title":"<code>set_features(feature_columns)</code>","text":"<p>Set the features for this Model</p> <p>Parameters:</p> Name Type Description Default <code>feature_columns</code> <code>list[str]</code> <p>List of feature columns</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def set_features(self, feature_columns: list[str]):\n    \"\"\"Set the features for this Model\n\n    Args:\n        feature_columns (list[str]): List of feature columns\n    \"\"\"\n    self.upsert_sageworks_meta({\"sageworks_model_features\": feature_columns})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.set_input","title":"<code>set_input(input, force=False)</code>","text":"<p>Override: Set the input data for this artifact</p> <p>Parameters:</p> Name Type Description Default <code>input</code> <code>str</code> <p>Name of input for this artifact</p> required <code>force</code> <code>bool</code> <p>Force the input to be set (default: False)</p> <code>False</code> <p>Note:     We're going to not allow this to be used for Models</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def set_input(self, input: str, force: bool = False):\n    \"\"\"Override: Set the input data for this artifact\n\n    Args:\n        input (str): Name of input for this artifact\n        force (bool, optional): Force the input to be set (default: False)\n    Note:\n        We're going to not allow this to be used for Models\n    \"\"\"\n    if not force:\n        self.log.warning(f\"Model {self.uuid}: Does not allow manual override of the input!\")\n        return\n\n    # Okay we're going to allow this to be set\n    self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n    self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n    self.upsert_sageworks_meta({\"sageworks_input\": input})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.set_pipeline","title":"<code>set_pipeline(pipeline)</code>","text":"<p>Set the pipeline for this model</p> <p>Parameters:</p> Name Type Description Default <code>pipeline</code> <code>str</code> <p>Pipeline that was used to create this model</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def set_pipeline(self, pipeline: str):\n    \"\"\"Set the pipeline for this model\n\n    Args:\n        pipeline (str): Pipeline that was used to create this model\n    \"\"\"\n    self.upsert_sageworks_meta({\"sageworks_pipeline\": pipeline})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.set_target","title":"<code>set_target(target_column)</code>","text":"<p>Set the target for this Model</p> <p>Parameters:</p> Name Type Description Default <code>target_column</code> <code>str</code> <p>Target column for this Model</p> required Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def set_target(self, target_column: str):\n    \"\"\"Set the target for this Model\n\n    Args:\n        target_column (str): Target column for this Model\n    \"\"\"\n    self.upsert_sageworks_meta({\"sageworks_model_target\": target_column})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.shapley_values","title":"<code>shapley_values(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the Shapely values for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: training_holdout)</p> <code>'auto_inference'</code> <p>Returns:</p> Type Description <code>Union[list[DataFrame], DataFrame, None]</code> <p>pd.DataFrame: Dataframe(s) of the shapley values or None if not found</p> Notes <p>This may or may not exist based on whether an Endpoint ran Shapley</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def shapley_values(self, capture_uuid: str = \"auto_inference\") -&gt; Union[list[pd.DataFrame], pd.DataFrame, None]:\n    \"\"\"Retrieve the Shapely values for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n    Returns:\n        pd.DataFrame: Dataframe(s) of the shapley values or None if not found\n\n    Notes:\n        This may or may not exist based on whether an Endpoint ran Shapley\n    \"\"\"\n\n    # Sanity check the inference path (which may or may not exist)\n    if self.endpoint_inference_path is None:\n        return None\n\n    # Construct the S3 path for the Shapley values\n    shapley_s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}\"\n\n    # Multiple CSV if classifier\n    if self.model_type == ModelType.CLASSIFIER:\n        # CSVs for shap values are indexed by prediction class\n        # Because we don't know how many classes there are, we need to search through\n        # a list of S3 objects in the parent folder\n        s3_paths = wr.s3.list_objects(shapley_s3_path)\n        return [pull_s3_data(f) for f in s3_paths if \"inference_shap_values\" in f]\n\n    # One CSV if regressor\n    if self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n        s3_path = f\"{shapley_s3_path}/inference_shap_values.csv\"\n        return pull_s3_data(s3_path)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.size","title":"<code>size()</code>","text":"<p>Return the size of this data in MegaBytes</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of this data in MegaBytes\"\"\"\n    return 0.0\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelCore.target","title":"<code>target()</code>","text":"<p>Return the target for this Model (if supervised, else None)</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>Target column for this Model (if supervised, else None)</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>def target(self) -&gt; Union[str, None]:\n    \"\"\"Return the target for this Model (if supervised, else None)\n\n    Returns:\n        str: Target column for this Model (if supervised, else None)\n    \"\"\"\n    return self.sageworks_meta().get(\"sageworks_model_target\")  # Returns None if not found\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#sageworks.core.artifacts.model_core.ModelType","title":"<code>ModelType</code>","text":"<p>               Bases: <code>Enum</code></p> <p>Enumerated Types for SageWorks Model Types</p> Source code in <code>src/sageworks/core/artifacts/model_core.py</code> <pre><code>class ModelType(Enum):\n    \"\"\"Enumerated Types for SageWorks Model Types\"\"\"\n\n    CLASSIFIER = \"classifier\"\n    REGRESSOR = \"regressor\"\n    CLUSTERER = \"clusterer\"\n    TRANSFORMER = \"transformer\"\n    PROJECTION = \"projection\"\n    UNSUPERVISED = \"unsupervised\"\n    QUANTILE_REGRESSOR = \"quantile_regressor\"\n    DETECTOR = \"detector\"\n    UNKNOWN = \"unknown\"\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/","title":"MonitorCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the Monitor API Class and voil\u00e0 it works the same.</p> <p>MonitorCore class for monitoring SageMaker endpoints</p>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore","title":"<code>MonitorCore</code>","text":"Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>class MonitorCore:\n    def __init__(self, endpoint_name, instance_type=\"ml.t3.large\"):\n        \"\"\"ExtractModelArtifact Class\n        Args:\n            endpoint_name (str): Name of the endpoint to set up monitoring for\n            instance_type (str): Instance type to use for monitoring. Defaults to \"ml.t3.large\".\n                                 Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...\n        \"\"\"\n        self.log = logging.getLogger(\"sageworks\")\n        self.endpoint_name = endpoint_name\n        self.endpoint = EndpointCore(self.endpoint_name)\n\n        # Initialize Class Attributes\n        self.sagemaker_session = self.endpoint.sm_session\n        self.sagemaker_client = self.endpoint.sm_client\n        self.data_capture_path = self.endpoint.endpoint_data_capture_path\n        self.monitoring_path = self.endpoint.endpoint_monitoring_path\n        self.instance_type = instance_type\n        self.monitoring_schedule_name = f\"{self.endpoint_name}-monitoring-schedule\"\n        self.monitoring_output_path = f\"{self.monitoring_path}/monitoring_reports\"\n        self.baseline_dir = f\"{self.monitoring_path}/baseline\"\n        self.baseline_csv_file = f\"{self.baseline_dir}/baseline.csv\"\n        self.constraints_json_file = f\"{self.baseline_dir}/constraints.json\"\n        self.statistics_json_file = f\"{self.baseline_dir}/statistics.json\"\n\n        # Initialize the DefaultModelMonitor\n        self.sageworks_role_arn = AWSAccountClamp().aws_session.get_sageworks_execution_role_arn()\n        self.model_monitor = DefaultModelMonitor(role=self.sageworks_role_arn, instance_type=self.instance_type)\n\n    def summary(self) -&gt; dict:\n        \"\"\"Return the summary of information about the endpoint monitor\n\n        Returns:\n            dict: Summary of information about the endpoint monitor\n        \"\"\"\n        if self.endpoint.is_serverless():\n            return {\n                \"endpoint_type\": \"serverless\",\n                \"data_capture\": \"not supported\",\n                \"baseline\": \"not supported\",\n                \"monitoring_schedule\": \"not supported\",\n            }\n        else:\n            summary = {\n                \"endpoint_type\": \"realtime\",\n                \"data_capture\": self.is_data_capture_configured(capture_percentage=100),\n                \"baseline\": self.baseline_exists(),\n                \"monitoring_schedule\": self.monitoring_schedule_exists(),\n            }\n            summary.update(self.last_run_details() or {})\n            return summary\n\n    def __repr__(self) -&gt; str:\n        \"\"\"String representation of this MonitorCore object\n\n        Returns:\n            str: String representation of this MonitorCore object\n        \"\"\"\n        summary_dict = self.summary()\n        summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items()]\n        summary_str = f\"{self.__class__.__name__}: {self.endpoint_name}\\n\" + \",\\n\".join(summary_items)\n        return summary_str\n\n    def last_run_details(self) -&gt; Union[dict, None]:\n        \"\"\"Return the details of the last monitoring run for the endpoint\n\n        Returns:\n            dict: The details of the last monitoring run for the endpoint (None if no monitoring schedule)\n        \"\"\"\n        # Check if we have a monitoring schedule\n        if not self.monitoring_schedule_exists():\n            return None\n\n        # Get the details of the last monitoring run\n        schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n            MonitoringScheduleName=self.monitoring_schedule_name\n        )\n        last_run_status = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"MonitoringExecutionStatus\")\n        last_run_time = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"ScheduledTime\")\n        failure_reason = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"FailureReason\")\n        return {\n            \"last_run_status\": last_run_status,\n            \"last_run_time\": str(last_run_time),\n            \"failure_reason\": failure_reason,\n        }\n\n    def details(self) -&gt; dict:\n        \"\"\"Return the details of the monitoring for the endpoint\n\n        Returns:\n            dict: The details of the monitoring for the endpoint\n        \"\"\"\n        # Check if we have data capture\n        if self.is_data_capture_configured(capture_percentage=100):\n            data_capture_path = self.data_capture_path\n        else:\n            data_capture_path = None\n\n        # Check if we have a baseline\n        if self.baseline_exists():\n            baseline_csv_file = self.baseline_csv_file\n            constraints_json_file = self.constraints_json_file\n            statistics_json_file = self.statistics_json_file\n        else:\n            baseline_csv_file = None\n            constraints_json_file = None\n            statistics_json_file = None\n\n        # Check if we have a monitoring schedule\n        if self.monitoring_schedule_exists():\n            schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n                MonitoringScheduleName=self.monitoring_schedule_name\n            )\n\n            # General monitoring details\n            schedule_name = schedule_details.get(\"MonitoringScheduleName\")\n            schedule_status = schedule_details.get(\"MonitoringScheduleStatus\")\n            output_path = self.monitoring_output_path\n            last_run_details = self.last_run_details()\n        else:\n            schedule_name = None\n            schedule_status = \"Not Scheduled\"\n            schedule_details = None\n            output_path = None\n            last_run_details = None\n\n        # General monitoring details\n        general = {\n            \"data_capture_path\": data_capture_path,\n            \"baseline_csv_file\": baseline_csv_file,\n            \"baseline_constraints_json_file\": constraints_json_file,\n            \"baseline_statistics_json_file\": statistics_json_file,\n            \"monitoring_schedule_name\": schedule_name,\n            \"monitoring_output_path\": output_path,\n            \"monitoring_schedule_status\": schedule_status,\n            \"monitoring_schedule_details\": schedule_details,\n        }\n        if last_run_details:\n            general.update(last_run_details)\n        return general\n\n    def add_data_capture(self, capture_percentage=100):\n        \"\"\"\n        Add data capture configuration for the SageMaker endpoint.\n\n        Args:\n            capture_percentage (int): Percentage of data to capture. Defaults to 100.\n        \"\"\"\n\n        # Check if this endpoint is a serverless endpoint\n        if self.endpoint.is_serverless():\n            self.log.warning(\"Data capture is not currently supported for serverless endpoints.\")\n            return\n\n        # Check if the endpoint already has data capture configured\n        if self.is_data_capture_configured(capture_percentage):\n            self.log.important(f\"Data capture {capture_percentage} already configured for {self.endpoint_name}.\")\n            return\n\n        # Get the current endpoint configuration name\n        current_endpoint_config_name = self.endpoint.endpoint_config_name()\n\n        # Log the data capture path\n        self.log.important(f\"Adding Data Capture to {self.endpoint_name} --&gt; {self.data_capture_path}\")\n        self.log.important(\"This normally redeploys the endpoint...\")\n\n        # Setup data capture config\n        data_capture_config = DataCaptureConfig(\n            enable_capture=True,\n            sampling_percentage=capture_percentage,\n            destination_s3_uri=self.data_capture_path,\n            capture_options=[\"Input\", \"Output\"],\n            csv_content_types=[\"text/csv\"],\n        )\n\n        # Create a Predictor instance and update data capture configuration\n        predictor = Predictor(self.endpoint_name, sagemaker_session=self.sagemaker_session)\n        predictor.update_data_capture_config(data_capture_config=data_capture_config)\n\n        # Delete the old endpoint configuration\n        self.log.important(f\"Deleting old endpoint configuration: {current_endpoint_config_name}\")\n        self.sagemaker_client.delete_endpoint_config(EndpointConfigName=current_endpoint_config_name)\n\n    def is_data_capture_configured(self, capture_percentage):\n        \"\"\"\n        Check if data capture is already configured on the endpoint.\n        Args:\n            capture_percentage (int): Expected data capture percentage.\n        Returns:\n            bool: True if data capture is already configured, False otherwise.\n        \"\"\"\n        try:\n            endpoint_config_name = self.endpoint.endpoint_config_name()\n            endpoint_config = self.sagemaker_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n            data_capture_config = endpoint_config.get(\"DataCaptureConfig\", {})\n\n            # Check if data capture is enabled and the percentage matches\n            is_enabled = data_capture_config.get(\"EnableCapture\", False)\n            current_percentage = data_capture_config.get(\"InitialSamplingPercentage\", 0)\n            return is_enabled and current_percentage == capture_percentage\n        except Exception as e:\n            self.log.error(f\"Error checking data capture configuration: {e}\")\n            return False\n\n    def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"\n        Get the latest data capture from S3.\n\n        Returns:\n            DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n        \"\"\"\n        # List files in the specified S3 path\n        files = wr.s3.list_objects(self.data_capture_path)\n\n        if files:\n            print(f\"Found {len(files)} files in {self.data_capture_path}. Reading the most recent file.\")\n\n            # Read the most recent file into a DataFrame\n            df = wr.s3.read_json(path=files[-1], lines=True)  # Reads the last file assuming it's the most recent one\n\n            # Process the captured data and return the input and output DataFrames\n            return self.process_captured_data(df)\n        else:\n            print(f\"No data capture files found in {self.data_capture_path}.\")\n            return None, None\n\n    @staticmethod\n    def process_captured_data(df: pd.DataFrame) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"\n        Process the captured data DataFrame to extract and flatten the nested data.\n\n        Args:\n            df (DataFrame): DataFrame with captured data.\n\n        Returns:\n            DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n        \"\"\"\n        processed_records = []\n\n        # Phase1: Process the AWS Data Capture format into a flatter DataFrame\n        for _, row in df.iterrows():\n            # Extract data from captureData dictionary\n            capture_data = row[\"captureData\"]\n            input_data = capture_data[\"endpointInput\"]\n            output_data = capture_data[\"endpointOutput\"]\n\n            # Process input and output, both meta and actual data\n            record = {\n                \"input_content_type\": input_data.get(\"observedContentType\"),\n                \"input_encoding\": input_data.get(\"encoding\"),\n                \"input\": input_data.get(\"data\"),\n                \"output_content_type\": output_data.get(\"observedContentType\"),\n                \"output_encoding\": output_data.get(\"encoding\"),\n                \"output\": output_data.get(\"data\"),\n            }\n            processed_records.append(record)\n        processed_df = pd.DataFrame(processed_records)\n\n        # Phase2: Process the input and output 'data' columns into separate DataFrames\n        input_df_list = []\n        output_df_list = []\n        for _, row in processed_df.iterrows():\n            input_df = pd.read_csv(StringIO(row[\"input\"]))\n            input_df_list.append(input_df)\n            output_df = pd.read_csv(StringIO(row[\"output\"]))\n            output_df_list.append(output_df)\n\n        # Return the input and output DataFrames\n        return pd.concat(input_df_list), pd.concat(output_df_list)\n\n    def baseline_exists(self) -&gt; bool:\n        \"\"\"\n        Check if baseline files exist in S3.\n\n        Returns:\n            bool: True if all files exist, False otherwise.\n        \"\"\"\n\n        files = [self.baseline_csv_file, self.constraints_json_file, self.statistics_json_file]\n        return all(wr.s3.does_object_exist(file) for file in files)\n\n    def create_baseline(self, recreate: bool = False):\n        \"\"\"Code to create a baseline for monitoring\n        Args:\n            recreate (bool): If True, recreate the baseline even if it already exists\n        Notes:\n            This will create/write three files to the baseline_dir:\n            - baseline.csv\n            - constraints.json\n            - statistics.json\n        \"\"\"\n        # Check if this endpoint is a serverless endpoint\n        if self.endpoint.is_serverless():\n            self.log.warning(\n                \"You can create a baseline but it can't be used/monitored for serverless endpoints, skipping...\"\n            )\n            return\n\n        if not self.baseline_exists() or recreate:\n            # Create a baseline for monitoring (training data from the FeatureSet)\n            baseline_df = endpoint_utils.fs_training_data(self.endpoint)\n            wr.s3.to_csv(baseline_df, self.baseline_csv_file, index=False)\n\n            self.log.important(f\"Creating baseline files for {self.endpoint_name} --&gt; {self.baseline_dir}\")\n            self.model_monitor.suggest_baseline(\n                baseline_dataset=self.baseline_csv_file,\n                dataset_format=DatasetFormat.csv(header=True),\n                output_s3_uri=self.baseline_dir,\n            )\n        else:\n            self.log.important(f\"Baseline already exists for {self.endpoint_name}\")\n\n    def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n        Returns:\n            pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n        \"\"\"\n        # Read the monitoring data from S3\n        if not wr.s3.does_object_exist(path=self.baseline_csv_file):\n            self.log.warning(\"baseline.csv data does not exist in S3.\")\n            return None\n        else:\n            return wr.s3.read_csv(self.baseline_csv_file)\n\n    def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the constraints from the baseline\n\n        Returns:\n           pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n        \"\"\"\n        return self._get_monitor_json_data(self.constraints_json_file)\n\n    def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the statistics from the baseline\n\n        Returns:\n            pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n        \"\"\"\n        return self._get_monitor_json_data(self.statistics_json_file)\n\n    def _get_monitor_json_data(self, s3_path: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Internal: Convert the JSON monitoring data into a DataFrame\n        Args:\n            s3_path(str): The S3 path to the monitoring data\n        Returns:\n            pd.DataFrame: Monitoring data in DataFrame form (None if it doesn't exist)\n        \"\"\"\n        # Read the monitoring data from S3\n        if not wr.s3.does_object_exist(path=s3_path):\n            self.log.warning(\"Monitoring data does not exist in S3.\")\n            return None\n        else:\n            raw_json = read_s3_file(s3_path=s3_path)\n            monitoring_data = json.loads(raw_json)\n            monitoring_df = pd.json_normalize(monitoring_data[\"features\"])\n            return monitoring_df\n\n    def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n        \"\"\"\n        Sets up the monitoring schedule for the model endpoint.\n        Args:\n            schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n            recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n        \"\"\"\n        # Check if this endpoint is a serverless endpoint\n        if self.endpoint.is_serverless():\n            self.log.warning(\"Monitoring Schedule is not currently supported for serverless endpoints.\")\n            return\n\n        # Set up the monitoring schedule, name, and output path\n        if schedule == \"daily\":\n            schedule = CronExpressionGenerator.daily()\n        else:\n            schedule = CronExpressionGenerator.hourly()\n\n        # Check if the baseline exists\n        if not self.baseline_exists():\n            self.log.warning(f\"Baseline does not exist for {self.endpoint_name}. Call create_baseline() first...\")\n            return\n\n        # Check if monitoring schedule already exists\n        schedule_exists = self.monitoring_schedule_exists()\n\n        # If the schedule exists, and we don't want to recreate it, return\n        if schedule_exists and not recreate:\n            return\n\n        # If the schedule exists, delete it\n        if schedule_exists:\n            self.log.important(f\"Deleting existing monitoring schedule for {self.endpoint_name}...\")\n            self.sagemaker_client.delete_monitoring_schedule(MonitoringScheduleName=self.monitoring_schedule_name)\n\n        # Set up a NEW monitoring schedule\n        self.model_monitor.create_monitoring_schedule(\n            monitor_schedule_name=self.monitoring_schedule_name,\n            endpoint_input=self.endpoint_name,\n            output_s3_uri=self.monitoring_output_path,\n            statistics=self.statistics_json_file,\n            constraints=self.constraints_json_file,\n            schedule_cron_expression=schedule,\n        )\n        self.log.important(f\"New Monitoring schedule created for {self.endpoint_name}.\")\n\n    def setup_alerts(self):\n        \"\"\"Code to set up alerts based on monitoring results\"\"\"\n        pass\n\n    def monitoring_schedule_exists(self):\n        \"\"\"Code to figure out if a monitoring schedule already exists for this endpoint\"\"\"\n        existing_schedules = self.sagemaker_client.list_monitoring_schedules(MaxResults=100).get(\n            \"MonitoringScheduleSummaries\", []\n        )\n        if any(schedule[\"MonitoringScheduleName\"] == self.monitoring_schedule_name for schedule in existing_schedules):\n            self.log.info(f\"Monitoring schedule already exists for {self.endpoint_name}.\")\n            return True\n        else:\n            self.log.info(f\"Could not find a Monitoring schedule for {self.endpoint_name}.\")\n            return False\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.__init__","title":"<code>__init__(endpoint_name, instance_type='ml.t3.large')</code>","text":"<p>ExtractModelArtifact Class Args:     endpoint_name (str): Name of the endpoint to set up monitoring for     instance_type (str): Instance type to use for monitoring. Defaults to \"ml.t3.large\".                          Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def __init__(self, endpoint_name, instance_type=\"ml.t3.large\"):\n    \"\"\"ExtractModelArtifact Class\n    Args:\n        endpoint_name (str): Name of the endpoint to set up monitoring for\n        instance_type (str): Instance type to use for monitoring. Defaults to \"ml.t3.large\".\n                             Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...\n    \"\"\"\n    self.log = logging.getLogger(\"sageworks\")\n    self.endpoint_name = endpoint_name\n    self.endpoint = EndpointCore(self.endpoint_name)\n\n    # Initialize Class Attributes\n    self.sagemaker_session = self.endpoint.sm_session\n    self.sagemaker_client = self.endpoint.sm_client\n    self.data_capture_path = self.endpoint.endpoint_data_capture_path\n    self.monitoring_path = self.endpoint.endpoint_monitoring_path\n    self.instance_type = instance_type\n    self.monitoring_schedule_name = f\"{self.endpoint_name}-monitoring-schedule\"\n    self.monitoring_output_path = f\"{self.monitoring_path}/monitoring_reports\"\n    self.baseline_dir = f\"{self.monitoring_path}/baseline\"\n    self.baseline_csv_file = f\"{self.baseline_dir}/baseline.csv\"\n    self.constraints_json_file = f\"{self.baseline_dir}/constraints.json\"\n    self.statistics_json_file = f\"{self.baseline_dir}/statistics.json\"\n\n    # Initialize the DefaultModelMonitor\n    self.sageworks_role_arn = AWSAccountClamp().aws_session.get_sageworks_execution_role_arn()\n    self.model_monitor = DefaultModelMonitor(role=self.sageworks_role_arn, instance_type=self.instance_type)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.__repr__","title":"<code>__repr__()</code>","text":"<p>String representation of this MonitorCore object</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>String representation of this MonitorCore object</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def __repr__(self) -&gt; str:\n    \"\"\"String representation of this MonitorCore object\n\n    Returns:\n        str: String representation of this MonitorCore object\n    \"\"\"\n    summary_dict = self.summary()\n    summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items()]\n    summary_str = f\"{self.__class__.__name__}: {self.endpoint_name}\\n\" + \",\\n\".join(summary_items)\n    return summary_str\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.add_data_capture","title":"<code>add_data_capture(capture_percentage=100)</code>","text":"<p>Add data capture configuration for the SageMaker endpoint.</p> <p>Parameters:</p> Name Type Description Default <code>capture_percentage</code> <code>int</code> <p>Percentage of data to capture. Defaults to 100.</p> <code>100</code> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def add_data_capture(self, capture_percentage=100):\n    \"\"\"\n    Add data capture configuration for the SageMaker endpoint.\n\n    Args:\n        capture_percentage (int): Percentage of data to capture. Defaults to 100.\n    \"\"\"\n\n    # Check if this endpoint is a serverless endpoint\n    if self.endpoint.is_serverless():\n        self.log.warning(\"Data capture is not currently supported for serverless endpoints.\")\n        return\n\n    # Check if the endpoint already has data capture configured\n    if self.is_data_capture_configured(capture_percentage):\n        self.log.important(f\"Data capture {capture_percentage} already configured for {self.endpoint_name}.\")\n        return\n\n    # Get the current endpoint configuration name\n    current_endpoint_config_name = self.endpoint.endpoint_config_name()\n\n    # Log the data capture path\n    self.log.important(f\"Adding Data Capture to {self.endpoint_name} --&gt; {self.data_capture_path}\")\n    self.log.important(\"This normally redeploys the endpoint...\")\n\n    # Setup data capture config\n    data_capture_config = DataCaptureConfig(\n        enable_capture=True,\n        sampling_percentage=capture_percentage,\n        destination_s3_uri=self.data_capture_path,\n        capture_options=[\"Input\", \"Output\"],\n        csv_content_types=[\"text/csv\"],\n    )\n\n    # Create a Predictor instance and update data capture configuration\n    predictor = Predictor(self.endpoint_name, sagemaker_session=self.sagemaker_session)\n    predictor.update_data_capture_config(data_capture_config=data_capture_config)\n\n    # Delete the old endpoint configuration\n    self.log.important(f\"Deleting old endpoint configuration: {current_endpoint_config_name}\")\n    self.sagemaker_client.delete_endpoint_config(EndpointConfigName=current_endpoint_config_name)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.baseline_exists","title":"<code>baseline_exists()</code>","text":"<p>Check if baseline files exist in S3.</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if all files exist, False otherwise.</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def baseline_exists(self) -&gt; bool:\n    \"\"\"\n    Check if baseline files exist in S3.\n\n    Returns:\n        bool: True if all files exist, False otherwise.\n    \"\"\"\n\n    files = [self.baseline_csv_file, self.constraints_json_file, self.statistics_json_file]\n    return all(wr.s3.does_object_exist(file) for file in files)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.create_baseline","title":"<code>create_baseline(recreate=False)</code>","text":"<p>Code to create a baseline for monitoring Args:     recreate (bool): If True, recreate the baseline even if it already exists Notes:     This will create/write three files to the baseline_dir:     - baseline.csv     - constraints.json     - statistics.json</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def create_baseline(self, recreate: bool = False):\n    \"\"\"Code to create a baseline for monitoring\n    Args:\n        recreate (bool): If True, recreate the baseline even if it already exists\n    Notes:\n        This will create/write three files to the baseline_dir:\n        - baseline.csv\n        - constraints.json\n        - statistics.json\n    \"\"\"\n    # Check if this endpoint is a serverless endpoint\n    if self.endpoint.is_serverless():\n        self.log.warning(\n            \"You can create a baseline but it can't be used/monitored for serverless endpoints, skipping...\"\n        )\n        return\n\n    if not self.baseline_exists() or recreate:\n        # Create a baseline for monitoring (training data from the FeatureSet)\n        baseline_df = endpoint_utils.fs_training_data(self.endpoint)\n        wr.s3.to_csv(baseline_df, self.baseline_csv_file, index=False)\n\n        self.log.important(f\"Creating baseline files for {self.endpoint_name} --&gt; {self.baseline_dir}\")\n        self.model_monitor.suggest_baseline(\n            baseline_dataset=self.baseline_csv_file,\n            dataset_format=DatasetFormat.csv(header=True),\n            output_s3_uri=self.baseline_dir,\n        )\n    else:\n        self.log.important(f\"Baseline already exists for {self.endpoint_name}\")\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.create_monitoring_schedule","title":"<code>create_monitoring_schedule(schedule='hourly', recreate=False)</code>","text":"<p>Sets up the monitoring schedule for the model endpoint. Args:     schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).     recreate (bool): If True, recreate the monitoring schedule even if it already exists.</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n    \"\"\"\n    Sets up the monitoring schedule for the model endpoint.\n    Args:\n        schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n        recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n    \"\"\"\n    # Check if this endpoint is a serverless endpoint\n    if self.endpoint.is_serverless():\n        self.log.warning(\"Monitoring Schedule is not currently supported for serverless endpoints.\")\n        return\n\n    # Set up the monitoring schedule, name, and output path\n    if schedule == \"daily\":\n        schedule = CronExpressionGenerator.daily()\n    else:\n        schedule = CronExpressionGenerator.hourly()\n\n    # Check if the baseline exists\n    if not self.baseline_exists():\n        self.log.warning(f\"Baseline does not exist for {self.endpoint_name}. Call create_baseline() first...\")\n        return\n\n    # Check if monitoring schedule already exists\n    schedule_exists = self.monitoring_schedule_exists()\n\n    # If the schedule exists, and we don't want to recreate it, return\n    if schedule_exists and not recreate:\n        return\n\n    # If the schedule exists, delete it\n    if schedule_exists:\n        self.log.important(f\"Deleting existing monitoring schedule for {self.endpoint_name}...\")\n        self.sagemaker_client.delete_monitoring_schedule(MonitoringScheduleName=self.monitoring_schedule_name)\n\n    # Set up a NEW monitoring schedule\n    self.model_monitor.create_monitoring_schedule(\n        monitor_schedule_name=self.monitoring_schedule_name,\n        endpoint_input=self.endpoint_name,\n        output_s3_uri=self.monitoring_output_path,\n        statistics=self.statistics_json_file,\n        constraints=self.constraints_json_file,\n        schedule_cron_expression=schedule,\n    )\n    self.log.important(f\"New Monitoring schedule created for {self.endpoint_name}.\")\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.details","title":"<code>details()</code>","text":"<p>Return the details of the monitoring for the endpoint</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The details of the monitoring for the endpoint</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def details(self) -&gt; dict:\n    \"\"\"Return the details of the monitoring for the endpoint\n\n    Returns:\n        dict: The details of the monitoring for the endpoint\n    \"\"\"\n    # Check if we have data capture\n    if self.is_data_capture_configured(capture_percentage=100):\n        data_capture_path = self.data_capture_path\n    else:\n        data_capture_path = None\n\n    # Check if we have a baseline\n    if self.baseline_exists():\n        baseline_csv_file = self.baseline_csv_file\n        constraints_json_file = self.constraints_json_file\n        statistics_json_file = self.statistics_json_file\n    else:\n        baseline_csv_file = None\n        constraints_json_file = None\n        statistics_json_file = None\n\n    # Check if we have a monitoring schedule\n    if self.monitoring_schedule_exists():\n        schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n            MonitoringScheduleName=self.monitoring_schedule_name\n        )\n\n        # General monitoring details\n        schedule_name = schedule_details.get(\"MonitoringScheduleName\")\n        schedule_status = schedule_details.get(\"MonitoringScheduleStatus\")\n        output_path = self.monitoring_output_path\n        last_run_details = self.last_run_details()\n    else:\n        schedule_name = None\n        schedule_status = \"Not Scheduled\"\n        schedule_details = None\n        output_path = None\n        last_run_details = None\n\n    # General monitoring details\n    general = {\n        \"data_capture_path\": data_capture_path,\n        \"baseline_csv_file\": baseline_csv_file,\n        \"baseline_constraints_json_file\": constraints_json_file,\n        \"baseline_statistics_json_file\": statistics_json_file,\n        \"monitoring_schedule_name\": schedule_name,\n        \"monitoring_output_path\": output_path,\n        \"monitoring_schedule_status\": schedule_status,\n        \"monitoring_schedule_details\": schedule_details,\n    }\n    if last_run_details:\n        general.update(last_run_details)\n    return general\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.get_baseline","title":"<code>get_baseline()</code>","text":"<p>Code to get the baseline CSV from the S3 baseline directory</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n    Returns:\n        pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n    \"\"\"\n    # Read the monitoring data from S3\n    if not wr.s3.does_object_exist(path=self.baseline_csv_file):\n        self.log.warning(\"baseline.csv data does not exist in S3.\")\n        return None\n    else:\n        return wr.s3.read_csv(self.baseline_csv_file)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.get_constraints","title":"<code>get_constraints()</code>","text":"<p>Code to get the constraints from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the constraints from the baseline\n\n    Returns:\n       pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n    \"\"\"\n    return self._get_monitor_json_data(self.constraints_json_file)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.get_latest_data_capture","title":"<code>get_latest_data_capture()</code>","text":"<p>Get the latest data capture from S3.</p> <p>Returns:</p> Name Type Description <code>DataFrame</code> <code>input), DataFrame(output</code> <p>Flattened and processed DataFrames for input and output data.</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n    \"\"\"\n    Get the latest data capture from S3.\n\n    Returns:\n        DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n    \"\"\"\n    # List files in the specified S3 path\n    files = wr.s3.list_objects(self.data_capture_path)\n\n    if files:\n        print(f\"Found {len(files)} files in {self.data_capture_path}. Reading the most recent file.\")\n\n        # Read the most recent file into a DataFrame\n        df = wr.s3.read_json(path=files[-1], lines=True)  # Reads the last file assuming it's the most recent one\n\n        # Process the captured data and return the input and output DataFrames\n        return self.process_captured_data(df)\n    else:\n        print(f\"No data capture files found in {self.data_capture_path}.\")\n        return None, None\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.get_statistics","title":"<code>get_statistics()</code>","text":"<p>Code to get the statistics from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the statistics from the baseline\n\n    Returns:\n        pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n    \"\"\"\n    return self._get_monitor_json_data(self.statistics_json_file)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.is_data_capture_configured","title":"<code>is_data_capture_configured(capture_percentage)</code>","text":"<p>Check if data capture is already configured on the endpoint. Args:     capture_percentage (int): Expected data capture percentage. Returns:     bool: True if data capture is already configured, False otherwise.</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def is_data_capture_configured(self, capture_percentage):\n    \"\"\"\n    Check if data capture is already configured on the endpoint.\n    Args:\n        capture_percentage (int): Expected data capture percentage.\n    Returns:\n        bool: True if data capture is already configured, False otherwise.\n    \"\"\"\n    try:\n        endpoint_config_name = self.endpoint.endpoint_config_name()\n        endpoint_config = self.sagemaker_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n        data_capture_config = endpoint_config.get(\"DataCaptureConfig\", {})\n\n        # Check if data capture is enabled and the percentage matches\n        is_enabled = data_capture_config.get(\"EnableCapture\", False)\n        current_percentage = data_capture_config.get(\"InitialSamplingPercentage\", 0)\n        return is_enabled and current_percentage == capture_percentage\n    except Exception as e:\n        self.log.error(f\"Error checking data capture configuration: {e}\")\n        return False\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.last_run_details","title":"<code>last_run_details()</code>","text":"<p>Return the details of the last monitoring run for the endpoint</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the last monitoring run for the endpoint (None if no monitoring schedule)</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def last_run_details(self) -&gt; Union[dict, None]:\n    \"\"\"Return the details of the last monitoring run for the endpoint\n\n    Returns:\n        dict: The details of the last monitoring run for the endpoint (None if no monitoring schedule)\n    \"\"\"\n    # Check if we have a monitoring schedule\n    if not self.monitoring_schedule_exists():\n        return None\n\n    # Get the details of the last monitoring run\n    schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n        MonitoringScheduleName=self.monitoring_schedule_name\n    )\n    last_run_status = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"MonitoringExecutionStatus\")\n    last_run_time = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"ScheduledTime\")\n    failure_reason = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"FailureReason\")\n    return {\n        \"last_run_status\": last_run_status,\n        \"last_run_time\": str(last_run_time),\n        \"failure_reason\": failure_reason,\n    }\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.monitoring_schedule_exists","title":"<code>monitoring_schedule_exists()</code>","text":"<p>Code to figure out if a monitoring schedule already exists for this endpoint</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def monitoring_schedule_exists(self):\n    \"\"\"Code to figure out if a monitoring schedule already exists for this endpoint\"\"\"\n    existing_schedules = self.sagemaker_client.list_monitoring_schedules(MaxResults=100).get(\n        \"MonitoringScheduleSummaries\", []\n    )\n    if any(schedule[\"MonitoringScheduleName\"] == self.monitoring_schedule_name for schedule in existing_schedules):\n        self.log.info(f\"Monitoring schedule already exists for {self.endpoint_name}.\")\n        return True\n    else:\n        self.log.info(f\"Could not find a Monitoring schedule for {self.endpoint_name}.\")\n        return False\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.process_captured_data","title":"<code>process_captured_data(df)</code>  <code>staticmethod</code>","text":"<p>Process the captured data DataFrame to extract and flatten the nested data.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>DataFrame with captured data.</p> required <p>Returns:</p> Name Type Description <code>DataFrame</code> <code>input), DataFrame(output</code> <p>Flattened and processed DataFrames for input and output data.</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>@staticmethod\ndef process_captured_data(df: pd.DataFrame) -&gt; (pd.DataFrame, pd.DataFrame):\n    \"\"\"\n    Process the captured data DataFrame to extract and flatten the nested data.\n\n    Args:\n        df (DataFrame): DataFrame with captured data.\n\n    Returns:\n        DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n    \"\"\"\n    processed_records = []\n\n    # Phase1: Process the AWS Data Capture format into a flatter DataFrame\n    for _, row in df.iterrows():\n        # Extract data from captureData dictionary\n        capture_data = row[\"captureData\"]\n        input_data = capture_data[\"endpointInput\"]\n        output_data = capture_data[\"endpointOutput\"]\n\n        # Process input and output, both meta and actual data\n        record = {\n            \"input_content_type\": input_data.get(\"observedContentType\"),\n            \"input_encoding\": input_data.get(\"encoding\"),\n            \"input\": input_data.get(\"data\"),\n            \"output_content_type\": output_data.get(\"observedContentType\"),\n            \"output_encoding\": output_data.get(\"encoding\"),\n            \"output\": output_data.get(\"data\"),\n        }\n        processed_records.append(record)\n    processed_df = pd.DataFrame(processed_records)\n\n    # Phase2: Process the input and output 'data' columns into separate DataFrames\n    input_df_list = []\n    output_df_list = []\n    for _, row in processed_df.iterrows():\n        input_df = pd.read_csv(StringIO(row[\"input\"]))\n        input_df_list.append(input_df)\n        output_df = pd.read_csv(StringIO(row[\"output\"]))\n        output_df_list.append(output_df)\n\n    # Return the input and output DataFrames\n    return pd.concat(input_df_list), pd.concat(output_df_list)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.setup_alerts","title":"<code>setup_alerts()</code>","text":"<p>Code to set up alerts based on monitoring results</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def setup_alerts(self):\n    \"\"\"Code to set up alerts based on monitoring results\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#sageworks.core.artifacts.monitor_core.MonitorCore.summary","title":"<code>summary()</code>","text":"<p>Return the summary of information about the endpoint monitor</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>Summary of information about the endpoint monitor</p> Source code in <code>src/sageworks/core/artifacts/monitor_core.py</code> <pre><code>def summary(self) -&gt; dict:\n    \"\"\"Return the summary of information about the endpoint monitor\n\n    Returns:\n        dict: Summary of information about the endpoint monitor\n    \"\"\"\n    if self.endpoint.is_serverless():\n        return {\n            \"endpoint_type\": \"serverless\",\n            \"data_capture\": \"not supported\",\n            \"baseline\": \"not supported\",\n            \"monitoring_schedule\": \"not supported\",\n        }\n    else:\n        summary = {\n            \"endpoint_type\": \"realtime\",\n            \"data_capture\": self.is_data_capture_configured(capture_percentage=100),\n            \"baseline\": self.baseline_exists(),\n            \"monitoring_schedule\": self.monitoring_schedule_exists(),\n        }\n        summary.update(self.last_run_details() or {})\n        return summary\n</code></pre>"},{"location":"core_classes/artifacts/overview/","title":"SageWorks Artifacts","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p>"},{"location":"core_classes/artifacts/overview/#welcome-to-the-sageworks-core-artifact-classes","title":"Welcome to the SageWorks Core Artifact Classes","text":"<p>These classes provide low-level APIs for the SageWorks package, they interact more directly with AWS Services and are therefore more complex with a fairly large number of methods. </p> <ul> <li>AthenaSource: Manages AWS Data Catalog and Athena</li> <li>FeatureSetCore: Manages AWS Feature Store and Feature Groups</li> <li>ModelCore: Manages the training and deployment of AWS Model Groups and Packages</li> <li>EndpointCore: Manages the deployment and invocations/inference on AWS Endpoints</li> </ul> <p></p>"},{"location":"core_classes/transforms/data_loaders_heavy/","title":"DataLoaders Heavy","text":"<p>These DataLoader Classes are intended to load larger dataset into AWS. For large data we need to use AWS Glue Jobs/Batch Jobs and in general the process is a bit more complicated and has less features.</p> <p>If you have smaller data please see DataLoaders Light</p> <p>Welcome to the SageWorks DataLoaders Heavy Classes</p> <p>These classes provide low-level APIs for loading larger data into AWS services</p> <ul> <li>S3HeavyToDataSource: Loads large data from S3 into a DataSource</li> </ul>"},{"location":"core_classes/transforms/data_loaders_heavy/#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource","title":"<code>S3HeavyToDataSource</code>","text":"Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>class S3HeavyToDataSource:\n    def __init__(self, glue_context: GlueContext, input_uuid: str, output_uuid: str):\n        \"\"\"S3HeavyToDataSource: Class to move HEAVY S3 Files into a SageWorks DataSource\n\n        Args:\n            glue_context: GlueContext, AWS Glue Specific wrapper around SparkContext\n            input_uuid (str): The S3 Path to the files to be loaded\n            output_uuid (str): The UUID of the SageWorks DataSource to be created\n        \"\"\"\n        self.log = glue_context.get_logger()\n\n        # FIXME: Pull these from Parameter Store or Config\n        self.input_uuid = input_uuid\n        self.output_uuid = output_uuid\n        self.output_meta = {\"sageworks_input\": self.input_uuid}\n        sageworks_bucket = \"s3://sandbox-sageworks-artifacts\"\n        self.data_sources_s3_path = sageworks_bucket + \"/data-sources\"\n\n        # Our Spark Context\n        self.glue_context = glue_context\n\n    @staticmethod\n    def resolve_choice_fields(dyf):\n        # Get schema fields\n        schema_fields = dyf.schema().fields\n\n        # Collect choice fields\n        choice_fields = [(field.name, \"cast:long\") for field in schema_fields if field.dataType.typeName() == \"choice\"]\n        print(f\"Choice Fields: {choice_fields}\")\n\n        # If there are choice fields, resolve them\n        if choice_fields:\n            dyf = dyf.resolveChoice(specs=choice_fields)\n\n        return dyf\n\n    def timestamp_conversions(self, dyf: DynamicFrame, time_columns: list = []) -&gt; DynamicFrame:\n        \"\"\"Convert columns in the DynamicFrame to the correct data types\n        Args:\n            dyf (DynamicFrame): The DynamicFrame to convert\n            time_columns (list): A list of column names to convert to timestamp\n        Returns:\n            DynamicFrame: The converted DynamicFrame\n        \"\"\"\n\n        # Convert the timestamp columns to timestamp types\n        spark_df = dyf.toDF()\n        for column in time_columns:\n            spark_df = spark_df.withColumn(column, to_timestamp(col(column)))\n\n        # Convert the Spark DataFrame back to a Glue DynamicFrame and return\n        return DynamicFrame.fromDF(spark_df, self.glue_context, \"output_dyf\")\n\n    @staticmethod\n    def remove_periods_from_columns(dyf: DynamicFrame) -&gt; DynamicFrame:\n        \"\"\"Remove periods from column names in the DynamicFrame\n        Args:\n            dyf (DynamicFrame): The DynamicFrame to convert\n        Returns:\n            DynamicFrame: The converted DynamicFrame\n        \"\"\"\n        # Extract the column names from the schema\n        old_column_names = [field.name for field in dyf.schema().fields]\n\n        # Create a new list of renamed column names\n        new_column_names = [name.replace(\".\", \"_\") for name in old_column_names]\n        print(old_column_names)\n        print(new_column_names)\n\n        # Create a new DynamicFrame with renamed columns\n        for c_old, c_new in zip(old_column_names, new_column_names):\n            dyf = dyf.rename_field(f\"`{c_old}`\", c_new)\n        return dyf\n\n    def transform(\n        self,\n        input_type: str = \"json\",\n        timestamp_columns: list = None,\n        output_format: str = \"parquet\",\n    ):\n        \"\"\"Convert the CSV or JSON data into Parquet Format in the SageWorks S3 Bucket, and\n        store the information about the data to the AWS Data Catalog sageworks database\n        Args:\n            input_type (str): The type of input files, either 'csv' or 'json'\n            timestamp_columns (list): A list of column names to convert to timestamp\n            output_format (str): The format of the output files, either 'parquet' or 'orc'\n        \"\"\"\n\n        # Add some tags here\n        tags = [\"heavy\"]\n\n        # Create the Output Parquet file S3 Storage Path\n        s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n        # Read JSONL files from S3 and infer schema dynamically\n        self.log.info(f\"Reading JSONL files from {self.input_uuid}...\")\n        input_dyf = self.glue_context.create_dynamic_frame.from_options(\n            connection_type=\"s3\",\n            connection_options={\n                \"paths\": [self.input_uuid],\n                \"recurse\": True,\n                \"gzip\": True,\n            },\n            format=input_type,\n            # format_options={'jsonPath': 'auto'}, Look into this later\n        )\n        self.log.info(\"Incoming DataFrame...\")\n        input_dyf.show(5)\n        input_dyf.printSchema()\n\n        # Resolve Choice fields\n        resolved_dyf = self.resolve_choice_fields(input_dyf)\n\n        # The next couple of lines of code is for un-nesting any nested JSON\n        # Create a Dynamic Frame Collection (dfc)\n        dfc = Relationalize.apply(resolved_dyf, name=\"root\")\n\n        # Aggregate the collection into a single dynamic frame\n        output_dyf = dfc.select(\"root\")\n\n        print(\"Before TimeStamp Conversions\")\n        output_dyf.printSchema()\n\n        # Convert any timestamp columns\n        output_dyf = self.timestamp_conversions(output_dyf, timestamp_columns)\n\n        # Relationalize will put periods in the column names. This will cause\n        # problems later when we try to create a FeatureSet from this DataSource\n        output_dyf = self.remove_periods_from_columns(output_dyf)\n\n        print(\"After TimeStamp Conversions and Removing Periods from column names\")\n        output_dyf.printSchema()\n\n        # Write Parquet files to S3\n        self.log.info(f\"Writing Parquet files to {s3_storage_path}...\")\n        self.glue_context.purge_s3_path(s3_storage_path, {\"retentionPeriod\": 0})\n        self.glue_context.write_dynamic_frame.from_options(\n            frame=output_dyf,\n            connection_type=\"s3\",\n            connection_options={\n                \"path\": s3_storage_path\n                # \"partitionKeys\": [\"year\", \"month\", \"day\"],\n            },\n            format=output_format,\n        )\n\n        # Set up our SageWorks metadata (description, tags, etc)\n        description = f\"SageWorks data source: {self.output_uuid}\"\n        sageworks_meta = {\"sageworks_tags\": self.tag_delimiter.join(tags)}\n        for key, value in self.output_meta.items():\n            sageworks_meta[key] = value\n\n        # Create a new table in the AWS Data Catalog\n        self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n\n        # Converting the Spark Types to Athena Types\n        def to_athena_type(col):\n            athena_type_map = {\"long\": \"bigint\"}\n            spark_type = col.dataType.typeName()\n            return athena_type_map.get(spark_type, spark_type)\n\n        column_name_types = [{\"Name\": col.name, \"Type\": to_athena_type(col)} for col in output_dyf.schema().fields]\n\n        # Our parameters for the Glue Data Catalog are different for Parquet and ORC\n        if output_format == \"parquet\":\n            glue_input_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat\"\n            glue_output_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat\"\n            serialization_library = \"org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe\"\n        else:\n            glue_input_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n            glue_output_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n            serialization_library = \"org.apache.hadoop.hive.ql.io.orc.OrcSerde\"\n\n        table_input = {\n            \"Name\": self.output_uuid,\n            \"Description\": description,\n            \"Parameters\": sageworks_meta,\n            \"TableType\": \"EXTERNAL_TABLE\",\n            \"StorageDescriptor\": {\n                \"Columns\": column_name_types,\n                \"Location\": s3_storage_path,\n                \"InputFormat\": glue_input_format,\n                \"OutputFormat\": glue_output_format,\n                \"Compressed\": True,\n                \"SerdeInfo\": {\n                    \"SerializationLibrary\": serialization_library,\n                },\n            },\n        }\n\n        # Delete the Data Catalog Table if it already exists\n        glue_client = boto3.client(\"glue\")\n        try:\n            glue_client.delete_table(DatabaseName=\"sageworks\", Name=self.output_uuid)\n            self.log.info(f\"Deleting Data Catalog Table: {self.output_uuid}...\")\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] != \"EntityNotFoundException\":\n                raise e\n\n        self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n        glue_client.create_table(DatabaseName=\"sageworks\", TableInput=table_input)\n\n        # All done!\n        self.log.info(f\"{self.input_uuid} --&gt; {self.output_uuid} complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init__","title":"<code>__init__(glue_context, input_uuid, output_uuid)</code>","text":"<p>S3HeavyToDataSource: Class to move HEAVY S3 Files into a SageWorks DataSource</p> <p>Parameters:</p> Name Type Description Default <code>glue_context</code> <code>GlueContext</code> <p>GlueContext, AWS Glue Specific wrapper around SparkContext</p> required <code>input_uuid</code> <code>str</code> <p>The S3 Path to the files to be loaded</p> required <code>output_uuid</code> <code>str</code> <p>The UUID of the SageWorks DataSource to be created</p> required Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>def __init__(self, glue_context: GlueContext, input_uuid: str, output_uuid: str):\n    \"\"\"S3HeavyToDataSource: Class to move HEAVY S3 Files into a SageWorks DataSource\n\n    Args:\n        glue_context: GlueContext, AWS Glue Specific wrapper around SparkContext\n        input_uuid (str): The S3 Path to the files to be loaded\n        output_uuid (str): The UUID of the SageWorks DataSource to be created\n    \"\"\"\n    self.log = glue_context.get_logger()\n\n    # FIXME: Pull these from Parameter Store or Config\n    self.input_uuid = input_uuid\n    self.output_uuid = output_uuid\n    self.output_meta = {\"sageworks_input\": self.input_uuid}\n    sageworks_bucket = \"s3://sandbox-sageworks-artifacts\"\n    self.data_sources_s3_path = sageworks_bucket + \"/data-sources\"\n\n    # Our Spark Context\n    self.glue_context = glue_context\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_periods_from_columns","title":"<code>remove_periods_from_columns(dyf)</code>  <code>staticmethod</code>","text":"<p>Remove periods from column names in the DynamicFrame Args:     dyf (DynamicFrame): The DynamicFrame to convert Returns:     DynamicFrame: The converted DynamicFrame</p> Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>@staticmethod\ndef remove_periods_from_columns(dyf: DynamicFrame) -&gt; DynamicFrame:\n    \"\"\"Remove periods from column names in the DynamicFrame\n    Args:\n        dyf (DynamicFrame): The DynamicFrame to convert\n    Returns:\n        DynamicFrame: The converted DynamicFrame\n    \"\"\"\n    # Extract the column names from the schema\n    old_column_names = [field.name for field in dyf.schema().fields]\n\n    # Create a new list of renamed column names\n    new_column_names = [name.replace(\".\", \"_\") for name in old_column_names]\n    print(old_column_names)\n    print(new_column_names)\n\n    # Create a new DynamicFrame with renamed columns\n    for c_old, c_new in zip(old_column_names, new_column_names):\n        dyf = dyf.rename_field(f\"`{c_old}`\", c_new)\n    return dyf\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timestamp_conversions","title":"<code>timestamp_conversions(dyf, time_columns=[])</code>","text":"<p>Convert columns in the DynamicFrame to the correct data types Args:     dyf (DynamicFrame): The DynamicFrame to convert     time_columns (list): A list of column names to convert to timestamp Returns:     DynamicFrame: The converted DynamicFrame</p> Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>def timestamp_conversions(self, dyf: DynamicFrame, time_columns: list = []) -&gt; DynamicFrame:\n    \"\"\"Convert columns in the DynamicFrame to the correct data types\n    Args:\n        dyf (DynamicFrame): The DynamicFrame to convert\n        time_columns (list): A list of column names to convert to timestamp\n    Returns:\n        DynamicFrame: The converted DynamicFrame\n    \"\"\"\n\n    # Convert the timestamp columns to timestamp types\n    spark_df = dyf.toDF()\n    for column in time_columns:\n        spark_df = spark_df.withColumn(column, to_timestamp(col(column)))\n\n    # Convert the Spark DataFrame back to a Glue DynamicFrame and return\n    return DynamicFrame.fromDF(spark_df, self.glue_context, \"output_dyf\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#sageworks.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform","title":"<code>transform(input_type='json', timestamp_columns=None, output_format='parquet')</code>","text":"<p>Convert the CSV or JSON data into Parquet Format in the SageWorks S3 Bucket, and store the information about the data to the AWS Data Catalog sageworks database Args:     input_type (str): The type of input files, either 'csv' or 'json'     timestamp_columns (list): A list of column names to convert to timestamp     output_format (str): The format of the output files, either 'parquet' or 'orc'</p> Source code in <code>src/sageworks/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>def transform(\n    self,\n    input_type: str = \"json\",\n    timestamp_columns: list = None,\n    output_format: str = \"parquet\",\n):\n    \"\"\"Convert the CSV or JSON data into Parquet Format in the SageWorks S3 Bucket, and\n    store the information about the data to the AWS Data Catalog sageworks database\n    Args:\n        input_type (str): The type of input files, either 'csv' or 'json'\n        timestamp_columns (list): A list of column names to convert to timestamp\n        output_format (str): The format of the output files, either 'parquet' or 'orc'\n    \"\"\"\n\n    # Add some tags here\n    tags = [\"heavy\"]\n\n    # Create the Output Parquet file S3 Storage Path\n    s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n    # Read JSONL files from S3 and infer schema dynamically\n    self.log.info(f\"Reading JSONL files from {self.input_uuid}...\")\n    input_dyf = self.glue_context.create_dynamic_frame.from_options(\n        connection_type=\"s3\",\n        connection_options={\n            \"paths\": [self.input_uuid],\n            \"recurse\": True,\n            \"gzip\": True,\n        },\n        format=input_type,\n        # format_options={'jsonPath': 'auto'}, Look into this later\n    )\n    self.log.info(\"Incoming DataFrame...\")\n    input_dyf.show(5)\n    input_dyf.printSchema()\n\n    # Resolve Choice fields\n    resolved_dyf = self.resolve_choice_fields(input_dyf)\n\n    # The next couple of lines of code is for un-nesting any nested JSON\n    # Create a Dynamic Frame Collection (dfc)\n    dfc = Relationalize.apply(resolved_dyf, name=\"root\")\n\n    # Aggregate the collection into a single dynamic frame\n    output_dyf = dfc.select(\"root\")\n\n    print(\"Before TimeStamp Conversions\")\n    output_dyf.printSchema()\n\n    # Convert any timestamp columns\n    output_dyf = self.timestamp_conversions(output_dyf, timestamp_columns)\n\n    # Relationalize will put periods in the column names. This will cause\n    # problems later when we try to create a FeatureSet from this DataSource\n    output_dyf = self.remove_periods_from_columns(output_dyf)\n\n    print(\"After TimeStamp Conversions and Removing Periods from column names\")\n    output_dyf.printSchema()\n\n    # Write Parquet files to S3\n    self.log.info(f\"Writing Parquet files to {s3_storage_path}...\")\n    self.glue_context.purge_s3_path(s3_storage_path, {\"retentionPeriod\": 0})\n    self.glue_context.write_dynamic_frame.from_options(\n        frame=output_dyf,\n        connection_type=\"s3\",\n        connection_options={\n            \"path\": s3_storage_path\n            # \"partitionKeys\": [\"year\", \"month\", \"day\"],\n        },\n        format=output_format,\n    )\n\n    # Set up our SageWorks metadata (description, tags, etc)\n    description = f\"SageWorks data source: {self.output_uuid}\"\n    sageworks_meta = {\"sageworks_tags\": self.tag_delimiter.join(tags)}\n    for key, value in self.output_meta.items():\n        sageworks_meta[key] = value\n\n    # Create a new table in the AWS Data Catalog\n    self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n\n    # Converting the Spark Types to Athena Types\n    def to_athena_type(col):\n        athena_type_map = {\"long\": \"bigint\"}\n        spark_type = col.dataType.typeName()\n        return athena_type_map.get(spark_type, spark_type)\n\n    column_name_types = [{\"Name\": col.name, \"Type\": to_athena_type(col)} for col in output_dyf.schema().fields]\n\n    # Our parameters for the Glue Data Catalog are different for Parquet and ORC\n    if output_format == \"parquet\":\n        glue_input_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat\"\n        glue_output_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat\"\n        serialization_library = \"org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe\"\n    else:\n        glue_input_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n        glue_output_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n        serialization_library = \"org.apache.hadoop.hive.ql.io.orc.OrcSerde\"\n\n    table_input = {\n        \"Name\": self.output_uuid,\n        \"Description\": description,\n        \"Parameters\": sageworks_meta,\n        \"TableType\": \"EXTERNAL_TABLE\",\n        \"StorageDescriptor\": {\n            \"Columns\": column_name_types,\n            \"Location\": s3_storage_path,\n            \"InputFormat\": glue_input_format,\n            \"OutputFormat\": glue_output_format,\n            \"Compressed\": True,\n            \"SerdeInfo\": {\n                \"SerializationLibrary\": serialization_library,\n            },\n        },\n    }\n\n    # Delete the Data Catalog Table if it already exists\n    glue_client = boto3.client(\"glue\")\n    try:\n        glue_client.delete_table(DatabaseName=\"sageworks\", Name=self.output_uuid)\n        self.log.info(f\"Deleting Data Catalog Table: {self.output_uuid}...\")\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] != \"EntityNotFoundException\":\n            raise e\n\n    self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n    glue_client.create_table(DatabaseName=\"sageworks\", TableInput=table_input)\n\n    # All done!\n    self.log.info(f\"{self.input_uuid} --&gt; {self.output_uuid} complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/","title":"DataLoaders Light","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>These DataLoader Classes are intended to load smaller dataset into AWS. If you have large data please see DataLoaders Heavy</p> <p>Welcome to the SageWorks DataLoaders Light Classes</p> <p>These classes provide low-level APIs for loading smaller data into AWS services</p> <ul> <li>CSVToDataSource: Loads local CSV data into a DataSource</li> <li>JSONToDataSource: Loads local JSON data into a DataSource</li> <li>S3ToDataSourceLight: Loads S3 data into a DataSource</li> </ul>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.CSVToDataSource","title":"<code>CSVToDataSource</code>","text":"<p>               Bases: <code>Transform</code></p> <p>CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource</p> Common Usage <pre><code>csv_to_data = CSVToDataSource(csv_file_path, data_uuid)\ncsv_to_data.set_output_tags([\"abalone\", \"csv\", \"whatever\"])\ncsv_to_data.transform()\n</code></pre> Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>class CSVToDataSource(Transform):\n    \"\"\"CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource\n\n    Common Usage:\n        ```python\n        csv_to_data = CSVToDataSource(csv_file_path, data_uuid)\n        csv_to_data.set_output_tags([\"abalone\", \"csv\", \"whatever\"])\n        csv_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, csv_file_path: str, data_uuid: str):\n        \"\"\"CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource\n\n        Args:\n            csv_file_path (str): The path to the CSV file to be transformed\n            data_uuid (str): The UUID of the SageWorks DataSource to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(csv_file_path, data_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.LOCAL_FILE\n        self.output_type = TransformOutput.DATA_SOURCE\n\n    def transform_impl(self, overwrite: bool = True):\n        \"\"\"Convert the local CSV file into Parquet Format in the SageWorks Data Sources Bucket, and\n        store the information about the data to the AWS Data Catalog sageworks database\n        \"\"\"\n\n        # Report the transformation initiation\n        csv_file = os.path.basename(self.input_uuid)\n        self.log.info(f\"Starting {csv_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n        # Read in the Local CSV as a Pandas DataFrame\n        df = pd.read_csv(self.input_uuid, low_memory=False)\n        df = convert_object_columns(df)\n\n        # Use the SageWorks Pandas to Data Source class\n        pandas_to_data = PandasToData(self.output_uuid)\n        pandas_to_data.set_input(df)\n        pandas_to_data.set_output_tags(self.output_tags)\n        pandas_to_data.add_output_meta(self.output_meta)\n        pandas_to_data.transform()\n\n        # Report the transformation results\n        self.log.info(f\"{csv_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform\"\"\"\n        self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.CSVToDataSource.__init__","title":"<code>__init__(csv_file_path, data_uuid)</code>","text":"<p>CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource</p> <p>Parameters:</p> Name Type Description Default <code>csv_file_path</code> <code>str</code> <p>The path to the CSV file to be transformed</p> required <code>data_uuid</code> <code>str</code> <p>The UUID of the SageWorks DataSource to be created</p> required Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>def __init__(self, csv_file_path: str, data_uuid: str):\n    \"\"\"CSVToDataSource: Class to move local CSV Files into a SageWorks DataSource\n\n    Args:\n        csv_file_path (str): The path to the CSV file to be transformed\n        data_uuid (str): The UUID of the SageWorks DataSource to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(csv_file_path, data_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.LOCAL_FILE\n    self.output_type = TransformOutput.DATA_SOURCE\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.CSVToDataSource.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform</p> Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform\"\"\"\n    self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.CSVToDataSource.transform_impl","title":"<code>transform_impl(overwrite=True)</code>","text":"<p>Convert the local CSV file into Parquet Format in the SageWorks Data Sources Bucket, and store the information about the data to the AWS Data Catalog sageworks database</p> Source code in <code>src/sageworks/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>def transform_impl(self, overwrite: bool = True):\n    \"\"\"Convert the local CSV file into Parquet Format in the SageWorks Data Sources Bucket, and\n    store the information about the data to the AWS Data Catalog sageworks database\n    \"\"\"\n\n    # Report the transformation initiation\n    csv_file = os.path.basename(self.input_uuid)\n    self.log.info(f\"Starting {csv_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n    # Read in the Local CSV as a Pandas DataFrame\n    df = pd.read_csv(self.input_uuid, low_memory=False)\n    df = convert_object_columns(df)\n\n    # Use the SageWorks Pandas to Data Source class\n    pandas_to_data = PandasToData(self.output_uuid)\n    pandas_to_data.set_input(df)\n    pandas_to_data.set_output_tags(self.output_tags)\n    pandas_to_data.add_output_meta(self.output_meta)\n    pandas_to_data.transform()\n\n    # Report the transformation results\n    self.log.info(f\"{csv_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.JSONToDataSource","title":"<code>JSONToDataSource</code>","text":"<p>               Bases: <code>Transform</code></p> <p>JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource</p> Common Usage <pre><code>json_to_data = JSONToDataSource(json_file_path, data_uuid)\njson_to_data.set_output_tags([\"abalone\", \"json\", \"whatever\"])\njson_to_data.transform()\n</code></pre> Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>class JSONToDataSource(Transform):\n    \"\"\"JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource\n\n    Common Usage:\n        ```python\n        json_to_data = JSONToDataSource(json_file_path, data_uuid)\n        json_to_data.set_output_tags([\"abalone\", \"json\", \"whatever\"])\n        json_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, json_file_path: str, data_uuid: str):\n        \"\"\"JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource\n\n        Args:\n            json_file_path (str): The path to the JSON file to be transformed\n            data_uuid (str): The UUID of the SageWorks DataSource to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(json_file_path, data_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.LOCAL_FILE\n        self.output_type = TransformOutput.DATA_SOURCE\n\n    def transform_impl(self, overwrite: bool = True):\n        \"\"\"Convert the local JSON file into Parquet Format in the SageWorks Data Sources Bucket, and\n        store the information about the data to the AWS Data Catalog sageworks database\n        \"\"\"\n\n        # Report the transformation initiation\n        json_file = os.path.basename(self.input_uuid)\n        self.log.info(f\"Starting {json_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n        # Read in the Local JSON as a Pandas DataFrame\n        df = pd.read_json(self.input_uuid, lines=True)\n\n        # Use the SageWorks Pandas to Data Source class\n        pandas_to_data = PandasToData(self.output_uuid)\n        pandas_to_data.set_input(df)\n        pandas_to_data.set_output_tags(self.output_tags)\n        pandas_to_data.add_output_meta(self.output_meta)\n        pandas_to_data.transform()\n\n        # Report the transformation results\n        self.log.info(f\"{json_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform\"\"\"\n        self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.JSONToDataSource.__init__","title":"<code>__init__(json_file_path, data_uuid)</code>","text":"<p>JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource</p> <p>Parameters:</p> Name Type Description Default <code>json_file_path</code> <code>str</code> <p>The path to the JSON file to be transformed</p> required <code>data_uuid</code> <code>str</code> <p>The UUID of the SageWorks DataSource to be created</p> required Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>def __init__(self, json_file_path: str, data_uuid: str):\n    \"\"\"JSONToDataSource: Class to move local JSON Files into a SageWorks DataSource\n\n    Args:\n        json_file_path (str): The path to the JSON file to be transformed\n        data_uuid (str): The UUID of the SageWorks DataSource to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(json_file_path, data_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.LOCAL_FILE\n    self.output_type = TransformOutput.DATA_SOURCE\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.JSONToDataSource.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform</p> Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform\"\"\"\n    self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.JSONToDataSource.transform_impl","title":"<code>transform_impl(overwrite=True)</code>","text":"<p>Convert the local JSON file into Parquet Format in the SageWorks Data Sources Bucket, and store the information about the data to the AWS Data Catalog sageworks database</p> Source code in <code>src/sageworks/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>def transform_impl(self, overwrite: bool = True):\n    \"\"\"Convert the local JSON file into Parquet Format in the SageWorks Data Sources Bucket, and\n    store the information about the data to the AWS Data Catalog sageworks database\n    \"\"\"\n\n    # Report the transformation initiation\n    json_file = os.path.basename(self.input_uuid)\n    self.log.info(f\"Starting {json_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n    # Read in the Local JSON as a Pandas DataFrame\n    df = pd.read_json(self.input_uuid, lines=True)\n\n    # Use the SageWorks Pandas to Data Source class\n    pandas_to_data = PandasToData(self.output_uuid)\n    pandas_to_data.set_input(df)\n    pandas_to_data.set_output_tags(self.output_tags)\n    pandas_to_data.add_output_meta(self.output_meta)\n    pandas_to_data.transform()\n\n    # Report the transformation results\n    self.log.info(f\"{json_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight","title":"<code>S3ToDataSourceLight</code>","text":"<p>               Bases: <code>Transform</code></p> <p>S3ToDataSourceLight: Class to move LIGHT S3 Files into a SageWorks DataSource</p> Common Usage <pre><code>s3_to_data = S3ToDataSourceLight(s3_path, data_uuid, datatype=\"csv/json\")\ns3_to_data.set_output_tags([\"abalone\", \"whatever\"])\ns3_to_data.transform()\n</code></pre> Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>class S3ToDataSourceLight(Transform):\n    \"\"\"S3ToDataSourceLight: Class to move LIGHT S3 Files into a SageWorks DataSource\n\n    Common Usage:\n        ```python\n        s3_to_data = S3ToDataSourceLight(s3_path, data_uuid, datatype=\"csv/json\")\n        s3_to_data.set_output_tags([\"abalone\", \"whatever\"])\n        s3_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, s3_path: str, data_uuid: str, datatype: str = \"csv\"):\n        \"\"\"S3ToDataSourceLight Initialization\n\n        Args:\n            s3_path (str): The S3 Path to the file to be transformed\n            data_uuid (str): The UUID of the SageWorks DataSource to be created\n            datatype (str): The datatype of the file to be transformed (defaults to \"csv\")\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(s3_path, data_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.S3_OBJECT\n        self.output_type = TransformOutput.DATA_SOURCE\n        self.datatype = datatype\n\n    def input_size_mb(self) -&gt; int:\n        \"\"\"Get the size of the input S3 object in MBytes\"\"\"\n        size_in_bytes = wr.s3.size_objects(self.input_uuid, boto3_session=self.boto3_session)[self.input_uuid]\n        size_in_mb = round(size_in_bytes / 1_000_000)\n        return size_in_mb\n\n    def transform_impl(self, overwrite: bool = True):\n        \"\"\"Convert the S3 CSV data into Parquet Format in the SageWorks Data Sources Bucket, and\n        store the information about the data to the AWS Data Catalog sageworks database\n        \"\"\"\n\n        # Sanity Check for S3 Object size\n        object_megabytes = self.input_size_mb()\n        if object_megabytes &gt; 100:\n            self.log.error(f\"S3 Object too big ({object_megabytes} MBytes): Use the S3ToDataSourceHeavy class!\")\n            return\n\n        # Read in the S3 CSV as a Pandas DataFrame\n        if self.datatype == \"csv\":\n            df = wr.s3.read_csv(self.input_uuid, low_memory=False, boto3_session=self.boto3_session)\n        else:\n            df = wr.s3.read_json(self.input_uuid, lines=True, boto3_session=self.boto3_session)\n\n        # Temporary hack to limit the number of columns in the dataframe\n        if len(df.columns) &gt; 40:\n            self.log.warning(f\"{self.input_uuid} Too Many Columns! Talk to SageWorks Support...\")\n\n        # Convert object columns before sending to SageWorks Data Source\n        df = convert_object_columns(df)\n\n        # Use the SageWorks Pandas to Data Source class\n        pandas_to_data = PandasToData(self.output_uuid)\n        pandas_to_data.set_input(df)\n        pandas_to_data.set_output_tags(self.output_tags)\n        pandas_to_data.add_output_meta(self.output_meta)\n        pandas_to_data.transform()\n\n        # Report the transformation results\n        self.log.info(f\"{self.input_uuid} --&gt;  DataSource: {self.output_uuid} Complete!\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform\"\"\"\n        self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__","title":"<code>__init__(s3_path, data_uuid, datatype='csv')</code>","text":"<p>S3ToDataSourceLight Initialization</p> <p>Parameters:</p> Name Type Description Default <code>s3_path</code> <code>str</code> <p>The S3 Path to the file to be transformed</p> required <code>data_uuid</code> <code>str</code> <p>The UUID of the SageWorks DataSource to be created</p> required <code>datatype</code> <code>str</code> <p>The datatype of the file to be transformed (defaults to \"csv\")</p> <code>'csv'</code> Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def __init__(self, s3_path: str, data_uuid: str, datatype: str = \"csv\"):\n    \"\"\"S3ToDataSourceLight Initialization\n\n    Args:\n        s3_path (str): The S3 Path to the file to be transformed\n        data_uuid (str): The UUID of the SageWorks DataSource to be created\n        datatype (str): The datatype of the file to be transformed (defaults to \"csv\")\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(s3_path, data_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.S3_OBJECT\n    self.output_type = TransformOutput.DATA_SOURCE\n    self.datatype = datatype\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb","title":"<code>input_size_mb()</code>","text":"<p>Get the size of the input S3 object in MBytes</p> Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def input_size_mb(self) -&gt; int:\n    \"\"\"Get the size of the input S3 object in MBytes\"\"\"\n    size_in_bytes = wr.s3.size_objects(self.input_uuid, boto3_session=self.boto3_session)[self.input_uuid]\n    size_in_mb = round(size_in_bytes / 1_000_000)\n    return size_in_mb\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform</p> Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform\"\"\"\n    self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#sageworks.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl","title":"<code>transform_impl(overwrite=True)</code>","text":"<p>Convert the S3 CSV data into Parquet Format in the SageWorks Data Sources Bucket, and store the information about the data to the AWS Data Catalog sageworks database</p> Source code in <code>src/sageworks/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def transform_impl(self, overwrite: bool = True):\n    \"\"\"Convert the S3 CSV data into Parquet Format in the SageWorks Data Sources Bucket, and\n    store the information about the data to the AWS Data Catalog sageworks database\n    \"\"\"\n\n    # Sanity Check for S3 Object size\n    object_megabytes = self.input_size_mb()\n    if object_megabytes &gt; 100:\n        self.log.error(f\"S3 Object too big ({object_megabytes} MBytes): Use the S3ToDataSourceHeavy class!\")\n        return\n\n    # Read in the S3 CSV as a Pandas DataFrame\n    if self.datatype == \"csv\":\n        df = wr.s3.read_csv(self.input_uuid, low_memory=False, boto3_session=self.boto3_session)\n    else:\n        df = wr.s3.read_json(self.input_uuid, lines=True, boto3_session=self.boto3_session)\n\n    # Temporary hack to limit the number of columns in the dataframe\n    if len(df.columns) &gt; 40:\n        self.log.warning(f\"{self.input_uuid} Too Many Columns! Talk to SageWorks Support...\")\n\n    # Convert object columns before sending to SageWorks Data Source\n    df = convert_object_columns(df)\n\n    # Use the SageWorks Pandas to Data Source class\n    pandas_to_data = PandasToData(self.output_uuid)\n    pandas_to_data.set_input(df)\n    pandas_to_data.set_output_tags(self.output_tags)\n    pandas_to_data.add_output_meta(self.output_meta)\n    pandas_to_data.transform()\n\n    # Report the transformation results\n    self.log.info(f\"{self.input_uuid} --&gt;  DataSource: {self.output_uuid} Complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/","title":"Data To Features","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas</p> <p>MolecularDescriptors: Compute a Feature Set based on RDKit Descriptors</p> An alternative to using this class is to use the <code>compute_molecular_descriptors</code> function directly. <p>df_features = compute_molecular_descriptors(df) to_features = PandasToFeatures(\"my_feature_set\")    to_features.set_input(df_features, id_column=\"id\")    to_features.set_output_tags([\"blah\", \"whatever\"])    to_features.transform()</p>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight","title":"<code>DataToFeaturesLight</code>","text":"<p>               Bases: <code>Transform</code></p> <p>DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas</p> Common Usage <pre><code>to_features = DataToFeaturesLight(data_uuid, feature_uuid)\nto_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\nto_features.transform(id_column=\"id\"/None, event_time_column=\"date\"/None, query=str/None)\n</code></pre> Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>class DataToFeaturesLight(Transform):\n    \"\"\"DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas\n\n    Common Usage:\n        ```python\n        to_features = DataToFeaturesLight(data_uuid, feature_uuid)\n        to_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\n        to_features.transform(id_column=\"id\"/None, event_time_column=\"date\"/None, query=str/None)\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid: str, feature_uuid: str):\n        \"\"\"DataToFeaturesLight Initialization\n\n        Args:\n            data_uuid (str): The UUID of the SageWorks DataSource to be transformed\n            feature_uuid (str): The UUID of the SageWorks FeatureSet to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(data_uuid, feature_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.DATA_SOURCE\n        self.output_type = TransformOutput.FEATURE_SET\n        self.input_df = None\n        self.output_df = None\n\n    def pre_transform(self, query: str = None, **kwargs):\n        \"\"\"Pull the input DataSource into our Input Pandas DataFrame\n        Args:\n            query(str): Optional query to filter the input DataFrame\n        \"\"\"\n\n        # Grab the Input (Data Source)\n        data_to_pandas = DataToPandas(self.input_uuid)\n        data_to_pandas.transform(query=query)\n        self.input_df = data_to_pandas.get_output()\n\n        # Check if there are any columns that are greater than 64 characters\n        for col in self.input_df.columns:\n            if len(col) &gt; 64:\n                raise ValueError(f\"Column name '{col}' &gt; 64 characters. AWS FeatureGroup limits to 64 characters.\")\n\n    def transform_impl(self, **kwargs):\n        \"\"\"Transform the input DataFrame into a Feature Set\"\"\"\n\n        # This is a reference implementation that should be overridden by the subclass\n        self.output_df = self.input_df\n\n    def post_transform(self, id_column, event_time_column=None, one_hot_columns=None, **kwargs):\n        \"\"\"At this point the output DataFrame should be populated, so publish it as a Feature Set\n\n        Args:\n            id_column (str): The ID column (must be specified, use \"auto\" for auto-generated IDs).\n            event_time_column (str, optional): The name of the event time column (default: None).\n            one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n        \"\"\"\n        # Now publish to the output location\n        output_features = PandasToFeatures(self.output_uuid)\n        output_features.set_input(\n            self.output_df, id_column=id_column, event_time_column=event_time_column, one_hot_columns=one_hot_columns\n        )\n        output_features.set_output_tags(self.output_tags)\n        output_features.add_output_meta(self.output_meta)\n        output_features.transform()\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__","title":"<code>__init__(data_uuid, feature_uuid)</code>","text":"<p>DataToFeaturesLight Initialization</p> <p>Parameters:</p> Name Type Description Default <code>data_uuid</code> <code>str</code> <p>The UUID of the SageWorks DataSource to be transformed</p> required <code>feature_uuid</code> <code>str</code> <p>The UUID of the SageWorks FeatureSet to be created</p> required Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def __init__(self, data_uuid: str, feature_uuid: str):\n    \"\"\"DataToFeaturesLight Initialization\n\n    Args:\n        data_uuid (str): The UUID of the SageWorks DataSource to be transformed\n        feature_uuid (str): The UUID of the SageWorks FeatureSet to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(data_uuid, feature_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.DATA_SOURCE\n    self.output_type = TransformOutput.FEATURE_SET\n    self.input_df = None\n    self.output_df = None\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform","title":"<code>post_transform(id_column, event_time_column=None, one_hot_columns=None, **kwargs)</code>","text":"<p>At this point the output DataFrame should be populated, so publish it as a Feature Set</p> <p>Parameters:</p> Name Type Description Default <code>id_column</code> <code>str</code> <p>The ID column (must be specified, use \"auto\" for auto-generated IDs).</p> required <code>event_time_column</code> <code>str</code> <p>The name of the event time column (default: None).</p> <code>None</code> <code>one_hot_columns</code> <code>list</code> <p>The list of columns to one-hot encode (default: None).</p> <code>None</code> Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def post_transform(self, id_column, event_time_column=None, one_hot_columns=None, **kwargs):\n    \"\"\"At this point the output DataFrame should be populated, so publish it as a Feature Set\n\n    Args:\n        id_column (str): The ID column (must be specified, use \"auto\" for auto-generated IDs).\n        event_time_column (str, optional): The name of the event time column (default: None).\n        one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n    \"\"\"\n    # Now publish to the output location\n    output_features = PandasToFeatures(self.output_uuid)\n    output_features.set_input(\n        self.output_df, id_column=id_column, event_time_column=event_time_column, one_hot_columns=one_hot_columns\n    )\n    output_features.set_output_tags(self.output_tags)\n    output_features.add_output_meta(self.output_meta)\n    output_features.transform()\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform","title":"<code>pre_transform(query=None, **kwargs)</code>","text":"<p>Pull the input DataSource into our Input Pandas DataFrame Args:     query(str): Optional query to filter the input DataFrame</p> Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def pre_transform(self, query: str = None, **kwargs):\n    \"\"\"Pull the input DataSource into our Input Pandas DataFrame\n    Args:\n        query(str): Optional query to filter the input DataFrame\n    \"\"\"\n\n    # Grab the Input (Data Source)\n    data_to_pandas = DataToPandas(self.input_uuid)\n    data_to_pandas.transform(query=query)\n    self.input_df = data_to_pandas.get_output()\n\n    # Check if there are any columns that are greater than 64 characters\n    for col in self.input_df.columns:\n        if len(col) &gt; 64:\n            raise ValueError(f\"Column name '{col}' &gt; 64 characters. AWS FeatureGroup limits to 64 characters.\")\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl","title":"<code>transform_impl(**kwargs)</code>","text":"<p>Transform the input DataFrame into a Feature Set</p> Source code in <code>src/sageworks/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def transform_impl(self, **kwargs):\n    \"\"\"Transform the input DataFrame into a Feature Set\"\"\"\n\n    # This is a reference implementation that should be overridden by the subclass\n    self.output_df = self.input_df\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors","title":"<code>MolecularDescriptors</code>","text":"<p>               Bases: <code>DataToFeaturesLight</code></p> <p>MolecularDescriptors: Create a FeatureSet (RDKit Descriptors) from a DataSource</p> Common Usage <pre><code>to_features = MolecularDescriptors(data_uuid, feature_uuid)\nto_features.set_output_tags([\"aqsol\", \"whatever\"])\nto_features.transform()\n</code></pre> Source code in <code>src/sageworks/core/transforms/data_to_features/light/molecular_descriptors.py</code> <pre><code>class MolecularDescriptors(DataToFeaturesLight):\n    \"\"\"MolecularDescriptors: Create a FeatureSet (RDKit Descriptors) from a DataSource\n\n    Common Usage:\n        ```python\n        to_features = MolecularDescriptors(data_uuid, feature_uuid)\n        to_features.set_output_tags([\"aqsol\", \"whatever\"])\n        to_features.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid: str, feature_uuid: str):\n        \"\"\"MolecularDescriptors Initialization\n\n        Args:\n            data_uuid (str): The UUID of the SageWorks DataSource to be transformed\n            feature_uuid (str): The UUID of the SageWorks FeatureSet to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(data_uuid, feature_uuid)\n\n    def transform_impl(self, **kwargs):\n        \"\"\"Compute a Feature Set based on RDKit Descriptors\"\"\"\n\n        # Compute/add all the Molecular Descriptors\n        self.output_df = compute_molecular_descriptors(self.input_df)\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__","title":"<code>__init__(data_uuid, feature_uuid)</code>","text":"<p>MolecularDescriptors Initialization</p> <p>Parameters:</p> Name Type Description Default <code>data_uuid</code> <code>str</code> <p>The UUID of the SageWorks DataSource to be transformed</p> required <code>feature_uuid</code> <code>str</code> <p>The UUID of the SageWorks FeatureSet to be created</p> required Source code in <code>src/sageworks/core/transforms/data_to_features/light/molecular_descriptors.py</code> <pre><code>def __init__(self, data_uuid: str, feature_uuid: str):\n    \"\"\"MolecularDescriptors Initialization\n\n    Args:\n        data_uuid (str): The UUID of the SageWorks DataSource to be transformed\n        feature_uuid (str): The UUID of the SageWorks FeatureSet to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(data_uuid, feature_uuid)\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#sageworks.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl","title":"<code>transform_impl(**kwargs)</code>","text":"<p>Compute a Feature Set based on RDKit Descriptors</p> Source code in <code>src/sageworks/core/transforms/data_to_features/light/molecular_descriptors.py</code> <pre><code>def transform_impl(self, **kwargs):\n    \"\"\"Compute a Feature Set based on RDKit Descriptors\"\"\"\n\n    # Compute/add all the Molecular Descriptors\n    self.output_df = compute_molecular_descriptors(self.input_df)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/","title":"Features To Model","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>FeaturesToModel: Train/Create a Model from a Feature Set</p>"},{"location":"core_classes/transforms/features_to_model/#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel","title":"<code>FeaturesToModel</code>","text":"<p>               Bases: <code>Transform</code></p> <p>FeaturesToModel: Train/Create a Model from a FeatureSet</p> Common Usage <pre><code>from sageworks.core.transforms.features_to_model.features_to_model import FeaturesToModel\nto_model = FeaturesToModel(feature_uuid, model_uuid, model_type=ModelType)\nto_model.set_output_tags([\"abalone\", \"public\", \"whatever\"])\nto_model.transform(target_column=\"class_number_of_rings\",\n                   feature_list=[\"my\", \"best\", \"features\"])\n</code></pre> Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code> <pre><code>class FeaturesToModel(Transform):\n    \"\"\"FeaturesToModel: Train/Create a Model from a FeatureSet\n\n    Common Usage:\n        ```python\n        from sageworks.core.transforms.features_to_model.features_to_model import FeaturesToModel\n        to_model = FeaturesToModel(feature_uuid, model_uuid, model_type=ModelType)\n        to_model.set_output_tags([\"abalone\", \"public\", \"whatever\"])\n        to_model.transform(target_column=\"class_number_of_rings\",\n                           feature_list=[\"my\", \"best\", \"features\"])\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        feature_uuid: str,\n        model_uuid: str,\n        model_type: ModelType,\n        scikit_model_class=None,\n        model_import_str=None,\n        custom_script=None,\n    ):\n        \"\"\"FeaturesToModel Initialization\n        Args:\n            feature_uuid (str): UUID of the FeatureSet to use as input\n            model_uuid (str): UUID of the Model to create as output\n            model_type (ModelType): ModelType.REGRESSOR or ModelType.CLASSIFIER, etc.\n            scikit_model_class (str, optional): The scikit model (e.g. KNeighborsRegressor) (default None)\n            model_import_str (str, optional): The import string for the model (default None)\n            custom_script (str, optional): Custom script to use for the model (default None)\n        \"\"\"\n\n        # Make sure the model_uuid is a valid name\n        Artifact.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call superclass init\n        super().__init__(feature_uuid, model_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.FEATURE_SET\n        self.output_type = TransformOutput.MODEL\n        self.model_type = model_type\n        self.scikit_model_class = scikit_model_class\n        self.model_import_str = model_import_str\n        self.custom_script = custom_script\n        self.estimator = None\n        self.model_description = None\n        self.model_training_root = self.models_s3_path + \"/training\"\n        self.model_feature_list = None\n        self.target_column = None\n        self.class_labels = None\n\n    def transform_impl(\n        self, target_column: str, description: str = None, feature_list: list = None, train_all_data=False\n    ):\n        \"\"\"Generic Features to Model: Note you should create a new class and inherit from\n        this one to include specific logic for your Feature Set/Model\n        Args:\n            target_column (str): Column name of the target variable\n            description (str): Description of the model (optional)\n            feature_list (list[str]): A list of columns for the features (default None, will try to guess)\n            train_all_data (bool): Train on ALL (100%) of the data (default False)\n        \"\"\"\n        # Delete the existing model (if it exists)\n        self.log.important(\"Trying to delete existing model...\")\n        ModelCore.managed_delete(self.output_uuid)\n\n        # Set our model description\n        self.model_description = description if description is not None else f\"Model created from {self.input_uuid}\"\n\n        # Get our Feature Set and create an S3 CSV Training dataset\n        feature_set = FeatureSetCore(self.input_uuid)\n        s3_training_path = feature_set.create_s3_training_data()\n        self.log.info(f\"Created new training data {s3_training_path}...\")\n\n        # Report the target column\n        self.target_column = target_column\n        self.log.info(f\"Target column: {self.target_column}\")\n\n        # Did they specify a feature list?\n        if feature_list:\n            # AWS Feature Groups will also add these implicit columns, so remove them\n            aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\", \"training\"]\n            feature_list = [c for c in feature_list if c not in aws_cols]\n\n        # If they didn't specify a feature list, try to guess it\n        else:\n            # Try to figure out features with this logic\n            # - Don't include id, event_time, __index_level_0__, or training columns\n            # - Don't include AWS generated columns (e.g. write_time, api_invocation_time, is_deleted)\n            # - Don't include the target columns\n            # - Don't include any columns that are of type string or timestamp\n            # - The rest of the columns are assumed to be features\n            self.log.warning(\"Guessing at the feature list, HIGHLY SUGGESTED to specify an explicit feature list!\")\n            all_columns = feature_set.columns\n            filter_list = [\n                \"id\",\n                \"__index_level_0__\",\n                \"write_time\",\n                \"api_invocation_time\",\n                \"is_deleted\",\n                \"event_time\",\n                \"training\",\n            ] + [self.target_column]\n            feature_list = [c for c in all_columns if c not in filter_list]\n\n            # AWS Feature Store has 3 user column types (String, Integral, Fractional)\n            # and two internal types (Timestamp and Boolean). A Feature List for\n            # modeling can only contain Integral and Fractional types.\n            remove_columns = []\n            column_details = feature_set.column_details()\n            for column_name in feature_list:\n                if column_details[column_name] not in [\"Integral\", \"Fractional\"]:\n                    self.log.warning(\n                        f\"Removing {column_name} from feature list, improper type {column_details[column_name]}\"\n                    )\n                    remove_columns.append(column_name)\n\n            # Remove the columns that are not Integral or Fractional\n            feature_list = [c for c in feature_list if c not in remove_columns]\n\n        # Set the final feature list\n        self.model_feature_list = feature_list\n        self.log.important(f\"Feature List for Modeling: {self.model_feature_list}\")\n\n        # Custom Script\n        if self.custom_script:\n            script_path = self.custom_script\n            self.log.info(\"Custom script path: {script_path}\")\n            # Fixme: We'll need to circle back to this later\n            copy_imports_to_script_dir(script_path, [\"sageworks.utils.chem_utils\"])\n\n        # We're using one of the built-in model script templates\n        else:\n            # Set up our parameters for the model script\n            template_params = {\n                \"model_imports\": self.model_import_str,\n                \"model_type\": self.model_type,\n                \"scikit_model_class\": self.scikit_model_class,\n                \"target_column\": self.target_column,\n                \"feature_list\": self.model_feature_list,\n                \"model_metrics_s3_path\": f\"{self.model_training_root}/{self.output_uuid}\",\n                \"train_all_data\": train_all_data,\n            }\n            # Generate our model script\n            script_path = generate_model_script(template_params)\n\n        # Metric Definitions for Regression\n        if self.model_type == ModelType.REGRESSOR or self.model_type == ModelType.QUANTILE_REGRESSOR:\n            metric_definitions = [\n                {\"Name\": \"RMSE\", \"Regex\": \"RMSE: ([0-9.]+)\"},\n                {\"Name\": \"MAE\", \"Regex\": \"MAE: ([0-9.]+)\"},\n                {\"Name\": \"R2\", \"Regex\": \"R2: ([0-9.]+)\"},\n                {\"Name\": \"NumRows\", \"Regex\": \"NumRows: ([0-9]+)\"},\n            ]\n\n        # Metric Definitions for Classification\n        elif self.model_type == ModelType.CLASSIFIER:\n            # We need to get creative with the Classification Metrics\n\n            # Grab all the target column class values (class labels)\n            table = feature_set.data_source.table\n            self.class_labels = feature_set.query(f'select DISTINCT {self.target_column} FROM \"{table}\"')[\n                self.target_column\n            ].to_list()\n\n            # Sanity check on the targets\n            if len(self.class_labels) &gt; 10:\n                msg = f\"Too many target classes ({len(self.class_labels)}) for classification, aborting!\"\n                self.log.critical(msg)\n                raise ValueError(msg)\n\n            # Dynamically create the metric definitions\n            metrics = [\"precision\", \"recall\", \"fscore\"]\n            metric_definitions = []\n            for t in self.class_labels:\n                for m in metrics:\n                    metric_definitions.append({\"Name\": f\"Metrics:{t}:{m}\", \"Regex\": f\"Metrics:{t}:{m} ([0-9.]+)\"})\n\n            # Add the confusion matrix metrics\n            for row in self.class_labels:\n                for col in self.class_labels:\n                    metric_definitions.append(\n                        {\"Name\": f\"ConfusionMatrix:{row}:{col}\", \"Regex\": f\"ConfusionMatrix:{row}:{col} ([0-9.]+)\"}\n                    )\n\n        # If the model type is UNKNOWN, our metric_definitions will be empty\n        else:\n            self.log.important(f\"ModelType is {self.model_type}, skipping metric_definitions...\")\n            metric_definitions = []\n\n        # Take the full script path and extract the entry point and source directory\n        entry_point = str(Path(script_path).name)\n        source_dir = str(Path(script_path).parent)\n\n        # Create a Sagemaker Model with our script\n        image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n        self.estimator = SKLearn(\n            entry_point=entry_point,\n            source_dir=source_dir,\n            role=self.sageworks_role_arn,\n            instance_type=\"ml.m5.large\",\n            sagemaker_session=self.sm_session,\n            framework_version=\"1.2-1\",\n            image_uri=image,\n            metric_definitions=metric_definitions,\n        )\n\n        # Training Job Name based on the Model UUID and today's date\n        training_date_time_utc = datetime.now(timezone.utc).strftime(\"%Y-%m-%d-%H-%M\")\n        training_job_name = f\"{self.output_uuid}-{training_date_time_utc}\"\n\n        # Train the estimator\n        self.estimator.fit({\"train\": s3_training_path}, job_name=training_job_name)\n\n        # Now delete the training data\n        self.log.info(f\"Deleting training data {s3_training_path}...\")\n        wr.s3.delete_objects(\n            [s3_training_path, s3_training_path.replace(\".csv\", \".csv.metadata\")],\n            boto3_session=self.boto3_session,\n        )\n\n        # Create Model and officially Register\n        self.log.important(f\"Creating new model {self.output_uuid}...\")\n        self.create_and_register_model()\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Calling onboard() on the Model\"\"\"\n        self.log.info(\"Post-Transform: Calling onboard() on the Model...\")\n\n        # Store the model feature_list and target_column in the sageworks_meta\n        output_model = ModelCore(self.output_uuid, model_type=self.model_type)\n        output_model.upsert_sageworks_meta({\"sageworks_model_features\": self.model_feature_list})\n        output_model.upsert_sageworks_meta({\"sageworks_model_target\": self.target_column})\n\n        # Store the class labels (if they exist)\n        if self.class_labels:\n            output_model.set_class_labels(self.class_labels)\n\n        # Call the Model onboard method\n        output_model.onboard_with_args(self.model_type, self.target_column, self.model_feature_list)\n\n    def create_and_register_model(self):\n        \"\"\"Create and Register the Model\"\"\"\n\n        # Get the metadata/tags to push into AWS\n        aws_tags = self.get_aws_tags()\n\n        # Create model group (if it doesn't already exist)\n        self.sm_client.create_model_package_group(\n            ModelPackageGroupName=self.output_uuid,\n            ModelPackageGroupDescription=self.model_description,\n            Tags=aws_tags,\n        )\n\n        # Register our model\n        image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n        self.log.important(f\"Registering model {self.output_uuid} with image {image}...\")\n        model = self.estimator.create_model(role=self.sageworks_role_arn)\n        model.register(\n            model_package_group_name=self.output_uuid,\n            framework_version=\"1.2.1\",\n            image_uri=image,\n            content_types=[\"text/csv\"],\n            response_types=[\"text/csv\"],\n            inference_instances=[\"ml.t2.medium\"],\n            transform_instances=[\"ml.m5.large\"],\n            approval_status=\"Approved\",\n            description=self.model_description,\n        )\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__","title":"<code>__init__(feature_uuid, model_uuid, model_type, scikit_model_class=None, model_import_str=None, custom_script=None)</code>","text":"<p>FeaturesToModel Initialization Args:     feature_uuid (str): UUID of the FeatureSet to use as input     model_uuid (str): UUID of the Model to create as output     model_type (ModelType): ModelType.REGRESSOR or ModelType.CLASSIFIER, etc.     scikit_model_class (str, optional): The scikit model (e.g. KNeighborsRegressor) (default None)     model_import_str (str, optional): The import string for the model (default None)     custom_script (str, optional): Custom script to use for the model (default None)</p> Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def __init__(\n    self,\n    feature_uuid: str,\n    model_uuid: str,\n    model_type: ModelType,\n    scikit_model_class=None,\n    model_import_str=None,\n    custom_script=None,\n):\n    \"\"\"FeaturesToModel Initialization\n    Args:\n        feature_uuid (str): UUID of the FeatureSet to use as input\n        model_uuid (str): UUID of the Model to create as output\n        model_type (ModelType): ModelType.REGRESSOR or ModelType.CLASSIFIER, etc.\n        scikit_model_class (str, optional): The scikit model (e.g. KNeighborsRegressor) (default None)\n        model_import_str (str, optional): The import string for the model (default None)\n        custom_script (str, optional): Custom script to use for the model (default None)\n    \"\"\"\n\n    # Make sure the model_uuid is a valid name\n    Artifact.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call superclass init\n    super().__init__(feature_uuid, model_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.FEATURE_SET\n    self.output_type = TransformOutput.MODEL\n    self.model_type = model_type\n    self.scikit_model_class = scikit_model_class\n    self.model_import_str = model_import_str\n    self.custom_script = custom_script\n    self.estimator = None\n    self.model_description = None\n    self.model_training_root = self.models_s3_path + \"/training\"\n    self.model_feature_list = None\n    self.target_column = None\n    self.class_labels = None\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model","title":"<code>create_and_register_model()</code>","text":"<p>Create and Register the Model</p> Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def create_and_register_model(self):\n    \"\"\"Create and Register the Model\"\"\"\n\n    # Get the metadata/tags to push into AWS\n    aws_tags = self.get_aws_tags()\n\n    # Create model group (if it doesn't already exist)\n    self.sm_client.create_model_package_group(\n        ModelPackageGroupName=self.output_uuid,\n        ModelPackageGroupDescription=self.model_description,\n        Tags=aws_tags,\n    )\n\n    # Register our model\n    image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n    self.log.important(f\"Registering model {self.output_uuid} with image {image}...\")\n    model = self.estimator.create_model(role=self.sageworks_role_arn)\n    model.register(\n        model_package_group_name=self.output_uuid,\n        framework_version=\"1.2.1\",\n        image_uri=image,\n        content_types=[\"text/csv\"],\n        response_types=[\"text/csv\"],\n        inference_instances=[\"ml.t2.medium\"],\n        transform_instances=[\"ml.m5.large\"],\n        approval_status=\"Approved\",\n        description=self.model_description,\n    )\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Calling onboard() on the Model</p> Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Calling onboard() on the Model\"\"\"\n    self.log.info(\"Post-Transform: Calling onboard() on the Model...\")\n\n    # Store the model feature_list and target_column in the sageworks_meta\n    output_model = ModelCore(self.output_uuid, model_type=self.model_type)\n    output_model.upsert_sageworks_meta({\"sageworks_model_features\": self.model_feature_list})\n    output_model.upsert_sageworks_meta({\"sageworks_model_target\": self.target_column})\n\n    # Store the class labels (if they exist)\n    if self.class_labels:\n        output_model.set_class_labels(self.class_labels)\n\n    # Call the Model onboard method\n    output_model.onboard_with_args(self.model_type, self.target_column, self.model_feature_list)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#sageworks.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl","title":"<code>transform_impl(target_column, description=None, feature_list=None, train_all_data=False)</code>","text":"<p>Generic Features to Model: Note you should create a new class and inherit from this one to include specific logic for your Feature Set/Model Args:     target_column (str): Column name of the target variable     description (str): Description of the model (optional)     feature_list (list[str]): A list of columns for the features (default None, will try to guess)     train_all_data (bool): Train on ALL (100%) of the data (default False)</p> Source code in <code>src/sageworks/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def transform_impl(\n    self, target_column: str, description: str = None, feature_list: list = None, train_all_data=False\n):\n    \"\"\"Generic Features to Model: Note you should create a new class and inherit from\n    this one to include specific logic for your Feature Set/Model\n    Args:\n        target_column (str): Column name of the target variable\n        description (str): Description of the model (optional)\n        feature_list (list[str]): A list of columns for the features (default None, will try to guess)\n        train_all_data (bool): Train on ALL (100%) of the data (default False)\n    \"\"\"\n    # Delete the existing model (if it exists)\n    self.log.important(\"Trying to delete existing model...\")\n    ModelCore.managed_delete(self.output_uuid)\n\n    # Set our model description\n    self.model_description = description if description is not None else f\"Model created from {self.input_uuid}\"\n\n    # Get our Feature Set and create an S3 CSV Training dataset\n    feature_set = FeatureSetCore(self.input_uuid)\n    s3_training_path = feature_set.create_s3_training_data()\n    self.log.info(f\"Created new training data {s3_training_path}...\")\n\n    # Report the target column\n    self.target_column = target_column\n    self.log.info(f\"Target column: {self.target_column}\")\n\n    # Did they specify a feature list?\n    if feature_list:\n        # AWS Feature Groups will also add these implicit columns, so remove them\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\", \"training\"]\n        feature_list = [c for c in feature_list if c not in aws_cols]\n\n    # If they didn't specify a feature list, try to guess it\n    else:\n        # Try to figure out features with this logic\n        # - Don't include id, event_time, __index_level_0__, or training columns\n        # - Don't include AWS generated columns (e.g. write_time, api_invocation_time, is_deleted)\n        # - Don't include the target columns\n        # - Don't include any columns that are of type string or timestamp\n        # - The rest of the columns are assumed to be features\n        self.log.warning(\"Guessing at the feature list, HIGHLY SUGGESTED to specify an explicit feature list!\")\n        all_columns = feature_set.columns\n        filter_list = [\n            \"id\",\n            \"__index_level_0__\",\n            \"write_time\",\n            \"api_invocation_time\",\n            \"is_deleted\",\n            \"event_time\",\n            \"training\",\n        ] + [self.target_column]\n        feature_list = [c for c in all_columns if c not in filter_list]\n\n        # AWS Feature Store has 3 user column types (String, Integral, Fractional)\n        # and two internal types (Timestamp and Boolean). A Feature List for\n        # modeling can only contain Integral and Fractional types.\n        remove_columns = []\n        column_details = feature_set.column_details()\n        for column_name in feature_list:\n            if column_details[column_name] not in [\"Integral\", \"Fractional\"]:\n                self.log.warning(\n                    f\"Removing {column_name} from feature list, improper type {column_details[column_name]}\"\n                )\n                remove_columns.append(column_name)\n\n        # Remove the columns that are not Integral or Fractional\n        feature_list = [c for c in feature_list if c not in remove_columns]\n\n    # Set the final feature list\n    self.model_feature_list = feature_list\n    self.log.important(f\"Feature List for Modeling: {self.model_feature_list}\")\n\n    # Custom Script\n    if self.custom_script:\n        script_path = self.custom_script\n        self.log.info(\"Custom script path: {script_path}\")\n        # Fixme: We'll need to circle back to this later\n        copy_imports_to_script_dir(script_path, [\"sageworks.utils.chem_utils\"])\n\n    # We're using one of the built-in model script templates\n    else:\n        # Set up our parameters for the model script\n        template_params = {\n            \"model_imports\": self.model_import_str,\n            \"model_type\": self.model_type,\n            \"scikit_model_class\": self.scikit_model_class,\n            \"target_column\": self.target_column,\n            \"feature_list\": self.model_feature_list,\n            \"model_metrics_s3_path\": f\"{self.model_training_root}/{self.output_uuid}\",\n            \"train_all_data\": train_all_data,\n        }\n        # Generate our model script\n        script_path = generate_model_script(template_params)\n\n    # Metric Definitions for Regression\n    if self.model_type == ModelType.REGRESSOR or self.model_type == ModelType.QUANTILE_REGRESSOR:\n        metric_definitions = [\n            {\"Name\": \"RMSE\", \"Regex\": \"RMSE: ([0-9.]+)\"},\n            {\"Name\": \"MAE\", \"Regex\": \"MAE: ([0-9.]+)\"},\n            {\"Name\": \"R2\", \"Regex\": \"R2: ([0-9.]+)\"},\n            {\"Name\": \"NumRows\", \"Regex\": \"NumRows: ([0-9]+)\"},\n        ]\n\n    # Metric Definitions for Classification\n    elif self.model_type == ModelType.CLASSIFIER:\n        # We need to get creative with the Classification Metrics\n\n        # Grab all the target column class values (class labels)\n        table = feature_set.data_source.table\n        self.class_labels = feature_set.query(f'select DISTINCT {self.target_column} FROM \"{table}\"')[\n            self.target_column\n        ].to_list()\n\n        # Sanity check on the targets\n        if len(self.class_labels) &gt; 10:\n            msg = f\"Too many target classes ({len(self.class_labels)}) for classification, aborting!\"\n            self.log.critical(msg)\n            raise ValueError(msg)\n\n        # Dynamically create the metric definitions\n        metrics = [\"precision\", \"recall\", \"fscore\"]\n        metric_definitions = []\n        for t in self.class_labels:\n            for m in metrics:\n                metric_definitions.append({\"Name\": f\"Metrics:{t}:{m}\", \"Regex\": f\"Metrics:{t}:{m} ([0-9.]+)\"})\n\n        # Add the confusion matrix metrics\n        for row in self.class_labels:\n            for col in self.class_labels:\n                metric_definitions.append(\n                    {\"Name\": f\"ConfusionMatrix:{row}:{col}\", \"Regex\": f\"ConfusionMatrix:{row}:{col} ([0-9.]+)\"}\n                )\n\n    # If the model type is UNKNOWN, our metric_definitions will be empty\n    else:\n        self.log.important(f\"ModelType is {self.model_type}, skipping metric_definitions...\")\n        metric_definitions = []\n\n    # Take the full script path and extract the entry point and source directory\n    entry_point = str(Path(script_path).name)\n    source_dir = str(Path(script_path).parent)\n\n    # Create a Sagemaker Model with our script\n    image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n    self.estimator = SKLearn(\n        entry_point=entry_point,\n        source_dir=source_dir,\n        role=self.sageworks_role_arn,\n        instance_type=\"ml.m5.large\",\n        sagemaker_session=self.sm_session,\n        framework_version=\"1.2-1\",\n        image_uri=image,\n        metric_definitions=metric_definitions,\n    )\n\n    # Training Job Name based on the Model UUID and today's date\n    training_date_time_utc = datetime.now(timezone.utc).strftime(\"%Y-%m-%d-%H-%M\")\n    training_job_name = f\"{self.output_uuid}-{training_date_time_utc}\"\n\n    # Train the estimator\n    self.estimator.fit({\"train\": s3_training_path}, job_name=training_job_name)\n\n    # Now delete the training data\n    self.log.info(f\"Deleting training data {s3_training_path}...\")\n    wr.s3.delete_objects(\n        [s3_training_path, s3_training_path.replace(\".csv\", \".csv.metadata\")],\n        boto3_session=self.boto3_session,\n    )\n\n    # Create Model and officially Register\n    self.log.important(f\"Creating new model {self.output_uuid}...\")\n    self.create_and_register_model()\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#supported-models","title":"Supported Models","text":"<p>Currently SageWorks supports XGBoost (classifier/regressor), and Scikit Learn models. Those models can be created by just specifying different parameters to the <code>FeaturesToModel</code> class. The main issue with the supported models is they are vanilla versions with default parameters, any customization should be done with Custom Models</p>"},{"location":"core_classes/transforms/features_to_model/#xgboost","title":"XGBoost","text":"<pre><code>from sageworks.core.transforms.features_to_model.features_to_model import FeaturesToModel\n\n# XGBoost Regression Model\ninput_uuid = \"abalone_features\"\noutput_uuid = \"abalone-regression\"\nto_model = FeaturesToModel(input_uuid, output_uuid, model_type=ModelType.REGRESSOR)\nto_model.set_output_tags([\"abalone\", \"public\"])\nto_model.transform(target_column=\"class_number_of_rings\", description=\"Abalone Regression\")\n\n# XGBoost Classification Model\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-classification\"\nto_model = FeaturesToModel(input_uuid, output_uuid, ModelType.CLASSIFIER)\nto_model.set_output_tags([\"wine\", \"public\"])\nto_model.transform(target_column=\"wine_class\", description=\"Wine Classification\")\n\n# Quantile Regression Model (Abalone)\ninput_uuid = \"abalone_features\"\noutput_uuid = \"abalone-quantile-reg\"\nto_model = FeaturesToModel(input_uuid, output_uuid, ModelType.QUANTILE_REGRESSOR)\nto_model.set_output_tags([\"abalone\", \"quantiles\"])\nto_model.transform(target_column=\"class_number_of_rings\", description=\"Abalone Quantile Regression\")\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#scikit-learn","title":"Scikit-Learn","text":"<pre><code>from sageworks.core.transforms.features_to_model.features_to_model import FeaturesToModel\n\n# Scikit-Learn Kmeans Clustering Model\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-clusters\"\nto_model = FeaturesToModel(\n    input_uuid,\n    output_uuid,\n    scikit_model_class=\"KMeans\",  # Clustering algorithm\n    model_import_str=\"from sklearn.cluster import KMeans\",  # Import statement for KMeans\n    model_type=ModelType.CLUSTERER,\n)\nto_model.set_output_tags([\"wine\", \"clustering\"])\nto_model.transform(target_column=None, description=\"Wine Clustering\", train_all_data=True)\n\n# Scikit-Learn HDBSCAN Clustering Model\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-clusters-hdbscan\"\nto_model = FeaturesToModel(\n    input_uuid,\n    output_uuid,\n    scikit_model_class=\"HDBSCAN\",  # Density-based clustering algorithm\n    model_import_str=\"from sklearn.cluster import HDBSCAN\",\n    model_type=ModelType.CLUSTERER,\n)\nto_model.set_output_tags([\"wine\", \"density-based clustering\"])\nto_model.transform(target_column=None, description=\"Wine Clustering with HDBSCAN\", train_all_data=True)\n\n# Scikit-Learn 2D Projection Model using UMAP\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-2d-projection\"\nto_model = FeaturesToModel(\n    input_uuid,\n    output_uuid,\n    scikit_model_class=\"UMAP\",\n    model_import_str=\"from umap import UMAP\",\n    model_type=ModelType.PROJECTION,\n)\nto_model.set_output_tags([\"wine\", \"2d-projection\"])\nto_model.transform(target_column=None, description=\"Wine 2D Projection\", train_all_data=True)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#custom-models","title":"Custom Models","text":"<p>For custom models we recommend the following steps:</p> <p>Experimental</p> <p>The SageWorks Custom Models are currently in experimental mode so have fun but expect issues. Requires <code>sageworks &gt;= 0.8.60</code>. Feel free to submit issues to SageWorks Github</p> <ul> <li>Copy the example custom model script into your own directory<ul> <li>See: Custom Model Script</li> </ul> </li> <li>Make a requirements.txt and put into the same directory</li> <li>Train/deploy the ^existing^ example<ul> <li>This is an important step, don't skip it</li> <li>If the existing model script trains/deploys your in great shape for the next step, if it doesn't then now is a good time to debug AWS account/permissions/etc.</li> </ul> </li> <li>Now customize the model script</li> <li>Train/deploy your custom script</li> </ul>"},{"location":"core_classes/transforms/features_to_model/#trainingdeploying-custom-models","title":"Training/Deploying Custom Models","text":"<pre><code>from sageworks.api import ModelType\nfrom sageworks.core.transforms.features_to_model.features_to_model import FeaturesToModel\n\n# Note this directory should also have a requirements.txt in it\nmy_custom_script = \"/full/path/to/my/directory/my_custom_script.py\"\ninput_uuid = \"wine_features\"    # FeatureSet you want to use\noutput_uuid = \"my-custom-model\" # change to whatever\ntarget_column = \"wine-class\"    # change to whatever\nto_model = FeaturesToModel(input_uuid, output_uuid,\n                           model_type=ModelType.CLASSIFIER, \n                           custom_script=my_custom_script)\nto_model.set_output_tags([\"your\", \"tags\"])\nto_model.transform(target_column=target_column, description=\"Custom Model\")\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#custom-models-create-an-endpointrun-inference","title":"Custom Models: Create an Endpoint/Run Inference","text":"<pre><code>from sageworks.api import Model, Endpoint\n\nmodel = Model(\"my-custom-model\")\nend = model.to_endpoint()   # Note: This takes a while\n\n# Now run inference on my custom model :)\nend.auto_inference(capture=True)\n\n# Run inference with my own dataframe\ndf = fs.pull_dataframe()  # Or whatever dataframe\nend.inference(df)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord</p>"},{"location":"core_classes/transforms/model_to_endpoint/","title":"Model to Endpoint","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>ModelToEndpoint: Deploy an Endpoint for a Model</p>"},{"location":"core_classes/transforms/model_to_endpoint/#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint","title":"<code>ModelToEndpoint</code>","text":"<p>               Bases: <code>Transform</code></p> <p>ModelToEndpoint: Deploy an Endpoint for a Model</p> Common Usage <pre><code>to_endpoint = ModelToEndpoint(model_uuid, endpoint_uuid)\nto_endpoint.set_output_tags([\"aqsol\", \"public\", \"whatever\"])\nto_endpoint.transform()\n</code></pre> Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>class ModelToEndpoint(Transform):\n    \"\"\"ModelToEndpoint: Deploy an Endpoint for a Model\n\n    Common Usage:\n        ```python\n        to_endpoint = ModelToEndpoint(model_uuid, endpoint_uuid)\n        to_endpoint.set_output_tags([\"aqsol\", \"public\", \"whatever\"])\n        to_endpoint.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, model_uuid: str, endpoint_uuid: str, serverless: bool = True):\n        \"\"\"ModelToEndpoint Initialization\n        Args:\n            model_uuid(str): The UUID of the input Model\n            endpoint_uuid(str): The UUID of the output Endpoint\n            serverless(bool): Deploy the Endpoint in serverless mode (default: True)\n        \"\"\"\n        # Make sure the endpoint_uuid is a valid name\n        Artifact.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call superclass init\n        super().__init__(model_uuid, endpoint_uuid)\n\n        # Set up all my instance attributes\n        self.serverless = serverless\n        self.instance_type = \"serverless\" if serverless else \"ml.t2.medium\"\n        self.input_type = TransformInput.MODEL\n        self.output_type = TransformOutput.ENDPOINT\n\n    def transform_impl(self):\n        \"\"\"Deploy an Endpoint for a Model\"\"\"\n\n        # Delete endpoint (if it already exists)\n        EndpointCore.managed_delete(self.output_uuid)\n\n        # Get the Model Package ARN for our input model\n        input_model = ModelCore(self.input_uuid)\n        model_package_arn = input_model.model_package_arn()\n\n        # Deploy the model\n        self._deploy_model(model_package_arn)\n\n        # Add this endpoint to the set of registered endpoints for the model\n        input_model.register_endpoint(self.output_uuid)\n\n        # This ensures that the endpoint is ready for use\n        time.sleep(5)  # We wait for AWS Lag\n        end = EndpointCore(self.output_uuid)\n        self.log.important(f\"Endpoint {end.uuid} is ready for use\")\n\n    def _deploy_model(self, model_package_arn: str):\n        \"\"\"Internal Method: Deploy the Model\n\n        Args:\n            model_package_arn(str): The Model Package ARN used to deploy the Endpoint\n        \"\"\"\n        # Grab the specified Model Package\n        model_package = ModelPackage(\n            role=self.sageworks_role_arn,\n            model_package_arn=model_package_arn,\n            sagemaker_session=self.sm_session,\n        )\n\n        # Get the metadata/tags to push into AWS\n        aws_tags = self.get_aws_tags()\n\n        # Is this a serverless deployment?\n        serverless_config = None\n        if self.serverless:\n            serverless_config = ServerlessInferenceConfig(\n                memory_size_in_mb=2048,\n                max_concurrency=5,\n            )\n\n        # Deploy the Endpoint\n        self.log.important(f\"Deploying the Endpoint {self.output_uuid}...\")\n        model_package.deploy(\n            initial_instance_count=1,\n            instance_type=self.instance_type,\n            serverless_inference_config=serverless_config,\n            endpoint_name=self.output_uuid,\n            serializer=CSVSerializer(),\n            deserializer=CSVDeserializer(),\n            tags=aws_tags,\n        )\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Calling onboard() for the Endpoint\"\"\"\n        self.log.info(\"Post-Transform: Calling onboard() for the Endpoint...\")\n\n        # Onboard the Endpoint\n        output_endpoint = EndpointCore(self.output_uuid)\n        output_endpoint.onboard_with_args(input_model=self.input_uuid)\n</code></pre>"},{"location":"core_classes/transforms/model_to_endpoint/#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__","title":"<code>__init__(model_uuid, endpoint_uuid, serverless=True)</code>","text":"<p>ModelToEndpoint Initialization Args:     model_uuid(str): The UUID of the input Model     endpoint_uuid(str): The UUID of the output Endpoint     serverless(bool): Deploy the Endpoint in serverless mode (default: True)</p> Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>def __init__(self, model_uuid: str, endpoint_uuid: str, serverless: bool = True):\n    \"\"\"ModelToEndpoint Initialization\n    Args:\n        model_uuid(str): The UUID of the input Model\n        endpoint_uuid(str): The UUID of the output Endpoint\n        serverless(bool): Deploy the Endpoint in serverless mode (default: True)\n    \"\"\"\n    # Make sure the endpoint_uuid is a valid name\n    Artifact.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call superclass init\n    super().__init__(model_uuid, endpoint_uuid)\n\n    # Set up all my instance attributes\n    self.serverless = serverless\n    self.instance_type = \"serverless\" if serverless else \"ml.t2.medium\"\n    self.input_type = TransformInput.MODEL\n    self.output_type = TransformOutput.ENDPOINT\n</code></pre>"},{"location":"core_classes/transforms/model_to_endpoint/#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Calling onboard() for the Endpoint</p> Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Calling onboard() for the Endpoint\"\"\"\n    self.log.info(\"Post-Transform: Calling onboard() for the Endpoint...\")\n\n    # Onboard the Endpoint\n    output_endpoint = EndpointCore(self.output_uuid)\n    output_endpoint.onboard_with_args(input_model=self.input_uuid)\n</code></pre>"},{"location":"core_classes/transforms/model_to_endpoint/#sageworks.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl","title":"<code>transform_impl()</code>","text":"<p>Deploy an Endpoint for a Model</p> Source code in <code>src/sageworks/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>def transform_impl(self):\n    \"\"\"Deploy an Endpoint for a Model\"\"\"\n\n    # Delete endpoint (if it already exists)\n    EndpointCore.managed_delete(self.output_uuid)\n\n    # Get the Model Package ARN for our input model\n    input_model = ModelCore(self.input_uuid)\n    model_package_arn = input_model.model_package_arn()\n\n    # Deploy the model\n    self._deploy_model(model_package_arn)\n\n    # Add this endpoint to the set of registered endpoints for the model\n    input_model.register_endpoint(self.output_uuid)\n\n    # This ensures that the endpoint is ready for use\n    time.sleep(5)  # We wait for AWS Lag\n    end = EndpointCore(self.output_uuid)\n    self.log.important(f\"Endpoint {end.uuid} is ready for use\")\n</code></pre>"},{"location":"core_classes/transforms/overview/","title":"Transforms","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>SageWorks currently has a large set of Transforms that go from one Artifact type to another (e.g. DataSource to FeatureSet). The Transforms will often have light and heavy versions depending on the scale of data that needs to be transformed.</p>"},{"location":"core_classes/transforms/overview/#transform-details","title":"Transform Details","text":"<ul> <li>DataLoaders Light: Loads various light/smaller data into AWS Data Catalog and Athena</li> <li>DataLoaders Heavy: Loads heavy/larger data (via Glue) into AWS Data Catalog and Athena</li> <li>DataToFeatures: Transforms a DataSource into a FeatureSet (AWS Feature Store/Group)</li> <li>FeaturesToModel: Trains and deploys an AWS Model Package/Group from a FeatureSet</li> <li>ModelToEndpoint: Manages the provisioning and deployment of a Model Endpoint</li> <li>PandasTransforms: Pandas DataFrame transforms and helper methods.</li> </ul>"},{"location":"core_classes/transforms/pandas_transforms/","title":"Pandas Transforms","text":"<p>API Classes</p> <p>The API Classes will often provide helpful methods that give you a DataFrame (data_source.query() for instance), so always check out the API Classes first.</p> <p>These Transforms will give you the ultimate in customization and flexibility when creating AWS Machine Learning Pipelines. Grab a Pandas DataFrame from a DataSource or FeatureSet process in whatever way for your use case and simply create another Sageworks DataSource or FeatureSet from the resulting DataFrame.</p> <p>Lots of Options:</p> <p>Not for Large Data</p> <p>Pandas Transforms can't handle large datasets (&gt; 4 GigaBytes). For doing transforma on large data see our Heavy Transforms</p> <ul> <li>S3 --&gt; DF --&gt; DataSource</li> <li>DataSource --&gt; DF --&gt; DataSource</li> <li>DataSoruce --&gt; DF --&gt; FeatureSet</li> <li>Get Creative!</li> </ul> <p>Welcome to the SageWorks Pandas Transform Classes</p> <p>These classes provide low-level APIs for using Pandas DataFrames</p> <ul> <li>DataToPandas: Pull a dataframe from a SageWorks DataSource</li> <li>FeaturesToPandas: Pull a dataframe from a SageWorks FeatureSet</li> <li>PandasToData: Create a SageWorks DataSource using a Pandas DataFrame as the source</li> <li>PandasToFeatures: Create a SageWorks FeatureSet using a Pandas DataFrame as the source</li> <li>PandasToFeaturesChunked: Create a SageWorks FeatureSet using a Chunked/Streaming Pandas DataFrame as the source</li> </ul>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.DataToPandas","title":"<code>DataToPandas</code>","text":"<p>               Bases: <code>Transform</code></p> <p>DataToPandas: Class to transform a Data Source into a Pandas DataFrame</p> Common Usage <pre><code>data_to_df = DataToPandas(data_source_uuid)\ndata_to_df.transform(query=&lt;optional SQL query to filter/process data&gt;)\ndata_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\nmy_df = data_to_df.get_output()\n\nNote: query is the best way to use this class, so use it :)\n</code></pre> Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>class DataToPandas(Transform):\n    \"\"\"DataToPandas: Class to transform a Data Source into a Pandas DataFrame\n\n    Common Usage:\n        ```python\n        data_to_df = DataToPandas(data_source_uuid)\n        data_to_df.transform(query=&lt;optional SQL query to filter/process data&gt;)\n        data_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\n        my_df = data_to_df.get_output()\n\n        Note: query is the best way to use this class, so use it :)\n        ```\n    \"\"\"\n\n    def __init__(self, input_uuid: str):\n        \"\"\"DataToPandas Initialization\"\"\"\n\n        # Call superclass init\n        super().__init__(input_uuid, \"DataFrame\")\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.DATA_SOURCE\n        self.output_type = TransformOutput.PANDAS_DF\n        self.output_df = None\n\n    def transform_impl(self, query: str = None, max_rows=100000):\n        \"\"\"Convert the DataSource into a Pandas DataFrame\n        Args:\n            query(str): The query to run against the DataSource (default: None)\n            max_rows(int): The maximum number of rows to return (default: 100000)\n        \"\"\"\n\n        # Grab the Input (Data Source)\n        input_data = DataSourceFactory(self.input_uuid)\n        if not input_data.exists():\n            self.log.critical(f\"Data Check on {self.input_uuid} failed!\")\n            return\n\n        # If a query is provided, that overrides the queries below\n        if query:\n            self.log.info(f\"Querying {self.input_uuid} with {query}...\")\n            self.output_df = input_data.query(query)\n            return\n\n        # If the data source has more rows than max_rows, do a sample query\n        num_rows = input_data.num_rows()\n        if num_rows &gt; max_rows:\n            percentage = round(max_rows * 100.0 / num_rows)\n            self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n            query = f\"SELECT * FROM {self.input_uuid} TABLESAMPLE BERNOULLI({percentage})\"\n        else:\n            query = f\"SELECT * FROM {self.input_uuid}\"\n\n        # Mark the transform as complete and set the output DataFrame\n        self.output_df = input_data.query(query)\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n        self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n        self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n\n    def get_output(self) -&gt; pd.DataFrame:\n        \"\"\"Get the DataFrame Output from this Transform\"\"\"\n        return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.DataToPandas.__init__","title":"<code>__init__(input_uuid)</code>","text":"<p>DataToPandas Initialization</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def __init__(self, input_uuid: str):\n    \"\"\"DataToPandas Initialization\"\"\"\n\n    # Call superclass init\n    super().__init__(input_uuid, \"DataFrame\")\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.DATA_SOURCE\n    self.output_type = TransformOutput.PANDAS_DF\n    self.output_df = None\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.DataToPandas.get_output","title":"<code>get_output()</code>","text":"<p>Get the DataFrame Output from this Transform</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def get_output(self) -&gt; pd.DataFrame:\n    \"\"\"Get the DataFrame Output from this Transform\"\"\"\n    return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.DataToPandas.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Any checks on the Pandas DataFrame that need to be done</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n    self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n    self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.DataToPandas.transform_impl","title":"<code>transform_impl(query=None, max_rows=100000)</code>","text":"<p>Convert the DataSource into a Pandas DataFrame Args:     query(str): The query to run against the DataSource (default: None)     max_rows(int): The maximum number of rows to return (default: 100000)</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def transform_impl(self, query: str = None, max_rows=100000):\n    \"\"\"Convert the DataSource into a Pandas DataFrame\n    Args:\n        query(str): The query to run against the DataSource (default: None)\n        max_rows(int): The maximum number of rows to return (default: 100000)\n    \"\"\"\n\n    # Grab the Input (Data Source)\n    input_data = DataSourceFactory(self.input_uuid)\n    if not input_data.exists():\n        self.log.critical(f\"Data Check on {self.input_uuid} failed!\")\n        return\n\n    # If a query is provided, that overrides the queries below\n    if query:\n        self.log.info(f\"Querying {self.input_uuid} with {query}...\")\n        self.output_df = input_data.query(query)\n        return\n\n    # If the data source has more rows than max_rows, do a sample query\n    num_rows = input_data.num_rows()\n    if num_rows &gt; max_rows:\n        percentage = round(max_rows * 100.0 / num_rows)\n        self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n        query = f\"SELECT * FROM {self.input_uuid} TABLESAMPLE BERNOULLI({percentage})\"\n    else:\n        query = f\"SELECT * FROM {self.input_uuid}\"\n\n    # Mark the transform as complete and set the output DataFrame\n    self.output_df = input_data.query(query)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.FeaturesToPandas","title":"<code>FeaturesToPandas</code>","text":"<p>               Bases: <code>Transform</code></p> <p>FeaturesToPandas: Class to transform a FeatureSet into a Pandas DataFrame</p> Common Usage <pre><code>feature_to_df = FeaturesToPandas(feature_set_uuid)\nfeature_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\nmy_df = feature_to_df.get_output()\n</code></pre> Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>class FeaturesToPandas(Transform):\n    \"\"\"FeaturesToPandas: Class to transform a FeatureSet into a Pandas DataFrame\n\n    Common Usage:\n        ```python\n        feature_to_df = FeaturesToPandas(feature_set_uuid)\n        feature_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\n        my_df = feature_to_df.get_output()\n        ```\n    \"\"\"\n\n    def __init__(self, feature_set_name: str):\n        \"\"\"FeaturesToPandas Initialization\"\"\"\n\n        # Call superclass init\n        super().__init__(input_uuid=feature_set_name, output_uuid=\"DataFrame\")\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.FEATURE_SET\n        self.output_type = TransformOutput.PANDAS_DF\n        self.output_df = None\n        self.transform_run = False\n\n    def transform_impl(self, max_rows=100000):\n        \"\"\"Convert the FeatureSet into a Pandas DataFrame\"\"\"\n\n        # Grab the Input (Feature Set)\n        input_data = FeatureSetCore(self.input_uuid)\n        if not input_data.exists():\n            self.log.critical(f\"Feature Set Check on {self.input_uuid} failed!\")\n            return\n\n        # Grab the table for this Feature Set\n        table = input_data.athena_table\n\n        # Get the list of columns (and subtract metadata columns that might get added)\n        columns = input_data.columns\n        filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n        columns = \", \".join([x for x in columns if x not in filter_columns])\n\n        # Get the number of rows in the Feature Set\n        num_rows = input_data.num_rows()\n\n        # If the data source has more rows than max_rows, do a sample query\n        if num_rows &gt; max_rows:\n            percentage = round(max_rows * 100.0 / num_rows)\n            self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n            query = f'SELECT {columns} FROM \"{table}\" TABLESAMPLE BERNOULLI({percentage})'\n        else:\n            query = f'SELECT {columns} FROM \"{table}\"'\n\n        # Mark the transform as complete and set the output DataFrame\n        self.transform_run = True\n        self.output_df = input_data.query(query)\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n        self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n        self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n\n    def get_output(self) -&gt; pd.DataFrame:\n        \"\"\"Get the DataFrame Output from this Transform\"\"\"\n        if not self.transform_run:\n            self.transform()\n        return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.__init__","title":"<code>__init__(feature_set_name)</code>","text":"<p>FeaturesToPandas Initialization</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def __init__(self, feature_set_name: str):\n    \"\"\"FeaturesToPandas Initialization\"\"\"\n\n    # Call superclass init\n    super().__init__(input_uuid=feature_set_name, output_uuid=\"DataFrame\")\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.FEATURE_SET\n    self.output_type = TransformOutput.PANDAS_DF\n    self.output_df = None\n    self.transform_run = False\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.get_output","title":"<code>get_output()</code>","text":"<p>Get the DataFrame Output from this Transform</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def get_output(self) -&gt; pd.DataFrame:\n    \"\"\"Get the DataFrame Output from this Transform\"\"\"\n    if not self.transform_run:\n        self.transform()\n    return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Any checks on the Pandas DataFrame that need to be done</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n    self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n    self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.FeaturesToPandas.transform_impl","title":"<code>transform_impl(max_rows=100000)</code>","text":"<p>Convert the FeatureSet into a Pandas DataFrame</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def transform_impl(self, max_rows=100000):\n    \"\"\"Convert the FeatureSet into a Pandas DataFrame\"\"\"\n\n    # Grab the Input (Feature Set)\n    input_data = FeatureSetCore(self.input_uuid)\n    if not input_data.exists():\n        self.log.critical(f\"Feature Set Check on {self.input_uuid} failed!\")\n        return\n\n    # Grab the table for this Feature Set\n    table = input_data.athena_table\n\n    # Get the list of columns (and subtract metadata columns that might get added)\n    columns = input_data.columns\n    filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n    columns = \", \".join([x for x in columns if x not in filter_columns])\n\n    # Get the number of rows in the Feature Set\n    num_rows = input_data.num_rows()\n\n    # If the data source has more rows than max_rows, do a sample query\n    if num_rows &gt; max_rows:\n        percentage = round(max_rows * 100.0 / num_rows)\n        self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n        query = f'SELECT {columns} FROM \"{table}\" TABLESAMPLE BERNOULLI({percentage})'\n    else:\n        query = f'SELECT {columns} FROM \"{table}\"'\n\n    # Mark the transform as complete and set the output DataFrame\n    self.transform_run = True\n    self.output_df = input_data.query(query)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData","title":"<code>PandasToData</code>","text":"<p>               Bases: <code>Transform</code></p> <p>PandasToData: Class to publish a Pandas DataFrame as a DataSource</p> Common Usage <pre><code>df_to_data = PandasToData(output_uuid)\ndf_to_data.set_output_tags([\"test\", \"small\"])\ndf_to_data.set_input(test_df)\ndf_to_data.transform()\n</code></pre> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>class PandasToData(Transform):\n    \"\"\"PandasToData: Class to publish a Pandas DataFrame as a DataSource\n\n    Common Usage:\n        ```python\n        df_to_data = PandasToData(output_uuid)\n        df_to_data.set_output_tags([\"test\", \"small\"])\n        df_to_data.set_input(test_df)\n        df_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, output_uuid: str, output_format: str = \"parquet\"):\n        \"\"\"PandasToData Initialization\n        Args:\n            output_uuid (str): The UUID of the DataSource to create\n            output_format (str): The file format to store the S3 object data in (default: \"parquet\")\n        \"\"\"\n\n        # Make sure the output_uuid is a valid name/id\n        Artifact.is_name_valid(output_uuid)\n\n        # Call superclass init\n        super().__init__(\"DataFrame\", output_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.PANDAS_DF\n        self.output_type = TransformOutput.DATA_SOURCE\n        self.output_df = None\n\n        # Give a message that Parquet is best in most cases\n        if output_format != \"parquet\":\n            self.log.warning(\"Parquet format works the best in most cases please consider using it\")\n        self.output_format = output_format\n\n    def set_input(self, input_df: pd.DataFrame):\n        \"\"\"Set the DataFrame Input for this Transform\"\"\"\n        self.output_df = input_df.copy()\n\n    def delete_existing(self):\n        # Delete the existing FeatureSet if it exists\n        self.log.info(f\"Deleting the {self.output_uuid} DataSource...\")\n        AthenaSource.managed_delete(self.output_uuid)\n        time.sleep(1)\n\n    def convert_object_to_string(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Try to automatically convert object columns to string columns\"\"\"\n        for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n            try:\n                df[c] = df[c].astype(\"string\")\n                df[c] = df[c].str.replace(\"'\", '\"')  # This is for nested JSON\n            except (ParserError, ValueError, TypeError):\n                self.log.info(f\"Column {c} could not be converted to string...\")\n        return df\n\n    def convert_object_to_datetime(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Try to automatically convert object columns to datetime or string columns\"\"\"\n        for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n            try:\n                df[c] = pd.to_datetime(df[c])\n            except (ParserError, ValueError, TypeError):\n                self.log.debug(f\"Column {c} could not be converted to datetime...\")\n        return df\n\n    @staticmethod\n    def convert_datetime_columns(df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Convert datetime columns to ISO-8601 string\"\"\"\n        datetime_type = [\"datetime\", \"datetime64\", \"datetime64[ns]\", \"datetimetz\"]\n        for c in df.select_dtypes(include=datetime_type).columns:\n            df[c] = df[c].map(datetime_to_iso8601)\n            df[c] = df[c].astype(pd.StringDtype())\n        return df\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Pre-Transform: Delete the existing DataSource if it exists\"\"\"\n        self.delete_existing()\n\n    def transform_impl(self, overwrite: bool = True, **kwargs):\n        \"\"\"Convert the Pandas DataFrame into Parquet Format in the SageWorks S3 Bucket, and\n        store the information about the data to the AWS Data Catalog sageworks database\n\n        Args:\n            overwrite (bool): Overwrite the existing data in the SageWorks S3 Bucket\n        \"\"\"\n        self.log.info(f\"DataFrame to SageWorks DataSource: {self.output_uuid}...\")\n\n        # Set up our metadata storage\n        sageworks_meta = {\"sageworks_tags\": self.output_tags}\n        sageworks_meta.update(self.output_meta)\n\n        # Create the Output Parquet file S3 Storage Path\n        s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n        # Convert columns names to lowercase, Athena will not work with uppercase column names\n        if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n            for c in self.output_df.columns:\n                if c != c.lower():\n                    self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n            self.output_df.columns = self.output_df.columns.str.lower()\n\n        # Convert Object Columns to String\n        self.output_df = self.convert_object_to_string(self.output_df)\n\n        # Note: Both of these conversions may not be necessary, so we're leaving them commented out\n        \"\"\"\n        # Convert Object Columns to Datetime\n        self.output_df = self.convert_object_to_datetime(self.output_df)\n\n        # Now convert datetime columns to ISO-8601 string\n        # self.output_df = self.convert_datetime_columns(self.output_df)\n        \"\"\"\n\n        # Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format\n        description = f\"SageWorks data source: {self.output_uuid}\"\n        glue_table_settings = {\"description\": description, \"parameters\": sageworks_meta}\n        if self.output_format == \"parquet\":\n            wr.s3.to_parquet(\n                self.output_df,\n                path=s3_storage_path,\n                dataset=True,\n                mode=\"overwrite\",\n                database=self.data_catalog_db,\n                table=self.output_uuid,\n                filename_prefix=f\"{self.output_uuid}_\",\n                boto3_session=self.boto3_session,\n                partition_cols=None,\n                glue_table_settings=glue_table_settings,\n                sanitize_columns=False,\n            )  # FIXME: Have some logic around partition columns\n\n        # Note: In general Parquet works will for most uses cases. We recommend using Parquet\n        #       You can use JSON_EXTRACT on Parquet string field, and it works great.\n        elif self.output_format == \"jsonl\":\n            self.log.warning(\"We recommend using Parquet format for most use cases\")\n            self.log.warning(\"If you have a use case that requires JSONL please contact SageWorks support\")\n            self.log.warning(\"We'd like to understand what functionality JSONL is providing that isn't already\")\n            self.log.warning(\"provided with Parquet and JSON_EXTRACT() for your Athena Queries\")\n            wr.s3.to_json(\n                self.output_df,\n                path=s3_storage_path,\n                orient=\"records\",\n                lines=True,\n                date_format=\"iso\",\n                dataset=True,\n                mode=\"overwrite\",\n                database=self.data_catalog_db,\n                table=self.output_uuid,\n                filename_prefix=f\"{self.output_uuid}_\",\n                boto3_session=self.boto3_session,\n                partition_cols=None,\n                glue_table_settings=glue_table_settings,\n            )\n        else:\n            raise ValueError(f\"Unsupported file format: {self.output_format}\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Calling onboard() fnr the DataSource\"\"\"\n        self.log.info(\"Post-Transform: Calling onboard() for the DataSource...\")\n\n        # Onboard the DataSource\n        output_data_source = DataSourceFactory(self.output_uuid)\n        output_data_source.onboard()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.__init__","title":"<code>__init__(output_uuid, output_format='parquet')</code>","text":"<p>PandasToData Initialization Args:     output_uuid (str): The UUID of the DataSource to create     output_format (str): The file format to store the S3 object data in (default: \"parquet\")</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def __init__(self, output_uuid: str, output_format: str = \"parquet\"):\n    \"\"\"PandasToData Initialization\n    Args:\n        output_uuid (str): The UUID of the DataSource to create\n        output_format (str): The file format to store the S3 object data in (default: \"parquet\")\n    \"\"\"\n\n    # Make sure the output_uuid is a valid name/id\n    Artifact.is_name_valid(output_uuid)\n\n    # Call superclass init\n    super().__init__(\"DataFrame\", output_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.PANDAS_DF\n    self.output_type = TransformOutput.DATA_SOURCE\n    self.output_df = None\n\n    # Give a message that Parquet is best in most cases\n    if output_format != \"parquet\":\n        self.log.warning(\"Parquet format works the best in most cases please consider using it\")\n    self.output_format = output_format\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.convert_datetime_columns","title":"<code>convert_datetime_columns(df)</code>  <code>staticmethod</code>","text":"<p>Convert datetime columns to ISO-8601 string</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>@staticmethod\ndef convert_datetime_columns(df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Convert datetime columns to ISO-8601 string\"\"\"\n    datetime_type = [\"datetime\", \"datetime64\", \"datetime64[ns]\", \"datetimetz\"]\n    for c in df.select_dtypes(include=datetime_type).columns:\n        df[c] = df[c].map(datetime_to_iso8601)\n        df[c] = df[c].astype(pd.StringDtype())\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_to_datetime","title":"<code>convert_object_to_datetime(df)</code>","text":"<p>Try to automatically convert object columns to datetime or string columns</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def convert_object_to_datetime(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Try to automatically convert object columns to datetime or string columns\"\"\"\n    for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n        try:\n            df[c] = pd.to_datetime(df[c])\n        except (ParserError, ValueError, TypeError):\n            self.log.debug(f\"Column {c} could not be converted to datetime...\")\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.convert_object_to_string","title":"<code>convert_object_to_string(df)</code>","text":"<p>Try to automatically convert object columns to string columns</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def convert_object_to_string(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Try to automatically convert object columns to string columns\"\"\"\n    for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n        try:\n            df[c] = df[c].astype(\"string\")\n            df[c] = df[c].str.replace(\"'\", '\"')  # This is for nested JSON\n        except (ParserError, ValueError, TypeError):\n            self.log.info(f\"Column {c} could not be converted to string...\")\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Calling onboard() fnr the DataSource</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Calling onboard() fnr the DataSource\"\"\"\n    self.log.info(\"Post-Transform: Calling onboard() for the DataSource...\")\n\n    # Onboard the DataSource\n    output_data_source = DataSourceFactory(self.output_uuid)\n    output_data_source.onboard()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Pre-Transform: Delete the existing DataSource if it exists</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Pre-Transform: Delete the existing DataSource if it exists\"\"\"\n    self.delete_existing()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.set_input","title":"<code>set_input(input_df)</code>","text":"<p>Set the DataFrame Input for this Transform</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def set_input(self, input_df: pd.DataFrame):\n    \"\"\"Set the DataFrame Input for this Transform\"\"\"\n    self.output_df = input_df.copy()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToData.transform_impl","title":"<code>transform_impl(overwrite=True, **kwargs)</code>","text":"<p>Convert the Pandas DataFrame into Parquet Format in the SageWorks S3 Bucket, and store the information about the data to the AWS Data Catalog sageworks database</p> <p>Parameters:</p> Name Type Description Default <code>overwrite</code> <code>bool</code> <p>Overwrite the existing data in the SageWorks S3 Bucket</p> <code>True</code> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def transform_impl(self, overwrite: bool = True, **kwargs):\n    \"\"\"Convert the Pandas DataFrame into Parquet Format in the SageWorks S3 Bucket, and\n    store the information about the data to the AWS Data Catalog sageworks database\n\n    Args:\n        overwrite (bool): Overwrite the existing data in the SageWorks S3 Bucket\n    \"\"\"\n    self.log.info(f\"DataFrame to SageWorks DataSource: {self.output_uuid}...\")\n\n    # Set up our metadata storage\n    sageworks_meta = {\"sageworks_tags\": self.output_tags}\n    sageworks_meta.update(self.output_meta)\n\n    # Create the Output Parquet file S3 Storage Path\n    s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n    # Convert columns names to lowercase, Athena will not work with uppercase column names\n    if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n        for c in self.output_df.columns:\n            if c != c.lower():\n                self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n        self.output_df.columns = self.output_df.columns.str.lower()\n\n    # Convert Object Columns to String\n    self.output_df = self.convert_object_to_string(self.output_df)\n\n    # Note: Both of these conversions may not be necessary, so we're leaving them commented out\n    \"\"\"\n    # Convert Object Columns to Datetime\n    self.output_df = self.convert_object_to_datetime(self.output_df)\n\n    # Now convert datetime columns to ISO-8601 string\n    # self.output_df = self.convert_datetime_columns(self.output_df)\n    \"\"\"\n\n    # Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format\n    description = f\"SageWorks data source: {self.output_uuid}\"\n    glue_table_settings = {\"description\": description, \"parameters\": sageworks_meta}\n    if self.output_format == \"parquet\":\n        wr.s3.to_parquet(\n            self.output_df,\n            path=s3_storage_path,\n            dataset=True,\n            mode=\"overwrite\",\n            database=self.data_catalog_db,\n            table=self.output_uuid,\n            filename_prefix=f\"{self.output_uuid}_\",\n            boto3_session=self.boto3_session,\n            partition_cols=None,\n            glue_table_settings=glue_table_settings,\n            sanitize_columns=False,\n        )  # FIXME: Have some logic around partition columns\n\n    # Note: In general Parquet works will for most uses cases. We recommend using Parquet\n    #       You can use JSON_EXTRACT on Parquet string field, and it works great.\n    elif self.output_format == \"jsonl\":\n        self.log.warning(\"We recommend using Parquet format for most use cases\")\n        self.log.warning(\"If you have a use case that requires JSONL please contact SageWorks support\")\n        self.log.warning(\"We'd like to understand what functionality JSONL is providing that isn't already\")\n        self.log.warning(\"provided with Parquet and JSON_EXTRACT() for your Athena Queries\")\n        wr.s3.to_json(\n            self.output_df,\n            path=s3_storage_path,\n            orient=\"records\",\n            lines=True,\n            date_format=\"iso\",\n            dataset=True,\n            mode=\"overwrite\",\n            database=self.data_catalog_db,\n            table=self.output_uuid,\n            filename_prefix=f\"{self.output_uuid}_\",\n            boto3_session=self.boto3_session,\n            partition_cols=None,\n            glue_table_settings=glue_table_settings,\n        )\n    else:\n        raise ValueError(f\"Unsupported file format: {self.output_format}\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures","title":"<code>PandasToFeatures</code>","text":"<p>               Bases: <code>Transform</code></p> <p>PandasToFeatures: Class to publish a Pandas DataFrame into a FeatureSet</p> Common Usage <pre><code>to_features = PandasToFeatures(output_uuid)\nto_features.set_output_tags([\"my\", \"awesome\", \"data\"])\nto_features.set_input(df, id_column=\"my_id\")\nto_features.transform()\n</code></pre> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>class PandasToFeatures(Transform):\n    \"\"\"PandasToFeatures: Class to publish a Pandas DataFrame into a FeatureSet\n\n    Common Usage:\n        ```python\n        to_features = PandasToFeatures(output_uuid)\n        to_features.set_output_tags([\"my\", \"awesome\", \"data\"])\n        to_features.set_input(df, id_column=\"my_id\")\n        to_features.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, output_uuid: str):\n        \"\"\"PandasToFeatures Initialization\n\n        Args:\n            output_uuid (str): The UUID of the FeatureSet to create\n        \"\"\"\n\n        # Make sure the output_uuid is a valid name\n        Artifact.is_name_valid(output_uuid)\n\n        # Call superclass init\n        super().__init__(\"DataFrame\", output_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.PANDAS_DF\n        self.output_type = TransformOutput.FEATURE_SET\n        self.id_column = None\n        self.event_time_column = None\n        self.one_hot_columns = []\n        self.categorical_dtypes = {}  # Used for streaming/chunking\n        self.output_df = None\n        self.table_format = TableFormatEnum.ICEBERG\n        self.incoming_hold_out_ids = None\n\n        # These will be set in the transform method\n        self.output_feature_group = None\n        self.output_feature_set = None\n        self.expected_rows = 0\n\n    def set_input(self, input_df: pd.DataFrame, id_column=None, event_time_column=None, one_hot_columns=None):\n        \"\"\"Set the Input DataFrame for this Transform\n\n        Args:\n            input_df (pd.DataFrame): The input DataFrame.\n            id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n            event_time_column (str, optional): The name of the event time column (default: None).\n            one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n        \"\"\"\n        self.id_column = id_column\n        self.event_time_column = event_time_column\n        self.output_df = input_df.copy()\n        self.one_hot_columns = one_hot_columns or []\n\n        # Now Prepare the DataFrame for its journey into an AWS FeatureGroup\n        self.prep_dataframe()\n\n    def delete_existing(self):\n        # Delete the existing FeatureSet if it exists\n        self.log.info(f\"Deleting the {self.output_uuid} FeatureSet...\")\n        FeatureSetCore.managed_delete(self.output_uuid)\n        time.sleep(1)\n\n    def _ensure_id_column(self):\n        \"\"\"Internal: AWS Feature Store requires an Id field\"\"\"\n        if self.id_column is None:\n            self.log.warning(\"Generating an 'auto_id' column, we highly recommended setting the 'id_column'\")\n            self.output_df[\"auto_id\"] = self.output_df.index\n            self.id_column = \"auto_id\"\n            return\n        if self.id_column not in self.output_df.columns:\n            error_msg = f\"Id column {self.id_column} not found in the DataFrame\"\n            self.log.critical(error_msg)\n            raise ValueError(error_msg)\n\n    def _ensure_event_time(self):\n        \"\"\"Internal: AWS Feature Store requires an event_time field for all data stored\"\"\"\n        if self.event_time_column is None or self.event_time_column not in self.output_df.columns:\n            self.log.info(\"Generating an event_time column before FeatureSet Creation...\")\n            self.event_time_column = \"event_time\"\n            self.output_df[self.event_time_column] = pd.Timestamp(\"now\", tz=\"UTC\")\n\n        # The event_time_column is defined, so we need to make sure it's in ISO-8601 string format\n        # Note: AWS Feature Store only a particular ISO-8601 format not ALL ISO-8601 formats\n        time_column = self.output_df[self.event_time_column]\n\n        # Check if the event_time_column is of type object or string convert it to DateTime\n        if time_column.dtypes == \"object\" or time_column.dtypes.name == \"string\":\n            self.log.info(f\"Converting {self.event_time_column} to DateTime...\")\n            time_column = pd.to_datetime(time_column)\n\n        # Let's make sure it the right type for Feature Store\n        if pd.api.types.is_datetime64_any_dtype(time_column):\n            self.log.info(f\"Converting {self.event_time_column} to ISOFormat Date String before FeatureSet Creation...\")\n\n            # Convert the datetime DType to ISO-8601 string\n            # TableFormat=ICEBERG does not support alternate formats for event_time field, it only supports String type.\n            time_column = time_column.map(datetime_to_iso8601)\n            self.output_df[self.event_time_column] = time_column.astype(\"string\")\n\n    def _convert_objs_to_string(self):\n        \"\"\"Internal: AWS Feature Store doesn't know how to store object dtypes, so convert to String\"\"\"\n        for col in self.output_df:\n            if pd.api.types.is_object_dtype(self.output_df[col].dtype):\n                self.output_df[col] = self.output_df[col].astype(pd.StringDtype())\n\n    def process_column_name(self, column: str, shorten: bool = False) -&gt; str:\n        \"\"\"Call various methods to make sure the column is ready for Feature Store\n        Args:\n            column (str): The column name to process\n            shorten (bool): Should we shorten the column name? (default: False)\n        \"\"\"\n        self.log.debug(f\"Processing column {column}...\")\n\n        # Make sure the column name is valid\n        column = self.sanitize_column_name(column)\n\n        # Make sure the column name isn't too long\n        if shorten:\n            column = self.shorten_column_name(column)\n\n        return column\n\n    def shorten_column_name(self, name, max_length=20):\n        if len(name) &lt;= max_length:\n            return name\n\n        # Start building the new name from the end\n        parts = name.split(\"_\")[::-1]\n        new_name = \"\"\n        for part in parts:\n            if len(new_name) + len(part) + 1 &lt;= max_length:  # +1 for the underscore\n                new_name = f\"{part}_{new_name}\" if new_name else part\n            else:\n                break\n\n        # If new_name is empty, just use the last part of the original name\n        if not new_name:\n            new_name = parts[0]\n\n        self.log.info(f\"Shortening {name} to {new_name}\")\n        return new_name\n\n    def sanitize_column_name(self, name):\n        # Remove all invalid characters\n        sanitized = re.sub(\"[^a-zA-Z0-9-_]\", \"_\", name)\n        sanitized = re.sub(\"_+\", \"_\", sanitized)\n        sanitized = sanitized.strip(\"_\")\n\n        # Log the change if the name was altered\n        if sanitized != name:\n            self.log.info(f\"Sanitizing {name} to {sanitized}\")\n\n        return sanitized\n\n    def one_hot_encode(self, df, one_hot_columns: list) -&gt; pd.DataFrame:\n        \"\"\"One Hot Encoding for Categorical Columns with additional column name management\n\n        Args:\n            df (pd.DataFrame): The DataFrame to process\n            one_hot_columns (list): The list of columns to one-hot encode\n\n        Returns:\n            pd.DataFrame: The DataFrame with one-hot encoded columns\n        \"\"\"\n\n        # Grab the current list of columns\n        current_columns = list(df.columns)\n\n        # Now convert the list of columns into Categorical and then One-Hot Encode\n        self.convert_columns_to_categorical(one_hot_columns)\n        self.log.important(f\"One-Hot encoding columns: {one_hot_columns}\")\n        df = pd.get_dummies(df, columns=one_hot_columns)\n\n        # Compute the new columns generated by get_dummies\n        new_columns = list(set(df.columns) - set(current_columns))\n        self.log.important(f\"New columns generated: {new_columns}\")\n\n        # Convert new columns to int32\n        df[new_columns] = df[new_columns].astype(\"int32\")\n\n        # For the new columns we're going to shorten the names\n        renamed_columns = {col: self.process_column_name(col) for col in new_columns}\n\n        # Rename the columns in the DataFrame\n        df.rename(columns=renamed_columns, inplace=True)\n\n        return df\n\n    # Helper Methods\n    def convert_columns_to_categorical(self, columns: list):\n        \"\"\"Convert column to Categorical type\"\"\"\n        for feature in columns:\n            if feature not in [self.event_time_column, self.id_column]:\n                unique_values = self.output_df[feature].nunique()\n                if 1 &lt; unique_values &lt; 10:\n                    self.log.important(f\"Converting column {feature} to categorical (unique {unique_values})\")\n                    self.output_df[feature] = self.output_df[feature].astype(\"category\")\n                else:\n                    self.log.warning(f\"Column {feature} too many unique values {unique_values} skipping...\")\n\n    def manual_categorical_converter(self):\n        \"\"\"Used for Streaming: Convert object and string types to Categorical\n\n        Note:\n            This method is used for streaming/chunking. You can set the\n            categorical_dtypes attribute to a dictionary of column names and\n            their respective categorical types.\n        \"\"\"\n        for column, cat_d_type in self.categorical_dtypes.items():\n            self.output_df[column] = self.output_df[column].astype(cat_d_type)\n\n    @staticmethod\n    def convert_column_types(df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Convert the types of the DataFrame to the correct types for the Feature Store\"\"\"\n        for column in list(df.select_dtypes(include=\"bool\").columns):\n            df[column] = df[column].astype(\"int32\")\n        for column in list(df.select_dtypes(include=\"category\").columns):\n            df[column] = df[column].astype(\"str\")\n\n        # Select all columns that are of datetime dtype and convert them to ISO-8601 strings\n        for column in [col for col in df.columns if pd.api.types.is_datetime64_any_dtype(df[col])]:\n            df[column] = df[column].map(datetime_to_iso8601).astype(\"string\")\n\n        \"\"\"FIXME Not sure we need these conversions\n        for column in list(df.select_dtypes(include=\"object\").columns):\n            df[column] = df[column].astype(\"string\")\n        for column in list(df.select_dtypes(include=[pd.Int64Dtype]).columns):\n            df[column] = df[column].astype(\"int64\")\n        for column in list(df.select_dtypes(include=[pd.Float64Dtype]).columns):\n            df[column] = df[column].astype(\"float64\")\n        \"\"\"\n        return df\n\n    def prep_dataframe(self):\n        \"\"\"Prep the DataFrame for Feature Store Creation\"\"\"\n        self.log.info(\"Prep the output_df (cat_convert, convert types, and lowercase columns)...\")\n\n        # Remove any columns generated from AWS\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        self.output_df = self.output_df.drop(columns=aws_cols, errors=\"ignore\")\n\n        # If one-hot columns are provided then one-hot encode them\n        if self.one_hot_columns:\n            self.output_df = self.one_hot_encode(self.output_df, self.one_hot_columns)\n\n        # Convert columns names to lowercase, Athena will not work with uppercase column names\n        if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n            for c in self.output_df.columns:\n                if c != c.lower():\n                    self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n            self.output_df.columns = self.output_df.columns.str.lower()\n\n        # Make sure we have the required id and event_time columns\n        self._ensure_id_column()\n        self._ensure_event_time()\n\n        # Check for a training column (SageWorks uses dynamic training columns)\n        if \"training\" in self.output_df.columns:\n            self.log.important(\n                \"\"\"Training column detected: Since FeatureSets are read-only, SageWorks creates a training view\n                that can be dynamically changed. We'll use this training column to create a training view.\"\"\"\n            )\n            self.incoming_hold_out_ids = self.output_df[~self.output_df[\"training\"]][self.id_column].tolist()\n            self.output_df = self.output_df.drop(columns=[\"training\"])\n\n        # We need to convert some of our column types to the correct types\n        # Feature Store only supports these data types:\n        # - Integral\n        # - Fractional\n        # - String (timestamp/datetime types need to be converted to string)\n        self.output_df = self.convert_column_types(self.output_df)\n\n    def create_feature_group(self):\n        \"\"\"Create a Feature Group, load our Feature Definitions, and wait for it to be ready\"\"\"\n\n        # Create a Feature Group and load our Feature Definitions\n        my_feature_group = FeatureGroup(name=self.output_uuid, sagemaker_session=self.sm_session)\n        my_feature_group.load_feature_definitions(data_frame=self.output_df)\n\n        # Create the Output S3 Storage Path for this Feature Set\n        s3_storage_path = f\"{self.feature_sets_s3_path}/{self.output_uuid}\"\n\n        # Get the metadata/tags to push into AWS\n        aws_tags = self.get_aws_tags()\n\n        # Create the Feature Group\n        my_feature_group.create(\n            s3_uri=s3_storage_path,\n            record_identifier_name=self.id_column,\n            event_time_feature_name=self.event_time_column,\n            role_arn=self.sageworks_role_arn,\n            enable_online_store=True,\n            table_format=self.table_format,\n            tags=aws_tags,\n        )\n\n        # Ensure/wait for the feature group to be created\n        self.ensure_feature_group_created(my_feature_group)\n        return my_feature_group\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Pre-Transform: Delete any existing FeatureSet and Create the Feature Group\"\"\"\n        self.delete_existing()\n        self.output_feature_group = self.create_feature_group()\n\n    def transform_impl(self):\n        \"\"\"Transform Implementation: Ingest the data into the Feature Group\"\"\"\n\n        # Now we actually push the data into the Feature Group (called ingestion)\n        self.log.important(f\"Ingesting rows into Feature Group {self.output_uuid}...\")\n        ingest_manager = self.output_feature_group.ingest(self.output_df, max_workers=8, max_processes=2, wait=False)\n        try:\n            ingest_manager.wait()\n        except IngestionError as exc:\n            self.log.warning(f\"Some rows had an ingesting error: {exc}\")\n\n        # Report on any rows that failed to ingest\n        if ingest_manager.failed_rows:\n            self.log.warning(f\"Number of Failed Rows: {len(ingest_manager.failed_rows)}\")\n\n            # FIXME: This may or may not give us the correct rows\n            # If any index is greater then the number of rows, then the index needs\n            # to be converted to a relative index in our current output_df\n            df_rows = len(self.output_df)\n            relative_indexes = [idx - df_rows if idx &gt;= df_rows else idx for idx in ingest_manager.failed_rows]\n            failed_data = self.output_df.iloc[relative_indexes]\n            for idx, row in failed_data.iterrows():\n                self.log.warning(f\"Failed Row {idx}: {row.to_dict()}\")\n\n        # Keep track of the number of rows we expect to be ingested\n        self.expected_rows += len(self.output_df) - len(ingest_manager.failed_rows)\n        self.log.info(f\"Added rows: {len(self.output_df)}\")\n        self.log.info(f\"Failed rows: {len(ingest_manager.failed_rows)}\")\n        self.log.info(f\"Total rows ingested: {self.expected_rows}\")\n\n        # We often need to wait a bit for AWS to fully register the new Feature Group\n        self.log.important(f\"Waiting for AWS to register the new Feature Group {self.output_uuid}...\")\n        time.sleep(30)\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Populating Offline Storage and onboard()\"\"\"\n        self.log.info(\"Post-Transform: Populating Offline Storage and onboard()...\")\n\n        # Feature Group Ingestion takes a while, so we need to wait for it to finish\n        self.output_feature_set = FeatureSetCore(self.output_uuid)\n        self.log.important(\"Waiting for AWS Feature Group Offline storage to be ready...\")\n        self.log.important(\"This will often take 10-20 minutes...go have coffee or lunch :)\")\n        self.output_feature_set.set_status(\"initializing\")\n        self.wait_for_rows(self.expected_rows)\n\n        # Call the FeatureSet onboard method to compute a bunch of EDA stuff\n        self.output_feature_set.onboard()\n\n        # Set Hold Out Ids (if we got them during creation)\n        if self.incoming_hold_out_ids:\n            self.output_feature_set.set_training_holdouts(self.id_column, self.incoming_hold_out_ids)\n\n    def ensure_feature_group_created(self, feature_group):\n        status = feature_group.describe().get(\"FeatureGroupStatus\")\n        while status == \"Creating\":\n            self.log.debug(\"FeatureSet being Created...\")\n            time.sleep(5)\n            status = feature_group.describe().get(\"FeatureGroupStatus\")\n        if status == \"Created\":\n            self.log.info(f\"FeatureSet {feature_group.name} successfully created\")\n        else:\n            self.log.critical(f\"FeatureSet {feature_group.name} creation failed with status: {status}\")\n\n    def wait_for_rows(self, expected_rows: int):\n        \"\"\"Wait for AWS Feature Group to fully populate the Offline Storage\"\"\"\n        rows = self.output_feature_set.num_rows()\n\n        # Wait for the rows to be populated\n        self.log.info(f\"Waiting for AWS Feature Group {self.output_uuid} Offline Storage...\")\n        max_retry = 20\n        num_retry = 0\n        sleep_time = 30\n        while rows &lt; expected_rows and num_retry &lt; max_retry:\n            num_retry += 1\n            time.sleep(sleep_time)\n            rows = self.output_feature_set.num_rows()\n            self.log.info(f\"Offline Storage {self.output_uuid}: {rows} rows out of {expected_rows}\")\n        if rows == expected_rows:\n            self.log.important(f\"Success: Reached Expected Rows ({rows} rows)...\")\n        else:\n            msg = f\"Did not reach expected rows ({rows}/{expected_rows})...(probably AWS lag)\"\n            self.log.warning(msg)\n            self.log.monitor(msg)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.__init__","title":"<code>__init__(output_uuid)</code>","text":"<p>PandasToFeatures Initialization</p> <p>Parameters:</p> Name Type Description Default <code>output_uuid</code> <code>str</code> <p>The UUID of the FeatureSet to create</p> required Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def __init__(self, output_uuid: str):\n    \"\"\"PandasToFeatures Initialization\n\n    Args:\n        output_uuid (str): The UUID of the FeatureSet to create\n    \"\"\"\n\n    # Make sure the output_uuid is a valid name\n    Artifact.is_name_valid(output_uuid)\n\n    # Call superclass init\n    super().__init__(\"DataFrame\", output_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.PANDAS_DF\n    self.output_type = TransformOutput.FEATURE_SET\n    self.id_column = None\n    self.event_time_column = None\n    self.one_hot_columns = []\n    self.categorical_dtypes = {}  # Used for streaming/chunking\n    self.output_df = None\n    self.table_format = TableFormatEnum.ICEBERG\n    self.incoming_hold_out_ids = None\n\n    # These will be set in the transform method\n    self.output_feature_group = None\n    self.output_feature_set = None\n    self.expected_rows = 0\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_column_types","title":"<code>convert_column_types(df)</code>  <code>staticmethod</code>","text":"<p>Convert the types of the DataFrame to the correct types for the Feature Store</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>@staticmethod\ndef convert_column_types(df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Convert the types of the DataFrame to the correct types for the Feature Store\"\"\"\n    for column in list(df.select_dtypes(include=\"bool\").columns):\n        df[column] = df[column].astype(\"int32\")\n    for column in list(df.select_dtypes(include=\"category\").columns):\n        df[column] = df[column].astype(\"str\")\n\n    # Select all columns that are of datetime dtype and convert them to ISO-8601 strings\n    for column in [col for col in df.columns if pd.api.types.is_datetime64_any_dtype(df[col])]:\n        df[column] = df[column].map(datetime_to_iso8601).astype(\"string\")\n\n    \"\"\"FIXME Not sure we need these conversions\n    for column in list(df.select_dtypes(include=\"object\").columns):\n        df[column] = df[column].astype(\"string\")\n    for column in list(df.select_dtypes(include=[pd.Int64Dtype]).columns):\n        df[column] = df[column].astype(\"int64\")\n    for column in list(df.select_dtypes(include=[pd.Float64Dtype]).columns):\n        df[column] = df[column].astype(\"float64\")\n    \"\"\"\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.convert_columns_to_categorical","title":"<code>convert_columns_to_categorical(columns)</code>","text":"<p>Convert column to Categorical type</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def convert_columns_to_categorical(self, columns: list):\n    \"\"\"Convert column to Categorical type\"\"\"\n    for feature in columns:\n        if feature not in [self.event_time_column, self.id_column]:\n            unique_values = self.output_df[feature].nunique()\n            if 1 &lt; unique_values &lt; 10:\n                self.log.important(f\"Converting column {feature} to categorical (unique {unique_values})\")\n                self.output_df[feature] = self.output_df[feature].astype(\"category\")\n            else:\n                self.log.warning(f\"Column {feature} too many unique values {unique_values} skipping...\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.create_feature_group","title":"<code>create_feature_group()</code>","text":"<p>Create a Feature Group, load our Feature Definitions, and wait for it to be ready</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def create_feature_group(self):\n    \"\"\"Create a Feature Group, load our Feature Definitions, and wait for it to be ready\"\"\"\n\n    # Create a Feature Group and load our Feature Definitions\n    my_feature_group = FeatureGroup(name=self.output_uuid, sagemaker_session=self.sm_session)\n    my_feature_group.load_feature_definitions(data_frame=self.output_df)\n\n    # Create the Output S3 Storage Path for this Feature Set\n    s3_storage_path = f\"{self.feature_sets_s3_path}/{self.output_uuid}\"\n\n    # Get the metadata/tags to push into AWS\n    aws_tags = self.get_aws_tags()\n\n    # Create the Feature Group\n    my_feature_group.create(\n        s3_uri=s3_storage_path,\n        record_identifier_name=self.id_column,\n        event_time_feature_name=self.event_time_column,\n        role_arn=self.sageworks_role_arn,\n        enable_online_store=True,\n        table_format=self.table_format,\n        tags=aws_tags,\n    )\n\n    # Ensure/wait for the feature group to be created\n    self.ensure_feature_group_created(my_feature_group)\n    return my_feature_group\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.manual_categorical_converter","title":"<code>manual_categorical_converter()</code>","text":"<p>Used for Streaming: Convert object and string types to Categorical</p> Note <p>This method is used for streaming/chunking. You can set the categorical_dtypes attribute to a dictionary of column names and their respective categorical types.</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def manual_categorical_converter(self):\n    \"\"\"Used for Streaming: Convert object and string types to Categorical\n\n    Note:\n        This method is used for streaming/chunking. You can set the\n        categorical_dtypes attribute to a dictionary of column names and\n        their respective categorical types.\n    \"\"\"\n    for column, cat_d_type in self.categorical_dtypes.items():\n        self.output_df[column] = self.output_df[column].astype(cat_d_type)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.one_hot_encode","title":"<code>one_hot_encode(df, one_hot_columns)</code>","text":"<p>One Hot Encoding for Categorical Columns with additional column name management</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The DataFrame to process</p> required <code>one_hot_columns</code> <code>list</code> <p>The list of columns to one-hot encode</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with one-hot encoded columns</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def one_hot_encode(self, df, one_hot_columns: list) -&gt; pd.DataFrame:\n    \"\"\"One Hot Encoding for Categorical Columns with additional column name management\n\n    Args:\n        df (pd.DataFrame): The DataFrame to process\n        one_hot_columns (list): The list of columns to one-hot encode\n\n    Returns:\n        pd.DataFrame: The DataFrame with one-hot encoded columns\n    \"\"\"\n\n    # Grab the current list of columns\n    current_columns = list(df.columns)\n\n    # Now convert the list of columns into Categorical and then One-Hot Encode\n    self.convert_columns_to_categorical(one_hot_columns)\n    self.log.important(f\"One-Hot encoding columns: {one_hot_columns}\")\n    df = pd.get_dummies(df, columns=one_hot_columns)\n\n    # Compute the new columns generated by get_dummies\n    new_columns = list(set(df.columns) - set(current_columns))\n    self.log.important(f\"New columns generated: {new_columns}\")\n\n    # Convert new columns to int32\n    df[new_columns] = df[new_columns].astype(\"int32\")\n\n    # For the new columns we're going to shorten the names\n    renamed_columns = {col: self.process_column_name(col) for col in new_columns}\n\n    # Rename the columns in the DataFrame\n    df.rename(columns=renamed_columns, inplace=True)\n\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Populating Offline Storage and onboard()</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Populating Offline Storage and onboard()\"\"\"\n    self.log.info(\"Post-Transform: Populating Offline Storage and onboard()...\")\n\n    # Feature Group Ingestion takes a while, so we need to wait for it to finish\n    self.output_feature_set = FeatureSetCore(self.output_uuid)\n    self.log.important(\"Waiting for AWS Feature Group Offline storage to be ready...\")\n    self.log.important(\"This will often take 10-20 minutes...go have coffee or lunch :)\")\n    self.output_feature_set.set_status(\"initializing\")\n    self.wait_for_rows(self.expected_rows)\n\n    # Call the FeatureSet onboard method to compute a bunch of EDA stuff\n    self.output_feature_set.onboard()\n\n    # Set Hold Out Ids (if we got them during creation)\n    if self.incoming_hold_out_ids:\n        self.output_feature_set.set_training_holdouts(self.id_column, self.incoming_hold_out_ids)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Pre-Transform: Delete any existing FeatureSet and Create the Feature Group</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Pre-Transform: Delete any existing FeatureSet and Create the Feature Group\"\"\"\n    self.delete_existing()\n    self.output_feature_group = self.create_feature_group()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.prep_dataframe","title":"<code>prep_dataframe()</code>","text":"<p>Prep the DataFrame for Feature Store Creation</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def prep_dataframe(self):\n    \"\"\"Prep the DataFrame for Feature Store Creation\"\"\"\n    self.log.info(\"Prep the output_df (cat_convert, convert types, and lowercase columns)...\")\n\n    # Remove any columns generated from AWS\n    aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n    self.output_df = self.output_df.drop(columns=aws_cols, errors=\"ignore\")\n\n    # If one-hot columns are provided then one-hot encode them\n    if self.one_hot_columns:\n        self.output_df = self.one_hot_encode(self.output_df, self.one_hot_columns)\n\n    # Convert columns names to lowercase, Athena will not work with uppercase column names\n    if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n        for c in self.output_df.columns:\n            if c != c.lower():\n                self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n        self.output_df.columns = self.output_df.columns.str.lower()\n\n    # Make sure we have the required id and event_time columns\n    self._ensure_id_column()\n    self._ensure_event_time()\n\n    # Check for a training column (SageWorks uses dynamic training columns)\n    if \"training\" in self.output_df.columns:\n        self.log.important(\n            \"\"\"Training column detected: Since FeatureSets are read-only, SageWorks creates a training view\n            that can be dynamically changed. We'll use this training column to create a training view.\"\"\"\n        )\n        self.incoming_hold_out_ids = self.output_df[~self.output_df[\"training\"]][self.id_column].tolist()\n        self.output_df = self.output_df.drop(columns=[\"training\"])\n\n    # We need to convert some of our column types to the correct types\n    # Feature Store only supports these data types:\n    # - Integral\n    # - Fractional\n    # - String (timestamp/datetime types need to be converted to string)\n    self.output_df = self.convert_column_types(self.output_df)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.process_column_name","title":"<code>process_column_name(column, shorten=False)</code>","text":"<p>Call various methods to make sure the column is ready for Feature Store Args:     column (str): The column name to process     shorten (bool): Should we shorten the column name? (default: False)</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def process_column_name(self, column: str, shorten: bool = False) -&gt; str:\n    \"\"\"Call various methods to make sure the column is ready for Feature Store\n    Args:\n        column (str): The column name to process\n        shorten (bool): Should we shorten the column name? (default: False)\n    \"\"\"\n    self.log.debug(f\"Processing column {column}...\")\n\n    # Make sure the column name is valid\n    column = self.sanitize_column_name(column)\n\n    # Make sure the column name isn't too long\n    if shorten:\n        column = self.shorten_column_name(column)\n\n    return column\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.set_input","title":"<code>set_input(input_df, id_column=None, event_time_column=None, one_hot_columns=None)</code>","text":"<p>Set the Input DataFrame for this Transform</p> <p>Parameters:</p> Name Type Description Default <code>input_df</code> <code>DataFrame</code> <p>The input DataFrame.</p> required <code>id_column</code> <code>str</code> <p>The ID column (if not specified, an 'auto_id' will be generated).</p> <code>None</code> <code>event_time_column</code> <code>str</code> <p>The name of the event time column (default: None).</p> <code>None</code> <code>one_hot_columns</code> <code>list</code> <p>The list of columns to one-hot encode (default: None).</p> <code>None</code> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def set_input(self, input_df: pd.DataFrame, id_column=None, event_time_column=None, one_hot_columns=None):\n    \"\"\"Set the Input DataFrame for this Transform\n\n    Args:\n        input_df (pd.DataFrame): The input DataFrame.\n        id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n        event_time_column (str, optional): The name of the event time column (default: None).\n        one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n    \"\"\"\n    self.id_column = id_column\n    self.event_time_column = event_time_column\n    self.output_df = input_df.copy()\n    self.one_hot_columns = one_hot_columns or []\n\n    # Now Prepare the DataFrame for its journey into an AWS FeatureGroup\n    self.prep_dataframe()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.transform_impl","title":"<code>transform_impl()</code>","text":"<p>Transform Implementation: Ingest the data into the Feature Group</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def transform_impl(self):\n    \"\"\"Transform Implementation: Ingest the data into the Feature Group\"\"\"\n\n    # Now we actually push the data into the Feature Group (called ingestion)\n    self.log.important(f\"Ingesting rows into Feature Group {self.output_uuid}...\")\n    ingest_manager = self.output_feature_group.ingest(self.output_df, max_workers=8, max_processes=2, wait=False)\n    try:\n        ingest_manager.wait()\n    except IngestionError as exc:\n        self.log.warning(f\"Some rows had an ingesting error: {exc}\")\n\n    # Report on any rows that failed to ingest\n    if ingest_manager.failed_rows:\n        self.log.warning(f\"Number of Failed Rows: {len(ingest_manager.failed_rows)}\")\n\n        # FIXME: This may or may not give us the correct rows\n        # If any index is greater then the number of rows, then the index needs\n        # to be converted to a relative index in our current output_df\n        df_rows = len(self.output_df)\n        relative_indexes = [idx - df_rows if idx &gt;= df_rows else idx for idx in ingest_manager.failed_rows]\n        failed_data = self.output_df.iloc[relative_indexes]\n        for idx, row in failed_data.iterrows():\n            self.log.warning(f\"Failed Row {idx}: {row.to_dict()}\")\n\n    # Keep track of the number of rows we expect to be ingested\n    self.expected_rows += len(self.output_df) - len(ingest_manager.failed_rows)\n    self.log.info(f\"Added rows: {len(self.output_df)}\")\n    self.log.info(f\"Failed rows: {len(ingest_manager.failed_rows)}\")\n    self.log.info(f\"Total rows ingested: {self.expected_rows}\")\n\n    # We often need to wait a bit for AWS to fully register the new Feature Group\n    self.log.important(f\"Waiting for AWS to register the new Feature Group {self.output_uuid}...\")\n    time.sleep(30)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeatures.wait_for_rows","title":"<code>wait_for_rows(expected_rows)</code>","text":"<p>Wait for AWS Feature Group to fully populate the Offline Storage</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def wait_for_rows(self, expected_rows: int):\n    \"\"\"Wait for AWS Feature Group to fully populate the Offline Storage\"\"\"\n    rows = self.output_feature_set.num_rows()\n\n    # Wait for the rows to be populated\n    self.log.info(f\"Waiting for AWS Feature Group {self.output_uuid} Offline Storage...\")\n    max_retry = 20\n    num_retry = 0\n    sleep_time = 30\n    while rows &lt; expected_rows and num_retry &lt; max_retry:\n        num_retry += 1\n        time.sleep(sleep_time)\n        rows = self.output_feature_set.num_rows()\n        self.log.info(f\"Offline Storage {self.output_uuid}: {rows} rows out of {expected_rows}\")\n    if rows == expected_rows:\n        self.log.important(f\"Success: Reached Expected Rows ({rows} rows)...\")\n    else:\n        msg = f\"Did not reach expected rows ({rows}/{expected_rows})...(probably AWS lag)\"\n        self.log.warning(msg)\n        self.log.monitor(msg)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked","title":"<code>PandasToFeaturesChunked</code>","text":"<p>               Bases: <code>Transform</code></p> <p>PandasToFeaturesChunked:  Class to manage a bunch of chunked Pandas DataFrames into a FeatureSet</p> Common Usage <pre><code>to_features = PandasToFeaturesChunked(output_uuid, id_column=\"id\"/None, event_time_column=\"date\"/None)\nto_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\ncat_column_info = {\"sex\": [\"M\", \"F\", \"I\"]}\nto_features.set_categorical_info(cat_column_info)\nto_features.add_chunk(df)\nto_features.add_chunk(df)\n...\nto_features.finalize()\n</code></pre> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>class PandasToFeaturesChunked(Transform):\n    \"\"\"PandasToFeaturesChunked:  Class to manage a bunch of chunked Pandas DataFrames into a FeatureSet\n\n    Common Usage:\n        ```python\n        to_features = PandasToFeaturesChunked(output_uuid, id_column=\"id\"/None, event_time_column=\"date\"/None)\n        to_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\n        cat_column_info = {\"sex\": [\"M\", \"F\", \"I\"]}\n        to_features.set_categorical_info(cat_column_info)\n        to_features.add_chunk(df)\n        to_features.add_chunk(df)\n        ...\n        to_features.finalize()\n        ```\n    \"\"\"\n\n    def __init__(self, output_uuid: str, id_column=None, event_time_column=None):\n        \"\"\"PandasToFeaturesChunked Initialization\"\"\"\n\n        # Make sure the output_uuid is a valid name\n        Artifact.is_name_valid(output_uuid)\n\n        # Call superclass init\n        super().__init__(\"DataFrame\", output_uuid)\n\n        # Set up all my instance attributes\n        self.id_column = id_column\n        self.event_time_column = event_time_column\n        self.first_chunk = None\n        self.pandas_to_features = PandasToFeatures(output_uuid)\n\n    def set_categorical_info(self, cat_column_info: dict[list[str]]):\n        \"\"\"Set the Categorical Columns\n        Args:\n            cat_column_info (dict[list[str]]): Dictionary of categorical columns and their possible values\n        \"\"\"\n\n        # Create the CategoricalDtypes\n        cat_d_types = {}\n        for col, vals in cat_column_info.items():\n            cat_d_types[col] = CategoricalDtype(categories=vals)\n\n        # Now set the CategoricalDtypes on our underlying PandasToFeatures\n        self.pandas_to_features.categorical_dtypes = cat_d_types\n\n    def add_chunk(self, chunk_df: pd.DataFrame):\n        \"\"\"Add a Chunk of Data to the FeatureSet\"\"\"\n\n        # Is this the first chunk? If so we need to run the pre_transform\n        if self.first_chunk is None:\n            self.log.info(f\"Adding first chunk {chunk_df.shape}...\")\n            self.first_chunk = chunk_df\n            self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n            self.pandas_to_features.pre_transform()\n            self.pandas_to_features.transform_impl()\n        else:\n            self.log.info(f\"Adding chunk {chunk_df.shape}...\")\n            self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n            self.pandas_to_features.transform_impl()\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Pre-Transform: Create the Feature Group with Chunked Data\"\"\"\n\n        # Loading data into a Feature Group takes a while, so set status to loading\n        FeatureSetCore(self.output_uuid).set_status(\"loading\")\n\n    def transform_impl(self):\n        \"\"\"Required implementation of the Transform interface\"\"\"\n        self.log.warning(\"PandasToFeaturesChunked.transform_impl() called.  This is a no-op.\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Any Post Transform Steps\"\"\"\n        self.pandas_to_features.post_transform()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.__init__","title":"<code>__init__(output_uuid, id_column=None, event_time_column=None)</code>","text":"<p>PandasToFeaturesChunked Initialization</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def __init__(self, output_uuid: str, id_column=None, event_time_column=None):\n    \"\"\"PandasToFeaturesChunked Initialization\"\"\"\n\n    # Make sure the output_uuid is a valid name\n    Artifact.is_name_valid(output_uuid)\n\n    # Call superclass init\n    super().__init__(\"DataFrame\", output_uuid)\n\n    # Set up all my instance attributes\n    self.id_column = id_column\n    self.event_time_column = event_time_column\n    self.first_chunk = None\n    self.pandas_to_features = PandasToFeatures(output_uuid)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.add_chunk","title":"<code>add_chunk(chunk_df)</code>","text":"<p>Add a Chunk of Data to the FeatureSet</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def add_chunk(self, chunk_df: pd.DataFrame):\n    \"\"\"Add a Chunk of Data to the FeatureSet\"\"\"\n\n    # Is this the first chunk? If so we need to run the pre_transform\n    if self.first_chunk is None:\n        self.log.info(f\"Adding first chunk {chunk_df.shape}...\")\n        self.first_chunk = chunk_df\n        self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n        self.pandas_to_features.pre_transform()\n        self.pandas_to_features.transform_impl()\n    else:\n        self.log.info(f\"Adding chunk {chunk_df.shape}...\")\n        self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n        self.pandas_to_features.transform_impl()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Any Post Transform Steps</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Any Post Transform Steps\"\"\"\n    self.pandas_to_features.post_transform()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Pre-Transform: Create the Feature Group with Chunked Data</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Pre-Transform: Create the Feature Group with Chunked Data\"\"\"\n\n    # Loading data into a Feature Group takes a while, so set status to loading\n    FeatureSetCore(self.output_uuid).set_status(\"loading\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_categorical_info","title":"<code>set_categorical_info(cat_column_info)</code>","text":"<p>Set the Categorical Columns Args:     cat_column_info (dict[list[str]]): Dictionary of categorical columns and their possible values</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def set_categorical_info(self, cat_column_info: dict[list[str]]):\n    \"\"\"Set the Categorical Columns\n    Args:\n        cat_column_info (dict[list[str]]): Dictionary of categorical columns and their possible values\n    \"\"\"\n\n    # Create the CategoricalDtypes\n    cat_d_types = {}\n    for col, vals in cat_column_info.items():\n        cat_d_types[col] = CategoricalDtype(categories=vals)\n\n    # Now set the CategoricalDtypes on our underlying PandasToFeatures\n    self.pandas_to_features.categorical_dtypes = cat_d_types\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#sageworks.core.transforms.pandas_transforms.PandasToFeaturesChunked.transform_impl","title":"<code>transform_impl()</code>","text":"<p>Required implementation of the Transform interface</p> Source code in <code>src/sageworks/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def transform_impl(self):\n    \"\"\"Required implementation of the Transform interface\"\"\"\n    self.log.warning(\"PandasToFeaturesChunked.transform_impl() called.  This is a no-op.\")\n</code></pre>"},{"location":"core_classes/transforms/transform/","title":"Transform","text":"<p>API Classes</p> <p>The API Classes will use Transforms internally. So model.to_endpoint() uses the ModelToEndpoint() transform. If you need more control over the Transform you can use the Core Classes directly.</p> <p>The SageWorks Transform class is a base/abstract class that defines API implemented by all the child classes (DataLoaders, DataSourceToFeatureSet, ModelToEndpoint, etc).</p> <p>Transform: Base Class for all transforms within SageWorks Inherited Classes must implement the abstract transform_impl() method</p>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform","title":"<code>Transform</code>","text":"<p>               Bases: <code>ABC</code></p> <p>Transform: Abstract Base Class for all transforms within SageWorks. Inherited Classes must implement the abstract transform_impl() method</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>class Transform(ABC):\n    \"\"\"Transform: Abstract Base Class for all transforms within SageWorks. Inherited Classes\n    must implement the abstract transform_impl() method\"\"\"\n\n    def __init__(self, input_uuid: str, output_uuid: str):\n        \"\"\"Transform Initialization\"\"\"\n\n        self.log = logging.getLogger(\"sageworks\")\n        self.input_type = None\n        self.output_type = None\n        self.output_tags = \"\"\n        self.input_uuid = str(input_uuid)  # Occasionally we get a pathlib.Path object\n        self.output_uuid = str(output_uuid)  # Occasionally we get a pathlib.Path object\n        self.output_meta = {\"sageworks_input\": self.input_uuid}\n        self.data_catalog_db = \"sageworks\"\n\n        # Grab our SageWorks Bucket\n        cm = ConfigManager()\n        if not cm.config_okay():\n            self.log.error(\"SageWorks Configuration Incomplete...\")\n            self.log.error(\"Run the 'sageworks' command and follow the prompts...\")\n            raise FatalConfigError()\n        self.sageworks_bucket = cm.get_config(\"SAGEWORKS_BUCKET\")\n        self.data_sources_s3_path = \"s3://\" + self.sageworks_bucket + \"/data-sources\"\n        self.feature_sets_s3_path = \"s3://\" + self.sageworks_bucket + \"/feature-sets\"\n        self.models_s3_path = \"s3://\" + self.sageworks_bucket + \"/models\"\n        self.endpoints_sets_s3_path = \"s3://\" + self.sageworks_bucket + \"/endpoints\"\n\n        # Grab a SageWorks Role ARN, Boto3, SageMaker Session, and SageMaker Client\n        self.aws_account_clamp = AWSAccountClamp()\n        self.sageworks_role_arn = self.aws_account_clamp.aws_session.get_sageworks_execution_role_arn()\n        self.boto3_session = self.aws_account_clamp.boto3_session\n        self.sm_session = self.aws_account_clamp.sagemaker_session()\n        self.sm_client = self.aws_account_clamp.sagemaker_client()\n\n        # Delimiter for storing lists in AWS Tags\n        self.tag_delimiter = \"::\"\n\n    @abstractmethod\n    def transform_impl(self, **kwargs):\n        \"\"\"Abstract Method: Implement the Transformation from Input to Output\"\"\"\n        pass\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Perform any Pre-Transform operations\"\"\"\n        self.log.debug(\"Pre-Transform...\")\n\n    @abstractmethod\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform ensures that the output Artifact is ready for use\"\"\"\n        pass\n\n    def set_output_tags(self, tags: Union[list, str]):\n        \"\"\"Set the tags that will be associated with the output object\n        Args:\n            tags (Union[list, str]): The list of tags or a '::' separated string of tags\"\"\"\n        if isinstance(tags, list):\n            self.output_tags = self.tag_delimiter.join(tags)\n        else:\n            self.output_tags = tags\n\n    def add_output_meta(self, meta: dict):\n        \"\"\"Add additional metadata that will be associated with the output artifact\n        Args:\n            meta (dict): A dictionary of metadata\"\"\"\n        self.output_meta = self.output_meta | meta\n\n    @staticmethod\n    def convert_to_aws_tags(metadata: dict):\n        \"\"\"Convert a dictionary to the AWS tag format (list of dicts)\n        [ {Key: key_name, Value: value}, {..}, ...]\"\"\"\n        return [{\"Key\": key, \"Value\": value} for key, value in metadata.items()]\n\n    def get_aws_tags(self):\n        \"\"\"Get the metadata/tags and convert them into AWS Tag Format\"\"\"\n        # Set up our metadata storage\n        sageworks_meta = {\"sageworks_tags\": self.output_tags}\n        for key, value in self.output_meta.items():\n            sageworks_meta[key] = value\n        aws_tags = self.convert_to_aws_tags(sageworks_meta)\n        return aws_tags\n\n    @final\n    def transform(self, **kwargs):\n        \"\"\"Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations\"\"\"\n        self.pre_transform(**kwargs)\n        self.transform_impl(**kwargs)\n        self.post_transform(**kwargs)\n\n    def input_type(self) -&gt; TransformInput:\n        \"\"\"What Input Type does this Transform Consume\"\"\"\n        return self.input_type\n\n    def output_type(self) -&gt; TransformOutput:\n        \"\"\"What Output Type does this Transform Produce\"\"\"\n        return self.output_type\n\n    def set_input_uuid(self, input_uuid: str):\n        \"\"\"Set the Input UUID (Name) for this Transform\"\"\"\n        self.input_uuid = input_uuid\n\n    def set_output_uuid(self, output_uuid: str):\n        \"\"\"Set the Output UUID (Name) for this Transform\"\"\"\n        self.output_uuid = output_uuid\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.__init__","title":"<code>__init__(input_uuid, output_uuid)</code>","text":"<p>Transform Initialization</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def __init__(self, input_uuid: str, output_uuid: str):\n    \"\"\"Transform Initialization\"\"\"\n\n    self.log = logging.getLogger(\"sageworks\")\n    self.input_type = None\n    self.output_type = None\n    self.output_tags = \"\"\n    self.input_uuid = str(input_uuid)  # Occasionally we get a pathlib.Path object\n    self.output_uuid = str(output_uuid)  # Occasionally we get a pathlib.Path object\n    self.output_meta = {\"sageworks_input\": self.input_uuid}\n    self.data_catalog_db = \"sageworks\"\n\n    # Grab our SageWorks Bucket\n    cm = ConfigManager()\n    if not cm.config_okay():\n        self.log.error(\"SageWorks Configuration Incomplete...\")\n        self.log.error(\"Run the 'sageworks' command and follow the prompts...\")\n        raise FatalConfigError()\n    self.sageworks_bucket = cm.get_config(\"SAGEWORKS_BUCKET\")\n    self.data_sources_s3_path = \"s3://\" + self.sageworks_bucket + \"/data-sources\"\n    self.feature_sets_s3_path = \"s3://\" + self.sageworks_bucket + \"/feature-sets\"\n    self.models_s3_path = \"s3://\" + self.sageworks_bucket + \"/models\"\n    self.endpoints_sets_s3_path = \"s3://\" + self.sageworks_bucket + \"/endpoints\"\n\n    # Grab a SageWorks Role ARN, Boto3, SageMaker Session, and SageMaker Client\n    self.aws_account_clamp = AWSAccountClamp()\n    self.sageworks_role_arn = self.aws_account_clamp.aws_session.get_sageworks_execution_role_arn()\n    self.boto3_session = self.aws_account_clamp.boto3_session\n    self.sm_session = self.aws_account_clamp.sagemaker_session()\n    self.sm_client = self.aws_account_clamp.sagemaker_client()\n\n    # Delimiter for storing lists in AWS Tags\n    self.tag_delimiter = \"::\"\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.add_output_meta","title":"<code>add_output_meta(meta)</code>","text":"<p>Add additional metadata that will be associated with the output artifact Args:     meta (dict): A dictionary of metadata</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def add_output_meta(self, meta: dict):\n    \"\"\"Add additional metadata that will be associated with the output artifact\n    Args:\n        meta (dict): A dictionary of metadata\"\"\"\n    self.output_meta = self.output_meta | meta\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.convert_to_aws_tags","title":"<code>convert_to_aws_tags(metadata)</code>  <code>staticmethod</code>","text":"<p>Convert a dictionary to the AWS tag format (list of dicts) [ {Key: key_name, Value: value}, {..}, ...]</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>@staticmethod\ndef convert_to_aws_tags(metadata: dict):\n    \"\"\"Convert a dictionary to the AWS tag format (list of dicts)\n    [ {Key: key_name, Value: value}, {..}, ...]\"\"\"\n    return [{\"Key\": key, \"Value\": value} for key, value in metadata.items()]\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.get_aws_tags","title":"<code>get_aws_tags()</code>","text":"<p>Get the metadata/tags and convert them into AWS Tag Format</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def get_aws_tags(self):\n    \"\"\"Get the metadata/tags and convert them into AWS Tag Format\"\"\"\n    # Set up our metadata storage\n    sageworks_meta = {\"sageworks_tags\": self.output_tags}\n    for key, value in self.output_meta.items():\n        sageworks_meta[key] = value\n    aws_tags = self.convert_to_aws_tags(sageworks_meta)\n    return aws_tags\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.input_type","title":"<code>input_type()</code>","text":"<p>What Input Type does this Transform Consume</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def input_type(self) -&gt; TransformInput:\n    \"\"\"What Input Type does this Transform Consume\"\"\"\n    return self.input_type\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.output_type","title":"<code>output_type()</code>","text":"<p>What Output Type does this Transform Produce</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def output_type(self) -&gt; TransformOutput:\n    \"\"\"What Output Type does this Transform Produce\"\"\"\n    return self.output_type\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.post_transform","title":"<code>post_transform(**kwargs)</code>  <code>abstractmethod</code>","text":"<p>Post-Transform ensures that the output Artifact is ready for use</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>@abstractmethod\ndef post_transform(self, **kwargs):\n    \"\"\"Post-Transform ensures that the output Artifact is ready for use\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Perform any Pre-Transform operations</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Perform any Pre-Transform operations\"\"\"\n    self.log.debug(\"Pre-Transform...\")\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.set_input_uuid","title":"<code>set_input_uuid(input_uuid)</code>","text":"<p>Set the Input UUID (Name) for this Transform</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def set_input_uuid(self, input_uuid: str):\n    \"\"\"Set the Input UUID (Name) for this Transform\"\"\"\n    self.input_uuid = input_uuid\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.set_output_tags","title":"<code>set_output_tags(tags)</code>","text":"<p>Set the tags that will be associated with the output object Args:     tags (Union[list, str]): The list of tags or a '::' separated string of tags</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def set_output_tags(self, tags: Union[list, str]):\n    \"\"\"Set the tags that will be associated with the output object\n    Args:\n        tags (Union[list, str]): The list of tags or a '::' separated string of tags\"\"\"\n    if isinstance(tags, list):\n        self.output_tags = self.tag_delimiter.join(tags)\n    else:\n        self.output_tags = tags\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.set_output_uuid","title":"<code>set_output_uuid(output_uuid)</code>","text":"<p>Set the Output UUID (Name) for this Transform</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>def set_output_uuid(self, output_uuid: str):\n    \"\"\"Set the Output UUID (Name) for this Transform\"\"\"\n    self.output_uuid = output_uuid\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.transform","title":"<code>transform(**kwargs)</code>","text":"<p>Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>@final\ndef transform(self, **kwargs):\n    \"\"\"Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations\"\"\"\n    self.pre_transform(**kwargs)\n    self.transform_impl(**kwargs)\n    self.post_transform(**kwargs)\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.Transform.transform_impl","title":"<code>transform_impl(**kwargs)</code>  <code>abstractmethod</code>","text":"<p>Abstract Method: Implement the Transformation from Input to Output</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>@abstractmethod\ndef transform_impl(self, **kwargs):\n    \"\"\"Abstract Method: Implement the Transformation from Input to Output\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.TransformInput","title":"<code>TransformInput</code>","text":"<p>               Bases: <code>Enum</code></p> <p>Enumerated Types for SageWorks Transform Inputs</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>class TransformInput(Enum):\n    \"\"\"Enumerated Types for SageWorks Transform Inputs\"\"\"\n\n    LOCAL_FILE = auto()\n    PANDAS_DF = auto()\n    SPARK_DF = auto()\n    S3_OBJECT = auto()\n    DATA_SOURCE = auto()\n    FEATURE_SET = auto()\n    MODEL = auto()\n</code></pre>"},{"location":"core_classes/transforms/transform/#sageworks.core.transforms.transform.TransformOutput","title":"<code>TransformOutput</code>","text":"<p>               Bases: <code>Enum</code></p> <p>Enumerated Types for SageWorks Transform Outputs</p> Source code in <code>src/sageworks/core/transforms/transform.py</code> <pre><code>class TransformOutput(Enum):\n    \"\"\"Enumerated Types for SageWorks Transform Outputs\"\"\"\n\n    PANDAS_DF = auto()\n    SPARK_DF = auto()\n    S3_OBJECT = auto()\n    DATA_SOURCE = auto()\n    FEATURE_SET = auto()\n    MODEL = auto()\n    ENDPOINT = auto()\n</code></pre>"},{"location":"core_classes/views/computation_view/","title":"Computation View","text":"<p>Experimental</p> <p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>Note: This class can be automatically invoked from DataSource/FeatureSet <code>set_computation_columns()</code> DataSource or FeatureSet. If you need more control then you can use this class directly.</p> <p>ComputationView Class: Create a View with a subset of columns for display purposes</p>"},{"location":"core_classes/views/computation_view/#sageworks.core.views.computation_view.ComputationView","title":"<code>ComputationView</code>","text":"<p>               Bases: <code>ColumnSubsetView</code></p> <p>ComputationView Class: Create a View with a subset of columns for computation purposes</p> Common Usage <pre><code># Create a default ComputationView\nfs = FeatureSet(\"test_features\")\ncomp_view = ComputationView.create(fs)\ndf = comp_view.pull_dataframe()\n\n# Create a ComputationView with a specific set of columns\ncomp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n</code></pre> Source code in <code>src/sageworks/core/views/computation_view.py</code> <pre><code>class ComputationView(ColumnSubsetView):\n    \"\"\"ComputationView Class: Create a View with a subset of columns for computation purposes\n\n    Common Usage:\n        ```python\n        # Create a default ComputationView\n        fs = FeatureSet(\"test_features\")\n        comp_view = ComputationView.create(fs)\n        df = comp_view.pull_dataframe()\n\n        # Create a ComputationView with a specific set of columns\n        comp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        artifact: Union[DataSource, FeatureSet],\n        source_table: str = None,\n        column_list: Union[list[str], None] = None,\n        column_limit: int = 30,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a ComputationView instance.\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None\n            column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n            column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n        return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/computation_view/#sageworks.core.views.computation_view.ComputationView.create","title":"<code>create(artifact, source_table=None, column_list=None, column_limit=30)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a ComputationView instance.</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>The DataSource or FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None</p> <code>None</code> <code>column_list</code> <code>Union[list[str], None]</code> <p>A list of columns to include. Defaults to None.</p> <code>None</code> <code>column_limit</code> <code>int</code> <p>The max number of columns to include. Defaults to 30.</p> <code>30</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/sageworks/core/views/computation_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    artifact: Union[DataSource, FeatureSet],\n    source_table: str = None,\n    column_list: Union[list[str], None] = None,\n    column_limit: int = 30,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a ComputationView instance.\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None\n        column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n        column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n    return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/computation_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/display_view/","title":"Display View","text":"<p>Experimental</p> <p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>Note: This class will be used in the future to fine tune what columns get displayed. For now just use the DataSource/FeatureSet <code>set_computation_columns()</code> DataSource or FeatureSet</p> <p>DisplayView Class: Create a View with a subset of columns for display purposes</p>"},{"location":"core_classes/views/display_view/#sageworks.core.views.display_view.DisplayView","title":"<code>DisplayView</code>","text":"<p>               Bases: <code>ColumnSubsetView</code></p> <p>DisplayView Class: Create a View with a subset of columns for display purposes</p> Common Usage <pre><code># Create a default DisplayView\nfs = FeatureSet(\"test_features\")\ndisplay_view = DisplayView.create(fs)\ndf = display_view.pull_dataframe()\n\n# Create a DisplayView with a specific set of columns\ndisplay_view = DisplayView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = display_view.query(f\"SELECT * FROM {display_view.table} where awesome = 'yes'\")\n</code></pre> Source code in <code>src/sageworks/core/views/display_view.py</code> <pre><code>class DisplayView(ColumnSubsetView):\n    \"\"\"DisplayView Class: Create a View with a subset of columns for display purposes\n\n    Common Usage:\n        ```python\n        # Create a default DisplayView\n        fs = FeatureSet(\"test_features\")\n        display_view = DisplayView.create(fs)\n        df = display_view.pull_dataframe()\n\n        # Create a DisplayView with a specific set of columns\n        display_view = DisplayView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = display_view.query(f\"SELECT * FROM {display_view.table} where awesome = 'yes'\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        artifact: Union[DataSource, FeatureSet],\n        source_table: str = None,\n        column_list: Union[list[str], None] = None,\n        column_limit: int = 30,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a DisplayView instance.\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None\n            column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n            column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Use the create logic directly from ColumnSubsetView with the \"display\" view name\n        return ColumnSubsetView.create(\"display\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/display_view/#sageworks.core.views.display_view.DisplayView.create","title":"<code>create(artifact, source_table=None, column_list=None, column_limit=30)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a DisplayView instance.</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>The DataSource or FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None</p> <code>None</code> <code>column_list</code> <code>Union[list[str], None]</code> <p>A list of columns to include. Defaults to None.</p> <code>None</code> <code>column_limit</code> <code>int</code> <p>The max number of columns to include. Defaults to 30.</p> <code>30</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/sageworks/core/views/display_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    artifact: Union[DataSource, FeatureSet],\n    source_table: str = None,\n    column_list: Union[list[str], None] = None,\n    column_limit: int = 30,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a DisplayView instance.\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None\n        column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n        column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Use the create logic directly from ColumnSubsetView with the \"display\" view name\n    return ColumnSubsetView.create(\"display\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/display_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/mdq_view/","title":"ModelDataQuality View","text":"<p>Experimental</p> <p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>MDQView Class: A View that computes various endpoint data quality metrics</p>"},{"location":"core_classes/views/mdq_view/#sageworks.core.views.mdq_view.MDQView","title":"<code>MDQView</code>","text":"<p>MDQView Class: A View that computes various endpoint data quality metrics</p> Common Usage <pre><code># Grab a FeatureSet and an Endpoint\nfs = FeatureSet(\"abalone_features\")\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# Create a ModelDataQuality View\nmdq_view = MDQView.create(fs, endpoint=endpoint, id_column=\"id\")\nmy_df = mdq_view.pull_dataframe(head=True)\n\n# Query the view\ndf = mdq_view.query(f\"SELECT * FROM {mdq_view.table} where residuals &gt; 0.5\")\n</code></pre> Source code in <code>src/sageworks/core/views/mdq_view.py</code> <pre><code>class MDQView:\n    \"\"\"MDQView Class: A View that computes various endpoint data quality metrics\n\n    Common Usage:\n        ```python\n        # Grab a FeatureSet and an Endpoint\n        fs = FeatureSet(\"abalone_features\")\n        endpoint = Endpoint(\"abalone-regression-end\")\n\n        # Create a ModelDataQuality View\n        mdq_view = MDQView.create(fs, endpoint=endpoint, id_column=\"id\")\n        my_df = mdq_view.pull_dataframe(head=True)\n\n        # Query the view\n        df = mdq_view.query(f\"SELECT * FROM {mdq_view.table} where residuals &gt; 0.5\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        fs: FeatureSet,\n        endpoint: Endpoint,\n        id_column: str,\n        use_reference_model: bool = False,\n    ) -&gt; Union[View, None]:\n        \"\"\"Create a Model Data Quality View with metrics\n\n        Args:\n            fs (FeatureSet): The FeatureSet object\n            endpoint (Endpoint): The Endpoint object to use for the target and features\n            id_column (str): The name of the id column (must be defined for join logic)\n            use_reference_model (bool): Use the reference model for inference (default: False)\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed)\n        \"\"\"\n        # Log view creation\n        fs.log.important(\"Creating Model Data Quality View...\")\n\n        # Get the target and feature columns from the endpoints model input\n        model_input = Model(endpoint.get_input())\n        target = model_input.target()\n        features = model_input.features()\n\n        # Pull in data from the source table\n        df = fs.data_source.query(f\"SELECT * FROM {fs.data_source.uuid}\")\n\n        # Check if the target and features are available in the data source\n        missing_columns = [col for col in [target] + features if col not in df.columns]\n        if missing_columns:\n            fs.log.error(f\"Missing columns in data source: {missing_columns}\")\n            return None\n\n        # Check if the target is categorical\n        categorical_target = not pd.api.types.is_numeric_dtype(df[target])\n\n        # Compute row tags with RowTagger\n        row_tagger = RowTagger(\n            df,\n            features=features,\n            id_column=id_column,\n            target_column=target,\n            within_dist=0.25,\n            min_target_diff=1.0,\n            outlier_df=fs.data_source.outliers(),\n            categorical_target=categorical_target,\n        )\n        mdq_df = row_tagger.tag_rows()\n\n        # Rename and compute data quality scores based on tags\n        mdq_df.rename(columns={\"tags\": \"data_quality_tags\"}, inplace=True)\n\n        # We're going to compute a data_quality score based on the tags.\n        mdq_df[\"data_quality\"] = mdq_df[\"data_quality_tags\"].apply(cls.calculate_data_quality)\n\n        # Compute residuals using ResidualsCalculator\n        if use_reference_model:\n            residuals_calculator = ResidualsCalculator()\n        else:\n            residuals_calculator = ResidualsCalculator(endpoint=endpoint)\n        residuals_df = residuals_calculator.fit_transform(df[features], df[target])\n\n        # Add id_column to the residuals dataframe and merge with mdq_df\n        residuals_df[id_column] = df[id_column]\n\n        # Drop overlapping columns in mdq_df (except for the id_column) to avoid _x and _y suffixes\n        overlap_columns = [col for col in residuals_df.columns if col in mdq_df.columns and col != id_column]\n        mdq_df = mdq_df.drop(columns=overlap_columns)\n\n        # Merge the DataFrames, with the id_column as the join key\n        mdq_df = mdq_df.merge(residuals_df, on=id_column, how=\"left\")\n\n        # Delegate view creation to PandasToView\n        view_name = \"mdq_ref\" if use_reference_model else \"mdq\"\n        return PandasToView.create(view_name, fs, df=mdq_df, id_column=id_column)\n\n    @staticmethod\n    def calculate_data_quality(tags):\n        score = 1.0  # Start with the default score\n        if \"coincident\" in tags:\n            score -= 1.0\n        if \"htg\" in tags:\n            score -= 0.5\n        if \"outlier\" in tags:\n            score -= 0.25\n        score = max(0.0, score)\n        return score\n</code></pre>"},{"location":"core_classes/views/mdq_view/#sageworks.core.views.mdq_view.MDQView.create","title":"<code>create(fs, endpoint, id_column, use_reference_model=False)</code>  <code>classmethod</code>","text":"<p>Create a Model Data Quality View with metrics</p> <p>Parameters:</p> Name Type Description Default <code>fs</code> <code>FeatureSet</code> <p>The FeatureSet object</p> required <code>endpoint</code> <code>Endpoint</code> <p>The Endpoint object to use for the target and features</p> required <code>id_column</code> <code>str</code> <p>The name of the id column (must be defined for join logic)</p> required <code>use_reference_model</code> <code>bool</code> <p>Use the reference model for inference (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed)</p> Source code in <code>src/sageworks/core/views/mdq_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    fs: FeatureSet,\n    endpoint: Endpoint,\n    id_column: str,\n    use_reference_model: bool = False,\n) -&gt; Union[View, None]:\n    \"\"\"Create a Model Data Quality View with metrics\n\n    Args:\n        fs (FeatureSet): The FeatureSet object\n        endpoint (Endpoint): The Endpoint object to use for the target and features\n        id_column (str): The name of the id column (must be defined for join logic)\n        use_reference_model (bool): Use the reference model for inference (default: False)\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed)\n    \"\"\"\n    # Log view creation\n    fs.log.important(\"Creating Model Data Quality View...\")\n\n    # Get the target and feature columns from the endpoints model input\n    model_input = Model(endpoint.get_input())\n    target = model_input.target()\n    features = model_input.features()\n\n    # Pull in data from the source table\n    df = fs.data_source.query(f\"SELECT * FROM {fs.data_source.uuid}\")\n\n    # Check if the target and features are available in the data source\n    missing_columns = [col for col in [target] + features if col not in df.columns]\n    if missing_columns:\n        fs.log.error(f\"Missing columns in data source: {missing_columns}\")\n        return None\n\n    # Check if the target is categorical\n    categorical_target = not pd.api.types.is_numeric_dtype(df[target])\n\n    # Compute row tags with RowTagger\n    row_tagger = RowTagger(\n        df,\n        features=features,\n        id_column=id_column,\n        target_column=target,\n        within_dist=0.25,\n        min_target_diff=1.0,\n        outlier_df=fs.data_source.outliers(),\n        categorical_target=categorical_target,\n    )\n    mdq_df = row_tagger.tag_rows()\n\n    # Rename and compute data quality scores based on tags\n    mdq_df.rename(columns={\"tags\": \"data_quality_tags\"}, inplace=True)\n\n    # We're going to compute a data_quality score based on the tags.\n    mdq_df[\"data_quality\"] = mdq_df[\"data_quality_tags\"].apply(cls.calculate_data_quality)\n\n    # Compute residuals using ResidualsCalculator\n    if use_reference_model:\n        residuals_calculator = ResidualsCalculator()\n    else:\n        residuals_calculator = ResidualsCalculator(endpoint=endpoint)\n    residuals_df = residuals_calculator.fit_transform(df[features], df[target])\n\n    # Add id_column to the residuals dataframe and merge with mdq_df\n    residuals_df[id_column] = df[id_column]\n\n    # Drop overlapping columns in mdq_df (except for the id_column) to avoid _x and _y suffixes\n    overlap_columns = [col for col in residuals_df.columns if col in mdq_df.columns and col != id_column]\n    mdq_df = mdq_df.drop(columns=overlap_columns)\n\n    # Merge the DataFrames, with the id_column as the join key\n    mdq_df = mdq_df.merge(residuals_df, on=id_column, how=\"left\")\n\n    # Delegate view creation to PandasToView\n    view_name = \"mdq_ref\" if use_reference_model else \"mdq\"\n    return PandasToView.create(view_name, fs, df=mdq_df, id_column=id_column)\n</code></pre>"},{"location":"core_classes/views/mdq_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/overview/","title":"Views","text":"<p>View Examples</p> <p>Examples of using the Views classes to extend the functionality of SageWorks Artifacts are in the Examples section at the bottom of this page. </p> <p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing SageWorks Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p> <p>If you're looking to read and pull data from a view please see the Views documentation.</p>"},{"location":"core_classes/views/overview/#view-constructor-classes","title":"View Constructor Classes","text":"<p>These classes provide APIs for creating Views for DataSources and FeatureSets.</p> <ul> <li>DisplayView: The Display View is leveraged by the web views/components and allows fine tuning of the UI for the SageWorks Dashboard.</li> <li>ComputationView: The Computation View controls which columns have descriptive stats, outliers, and correlation calculations. Typically the computation view is a superset of the display view.</li> <li>TrainingView: The Training View will add a 'training' column to the data for model training, validation, and testing. Each row will have a 1 or 0 indicated whether is was used in the model training.</li> <li>MDQView: The Model Data Quality View computes various data quality metrics that include modeling inference results.</li> </ul>"},{"location":"core_classes/views/overview/#examples","title":"Examples","text":"<p>All of the SageWorks Examples are in the Sageworks Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p> <p>Listing Views</p> views.py<pre><code>from sageworks.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\ntest_data = DataSource('test_data')\ntest_data.views()\n[\"display\", \"training\", \"computation\"]\n</code></pre> <p>Getting a Particular View</p> views.py<pre><code>from sageworks.api.feature_set import FeatureSet\n\nfs = FeatureSet('test_features')\n\n# Grab the columns for the display view\ndisplay_view = fs.view(\"display\")\ndisplay_view.columns\n['id', 'name', 'height', 'weight', 'salary', ...]\n\n# Pull the dataframe for this view\ndf = display_view.pull_dataframe()\n    id       name     height      weight         salary ...\n0   58  Person 58  71.781227  275.088196  162053.140625  \n</code></pre> <p>View Queries</p> <p>All SageWorks Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p> view_query.py<pre><code>from sageworks.api.feature_set import FeatureSet\n\n# Grab a FeatureSet View\nfs = FeatureSet(\"abalone_features\")\nt_view = fs.view(\"training\")\n\n# Make some queries using the Athena backend\ndf = t_view(f\"select * from {t_view.table} where height &gt; .3\")\nprint(df.head())\n\ndf = t_view.query(\"select * from abalone_features where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040         0.0050                      2\n</code></pre> <p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/training_view/","title":"Training View","text":"<p>Experimental</p> <p>The SageWorks View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>TrainingView Class: A View with an additional training column that marks holdout ids</p>"},{"location":"core_classes/views/training_view/#sageworks.core.views.training_view.TrainingView","title":"<code>TrainingView</code>","text":"<p>               Bases: <code>CreateView</code></p> <p>TrainingView Class: A View with an additional training column that marks holdout ids</p> Common Usage <pre><code># Create a default TrainingView\nfs = FeatureSet(\"test_features\")\ntraining_view = TrainingView.create(fs)\ndf = training_view.pull_dataframe()\n\n# Create a TrainingView with a specific set of columns\ntraining_view = TrainingView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = training_view.query(f\"SELECT * FROM {training_view.table} where training = TRUE\")\n</code></pre> Source code in <code>src/sageworks/core/views/training_view.py</code> <pre><code>class TrainingView(CreateView):\n    \"\"\"TrainingView Class: A View with an additional training column that marks holdout ids\n\n    Common Usage:\n        ```python\n        # Create a default TrainingView\n        fs = FeatureSet(\"test_features\")\n        training_view = TrainingView.create(fs)\n        df = training_view.pull_dataframe()\n\n        # Create a TrainingView with a specific set of columns\n        training_view = TrainingView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = training_view.query(f\"SELECT * FROM {training_view.table} where training = TRUE\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        feature_set: FeatureSet,\n        source_table: str = None,\n        id_column: str = None,\n        holdout_ids: Union[list[str], list[int], None] = None,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a TrainingView instance.\n\n        Args:\n            feature_set (FeatureSet): A FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None.\n            id_column (str, optional): The name of the id column. Defaults to None.\n            holdout_ids (Union[list[str], list[int], None], optional): A list of holdout ids. Defaults to None.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Instantiate the TrainingView with \"training\" as the view name\n        instance = cls(\"training\", feature_set, source_table)\n\n        # Drop any columns generated from AWS\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        source_table_columns = get_column_list(instance.data_source, instance.source_table)\n        column_list = [col for col in source_table_columns if col not in aws_cols]\n\n        # Sanity check on the id column\n        if not id_column:\n            instance.log.important(\"No id column specified, we'll try the auto_id_column ..\")\n            if not instance.auto_id_column:\n                instance.log.error(\"No id column specified and no auto_id_column found, aborting ..\")\n                return None\n            else:\n                if instance.auto_id_column not in column_list:\n                    instance.log.error(\n                        f\"Auto id column {instance.auto_id_column} not found in column list, aborting ..\"\n                    )\n                    return None\n                else:\n                    id_column = instance.auto_id_column\n\n        # If we don't have holdout ids, create a default training view\n        if not holdout_ids:\n            instance._default_training_view(instance.data_source, id_column)\n            return View(instance.data_source, instance.view_name, auto_create_view=False)\n\n        # Format the list of holdout ids for SQL IN clause\n        if holdout_ids and all(isinstance(id, str) for id in holdout_ids):\n            formatted_holdout_ids = \", \".join(f\"'{id}'\" for id in holdout_ids)\n        else:\n            formatted_holdout_ids = \", \".join(map(str, holdout_ids))\n\n        # Enclose each column name in double quotes\n        sql_columns = \", \".join([f'\"{column}\"' for column in column_list])\n\n        # Construct the CREATE VIEW query\n        create_view_query = f\"\"\"\n        CREATE OR REPLACE VIEW {instance.table} AS\n        SELECT {sql_columns}, CASE\n            WHEN {id_column} IN ({formatted_holdout_ids}) THEN False\n            ELSE True\n        END AS training\n        FROM {instance.source_table}\n        \"\"\"\n\n        # Execute the CREATE VIEW query\n        instance.data_source.execute_statement(create_view_query)\n\n        # Return the View\n        return View(instance.data_source, instance.view_name, auto_create_view=False)\n\n    # This is an internal method that's used to create a default training view\n    def _default_training_view(self, data_source: DataSource, id_column: str):\n        \"\"\"Create a default view in Athena that assigns roughly 80% of the data to training\n\n        Args:\n            data_source (DataSource): The SageWorks DataSource object\n            id_column (str): The name of the id column\n        \"\"\"\n        self.log.important(f\"Creating default Training View {self.table}...\")\n\n        # Drop any columns generated from AWS\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        column_list = [col for col in data_source.columns if col not in aws_cols]\n\n        # Enclose each column name in double quotes\n        sql_columns = \", \".join([f'\"{column}\"' for column in column_list])\n\n        # Construct the CREATE VIEW query with a simple modulo operation for the 80/20 split\n        create_view_query = f\"\"\"\n        CREATE OR REPLACE VIEW \"{self.table}\" AS\n        SELECT {sql_columns}, CASE\n            WHEN MOD(ROW_NUMBER() OVER (ORDER BY {id_column}), 10) &lt; 8 THEN True  -- Assign 80% to training\n            ELSE False  -- Assign roughly 20% to validation/test\n        END AS training\n        FROM {self.base_table_name}\n        \"\"\"\n\n        # Execute the CREATE VIEW query\n        data_source.execute_statement(create_view_query)\n</code></pre>"},{"location":"core_classes/views/training_view/#sageworks.core.views.training_view.TrainingView.create","title":"<code>create(feature_set, source_table=None, id_column=None, holdout_ids=None)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a TrainingView instance.</p> <p>Parameters:</p> Name Type Description Default <code>feature_set</code> <code>FeatureSet</code> <p>A FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None.</p> <code>None</code> <code>id_column</code> <code>str</code> <p>The name of the id column. Defaults to None.</p> <code>None</code> <code>holdout_ids</code> <code>Union[list[str], list[int], None]</code> <p>A list of holdout ids. Defaults to None.</p> <code>None</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/sageworks/core/views/training_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    feature_set: FeatureSet,\n    source_table: str = None,\n    id_column: str = None,\n    holdout_ids: Union[list[str], list[int], None] = None,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a TrainingView instance.\n\n    Args:\n        feature_set (FeatureSet): A FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None.\n        id_column (str, optional): The name of the id column. Defaults to None.\n        holdout_ids (Union[list[str], list[int], None], optional): A list of holdout ids. Defaults to None.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Instantiate the TrainingView with \"training\" as the view name\n    instance = cls(\"training\", feature_set, source_table)\n\n    # Drop any columns generated from AWS\n    aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n    source_table_columns = get_column_list(instance.data_source, instance.source_table)\n    column_list = [col for col in source_table_columns if col not in aws_cols]\n\n    # Sanity check on the id column\n    if not id_column:\n        instance.log.important(\"No id column specified, we'll try the auto_id_column ..\")\n        if not instance.auto_id_column:\n            instance.log.error(\"No id column specified and no auto_id_column found, aborting ..\")\n            return None\n        else:\n            if instance.auto_id_column not in column_list:\n                instance.log.error(\n                    f\"Auto id column {instance.auto_id_column} not found in column list, aborting ..\"\n                )\n                return None\n            else:\n                id_column = instance.auto_id_column\n\n    # If we don't have holdout ids, create a default training view\n    if not holdout_ids:\n        instance._default_training_view(instance.data_source, id_column)\n        return View(instance.data_source, instance.view_name, auto_create_view=False)\n\n    # Format the list of holdout ids for SQL IN clause\n    if holdout_ids and all(isinstance(id, str) for id in holdout_ids):\n        formatted_holdout_ids = \", \".join(f\"'{id}'\" for id in holdout_ids)\n    else:\n        formatted_holdout_ids = \", \".join(map(str, holdout_ids))\n\n    # Enclose each column name in double quotes\n    sql_columns = \", \".join([f'\"{column}\"' for column in column_list])\n\n    # Construct the CREATE VIEW query\n    create_view_query = f\"\"\"\n    CREATE OR REPLACE VIEW {instance.table} AS\n    SELECT {sql_columns}, CASE\n        WHEN {id_column} IN ({formatted_holdout_ids}) THEN False\n        ELSE True\n    END AS training\n    FROM {instance.source_table}\n    \"\"\"\n\n    # Execute the CREATE VIEW query\n    instance.data_source.execute_statement(create_view_query)\n\n    # Return the View\n    return View(instance.data_source, instance.view_name, auto_create_view=False)\n</code></pre>"},{"location":"core_classes/views/training_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord</p>"},{"location":"data_algorithms/overview/","title":"Data Algorithms","text":"<p>Data Algorithms</p> <p>WIP: These classes are currently actively being developed and are subject to change in both API and functionality over time. They provide a set of data algorithms for various types of data storage. We currently have subdirectorys for:</p> <ul> <li>DataFrames: A set of algorithms that consume/return a Panda dataFrame.</li> <li>Graphs: Algorithms for node/edge graphs, specifically focused on NetworkX graphs.</li> <li>Spark: A set of algorithms that consume/return a Spark dataFrame.</li> <li> <p>SQL: SQL queries that provide a wide range of functionality:</p> <ul> <li>Outliers</li> <li>Descriptive Stats</li> <li>Correlations</li> <li>and More</li> </ul> </li> </ul> <p>Welcome to the SageWorks Data Algorithms</p> <p>Docs TBD</p>"},{"location":"data_algorithms/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/dataframes/overview/","title":"Pandas Dataframe Algorithms","text":"<p>Pandas Dataframes</p> <p>Pandas dataframes are obviously not going to scale as well as our Spark and SQL Algorithms, but for 'moderate' sized data these algorithms provide some nice functionality.</p> <p>Pandas Dataframe Algorithms</p> <p>SageWorks has a growing set of algorithms and data processing tools for Pandas Dataframes. In general these algorithm will take a dataframe as input and give you back a dataframe with additional columns.</p> <p>FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.</p> <p>DimensionalityReduction: Perform Dimensionality Reduction on a DataFrame</p>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity","title":"<code>FeatureSpaceProximity</code>","text":"Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>class FeatureSpaceProximity:\n    def __init__(self, df: pd.DataFrame, features: list, id_column: str, target: str = None, neighbors: int = 10):\n        \"\"\"FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.\n\n        Args:\n            df: Pandas DataFrame\n            features: List of feature column names\n            id_column: Name of the ID column\n            target: Optional name of the target column to include target-based functionality (default: None)\n            neighbors: Number of neighbors to use in the KNN model (default: 10)\n        \"\"\"\n        self.log = logging.getLogger(\"sageworks\")\n        self.df = df\n        self.features = features\n        self.id_column = id_column\n        self.target = target\n        self.knn_neighbors = neighbors\n\n        # Standardize the feature values and build the KNN model\n        self.log.info(\"Building KNN model for FeatureSpaceProximity...\")\n        self.scaler = StandardScaler().fit(df[features])\n        scaled_features = self.scaler.transform(df[features])\n        self.knn_model = NearestNeighbors(n_neighbors=neighbors, algorithm=\"auto\").fit(scaled_features)\n\n        # Compute Z-Scores or Consistency Scores for the target values\n        if self.target and is_numeric_dtype(self.df[self.target]):\n            self.log.info(\"Computing Z-Scores for target values...\")\n            self.target_z_scores()\n        else:\n            self.log.info(\"Computing target consistency scores...\")\n            self.target_consistency()\n\n        # Now compute the outlier scores\n        self.log.info(\"Computing outlier scores...\")\n        self.outliers()\n\n    @classmethod\n    def from_model(cls, model) -&gt; \"FeatureSpaceProximity\":\n        \"\"\"Create a FeatureSpaceProximity instance from a SageWorks model object.\n\n        Args:\n            model (Model): A SageWorks model object.\n\n        Returns:\n            FeatureSpaceProximity: A new instance of the FeatureSpaceProximity class.\n        \"\"\"\n        from sageworks.api import FeatureSet\n\n        # Extract necessary attributes from the SageWorks model\n        fs = FeatureSet(model.get_input())\n        features = model.features()\n        target = model.target()\n\n        # Retrieve the training DataFrame from the feature set\n        df = fs.view(\"training\").pull_dataframe()\n\n        # Create and return a new instance of FeatureSpaceProximity\n        return cls(df=df, features=features, id_column=fs.id_column, target=target)\n\n    def neighbors(self, query_id: Union[str, int], radius: float = None, include_self: bool = True) -&gt; pd.DataFrame:\n        \"\"\"Return neighbors of the given query ID, either by fixed neighbors or within a radius.\n\n        Args:\n            query_id (Union[str, int]): The ID of the query point.\n            radius (float): Optional radius within which neighbors are to be searched, else use fixed neighbors.\n            include_self (bool): Whether to include the query ID itself in the neighbor results.\n\n        Returns:\n            pd.DataFrame: Filtered DataFrame that includes the query ID, its neighbors, and optionally target values.\n        \"\"\"\n        if query_id not in self.df[self.id_column].values:\n            self.log.warning(f\"Query ID '{query_id}' not found in the DataFrame. Returning an empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Get a single-row DataFrame for the query ID\n        query_df = self.df[self.df[self.id_column] == query_id]\n\n        # Use the neighbors_bulk method with the appropriate radius\n        neighbors_info_df = self.neighbors_bulk(query_df, radius=radius, include_self=include_self)\n\n        # Extract the neighbor IDs and distances from the results\n        neighbor_ids = neighbors_info_df[\"neighbor_ids\"].iloc[0]\n        neighbor_distances = neighbors_info_df[\"neighbor_distances\"].iloc[0]\n\n        # Sort neighbors by distance (ascending order)\n        sorted_neighbors = sorted(zip(neighbor_ids, neighbor_distances), key=lambda x: x[1])\n        sorted_ids, sorted_distances = zip(*sorted_neighbors)\n\n        # Filter the internal DataFrame to include only the sorted neighbors\n        neighbors_df = self.df[self.df[self.id_column].isin(sorted_ids)]\n        neighbors_df = neighbors_df.set_index(self.id_column).reindex(sorted_ids).reset_index()\n        neighbors_df[\"knn_distance\"] = sorted_distances\n        return neighbors_df\n\n    def neighbors_bulk(self, query_df: pd.DataFrame, radius: float = None, include_self: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Return neighbors for each row in the given query dataframe, either by fixed neighbors or within a radius.\n\n        Args:\n            query_df: Pandas DataFrame with the same features as the training data.\n            radius: Optional radius within which neighbors are to be searched, else use fixed neighbors.\n            include_self: Boolean indicating whether to include the query ID in the neighbor results.\n\n        Returns:\n            pd.DataFrame: DataFrame with query ID, neighbor IDs, neighbor targets, and neighbor distances.\n        \"\"\"\n        # Scale the query data using the same scaler as the training data\n        query_scaled = self.scaler.transform(query_df[self.features])\n\n        # Retrieve neighbors based on radius or standard neighbors\n        if radius is not None:\n            distances, indices = self.knn_model.radius_neighbors(query_scaled, radius=radius)\n        else:\n            distances, indices = self.knn_model.kneighbors(query_scaled)\n\n        # Collect neighbor information (IDs, target values, and distances)\n        query_ids = query_df[self.id_column].values\n        neighbor_ids = [[self.df.iloc[idx][self.id_column] for idx in index_list] for index_list in indices]\n        neighbor_targets = (\n            [\n                [self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0] for neighbor in index_list]\n                for index_list in neighbor_ids\n            ]\n            if self.target\n            else None\n        )\n        neighbor_distances = [list(dist_list) for dist_list in distances]\n\n        # Automatically remove the query ID itself from the neighbor results if include_self is False\n        for i, query_id in enumerate(query_ids):\n            if query_id in neighbor_ids[i] and not include_self:\n                idx_to_remove = neighbor_ids[i].index(query_id)\n                neighbor_ids[i].pop(idx_to_remove)\n                neighbor_distances[i].pop(idx_to_remove)\n                if neighbor_targets:\n                    neighbor_targets[i].pop(idx_to_remove)\n\n            # Sort neighbors by distance (ascending order)\n            sorted_neighbors = sorted(zip(neighbor_ids[i], neighbor_distances[i]), key=lambda x: x[1])\n            neighbor_ids[i], neighbor_distances[i] = list(zip(*sorted_neighbors)) if sorted_neighbors else ([], [])\n            if neighbor_targets:\n                neighbor_targets[i] = [\n                    self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0]\n                    for neighbor in neighbor_ids[i]\n                ]\n\n        # Create and return a results DataFrame with the updated neighbor information\n        result_df = pd.DataFrame(\n            {\n                \"query_id\": query_ids,\n                \"neighbor_ids\": neighbor_ids,\n                \"neighbor_distances\": neighbor_distances,\n            }\n        )\n\n        if neighbor_targets:\n            result_df[\"neighbor_targets\"] = neighbor_targets\n\n        return result_df\n\n    def outliers(self) -&gt; None:\n        \"\"\"Compute a unified 'outlier' score based on either 'target_z' or 'target_consistency'.\"\"\"\n        if \"target_z\" in self.df.columns:\n            # Normalize Z-Scores to a 0-1 range\n            self.df[\"outlier\"] = (self.df[\"target_z\"].abs() / (self.df[\"target_z\"].abs().max() + 1e-6)).clip(0, 1)\n\n        elif \"target_consistency\" in self.df.columns:\n            # Calculate outlier score as 1 - consistency\n            self.df[\"outlier\"] = 1 - self.df[\"target_consistency\"]\n\n        else:\n            self.log.warning(\"No 'target_z' or 'target_consistency' column found to compute outlier scores.\")\n\n    def target_z_scores(self) -&gt; None:\n        \"\"\"Compute Z-Scores for NUMERIC target values.\"\"\"\n        if not self.target:\n            self.log.warning(\"No target column defined for Z-Score computation.\")\n            return\n\n        # Get the neighbors and distances for each internal observation\n        distances, indices = self.knn_model.kneighbors()\n\n        # Retrieve all neighbor target values in a single operation\n        neighbor_targets = self.df[self.target].values[indices]  # Shape will be (n_samples, n_neighbors)\n\n        # Compute the mean and std along the neighbors axis (axis=1)\n        neighbor_means = neighbor_targets.mean(axis=1)\n        neighbor_stds = neighbor_targets.std(axis=1, ddof=0)\n\n        # Vectorized Z-score calculation\n        current_targets = self.df[self.target].values\n        z_scores = np.where(neighbor_stds == 0, 0.0, (current_targets - neighbor_means) / neighbor_stds)\n\n        # Assign the computed Z-Scores back to the DataFrame\n        self.df[\"target_z\"] = z_scores\n\n    def target_consistency(self) -&gt; None:\n        \"\"\"Compute a Neighborhood Consistency Score for CATEGORICAL targets.\"\"\"\n        if not self.target:\n            self.log.warning(\"No target column defined for neighborhood consistency computation.\")\n            return\n\n        # Get the neighbors and distances for each internal observation (already excludes the query)\n        distances, indices = self.knn_model.kneighbors()\n\n        # Calculate the Neighborhood Consistency Score for each observation\n        consistency_scores = []\n        for idx, idx_list in enumerate(indices):\n            query_target = self.df.iloc[idx][self.target]  # Get current observation's target value\n\n            # Get the neighbors' target values\n            neighbor_targets = self.df.iloc[idx_list][self.target]\n\n            # Calculate the proportion of neighbors that have the same category as the query observation\n            consistency_score = (neighbor_targets == query_target).mean()\n            consistency_scores.append(consistency_score)\n\n        # Add the 'target_consistency' column to the internal dataframe\n        self.df[\"target_consistency\"] = consistency_scores\n\n    def get_neighbor_indices_and_distances(self):\n        \"\"\"Retrieve neighbor indices and distances for all points in the dataset.\"\"\"\n        distances, indices = self.knn_model.kneighbors()\n        return indices, distances\n\n    def target_summary(self, query_id: Union[str, int]) -&gt; pd.DataFrame:\n        \"\"\"WIP: Provide a summary of target values in the neighborhood of the given query ID\"\"\"\n        neighbors_df = self.neighbors(query_id, include_self=False)\n        if self.target and not neighbors_df.empty:\n            summary_stats = neighbors_df[self.target].describe()\n            return pd.DataFrame(summary_stats).transpose()\n        else:\n            self.log.warning(f\"No target values found for neighbors of Query ID '{query_id}'.\")\n            return pd.DataFrame()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__","title":"<code>__init__(df, features, id_column, target=None, neighbors=10)</code>","text":"<p>FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>Pandas DataFrame</p> required <code>features</code> <code>list</code> <p>List of feature column names</p> required <code>id_column</code> <code>str</code> <p>Name of the ID column</p> required <code>target</code> <code>str</code> <p>Optional name of the target column to include target-based functionality (default: None)</p> <code>None</code> <code>neighbors</code> <code>int</code> <p>Number of neighbors to use in the KNN model (default: 10)</p> <code>10</code> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def __init__(self, df: pd.DataFrame, features: list, id_column: str, target: str = None, neighbors: int = 10):\n    \"\"\"FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.\n\n    Args:\n        df: Pandas DataFrame\n        features: List of feature column names\n        id_column: Name of the ID column\n        target: Optional name of the target column to include target-based functionality (default: None)\n        neighbors: Number of neighbors to use in the KNN model (default: 10)\n    \"\"\"\n    self.log = logging.getLogger(\"sageworks\")\n    self.df = df\n    self.features = features\n    self.id_column = id_column\n    self.target = target\n    self.knn_neighbors = neighbors\n\n    # Standardize the feature values and build the KNN model\n    self.log.info(\"Building KNN model for FeatureSpaceProximity...\")\n    self.scaler = StandardScaler().fit(df[features])\n    scaled_features = self.scaler.transform(df[features])\n    self.knn_model = NearestNeighbors(n_neighbors=neighbors, algorithm=\"auto\").fit(scaled_features)\n\n    # Compute Z-Scores or Consistency Scores for the target values\n    if self.target and is_numeric_dtype(self.df[self.target]):\n        self.log.info(\"Computing Z-Scores for target values...\")\n        self.target_z_scores()\n    else:\n        self.log.info(\"Computing target consistency scores...\")\n        self.target_consistency()\n\n    # Now compute the outlier scores\n    self.log.info(\"Computing outlier scores...\")\n    self.outliers()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model","title":"<code>from_model(model)</code>  <code>classmethod</code>","text":"<p>Create a FeatureSpaceProximity instance from a SageWorks model object.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>Model</code> <p>A SageWorks model object.</p> required <p>Returns:</p> Name Type Description <code>FeatureSpaceProximity</code> <code>FeatureSpaceProximity</code> <p>A new instance of the FeatureSpaceProximity class.</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>@classmethod\ndef from_model(cls, model) -&gt; \"FeatureSpaceProximity\":\n    \"\"\"Create a FeatureSpaceProximity instance from a SageWorks model object.\n\n    Args:\n        model (Model): A SageWorks model object.\n\n    Returns:\n        FeatureSpaceProximity: A new instance of the FeatureSpaceProximity class.\n    \"\"\"\n    from sageworks.api import FeatureSet\n\n    # Extract necessary attributes from the SageWorks model\n    fs = FeatureSet(model.get_input())\n    features = model.features()\n    target = model.target()\n\n    # Retrieve the training DataFrame from the feature set\n    df = fs.view(\"training\").pull_dataframe()\n\n    # Create and return a new instance of FeatureSpaceProximity\n    return cls(df=df, features=features, id_column=fs.id_column, target=target)\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances","title":"<code>get_neighbor_indices_and_distances()</code>","text":"<p>Retrieve neighbor indices and distances for all points in the dataset.</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def get_neighbor_indices_and_distances(self):\n    \"\"\"Retrieve neighbor indices and distances for all points in the dataset.\"\"\"\n    distances, indices = self.knn_model.kneighbors()\n    return indices, distances\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors","title":"<code>neighbors(query_id, radius=None, include_self=True)</code>","text":"<p>Return neighbors of the given query ID, either by fixed neighbors or within a radius.</p> <p>Parameters:</p> Name Type Description Default <code>query_id</code> <code>Union[str, int]</code> <p>The ID of the query point.</p> required <code>radius</code> <code>float</code> <p>Optional radius within which neighbors are to be searched, else use fixed neighbors.</p> <code>None</code> <code>include_self</code> <code>bool</code> <p>Whether to include the query ID itself in the neighbor results.</p> <code>True</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: Filtered DataFrame that includes the query ID, its neighbors, and optionally target values.</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def neighbors(self, query_id: Union[str, int], radius: float = None, include_self: bool = True) -&gt; pd.DataFrame:\n    \"\"\"Return neighbors of the given query ID, either by fixed neighbors or within a radius.\n\n    Args:\n        query_id (Union[str, int]): The ID of the query point.\n        radius (float): Optional radius within which neighbors are to be searched, else use fixed neighbors.\n        include_self (bool): Whether to include the query ID itself in the neighbor results.\n\n    Returns:\n        pd.DataFrame: Filtered DataFrame that includes the query ID, its neighbors, and optionally target values.\n    \"\"\"\n    if query_id not in self.df[self.id_column].values:\n        self.log.warning(f\"Query ID '{query_id}' not found in the DataFrame. Returning an empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Get a single-row DataFrame for the query ID\n    query_df = self.df[self.df[self.id_column] == query_id]\n\n    # Use the neighbors_bulk method with the appropriate radius\n    neighbors_info_df = self.neighbors_bulk(query_df, radius=radius, include_self=include_self)\n\n    # Extract the neighbor IDs and distances from the results\n    neighbor_ids = neighbors_info_df[\"neighbor_ids\"].iloc[0]\n    neighbor_distances = neighbors_info_df[\"neighbor_distances\"].iloc[0]\n\n    # Sort neighbors by distance (ascending order)\n    sorted_neighbors = sorted(zip(neighbor_ids, neighbor_distances), key=lambda x: x[1])\n    sorted_ids, sorted_distances = zip(*sorted_neighbors)\n\n    # Filter the internal DataFrame to include only the sorted neighbors\n    neighbors_df = self.df[self.df[self.id_column].isin(sorted_ids)]\n    neighbors_df = neighbors_df.set_index(self.id_column).reindex(sorted_ids).reset_index()\n    neighbors_df[\"knn_distance\"] = sorted_distances\n    return neighbors_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk","title":"<code>neighbors_bulk(query_df, radius=None, include_self=False)</code>","text":"<p>Return neighbors for each row in the given query dataframe, either by fixed neighbors or within a radius.</p> <p>Parameters:</p> Name Type Description Default <code>query_df</code> <code>DataFrame</code> <p>Pandas DataFrame with the same features as the training data.</p> required <code>radius</code> <code>float</code> <p>Optional radius within which neighbors are to be searched, else use fixed neighbors.</p> <code>None</code> <code>include_self</code> <code>bool</code> <p>Boolean indicating whether to include the query ID in the neighbor results.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: DataFrame with query ID, neighbor IDs, neighbor targets, and neighbor distances.</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def neighbors_bulk(self, query_df: pd.DataFrame, radius: float = None, include_self: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Return neighbors for each row in the given query dataframe, either by fixed neighbors or within a radius.\n\n    Args:\n        query_df: Pandas DataFrame with the same features as the training data.\n        radius: Optional radius within which neighbors are to be searched, else use fixed neighbors.\n        include_self: Boolean indicating whether to include the query ID in the neighbor results.\n\n    Returns:\n        pd.DataFrame: DataFrame with query ID, neighbor IDs, neighbor targets, and neighbor distances.\n    \"\"\"\n    # Scale the query data using the same scaler as the training data\n    query_scaled = self.scaler.transform(query_df[self.features])\n\n    # Retrieve neighbors based on radius or standard neighbors\n    if radius is not None:\n        distances, indices = self.knn_model.radius_neighbors(query_scaled, radius=radius)\n    else:\n        distances, indices = self.knn_model.kneighbors(query_scaled)\n\n    # Collect neighbor information (IDs, target values, and distances)\n    query_ids = query_df[self.id_column].values\n    neighbor_ids = [[self.df.iloc[idx][self.id_column] for idx in index_list] for index_list in indices]\n    neighbor_targets = (\n        [\n            [self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0] for neighbor in index_list]\n            for index_list in neighbor_ids\n        ]\n        if self.target\n        else None\n    )\n    neighbor_distances = [list(dist_list) for dist_list in distances]\n\n    # Automatically remove the query ID itself from the neighbor results if include_self is False\n    for i, query_id in enumerate(query_ids):\n        if query_id in neighbor_ids[i] and not include_self:\n            idx_to_remove = neighbor_ids[i].index(query_id)\n            neighbor_ids[i].pop(idx_to_remove)\n            neighbor_distances[i].pop(idx_to_remove)\n            if neighbor_targets:\n                neighbor_targets[i].pop(idx_to_remove)\n\n        # Sort neighbors by distance (ascending order)\n        sorted_neighbors = sorted(zip(neighbor_ids[i], neighbor_distances[i]), key=lambda x: x[1])\n        neighbor_ids[i], neighbor_distances[i] = list(zip(*sorted_neighbors)) if sorted_neighbors else ([], [])\n        if neighbor_targets:\n            neighbor_targets[i] = [\n                self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0]\n                for neighbor in neighbor_ids[i]\n            ]\n\n    # Create and return a results DataFrame with the updated neighbor information\n    result_df = pd.DataFrame(\n        {\n            \"query_id\": query_ids,\n            \"neighbor_ids\": neighbor_ids,\n            \"neighbor_distances\": neighbor_distances,\n        }\n    )\n\n    if neighbor_targets:\n        result_df[\"neighbor_targets\"] = neighbor_targets\n\n    return result_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers","title":"<code>outliers()</code>","text":"<p>Compute a unified 'outlier' score based on either 'target_z' or 'target_consistency'.</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def outliers(self) -&gt; None:\n    \"\"\"Compute a unified 'outlier' score based on either 'target_z' or 'target_consistency'.\"\"\"\n    if \"target_z\" in self.df.columns:\n        # Normalize Z-Scores to a 0-1 range\n        self.df[\"outlier\"] = (self.df[\"target_z\"].abs() / (self.df[\"target_z\"].abs().max() + 1e-6)).clip(0, 1)\n\n    elif \"target_consistency\" in self.df.columns:\n        # Calculate outlier score as 1 - consistency\n        self.df[\"outlier\"] = 1 - self.df[\"target_consistency\"]\n\n    else:\n        self.log.warning(\"No 'target_z' or 'target_consistency' column found to compute outlier scores.\")\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency","title":"<code>target_consistency()</code>","text":"<p>Compute a Neighborhood Consistency Score for CATEGORICAL targets.</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def target_consistency(self) -&gt; None:\n    \"\"\"Compute a Neighborhood Consistency Score for CATEGORICAL targets.\"\"\"\n    if not self.target:\n        self.log.warning(\"No target column defined for neighborhood consistency computation.\")\n        return\n\n    # Get the neighbors and distances for each internal observation (already excludes the query)\n    distances, indices = self.knn_model.kneighbors()\n\n    # Calculate the Neighborhood Consistency Score for each observation\n    consistency_scores = []\n    for idx, idx_list in enumerate(indices):\n        query_target = self.df.iloc[idx][self.target]  # Get current observation's target value\n\n        # Get the neighbors' target values\n        neighbor_targets = self.df.iloc[idx_list][self.target]\n\n        # Calculate the proportion of neighbors that have the same category as the query observation\n        consistency_score = (neighbor_targets == query_target).mean()\n        consistency_scores.append(consistency_score)\n\n    # Add the 'target_consistency' column to the internal dataframe\n    self.df[\"target_consistency\"] = consistency_scores\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary","title":"<code>target_summary(query_id)</code>","text":"<p>WIP: Provide a summary of target values in the neighborhood of the given query ID</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def target_summary(self, query_id: Union[str, int]) -&gt; pd.DataFrame:\n    \"\"\"WIP: Provide a summary of target values in the neighborhood of the given query ID\"\"\"\n    neighbors_df = self.neighbors(query_id, include_self=False)\n    if self.target and not neighbors_df.empty:\n        summary_stats = neighbors_df[self.target].describe()\n        return pd.DataFrame(summary_stats).transpose()\n    else:\n        self.log.warning(f\"No target values found for neighbors of Query ID '{query_id}'.\")\n        return pd.DataFrame()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores","title":"<code>target_z_scores()</code>","text":"<p>Compute Z-Scores for NUMERIC target values.</p> Source code in <code>src/sageworks/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def target_z_scores(self) -&gt; None:\n    \"\"\"Compute Z-Scores for NUMERIC target values.\"\"\"\n    if not self.target:\n        self.log.warning(\"No target column defined for Z-Score computation.\")\n        return\n\n    # Get the neighbors and distances for each internal observation\n    distances, indices = self.knn_model.kneighbors()\n\n    # Retrieve all neighbor target values in a single operation\n    neighbor_targets = self.df[self.target].values[indices]  # Shape will be (n_samples, n_neighbors)\n\n    # Compute the mean and std along the neighbors axis (axis=1)\n    neighbor_means = neighbor_targets.mean(axis=1)\n    neighbor_stds = neighbor_targets.std(axis=1, ddof=0)\n\n    # Vectorized Z-score calculation\n    current_targets = self.df[self.target].values\n    z_scores = np.where(neighbor_stds == 0, 0.0, (current_targets - neighbor_means) / neighbor_stds)\n\n    # Assign the computed Z-Scores back to the DataFrame\n    self.df[\"target_z\"] = z_scores\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator","title":"<code>ResidualsCalculator</code>","text":"<p>               Bases: <code>BaseEstimator</code>, <code>TransformerMixin</code></p> <p>A custom transformer for calculating residuals using cross-validation or an endpoint.</p> <p>This transformer performs K-Fold cross-validation (if no endpoint is provided), or it uses the endpoint to generate predictions and compute residuals. It adds 'prediction', 'residuals', 'residuals_abs', 'prediction_100', 'residuals_100', and 'residuals_100_abs' columns to the input DataFrame.</p> <p>Attributes:</p> Name Type Description <code>model_class</code> <code>Union[RegressorMixin, XGBRegressor]</code> <p>The machine learning model class used for predictions.</p> <code>n_splits</code> <code>int</code> <p>Number of splits for cross-validation.</p> <code>random_state</code> <code>int</code> <p>Random state for reproducibility.</p> <code>endpoint</code> <code>Optional</code> <p>The SageWorks endpoint object for running inference, if provided.</p> Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code> <pre><code>class ResidualsCalculator(BaseEstimator, TransformerMixin):\n    \"\"\"\n    A custom transformer for calculating residuals using cross-validation or an endpoint.\n\n    This transformer performs K-Fold cross-validation (if no endpoint is provided), or it uses the endpoint\n    to generate predictions and compute residuals. It adds 'prediction', 'residuals', 'residuals_abs',\n    'prediction_100', 'residuals_100', and 'residuals_100_abs' columns to the input DataFrame.\n\n    Attributes:\n        model_class (Union[RegressorMixin, XGBRegressor]): The machine learning model class used for predictions.\n        n_splits (int): Number of splits for cross-validation.\n        random_state (int): Random state for reproducibility.\n        endpoint (Optional): The SageWorks endpoint object for running inference, if provided.\n    \"\"\"\n\n    def __init__(\n        self,\n        endpoint: Optional[object] = None,\n        reference_model_class: Union[RegressorMixin, XGBRegressor] = XGBRegressor,\n    ):\n        \"\"\"\n        Initializes the ResidualsCalculator with the specified parameters.\n\n        Args:\n            endpoint (Optional): A SageWorks endpoint object to run inference, if available.\n            reference_model_class (Union[RegressorMixin, XGBRegressor]): The reference model class for predictions.\n        \"\"\"\n        self.n_splits = 5\n        self.random_state = 42\n        self.reference_model_class = reference_model_class  # Store the class, instantiate the model later\n        self.reference_model = None  # Lazy model initialization\n        self.endpoint = endpoint  # Use this endpoint for inference if provided\n        self.X = None\n        self.y = None\n        super().__init__()\n\n    def fit(self, X: pd.DataFrame, y: pd.Series) -&gt; BaseEstimator:\n        \"\"\"\n        Fits the model. If no endpoint is provided, fitting involves storing the input data\n        and initializing a reference model.\n\n        Args:\n            X (pd.DataFrame): The input features.\n            y (pd.Series): The target variable.\n\n        Returns:\n            self: Returns an instance of self.\n        \"\"\"\n        self.X = X\n        self.y = y\n\n        if self.endpoint is None:\n            # Only initialize the reference model if no endpoint is provided\n            self.reference_model = self.reference_model_class()\n        return self\n\n    def transform(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"\n        Transforms the input DataFrame by adding 'prediction', 'residuals', 'residuals_abs',\n        'prediction_100', 'residuals_100', and 'residuals_100_abs' columns.\n\n        Args:\n            X (pd.DataFrame): The input features.\n\n        Returns:\n            pd.DataFrame: The transformed DataFrame with additional columns.\n        \"\"\"\n        check_is_fitted(self, [\"X\", \"y\"])  # Ensure fit has been called\n\n        if self.endpoint:\n            # If an endpoint is provided, run inference on the full data\n            result_df = self._run_inference_via_endpoint(X)\n        else:\n            # If no endpoint, perform cross-validation and full model fitting\n            result_df = self._run_cross_validation(X)\n\n        return result_df\n\n    def _run_cross_validation(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"\n        Handles the cross-validation process when no endpoint is provided.\n\n        Args:\n            X (pd.DataFrame): The input features.\n\n        Returns:\n            pd.DataFrame: DataFrame with predictions and residuals from cross-validation and full model fit.\n        \"\"\"\n        kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)\n\n        # Initialize pandas Series to store predictions and residuals, aligned by index\n        predictions = pd.Series(index=self.y.index, dtype=np.float64)\n        residuals = pd.Series(index=self.y.index, dtype=np.float64)\n        residuals_abs = pd.Series(index=self.y.index, dtype=np.float64)\n\n        # Perform cross-validation and collect predictions and residuals\n        for train_index, test_index in kf.split(self.X):\n            X_train, X_test = self.X.iloc[train_index], self.X.iloc[test_index]\n            y_train, y_test = self.y.iloc[train_index], self.y.iloc[test_index]\n\n            # Fit the model on the training data\n            self.reference_model.fit(X_train, y_train)\n\n            # Predict on the test data\n            y_pred = self.reference_model.predict(X_test)\n\n            # Compute residuals and absolute residuals\n            residuals_fold = y_test - y_pred\n            residuals_abs_fold = np.abs(residuals_fold)\n\n            # Place the predictions and residuals in the correct positions based on index\n            predictions.iloc[test_index] = y_pred\n            residuals.iloc[test_index] = residuals_fold\n            residuals_abs.iloc[test_index] = residuals_abs_fold\n\n        # Train on all data and compute residuals for 100% training\n        self.reference_model.fit(self.X, self.y)\n        y_pred_100 = self.reference_model.predict(self.X)\n        residuals_100 = self.y - y_pred_100\n        residuals_100_abs = np.abs(residuals_100)\n\n        # Create a copy of the provided DataFrame and add the new columns\n        result_df = X.copy()\n        result_df[\"prediction\"] = predictions\n        result_df[\"residuals\"] = residuals\n        result_df[\"residuals_abs\"] = residuals_abs\n        result_df[\"prediction_100\"] = y_pred_100\n        result_df[\"residuals_100\"] = residuals_100\n        result_df[\"residuals_100_abs\"] = residuals_100_abs\n        result_df[self.y.name] = self.y  # Add the target column back\n\n        return result_df\n\n    def _run_inference_via_endpoint(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"\n        Handles the inference process when an endpoint is provided.\n\n        Args:\n            X (pd.DataFrame): The input features.\n\n        Returns:\n            pd.DataFrame: DataFrame with predictions and residuals from the endpoint.\n        \"\"\"\n        # Run inference on all data using the endpoint (include the target column)\n        X = X.copy()\n        X.loc[:, self.y.name] = self.y\n        results_df = self.endpoint.inference(X)\n        predictions = results_df[\"prediction\"]\n\n        # Compute residuals and residuals_abs based on the endpoint's predictions\n        residuals = self.y - predictions\n        residuals_abs = np.abs(residuals)\n\n        # To maintain consistency, populate both 'prediction' and 'prediction_100' with the same values\n        result_df = X.copy()\n        result_df[\"prediction\"] = predictions\n        result_df[\"residuals\"] = residuals\n        result_df[\"residuals_abs\"] = residuals_abs\n        result_df[\"prediction_100\"] = predictions\n        result_df[\"residuals_100\"] = residuals\n        result_df[\"residuals_100_abs\"] = residuals_abs\n\n        return result_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__","title":"<code>__init__(endpoint=None, reference_model_class=XGBRegressor)</code>","text":"<p>Initializes the ResidualsCalculator with the specified parameters.</p> <p>Parameters:</p> Name Type Description Default <code>endpoint</code> <code>Optional</code> <p>A SageWorks endpoint object to run inference, if available.</p> <code>None</code> <code>reference_model_class</code> <code>Union[RegressorMixin, XGBRegressor]</code> <p>The reference model class for predictions.</p> <code>XGBRegressor</code> Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code> <pre><code>def __init__(\n    self,\n    endpoint: Optional[object] = None,\n    reference_model_class: Union[RegressorMixin, XGBRegressor] = XGBRegressor,\n):\n    \"\"\"\n    Initializes the ResidualsCalculator with the specified parameters.\n\n    Args:\n        endpoint (Optional): A SageWorks endpoint object to run inference, if available.\n        reference_model_class (Union[RegressorMixin, XGBRegressor]): The reference model class for predictions.\n    \"\"\"\n    self.n_splits = 5\n    self.random_state = 42\n    self.reference_model_class = reference_model_class  # Store the class, instantiate the model later\n    self.reference_model = None  # Lazy model initialization\n    self.endpoint = endpoint  # Use this endpoint for inference if provided\n    self.X = None\n    self.y = None\n    super().__init__()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.fit","title":"<code>fit(X, y)</code>","text":"<p>Fits the model. If no endpoint is provided, fitting involves storing the input data and initializing a reference model.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>DataFrame</code> <p>The input features.</p> required <code>y</code> <code>Series</code> <p>The target variable.</p> required <p>Returns:</p> Name Type Description <code>self</code> <code>BaseEstimator</code> <p>Returns an instance of self.</p> Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code> <pre><code>def fit(self, X: pd.DataFrame, y: pd.Series) -&gt; BaseEstimator:\n    \"\"\"\n    Fits the model. If no endpoint is provided, fitting involves storing the input data\n    and initializing a reference model.\n\n    Args:\n        X (pd.DataFrame): The input features.\n        y (pd.Series): The target variable.\n\n    Returns:\n        self: Returns an instance of self.\n    \"\"\"\n    self.X = X\n    self.y = y\n\n    if self.endpoint is None:\n        # Only initialize the reference model if no endpoint is provided\n        self.reference_model = self.reference_model_class()\n    return self\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.residuals_calculator.ResidualsCalculator.transform","title":"<code>transform(X)</code>","text":"<p>Transforms the input DataFrame by adding 'prediction', 'residuals', 'residuals_abs', 'prediction_100', 'residuals_100', and 'residuals_100_abs' columns.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>DataFrame</code> <p>The input features.</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The transformed DataFrame with additional columns.</p> Source code in <code>src/sageworks/algorithms/dataframe/residuals_calculator.py</code> <pre><code>def transform(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"\n    Transforms the input DataFrame by adding 'prediction', 'residuals', 'residuals_abs',\n    'prediction_100', 'residuals_100', and 'residuals_100_abs' columns.\n\n    Args:\n        X (pd.DataFrame): The input features.\n\n    Returns:\n        pd.DataFrame: The transformed DataFrame with additional columns.\n    \"\"\"\n    check_is_fitted(self, [\"X\", \"y\"])  # Ensure fit has been called\n\n    if self.endpoint:\n        # If an endpoint is provided, run inference on the full data\n        result_df = self._run_inference_via_endpoint(X)\n    else:\n        # If no endpoint, perform cross-validation and full model fitting\n        result_df = self._run_cross_validation(X)\n\n    return result_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction","title":"<code>DimensionalityReduction</code>","text":"Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>class DimensionalityReduction:\n    def __init__(self):\n        \"\"\"DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame\"\"\"\n        self.log = logging.getLogger(\"sageworks\")\n        self.projection_model = None\n        self.features = None\n\n    def fit_transform(self, df: pd.DataFrame, features: list = None, projection: str = \"TSNE\") -&gt; pd.DataFrame:\n        \"\"\"Fit and Transform the DataFrame\n        Args:\n            df: Pandas DataFrame\n            features: List of feature column names (default: None)\n            projection: The projection model to use (TSNE, MDS or PCA, default: PCA)\n        Returns:\n            Pandas DataFrame with new columns x and y\n        \"\"\"\n\n        # If no features are given, indentify all numeric columns\n        if features is None:\n            features = [x for x in df.select_dtypes(include=\"number\").columns.tolist() if not x.endswith(\"id\")]\n            # Also drop group_count if it exists\n            features = [x for x in features if x != \"group_count\"]\n            self.log.info(\"No features given, auto identifying numeric columns...\")\n            self.log.info(f\"{features}\")\n        self.features = features\n\n        # Sanity checks\n        if not all(column in df.columns for column in self.features):\n            self.log.critical(\"Some features are missing in the DataFrame\")\n            return df\n        if len(self.features) &lt; 2:\n            self.log.critical(\"At least two features are required\")\n            return df\n        if df.empty:\n            self.log.critical(\"DataFrame is empty\")\n            return df\n\n        # Most projection models will fail if there are any NaNs in the data\n        # So we'll fill NaNs with the mean value for that column\n        for col in df[self.features].columns:\n            df[col].fillna(df[col].mean(), inplace=True)\n\n        # Normalize the features\n        scaler = StandardScaler()\n        normalized_data = scaler.fit_transform(df[self.features])\n        df[self.features] = normalized_data\n\n        # Project the multidimensional features onto an x,y plane\n        self.log.info(\"Projecting features onto an x,y plane...\")\n\n        # Perform the projection\n        if projection == \"TSNE\":\n            # Perplexity is a hyperparameter that controls the number of neighbors used to compute the manifold\n            # The number of neighbors should be less than the number of samples\n            perplexity = min(40, len(df) - 1)\n            self.log.info(f\"Perplexity: {perplexity}\")\n            self.projection_model = TSNE(perplexity=perplexity)\n        elif projection == \"MDS\":\n            self.projection_model = MDS(n_components=2, random_state=0)\n        elif projection == \"PCA\":\n            self.projection_model = PCA(n_components=2)\n\n        # Fit the projection model\n        # Hack PCA + TSNE to work together\n        projection = self.projection_model.fit_transform(df[self.features])\n\n        # Put the projection results back into the given DataFrame\n        df[\"x\"] = projection[:, 0]  # Projection X Column\n        df[\"y\"] = projection[:, 1]  # Projection Y Column\n\n        # Jitter the data to resolve coincident points\n        # df = self.resolve_coincident_points(df)\n\n        # Return the DataFrame with the new columns\n        return df\n\n    @staticmethod\n    def resolve_coincident_points(df: pd.DataFrame):\n        \"\"\"Resolve coincident points in a DataFrame\n        Args:\n            df(pd.DataFrame): The DataFrame to resolve coincident points in\n        Returns:\n            pd.DataFrame: The DataFrame with resolved coincident points\n        \"\"\"\n        # Adding Jitter to the projection\n        x_scale = (df[\"x\"].max() - df[\"x\"].min()) * 0.1\n        y_scale = (df[\"y\"].max() - df[\"y\"].min()) * 0.1\n        df[\"x\"] += np.random.normal(-x_scale, +x_scale, len(df))\n        df[\"y\"] += np.random.normal(-y_scale, +y_scale, len(df))\n        return df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.__init__","title":"<code>__init__()</code>","text":"<p>DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame</p> Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>def __init__(self):\n    \"\"\"DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame\"\"\"\n    self.log = logging.getLogger(\"sageworks\")\n    self.projection_model = None\n    self.features = None\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.fit_transform","title":"<code>fit_transform(df, features=None, projection='TSNE')</code>","text":"<p>Fit and Transform the DataFrame Args:     df: Pandas DataFrame     features: List of feature column names (default: None)     projection: The projection model to use (TSNE, MDS or PCA, default: PCA) Returns:     Pandas DataFrame with new columns x and y</p> Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>def fit_transform(self, df: pd.DataFrame, features: list = None, projection: str = \"TSNE\") -&gt; pd.DataFrame:\n    \"\"\"Fit and Transform the DataFrame\n    Args:\n        df: Pandas DataFrame\n        features: List of feature column names (default: None)\n        projection: The projection model to use (TSNE, MDS or PCA, default: PCA)\n    Returns:\n        Pandas DataFrame with new columns x and y\n    \"\"\"\n\n    # If no features are given, indentify all numeric columns\n    if features is None:\n        features = [x for x in df.select_dtypes(include=\"number\").columns.tolist() if not x.endswith(\"id\")]\n        # Also drop group_count if it exists\n        features = [x for x in features if x != \"group_count\"]\n        self.log.info(\"No features given, auto identifying numeric columns...\")\n        self.log.info(f\"{features}\")\n    self.features = features\n\n    # Sanity checks\n    if not all(column in df.columns for column in self.features):\n        self.log.critical(\"Some features are missing in the DataFrame\")\n        return df\n    if len(self.features) &lt; 2:\n        self.log.critical(\"At least two features are required\")\n        return df\n    if df.empty:\n        self.log.critical(\"DataFrame is empty\")\n        return df\n\n    # Most projection models will fail if there are any NaNs in the data\n    # So we'll fill NaNs with the mean value for that column\n    for col in df[self.features].columns:\n        df[col].fillna(df[col].mean(), inplace=True)\n\n    # Normalize the features\n    scaler = StandardScaler()\n    normalized_data = scaler.fit_transform(df[self.features])\n    df[self.features] = normalized_data\n\n    # Project the multidimensional features onto an x,y plane\n    self.log.info(\"Projecting features onto an x,y plane...\")\n\n    # Perform the projection\n    if projection == \"TSNE\":\n        # Perplexity is a hyperparameter that controls the number of neighbors used to compute the manifold\n        # The number of neighbors should be less than the number of samples\n        perplexity = min(40, len(df) - 1)\n        self.log.info(f\"Perplexity: {perplexity}\")\n        self.projection_model = TSNE(perplexity=perplexity)\n    elif projection == \"MDS\":\n        self.projection_model = MDS(n_components=2, random_state=0)\n    elif projection == \"PCA\":\n        self.projection_model = PCA(n_components=2)\n\n    # Fit the projection model\n    # Hack PCA + TSNE to work together\n    projection = self.projection_model.fit_transform(df[self.features])\n\n    # Put the projection results back into the given DataFrame\n    df[\"x\"] = projection[:, 0]  # Projection X Column\n    df[\"y\"] = projection[:, 1]  # Projection Y Column\n\n    # Jitter the data to resolve coincident points\n    # df = self.resolve_coincident_points(df)\n\n    # Return the DataFrame with the new columns\n    return df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points","title":"<code>resolve_coincident_points(df)</code>  <code>staticmethod</code>","text":"<p>Resolve coincident points in a DataFrame Args:     df(pd.DataFrame): The DataFrame to resolve coincident points in Returns:     pd.DataFrame: The DataFrame with resolved coincident points</p> Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>@staticmethod\ndef resolve_coincident_points(df: pd.DataFrame):\n    \"\"\"Resolve coincident points in a DataFrame\n    Args:\n        df(pd.DataFrame): The DataFrame to resolve coincident points in\n    Returns:\n        pd.DataFrame: The DataFrame with resolved coincident points\n    \"\"\"\n    # Adding Jitter to the projection\n    x_scale = (df[\"x\"].max() - df[\"x\"].min()) * 0.1\n    y_scale = (df[\"y\"].max() - df[\"y\"].min()) * 0.1\n    df[\"x\"] += np.random.normal(-x_scale, +x_scale, len(df))\n    df[\"y\"] += np.random.normal(-y_scale, +y_scale, len(df))\n    return df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#sageworks.algorithms.dataframe.dimensionality_reduction.test","title":"<code>test()</code>","text":"<p>Test for the Dimensionality Reduction Class</p> Source code in <code>src/sageworks/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>def test():\n    \"\"\"Test for the Dimensionality Reduction Class\"\"\"\n    # Set some pandas options\n    pd.set_option(\"display.max_columns\", None)\n    pd.set_option(\"display.width\", 1000)\n\n    # Make some fake data\n    data = {\n        \"ID\": [\n            \"id_0\",\n            \"id_0\",\n            \"id_2\",\n            \"id_3\",\n            \"id_4\",\n            \"id_5\",\n            \"id_6\",\n            \"id_7\",\n            \"id_8\",\n            \"id_9\",\n        ],\n        \"feat1\": [1.0, 1.0, 1.1, 3.0, 4.0, 1.0, 1.0, 1.1, 3.0, 4.0],\n        \"feat2\": [1.0, 1.0, 1.1, 3.0, 4.0, 1.0, 1.0, 1.1, 3.0, 4.0],\n        \"feat3\": [0.1, 0.1, 0.2, 1.6, 2.5, 0.1, 0.1, 0.2, 1.6, 2.5],\n        \"price\": [31, 60, 62, 40, 20, 31, 61, 60, 40, 20],\n    }\n    data_df = pd.DataFrame(data)\n    features = [\"feat1\", \"feat2\", \"feat3\"]\n\n    # Create the class and run the dimensionality reduction\n    projection = DimensionalityReduction()\n    new_df = projection.fit_transform(data_df, features=features, projection=\"TSNE\")\n\n    # Check that the x and y columns were added\n    assert \"x\" in new_df.columns\n    assert \"y\" in new_df.columns\n\n    # Output the DataFrame\n    print(new_df)\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/graphs/overview/","title":"Graph Algorithms","text":"<p>Graph Algorithms</p> <p>WIP: These classes are currently actively being developed and are subject to change in both API and functionality over time.</p> <ul> <li>TBD: TBD</li> </ul> <p>Graph Algorithms</p> <p>Docs TBD</p>"},{"location":"data_algorithms/graphs/overview/#sageworks.algorithms.graph.light.proximity_graph.ProximityGraph","title":"<code>ProximityGraph</code>","text":"<p>Build a proximity graph of the nearest neighbors based on feature space.</p> Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code> <pre><code>class ProximityGraph:\n    \"\"\"\n    Build a proximity graph of the nearest neighbors based on feature space.\n    \"\"\"\n\n    def __init__(self, n_neighbors: int = 5):\n        \"\"\"Initialize the ProximityGraph with the specified parameters.\n\n        Args:\n            n_neighbors (int): Number of neighbors to consider (default: 5)\n        \"\"\"\n        self.n_neighbors = n_neighbors\n        self.nx_graph = nx.Graph()\n\n    def build_graph(\n        self,\n        df: pd.DataFrame,\n        features: list,\n        id_column: str,\n        target: str,\n        store_features=True,\n    ) -&gt; nx.Graph:\n        \"\"\"\n        Processes the input DataFrame and builds a proximity graph.\n\n        Args:\n            df (pd.DataFrame): The input DataFrame containing feature columns.\n            features (list): List of feature column names to be used for building the proximity graph.\n            id_column (str): Name of the ID column in the DataFrame.\n            target (str): Name of the target column in the DataFrame.\n            store_features (bool): Whether to store the features as node attributes (default: True).\n\n        Returns:\n            nx.Graph: The proximity graph as a NetworkX graph.\n        \"\"\"\n        # Drop NaNs from the DataFrame using the provided utility\n        df = drop_nans(df)\n\n        # Initialize FeatureSpaceProximity with the input DataFrame and the specified features\n        knn_spider = FeatureSpaceProximity(\n            df,\n            features=features,\n            id_column=id_column,\n            target=target,\n            neighbors=self.n_neighbors,\n        )\n\n        # Use FeatureSpaceProximity to get all neighbor indices and distances\n        indices, distances = knn_spider.get_neighbor_indices_and_distances()\n\n        # Compute max distance for scaling (to [0, 1])\n        max_distance = distances.max()\n\n        # Initialize an empty graph\n        self.nx_graph = nx.Graph()\n\n        # Use the ID column for node IDs instead of relying on the DataFrame index\n        node_ids = df[id_column].values\n\n        # Add nodes with their features as attributes using the ID column\n        for node_id in node_ids:\n            if store_features:\n                self.nx_graph.add_node(\n                    node_id, **df[df[id_column] == node_id].iloc[0].to_dict()\n                )  # Use .iloc[0] for correct node attributes\n            else:\n                self.nx_graph.add_node(node_id)\n\n        # Add edges with weights based on inverse distance\n        for i, neighbors in enumerate(indices):\n            one_edge_added = False\n            for j, neighbor_idx in enumerate(neighbors):\n                if i != neighbor_idx:\n                    # Compute the weight of the edge (inverse of distance)\n                    weight = 1.0 - (distances[i][j] / max_distance)  # Scale to [0, 1]\n\n                    # Map back to the ID column instead of the DataFrame index\n                    src_node = node_ids[i]\n                    dst_node = node_ids[neighbor_idx]\n\n                    # Add the edge to the graph (if the weight is greater than 0.1)\n                    if weight &gt; 0.1 or not one_edge_added:\n                        self.nx_graph.add_edge(src_node, dst_node, weight=weight)\n                        one_edge_added = True\n\n        # Return the NetworkX graph\n        return self.nx_graph\n\n    def get_neighborhood(self, node_id: Union[str, int], radius: int = 1) -&gt; nx.Graph:\n        \"\"\"\n        Get a subgraph containing nodes within a given number of hops around a specific node.\n\n        Args:\n            node_id: The ID of the node to center the neighborhood around.\n            radius: The number of hops to consider around the node (default: 1).\n\n        Returns:\n            nx.Graph: A subgraph containing the specified neighborhood.\n        \"\"\"\n        # Use NetworkX's ego_graph to extract the neighborhood within the given radius\n        if node_id in self.nx_graph:\n            return nx.ego_graph(self.nx_graph, node_id, radius=radius)\n        else:\n            raise ValueError(f\"Node ID '{node_id}' not found in the graph.\")\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#sageworks.algorithms.graph.light.proximity_graph.ProximityGraph.__init__","title":"<code>__init__(n_neighbors=5)</code>","text":"<p>Initialize the ProximityGraph with the specified parameters.</p> <p>Parameters:</p> Name Type Description Default <code>n_neighbors</code> <code>int</code> <p>Number of neighbors to consider (default: 5)</p> <code>5</code> Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code> <pre><code>def __init__(self, n_neighbors: int = 5):\n    \"\"\"Initialize the ProximityGraph with the specified parameters.\n\n    Args:\n        n_neighbors (int): Number of neighbors to consider (default: 5)\n    \"\"\"\n    self.n_neighbors = n_neighbors\n    self.nx_graph = nx.Graph()\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#sageworks.algorithms.graph.light.proximity_graph.ProximityGraph.build_graph","title":"<code>build_graph(df, features, id_column, target, store_features=True)</code>","text":"<p>Processes the input DataFrame and builds a proximity graph.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The input DataFrame containing feature columns.</p> required <code>features</code> <code>list</code> <p>List of feature column names to be used for building the proximity graph.</p> required <code>id_column</code> <code>str</code> <p>Name of the ID column in the DataFrame.</p> required <code>target</code> <code>str</code> <p>Name of the target column in the DataFrame.</p> required <code>store_features</code> <code>bool</code> <p>Whether to store the features as node attributes (default: True).</p> <code>True</code> <p>Returns:</p> Type Description <code>Graph</code> <p>nx.Graph: The proximity graph as a NetworkX graph.</p> Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code> <pre><code>def build_graph(\n    self,\n    df: pd.DataFrame,\n    features: list,\n    id_column: str,\n    target: str,\n    store_features=True,\n) -&gt; nx.Graph:\n    \"\"\"\n    Processes the input DataFrame and builds a proximity graph.\n\n    Args:\n        df (pd.DataFrame): The input DataFrame containing feature columns.\n        features (list): List of feature column names to be used for building the proximity graph.\n        id_column (str): Name of the ID column in the DataFrame.\n        target (str): Name of the target column in the DataFrame.\n        store_features (bool): Whether to store the features as node attributes (default: True).\n\n    Returns:\n        nx.Graph: The proximity graph as a NetworkX graph.\n    \"\"\"\n    # Drop NaNs from the DataFrame using the provided utility\n    df = drop_nans(df)\n\n    # Initialize FeatureSpaceProximity with the input DataFrame and the specified features\n    knn_spider = FeatureSpaceProximity(\n        df,\n        features=features,\n        id_column=id_column,\n        target=target,\n        neighbors=self.n_neighbors,\n    )\n\n    # Use FeatureSpaceProximity to get all neighbor indices and distances\n    indices, distances = knn_spider.get_neighbor_indices_and_distances()\n\n    # Compute max distance for scaling (to [0, 1])\n    max_distance = distances.max()\n\n    # Initialize an empty graph\n    self.nx_graph = nx.Graph()\n\n    # Use the ID column for node IDs instead of relying on the DataFrame index\n    node_ids = df[id_column].values\n\n    # Add nodes with their features as attributes using the ID column\n    for node_id in node_ids:\n        if store_features:\n            self.nx_graph.add_node(\n                node_id, **df[df[id_column] == node_id].iloc[0].to_dict()\n            )  # Use .iloc[0] for correct node attributes\n        else:\n            self.nx_graph.add_node(node_id)\n\n    # Add edges with weights based on inverse distance\n    for i, neighbors in enumerate(indices):\n        one_edge_added = False\n        for j, neighbor_idx in enumerate(neighbors):\n            if i != neighbor_idx:\n                # Compute the weight of the edge (inverse of distance)\n                weight = 1.0 - (distances[i][j] / max_distance)  # Scale to [0, 1]\n\n                # Map back to the ID column instead of the DataFrame index\n                src_node = node_ids[i]\n                dst_node = node_ids[neighbor_idx]\n\n                # Add the edge to the graph (if the weight is greater than 0.1)\n                if weight &gt; 0.1 or not one_edge_added:\n                    self.nx_graph.add_edge(src_node, dst_node, weight=weight)\n                    one_edge_added = True\n\n    # Return the NetworkX graph\n    return self.nx_graph\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#sageworks.algorithms.graph.light.proximity_graph.ProximityGraph.get_neighborhood","title":"<code>get_neighborhood(node_id, radius=1)</code>","text":"<p>Get a subgraph containing nodes within a given number of hops around a specific node.</p> <p>Parameters:</p> Name Type Description Default <code>node_id</code> <code>Union[str, int]</code> <p>The ID of the node to center the neighborhood around.</p> required <code>radius</code> <code>int</code> <p>The number of hops to consider around the node (default: 1).</p> <code>1</code> <p>Returns:</p> Type Description <code>Graph</code> <p>nx.Graph: A subgraph containing the specified neighborhood.</p> Source code in <code>src/sageworks/algorithms/graph/light/proximity_graph.py</code> <pre><code>def get_neighborhood(self, node_id: Union[str, int], radius: int = 1) -&gt; nx.Graph:\n    \"\"\"\n    Get a subgraph containing nodes within a given number of hops around a specific node.\n\n    Args:\n        node_id: The ID of the node to center the neighborhood around.\n        radius: The number of hops to consider around the node (default: 1).\n\n    Returns:\n        nx.Graph: A subgraph containing the specified neighborhood.\n    \"\"\"\n    # Use NetworkX's ego_graph to extract the neighborhood within the given radius\n    if node_id in self.nx_graph:\n        return nx.ego_graph(self.nx_graph, node_id, radius=radius)\n    else:\n        raise ValueError(f\"Node ID '{node_id}' not found in the graph.\")\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/spark/overview/","title":"Graph Algorithms","text":"<p>Graph Algorithms</p> <p>WIP: These classes are currently actively being developed and are subject to change in both API and functionality over time.</p> <ul> <li>TBD: TBD</li> </ul> <p>Graph Algorithms</p> <p>Docs TBD</p> <p>ComputationView Class: Create a View with a subset of columns for display purposes</p>"},{"location":"data_algorithms/spark/overview/#sageworks.core.views.computation_view.ComputationView","title":"<code>ComputationView</code>","text":"<p>               Bases: <code>ColumnSubsetView</code></p> <p>ComputationView Class: Create a View with a subset of columns for computation purposes</p> Common Usage <pre><code># Create a default ComputationView\nfs = FeatureSet(\"test_features\")\ncomp_view = ComputationView.create(fs)\ndf = comp_view.pull_dataframe()\n\n# Create a ComputationView with a specific set of columns\ncomp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n</code></pre> Source code in <code>src/sageworks/core/views/computation_view.py</code> <pre><code>class ComputationView(ColumnSubsetView):\n    \"\"\"ComputationView Class: Create a View with a subset of columns for computation purposes\n\n    Common Usage:\n        ```python\n        # Create a default ComputationView\n        fs = FeatureSet(\"test_features\")\n        comp_view = ComputationView.create(fs)\n        df = comp_view.pull_dataframe()\n\n        # Create a ComputationView with a specific set of columns\n        comp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        artifact: Union[DataSource, FeatureSet],\n        source_table: str = None,\n        column_list: Union[list[str], None] = None,\n        column_limit: int = 30,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a ComputationView instance.\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None\n            column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n            column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n        return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"data_algorithms/spark/overview/#sageworks.core.views.computation_view.ComputationView.create","title":"<code>create(artifact, source_table=None, column_list=None, column_limit=30)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a ComputationView instance.</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>The DataSource or FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None</p> <code>None</code> <code>column_list</code> <code>Union[list[str], None]</code> <p>A list of columns to include. Defaults to None.</p> <code>None</code> <code>column_limit</code> <code>int</code> <p>The max number of columns to include. Defaults to 30.</p> <code>30</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/sageworks/core/views/computation_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    artifact: Union[DataSource, FeatureSet],\n    source_table: str = None,\n    column_list: Union[list[str], None] = None,\n    column_limit: int = 30,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a ComputationView instance.\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None\n        column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n        column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n    return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"data_algorithms/spark/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/sql/overview/","title":"SQL Algorithms","text":"<p>SQL Algorithms</p> <p>One of the main benefit of SQL Algorithms is that the 'heavy lifting' is all done on the SQL Database, so if you have large datassets this is the place for you.</p> <ul> <li> <p>SQL: SQL queries that provide a wide range of functionality:</p> <ul> <li>Outliers</li> <li>Descriptive Stats</li> <li>Correlations</li> <li>and More Oulliers</li> </ul> </li> </ul> <p>SQL based Outliers: Compute outliers for all the columns in a DataSource using SQL</p> <p>SQL based Descriptive Stats: Compute Descriptive Stats for all the numeric columns in a DataSource using SQL</p> <p>SQL based Correlations: Compute Correlations for all the numeric columns in a DataSource using SQL</p>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.outliers.Outliers","title":"<code>Outliers</code>","text":"<p>Outliers: Class to compute outliers for all the columns in a DataSource using SQL</p> Source code in <code>src/sageworks/algorithms/sql/outliers.py</code> <pre><code>class Outliers:\n    \"\"\"Outliers: Class to compute outliers for all the columns in a DataSource using SQL\"\"\"\n\n    def __init__(self):\n        \"\"\"SQLOutliers Initialization\"\"\"\n        self.outlier_group = \"unknown\"\n\n    def compute_outliers(\n        self, data_source: DataSourceAbstract, scale: float = 1.5, use_stddev: bool = False\n    ) -&gt; pd.DataFrame:\n        \"\"\"Compute outliers for all the numeric columns in a DataSource\n        Args:\n            data_source(DataSource): The DataSource that we're computing outliers on\n            scale (float): The scale to use for either the IQR or stddev outlier calculation (default: 1.5)\n            use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False)\n        Returns:\n            pd.DataFrame: A DataFrame of outliers for this DataSource\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n\n        # Note: If use_stddev is True, then the scale parameter needs to be adjusted\n        if use_stddev and scale == 1.5:  # If the default scale is used, adjust it\n            scale = 2.5\n\n        # Compute the numeric outliers\n        outlier_df = self._numeric_outliers(data_source, scale, use_stddev)\n\n        # If there are no outliers, return a DataFrame with the computation columns but no rows\n        if outlier_df is None:\n            columns = data_source.view(\"computation\").columns\n            return pd.DataFrame(columns=columns + [\"outlier_group\"])\n\n        # Get the top N outliers for each outlier group\n        outlier_df = self.get_top_n_outliers(outlier_df)\n\n        # Make sure the dataframe isn't too big, if it's too big sample it down\n        if len(outlier_df) &gt; 300:\n            log.important(f\"Outliers DataFrame is too large {len(outlier_df)}, sampling down to 300 rows\")\n            outlier_df = outlier_df.sample(300)\n\n        # Sort by outlier_group and reset the index\n        outlier_df = outlier_df.sort_values(\"outlier_group\").reset_index(drop=True)\n\n        # Shorten any long string values\n        outlier_df = shorten_values(outlier_df)\n        return outlier_df\n\n    def _numeric_outliers(self, data_source: DataSourceAbstract, scale: float, use_stddev=False) -&gt; pd.DataFrame:\n        \"\"\"Internal method to compute outliers for all numeric columns\n        Args:\n            data_source(DataSource): The DataSource that we're computing outliers on\n            scale (float): The scale to use for the IQR outlier calculation\n            use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False)\n        Returns:\n            pd.DataFrame: A DataFrame of all the outliers combined\n        \"\"\"\n\n        # Grab the column stats and descriptive stats for this DataSource\n        column_stats = data_source.column_stats()\n        descriptive_stats = data_source.descriptive_stats()\n\n        # If there are no numeric columns, return None\n        if not descriptive_stats:\n            log.warning(\"No numeric columns found in the current computation view of the DataSource\")\n            log.warning(\"If the data source was created from a DataFrame, ensure that the DataFrame was properly typed\")\n            log.warning(\"Recommendation: Properly type the DataFrame and recreate the SageWorks artifact\")\n            return None\n\n        # Get the column names and types from the DataSource\n        column_details = data_source.view(\"computation\").column_details()\n\n        # For every column in the data_source that is numeric get the outliers\n        # This loop computes the columns, lower bounds, and upper bounds for the SQL query\n        log.info(\"Computing Outliers for numeric columns...\")\n        numeric = [\"tinyint\", \"smallint\", \"int\", \"bigint\", \"float\", \"double\", \"decimal\"]\n        columns = []\n        lower_bounds = []\n        upper_bounds = []\n        for column, data_type in column_details.items():\n            if data_type in numeric:\n                # Skip columns that just have one value (or are all nans)\n                if column_stats[column][\"unique\"] &lt;= 1:\n                    log.info(f\"Skipping unary column {column} with value {descriptive_stats[column]['min']}\")\n                    continue\n\n                # Skip columns that are 'binary' columns\n                if column_stats[column][\"unique\"] == 2:\n                    log.info(f\"Skipping binary column {column}\")\n                    continue\n\n                # Do they want to use the stddev instead of IQR?\n                if use_stddev:\n                    mean = descriptive_stats[column][\"mean\"]\n                    stddev = descriptive_stats[column][\"stddev\"]\n                    lower_bound = mean - (stddev * scale)\n                    upper_bound = mean + (stddev * scale)\n\n                # Compute the IQR for this column\n                else:\n                    iqr = descriptive_stats[column][\"q3\"] - descriptive_stats[column][\"q1\"]\n                    lower_bound = descriptive_stats[column][\"q1\"] - (iqr * scale)\n                    upper_bound = descriptive_stats[column][\"q3\"] + (iqr * scale)\n\n                # Add the column, lower bound, and upper bound to the lists\n                columns.append(column)\n                lower_bounds.append(lower_bound)\n                upper_bounds.append(upper_bound)\n\n        # Compute the SQL query\n        query = self._multi_column_outlier_query(data_source, columns, lower_bounds, upper_bounds)\n        outlier_df = data_source.query(query)\n\n        # Label the outlier groups\n        outlier_df = self._label_outlier_groups(outlier_df, columns, lower_bounds, upper_bounds)\n        return outlier_df\n\n    @staticmethod\n    def _multi_column_outlier_query(\n        data_source: DataSourceAbstract, columns: list, lower_bounds: list, upper_bounds: list\n    ) -&gt; str:\n        \"\"\"Internal method to compute outliers for multiple columns\n        Args:\n            data_source(DataSource): The DataSource that we're computing outliers on\n            columns(list): The columns to compute outliers on\n            lower_bounds(list): The lower bounds for outliers\n            upper_bounds(list): The upper bounds for outliers\n        Returns:\n            str: A SQL query to compute outliers for multiple columns\n        \"\"\"\n        # Grab the DataSource computation table name\n        table = data_source.view(\"computation\").table\n\n        # Get the column names and types from the DataSource\n        column_details = data_source.view(\"computation\").column_details()\n        sql_columns = \", \".join([f'\"{col}\"' for col in column_details.keys()])\n\n        query = f'SELECT {sql_columns} FROM \"{table}\" WHERE '\n        for col, lb, ub in zip(columns, lower_bounds, upper_bounds):\n            query += f\"({col} &lt; {lb} OR {col} &gt; {ub}) OR \"\n        query = query[:-4]\n\n        # Add a limit just in case\n        query += \" LIMIT 5000\"\n        return query\n\n    @staticmethod\n    def _label_outlier_groups(\n        outlier_df: pd.DataFrame, columns: list, lower_bounds: list, upper_bounds: list\n    ) -&gt; pd.DataFrame:\n        \"\"\"Internal method to label outliers by group.\n        Args:\n            outlier_df(pd.DataFrame): The DataFrame of outliers\n            columns(list): The columns for which to compute outliers\n            lower_bounds(list): The lower bounds for each column\n            upper_bounds(list): The upper bounds for each column\n        Returns:\n            pd.DataFrame: A DataFrame with an added 'outlier_group' column, indicating the type of outlier.\n        \"\"\"\n\n        column_outlier_dfs = []\n        for col, lb, ub in zip(columns, lower_bounds, upper_bounds):\n            mask_low = outlier_df[col] &lt; lb\n            mask_high = outlier_df[col] &gt; ub\n\n            low_df = outlier_df[mask_low].copy()\n            low_df[\"outlier_group\"] = f\"{col}_low\"\n\n            high_df = outlier_df[mask_high].copy()\n            high_df[\"outlier_group\"] = f\"{col}_high\"\n\n            column_outlier_dfs.extend([low_df, high_df])\n\n        # If there are no outliers, return the original DataFrame with an empty 'outlier_group' column\n        if not column_outlier_dfs:\n            log.critical(\"No outliers found in the data source.. probably something is wrong\")\n            return outlier_df.assign(outlier_group=\"\")\n\n        # Concatenate the DataFrames and return\n        return pd.concat(column_outlier_dfs, ignore_index=True)\n\n    @staticmethod\n    def get_top_n_outliers(outlier_df: pd.DataFrame, n: int = 10) -&gt; pd.DataFrame:\n        \"\"\"Function to retrieve the top N highest and lowest outliers for each outlier group.\n\n        Args:\n            outlier_df (pd.DataFrame): The DataFrame of outliers with 'outlier_group' column\n            n (int): Number of top outliers to retrieve for each group, defaults to 10\n\n        Returns:\n            pd.DataFrame: A DataFrame containing the top N outliers for each outlier group\n        \"\"\"\n\n        def get_extreme_values(group: pd.DataFrame) -&gt; pd.DataFrame:\n            \"\"\"Helper function to get the top N extreme values from a group.\"\"\"\n            col, extreme_type = group.name.rsplit(\"_\", 1)\n            if extreme_type == \"low\":\n                return group.nsmallest(n, col)\n            else:\n                return group.nlargest(n, col)\n\n        # Group by 'outlier_group' and apply the helper function, explicitly selecting columns\n        top_outliers = outlier_df.groupby(\"outlier_group\", group_keys=False).apply(\n            get_extreme_values, include_groups=True\n        )\n        return top_outliers.reset_index(drop=True)\n</code></pre>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.outliers.Outliers.__init__","title":"<code>__init__()</code>","text":"<p>SQLOutliers Initialization</p> Source code in <code>src/sageworks/algorithms/sql/outliers.py</code> <pre><code>def __init__(self):\n    \"\"\"SQLOutliers Initialization\"\"\"\n    self.outlier_group = \"unknown\"\n</code></pre>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.outliers.Outliers.compute_outliers","title":"<code>compute_outliers(data_source, scale=1.5, use_stddev=False)</code>","text":"<p>Compute outliers for all the numeric columns in a DataSource Args:     data_source(DataSource): The DataSource that we're computing outliers on     scale (float): The scale to use for either the IQR or stddev outlier calculation (default: 1.5)     use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False) Returns:     pd.DataFrame: A DataFrame of outliers for this DataSource Notes:     Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)     The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/sageworks/algorithms/sql/outliers.py</code> <pre><code>def compute_outliers(\n    self, data_source: DataSourceAbstract, scale: float = 1.5, use_stddev: bool = False\n) -&gt; pd.DataFrame:\n    \"\"\"Compute outliers for all the numeric columns in a DataSource\n    Args:\n        data_source(DataSource): The DataSource that we're computing outliers on\n        scale (float): The scale to use for either the IQR or stddev outlier calculation (default: 1.5)\n        use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False)\n    Returns:\n        pd.DataFrame: A DataFrame of outliers for this DataSource\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n\n    # Note: If use_stddev is True, then the scale parameter needs to be adjusted\n    if use_stddev and scale == 1.5:  # If the default scale is used, adjust it\n        scale = 2.5\n\n    # Compute the numeric outliers\n    outlier_df = self._numeric_outliers(data_source, scale, use_stddev)\n\n    # If there are no outliers, return a DataFrame with the computation columns but no rows\n    if outlier_df is None:\n        columns = data_source.view(\"computation\").columns\n        return pd.DataFrame(columns=columns + [\"outlier_group\"])\n\n    # Get the top N outliers for each outlier group\n    outlier_df = self.get_top_n_outliers(outlier_df)\n\n    # Make sure the dataframe isn't too big, if it's too big sample it down\n    if len(outlier_df) &gt; 300:\n        log.important(f\"Outliers DataFrame is too large {len(outlier_df)}, sampling down to 300 rows\")\n        outlier_df = outlier_df.sample(300)\n\n    # Sort by outlier_group and reset the index\n    outlier_df = outlier_df.sort_values(\"outlier_group\").reset_index(drop=True)\n\n    # Shorten any long string values\n    outlier_df = shorten_values(outlier_df)\n    return outlier_df\n</code></pre>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.outliers.Outliers.get_top_n_outliers","title":"<code>get_top_n_outliers(outlier_df, n=10)</code>  <code>staticmethod</code>","text":"<p>Function to retrieve the top N highest and lowest outliers for each outlier group.</p> <p>Parameters:</p> Name Type Description Default <code>outlier_df</code> <code>DataFrame</code> <p>The DataFrame of outliers with 'outlier_group' column</p> required <code>n</code> <code>int</code> <p>Number of top outliers to retrieve for each group, defaults to 10</p> <code>10</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame containing the top N outliers for each outlier group</p> Source code in <code>src/sageworks/algorithms/sql/outliers.py</code> <pre><code>@staticmethod\ndef get_top_n_outliers(outlier_df: pd.DataFrame, n: int = 10) -&gt; pd.DataFrame:\n    \"\"\"Function to retrieve the top N highest and lowest outliers for each outlier group.\n\n    Args:\n        outlier_df (pd.DataFrame): The DataFrame of outliers with 'outlier_group' column\n        n (int): Number of top outliers to retrieve for each group, defaults to 10\n\n    Returns:\n        pd.DataFrame: A DataFrame containing the top N outliers for each outlier group\n    \"\"\"\n\n    def get_extreme_values(group: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Helper function to get the top N extreme values from a group.\"\"\"\n        col, extreme_type = group.name.rsplit(\"_\", 1)\n        if extreme_type == \"low\":\n            return group.nsmallest(n, col)\n        else:\n            return group.nlargest(n, col)\n\n    # Group by 'outlier_group' and apply the helper function, explicitly selecting columns\n    top_outliers = outlier_df.groupby(\"outlier_group\", group_keys=False).apply(\n        get_extreme_values, include_groups=True\n    )\n    return top_outliers.reset_index(drop=True)\n</code></pre>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.descriptive_stats.descriptive_stats","title":"<code>descriptive_stats(data_source)</code>","text":"<p>Compute Descriptive Stats for all the numeric columns in a DataSource Args:     data_source(DataSource): The DataSource that we're computing descriptive stats on Returns:     dict(dict): A dictionary of descriptive stats for each column in this format          {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4, 'mean': 2.5, 'stddev': 1.5},           'col2': ...}</p> Source code in <code>src/sageworks/algorithms/sql/descriptive_stats.py</code> <pre><code>def descriptive_stats(data_source: DataSourceAbstract) -&gt; dict[dict]:\n    \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n    Args:\n        data_source(DataSource): The DataSource that we're computing descriptive stats on\n    Returns:\n        dict(dict): A dictionary of descriptive stats for each column in this format\n             {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4, 'mean': 2.5, 'stddev': 1.5},\n              'col2': ...}\n    \"\"\"\n    # Grab the DataSource computation view table name\n    table = data_source.view(\"computation\").table\n\n    # Figure out which columns are numeric\n    num_type = [\"double\", \"float\", \"int\", \"bigint\", \"smallint\", \"tinyint\"]\n    details = data_source.view(\"computation\").column_details()\n    numeric = [column for column, data_type in details.items() if data_type in num_type]\n\n    # Sanity Check for numeric columns\n    if len(numeric) == 0:\n        log.warning(\"No numeric columns found in the current computation view of the DataSource\")\n        log.warning(\"If the data source was created from a DataFrame, ensure that the DataFrame was properly typed\")\n        log.warning(\"Recommendation: Properly type the DataFrame and recreate the SageWorks artifact\")\n        return {}\n\n    # Build the query\n    query = descriptive_stats_query(numeric, table)\n\n    # Run the query\n    log.debug(query)\n    result_df = data_source.query(query)\n\n    # Process the results\n    # Note: The result_df is a DataFrame with a single row and a column for each stat metric\n    stats_dict = result_df.to_dict(orient=\"index\")[0]\n\n    # Convert the dictionary to a nested dictionary\n    # Note: The keys are in the format col1__col2\n    nested_descriptive_stats = defaultdict(dict)\n    for key, value in stats_dict.items():\n        col1, col2 = key.split(\"___\")\n        nested_descriptive_stats[col1][col2] = value\n\n    # Return the nested dictionary\n    return dict(nested_descriptive_stats)\n</code></pre>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.descriptive_stats.descriptive_stats_query","title":"<code>descriptive_stats_query(columns, table_name)</code>","text":"<p>Build a query to compute the descriptive stats for all columns in a table Args:     columns(list(str)): The columns to compute descriptive stats on     table_name(str): The table to compute descriptive stats on Returns:     str: The SQL query to compute descriptive stats</p> Source code in <code>src/sageworks/algorithms/sql/descriptive_stats.py</code> <pre><code>def descriptive_stats_query(columns: list[str], table_name: str) -&gt; str:\n    \"\"\"Build a query to compute the descriptive stats for all columns in a table\n    Args:\n        columns(list(str)): The columns to compute descriptive stats on\n        table_name(str): The table to compute descriptive stats on\n    Returns:\n        str: The SQL query to compute descriptive stats\n    \"\"\"\n    query = f'SELECT &lt;&lt;column_descriptive_stats&gt;&gt; FROM \"{table_name}\"'\n    column_descriptive_stats = \"\"\n    for c in columns:\n        column_descriptive_stats += (\n            f'min(\"{c}\") AS \"{c}___min\", '\n            f'approx_percentile(\"{c}\", 0.25) AS \"{c}___q1\", '\n            f'approx_percentile(\"{c}\", 0.5) AS \"{c}___median\", '\n            f'approx_percentile(\"{c}\", 0.75) AS \"{c}___q3\", '\n            f'max(\"{c}\") AS \"{c}___max\", '\n            f'avg(\"{c}\") AS \"{c}___mean\", '\n            f'stddev(\"{c}\") AS \"{c}___stddev\", '\n        )\n    query = query.replace(\"&lt;&lt;column_descriptive_stats&gt;&gt;\", column_descriptive_stats[:-2])\n\n    # Return the query\n    return query\n</code></pre>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.correlations.correlation_query","title":"<code>correlation_query(columns, table_name)</code>","text":"<p>Build a query to compute the correlations between columns in a table</p> <p>Parameters:</p> Name Type Description Default <code>columns</code> <code>list(str</code> <p>The columns to compute correlations on</p> required <code>table_name</code> <code>str</code> <p>The table to compute correlations on</p> required <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>The SQL query to compute correlations</p> Pearson correlation coefficient ranges from -1 to 1: <p>+1 indicates a perfect positive linear relationship. -1 indicates a perfect negative linear relationship.  0 indicates no linear relationship.</p> Source code in <code>src/sageworks/algorithms/sql/correlations.py</code> <pre><code>def correlation_query(columns: list[str], table_name: str) -&gt; str:\n    \"\"\"Build a query to compute the correlations between columns in a table\n\n    Args:\n        columns (list(str)): The columns to compute correlations on\n        table_name (str): The table to compute correlations on\n\n    Returns:\n        str: The SQL query to compute correlations\n\n    Notes: Pearson correlation coefficient ranges from -1 to 1:\n           +1 indicates a perfect positive linear relationship.\n           -1 indicates a perfect negative linear relationship.\n            0 indicates no linear relationship.\n    \"\"\"\n    query = f'SELECT &lt;&lt;cross_correlations&gt;&gt; FROM \"{table_name}\"'\n    cross_correlations = \"\"\n    for c in columns:\n        for d in columns:\n            if c != d:\n                cross_correlations += f'corr(\"{c}\", \"{d}\") AS \"{c}__{d}\", '\n    query = query.replace(\"&lt;&lt;cross_correlations&gt;&gt;\", cross_correlations[:-2])\n\n    # Return the query\n    return query\n</code></pre>"},{"location":"data_algorithms/sql/overview/#sageworks.algorithms.sql.correlations.correlations","title":"<code>correlations(data_source)</code>","text":"<p>Compute Correlations for all the numeric columns in a DataSource Args:     data_source(DataSource): The DataSource that we're computing correlations on Returns:     dict(dict): A dictionary of correlations for each column in this format          {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},           'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}</p> Source code in <code>src/sageworks/algorithms/sql/correlations.py</code> <pre><code>def correlations(data_source: DataSourceAbstract) -&gt; dict[dict]:\n    \"\"\"Compute Correlations for all the numeric columns in a DataSource\n    Args:\n        data_source(DataSource): The DataSource that we're computing correlations on\n    Returns:\n        dict(dict): A dictionary of correlations for each column in this format\n             {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n              'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n    \"\"\"\n    data_source.log.info(\"Computing Correlations for numeric columns...\")\n\n    # Figure out which columns are numeric\n    num_type = [\"double\", \"float\", \"int\", \"bigint\", \"smallint\", \"tinyint\"]\n    details = data_source.view(\"computation\").column_details()\n\n    # Get the numeric columns\n    numeric = [column for column, data_type in details.items() if data_type in num_type]\n\n    # If we have at least two numeric columns, compute the correlations\n    if len(numeric) &lt; 2:\n        return {}\n\n    # Grab the DataSource computation table name\n    table = data_source.view(\"computation\").table\n\n    # Build the query\n    query = correlation_query(numeric, table)\n\n    # Run the query\n    log.debug(query)\n    result_df = data_source.query(query)\n\n    # Drop any columns that have NaNs\n    result_df = result_df.dropna(axis=1)\n\n    # Process the results\n    # Note: The result_df is a DataFrame with a single row and a column for each pairwise correlation\n    correlation_dict = result_df.to_dict(orient=\"index\")[0]\n\n    # Convert the dictionary to a nested dictionary\n    # Note: The keys are in the format col1__col2\n    nested_corr = defaultdict(dict)\n    for key, value in correlation_dict.items():\n        col1, col2 = key.split(\"__\")\n        nested_corr[col1][col2] = value\n\n    # Sort the nested dictionaries\n    sorted_dict = {}\n    for key, sub_dict in nested_corr.items():\n        sorted_dict[key] = {k: v for k, v in sorted(sub_dict.items(), key=lambda item: item[1], reverse=True)}\n    return sorted_dict\n</code></pre>"},{"location":"data_algorithms/sql/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"enterprise/","title":"SageWorks Enterprise","text":"<p>The SageWorks API and User Interfaces cover a broad set of AWS Machine Learning services and provide easy to use abstractions and visualizations of your AWS ML data. We offer a wide range of options to best fit your companies needs.</p> Accelerate ML Pipeline development with an Enterprise License! Free Enterprise: Lite Enterprise: Standard Enterprise: Pro Python API \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 SageWorks REPL \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 Dashboard \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 AWS Onboarding \u2796 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 Dashboard Plugins \u2796 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 Custom Pages \u2796 \u2796 \ud83d\udfe2 \ud83d\udfe2 Themes \u2796 \u2796 \ud83d\udfe2 \ud83d\udfe2 ML Pipelines \u2796 \u2796 \u2796 \ud83d\udfe2 Project Branding \u2796 \u2796 \u2796 \ud83d\udfe2 Prioritized Feature Requests \u2796 \u2796 \u2796 \ud83d\udfe2 Pricing \u2796 $1500* $3000* $4000* <p>*USD per month, includes AWS setup, support, and training: Everything needed to accelerate your AWS ML Development team. Interested in Data Science/Engineering consulting? We have top notch Consultants with a depth and breadth of AWS ML/DS/Engineering expertise.</p>"},{"location":"enterprise/#try-sageworks","title":"Try SageWorks","text":"<p>We encourage new users to try out the free version, first. We offer support in our Discord channel and our Documentation has instructions for how to get started with SageWorks. So try it out and when you're ready to accelerate your AWS ML Adventure with an Enterprise licence contact us at SageWorks Sales</p>"},{"location":"enterprise/#data-engineeringscience-consulting","title":"Data Engineering/Science Consulting","text":"<p>Alongside our SageWorks Enterprise offerings, we provide comprehensive consulting services and domain expertise through our Partnerships. We specialize in AWS Machine Learning Systems and our extended team of Data Scientists and Engineers, have Masters and Ph.D. degrees in Computer Science, Chemistry, and Pharmacology. We also have a parntership with Nomic Networks to support our Network Security Clients.</p> <p>Using AWS and SageWorks, our experts are equipped to deliver tailored solutions that are focused on your project needs and deliverables. For more information please touch base and we'll set up a free initial consultation SageWorks Consulting</p>"},{"location":"enterprise/#contact-us","title":"Contact Us","text":"<p>Contact us on our Discord channel, we're happy to answer any questions that you might have about SageWorks and accelerating your AWS ML Pipelines. You can also send us email at SageWorks Info or  SageWorks Sales</p>"},{"location":"enterprise/private_saas/","title":"Benefits of a Private SaaS Architecture","text":""},{"location":"enterprise/private_saas/#self-hosted-vs-private-saas-vs-public-saas","title":"Self Hosted vs Private SaaS vs Public SaaS?","text":"<p>At the top level your team/project is making a decision about how they are going to build, expand, support, and maintain a machine learning pipeline.</p> <p>Conceptual ML Pipeline</p> <pre><code>Data \u2b95 Features \u2b95 Models \u2b95 Deployment (end-user application)\n</code></pre> <p>Concrete/Real World Example</p> <pre><code>S3 \u2b95 Glue Job \u2b95 Data Catalog \u2b95 FeatureGroups \u2b95 Models \u2b95 Endpoints \u2b95 App\n</code></pre> <p>When building out a framework to support ML Pipelines there are three main options:</p> <ul> <li>Self Hosted</li> <li>Private SaaS</li> <li>Public SaaS</li> </ul> <p>The other choice, that we're not going to cover here, is whether you use AWS, Azure, GCP, or something else. SageWorks is architected and powered by a broad and rich set of AWS ML Pipeline services. We believe that AWS provides the best set of functionality and APIs for flexible, real world ML architectures.</p> <p></p>"},{"location":"enterprise/private_saas/#resources","title":"Resources","text":"<p>See our full presentation on the SageWorks Private SaaS Architecture</p>"},{"location":"enterprise/project_branding/","title":"Project Branding","text":"<p>The SageWorks Dashboard can be customized extensively. Using SageWorks Project Branding allows you to change page headers, titles, and logos to match your project. All user interfaces will reflect your project name and company logos. </p>"},{"location":"enterprise/project_branding/#contact-us","title":"Contact Us","text":"<p>Contact us on our Discord channel, we're happy to answer any questions that you might have about SageWorks and accelerating your AWS ML Pipelines. You can also send us email at SageWorks Info or  SageWorks Sales.</p>"},{"location":"enterprise/themes/","title":"SageWorks Themes","text":"<p>The SageWorks Dashboard can be customized extensively. Using SageWorks Themes allows you to customize the User Interfaces to suit your preferences, including completely customized color palettes and fonts. We offer a set of default 'dark' and 'light' themes, but we'll also customize the theme to match your company's color palette and logos.</p>"},{"location":"enterprise/themes/#contact-us","title":"Contact Us","text":"<p>Contact us on our Discord channel, we're happy to answer any questions that you might have about SageWorks and accelerating your AWS ML Pipelines. You can also send us email at SageWorks Info or  SageWorks Sales.</p>"},{"location":"getting_started/","title":"Getting Started","text":"<p>For the initial setup of SageWorks we'll be using the SageWorks REPL. When you start <code>sageworks</code> it will recognize that it needs to complete the initial configuration and will guide you through that process.</p> <p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"getting_started/#initial-setupconfig","title":"Initial Setup/Config","text":"<p>Notes: SageWorks uses your existing AWS account/profile/SSO. So if you don't already have an AWS Profile or SSO Setup you'll need to do that first AWS Setup</p> <p>Okay so after you've completed your AWS Setup you can now install SageWorks.</p> <p><pre><code>&gt; pip install sageworks\n&gt; sageworks &lt;-- This starts the REPL\n\nWelcome to SageWorks!\nLooks like this is your first time using SageWorks...\nLet's get you set up...\nAWS_PROFILE: my_aws_profile\nSAGEWORKS_BUCKET: my-company-sageworks\n[optional] REDIS_HOST(localhost): my-redis.cache.amazon (or leave blank)\n[optional] REDIS_PORT(6379):\n[optional] REDIS_PASSWORD():\n[optional] SAGEWORKS_API_KEY(open_source): my_api_key (or leave blank)\n</code></pre> That's It: You're now all set. This configuration only needs to be ONCE :)</p>"},{"location":"getting_started/#data-scientistsengineers","title":"Data Scientists/Engineers","text":"<ul> <li>SageWorks REPL: SageWorks REPL</li> <li>Using SageWorks for ML Pipelines: SageWorks API Classes</li> <li>SCP SageWorks Github: Github Repo</li> </ul>"},{"location":"getting_started/#aws-administrators","title":"AWS Administrators","text":"<p>For companies that are setting up SageWorks on an internal AWS Account: Company AWS Setup</p>"},{"location":"getting_started/#additional-resources","title":"Additional Resources","text":"<ul> <li>SageWorks Core Classes: Core Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"glue/","title":"AWS Glue Jobs","text":"<p>AWS Glue Simplified</p> <p>AWS Glue Jobs are a great way to automate ETL and data processing. SageWorks takes all the hassle out of creating and debugging Glue Jobs. Follow this guide and empower your Glue Jobs with SageWorks!</p> <p>SageWorks make creating, testing, and debugging of AWS Glue Jobs easy. The exact same SageWorks API Classes are used in your Glue Jobs. Also since SageWorks manages the roles for both API and Glue Jobs you'll be able to test new Glue Jobs locally and minimizes surprises when deploying your Glue Job.</p>"},{"location":"glue/#glue-job-setup","title":"Glue Job Setup","text":"<p>Setting up a AWS Glue Job that uses SageWorks is straight forward. SageWorks can be 'installed' on AWS Glue via the <code>--additional-python-modules</code> parameter and then you can use the Sageworks API just like normal. </p> <p></p> <p>Here are the settings and a screen shot to guide you. There are several ways to set up and run Glue Jobs, with either the SageWorks-ExecutionRole or using the SageWorksAPIPolicy. Please feel free to contact SageWorks support if you need any help with setting up Glue Jobs.</p> <ul> <li>IAM Role: SageWorks-ExecutionRole</li> <li>Type: Spark</li> <li>Glue Version: Glue 4.0</li> <li>Worker Type: G.1X</li> <li>Number of Workers: 2</li> <li>Job Parameters</li> <li>--additional-python-modules: sageworks&gt;=0.4.6</li> <li>--sageworks-bucket: &lt;your sageworks bucket&gt;</li> </ul> <p>Glue IAM Role Details</p> <p>If your Glue Jobs already use an existing IAM Role then you can add the <code>SageWorksAPIPolicy</code> to that Role to enable the Glue Job to perform SageWorks API Tasks.</p>"},{"location":"glue/#sageworks-glue-example","title":"SageWorks Glue Example","text":"<p>Anyone familiar with a typical Glue Job should be pleasantly surpised by how simple the example below is. Also SageWorks allows you to test Glue Jobs locally using the same code that you use for script and Notebooks (see Glue Testing)</p> <p>Glue Job Arguments</p> <p>AWS Glue Jobs take arguments in the form of Job Parameters (see screenshot above). There's a SageWorks utility function <code>get_resolved_options</code> that turns these Job Parameters into a nice dictionary for ease of use.</p> examples/glue_hello_world.py<pre><code>import sys\n\n# SageWorks Imports\nfrom sageworks.api.data_source import DataSource\nfrom sageworks.utils.config_manager import ConfigManager\nfrom sageworks.utils.glue_utils import get_resolved_options\n\n# Convert Glue Job Args to a Dictionary\nglue_args = get_resolved_options(sys.argv)\n\n# Set the SAGEWORKS_BUCKET for the ConfigManager\ncm = ConfigManager()\ncm.set_config(\"SAGEWORKS_BUCKET\", glue_args[\"sageworks-bucket\"])\n\n# Create a new Data Source from an S3 Path\nsource_path = \"s3://sageworks-public-data/common/abalone.csv\"\nmy_data = DataSource(source_path, name=\"abalone_glue_test\")\n</code></pre>"},{"location":"glue/#glue-example-2","title":"Glue Example 2","text":"<p>This example takes two 'Job Parameters'</p> <ul> <li>--sageworks-bucket : &lt;your sageworks bucket&gt;</li> <li>--input-s3-path : &lt;your S3 input path&gt;</li> </ul> <p>The example will convert all CSV files in an S3 bucket/prefix and load them up as DataSources in SageWorks.</p> examples/glue_load_s3_bucket.py<pre><code>import sys\n\n# SageWorks Imports\nfrom sageworks.api.data_source import DataSource\nfrom sageworks.utils.config_manager import ConfigManager\nfrom sageworks.utils.glue_utils import get_resolved_options, list_s3_files\n\n# Convert Glue Job Args to a Dictionary\nglue_args = get_resolved_options(sys.argv)\n\n# Set the SAGEWORKS_BUCKET for the ConfigManager\ncm = ConfigManager()\ncm.set_config(\"SAGEWORKS_BUCKET\", glue_args[\"sageworks-bucket\"])\n\n# List all the CSV files in the given S3 Path\ninput_s3_path = glue_args[\"input-s3-path\"]\nfor input_file in list_s3_files(input_s3_path):\n\n    # Note: If we don't specify a name, one will be 'auto-generated'\n    my_data = DataSource(input_file, name=None)\n</code></pre>"},{"location":"glue/#exception-log-forwarding","title":"Exception Log Forwarding","text":"<p>When a Glue Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use SageWorks log forwarding the exception/stack will be forwarded to CloudWatch.</p> <p><pre><code>from sageworks.utils.sageworks_logging import exception_log_forward\n\nwith exception_log_forward():\n   &lt;my glue code&gt;\n   ...\n   &lt;exception happens&gt;\n   &lt;more of my code&gt;\n</code></pre> The <code>exception_log_forward</code> sets up a context manager that will trap exceptions and forward the exception/stack to CloudWatch for diagnosis. </p>"},{"location":"glue/#glue-job-local-testing","title":"Glue Job Local Testing","text":"<p>Glue Power without the Pain. SageWorks manages the AWS Execution Role, so local API and Glue Jobs will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Glue Jobs a breeze.</p> <pre><code>export SAGEWORKS_CONFIG=&lt;your config&gt;  # Only if not already set up\npython my_glue_job.py --sageworks-bucket &lt;your bucket&gt;\n</code></pre>"},{"location":"glue/#additional-resources","title":"Additional Resources","text":"<ul> <li>SageWorks Glue Jobs: SageWorks Glue</li> <li>Setting up SageWorks on your AWS Account: AWS Setup</li> <li>Using SageWorks for ML Pipelines: SageWorks API Classes</li> </ul> <ul> <li>SageWorks Core Classes: Core Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"lambda_layer/","title":"AWS Lambda Layer","text":"<p>SageWorks Lambda Layers</p> <p>AWS Lambda Jobs are a great way to spin up data processing jobs. Follow this guide and empower AWS Lambda with SageWorks!</p> <p>SageWorks makes creating, testing, and debugging of AWS Lambda Functions easy. The exact same SageWorks API Classes are used in your AWS Lambda Functions. Also since SageWorks manages the access policies you'll be able to test new Lambda Jobs locally and minimizes surprises when deploying.</p> <p>Work In Progress</p> <p>The SageWorks Lambda Layers are a great way to use SageWorks but they are still in 'beta' mode so please let us know if you have any issues.</p>"},{"location":"lambda_layer/#lambda-job-setup","title":"Lambda Job Setup","text":"<p>Setting up a AWS Lambda Job that uses SageWorks is straight forward. SageWorks can be 'installed' using a Lambda Layer and then you can use the Sageworks API just like normal.</p> <p>Here are the ARNs for the current SageWorks Lambda Layers, please note they are specified with region and Python version in the name, so if your lambda is us-east-1, python 3.12, pick this ARN with those values in it.</p>"},{"location":"lambda_layer/#python-312-if-you-need-another-versionregion-let-us-know","title":"Python 3.12 (if you need another version/region let us know)","text":"<p>us-east-1</p> <ul> <li>arn:aws:lambda:us-east-1:507740646243:layer:sageworks_lambda_layer-us-east-1-python312:7</li> </ul> <p>us-west-2</p> <ul> <li>arn:aws:lambda:us-west-2:507740646243:layer:sageworks_lambda_layer-us-west-2-python312:8</li> </ul> <p>Note: If you're using lambdas on a different region or with a different Python version, just let us know and we'll publish some additional layers.</p> <p></p> <p>At the bottom of the Lambda page there's an 'Add Layer' button. You can click that button and specify the layer using the ARN above. Also in the 'General Configuration' set these parameters:</p> <ul> <li>Timeout: 5 Minutes</li> <li>Memory: 4096</li> <li>Ephemeral storage: 2048</li> </ul> <p>Set the SAGEWORKS_BUCKET ENV SageWorks will need to know what bucket to work out of, so go into the Configuration...Environment Variables... and add one for the SageWorks bucket that your are using for AWS Account (dev, prod, etc). </p> <p>Lambda Role Details</p> <p>If your Lambda Function already use an existing IAM Role then you can add the SageWorks policies to that Role to enable the Lambda Job to perform SageWorks API Tasks. See SageWorks Access Controls</p>"},{"location":"lambda_layer/#sageworks-lambda-example","title":"SageWorks Lambda Example","text":"<p>Here's a simple example of using SageWorks in your Lambda Function. </p> <p>SageWorks Layer is Compressed</p> <p>The SageWorks Lambda Layer is compressed (to fit all the awesome). This means that the <code>load_lambda_layer()</code> method must be called before using any other SageWorks imports, see the example below. If you do not do this you'll probably get a <code>No module named 'numpy'</code> error or something like that.</p> examples/lambda_hello_world.py<pre><code>import json\nfrom pprint import pprint\nfrom sageworks.utils.lambda_utils import load_lambda_layer\n\n# Load/Decompress the SageWorks Lambda Layer\nload_lambda_layer()\n\n# After 'load_lambda_layer()' we can use other SageWorks imports\nfrom sageworks.api import Meta\nfrom sageworks.api import Model \n\ndef lambda_handler(event, context):\n\n    # Create our Meta Class and get a list of our Models\n    meta = Meta()\n    models = meta.models()\n\n    print(f\"Number of Models: {len(models)}\")\n    print(models)\n\n    # Onboard a model\n    model = Model(\"abalone-regression\")\n    pprint(model.details())\n\n    # Return success\n    return {\n        'statusCode': 200,\n        'body': { \"incoming_event\": event}\n    }\n</code></pre>"},{"location":"lambda_layer/#exception-log-forwarding","title":"Exception Log Forwarding","text":"<p>When a Lambda Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use SageWorks log forwarding the exception/stack will be forwarded to CloudWatch.</p> <p><pre><code>from sageworks.utils.sageworks_logging import exception_log_forward\n\nwith exception_log_forward():\n   &lt;my lambda code&gt;\n   ...\n   &lt;exception happens&gt;\n   &lt;more of my code&gt;\n</code></pre> The <code>exception_log_forward</code> sets up a context manager that will trap exceptions and forward the exception/stack to CloudWatch for diagnosis. </p>"},{"location":"lambda_layer/#lambda-function-local-testing","title":"Lambda Function Local Testing","text":"<p>Lambda Power without the Pain. SageWorks manages the AWS Execution Role/Policies, so local API and Lambda Functions will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Lambda Functions a breeze.</p> <pre><code>python my_lambda_function.py --sageworks-bucket &lt;your bucket&gt;\n</code></pre>"},{"location":"lambda_layer/#additional-resources","title":"Additional Resources","text":"<ul> <li>SageWorks Access Management: SageWorks Access Management</li> <li>Setting up SageWorks on your AWS Account: AWS Setup</li> </ul> <ul> <li> <p>Using SageWorks for ML Pipelines: SageWorks API Classes</p> </li> <li> <p>Consulting Available: SuperCowPowers LLC</p> </li> </ul>"},{"location":"misc/faq/","title":"SageWorks: FAQ","text":"<p>Artifact and Column Naming?</p> <p>You might have noticed that SageWorks has some unintuitive constraints when naming Artifacts and restrictions on column names. All of these restrictions come from AWS. SageWorks uses Glue, Athena, Feature Store, Models and Endpoints, each of these services have their own constraints, SageWorks simply 'reflects' those contraints.</p>"},{"location":"misc/faq/#naming-underscores-dashes-and-lower-case","title":"Naming: Underscores, Dashes, and Lower Case","text":"<p>Data Sources and Feature Sets must adhere to AWS restrictions on table names and columns names (here is a snippet from the AWS documentation)</p> <p>Database, table, and column names</p> <p>When you create schema in AWS Glue to query in Athena, consider the following:</p> <p>A database name cannot be longer than 255 characters. A table name cannot be longer than 255 characters. A column name cannot be longer than 255 characters.</p> <p>The only acceptable characters for database names, table names, and column names are lowercase letters, numbers, and the underscore character.</p> <p>For more info see: Glue Best Practices</p>"},{"location":"misc/faq/#datasourcefeatureset-use-_-and-modelendpoint-use-","title":"DataSource/FeatureSet use '_'  and Model/Endpoint use '-'","text":"<p>You may notice that DataSource and FeatureSet uuid/name examples have underscores but the model and endpoints have dashes. Yes, it\u2019s super annoying to have one convention for DataSources and FeatureSets and another for Models and Endpoints but this is an AWS restriction and not something that SageWorks can control.</p> <p>DataSources and FeatureSet: Underscores. You cannot use a dash because both classes use Athena for Storage and Athena tables names cannot have a dash.</p> <p>Models and Endpoints: Dashes. You cannot use an underscores because AWS imposes a restriction on the naming.</p>"},{"location":"misc/faq/#additional-information-on-the-lower-case-issue","title":"Additional information on the lower case issue","text":"<p>We\u2019ve tried to create a glue table with Mixed Case column names and haven\u2019t had any luck. We\u2019ve bypassed wrangler and used the boto3 low level calls directly. In all cases when it shows up in the Glue Table the columns have always been converted to lower case. We've also tried uses the Athena DDL directly, that also doesn't work. Here's the relevant AWS documentation and the two scripts that reproduce the issue.</p> <p>AWS Docs</p> <ul> <li>Athena Naming Restrictions</li> <li>Glue Best Practices</li> </ul> <p>Scripts to Reproduce</p> <ul> <li>scripts/athena_ddl_mixed_case.py</li> <li>scripts/glue_mixed_case.py</li> </ul>"},{"location":"misc/general_info/","title":"General info","text":""},{"location":"misc/general_info/#general-info","title":"General Info","text":""},{"location":"misc/general_info/#sageworks-the-scientists-workbench-powered-by-aws-for-scalability-flexibility-and-security","title":"SageWorks: The scientist's workbench powered by AWS\u00ae for scalability, flexibility, and security.","text":"<p>SageWorks is a medium granularity framework that manages and aggregates AWS\u00ae Services into classes and concepts. When you use SageWorks you think about DataSources, FeatureSets, Models, and Endpoints. Underneath the hood those classes handle all the details around updating and managing a complex set of AWS Services. All the power and none of the pain so that your team can Do Science Faster!</p>"},{"location":"misc/general_info/#sageworks-documentation","title":"SageWorks Documentation","text":"<p>See our Python API and AWS documentation here: SageWorks Documentation</p>"},{"location":"misc/general_info/#full-sageworks-overview","title":"Full SageWorks OverView","text":"<p>SageWorks Architected FrameWork</p>"},{"location":"misc/general_info/#why-sageworks","title":"Why SageWorks?","text":"<ul> <li>The AWS SageMaker\u00ae ecosystem is awesome but has a large number of services with significant complexity</li> <li>SageWorks provides rapid prototyping through easy to use classes and transforms</li> <li>SageWorks provides visibility and transparency into AWS SageMaker\u00ae Pipelines<ul> <li>What S3 data sources are getting pulled?</li> <li>What Features Store/Group is the Model Using?</li> <li>What's the Provenance of a Model in Model Registry?</li> <li>What SageMaker Endpoints are associated with this model?</li> </ul> </li> </ul>"},{"location":"misc/general_info/#single-pane-of-glass","title":"Single Pane of Glass","text":"<p>Visibility into the AWS Services that underpin the SageWorks Classes. We can see that SageWorks automatically tags and tracks the inputs of all artifacts providing 'data provenance' for all steps in the AWS modeling pipeline.</p> <p>Image TBD</p> <p> Clearly illustrated: SageWorks provides intuitive and transparent visibility into the full pipeline of your AWS Sagemaker Deployments.</p>"},{"location":"misc/general_info/#getting-started","title":"Getting Started","text":"<ul> <li>SageWorks Overview Slides that cover and illustrate the SageWorks Modeling Pipeline.</li> <li>SageWorks Docs/Wiki Our general documentation for getting started with SageWorks.</li> <li>SageWorks AWS Onboarding Deploy the SageWorks Stack to your AWS Account. </li> <li>Notebook: Start to Finish AWS ML Pipeline Building an AWS\u00ae ML Pipeline from start to finish.</li> <li>Video: Coding with SageWorks Informal coding + chatting while building a full ML pipeline.</li> <li>Join our Discord for questions and advice on using SageWorks within your organization.</li> </ul>"},{"location":"misc/general_info/#sageworks-zen","title":"SageWorks Zen","text":"<ul> <li>The AWS SageMaker\u00ae set of services is vast and complex.</li> <li>SageWorks Classes encapsulate, organize, and manage sets of AWS\u00ae Services.</li> <li>Heavy transforms typically use AWS Athena or Apache Spark (AWS Glue/EMR Serverless).</li> <li>Light transforms will typically use Pandas.</li> <li>Heavy and Light transforms both update AWS Artifacts (collections of AWS Services).</li> <li>Quick prototypes are typically built with the light path and then flipped to the heavy path as the system matures and usage grows.</li> </ul>"},{"location":"misc/general_info/#classes-and-concepts","title":"Classes and Concepts","text":"<p>The SageWorks Classes are organized to work in concert with AWS Services. For more details on the current classes and class hierarchies see SageWorks Classes and Concepts.</p>"},{"location":"misc/general_info/#contributions","title":"Contributions","text":"<p>If you'd like to contribute to the SageWorks project, you're more than welcome. All contributions will fall under the existing project license. If you are interested in contributing or have questions please feel free to contact us at sageworks@supercowpowers.com.</p>"},{"location":"misc/general_info/#sageworks-alpha-testers-wanted","title":"SageWorks Alpha Testers Wanted","text":"<p>Our experienced team can provide development and consulting services to help you effectively use Amazon\u2019s Machine Learning services within your organization.</p> <p>The popularity of cloud based Machine Learning services is booming. The problem many companies face is how that capability gets effectively used and harnessed to drive real business decisions and provide concrete value for their organization.</p> <p>Using SageWorks will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Alpha Tester, contact us at sageworks@supercowpowers.com.</p> <p>\u00ae Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates.</p>"},{"location":"misc/sageworks_classes_concepts/","title":"SageWorks Classes and Concepts","text":"<p>A flexible, rapid, and customizable AWS\u00ae ML Sandbox. Here's some of the classes and concepts we use in the SageWorks system:</p> <p></p> <ul> <li>Artifacts</li> <li>DataLoader</li> <li>DataSource</li> <li>FeatureSet</li> <li>Model</li> <li> <p>Endpoint</p> </li> <li> <p>Transforms</p> </li> <li>DataSource to DataSource<ul> <li>Heavy <ul> <li>AWS Glue Jobs</li> <li>AWS EMR Serverless</li> </ul> </li> <li>Light<ul> <li>Local/Laptop</li> <li>Lambdas</li> <li>StepFunctions</li> </ul> </li> </ul> </li> <li>DataSource to FeatureSet<ul> <li>Heavy/Light (see above breakout)</li> </ul> </li> <li>FeatureSet to FeatureSet<ul> <li>Heavy/Light (see above breakout)</li> </ul> </li> <li>FeatureSet to Model</li> <li>Model to Endpoint</li> </ul>"},{"location":"misc/scp_consulting/","title":"Scp consulting","text":""},{"location":"misc/scp_consulting/#consulting","title":"Consulting","text":""},{"location":"misc/scp_consulting/#sageworks-scp-consulting-awesome","title":"SageWorks + SCP Consulting = Awesome","text":"<p>Our experienced team can provide development and consulting services to help you effectively use Amazon\u2019s Machine Learning services within your organization.</p> <p>The popularity of cloud based Machine Learning services is booming. The problem many companies face is how that capability gets effectively used and harnessed to drive real business decisions and provide concrete value for their organization.</p> <p>Using SageWorks will minimizize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a SageWorks Alpha Tester, contact us at sageworks@supercowpowers.com.</p>"},{"location":"misc/scp_consulting/#typical-engagements","title":"Typical Engagements","text":"<p>SageWorks clients typically want a tailored web_interface that helps to drive business decisions and provides value for their organization.</p> <ul> <li>SageWorks components provide a set of classes and transforms the will dramatically reduce time and increase productivity when building AWS ML Systems.</li> <li>SageWorks enables rapid prototyping via it's light paths and provides AWS production workflows on large scale data through it's heavy paths.</li> <li> <p>Rapid Prototyping is typically done via these steps.</p> </li> <li> <p>Quick Construction of Web Interface (tailored)</p> </li> <li>Custom Components (tailored)</li> <li>Demo/Review the Application with the Client</li> <li>Get Feedback/Changes/Improvements</li> <li> <p>Goto Step 1</p> </li> <li> <p>When the client is happy/excited about the ProtoType we then bolt down the system, test the heavy paths, review AWS access, security and ensure 'least privileged' roles and policies.</p> </li> </ul> <p>Contact us for a free initial consultation on how we can accelerate the use of AWS ML at your company sageworks@supercowpowers.com.</p>"},{"location":"plugins/","title":"OverView","text":"<p>SageWorks Plugins</p> <p>The SageWorks toolkit provides a flexible plugin architecture to expand, enhance, or even replace the Dashboard. Make custom UI components, views, and entire pages with the plugin classes described here.</p> <p>The SageWorks Plugin system allows clients to customize how their AWS Machine Learning Pipeline is displayed, analyzed, and visualized. Our easy to use Python API enables developers to make new Dash/Plotly components, data views, and entirely new web pages focused on business use cases.</p>"},{"location":"plugins/#concept-docs","title":"Concept Docs","text":"<p>Many classes in SageWorks need additional high-level material that covers class design and illustrates class usage. Here's the Concept Docs for Plugins:</p> <ul> <li>Plugin Concepts: Read First!</li> <li>How to Write a Plugin </li> <li>Plugin Pages</li> <li>Plugins Advanced</li> </ul>"},{"location":"plugins/#make-a-plugin","title":"Make a plugin","text":"<p>Each plugin class inherits from the SageWorks PluginInterface class and needs to set two attributes and implement two methods. These requirements are set so that each Plugin will conform to the Sageworks infrastructure; if the required attributes and methods aren\u2019t included in the class definition, errors will be raised during tests and at runtime.</p> <p>Note: For full code see Model Plugin Example</p> <pre><code>class ModelPlugin(PluginInterface):\n    \"\"\"MyModelPlugin Component\"\"\"\n\n    \"\"\"Initialize this Plugin Component \"\"\"\n    auto_load_page = PluginPage.MODEL\n    plugin_input_type = PluginInputType.MODEL\n\n    def create_component(self, component_id: str) -&gt; dcc.Graph:\n        \"\"\"Create the container for this component\n        Args:\n            component_id (str): The ID of the web component\n        Returns:\n            dcc.Graph: The EndpointTurbo Component\n        \"\"\"\n        self.component_id = component_id\n        self.container = dcc.Graph(id=component_id, ...)\n\n        # Fill in plugin properties\n        self.properties = [(self.component_id, \"figure\")]\n\n        # Return the container\n        return self.container\n\n    def update_properties(self, model: Model, **kwargs) -&gt; list:\n        \"\"\"Update the properties for the plugin.\n\n        Args:\n            model (Model): An instantiated Model object\n            **kwargs: Additional keyword arguments\n\n        Returns:\n            list: A list of the updated property values\n        \"\"\"\n\n        # Create a pie chart with the endpoint name as the title\n        pie_figure = go.Figure(data=..., ...)\n\n        # Return the updated property values for the plugin\n        return [pie_figure]\n</code></pre>"},{"location":"plugins/#required-attributes","title":"Required Attributes","text":"<p>The class variable plugin_page determines what type of plugin the MyPlugin class is. This variable is inspected during plugin loading at runtime in order to load the plugin to the correct artifact page in the Sageworks dashboard. The PluginPage class can be DATA_SOURCE, FEATURE_SET, MODEL, or ENDPOINT.</p>"},{"location":"plugins/#s3-bucket-plugins-work-in-progress","title":"S3 Bucket Plugins (Work in Progress)","text":"<p>Note: This functionality is coming soon</p> <p>Offers the most flexibility and fast prototyping. Simple set your config/env for  blah to an S3 Path and SageWorks will load the plugins from S3 directly.</p> <p>Helpful Tip</p> <p>You can copy files from your local system up to S3 with this handy AWS CLI call</p> <pre><code> aws s3 cp . s3://my-sageworks/sageworks_plugins \\\n --recursive --exclude \"*\" --include \"*.py\"\n</code></pre>"},{"location":"plugins/#additional-resources","title":"Additional Resources","text":"<p>Need help with plugins? Want to develop a customized application tailored to your business needs?</p> <ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"presentations/","title":"SageWorks Presentations","text":"<p>The SageWorks framework makes AWS\u00ae both easier to use and more powerful. SageWorks handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, SageWorks makes creating AWS ML pipelines a snap.</p> <p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"presentations/#sageworks-presentations_1","title":"SageWorks Presentations","text":"<ul> <li>SageWorks Overview</li> <li>Private SaaS Architecture</li> <li>Python API</li> <li>Plugins Overview</li> <li>Plugins: Getting Started</li> <li>Plugins: Pages</li> <li>Plugins: Advanced</li> <li>Exploratory Data Analysis</li> <li>Architected ML Framework</li> <li>AWS Access Management</li> <li>Sageworks Config</li> <li>SageWorks REPL</li> </ul>"},{"location":"presentations/#sageworks-python-api-docs","title":"SageWorks Python API Docs","text":"<p>The SageWorks API documentation SageWorks API covers our in-depth Python API and contains code examples. The code examples are provided in the Github repo <code>examples/</code> directory. For a full code listing of any example please visit our SageWorks Examples</p>"},{"location":"presentations/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p></p> <p>\u00ae Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates</p>"},{"location":"release_notes/0_8_50/","title":"Release 0.8.50","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.46</code> to <code>0.8.50</code></p>"},{"location":"release_notes/0_8_50/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_50/#featureset-id_column-lock-in","title":"FeatureSet: id_column lock in","text":"<p>We're going to lock in id_columns when FeatureSets are created, AWS FeatureGroup requires an id column, so this is the best place to do it, see API Changes below.</p>"},{"location":"release_notes/0_8_50/#featureset-robust-handling-of-training-column","title":"FeatureSet: Robust handling of training column","text":"<p>In the past we haven't supported giving a training column as input data. FeatureSets are read-only, so locking in the training rows is 'suboptimal'. In general you might want to use the FeatureSet for several models with different training/hold_out sets. Now if a FeatureSet detects a training column it will give the follow message:</p> <pre><code>Training column detected: Since FeatureSets are read only, SageWorks \ncreates training views that can be dynamically changed. We'll use \nthis training column to create a training view.\n</code></pre>"},{"location":"release_notes/0_8_50/#endpoint-auto_inference","title":"Endpoint: auto_inference()","text":"<p>We're changing the internal logic for the <code>auto_inference()</code> method to include the id_column in it's output.</p>"},{"location":"release_notes/0_8_50/#api-changes","title":"API Changes","text":"<p>FeatureSet</p> <p>When creating a FeatureSet the <code>id_column</code> is now a required argument.</p> <pre><code>ds = DataSource(\"test_data\")\nfs = ds.to_features(\"test_features\", id_column=\"my_id\") &lt;-- Required\n</code></pre> <p><pre><code>to_features = PandasToFeatures(\"my_feature_set\")\nto_features.set_input(df_features, id_column=\"my_id\")  &lt;-- Required\nto_features.set_output_tags([\"blah\", \"whatever\"])\nto_features.transform()\n</code></pre> If you're data doesn't have a id column you can specify \"auto\"</p> <pre><code>to_features = PandasToFeatures(\"my_feature_set\")\nto_features.set_input(df_features, id_column=\"auto\")  &lt;-- Auto Id (index)\n</code></pre> <p>For more details see: FeatureSet Class</p> <p>The new Meta() API will be used inside of the Artifact classes (see Internal Changes...Artifacts... below)</p>"},{"location":"release_notes/0_8_50/#improvements","title":"Improvements","text":"<p>DFStore</p> <p>Robust handling of slashes, so now it will 'just work' with various upserts and gets:</p> <pre><code>```\n# These all give you /ml/shap_value dataframe\ndf_store.get(\"/ml/shap_values\")\ndf_store.get(\"ml/shap_values\")\ndf_store.get(\"//ml/shap_values\")\n```\n</code></pre>"},{"location":"release_notes/0_8_50/#internal-changes","title":"Internal Changes","text":"<p>There's a whole new directory structure that helps isolate Cloud Platform specific funcitonality.</p> <pre><code>- sageworks/src\n     - core/cloud_platform\n        - aws\n        - azure\n        - gcp\n</code></pre> <ul> <li>The <code>DFStore</code> now uses <code>AWSDFStore</code> as its concrete implementation class.</li> <li>Both <code>CachedMeta</code> and <code>AWSAccountClamp</code> have had a revamp of their singleton logic.</li> </ul>"},{"location":"release_notes/0_8_50/#internal-caching","title":"Internal Caching","text":"<p>So as part of our v0.9.0 Roadmap we're continuing to revamp caching. We're experimenting with CachedMeta Class inside the Artifact classes. Caching continues to be challenging for the framework, it's an absolute must for Web Inferface/UI performance and then it needs to get out of the way for batch jobs and the concurrent building of ML pipelines.</p>"},{"location":"release_notes/0_8_50/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.46 --&gt; v0.8.50 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/0_8_50/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_55/","title":"Release 0.8.55","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.50</code> to <code>0.8.55</code></p>"},{"location":"release_notes/0_8_55/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_55/#featureset-training-column-values","title":"FeatureSet: training column values","text":"<p>We're got a good suggestion from one of our beta customers to change the training column to use True/False values instead of 1/0. Having boolean values make semantic sense and make filtering easier and more intuitive.</p>"},{"location":"release_notes/0_8_55/#api-changes","title":"API Changes","text":"<p>FeatureSet Queries</p> <p>Since the training column now contains True/False, any code that you have where you're doing a query against the training view.</p> <pre><code>fs.query(f'SELECT * FROM \"{table}\" where training = 1')\n&lt;changed to&gt;\nfs.query(f'SELECT * FROM \"{table}\" where training = TRUE')\n\nfs.query(f'SELECT * FROM \"{table}\" where training = 0')\n&lt;changed to&gt;\nfs.query(f'SELECT * FROM \"{table}\" where training = FALSE')\n</code></pre> <p>Also dataframe filtering is easier now, so if you have a call to filter the dataframe that also needs to change.</p> <p><pre><code>df_train = all_df[all_df[\"training\"] == 1].copy()\n&lt;changed to&gt;\ndf_train = all_df[all_df[\"training\"]].copy()\n\ndf_val = all_df[all_df[\"training\"] == 0].copy()\n&lt;changed to&gt;\ndf_val = all_df[~all_df[\"training\"]].copy()\n</code></pre> For more details see: Training View</p> <p>Model Instantiation</p> <p>We got a request to reduce the time for Model() object instantiation. So we created a new <code>CachedModel()</code> class that is much faster to instantiate.</p> <p><pre><code>%time Model(\"abalone-regression\")\nCPU times: user 227 ms, sys: 19.5 ms, total: 246 ms\nWall time: 2.97 s\n\n%time CachedModel(\"abalone-regression\")\nCPU times: user 8.83 ms, sys: 2.64 ms, total: 11.5 ms\nWall time: 22.7 ms\n</code></pre> For more details see: CachedModel</p>"},{"location":"release_notes/0_8_55/#improvements","title":"Improvements","text":"<p>SageWorks REPL Onboarding</p> <p>At some point the onboarding with SageWorks REPL got broken and wasn't properly responding when the user didn't have a complete AWS/SageWorks setup.</p>"},{"location":"release_notes/0_8_55/#internal-changes","title":"Internal Changes","text":"<p>The decorator for the CachedMeta class did not work properly in Python 3.9 so had to be slightly refactored.</p>"},{"location":"release_notes/0_8_55/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.50 --&gt; v0.8.55 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/0_8_55/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_58/","title":"Release 0.8.58","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.55</code> to <code>0.8.58</code></p>"},{"location":"release_notes/0_8_58/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_58/#caching-refactoring","title":"Caching Refactoring","text":"<p>We've created a new set of Cached Classes:</p> <ul> <li>CachedDataSource</li> <li>CachedFeatureSet</li> <li>CachedModel</li> <li>CacheEndpoint</li> </ul> <p>As part of this there's now a <code>sageworks/cached</code> directory that housed these classes and the <code>CachedMeta</code> class.</p>"},{"location":"release_notes/0_8_58/#api-changes","title":"API Changes","text":"<p>Meta Imports Yes, this changed AGAIN :)</p> <pre><code>from sageworks.meta import Meta\n&lt;change to&gt;\nfrom sageworks.api import Meta\n</code></pre> <p>CachedModel Import <pre><code>from sageworks.api import CachedModel\n&lt;change to&gt;\nfrom sageworks.cached.cached_model import CachedModel\n</code></pre> For more details see: CachedModel</p>"},{"location":"release_notes/0_8_58/#improvements","title":"Improvements","text":"<p>Dashboard Responsiveness</p> <p>The whole point of these Cached Classes is to improve Dashboard/Web Interface responsiveness. The Dashboard uses both the CachedMeta and Cached(Artifact) classes to make both overview and drilldowns faster.</p> <p>Supporting a Client Use Case There was a use case where a set of plugin pages needed to iterate over all the models to gather and aggregate information. We've supported that use case with a new decorator that avoids overloading AWS/Throttling issues.</p> <p>Internal The Dashboard now refreshes all data every 90 seconds, so if you don't see you're new model on the dashboard... just wait longer. :)</p>"},{"location":"release_notes/0_8_58/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.55 --&gt; v0.8.58 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call a nonsense meeting?       .... Moo-larkey</p>"},{"location":"release_notes/0_8_58/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_60/","title":"Release 0.8.60","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.58</code> to <code>0.8.60</code></p>"},{"location":"release_notes/0_8_60/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_60/#custom-models","title":"Custom Models","text":"<p>We've now exposed additional functionality and API around adding your own custom models. The new custom model support is documented on the Features to Models page.  </p>"},{"location":"release_notes/0_8_60/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/0_8_60/#notes","title":"Notes","text":"<p>Custom models introduce models that don't have model metrics or inference runs, so you'll see a lot of log messages complaining about not finding metrics or inference results, please just ignore those, we'll put in additional logic to address those cases.</p>"},{"location":"release_notes/0_8_60/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.58 --&gt; v0.8.60 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call a nonsense meeting?       .... Moo-larkey</p>"},{"location":"release_notes/0_8_60/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_71/","title":"Release 0.8.71","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.60</code> to <code>0.8.71</code></p>"},{"location":"release_notes/0_8_71/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_71/#dash-thread-safety","title":"Dash Thread Safety","text":"<p>We learned that thread safety is good when using plugin classes. We had a model plugin class that was setting an attribute in one callback and then using that attribute in another callback, this mostly worked until it didn't. Anyway so the Inference Run dropdown box on the Models page now actually works correctly.</p>"},{"location":"release_notes/0_8_71/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/0_8_71/#internal-changes","title":"Internal Changes","text":"<p>When using PandasToFeatures it will overwrite FeatureSets if you give the same name. This behavior is expected. The issue was that it was super eager about doing that and would do it during class initiation, so we've moved that logic to when <code>transform()</code> is called.</p> <pre><code># Create a Feature Set from a DataFrame\ndf_to_features = PandasToFeatures(\"test_features\")\ndf_to_features.set_input(data_df, id_column=\"id\", one_hot_columns=[\"food\"])\ndf_to_features.set_output_tags([\"test\", \"small\"])\ndf_to_features.transform()      &lt;--- Overwrite happens here\n</code></pre>"},{"location":"release_notes/0_8_71/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.60 --&gt; v0.8.71 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call that feeling like you\u2019ve done this before?               Deja-moo</p>"},{"location":"release_notes/0_8_71/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_74/","title":"Release 0.8.74","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.71</code> to <code>0.8.74</code></p>"},{"location":"release_notes/0_8_74/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_74/#s3-based-plugins","title":"S3 based Plugins","text":"<p>We've added the ability to grab Dashboard plugins directly from an S3 bucket. Please see Dashboard S3 Plugin</p>"},{"location":"release_notes/0_8_74/#artifact-hashes","title":"Artifact Hashes","text":"<p>For content verification purposes we've added a <code>hash()</code> method to all of the SageWorks Artifact classes (DataSource, FeatureSet, Model, Endpoint, Graph, etc). Also for DataSources and FeatureSets there is a <code>table_hash()</code> method that will compute a total hash of all data in the Athena table.</p> <pre><code>ds = DataSource(\"abalone_data\")\n\nds.modified()\nOut[2]: datetime.datetime(2024, 11, 17, 19, 45, 58, tzinfo=tzlocal())\n\nds.hash()\nOut[3]: '67a9ebb495af573604794aa9c31eded8'\n\nds.table_hash()\nOut[4]: '622f5ddba9d4cad2cf642d1ea5555de9'\n\nfs = FeatureSet(\"test_features\")\n\nfs.hash()\nOut[5]: '1571eee207b72f14bd5065d6c4acdaaf'\n\n# Note: Model/Endpoint hashes will backtrack to model.tar.gz and can be used for validation\nmodel = Model(\"abalone-regression\")\nend = Endpoint(\"abalone-regression-end\")\n\nmodel.get_model_data_url()\nOut[6]: 's3://sagemaker-us-west-2-507740646243/abalone-regression-2024-11-18-03-09/output/model.tar.gz'\n\nmodel.hash()\nOut[7]: '00def9381366cdd062413d0b395ba70c'\n\n# Verify endpoint is using expected model\nend.hash()\nOut[7]: '00def9381366cdd062413d0b395ba70c'\n\n# Realtime endpoint created from the same model\nend = Endpoint(\"abalone-regression-end-rt\")\nend.hash()\nOut[8]: '00def9381366cdd062413d0b395ba70c'\n</code></pre> <p>Note: You will get a performance warning when running hash() or table_hash() as these operations compute actual content hashes on the underlying data.</p>"},{"location":"release_notes/0_8_74/#custom-model-scripts","title":"Custom Model Scripts","text":"<p>We're expanding our custom model script to include some additional chemical informatics capabilities. In addition to our molecular descriptors and Morgan fingerprints we also have RDKIT canonicalization and tautomerization.</p>"},{"location":"release_notes/0_8_74/#api-changes","title":"API Changes","text":"<p>FeatureSet</p> <p>When creating a FeatureSet the <code>id_column</code> is now NOT required. The old \"auto\" functionality is gone, if you don't specify an <code>id_column</code> and id column named <code>auto_id</code> will be automatically generated.</p> <pre><code>ds = DataSource(\"test_data\")\nfs = ds.to_features(\"test_features\", id_column=\"auto\")\n&lt;replaced by&gt;\nfs = ds.to_features(\"test_features\")\n\n# We do recommend that you specify an id :)\nfs = ds.to_features(\"test_features\", id_column=\"my_id\")\n</code></pre> <ul> <li> <p><code>get_database()</code> has a deprecation warning, it's replaced with just the <code>database</code> property.</p> <pre><code>ds.get_database()\n&lt;replaced by&gt;\nds.database\n</code></pre> </li> <li> <p>Added the <code>hash()</code> method to Artifacts (see above).</p> </li> <li>Added <code>table_hash()</code> method to DataSources and FeatuerSet (see above).</li> </ul>"},{"location":"release_notes/0_8_74/#internal-changes","title":"Internal Changes","text":"<p>There was a small refactor of the cache decorator. We fixed a case where if we blocked on getting a value we also spun up a background thread to get it. This chance will not affect existing code or APIs.</p>"},{"location":"release_notes/0_8_74/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.71 --&gt; v0.8.74 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call that feeling like you\u2019ve done this before?               Deja-moo</p>"},{"location":"release_notes/0_8_74/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_78/","title":"Release 0.8.78","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.74</code> to <code>0.8.78</code></p>"},{"location":"release_notes/0_8_78/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_78/#s3-based-plugins","title":"S3 based Plugins","text":"<p>We've added the ability to grab Dashboard plugins directly from an S3 bucket. Please see Dashboard S3 Plugin</p>"},{"location":"release_notes/0_8_78/#tbd-stuff","title":"TBD Stuff","text":""},{"location":"release_notes/0_8_78/#api-changes","title":"API Changes","text":""},{"location":"release_notes/0_8_78/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.74 --&gt; v0.8.78 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call that feeling like you\u2019ve done this before?               Deja-moo</p>"},{"location":"release_notes/0_8_78/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_7_8/","title":"Release 0.7.8","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on [Discord](https://discord.gg/WHAJuz8sw8</p> <p>Since we've recently introduced a View() class for DataSources and FeatureSets we needed to rename a few classes/modules.</p>"},{"location":"release_notes/archived/0_7_8/#featuresets","title":"FeatureSets","text":"<p>For setting holdout ids we've changed/combined to just one method <code>set_training_holdouts()</code>, so if you're using <code>create_training_view()</code> or <code>set_holdout_ids()</code> you can now just use the unified method <code>set_training_holdouts()</code>.</p> <p>There's also a change to getting the training view table method.</p> <pre><code>old: fs.get_training_view_table(create=False)\nnew: fs.get_training_view_table(), does not need the create=False\n</code></pre>"},{"location":"release_notes/archived/0_7_8/#models","title":"Models","text":"<pre><code>inference_predictions() --&gt; get_inference_predictions()\n</code></pre>"},{"location":"release_notes/archived/0_7_8/#webplugins","title":"Web/Plugins","text":"<p>We've changed the Web/UI View class to 'WebView'. So anywhere where you used to have view just replace with web_view</p> <p><pre><code>from sageworks.views.artifacts_view import ArtifactsView\n</code></pre> is now</p> <pre><code>from sageworks.web_interface.page_views.all_artifacts import AllArtifacts\n</code></pre>"},{"location":"release_notes/archived/0_7_8/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_11/","title":"Release 0.8.11","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover all the changes from <code>0.8.8</code> to <code>0.8.11</code></p>"},{"location":"release_notes/archived/0_8_11/#general","title":"General","text":"<p>The AWSAccountClamp had too many responsibilities so that class has been split up into two classes and a set of utilities:</p> <ul> <li>AWSAccountClamp</li> <li>AWSSession</li> <li>utils/execution_environment.py</li> </ul>"},{"location":"release_notes/archived/0_8_11/#api-changes","title":"API Changes","text":"<p>For all/most of these API changes they include both DataSources and FeatureSets. We're using a FeatureSet (fs) in the examples below but also applies to DataSoources.</p> <ul> <li> <p>Column Names/Table Names</p> <pre><code>fs.column_names() -&gt; fs.columns\nfs.get_table_name() -&gt; fs.table_name\n</code></pre> </li> <li> <p>Display/Training/Computation Views</p> <p>In general methods for FS/DS are now part of the View API, here's a change list:</p> <pre><code>fs.get_display_view() -&gt; fs.view(\"display\")\nfs.get_training_view() -&gt; fs.view(\"training\")\nfs.get_display_columns() -&gt; fs.view(\"display\").columns\nfs.get_computation_columns() -&gt; fs.view(\"computation\").columns\nfs.get_training_view_table() -&gt; fs.view(\"training\").table_name\nfs.get_training_data(self) -&gt; fs.view(\"training\").pull_dataframe()\n</code></pre> <p>Some FS/DS methods have also been removed</p> <p><code>num_display_columns() -&gt; gone    num_computation_columns() -&gt; gone</code></p> </li> <li> <p>Views: Methods that we're Keeping</p> <p>We're keeping the methods below since they handle some underlying mechanics and serve as nice convenience methods. </p> <pre><code>ds/fs.set_display_columns()\nds/fs.set_computation_columns()\n</code></pre> </li> <li> <p>AWSAccountClamp</p> <pre><code>AWSAccountClamp().boto_session() --&gt; AWSAccountClamp().boto3_session\n</code></pre> </li> <li> <p>All Classes</p> <p>If the class previously had a <code>boto_session</code> attribute that has been renamed to <code>boto3_session</code></p> </li> </ul>"},{"location":"release_notes/archived/0_8_11/#glue-job-fixes","title":"Glue Job Fixes","text":"<p>For <code>sageworks==0.8.8</code> you needed to be careful about when/where you set your config/ENV vars. With <code>&gt;=0.8.9</code> you can now use the typical setup like this:</p> <pre><code>```\nfrom sageworks.utils.config_manager import ConfigManager\n\n# Set the SageWorks Config\ncm = ConfigManager()\ncm.set_config(\"SAGEWORKS_BUCKET\", args_dict[\"sageworks-bucket\"])\ncm.set_config(\"REDIS_HOST\", args_dict[\"redis-host\"])\n```\n</code></pre>"},{"location":"release_notes/archived/0_8_11/#robust-modelnotreadyexception-handling","title":"Robust ModelNotReadyException Handling","text":"<p>AWS will 'deep freeze' Serverless Endpoints and if that endpoint hasn't been used for a while it can sometimes take a long time to come up and be ready for inference. SageWorks now properly manages this AWS error condition, it will report the issue, wait 60 seconds, and try again 5 times before raising the exception.</p> <pre><code>(endpoint_core.py:502) ERROR Endpoint model not ready\n(endpoint_core.py:503) ERROR Waiting and Retrying...\n...\nAfter a while, inference will run successfully :)\n</code></pre>"},{"location":"release_notes/archived/0_8_11/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_20/","title":"Release 0.8.20","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.11</code> to <code>0.8.20</code></p>"},{"location":"release_notes/archived/0_8_20/#general","title":"General","text":"<p>The <code>cloud_watch</code> AWS log aggregator, is now officially awesome. It provides a fairly sophisticated way of both doing broad scanning and deep dives on individual streams. Please see our Cloud Watch documentation.</p> <p>The View classes have finished their refactoring. The 'read' class <code>View()</code> can be constructed either directly or with the <code>ds/fs.view(\"display\")</code> methods. See Views for more details. There also a set of classes for constructing views, please see View Overview</p>"},{"location":"release_notes/archived/0_8_20/#api-changes","title":"API Changes","text":"<ul> <li> <p>Table Name attribute</p> <p>The <code>table_name</code> attribute/property has been replaced with just <code>table</code></p> <pre><code>ds.table_name -&gt; ds.table\nfs.table_name -&gt; fs.table\nview.table_name -&gt; view.table\n</code></pre> </li> <li> <p>Endpoint Confusion Matrix</p> <p>The <code>endpoint</code> class had a method called <code>confusion_matrix()</code> this has been renamed to the more descriptive <code>generate_confusion_matrix()</code>. Note: The model method, of the same name, has NOT changed.</p> <pre><code>end.confusion_matrix() -&gt; end.generate_confusion_matrix()\nmodel.confusion_matrix() == no change\n</code></pre> </li> </ul>"},{"location":"release_notes/archived/0_8_20/#bug-fixes","title":"Bug Fixes","text":"<p>Fixed: There was a corner case where if you had the following sequence:</p> <ul> <li>New FeatureSet without a training view</li> <li>Created a traiing view (like with <code>set_training_holdouts()</code> </li> <li>Immediately query/pull from the FS training view</li> </ul> <p>The corner case was a race-condition where the FeatureSet would not 'know' that a training view was already there and would create a default training view.</p>"},{"location":"release_notes/archived/0_8_20/#improvements","title":"Improvements","text":"<p>The log messages that you receive on a plugin validation failure should now be more distinguishable and more informative. They will look like this and in some cases even tell you the line to look at.</p> <pre><code>ERROR Plugin 'MyPlugin' failed validation:\nERROR    File: ../sageworks_plugins/components/my_plugin.py\nERROR    Class: MyPlugin\nERROR    Details: my_plugin.py (line 35): Incorrect return type for update_properties (expected list, got Figure)\n</code></pre>"},{"location":"release_notes/archived/0_8_20/#internal-api-changes","title":"Internal API Changes","text":"<p>In theory these API should not affect end user of the SageWorks API but are documented here for completeness.</p> <ul> <li>Artifact Name Validation</li> </ul> <p>The internal method used by Artifact subclasses has changed names from <code>ensure_valid_name</code> to <code>is_name_valid</code>, we've also introduced an optional argument to turn on/off lowercase enforcement, this will be used later when we support uppercase for models, endpoints, and graphs.</p>"},{"location":"release_notes/archived/0_8_20/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_22/","title":"Release 0.8.22","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.20</code> to <code>0.8.22</code></p>"},{"location":"release_notes/archived/0_8_22/#general","title":"General","text":"<p>Mostly bug fixes and minor API changes.</p>"},{"location":"release_notes/archived/0_8_22/#api-changes","title":"API Changes","text":"<ul> <li> <p>Removing <code>target_column</code> arg when creating FeatureSets</p> <p>When creating a FeatureSet via DataSource or Pandas Dataframe there was an optional argument for the <code>target_column</code> after some discussion we decided to remove this argument. In general <code>FeatureSets</code> are often used to create multiple models with different targets, so it doesn't make sense to specify a <code>target</code> at the FeatureSet level.</p> <p>Changed for both <code>DataSource.to_features()</code> and the <code>PandasToFeatures()</code> classes.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_22/#minor-bug-fixes","title":"Minor Bug Fixes","text":"<p>Fixed: The SHAP computation was occasionally complaining about the additivity check so we flipped that flag to False</p> <pre><code>shap_vals = explainer.shap_values(X_pred, check_additivity=False)\n</code></pre>"},{"location":"release_notes/archived/0_8_22/#improvements","title":"Improvements","text":"<p>The optional requirements for <code>[UI]</code> now include matplotlib since it will probably be useful in the future.</p>"},{"location":"release_notes/archived/0_8_22/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_23/","title":"Release 0.8.23","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.22</code> to <code>0.8.23</code></p>"},{"location":"release_notes/archived/0_8_23/#general","title":"General","text":"<p>Mostly bug fixes and minor API changes.</p>"},{"location":"release_notes/archived/0_8_23/#api-changes","title":"API Changes","text":"<ul> <li> <p>Removing <code>auto_one_hot</code> arg from <code>PandasToFeatures</code> and <code>DataSource.to_features()</code></p> <p>When creating a <code>PandasToFeatures</code> object or using <code>DataSource.to_features()</code> there was an optional argument <code>auto_one_hot</code>. This would try to automatically convert object/string columns to be one-hot encoded. In general this was only useful for 'toy' datasets but for more complex data we need to specify exactly which columns we want converted.</p> </li> <li> <p>Adding optional <code>one_hot_columns</code> arg to <code>PandasToFeatures.set_input()</code> and <code>DataSource.to_features()</code> </p> <p>When calling either of these FeatureSet creation methods you can now add an option arg <code>one_hot_columns</code> as a list of columns that you would like to be one-hot encoded.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_23/#minor-bug-fixes","title":"Minor Bug Fixes","text":"<p>Our pandas dependency was outdated and causing an issue with an <code>include_groups</code> arg when outlier groups were computed. We've changed the requirements:</p> <p><pre><code>pandas&gt;=2.1.2\nto\npandas&gt;=2.2.1\n</code></pre> We also have a ticket for the logic change so that we avoid the deprecation warning.</p>"},{"location":"release_notes/archived/0_8_23/#improvements","title":"Improvements","text":"<p>The time to <code>ingest</code> new rows into a FeatureSet can take a LONG time. Calling the FeatureGroup AWS API and waiting on the results is what takes all the time.</p> <p>There will hopefully be a series of optimizations around this process, the first one is simply increasing the number of workers/processes for the ingestion manager class.</p> <pre><code>feature_group.ingest(.., max_processes=8)\n(has been changed to)\nfeature_group.ingest(..., max_processes=16, num_workers=4)\n</code></pre>"},{"location":"release_notes/archived/0_8_23/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_27/","title":"Release 0.8.27","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.23</code> to <code>0.8.27</code></p>"},{"location":"release_notes/archived/0_8_27/#general","title":"General","text":"<ul> <li>Plugin Launcher: A flexible way to test plugins</li> <li>FeatureSpaceProximity (FSP): A Class for investigating feature space, neighbors, distances, etc.</li> <li>ProximityGraph: A class to construct NetworkX Graphs. Uses the FSP class to pull neighbors, contruct edges via 'distances', etc.</li> <li>Case</li> </ul>"},{"location":"release_notes/archived/0_8_27/#api-changes","title":"API Changes","text":"<ul> <li> <p>KNNSpider() --&gt; FeatureSpaceProximity()</p> <p>If you were previously using the <code>KNNSpider</code> that class has been replaced with <code>FeatureSpaceProximity</code>. The API is also a bit different please see the documentation on the FeatureSpaceProximity Class.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_27/#minor-improvements","title":"Minor Improvements","text":"<p>The model scripts used in deployed AWS Endpoints are now case-insensitive. In general this should make the use of the endpoints a bit more flexible for End-User Applications to hit the endpoints with less pre-processing of their column names.</p> <p>CloudWatch default buffers have been increased to 60 seconds as we appears to have been hitting some AWS limits with running 10 concurrent glue jobs.</p>"},{"location":"release_notes/archived/0_8_27/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_29/","title":"Release 0.8.29","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.27</code> to <code>0.8.29</code></p>"},{"location":"release_notes/archived/0_8_29/#general","title":"General","text":"<p>Locking AWS Model Training Image: AWS will randomly update the images associated with training and model registration. In particular the SKLearn Estimator has been updated into a non-working state for our use cases. So for both training and registration we're now explicitly specifying the image that we want to use.</p> <pre><code> self.estimator = SKLearn(\n     ...\n     framework_version=\"1.2-1\",\n     image_uri=image,  # New\n )\n</code></pre>"},{"location":"release_notes/archived/0_8_29/#api-changes","title":"API Changes","text":"<ul> <li> <p>delete() --&gt; class.delete(uuid)</p> <p>We've changed the API for deleting artifacts in AWS (DataSource, FeatureSet, etc). This is part of our efforts to minimize race-conditions when objects are deleted. </p> <pre><code>my_model = Model(\"xyz\")  # Creating object\nmy_model.delete()        # just to delete\n\n&lt;Now just one line&gt;\nModel.delete(\"xyz\")      # Delete\n</code></pre> </li> </ul>"},{"location":"release_notes/archived/0_8_29/#minor-improvements","title":"Minor Improvements","text":"<p>Bulk Delete: Added a Bulk Delete utility</p> <pre><code>from sageworks.utils.bulk_utils import bulk_delete\n\ndelete_list = [(\"DataSource\", \"abc\"), (\"FeatureSet\", \"abc_features\")]\nbulk_delete(delete_list)\n</code></pre>"},{"location":"release_notes/archived/0_8_29/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_33/","title":"Release 0.8.33","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.29</code> to <code>0.8.33</code></p>"},{"location":"release_notes/archived/0_8_33/#general","title":"General","text":"<p>Replaced WatchTower Code: Had lots of issues with WatchTower on Glue/Lambda, the use of forks/threads was overkill for our logging needs, so simply replaced the code with boto3 <code>put_log_events()</code> calls and some simple token handling and buffering.</p>"},{"location":"release_notes/archived/0_8_33/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_33/#improvementsfixes","title":"Improvements/Fixes","text":"<p>DataSource from DataFrame: When creating a DataSource from a Pandas Dataframe, the internal <code>transform()</code> was not deleting the existing DataSource (if it existed).</p> <p>ROCAUC on subset of classes: When running inference on input data that only had a subset of the classification labels (e.g. rows only had \"low\" and \"medium\" when model was trained on \"low\", \"medium\", \"high\"). The input to ROCAUC needed to be adjusted so that ROCAUC doesn't crash. When this case happens we're returning proper defaults based on scikit learn docs.</p>"},{"location":"release_notes/archived/0_8_33/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_35/","title":"Release 0.8.35","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.33</code> to <code>0.8.35</code></p>"},{"location":"release_notes/archived/0_8_35/#general","title":"General","text":"<p>SageWorks REPL: The REPL now has a workaround for the current iPython embedded shell namespace scoping issue. See: iPython Embedded Shell Scoping Issue. So this pretty much means the REPL is 110% more awesome now!</p>"},{"location":"release_notes/archived/0_8_35/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_35/#improvementsfixes","title":"Improvements/Fixes","text":"<p>AWS Service Broker: The AWS service broker was dramatic when it pulls meta data for something that just got deleted (or partially deleted), it was throwing CRITICAL log messages. We've refined the AWS error handling so that it's more granular about the error_codes for Validation or ResourceNotFound exceptions those are reduced to WARNINGS.</p> <p>ROCAUC modifications: Version <code>0.8.33</code> put in quite a few changes, for <code>0.8.35</code> we've also added logic to both validate and ensure proper order of the probability columns with the class labels.</p>"},{"location":"release_notes/archived/0_8_35/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.33 --&gt; v0.8.35 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call a cow with no legs?     ........Ground beef.</p>"},{"location":"release_notes/archived/0_8_35/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_36/","title":"Release 0.8.36","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.35</code> to <code>0.8.36</code></p>"},{"location":"release_notes/archived/0_8_36/#general","title":"General","text":"<p>Fast Inference: The current inference method for endpoints provides error handling, metrics calculations and capture mechanics. There are use cases where the inference needs to happen as fast as possible without all the additional features. So we've added a <code>fast_inference()</code> method that streamlines the calls to the endpoint.</p> <pre><code>end = Endpoint(\"my_endpoint\")\nend.inference(df)  # Metrics, Capture, Error Handling\nWall time: 5.07 s\n\nend.fast_inference(df)  # No frills, but Fast!\nWall time: 308 ms\n</code></pre>"},{"location":"release_notes/archived/0_8_36/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_36/#improvementsfixes","title":"Improvements/Fixes","text":"<p>Version Update Check: Added functionality that checks the current SageWorks version against the latest released and gives a log message for update available.</p> <p>ROCAUC modifications: Functionality now includes 'per label' rocauc calculation along with label order and alignment from previous versions.</p>"},{"location":"release_notes/archived/0_8_36/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.35 --&gt; v0.8.36 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What\u2019s a cow\u2019s best subject in school?     ......Cow-culus.</p>"},{"location":"release_notes/archived/0_8_36/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_39/","title":"Release 0.8.39","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.36</code> to <code>0.8.39</code></p>"},{"location":"release_notes/archived/0_8_39/#general","title":"General","text":"<p>Just a small set of error handling and bug fixes.</p>"},{"location":"release_notes/archived/0_8_39/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_39/#improvementsfixes","title":"Improvements/Fixes","text":"<p>Scatter Plot: Fixed a corner case where the hoover columns included AWS generated fields.</p> <p>Athena Queries: Put in additional error handling and retries when looking for and querying Athena/Glue Catalogs. These changes affect both DataSource and Features (which have DataSources internally for offline storage).</p> <p>FeatureSet Creation: Put in additional error handling and retries when pulling AWS meta data for FeatureSets (and internal DataSources).</p>"},{"location":"release_notes/archived/0_8_39/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.36 --&gt; v0.8.39 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/archived/0_8_39/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_42/","title":"Release 0.8.42","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.39</code> to <code>0.8.42</code></p>"},{"location":"release_notes/archived/0_8_42/#general","title":"General","text":"<p>Artifact deletion got a substantial overhaul. The 4 main classes received internal code changes for how they get deleted. Specifically deletion is now handled via a class method that allows an artifact to be delteed without instantiating an object. The API for deletion is actually more flexible now, please see API Changes below.</p>"},{"location":"release_notes/archived/0_8_42/#api-changes","title":"API Changes","text":"<p>Artifact Deletion</p> <p>The API for Artifact deletion is more flexible, if you already have an instantiated object, you can simply call <code>delete()</code> on it. If you're deleting an object in bulk/batch mode, you can call the class method <code>managed_delete()</code>, see code example below.</p> <p><pre><code>fs = FeatureSet(\"my_fs\")\nfs.delete()                        # Used for notebooks, scripts, etc.. \nOR\nFeatureSet.managed_delete(\"my_fs\") # Bulk/batch/internal use\n\n&lt;Same API for DataSources, Models, and Endpoints&gt;\n</code></pre> Note: Internally these use the same functionality, the dual API is simply for ease-of-use.</p>"},{"location":"release_notes/archived/0_8_42/#improvementsfixes","title":"Improvements/Fixes","text":"<p>Race Conditions</p> <p>In theory, the changes to a class based delete will reduce race conditions where an object would try to create itself (just to be deleted) and the AWS Service Broker was encountering partially created (or partially deleted objects) and would barf error messages.</p> <p>Slightly Better Throttling Logic</p> <p>The AWS Throttles have been 'tuned' a bit to back off a bit faster and also not retry the list_tags request when the ARN isn't found.</p>"},{"location":"release_notes/archived/0_8_42/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.39 --&gt; v0.8.42 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/archived/0_8_42/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_46/","title":"Release 0.8.46","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.42</code> to <code>0.8.46</code></p>"},{"location":"release_notes/archived/0_8_46/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/archived/0_8_46/#new-deprecation-warnings","title":"New Deprecation Warnings","text":"<p>We're starting to put in deprecation warning as we streamline classes and APIs. If you're using a class or method that's going to be deprecated you'll see a log message like this:</p> <pre><code>my_class = SomeOldClass()\nWARNING SomeOldClass is deprecated and will be removed in version 0.9.\n</code></pre> <p>In general these warning messages will be annoying but they will help us smoothly transistion and streamline our Classes and APIs.</p>"},{"location":"release_notes/archived/0_8_46/#deprecations","title":"Deprecations","text":"<ul> <li>AWSServiceBroker: Replaced by the Meta() class</li> </ul>"},{"location":"release_notes/archived/0_8_46/#api-changes","title":"API Changes","text":"<p>Meta()</p> <p>The new <code>Meta()</code> class will provide API that aligns with the AWS <code>list</code> and <code>describe</code> API. We'll have functionality for listing objects (models, feature sets, etc) and then functionality around the details for a named artifact.</p> <pre><code>meta = Meta()\nmodels_list = meta.models()  # List API\nend_list = meta.endpoints()  # List API\n\nfs_dict = meta.feature_set(\"my_fs\") # Describe API\nmodel_dict = meta.model(\"my_model\") # Describe API\n</code></pre> <p>For more details see: Meta Class</p> <p>The new Meta() API will be used inside of the Artifact classes (see Internal Changes...Artifacts... below)</p> <p>Artifact Classes</p> <p>The artifact classes (DataSource, FeatureSet, Model, Endpoint) have had some old arguments removed.</p> <pre><code>DataSource(force_refresh=True)  -&gt; Gone (remove it)\nFeatureSet(force_refresh=True)  -&gt; Gone (remove it)\nModel(force_refresh=True)       -&gt; Gone (remove it)\nModel(legacy=True)              -&gt; Gone (remove it)\n</code></pre>"},{"location":"release_notes/archived/0_8_46/#improvements","title":"Improvements","text":"<p>Scalability</p> <p>The changes to caching and the Meta() class should allow better horizontal scaling, we'll flex out the stress tests for upcoming releases before <code>0.9.0</code>.</p> <p>Table Names starting with Numbers</p> <p>Some of the Athena queries didn't properly escape the tables names and if you created a DataSource/FeatureSet with a name that started with a number the query would fail. Fixed now. :)</p>"},{"location":"release_notes/archived/0_8_46/#internal-changes","title":"Internal Changes","text":"<p>Meta()</p> <p><code>Meta()</code> doesn't do any caching now. If you want to use Caching as part of your meta data retrieval use the <code>CachedMeta()</code> class.</p> <p>Artifacts</p> <p>We're got rid of most (soon all) caching for individual Artifacts, if you're constructing an artifact object, you probably want detailed information that's 'up to date' and waiting a bit is probably fine. Note: We'll still make these instantiations as fast as we can, removing the caching logic will as least simplify the implementations.</p>"},{"location":"release_notes/archived/0_8_46/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.42 --&gt; v0.8.46 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/archived/0_8_46/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_6/","title":"Release 0.8.6","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. We've also fixed various corner cases mostly around 'half constructed' AWS artifacts (models/endpoints).</p>"},{"location":"release_notes/archived/0_8_6/#additional-functionality","title":"Additional Functionality","text":"<ul> <li>View Support (phase 1)</li> <li>CloudWatch (phase 1)</li> <li>Exception Log Forwarding Exception Handling</li> <li>Lambda Layers SageWorks Lambda Layers</li> <li>Better Docs for Deploying Plugins Deploying Plugins</li> </ul>"},{"location":"release_notes/archived/0_8_6/#issues-addressed","title":"Issues Addressed","text":"<ul> <li> <p>Model to Endpoint under AWS Throttle</p> <p>A corner case where the <code>to_endpoint()</code> method would fail when not 'knowing' the model input. This happened when AWS was throttling responses and the <code>get_input()</code> of the Endpoint returned <code>unknown</code> which caused a <code>NoneType</code> error when using the 'unknown' model.</p> </li> <li> <p>Empty Model Package Groups</p> <p>There are cases where customers might construct a Model Package Group (MPG) container and not put any Model Packages in that Group. SageWorks has assumed that all MPGs would have at least one model package. The current 'support' for empty MPGs treats it as an error condition but the API tries to accommodate the condition and will properly display the model group. The group will indicate that it's 'empty' and provides an alert health icons.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_6/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_8/","title":"Release 0.8.8","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>"},{"location":"release_notes/archived/0_8_8/#additional-functionality","title":"Additional Functionality","text":"<ul> <li>View Support (Views)</li> <li>CloudWatch (CloudWatch Docs)</li> </ul>"},{"location":"release_notes/archived/0_8_8/#api-changes","title":"API Changes","text":"<ul> <li> <p>Auto Inference name change</p> <p>When auto_inference is run on an endpoint the name of that inference run is currently <code>training_holdout</code>. That is too close to <code>model_training</code> and is confusing. So we're going to change the name to <code>auto_inference</code> which is way more explanatory and intuitive.</p> <p>Porting plugins: There should really not be any hard coding for <code>training_holdout</code>, plugins should just call <code>list_inference_runs()</code> (see below) and use the first one on the list.</p> </li> <li> <p><code>list_inference_runs()</code></p> <p>The <code>list_inference_runs()</code> method on Models has been improved. It now handles error states better (no model, no model training data) and will return 'model_training' LAST on the list, this should improve UX for plugin components.</p> </li> </ul> <p>Examples</p> <pre><code> model = Model(\"abalone-regression\")\n model.list_inference_runs()\n Out[1]: ['auto_inference', 'model_training']\n\n model = Model(\"wine-classification\")\n model.list_inference_runs()\n Out[2]: ['auto_inference', 'training_holdout', 'model_training']\n\n model = Model(\"aqsol-mol-regression\")\n model.list_inference_runs()\n Out[3]: ['training_holdout', 'model_training']\n\n model = Model(\"empty-model-group\")\n model.list_inference_runs()\n Out[4]: []\n</code></pre>"},{"location":"release_notes/archived/0_8_8/#glue-job-changes","title":"Glue Job Changes","text":"<p>We're spinning up the CloudWatch Handler much earlier now, so if you're setting config like this:</p> <pre><code>from sageworks.utils.config_manager import ConfigManager\n\n# Set the SageWorks Config\ncm = ConfigManager()\ncm.set_config(\"SAGEWORKS_BUCKET\", args_dict[\"sageworks-bucket\"])\ncm.set_config(\"REDIS_HOST\", args_dict[\"redis-host\"])\n</code></pre> <p>Just switch out that code for this code. Note: these need to be set before importing sageworks</p> <pre><code># Set these ENV vars for SageWorks \nos.environ['SAGEWORKS_BUCKET'] = args_dict[\"sageworks-bucket\"]\nos.environ[\"REDIS_HOST\"] = args_dict[\"redis-host\"]\n</code></pre>"},{"location":"release_notes/archived/0_8_8/#misc","title":"Misc","text":"<p>Confusion Matrix support for 'ordinal' labels</p> <p>Pandas has an \u2018ordinal\u2019 type, so the confusion matrix method <code>endpoint.confusion_matrix()</code> now checks the label column to see if it\u2019s ordinal and uses that order, if not just it will alphabetically sort. </p> <p>Note: This change may not affect your UI experience. Confusion matricies are saved in the Sageworks/S3 meta data storage, so a bunch of stuff upstream will also need to happen. FeatureSet object/api for setting the label order, recreation of the model/endpoint and confustion matrix, etc. In general this is a forwarding looking change that will be useful later. :)</p>"},{"location":"release_notes/archived/0_8_8/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"repl/","title":"SageWorks REPL","text":"<p>Visibility and Control</p> <p>The SageWorks REPL provides AWS ML Pipeline visibility just like the SageWorks Dashboard but also provides control over the creation, modification, and deletion of artifacts through the Python API.</p> <p>The SageWorks REPL is a customized iPython shell. It provides tailored functionality for easy interaction with SageWorks objects and since it's based on iPython developers will feel right at home using autocomplete, history, help, etc. Both easy and powerful, the SageWorks REPL puts control of AWS ML Pipelines at your fingertips.</p>"},{"location":"repl/#installation","title":"Installation","text":"<p><code>pip install sageworks</code></p>"},{"location":"repl/#usage","title":"Usage","text":"<p>Just type <code>sageworks</code> at the command line and the SageWorks shell will spin up and provide a command view of your AWS Machine Learning Pipelines.</p> <p>At startup the SageWorks shell, will connect to your AWS Account and create a summary of the Machine Learning artifacts currently residing on the account.</p> <p></p> <p>Available Commands:</p> <ul> <li>status</li> <li>config</li> <li>incoming_data</li> <li>glue_jobs</li> <li>data_sources</li> <li>feature_sets</li> <li>models</li> <li>endpoints</li> <li>and more...</li> </ul> <p>All of the API Classes are auto-loaded, so drilling down on an individual artifact is easy. The same Python API is provided so if you want additional info on a model, for instance, simply create a model object and use any of the documented API methods.</p> <pre><code>m = Model(\"abalone-regression\")\nm.details()\n&lt;shows info about the model&gt;\n</code></pre>"},{"location":"repl/#additional-resources","title":"Additional Resources","text":"<ul> <li>Setting up SageWorks on your AWS Account: AWS Setup</li> <li>Using SageWorks for ML Pipelines: SageWorks API Classes</li> </ul> <ul> <li>SageWorks Core Classes: Core Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"road_maps/0_9_0/","title":"Road Map v0.9.0","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>"},{"location":"road_maps/0_9_0/#general","title":"General","text":"<p>Streamlining</p> <p>We've learned a lot from our beta testers!</p> <p>One of the important lessons is not to 'over manage' AWS. We want to provide useful, granular Classes and APIs. Getting out of the way is just as important as providing functionality. So streamlining will be a big part of our <code>0.9.0</code> roadmap.</p> <p>Horizontal Scaling</p> <p>The framwork is currently struggling with 10 parallel ML pipelines being run concurrently. When running simultaneous pipelines we're seeing AWS service/query contention, throttling, and the occasional 'partial state' that we get back from AWS.</p> <p>Our plan for <code>0.9.0</code> is to have formalized horizontal stress testing that tests everything from 4 to 32 concurrent ML pipelines. Even though 32 may not seem like much, AWS has various quotas and limits that we'll be hitting, so 32 is a good goal for <code>0.9.0</code>. Obviously once we get to 32 we'll look forward to an architecture that will support 100's of concurrent pipelines.</p> <ul> <li>Feature Processor Quotas</li> <li>Sagemaker Endpoint Quotas</li> </ul> <p>Full Artifact Load or None</p> <p>For the SageWorks\u2019 DataSource, FeatureSet, Model, and Endpoint classes the new functionality will ensure that objects are only instantiated when all required data is fully available, returning None if the artifact ID is invalid or if the object is only partially constructed in AWS. </p> <p>By preventing partially constructed objects, this approach reduces runtime errors when accessing incomplete attributes and simplifies error handling for clients, enhancing robustness and reliability. We are looking at Pydantic for formally capturing schema and types (see Pydantic).</p> <p>Onboarding</p> <p>We'll have to balance the 'full artifact or None' with the need to <code>onboard()</code> artifacts created outside of SageWorks. We'll probably have a class method for onboarding, something like:</p> <pre><code>my_model = Model.onboard(\"some_random_model\")\nif my_model is None:\n    &lt;handle failure to onboard&gt;\n</code></pre> <p>Caching</p> <p>Caching needs a substantial overhaul. Right now SageWorks over uses caching. We baked it into our AWSServiceBroker and that gets used by everything.</p> <p>Caching only really makes sense when we can't wait for AWS to respond to requests. The Dashboard and Web Interfaces are the only use case where responsiveness is important. Other use cases like nightly batch processing, scripts or notebooks, will work totally fine waiting for AWS responses.</p> <p>Class/API Reductions</p> <p>The organic growth of SageWorks was based on user feedback and testing, that organic growth has led to an abundance of Classes and API calls. We'll be identifying classes and methods that are 'cruft' from some development push and will be deprecating those.</p>"},{"location":"road_maps/0_9_0/#deprecation-warnings","title":"Deprecation Warnings","text":"<p>We're starting to put in deprecation warning as we streamline classes and APIs. If you're using a class or method that's going to be deprecated you'll see a log message like this:</p> <pre><code>broker = AWSServiceBroker()\nWARNING AWSServiceBroker is deprecated and will be removed in version 0.9.\n</code></pre> <p>If you're using a class that's NOT going to be deprecated but currently uses/relies on one that is you'll still get a warning that you can ignore (developers will take care of it).</p> <pre><code># This class is NOT deprecated but an internal class is\nmeta = Meta() \nWARNING AWSServiceBroker is deprecated and will be removed in version 0.9.\n</code></pre> <p>In general these warning messages will be annoying but they will help us smoothly transistion and streamline our Classes and APIs.</p>"},{"location":"road_maps/0_9_0/#deprecations","title":"Deprecations","text":"<ul> <li>AWSServiceBroker: Replaced by the Meta() class</li> <li>Other Stuff</li> </ul>"},{"location":"road_maps/0_9_0/#api-changes","title":"API Changes","text":"<p>Meta()</p> <p>The new <code>Meta()</code> class will provide API that aligns with the AWS <code>list</code> and <code>describe</code> API. We'll have functionality for listing objects (models, feature sets, etc) and then functionality around the details for a named artifact.</p> <pre><code>meta = Meta()\nmodels_list = meta.models()  # List API\nend_list = meta.endpoints()  # List API\n\nfs_dict = meta.feature_set(\"my_fs\") # Describe API\nmodel_dict = meta.model(\"my_model\") # Describe API\n</code></pre> <p>The new Meta() API will be used inside of the Artifact classes (see Internal Changes...Artifacts... below)</p>"},{"location":"road_maps/0_9_0/#improvementsfixes","title":"Improvements/Fixes","text":"<p>FeatureSet</p> <p>When running concurrent ML pipelines we occasion get a partially constructed FeatureSet, FeatureSets will now 'wait and fail' if they detect partially constructed data (like offline storage not being ready).</p>"},{"location":"road_maps/0_9_0/#internal-changes","title":"Internal Changes","text":"<p>Meta()</p> <p>We're going to make a bunch of changes to <code>Meta()</code> specifically around more granular (LESS) caching. Also there will be an <code>AWSMeta()</code> subclass that manages the AWS specific API calls. We'll also put stubs in for <code>AzureMeta()</code> and <code>GCPMeta()</code>, cause hey we might have a client who really wants that flexibility.</p> <p>The new Meta class will also include API that's more aligned to the AWS <code>list</code> and <code>describe</code> interfacts. Allowing both broad and deep queries of the Machine Learning Artifacts within AWS.</p> <p>Artifacts</p> <p>We're getting rid of caching for individual Artifacts, if you're constructing an artifact object, you probably want detailed information that's 'up to date' and waiting a bit is probably fine. Note: We'll still make these instantiations as fast as we can, removing the caching logic will as least simplify the implementations.</p>"},{"location":"road_maps/0_9_0/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"road_maps/0_9_5/","title":"Road Map v0.9.5","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>The SageWorks framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>"},{"location":"road_maps/0_9_5/#general","title":"General","text":"<p>ML Pipelines</p> <p>We've learned a lot from our beta testers!</p> <p>One of the important lessons is that when you make it easier to build ML Pipelines the users are going to build lots of pipelines.</p> <p>For the creation, monitoring, and deployment of 50-100 of pipelines, we need to focus on the consoldation of artifacts into <code>Pipelines</code>. </p> <p>Pipelines are DAGs</p> <p>The use of Directed Acyclic Graphs for the storage and management of ML Pipelines will provide a good abstraction. Real world ML Pipelines will often branch multiple times, 1 DataSource may become 2 FeatureSets might become 3 Models/Endpoints. </p> <p>New Pipeline Dashboard Top Page</p> <p>The current main page shows all the individual artifacts, as we scale up to 100's models we need 2 additional levels of aggregation:</p> <ul> <li>Pipeline Groups<ul> <li>Group_1<ul> <li>Pipeline_1 (DS, FS, Model, Endpoint)</li> <li>Pipeline_2 (DS, FS, Model, Endpoint)</li> </ul> </li> <li>Group_2<ul> <li>Pipeline_3 (DS, FS...)</li> <li>Pipeline_4 (DS, FS...)</li> </ul> </li> </ul> </li> </ul> <p>New Pipeline Details Page</p> <p>When a pipeline is clicked on the top page, a Pipeline details page comes up for that specific pipeline. This page will give all relevant information about the pipeline, including model performance, monitoring, and endpoint status.</p> <p>Awesome image TBD</p>"},{"location":"road_maps/0_9_5/#versioned-artifacts","title":"Versioned Artifacts","text":"<p>Our beta customers have requested versioning for artifacts, so we support versioning for both Model and FeatureSets. Endpoints and DataSources typically do not need versioning, so we may wait on the versioning support for those artifact until a later version.</p>"},{"location":"road_maps/0_9_5/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and SageWorks. Please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p>"},{"location":"themes/","title":"Theme Support for SageWorks","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at sageworks@supercowpowers.com or on chat us up on Discord </p> <p>SageWorks interfaces are going to have full support for theming and branding to enable customizations of components, and pages. The theming support will include all of the default pages/components but will also support full theming for plugin components and pages SageWorks Plugins</p>"},{"location":"themes/#summary-of-theming-mechanisms","title":"Summary of Theming Mechanisms","text":""},{"location":"themes/#1-css-files","title":"1. CSS Files","text":"<p>CSS affects global styling of the entire application, including:</p> <ul> <li>Font styles and sizes.</li> <li>Layout styles (e.g., margins, padding, spacing).</li> <li>Background and text colors for non-Bootstrap or custom elements.</li> <li>Borders and shadows.</li> <li>Interactive element styling (e.g., hover effects, link colors).</li> <li>Dash custom components.</li> </ul>"},{"location":"themes/#2-plotly-figure-templates","title":"2. Plotly Figure Templates","text":"<p>Plotly templates control figure-specific styling for Plotly charts and graphs:</p> <ul> <li>Axis styling (e.g., line colors, grid lines, and ticks).</li> <li>Color scales for heatmaps, bar charts, and scatter plots.*</li> <li>Marker and line styles.</li> <li>Background colors for the figure and plot areas.</li> <li>Font styles inside the figures.</li> </ul>"},{"location":"themes/#3-dbc-components-lightdark","title":"3. DBC Components Light/Dark","text":"<p>The <code>data-bs-theme=light/dark</code> html attribute affects Bootstrap-specific components, primarily from Dash Bootstrap Components (DBC):</p> <ul> <li>Buttons.</li> <li>Tables.</li> <li>Forms (e.g., input boxes, checkboxes, radio buttons).</li> <li>Cards, modals, and alerts.</li> </ul>"},{"location":"themes/#key-differences","title":"Key Differences","text":"Aspect CSS Files Plotly Templates DBC Dark/Light Scope Global app styling Plotly figures DBC Components Level of Control Full Range Partial/Full* Just Light and Dark Dynamic Switching? Not easily (reloading needed) Can update dynamically Supports dynamic switching <ul> <li>Some figure parameters will automatically work, but for stuff like colorscales the component code needs to 'pull' that from the template meta data when it updates it's figure.</li> </ul>"},{"location":"themes/#additional-resources","title":"Additional Resources","text":"<p>Need help with plugins? Want to develop a customized application tailored to your business needs?</p> <ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"themes/details/","title":"Understanding Bootstrap and DBC Theme Hooks in Dash","text":""},{"location":"themes/details/#overview","title":"Overview","text":"<p>Bootstrap and Dash Bootstrap Components (DBC) provide mechanisms to control themes and styling through CSS classes, CSS variables, and data attributes. These hooks enable consistent theming across applications.</p>"},{"location":"themes/details/#theme-hooks-overview","title":"Theme Hooks Overview","text":""},{"location":"themes/details/#1-css-classes","title":"1. CSS Classes","text":"<ul> <li>Description: Bootstrap provides pre-defined classes like <code>container</code>, <code>row</code>, <code>col</code>, <code>btn</code>, etc., for responsive layouts and component styling.</li> <li>Usage: DBC components automatically apply many of these classes, and you can also customize them using the <code>className</code> property.</li> </ul>"},{"location":"themes/details/#2-css-variables","title":"2. CSS Variables","text":"<ul> <li>Description: Bootstrap uses CSS variables (e.g., <code>--bs-body-bg</code>, <code>--bs-primary</code>) to allow flexible theming and styling.</li> <li>How They Work: These variables dynamically adjust based on the theme (e.g., light or dark) or can be customized via custom stylesheets.</li> <li>Common Variables:</li> <li><code>--bs-body-bg</code>: Background color.</li> <li><code>--bs-body-color</code>: Text color.</li> <li><code>--bs-border-color</code>: Border color.</li> </ul>"},{"location":"themes/details/#3-data-attributes","title":"3. Data Attributes","text":"<ul> <li>Description: The <code>data-bs-theme</code> attribute enables quick switching between light and dark themes.</li> <li>Usage: Setting <code>data-bs-theme=\"light\"</code> or <code>data-bs-theme=\"dark\"</code> on a root container (e.g., <code>&lt;body&gt;</code> or a main <code>html.Div</code>) dynamically adjusts Bootstrap variables globally.</li> </ul>"},{"location":"themes/details/#how-these-hooks-work-together","title":"How These Hooks Work Together","text":"<ol> <li>Class Names:</li> <li>Used in DBC components to apply Bootstrap styling.</li> <li> <p>Affects layout, grid, and overall component behavior.</p> </li> <li> <p>CSS Variables:</p> </li> <li>Allow fine-grained control over the visual appearance based on the <code>data-bs-theme</code>.</li> <li> <p>Ensure consistent styling for text, backgrounds, borders, etc.</p> </li> <li> <p>Data Attributes:</p> </li> <li>Act as a high-level toggle for light/dark theming, affecting all components globally.</li> <li>Automatically updates the values of Bootstrap CSS variables.</li> </ol>"},{"location":"themes/details/#notes-on-redundancy","title":"Notes on Redundancy","text":"<ul> <li>When using <code>data-bs-theme</code>, explicitly setting styles (e.g., <code>backgroundColor</code>) might be redundant since the Bootstrap CSS variables (e.g., <code>--bs-body-bg</code>) automatically adjust based on the selected theme.</li> <li>DBC components like <code>dbc.Container</code> do not automatically set <code>data-bs-theme</code> or specific class names, so you may need to explicitly define these for global theming.</li> </ul>"},{"location":"themes/details/#conclusion","title":"Conclusion","text":"<ul> <li>Use data attributes (<code>data-bs-theme</code>) for global light/dark switching.</li> <li>Leverage CSS variables (<code>--bs-*</code>) for consistent theming across components.</li> <li>Apply CSS classes (<code>className</code>) for additional customization or integration with custom styles.</li> </ul>"},{"location":"themes/details/#theming-strategy-for-sageworks-dashboard","title":"Theming Strategy for SageWorks Dashboard","text":""},{"location":"themes/details/#phase-1-application-start-up-theming","title":"Phase 1: Application Start-Up Theming","text":"<p>This focuses on setting a polished, cohesive interface at startup. It ensures:</p> <ol> <li>CSS and Plotly Templates at Start-Up:</li> <li>The current system handles global styles via CSS (including Flask-served <code>custom.css</code>) and Plotly templates (via <code>pio.templates.default</code>) during app initialization.</li> <li>Highly Customizable Interface:</li> <li>By leveraging both custom CSS and Plotly templates, you can style everything to match branding and preferences.</li> </ol>"},{"location":"themes/details/#why-phase-1-works-well","title":"Why Phase 1 Works Well","text":"<ul> <li>Plotly Templates: Figures are automatically styled if <code>pio.templates.default</code> is set before app initialization.</li> <li>CSS: Dynamically affects the entire app without additional callbacks.</li> <li>Scalability: Suitable for large apps since styles and templates are applied globally.</li> </ul>"},{"location":"themes/details/#phase-2-dynamic-theme-switching","title":"Phase 2: Dynamic Theme Switching","text":"<p>Dynamic theme switching allows users to toggle between themes (e.g., light and dark) at runtime. This is more complex, and splitting it into its own phase will reduce the complexity of each Phase.</p>"},{"location":"themes/details/#challenges-with-dynamic-switching","title":"Challenges with Dynamic Switching","text":"<ol> <li>Encapsulation Barriers:</li> <li>A centralized callback for theme switching needs to know about every figure or component in the app, which is not scalable.</li> <li>Dynamic Content/Plugins:</li> <li>We have plugin pages, views, and components. A Typical app has 20 figures dynamically created, adding complexity to centralized callbacks.</li> </ol>"},{"location":"themes/details/#proposed-solution-set_theme-method","title":"Proposed Solution: <code>set_theme()</code> Method","text":"<p>Introduce a <code>set_theme()</code> method in each component to manage its own updates:</p> <ul> <li>Figures: Update their <code>layout.template</code> or regenerate themselves.</li> <li>HTML/CSS Components: Dynamically update styles or toggle CSS classes.</li> </ul>"},{"location":"themes/details/#advantages-of-set_theme","title":"Advantages of <code>set_theme()</code>","text":"<ol> <li>Encapsulation:</li> <li>Each component knows how to update itself based on the theme.</li> <li>Simple Callback:</li> <li>The theme switch callback iterates through all registered components and calls their <code>set_theme()</code> method.</li> <li>Scalable:</li> <li>Suitable for multi-page apps with many dynamic components.</li> </ol>"},{"location":"themes/details/#how-the-two-phases-work-together","title":"How the Two Phases Work Together","text":""},{"location":"themes/details/#phase-1","title":"Phase 1:","text":"<ul> <li>Focus: Global theming at app start-up.</li> <li>What Works:</li> <li>Plotly templates are applied to all figures by default.</li> <li>CSS styles are applied across the app.</li> </ul>"},{"location":"themes/details/#phase-2","title":"Phase 2:","text":"<ul> <li>Focus: Extend components to dynamically update their theme.</li> <li>What to Add:</li> <li>Extend components with a <code>set_theme(theme_name: str)</code> method.</li> <li>The callback triggers <code>set_theme()</code> for each registered component.</li> </ul>"},{"location":"themes/details/#next-steps","title":"Next Steps","text":"<ul> <li>Phase 1: Finalize CSS and Plotly template integration for start-up theming.</li> <li>Phase 2: Begin implementing a <code>set_theme()</code> system for individual components.</li> </ul>"},{"location":"themes/details/#storage","title":"Storage","text":""},{"location":"themes/details/#theme-support-in-sageworks","title":"Theme Support in SageWorks","text":"<p>For a Dash/Plotly web applications there are two main concepts that fall under the general umbrella of 'theme support'.</p>"},{"location":"themes/details/#css-for-high-level-theming","title":"CSS for High-Level Theming","text":"<ul> <li>Purpose: Styles the overall web interface (e.g., layout, buttons, dropdowns, fonts, background colors).</li> <li>Scope: Applies to Dash components (e.g., <code>dcc.Graph</code>, <code>dbc.Button</code>) and layout elements.</li> <li>Implementation:</li> <li>Use a CSS file or a framework like Bootstrap.</li> <li>Dynamically switch themes (e.g., light/dark) using CSS class changes.</li> </ul>"},{"location":"themes/details/#templates-for-plotly-figures","title":"Templates for Plotly Figures","text":"<ul> <li>Purpose: Styles Plotly figures (e.g., background, gridlines, colorscales, font styles) to match the app's theme.</li> <li>Scope: Only affects Plotly-generated visuals like charts, graphs, and heatmaps.</li> <li>Implementation:</li> <li>Use predefined templates (e.g., <code>darkly</code>, <code>mintly</code>) or create custom JSON templates.</li> <li>Apply globally or on a per-figure basis.</li> <li>Great Resource:     dash-bootstrap-templates</li> </ul>"},{"location":"themes/details/#how-they-complement-each-other","title":"How They Complement Each Other","text":"<ol> <li> <p>CSS handles the web app\u2019s overall look:</p> <p>Example: A \"dark mode\" CSS class changes the app's background, text color, and component styles.</p> </li> <li> <p>Templates handle Plotly-specific figure styling:</p> <p>Example: A \"dark mode\" Plotly template ensures charts have dark backgrounds, white gridlines, and matching font colors.</p> </li> </ol>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Welcome to Workbench","text":"<p>The Workbench framework makes AWS\u00ae both easier to use and more powerful. Workbench handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, Workbench makes creating AWS ML pipelines a snap. It also dramatically improves both the usability and visibility across the entire spectrum of services: Glue Jobs, Athena, Feature Store, Models, and Endpoints. Workbench makes it easy to build production ready, AWS powered, machine learning pipelines.</p> Workbench Dashboard: AWS Pipelines in a Whole New Light!"},{"location":"#full-aws-overview","title":"Full AWS OverView","text":"<ul> <li>Health Monitoring \ud83d\udfe2</li> <li>Dynamic Updates</li> <li>High Level Summary</li> </ul>"},{"location":"#drill-down-views","title":"Drill-Down Views","text":"<ul> <li>Glue Jobs</li> <li>DataSources</li> <li>FeatureSets</li> <li>Models</li> <li>Endpoints</li> </ul>"},{"location":"#private-saas-architecture","title":"Private SaaS Architecture","text":"<p>Secure your Data, Empower your ML Pipelines</p> <p>Workbench is architected as a Private SaaS. This hybrid architecture is the ultimate solution for businesses that prioritize data control and security. Workbench deploys as an AWS Stack within your own cloud environment, ensuring compliance with stringent corporate and regulatory standards. It offers the flexibility to tailor solutions to your specific business needs through our comprehensive plugin support, both components and full web interfaces. By using Workbench, you maintain absolute control over your data while benefiting from the power, security, and scalability of AWS cloud services. Workbench Private SaaS Architecture</p>"},{"location":"#dashboard-and-api","title":"Dashboard and API","text":"<p>The Workbench package has two main components, a Web Interface that provides visibility into AWS ML PIpelines and a Python API that makes creation and usage of the AWS ML Services easier than using/learning the services directly.</p>"},{"location":"#web-interfaces","title":"Web Interfaces","text":"<p>The Workbench Dashboard has a set of web interfaces that give visibility into the AWS Glue and SageMaker Services. There are currently 5 web interfaces available:</p> <ul> <li>Top Level Dashboard: Shows all AWS ML Artifacts (Glue and SageMaker)</li> <li>DataSources: DataSource Column Details, Distributions and Correlations</li> <li>FeatureSets: FeatureSet Details, Distributions and Correlations</li> <li>Model: Model details, performance metric, and inference plots</li> <li>Endpoints: Endpoint details, realtime charts of endpoint performance and latency</li> </ul>"},{"location":"#python-api","title":"Python API","text":"<p>Workbench API Documentation: Workbench API Classes </p> <p>The main functionality of the Python API is to encapsulate and manage a set of AWS services underneath a Python Object interface. The Python Classes are used to create and interact with Machine Learning Pipeline Artifacts.</p>"},{"location":"#getting-started","title":"Getting Started","text":"<p>Workbench will need some initial setup when you first start using it. See our Getting Started guide on how to connect Workbench to your AWS Account.</p>"},{"location":"#additional-resources","title":"Additional Resources","text":"<ul> <li>Getting Started: Getting Started </li> <li>Workbench API Classes: API Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"admin/aws_service_limits/","title":"AWS Service Limits","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>In general Workbench works well, out of the box, with the standard set of limits for AWS accounts. Workbench supports throttling, timeouts, and a broad set of AWS error handling routines for general purpose usage.</p> <p>When using Workbench for large scale deployments there are a set of AWS Service limits that will need to be increased.</p>"},{"location":"admin/aws_service_limits/#serverless-endpoints","title":"ServerLess Endpoints","text":"<p>There are two serverless endpoint quotas that will need to be adjusted.</p> <ul> <li>Maximum number of serverless endpoints (defaults to 5): Set to 50-100</li> <li>Maximum total concurrency serverless endpoints (defaults to 10): Set to 100-200</li> </ul> <p></p>"},{"location":"admin/aws_service_limits/#parallel-featuregroup-creation","title":"Parallel FeatureGroup Creation","text":"<p>When running a large set of parallel Glue/Batch Jobs that are creating FeatureGroups, some clients have hit this limit. </p> <p>\"ResourceLimitExceeded: An error occurred (ResourceLimitExceeded) when calling the CreateFeatureGroup operation: The account-level service limit 'Maximum number of feature group creation workflows executing in parallel' is 4 FeatureGroups, with current utilization of 4 FeatureGroups and a request delta of 1 FeatureGroups. Please use AWS Service Quotas to request an increase for this quota. If AWS Service Quotas is not available, contact AWS support to request an increase for this quota.\"}</p> <p>Unfortunately this one is not adjustable through the AWS Service Quota console and you'll have to initiate an AWS Support ticket.</p>"},{"location":"admin/aws_service_limits/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"admin/base_docker_push/","title":"Workbench Base Docker Build and Push","text":"<p>Notes and information on how to do the Docker Builds and Push to AWS ECR.</p>"},{"location":"admin/base_docker_push/#update-workbench-version","title":"Update Workbench Version","text":"<pre><code>vi Dockerfile\n\n# Install latest Workbench\nRUN pip install --no-cache-dir 'workbench[ml-tool,chem]'==0.7.0\n</code></pre>"},{"location":"admin/base_docker_push/#build-the-docker-image","title":"Build the Docker Image","text":"<p>Note: For a client specific config file you'll need to copy it locally so that it's within Dockers 'build context'. If you're building the 'vanilla' open source Docker image, then you can use the <code>open_source_config.json</code> that's in the directory already.</p> <pre><code>docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \\\nworkbench_base:v0_7_0_amd64 --platform linux/amd64 .\n</code></pre>"},{"location":"admin/base_docker_push/#test-the-image-locally","title":"Test the Image Locally","text":"<p>You have a <code>docker_local_base</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/base_docker_push/#login-to-ecr","title":"Login to ECR","text":"<pre><code>aws ecr-public get-login-password --region us-east-1 --profile \\\nscp_sandbox_admin | docker login --username AWS \\\n--password-stdin public.ecr.aws\n</code></pre>"},{"location":"admin/base_docker_push/#tagpush-the-image-to-aws-ecr","title":"Tag/Push the Image to AWS ECR","text":"<p><pre><code>docker tag workbench_base:v0_7_0_amd64 \\\npublic.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64\n</code></pre></p>"},{"location":"admin/base_docker_push/#update-the-latest-tag","title":"Update the 'latest' tag","text":"<p><pre><code>docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \\\npublic.ecr.aws/m6i5k1r2/workbench_base:latest\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/workbench_base:latest\n</code></pre></p>"},{"location":"admin/base_docker_push/#update-the-stable-tag","title":"Update the 'stable' tag","text":"<p>This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :)</p> <p><pre><code>docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \\\npublic.ecr.aws/m6i5k1r2/workbench_base:stable\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/workbench_base:stable\n</code></pre></p>"},{"location":"admin/base_docker_push/#test-the-ecr-image","title":"Test the ECR Image","text":"<p>You have a <code>docker_ecr_base</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/dashboard_docker_push/","title":"Dashboard Docker Build and Push","text":"<p>Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR.</p>"},{"location":"admin/dashboard_docker_push/#update-workbench-version","title":"Update Workbench Version","text":"<pre><code>cd applications/aws_dashboard\nvi Dockerfile\n\n# Install Workbench (changes often)\nRUN pip install --no-cache-dir workbench==0.4.13 &lt;-- change this\n</code></pre>"},{"location":"admin/dashboard_docker_push/#build-the-docker-image","title":"Build the Docker Image","text":"<p>Note: For a client specific config file you'll need to copy it locally so that it's within Dockers 'build context'. If you're building the 'vanilla' open source Docker image, then you can use the <code>open_source_config.json</code> that's in the directory already.</p> <pre><code>docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \\\nworkbench_dashboard:v0_4_13_amd64 --platform linux/amd64 .\n</code></pre> <p>Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page.</p>"},{"location":"admin/dashboard_docker_push/#test-the-image-locally","title":"Test the Image Locally","text":"<p>You have a <code>docker_local_dashboard</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/dashboard_docker_push/#login-to-ecr","title":"Login to ECR","text":"<pre><code>aws ecr-public get-login-password --region us-east-1 --profile \\\nscp_sandbox_admin | docker login --username AWS \\\n--password-stdin public.ecr.aws\n</code></pre>"},{"location":"admin/dashboard_docker_push/#tagpush-the-image-to-aws-ecr","title":"Tag/Push the Image to AWS ECR","text":"<p><pre><code>docker tag workbench_dashboard:v0_4_13_amd64 \\\npublic.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64\n</code></pre></p>"},{"location":"admin/dashboard_docker_push/#update-the-latest-tag","title":"Update the 'latest' tag","text":"<p><pre><code>docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \\\npublic.ecr.aws/m6i5k1r2/workbench_dashboard:latest\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest\n</code></pre></p>"},{"location":"admin/dashboard_docker_push/#update-the-stable-tag","title":"Update the 'stable' tag","text":"<p>This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :)</p> <p><pre><code>docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \\\npublic.ecr.aws/m6i5k1r2/workbench_dashboard:stable\n</code></pre> <pre><code>docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable\n</code></pre></p>"},{"location":"admin/dashboard_docker_push/#test-the-ecr-image","title":"Test the ECR Image","text":"<p>You have a <code>docker_ecr_dashboard</code> alias in your <code>~/.zshrc</code> :)</p>"},{"location":"admin/dashboard_s3_plugins/","title":"Deploying S3 Plugins with the Dashboard","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>Notes and information on how to include S3 based plugins with your Workbench Dashboard. </p> <p>Deploying your Dashboard plugins via an S3 bucket allows plugin developers to modify and improve plugins without a bunch of Docker builds, ECR, and CDK Deploy.</p> <p>On, the other hand when you have a 'final build' you might want to lock it in with a Docker image. If you'd like to bundle up your plugins in a Docker image and deploy please see Dashboard with Plugins.</p>"},{"location":"admin/dashboard_s3_plugins/#check-your-dashboard","title":"Check your Dashboard","text":"<p>Make sure your Dashboard is configured to pull plugin pages, views, and components from an S3 bucket. Go to your main dashboard page and there's a 'secret link' when you click on the main title that brings up the Dashboard Status Page.</p> <p></p> <p>Dashboard Status Page Okay now check your Plugin Path: config and make sure it points to the S3 bucket you're expecting.</p> <p></p>"},{"location":"admin/dashboard_s3_plugins/#develop-your-plugins","title":"Develop your Plugins","text":"<p>During development it's good to both unit testing and local dashboard testing. Please see our main plugin docs for how to do local testing Plugins General.</p> <p>When you're ready to 'deploy' the plugins you can copy them up to the S3 bucket/prefix. You want to copy recursively so if you're plugin directory looks like the listing below you want to copy all the files/directories, so that the dashboard picks up everything.</p> <pre><code>- my_plugins\n   - pages\n      - page_1.py\n   - views\n      - view_1.py\n   - components\n       -component_1.py\n       -component_2.py\n</code></pre> <p>Careful with Plugin Copy</p> <p>In particular, pay attention to additional files in the directory structure. You do not want to copy _pycache_ and *.pyc files. So we recommend using this CLI.</p> <pre><code>cd my_plugins\naws s3 cp . s3://my_bucket/prefix --recursive --exclude \"*\" --include \"*.py\"\n</code></pre>"},{"location":"admin/dashboard_s3_plugins/#restart-the-ecs-service","title":"Restart the ECS Service","text":"<p>Okay, so this is a bit heavy handed, but automatically removing/adding/modifying the plugin pages, views, and components was 'amazingly complicated' and will be a feature request for a later date. :)</p> <p>Getting Cluster and Service Names</p> <p>You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service.</p> <p>The cluster will be something like:</p> <pre><code>WorkbenchDashboard-WorkbenchCluster123456\n</code></pre> <p>and the service will be something like:</p> <pre><code>WorkbenchDashboard-WorkbenchService789123\n</code></pre> <p>Anyway, find those two things and run this command below (Note: You probably need admin permisions)</p> <pre><code>aws ecs update-service --cluster your-cluster-name \\\n--service your-service-name --force-new-deployment\n</code></pre> <p>Important: Even though this command will finish immediately, the ECS service will slowly flip over to the new instance (like 5-10 minutes), so wait a bit before testing the changes.</p>"},{"location":"admin/dashboard_s3_plugins/#verify-new-plugin-changes","title":"Verify new Plugin changes","text":"<p>Okay now that the ECS service has restarted (which can take a bit) you can now go to the Dashboard and test/verify that the changes you made now show up on the Dashboard.</p>"},{"location":"admin/dashboard_s3_plugins/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"admin/dashboard_with_plugins/","title":"Deploying Plugins with the Dashboard","text":"<p>Notes and information on how to include plugins with your Workbench Dashboard.</p> <ul> <li>ECR: AWS Elastic Container Registry (stores Docker images)</li> <li>ECS: AWS Elastic Container Service (uses Docker images)</li> </ul>"},{"location":"admin/dashboard_with_plugins/#install-docker","title":"Install Docker","text":"<ul> <li>Linux: https://docs.docker.com/engine/install/ubuntu/</li> <li>Windows: https://docs.docker.com/desktop/install/windows-install/</li> </ul>"},{"location":"admin/dashboard_with_plugins/#build-the-docker-image","title":"Build the Docker Image","text":"<p>If you don't already have a Dockerfile, here's one to get you started, just place this into your repo/directory that has the plugins. </p> <pre><code># Pull base workbench dashboard image with specific tag (pick latest or stable)\nFROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest\n\n# Copy the plugin files into the Dashboard plugins dir\nCOPY ./workbench_plugins /app/workbench_plugins\nENV WORKBENCH_PLUGINS=/app/workbench_plugins\n</code></pre> <p>Note: Your plugins directory should looks like this</p> <pre><code>workbench_plugins/\n   pages/\n      my_plugin_page.py\n      ...\n   views/\n      my_plugin_view.py\n      ...\n   components/\n      my_component.py\n      ...\n</code></pre>"},{"location":"admin/dashboard_with_plugins/#build-it","title":"Build it","text":"<pre><code>docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 .\n</code></pre>"},{"location":"admin/dashboard_with_plugins/#test-the-image-locally","title":"Test the Image Locally","text":"<p>You'll need to use AWS Credentials for this, it's a bit complicated, please contact Workbench Support workbench@supercowpowers.com or chat us up on Discord </p>"},{"location":"admin/dashboard_with_plugins/#login-to-your-ecr","title":"Login to your ECR","text":"<p>Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR.</p> <p>Note: This ECR should be private as your plugins are customized for specific business use cases.</p> <p>Your ECR location will have this form <pre><code>&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com\n</code></pre></p> <pre><code>aws ecr get-login-password --region us-east-1 --profile &lt;aws_profile&gt; \\\n| docker login --username AWS --password-stdin \\\n&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com\n</code></pre>"},{"location":"admin/dashboard_with_plugins/#tagpush-the-image-to-aws-ecr","title":"Tag/Push the Image to AWS ECR","text":"<p><pre><code>docker tag my_workbench_with_plugins:v1_0 \\\n&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0\n</code></pre> <pre><code>docker push \\\n&lt;aws_account_id&gt;.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0\n</code></pre></p>"},{"location":"admin/dashboard_with_plugins/#deploying-plugin-docker-image-to-aws","title":"Deploying Plugin Docker Image to AWS","text":"<p>Okay now that you have your plugin Docker Image you can deploy to your AWS account:</p> <p>Copy the Dashboard CDK files</p> <p>This is cheesy but just copy all the CDK files into your repo/directory.</p> <pre><code>cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/\n</code></pre> <p>Change the Docker Image to Deploy</p> <p>Now open up the <code>app.py</code> file and change this line to your Docker Image</p> <pre><code># When you want a different docker image change this line\ndashboard_image = \"public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64\"\n</code></pre> <p>Make sure your <code>WORKBENCH_CONFIG</code> is properly set, and run the following commands:</p> <pre><code>export WORKBENCH_CONFIG=/Users/&lt;user_name&gt;/.workbench/workbench_config.json\ncdk diff\ncdk deploy\n</code></pre> <p>CDK Diff</p> <p>In particular, pay attention to the <code>cdk diff</code> it should ONLY have the image name as a difference. <pre><code>cdk diff\n[-] \"Image\": \"&lt;account&gt;.dkr.ecr.us-east-1/my-plugins:latest_123\",\n[+] \"Image\": \"&lt;account&gt;.dkr.ecr.us-east-1/my-plugins:latest_456\",\n</code></pre></p>"},{"location":"admin/dashboard_with_plugins/#note-on-workbench-configuration","title":"Note on Workbench Configuration","text":"<p>All Configuration is managed by the CDK Python Script and the <code>WORKBENCH_CONFIG</code> ENV var. If you want to change things like <code>REDIS_HOST</code> or <code>WORKBENCH_BUCKET</code> you should do that with a <code>workbench.config</code> file and then point the <code>WORKBENCH_CONFIG</code> ENV var to that file.</p>"},{"location":"admin/pypi_release/","title":"PyPI Release Notes","text":"<p>Notes and information on how to do the PyPI release for the SageMaker project. For full details on packaging you can reference this page Packaging</p> <p>The following instructions should work, but things change :)</p>"},{"location":"admin/pypi_release/#package-requirements","title":"Package Requirements","text":"<ul> <li>pip install tox</li> <li>pip install --upgrade wheel build twine</li> </ul>"},{"location":"admin/pypi_release/#setup-pypirc","title":"Setup pypirc","text":"<p>The easiest thing to do is setup a \\~/.pypirc file with the following contents</p> <pre><code>[distutils]\nindex-servers =\n  pypi\n  testpypi\n\n[pypi]\nusername = __token__\npassword = pypi-AgEIcH...\n\n[testpypi]\nusername = __token__\npassword = pypi-AgENdG...\n</code></pre>"},{"location":"admin/pypi_release/#tox-background","title":"Tox Background","text":"<p>Tox will install the SageMaker Sandbox package into a blank virtualenv and then execute all the tests against the newly installed package. So if everything goes okay, you know the pypi package installed fine and the tests (which puls from the installed <code>workbench</code> package) also ran okay.</p>"},{"location":"admin/pypi_release/#make-sure-all-tests-pass","title":"Make sure ALL tests pass","text":"<pre><code>$ cd workbench\n$ tox \n</code></pre> <p>If ALL the test above pass...</p>"},{"location":"admin/pypi_release/#clean-any-previous-distribution-files","title":"Clean any previous distribution files","text":"<pre><code>make clean\n</code></pre>"},{"location":"admin/pypi_release/#tag-the-new-version","title":"Tag the New Version","text":"<pre><code>git tag v0.1.8 (or whatever)\ngit push --tags\n</code></pre>"},{"location":"admin/pypi_release/#create-the-test-pypi-release","title":"Create the TEST PyPI Release","text":"<pre><code>python -m build\ntwine upload dist/* -r testpypi\n</code></pre>"},{"location":"admin/pypi_release/#install-the-test-pypi-release","title":"Install the TEST PyPI Release","text":"<pre><code>pip install --index-url https://test.pypi.org/simple workbench\n</code></pre>"},{"location":"admin/pypi_release/#create-the-real-pypi-release","title":"Create the REAL PyPI Release","text":"<pre><code>twine upload dist/* -r pypi\n</code></pre>"},{"location":"admin/pypi_release/#push-any-possible-changes-to-github","title":"Push any possible changes to Github","text":"<pre><code>git push\n</code></pre>"},{"location":"admin/workbench_docker_for_lambdas/","title":"Workbench Docker Image for Lambdas","text":"<p>Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench.</p> <p>AWS, for some reason, does not allow Public ECRs to be used for Lambda Docker images. So you'll have to copy the Docker image into your private ECR. </p>"},{"location":"admin/workbench_docker_for_lambdas/#creating-a-private-ecr","title":"Creating a Private ECR","text":"<p>You only need to do this if you don't already have a private ECR.</p>"},{"location":"admin/workbench_docker_for_lambdas/#aws-console-to-create-private-ecr","title":"AWS Console to create Private ECR","text":"<ol> <li>Open the Amazon ECR console.</li> <li>Choose \"Create repository\".</li> <li>For \"Repository name\", enter <code>workbench_base</code>.</li> <li>Ensure \"Private\" is selected.</li> <li>Choose \"Create repository\".</li> </ol>"},{"location":"admin/workbench_docker_for_lambdas/#command-line-to-create-private-ecr","title":"Command Line to create Private ECR","text":"<p>Create the ECR repository using the AWS CLI:</p> <pre><code>aws ecr create-repository --repository-name workbench_base --region &lt;region&gt;\n</code></pre>"},{"location":"admin/workbench_docker_for_lambdas/#pulling-docker-image-into-private-ecr","title":"Pulling Docker Image into Private ECR","text":"<p>Note: You'll only need to do this when you want to update the Workbench Docker image</p> <p>Pull the Workbench Public ECR Image</p> <pre><code>docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest\n</code></pre> <p>Tag the image for your private ECR</p> <pre><code>docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \\\n&lt;your-account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:latest\n</code></pre> <p>Push the image to your private ECR</p> <pre><code>aws ecr get-login-password --region &lt;region&gt; --profile &lt;profile&gt; | \\\ndocker login --username AWS --password-stdin &lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com\n\ndocker push &lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;\n</code></pre>"},{"location":"admin/workbench_docker_for_lambdas/#using-the-docker-image-for-your-lambdas","title":"Using the Docker Image for your Lambdas","text":"<p>Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs.</p>"},{"location":"admin/workbench_docker_for_lambdas/#aws-console","title":"AWS Console","text":"<ol> <li>Open the AWS Lambda console.</li> <li>Create a new function.</li> <li>Select \"Container image\".</li> <li>Use the ECR image URI: <code>&lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;</code>.</li> </ol>"},{"location":"admin/workbench_docker_for_lambdas/#command-line","title":"Command Line","text":"<p>Create the Lambda function using the AWS CLI:</p> <pre><code>aws lambda create-function \\\n --region &lt;region&gt; \\\n --function-name myLambdaFunction \\\n --package-type Image \\\n --code ImageUri=&lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt; \\\n --role arn:aws:iam::&lt;account-id&gt;:role/&lt;execution-role&gt;\n</code></pre>"},{"location":"admin/workbench_docker_for_lambdas/#python-cdk","title":"Python CDK","text":"<p>Define the Lambda function in your CDK app:</p> <pre><code>from aws_cdk import (\n   aws_lambda as _lambda,\n   core\n)\n\nclass MyLambdaStack(core.Stack):\n   def __init__(self, scope: core.Construct, id: str, **kwargs) -&gt; None:\n       super().__init__(scope, id, **kwargs)\n\n       _lambda.Function(self, \"MyLambdaFunction\",\n                        code=_lambda.Code.from_ecr_image(\"&lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;\"),\n                        handler=_lambda.Handler.FROM_IMAGE,\n                        runtime=_lambda.Runtime.FROM_IMAGE,\n                        role=iam.Role.from_role_arn(self, \"LambdaRole\", \"arn:aws:iam::&lt;account-id&gt;:role/&lt;execution-role&gt;\"))\n\napp = core.App()\nMyLambdaStack(app, \"MyLambdaStack\")\napp.synth()\n</code></pre>"},{"location":"admin/workbench_docker_for_lambdas/#cloudformation","title":"Cloudformation","text":"<p>Define the Lambda function in your CloudFormation template.</p> <pre><code>Resources:\n MyLambdaFunction:\n   Type: AWS::Lambda::Function\n   Properties:\n     Code:\n       ImageUri: &lt;account-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;private-repo&gt;:&lt;tag&gt;\n     Role: arn:aws:iam::&lt;account-id&gt;:role/&lt;execution-role&gt;\n     PackageType: Image\n</code></pre>"},{"location":"api_classes/data_source/","title":"DataSource","text":"<p>DataSource Examples</p> <p>Examples of using the DataSource class are in the Examples section at the bottom of this page. S3 data, local files, and Pandas dataframes, DataSource can read data from many different sources.</p> <p>DataSource: Manages AWS Data Catalog creation and management. DataSources are set up so that can easily be queried with AWS Athena. All DataSources are run through a full set of Exploratory Data Analysis (EDA) techniques (data quality, distributions, stats, outliers, etc.) DataSources can be viewed and explored within the Workbench Dashboard UI.</p>"},{"location":"api_classes/data_source/#workbench.api.data_source.DataSource","title":"<code>DataSource</code>","text":"<p>               Bases: <code>AthenaSource</code></p> <p>DataSource: Workbench DataSource API Class</p> Common Usage <pre><code>my_data = DataSource(name_of_source)\nmy_data.details()\nmy_features = my_data.to_features()\n</code></pre> Source code in <code>src/workbench/api/data_source.py</code> <pre><code>class DataSource(AthenaSource):\n    \"\"\"DataSource: Workbench DataSource API Class\n\n    Common Usage:\n        ```python\n        my_data = DataSource(name_of_source)\n        my_data.details()\n        my_features = my_data.to_features()\n        ```\n    \"\"\"\n\n    def __init__(self, source: Union[str, pd.DataFrame], name: str = None, tags: list = None, **kwargs):\n        \"\"\"\n        Initializes a new DataSource object.\n\n        Args:\n            source (Union[str, pd.DataFrame]): Source of data (existing name, filepath, S3 path, or a Pandas DataFrame)\n            name (str): The name of the data source (must be lowercase). If not specified, a name will be generated\n            tags (list[str]): A list of tags associated with the data source. If not specified tags will be generated.\n        \"\"\"\n\n        # Ensure the ds_name is valid\n        if name:\n            Artifact.is_name_valid(name)\n\n        # If the data source name wasn't given, generate it\n        else:\n            name = extract_data_source_basename(source)\n            name = Artifact.generate_valid_name(name)\n\n            # Sanity check for dataframe sources\n            if name == \"dataframe\":\n                msg = \"Set the 'name' argument in the constructor: DataSource(df, name='my_data')\"\n                self.log.critical(msg)\n                raise ValueError(msg)\n\n        # Set the tags and load the source\n        tags = [name] if tags is None else tags\n        self._load_source(source, name, tags)\n\n        # Call superclass init\n        super().__init__(name, **kwargs)\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"DataSource Details\n\n        Returns:\n            dict: A dictionary of details about the DataSource\n        \"\"\"\n        return super().details(**kwargs)\n\n    def query(self, query: str) -&gt; pd.DataFrame:\n        \"\"\"Query the AthenaSource\n\n        Args:\n            query (str): The query to run against the DataSource\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        return super().query(query)\n\n    def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame of ALL the data from this DataSource\n\n        Args:\n            include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n        Returns:\n            pd.DataFrame: A DataFrame of ALL the data from this DataSource\n\n        Note:\n            Obviously this is not recommended for large datasets :)\n        \"\"\"\n\n        # Get the table associated with the data\n        self.log.info(f\"Pulling all data from {self.uuid}...\")\n        table = super().table\n        query = f'SELECT * FROM \"{table}\"'\n        df = self.query(query)\n\n        # Drop any columns generated from AWS\n        if not include_aws_columns:\n            aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n            df = df.drop(columns=aws_cols, errors=\"ignore\")\n        return df\n\n    def to_features(\n        self,\n        name: str,\n        id_column: str = None,\n        tags: list = None,\n        event_time_column: str = None,\n        one_hot_columns: list = None,\n    ) -&gt; Union[FeatureSet, None]:\n        \"\"\"\n        Convert the DataSource to a FeatureSet\n\n        Args:\n            name (str): Set the name for feature set (must be lowercase).\n            id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n            tags (list, optional: Set the tags for the feature set. If not specified tags will be generated\n            event_time_column (str, optional): Set the event time for feature set. If not specified will be generated\n            one_hot_columns (list, optional): Set the columns to be one-hot encoded. (default: None)\n\n        Returns:\n            FeatureSet: The FeatureSet created from the DataSource (or None if the FeatureSet isn't created)\n        \"\"\"\n\n        # Ensure the feature_set_name is valid\n        if not Artifact.is_name_valid(name):\n            self.log.critical(f\"Invalid FeatureSet name: {name}, not creating FeatureSet!\")\n            return None\n\n        # Set the Tags\n        tags = [name] if tags is None else tags\n\n        # Transform the DataSource to a FeatureSet\n        data_to_features = DataToFeaturesLight(self.uuid, name)\n        data_to_features.set_output_tags(tags)\n        data_to_features.transform(\n            id_column=id_column,\n            event_time_column=event_time_column,\n            one_hot_columns=one_hot_columns,\n        )\n\n        # Return the FeatureSet (which will now be up-to-date)\n        return FeatureSet(name)\n\n    def _load_source(self, source: str, name: str, tags: list):\n        \"\"\"Load the source of the data\"\"\"\n        self.log.info(f\"Loading source: {source}...\")\n\n        # Pandas DataFrame Source\n        if isinstance(source, pd.DataFrame):\n            my_loader = PandasToData(name)\n            my_loader.set_input(source)\n            my_loader.set_output_tags(tags)\n            my_loader.transform()\n\n        # S3 Source\n        source = source if isinstance(source, str) else str(source)\n        if source.startswith(\"s3://\"):\n            my_loader = S3ToDataSourceLight(source, name)\n            my_loader.set_output_tags(tags)\n            my_loader.transform()\n\n        # File Source\n        elif os.path.isfile(source):\n            my_loader = CSVToDataSource(source, name)\n            my_loader.set_output_tags(tags)\n            my_loader.transform()\n</code></pre>"},{"location":"api_classes/data_source/#workbench.api.data_source.DataSource.__init__","title":"<code>__init__(source, name=None, tags=None, **kwargs)</code>","text":"<p>Initializes a new DataSource object.</p> <p>Parameters:</p> Name Type Description Default <code>source</code> <code>Union[str, DataFrame]</code> <p>Source of data (existing name, filepath, S3 path, or a Pandas DataFrame)</p> required <code>name</code> <code>str</code> <p>The name of the data source (must be lowercase). If not specified, a name will be generated</p> <code>None</code> <code>tags</code> <code>list[str]</code> <p>A list of tags associated with the data source. If not specified tags will be generated.</p> <code>None</code> Source code in <code>src/workbench/api/data_source.py</code> <pre><code>def __init__(self, source: Union[str, pd.DataFrame], name: str = None, tags: list = None, **kwargs):\n    \"\"\"\n    Initializes a new DataSource object.\n\n    Args:\n        source (Union[str, pd.DataFrame]): Source of data (existing name, filepath, S3 path, or a Pandas DataFrame)\n        name (str): The name of the data source (must be lowercase). If not specified, a name will be generated\n        tags (list[str]): A list of tags associated with the data source. If not specified tags will be generated.\n    \"\"\"\n\n    # Ensure the ds_name is valid\n    if name:\n        Artifact.is_name_valid(name)\n\n    # If the data source name wasn't given, generate it\n    else:\n        name = extract_data_source_basename(source)\n        name = Artifact.generate_valid_name(name)\n\n        # Sanity check for dataframe sources\n        if name == \"dataframe\":\n            msg = \"Set the 'name' argument in the constructor: DataSource(df, name='my_data')\"\n            self.log.critical(msg)\n            raise ValueError(msg)\n\n    # Set the tags and load the source\n    tags = [name] if tags is None else tags\n    self._load_source(source, name, tags)\n\n    # Call superclass init\n    super().__init__(name, **kwargs)\n</code></pre>"},{"location":"api_classes/data_source/#workbench.api.data_source.DataSource.details","title":"<code>details(**kwargs)</code>","text":"<p>DataSource Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the DataSource</p> Source code in <code>src/workbench/api/data_source.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"DataSource Details\n\n    Returns:\n        dict: A dictionary of details about the DataSource\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/data_source/#workbench.api.data_source.DataSource.pull_dataframe","title":"<code>pull_dataframe(include_aws_columns=False)</code>","text":"<p>Return a DataFrame of ALL the data from this DataSource</p> <p>Parameters:</p> Name Type Description Default <code>include_aws_columns</code> <code>bool</code> <p>Include the AWS columns in the DataFrame (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of ALL the data from this DataSource</p> Note <p>Obviously this is not recommended for large datasets :)</p> Source code in <code>src/workbench/api/data_source.py</code> <pre><code>def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame of ALL the data from this DataSource\n\n    Args:\n        include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n    Returns:\n        pd.DataFrame: A DataFrame of ALL the data from this DataSource\n\n    Note:\n        Obviously this is not recommended for large datasets :)\n    \"\"\"\n\n    # Get the table associated with the data\n    self.log.info(f\"Pulling all data from {self.uuid}...\")\n    table = super().table\n    query = f'SELECT * FROM \"{table}\"'\n    df = self.query(query)\n\n    # Drop any columns generated from AWS\n    if not include_aws_columns:\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        df = df.drop(columns=aws_cols, errors=\"ignore\")\n    return df\n</code></pre>"},{"location":"api_classes/data_source/#workbench.api.data_source.DataSource.query","title":"<code>query(query)</code>","text":"<p>Query the AthenaSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the DataSource</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/workbench/api/data_source.py</code> <pre><code>def query(self, query: str) -&gt; pd.DataFrame:\n    \"\"\"Query the AthenaSource\n\n    Args:\n        query (str): The query to run against the DataSource\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    return super().query(query)\n</code></pre>"},{"location":"api_classes/data_source/#workbench.api.data_source.DataSource.to_features","title":"<code>to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None)</code>","text":"<p>Convert the DataSource to a FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>Set the name for feature set (must be lowercase).</p> required <code>id_column</code> <code>str</code> <p>The ID column (if not specified, an 'auto_id' will be generated).</p> <code>None</code> <code>tags</code> <code>list</code> <p>Set the tags for the feature set. If not specified tags will be generated</p> <code>None</code> <code>event_time_column</code> <code>str</code> <p>Set the event time for feature set. If not specified will be generated</p> <code>None</code> <code>one_hot_columns</code> <code>list</code> <p>Set the columns to be one-hot encoded. (default: None)</p> <code>None</code> <p>Returns:</p> Name Type Description <code>FeatureSet</code> <code>Union[FeatureSet, None]</code> <p>The FeatureSet created from the DataSource (or None if the FeatureSet isn't created)</p> Source code in <code>src/workbench/api/data_source.py</code> <pre><code>def to_features(\n    self,\n    name: str,\n    id_column: str = None,\n    tags: list = None,\n    event_time_column: str = None,\n    one_hot_columns: list = None,\n) -&gt; Union[FeatureSet, None]:\n    \"\"\"\n    Convert the DataSource to a FeatureSet\n\n    Args:\n        name (str): Set the name for feature set (must be lowercase).\n        id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n        tags (list, optional: Set the tags for the feature set. If not specified tags will be generated\n        event_time_column (str, optional): Set the event time for feature set. If not specified will be generated\n        one_hot_columns (list, optional): Set the columns to be one-hot encoded. (default: None)\n\n    Returns:\n        FeatureSet: The FeatureSet created from the DataSource (or None if the FeatureSet isn't created)\n    \"\"\"\n\n    # Ensure the feature_set_name is valid\n    if not Artifact.is_name_valid(name):\n        self.log.critical(f\"Invalid FeatureSet name: {name}, not creating FeatureSet!\")\n        return None\n\n    # Set the Tags\n    tags = [name] if tags is None else tags\n\n    # Transform the DataSource to a FeatureSet\n    data_to_features = DataToFeaturesLight(self.uuid, name)\n    data_to_features.set_output_tags(tags)\n    data_to_features.transform(\n        id_column=id_column,\n        event_time_column=event_time_column,\n        one_hot_columns=one_hot_columns,\n    )\n\n    # Return the FeatureSet (which will now be up-to-date)\n    return FeatureSet(name)\n</code></pre>"},{"location":"api_classes/data_source/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Create a DataSource from an S3 Path or File Path</p> datasource_from_s3.py<pre><code>from workbench.api.data_source import DataSource\n\n# Create a DataSource from an S3 Path (or a local file)\nsource_path = \"s3://workbench-public-data/common/abalone.csv\"\n# source_path = \"/full/path/to/local/file.csv\"\n\nmy_data = DataSource(source_path)\nprint(my_data.details())\n</code></pre> <p>Create a DataSource from a Pandas Dataframe</p> datasource_from_df.py<pre><code>from workbench.utils.test_data_generator import TestDataGenerator\nfrom workbench.api.data_source import DataSource\n\n# Create a DataSource from a Pandas DataFrame\ngen_data = TestDataGenerator()\ndf = gen_data.person_data()\n\ntest_data = DataSource(df, name=\"test_data\")\nprint(test_data.details())\n</code></pre> <p>Query a DataSource</p> <p>All Workbench DataSources use AWS Athena, so any query that you can make with Athena is accessible through the DataSource API.</p> datasource_query.py<pre><code>from workbench.api.data_source import DataSource\n\n# Grab a DataSource\nmy_data = DataSource(\"abalone_data\")\n\n# Make some queries using the Athena backend\ndf = my_data.query(\"select * from abalone_data where height &gt; .3\")\nprint(df.head())\n\ndf = my_data.query(\"select * from abalone_data where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040        0.0050                      2\n</code></pre> <p>Create a FeatureSet from a DataSource</p> datasource_to_featureset.py<pre><code>from workbench.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\ntest_data = DataSource('test_data')\nmy_features = test_data.to_features(\"test_features\")\nprint(my_features.details())\n</code></pre>"},{"location":"api_classes/data_source/#workbench-ui","title":"Workbench UI","text":"<p>Whenever a DataSource is created Workbench performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p> Workbench Dashboard: DataSources <p>Not Finding a particular method?</p> <p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: Workbench Core Classes</p>"},{"location":"api_classes/df_store/","title":"Workbench DataFrame Storage","text":"<p>Examples</p> <p>Examples of using the Parameter Storage class are listed at the bottom of this page Examples.</p>"},{"location":"api_classes/df_store/#why-dataframe-storage","title":"Why DataFrame Storage?","text":"<p>Great question, there's a couple of reasons. The first is that the Parameter Store in AWS has a 4KB limit, so that won't support any kind of 'real data'. The second reason is that DataFrames are commonly used as part of the data engineering, science, and ML pipeline construction process. Providing storage of named DataFrames in an accessible location that can be inspected and used by your ML Team comes in super handy.</p>"},{"location":"api_classes/df_store/#efficient-storage","title":"Efficient Storage","text":"<p>All DataFrames are stored in the Parquet format using 'snappy' storage. Parquet is a columnar storage format that efficiently handles large datasets, and using Snappy compression reduces file size while maintaining fast read/write speeds.</p> <p>DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy</p>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore","title":"<code>DFStore</code>","text":"<p>               Bases: <code>AWSDFStore</code></p> <p>DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy</p> Common Usage <pre><code>df_store = DFStore()\n\n# List Data\ndf_store.list()\n\n# Add DataFrame\ndf = pd.DataFrame({\"A\": [1, 2], \"B\": [3, 4]})\ndf_store.upsert(\"/test/my_data\", df)\n\n# Retrieve DataFrame\ndf = df_store.get(\"/test/my_data\")\nprint(df)\n\n# Delete Data\ndf_store.delete(\"/test/my_data\")\n</code></pre> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>class DFStore(AWSDFStore):\n    \"\"\"DFStore: Fast/efficient storage of DataFrames using AWS S3/Parquet/Snappy\n\n    Common Usage:\n        ```python\n        df_store = DFStore()\n\n        # List Data\n        df_store.list()\n\n        # Add DataFrame\n        df = pd.DataFrame({\"A\": [1, 2], \"B\": [3, 4]})\n        df_store.upsert(\"/test/my_data\", df)\n\n        # Retrieve DataFrame\n        df = df_store.get(\"/test/my_data\")\n        print(df)\n\n        # Delete Data\n        df_store.delete(\"/test/my_data\")\n        ```\n    \"\"\"\n\n    def __init__(self, path_prefix: Union[str, None] = None):\n        \"\"\"DFStore Init Method\n\n        Args:\n            path_prefix (Union[str, None], optional): Add a path prefix to storage locations (Defaults to None)\n        \"\"\"\n        self.log = logging.getLogger(\"workbench\")\n\n        # Initialize the SuperClass\n        super().__init__(path_prefix=path_prefix)\n\n    def list(self, include_cache: bool = False) -&gt; list:\n        \"\"\"List all the objects in the data_store prefix.\n\n        Args:\n            include_cache (bool, optional): Include cache objects in the list (Defaults to False).\n\n        Returns:\n            list: A list of all the objects in the data_store prefix.\n        \"\"\"\n        return super().list(include_cache=include_cache)\n\n    def summary(self, include_cache: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Return a nicely formatted summary of object locations, sizes (in MB), and modified dates.\n\n        Args:\n            include_cache (bool, optional): Include cache objects in the summary (Defaults to False).\n\n        Returns:\n            pd.DataFrame: A formatted DataFrame with the summary details.\n        \"\"\"\n        return super().summary(include_cache=include_cache)\n\n    def details(self, include_cache: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame with detailed metadata for all objects in the data_store prefix.\n\n        Args:\n            include_cache (bool, optional): Include cache objects in the details (Defaults to False).\n\n        Returns:\n            pd.DataFrame: A DataFrame with detailed metadata for all objects in the data_store prefix.\n        \"\"\"\n        return super().details(include_cache=include_cache)\n\n    def check(self, location: str) -&gt; bool:\n        \"\"\"Check if a DataFrame exists at the specified location\n\n        Args:\n            location (str): The location of the data to check.\n\n        Returns:\n            bool: True if the data exists, False otherwise.\n        \"\"\"\n        return super().check(location)\n\n    def get(self, location: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve a DataFrame from AWS S3.\n\n        Args:\n            location (str): The location of the data to retrieve.\n\n        Returns:\n            pd.DataFrame: The retrieved DataFrame or None if not found.\n        \"\"\"\n        _df = super().get(location)\n        if _df is None:\n            self.log.error(f\"Dataframe not found at location: {location}\")\n        return _df\n\n    def upsert(self, location: str, data: Union[pd.DataFrame, pd.Series]):\n        \"\"\"Insert or update a DataFrame or Series in the AWS S3.\n\n        Args:\n            location (str): The location of the data.\n            data (Union[pd.DataFrame, pd.Series]): The data to be stored.\n        \"\"\"\n        super().upsert(location, data)\n\n    def last_modified(self, location: str) -&gt; Union[datetime, None]:\n        \"\"\"Get the last modified date of the DataFrame at the specified location.\n\n        Args:\n            location (str): The location of the data to check.\n\n        Returns:\n            Union[datetime, None]: The last modified date of the DataFrame or None if not found.\n        \"\"\"\n        return super().last_modified(location)\n\n    def delete(self, location: str):\n        \"\"\"Delete a DataFrame from the AWS S3.\n\n        Args:\n            location (str): The location of the data to delete.\n        \"\"\"\n        super().delete(location)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.__init__","title":"<code>__init__(path_prefix=None)</code>","text":"<p>DFStore Init Method</p> <p>Parameters:</p> Name Type Description Default <code>path_prefix</code> <code>Union[str, None]</code> <p>Add a path prefix to storage locations (Defaults to None)</p> <code>None</code> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def __init__(self, path_prefix: Union[str, None] = None):\n    \"\"\"DFStore Init Method\n\n    Args:\n        path_prefix (Union[str, None], optional): Add a path prefix to storage locations (Defaults to None)\n    \"\"\"\n    self.log = logging.getLogger(\"workbench\")\n\n    # Initialize the SuperClass\n    super().__init__(path_prefix=path_prefix)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.check","title":"<code>check(location)</code>","text":"<p>Check if a DataFrame exists at the specified location</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to check.</p> required <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the data exists, False otherwise.</p> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def check(self, location: str) -&gt; bool:\n    \"\"\"Check if a DataFrame exists at the specified location\n\n    Args:\n        location (str): The location of the data to check.\n\n    Returns:\n        bool: True if the data exists, False otherwise.\n    \"\"\"\n    return super().check(location)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.delete","title":"<code>delete(location)</code>","text":"<p>Delete a DataFrame from the AWS S3.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to delete.</p> required Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def delete(self, location: str):\n    \"\"\"Delete a DataFrame from the AWS S3.\n\n    Args:\n        location (str): The location of the data to delete.\n    \"\"\"\n    super().delete(location)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.details","title":"<code>details(include_cache=False)</code>","text":"<p>Return a DataFrame with detailed metadata for all objects in the data_store prefix.</p> <p>Parameters:</p> Name Type Description Default <code>include_cache</code> <code>bool</code> <p>Include cache objects in the details (Defaults to False).</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame with detailed metadata for all objects in the data_store prefix.</p> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def details(self, include_cache: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame with detailed metadata for all objects in the data_store prefix.\n\n    Args:\n        include_cache (bool, optional): Include cache objects in the details (Defaults to False).\n\n    Returns:\n        pd.DataFrame: A DataFrame with detailed metadata for all objects in the data_store prefix.\n    \"\"\"\n    return super().details(include_cache=include_cache)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.get","title":"<code>get(location)</code>","text":"<p>Retrieve a DataFrame from AWS S3.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to retrieve.</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The retrieved DataFrame or None if not found.</p> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def get(self, location: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve a DataFrame from AWS S3.\n\n    Args:\n        location (str): The location of the data to retrieve.\n\n    Returns:\n        pd.DataFrame: The retrieved DataFrame or None if not found.\n    \"\"\"\n    _df = super().get(location)\n    if _df is None:\n        self.log.error(f\"Dataframe not found at location: {location}\")\n    return _df\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.last_modified","title":"<code>last_modified(location)</code>","text":"<p>Get the last modified date of the DataFrame at the specified location.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data to check.</p> required <p>Returns:</p> Type Description <code>Union[datetime, None]</code> <p>Union[datetime, None]: The last modified date of the DataFrame or None if not found.</p> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def last_modified(self, location: str) -&gt; Union[datetime, None]:\n    \"\"\"Get the last modified date of the DataFrame at the specified location.\n\n    Args:\n        location (str): The location of the data to check.\n\n    Returns:\n        Union[datetime, None]: The last modified date of the DataFrame or None if not found.\n    \"\"\"\n    return super().last_modified(location)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.list","title":"<code>list(include_cache=False)</code>","text":"<p>List all the objects in the data_store prefix.</p> <p>Parameters:</p> Name Type Description Default <code>include_cache</code> <code>bool</code> <p>Include cache objects in the list (Defaults to False).</p> <code>False</code> <p>Returns:</p> Name Type Description <code>list</code> <code>list</code> <p>A list of all the objects in the data_store prefix.</p> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def list(self, include_cache: bool = False) -&gt; list:\n    \"\"\"List all the objects in the data_store prefix.\n\n    Args:\n        include_cache (bool, optional): Include cache objects in the list (Defaults to False).\n\n    Returns:\n        list: A list of all the objects in the data_store prefix.\n    \"\"\"\n    return super().list(include_cache=include_cache)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.summary","title":"<code>summary(include_cache=False)</code>","text":"<p>Return a nicely formatted summary of object locations, sizes (in MB), and modified dates.</p> <p>Parameters:</p> Name Type Description Default <code>include_cache</code> <code>bool</code> <p>Include cache objects in the summary (Defaults to False).</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A formatted DataFrame with the summary details.</p> Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def summary(self, include_cache: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Return a nicely formatted summary of object locations, sizes (in MB), and modified dates.\n\n    Args:\n        include_cache (bool, optional): Include cache objects in the summary (Defaults to False).\n\n    Returns:\n        pd.DataFrame: A formatted DataFrame with the summary details.\n    \"\"\"\n    return super().summary(include_cache=include_cache)\n</code></pre>"},{"location":"api_classes/df_store/#workbench.api.df_store.DFStore.upsert","title":"<code>upsert(location, data)</code>","text":"<p>Insert or update a DataFrame or Series in the AWS S3.</p> <p>Parameters:</p> Name Type Description Default <code>location</code> <code>str</code> <p>The location of the data.</p> required <code>data</code> <code>Union[DataFrame, Series]</code> <p>The data to be stored.</p> required Source code in <code>src/workbench/api/df_store.py</code> <pre><code>def upsert(self, location: str, data: Union[pd.DataFrame, pd.Series]):\n    \"\"\"Insert or update a DataFrame or Series in the AWS S3.\n\n    Args:\n        location (str): The location of the data.\n        data (Union[pd.DataFrame, pd.Series]): The data to be stored.\n    \"\"\"\n    super().upsert(location, data)\n</code></pre>"},{"location":"api_classes/df_store/#examples","title":"Examples","text":"<p>These example show how to use the <code>DFStore()</code> class to list, add, and get dataframes from AWS Storage.</p> <p>Workbench REPL</p> <p>If you'd like to experiment with listing, adding, and getting dataframe with the <code>DFStore()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! Workbench REPL</p> Using DataFrame Store<pre><code>from workbench.api.df_store import DFStore\ndf_store = DFStore()\n\n# List DataFrames\ndf_store().list()\n\nOut[1]:\nml/confustion_matrix  (0.002MB/2024-09-23 16:44:48)\nml/hold_out_ids  (0.094MB/2024-09-23 16:57:01)\nml/my_awesome_df  (0.002MB/2024-09-23 16:43:30)\nml/shap_values  (0.019MB/2024-09-23 16:57:21)\n\n# Add a DataFrame\ndf = pd.DataFrame({\"A\": [1]*1000, \"B\": [3]*1000})\ndf_store.upsert(\"test/test_df\", df)\n\n# List DataFrames (we can just use the REPR)\ndf_store\n\nOut[2]:\nml/confustion_matrix  (0.002MB/2024-09-23 16:44:48)\nml/hold_out_ids  (0.094MB/2024-09-23 16:57:01)\nml/my_awesome_df  (0.002MB/2024-09-23 16:43:30)\nml/shap_values  (0.019MB/2024-09-23 16:57:21)\ntest/test_df  (0.002MB/2024-09-23 16:59:27)\n\n# Retrieve dataframes\nreturn_df = df_store.get(\"test/test_df\")\nreturn_df.head()\n\nOut[3]:\n   A  B\n0  1  3\n1  1  3\n2  1  3\n3  1  3\n4  1  3\n\n# Delete dataframes\ndf_store.delete(\"test/test_df\")\n</code></pre> <p>Compressed Storage is Automatic</p> <p>All DataFrames are stored in the Parquet format using 'snappy' storage. Parquet is a columnar storage format that efficiently handles large datasets, and using Snappy compression reduces file size while maintaining fast read/write speeds.</p>"},{"location":"api_classes/endpoint/","title":"Endpoint","text":"<p>Endpoint Examples</p> <p>Examples of using the Endpoint class are listed at the bottom of this page Examples.</p> <p>Endpoint: Manages AWS Endpoint creation and deployment. Endpoints are automatically set up and provisioned for deployment into AWS. Endpoints can be viewed in the AWS Sagemaker interfaces or in the Workbench Dashboard UI, which provides additional model details and performance metrics</p>"},{"location":"api_classes/endpoint/#workbench.api.endpoint.Endpoint","title":"<code>Endpoint</code>","text":"<p>               Bases: <code>EndpointCore</code></p> <p>Endpoint: Workbench Endpoint API Class</p> Common Usage <pre><code>my_endpoint = Endpoint(name)\nmy_endpoint.details()\nmy_endpoint.inference(eval_df)\n</code></pre> Source code in <code>src/workbench/api/endpoint.py</code> <pre><code>class Endpoint(EndpointCore):\n    \"\"\"Endpoint: Workbench Endpoint API Class\n\n    Common Usage:\n        ```python\n        my_endpoint = Endpoint(name)\n        my_endpoint.details()\n        my_endpoint.inference(eval_df)\n        ```\n    \"\"\"\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Endpoint Details\n\n        Returns:\n            dict: A dictionary of details about the Endpoint\n        \"\"\"\n        return super().details(**kwargs)\n\n    def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n        Args:\n            eval_df (pd.DataFrame): The DataFrame to run predictions on\n            capture_uuid (str, optional): The UUID of the capture to use (default: None)\n            id_column (str, optional): The name of the column to use as the ID (default: None)\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n        \"\"\"\n        return super().inference(eval_df, capture_uuid, id_column)\n\n    def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the FeatureSet evaluation data\n\n        Args:\n            capture (bool): Capture the inference results\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n        \"\"\"\n        return super().auto_inference(capture)\n\n    def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n        Args:\n            eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n\n        Note:\n            There's no sanity checks or error handling... just FAST Inference!\n        \"\"\"\n        return super().fast_inference(eval_df)\n</code></pre>"},{"location":"api_classes/endpoint/#workbench.api.endpoint.Endpoint.auto_inference","title":"<code>auto_inference(capture=False)</code>","text":"<p>Run inference on the Endpoint using the FeatureSet evaluation data</p> <p>Parameters:</p> Name Type Description Default <code>capture</code> <code>bool</code> <p>Capture the inference results</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Source code in <code>src/workbench/api/endpoint.py</code> <pre><code>def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the FeatureSet evaluation data\n\n    Args:\n        capture (bool): Capture the inference results\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n    \"\"\"\n    return super().auto_inference(capture)\n</code></pre>"},{"location":"api_classes/endpoint/#workbench.api.endpoint.Endpoint.details","title":"<code>details(**kwargs)</code>","text":"<p>Endpoint Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Endpoint</p> Source code in <code>src/workbench/api/endpoint.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"Endpoint Details\n\n    Returns:\n        dict: A dictionary of details about the Endpoint\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/endpoint/#workbench.api.endpoint.Endpoint.fast_inference","title":"<code>fast_inference(eval_df)</code>","text":"<p>Run inference on the Endpoint using the provided DataFrame</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>The DataFrame to run predictions on</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Note <p>There's no sanity checks or error handling... just FAST Inference!</p> Source code in <code>src/workbench/api/endpoint.py</code> <pre><code>def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n    Args:\n        eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n\n    Note:\n        There's no sanity checks or error handling... just FAST Inference!\n    \"\"\"\n    return super().fast_inference(eval_df)\n</code></pre>"},{"location":"api_classes/endpoint/#workbench.api.endpoint.Endpoint.inference","title":"<code>inference(eval_df, capture_uuid=None, id_column=None)</code>","text":"<p>Run inference on the Endpoint using the provided DataFrame</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>The DataFrame to run predictions on</p> required <code>capture_uuid</code> <code>str</code> <p>The UUID of the capture to use (default: None)</p> <code>None</code> <code>id_column</code> <code>str</code> <p>The name of the column to use as the ID (default: None)</p> <code>None</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Source code in <code>src/workbench/api/endpoint.py</code> <pre><code>def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n    Args:\n        eval_df (pd.DataFrame): The DataFrame to run predictions on\n        capture_uuid (str, optional): The UUID of the capture to use (default: None)\n        id_column (str, optional): The name of the column to use as the ID (default: None)\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n    \"\"\"\n    return super().inference(eval_df, capture_uuid, id_column)\n</code></pre>"},{"location":"api_classes/endpoint/#examples","title":"Examples","text":"<p>Run Inference on an Endpoint</p> endpoint_inference.py<pre><code>from workbench.api.feature_set import FeatureSet\nfrom workbench.api.model import Model\nfrom workbench.api.endpoint import Endpoint\n\n# Grab an existing Endpoint\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# Workbench has full ML Pipeline provenance, so we can backtrack the inputs,\n# get a DataFrame of data (not used for training) and run inference\nmodel = Model(endpoint.get_input())\nfs = FeatureSet(model.get_input())\nathena_table = fs.view(\"training\").table\ndf = fs.query(f\"SELECT * FROM {athena_table} where training = FALSE\")\n\n# Run inference/predictions on the Endpoint\nresults_df = endpoint.inference(df)\n\n# Run inference/predictions and capture the results\nresults_df = endpoint.inference(df, capture=True)\n\n# Run inference/predictions using the FeatureSet evaluation data\nresults_df = endpoint.auto_inference(capture=True)\n</code></pre> <p>Output</p> <p><pre><code>Processing...\n     class_number_of_rings  prediction\n0                       13   11.477922\n1                       12   12.316887\n2                        8    7.612847\n3                        8    9.663341\n4                        9    9.075263\n..                     ...         ...\n839                      8    8.069856\n840                     15   14.915502\n841                     11   10.977605\n842                     10   10.173433\n843                      7    7.297976\n</code></pre> Endpoint Details</p> <p>The details() method</p> <p>The <code>detail()</code> method on the Endpoint class provides a lot of useful information. All of the Workbench classes have a <code>details()</code> method try it out!</p> endpoint_details.py<pre><code>from workbench.api.endpoint import Endpoint\nfrom pprint import pprint\n\n# Get Endpoint and print out it's details\nendpoint = Endpoint(\"abalone-regression-end\")\npprint(endpoint.details())\n</code></pre> <p>Output</p> <pre><code>{\n 'input': 'abalone-regression',\n 'instance': 'Serverless (2GB/5)',\n 'model_metrics':   metric_name  value\n            0        RMSE  2.190\n            1         MAE  1.544\n            2          R2  0.504,\n 'model_name': 'abalone-regression',\n 'model_type': 'regressor',\n 'modified': datetime.datetime(2023, 12, 29, 17, 48, 35, 115000, tzinfo=datetime.timezone.utc),\n     class_number_of_rings  prediction\n0                        9    8.648378\n1                       11    9.717787\n2                       11   10.933070\n3                       10    9.899738\n4                        9   10.014504\n..                     ...         ...\n495                     10   10.261657\n496                      9   10.788254\n497                     13    7.779886\n498                     12   14.718514\n499                     13   10.637320\n 'workbench_tags': ['abalone', 'regression'],\n 'status': 'InService',\n 'uuid': 'abalone-regression-end',\n 'variant': 'AllTraffic'}\n</code></pre> <p>Endpoint Metrics</p> endpoint_metrics.py<pre><code>from workbench.api.endpoint import Endpoint\n\n# Grab an existing Endpoint\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# Workbench tracks both Model performance and Endpoint Metrics\nmodel_metrics = endpoint.details()[\"model_metrics\"]\nendpoint_metrics = endpoint.endpoint_metrics()\nprint(model_metrics)\nprint(endpoint_metrics)\n</code></pre> <p>Output</p> <pre><code>  metric_name  value\n0        RMSE  2.190\n1         MAE  1.544\n2          R2  0.504\n\n    Invocations  ModelLatency  OverheadLatency  ModelSetupTime  Invocation5XXErrors\n29          0.0          0.00             0.00            0.00                  0.0\n30          1.0          1.11            23.73           23.34                  0.0\n31          0.0          0.00             0.00            0.00                  0.0\n48          0.0          0.00             0.00            0.00                  0.0\n49          5.0          0.45             9.64           23.57                  0.0\n50          2.0          0.57             0.08            0.00                  0.0\n51          0.0          0.00             0.00            0.00                  0.0\n60          4.0          0.33             5.80           22.65                  0.0\n61          1.0          1.11            23.35           23.10                  0.0\n62          0.0          0.00             0.00            0.00                  0.0\n...\n</code></pre>"},{"location":"api_classes/endpoint/#workbench-ui","title":"Workbench UI","text":"<p>Running these few lines of code creates and deploys an AWS Endpoint. The Endpoint artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the Workbench Dashboard UI. Workbench will monitor the endpoint, plot invocations, latencies, and tracks error metrics.</p> Workbench Dashboard: Endpoints <p>Not Finding a particular method?</p> <p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: Workbench Core Classes</p>"},{"location":"api_classes/feature_set/","title":"FeatureSet","text":"<p>FeatureSet Examples</p> <p>Examples of using the FeatureSet Class are in the Examples section at the bottom of this page. AWS Feature Store and Feature Groups are quite complicated to set up manually but the Workbench FeatureSet makes it a breeze!</p> <p>FeatureSet: Manages AWS Feature Store/Group creation and management. FeatureSets are set up so they can easily be queried with AWS Athena. All FeatureSets are run through a full set of Exploratory Data Analysis (EDA) techniques (data quality, distributions, stats, outliers, etc.) FeatureSets can be viewed and explored within the Workbench Dashboard UI.</p>"},{"location":"api_classes/feature_set/#workbench.api.feature_set.FeatureSet","title":"<code>FeatureSet</code>","text":"<p>               Bases: <code>FeatureSetCore</code></p> <p>FeatureSet: Workbench FeatureSet API Class</p> Common Usage <pre><code>my_features = FeatureSet(name)\nmy_features.details()\nmy_features.to_model(\n    name=\"abalone-regression\",\n    model_type=ModelType.REGRESSOR,\n    target_column=\"class_number_of_rings\"\n    feature_list=[\"my\", \"best\", \"features\"])\n)\n</code></pre> Source code in <code>src/workbench/api/feature_set.py</code> <pre><code>class FeatureSet(FeatureSetCore):\n    \"\"\"FeatureSet: Workbench FeatureSet API Class\n\n    Common Usage:\n        ```python\n        my_features = FeatureSet(name)\n        my_features.details()\n        my_features.to_model(\n            name=\"abalone-regression\",\n            model_type=ModelType.REGRESSOR,\n            target_column=\"class_number_of_rings\"\n            feature_list=[\"my\", \"best\", \"features\"])\n        )\n        ```\n    \"\"\"\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"FeatureSet Details\n\n        Returns:\n            dict: A dictionary of details about the FeatureSet\n        \"\"\"\n        return super().details(**kwargs)\n\n    def query(self, query: str, **kwargs) -&gt; pd.DataFrame:\n        \"\"\"Query the AthenaSource\n\n        Args:\n            query (str): The query to run against the FeatureSet\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        return super().query(query, **kwargs)\n\n    def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame of ALL the data from this FeatureSet\n\n        Args:\n            include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n        Returns:\n            pd.DataFrame: A DataFrame of ALL the data from this FeatureSet\n\n        Note:\n            Obviously this is not recommended for large datasets :)\n        \"\"\"\n\n        # Get the table associated with the data\n        self.log.info(f\"Pulling all data from {self.uuid}...\")\n        query = f\"SELECT * FROM {self.athena_table}\"\n        df = self.query(query)\n\n        # Drop any columns generated from AWS\n        if not include_aws_columns:\n            aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n            df = df.drop(columns=aws_cols, errors=\"ignore\")\n        return df\n\n    def to_model(\n        self,\n        name: str,\n        model_type: ModelType,\n        tags: list = None,\n        description: str = None,\n        feature_list: list = None,\n        target_column: str = None,\n        scikit_model_class: str = None,\n        model_import_str: str = None,\n        **kwargs,\n    ) -&gt; Union[Model, None]:\n        \"\"\"Create a Model from the FeatureSet\n\n        Args:\n\n            name (str): The name of the Model to create\n            model_type (ModelType): The type of model to create (See workbench.model.ModelType)\n            tags (list, optional): Set the tags for the model.  If not given tags will be generated.\n            description (str, optional): Set the description for the model. If not give a description is generated.\n            feature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.\n            target_column (str, optional): The target column for the model (use None for unsupervised model)\n            scikit_model_class (str, optional): Scikit model class to use (e.g. \"KMeans\", default: None)\n            model_import_str (str, optional): The import for the model (e.g. \"from sklearn.cluster import KMeans\")\n\n        Returns:\n            Model: The Model created from the FeatureSet (or None if the Model could not be created)\n        \"\"\"\n\n        # Ensure the model_name is valid\n        if name:\n            if not Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False):\n                self.log.critical(f\"Invalid Model name: {name}, not creating Model!\")\n                return None\n\n        # If the model_name wasn't given generate it\n        else:\n            name = self.uuid.replace(\"_features\", \"\") + \"-model\"\n            name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n        # Create the Model Tags\n        tags = [name] if tags is None else tags\n\n        # Transform the FeatureSet into a Model\n        features_to_model = FeaturesToModel(\n            feature_uuid=self.uuid,\n            model_uuid=name,\n            model_type=model_type,\n            scikit_model_class=scikit_model_class,\n            model_import_str=model_import_str,\n        )\n        features_to_model.set_output_tags(tags)\n        features_to_model.transform(\n            target_column=target_column, description=description, feature_list=feature_list, **kwargs\n        )\n\n        # Return the Model\n        return Model(name)\n</code></pre>"},{"location":"api_classes/feature_set/#workbench.api.feature_set.FeatureSet.details","title":"<code>details(**kwargs)</code>","text":"<p>FeatureSet Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the FeatureSet</p> Source code in <code>src/workbench/api/feature_set.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"FeatureSet Details\n\n    Returns:\n        dict: A dictionary of details about the FeatureSet\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/feature_set/#workbench.api.feature_set.FeatureSet.pull_dataframe","title":"<code>pull_dataframe(include_aws_columns=False)</code>","text":"<p>Return a DataFrame of ALL the data from this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>include_aws_columns</code> <code>bool</code> <p>Include the AWS columns in the DataFrame (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of ALL the data from this FeatureSet</p> Note <p>Obviously this is not recommended for large datasets :)</p> Source code in <code>src/workbench/api/feature_set.py</code> <pre><code>def pull_dataframe(self, include_aws_columns=False) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame of ALL the data from this FeatureSet\n\n    Args:\n        include_aws_columns (bool): Include the AWS columns in the DataFrame (default: False)\n\n    Returns:\n        pd.DataFrame: A DataFrame of ALL the data from this FeatureSet\n\n    Note:\n        Obviously this is not recommended for large datasets :)\n    \"\"\"\n\n    # Get the table associated with the data\n    self.log.info(f\"Pulling all data from {self.uuid}...\")\n    query = f\"SELECT * FROM {self.athena_table}\"\n    df = self.query(query)\n\n    # Drop any columns generated from AWS\n    if not include_aws_columns:\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        df = df.drop(columns=aws_cols, errors=\"ignore\")\n    return df\n</code></pre>"},{"location":"api_classes/feature_set/#workbench.api.feature_set.FeatureSet.query","title":"<code>query(query, **kwargs)</code>","text":"<p>Query the AthenaSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the FeatureSet</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/workbench/api/feature_set.py</code> <pre><code>def query(self, query: str, **kwargs) -&gt; pd.DataFrame:\n    \"\"\"Query the AthenaSource\n\n    Args:\n        query (str): The query to run against the FeatureSet\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    return super().query(query, **kwargs)\n</code></pre>"},{"location":"api_classes/feature_set/#workbench.api.feature_set.FeatureSet.to_model","title":"<code>to_model(name, model_type, tags=None, description=None, feature_list=None, target_column=None, scikit_model_class=None, model_import_str=None, **kwargs)</code>","text":"<p>Create a Model from the FeatureSet</p> <p>Args:</p> <pre><code>name (str): The name of the Model to create\nmodel_type (ModelType): The type of model to create (See workbench.model.ModelType)\ntags (list, optional): Set the tags for the model.  If not given tags will be generated.\ndescription (str, optional): Set the description for the model. If not give a description is generated.\nfeature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.\ntarget_column (str, optional): The target column for the model (use None for unsupervised model)\nscikit_model_class (str, optional): Scikit model class to use (e.g. \"KMeans\", default: None)\nmodel_import_str (str, optional): The import for the model (e.g. \"from sklearn.cluster import KMeans\")\n</code></pre> <p>Returns:</p> Name Type Description <code>Model</code> <code>Union[Model, None]</code> <p>The Model created from the FeatureSet (or None if the Model could not be created)</p> Source code in <code>src/workbench/api/feature_set.py</code> <pre><code>def to_model(\n    self,\n    name: str,\n    model_type: ModelType,\n    tags: list = None,\n    description: str = None,\n    feature_list: list = None,\n    target_column: str = None,\n    scikit_model_class: str = None,\n    model_import_str: str = None,\n    **kwargs,\n) -&gt; Union[Model, None]:\n    \"\"\"Create a Model from the FeatureSet\n\n    Args:\n\n        name (str): The name of the Model to create\n        model_type (ModelType): The type of model to create (See workbench.model.ModelType)\n        tags (list, optional): Set the tags for the model.  If not given tags will be generated.\n        description (str, optional): Set the description for the model. If not give a description is generated.\n        feature_list (list, optional): Set the feature list for the model. If not given a feature list is generated.\n        target_column (str, optional): The target column for the model (use None for unsupervised model)\n        scikit_model_class (str, optional): Scikit model class to use (e.g. \"KMeans\", default: None)\n        model_import_str (str, optional): The import for the model (e.g. \"from sklearn.cluster import KMeans\")\n\n    Returns:\n        Model: The Model created from the FeatureSet (or None if the Model could not be created)\n    \"\"\"\n\n    # Ensure the model_name is valid\n    if name:\n        if not Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False):\n            self.log.critical(f\"Invalid Model name: {name}, not creating Model!\")\n            return None\n\n    # If the model_name wasn't given generate it\n    else:\n        name = self.uuid.replace(\"_features\", \"\") + \"-model\"\n        name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n    # Create the Model Tags\n    tags = [name] if tags is None else tags\n\n    # Transform the FeatureSet into a Model\n    features_to_model = FeaturesToModel(\n        feature_uuid=self.uuid,\n        model_uuid=name,\n        model_type=model_type,\n        scikit_model_class=scikit_model_class,\n        model_import_str=model_import_str,\n    )\n    features_to_model.set_output_tags(tags)\n    features_to_model.transform(\n        target_column=target_column, description=description, feature_list=feature_list, **kwargs\n    )\n\n    # Return the Model\n    return Model(name)\n</code></pre>"},{"location":"api_classes/feature_set/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Create a FeatureSet from a Datasource</p> datasource_to_featureset.py<pre><code>from workbench.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\nds = DataSource('test_data')\nfs = ds.to_features(\"test_features\", id_column=\"id\")\nprint(fs.details())\n</code></pre> <p>FeatureSet EDA Statistics</p> <p>featureset_eda.py<pre><code>from workbench.api.feature_set import FeatureSet\nimport pandas as pd\n\n# Grab a FeatureSet and pull some of the EDA Stats\nmy_features = FeatureSet('test_features')\n\n# Grab some of the EDA Stats\ncorr_data = my_features.correlations()\ncorr_df = pd.DataFrame(corr_data)\nprint(corr_df)\n\n# Get some outliers\noutliers = my_features.outliers()\npprint(outliers.head())\n\n# Full set of EDA Stats\neda_stats = my_features.column_stats()\npprint(eda_stats)\n</code></pre> Output</p> <pre><code>                 age  food_pizza  food_steak  food_sushi  food_tacos    height        id  iq_score\nage              NaN   -0.188645   -0.256356    0.263048    0.054211  0.439678 -0.054948 -0.295513\nfood_pizza -0.188645         NaN   -0.288175   -0.229591   -0.196818 -0.494380  0.137282  0.395378\nfood_steak -0.256356   -0.288175         NaN   -0.374920   -0.321403 -0.002542 -0.005199  0.076477\nfood_sushi  0.263048   -0.229591   -0.374920         NaN   -0.256064  0.536396  0.038279 -0.435033\nfood_tacos  0.054211   -0.196818   -0.321403   -0.256064         NaN -0.091493 -0.051398  0.033364\nheight      0.439678   -0.494380   -0.002542    0.536396   -0.091493       NaN -0.117372 -0.655210\nid         -0.054948    0.137282   -0.005199    0.038279   -0.051398 -0.117372       NaN  0.106020\niq_score   -0.295513    0.395378    0.076477   -0.435033    0.033364 -0.655210  0.106020       NaN\n\n        name     height      weight         salary  age    iq_score  likes_dogs  food_pizza  food_steak  food_sushi  food_tacos outlier_group\n0  Person 96  57.582840  148.461349   80000.000000   43  150.000000           1           0           0           0           0    height_low\n1  Person 68  73.918663  189.527313  219994.000000   80  100.000000           0           0           0           1           0  iq_score_low\n2  Person 49  70.381790  261.237000  175633.703125   49  107.933998           0           0           0           1           0  iq_score_low\n3  Person 90  73.488739  193.840698  227760.000000   72  110.821541           1           0           0           0           0   salary_high\n\n&lt;lots of EDA data and statistics&gt;\n</code></pre> <p>Query a FeatureSet</p> <p>All Workbench FeatureSet have an 'offline' store that uses AWS Athena, so any query that you can make with Athena is accessible through the FeatureSet API.</p> featureset_query.py<pre><code>from workbench.api.feature_set import FeatureSet\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"abalone_features\")\n\n# Make some queries using the Athena backend\ndf = my_features.query(\"select * from abalone_features where height &gt; .3\")\nprint(df.head())\n\ndf = my_features.query(\"select * from abalone_features where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040         0.0050                      2\n</code></pre> <p>Create a Model from a FeatureSet</p> featureset_to_model.py<pre><code>from workbench.api.feature_set import FeatureSet\nfrom workbench.api.model import ModelType\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet('test_features')\n\n# Create a Model from the FeatureSet\n# Note: ModelTypes can be CLASSIFIER, REGRESSOR, \n#       UNSUPERVISED, or TRANSFORMER\nmy_model = my_features.to_model(name=\"test-model\", \n                                model_type=ModelType.REGRESSOR, \n                                target_column=\"iq_score\")\npprint(my_model.details())\n</code></pre> <p>Output</p> <pre><code>{'approval_status': 'Approved',\n 'content_types': ['text/csv'],\n ...\n 'inference_types': ['ml.t2.medium'],\n 'input': 'test_features',\n 'model_metrics':   metric_name  value\n                0        RMSE  7.924\n                1         MAE  6.554,\n                2          R2  0.604,\n 'regression_predictions':       iq_score  prediction\n                            0   136.519012  139.964460\n                            1   133.616974  130.819950\n                            2   122.495415  124.967834\n                            3   133.279510  121.010284\n                            4   127.881073  113.825005\n    ...\n 'response_types': ['text/csv'],\n 'workbench_tags': ['test-model'],\n 'shapley_values': None,\n 'size': 0.0,\n 'status': 'Completed',\n 'transform_types': ['ml.m5.large'],\n 'uuid': 'test-model',\n 'version': 1}\n</code></pre>"},{"location":"api_classes/feature_set/#workbench-ui","title":"Workbench UI","text":"<p>Whenever a FeatureSet is created Workbench performs a comprehensive set of Exploratory Data Analysis techniques on your data, pushes the results into AWS, and provides a detailed web visualization of the results.</p> Workbench Dashboard: FeatureSets <p>Not Finding a particular method?</p> <p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: Workbench Core Classes</p>"},{"location":"api_classes/meta/","title":"Meta","text":"<p>Meta Examples</p> <p>Examples of using the Meta class are listed at the bottom of this page Examples.</p> <p>Meta: A class that provides high level information and summaries of Cloud Platform Artifacts. The Meta class provides 'account' information, configuration, etc. It also provides metadata for Artifacts, such as Data Sources, Feature Sets, Models, and Endpoints.</p>"},{"location":"api_classes/meta/#workbench.api.meta.Meta","title":"<code>Meta</code>","text":"<p>               Bases: <code>CloudMeta</code></p> <p>Meta: A class that provides metadata functionality for Cloud Platform Artifacts.</p> Common Usage <pre><code>from workbench.api import Meta\nmeta = Meta()\n\n# Get the AWS Account Info\nmeta.account()\nmeta.config()\n\n# These are 'list' methods\nmeta.etl_jobs()\nmeta.data_sources()\nmeta.feature_sets(details=True/False)\nmeta.models(details=True/False)\nmeta.endpoints()\nmeta.views()\nmeta.pipelines()\n\n# These are 'describe' methods\nmeta.data_source(\"abalone_data\")\nmeta.feature_set(\"abalone_features\")\nmeta.model(\"abalone-regression\")\nmeta.endpoint(\"abalone-endpoint\")\n</code></pre> Source code in <code>src/workbench/api/meta.py</code> <pre><code>class Meta(CloudMeta):\n    \"\"\"Meta: A class that provides metadata functionality for Cloud Platform Artifacts.\n\n    Common Usage:\n       ```python\n       from workbench.api import Meta\n       meta = Meta()\n\n       # Get the AWS Account Info\n       meta.account()\n       meta.config()\n\n       # These are 'list' methods\n       meta.etl_jobs()\n       meta.data_sources()\n       meta.feature_sets(details=True/False)\n       meta.models(details=True/False)\n       meta.endpoints()\n       meta.views()\n       meta.pipelines()\n\n       # These are 'describe' methods\n       meta.data_source(\"abalone_data\")\n       meta.feature_set(\"abalone_features\")\n       meta.model(\"abalone-regression\")\n       meta.endpoint(\"abalone-endpoint\")\n       ```\n    \"\"\"\n\n    def account(self) -&gt; dict:\n        \"\"\"Cloud Platform Account Info\n\n        Returns:\n            dict: Cloud Platform Account Info\n        \"\"\"\n        return super().account()\n\n    def config(self) -&gt; dict:\n        \"\"\"Return the current Workbench Configuration\n\n        Returns:\n            dict: The current Workbench Configuration\n        \"\"\"\n        return super().config()\n\n    def incoming_data(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about data in the incoming raw data\n\n        Returns:\n            pd.DataFrame: A summary of the incoming raw data\n        \"\"\"\n        return super().incoming_data()\n\n    def etl_jobs(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n        Returns:\n            pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n        \"\"\"\n        return super().etl_jobs()\n\n    def data_sources(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n        \"\"\"\n        return super().data_sources()\n\n    def views(self, database: str = \"workbench\") -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n        Args:\n            database (str, optional): Glue database. Defaults to 'workbench'.\n\n        Returns:\n            pd.DataFrame: A summary of all the Views, for the given database, in AWS\n        \"\"\"\n        return super().views(database=database)\n\n    def feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n        \"\"\"\n        return super().feature_sets(details=details)\n\n    def models(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n        \"\"\"\n        return super().models(details=details)\n\n    def endpoints(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n        \"\"\"\n        return super().endpoints()\n\n    def pipelines(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the ML Pipelines deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Pipelines in the Cloud Platform\n        \"\"\"\n        return super().pipelines()\n\n    def glue_job(self, job_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Glue Job\n\n        Args:\n            job_name (str): The name of the Glue Job\n\n        Returns:\n            dict: The details of the Glue Job (None if not found)\n        \"\"\"\n        return super().glue_job(job_name=job_name)\n\n    def data_source(self, data_source_name: str, database: str = \"workbench\") -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Data Source\n\n        Args:\n            data_source_name (str): The name of the Data Source\n            database (str, optional): The Glue database. Defaults to 'workbench'.\n\n        Returns:\n            dict: The details of the Data Source (None if not found)\n        \"\"\"\n        return super().data_source(table_name=data_source_name, database=database)\n\n    def feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Feature Set\n\n        Args:\n            feature_set_name (str): The name of the Feature Set\n\n        Returns:\n            dict: The details of the Feature Set (None if not found)\n        \"\"\"\n        return super().feature_set(feature_group_name=feature_set_name)\n\n    def model(self, model_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Model\n\n        Args:\n            model_name (str): The name of the Model\n\n        Returns:\n            dict: The details of the Model (None if not found)\n        \"\"\"\n        return super().model(model_group_name=model_name)\n\n    def endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Endpoint\n\n        Args:\n            endpoint_name (str): The name of the Endpoint\n\n        Returns:\n            dict: The details of the Endpoint (None if not found)\n        \"\"\"\n        return super().endpoint(endpoint_name=endpoint_name)\n\n    def __repr__(self):\n        return f\"Meta()\\n\\t{super().__repr__()}\"\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.account","title":"<code>account()</code>","text":"<p>Cloud Platform Account Info</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>Cloud Platform Account Info</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def account(self) -&gt; dict:\n    \"\"\"Cloud Platform Account Info\n\n    Returns:\n        dict: Cloud Platform Account Info\n    \"\"\"\n    return super().account()\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.config","title":"<code>config()</code>","text":"<p>Return the current Workbench Configuration</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The current Workbench Configuration</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def config(self) -&gt; dict:\n    \"\"\"Return the current Workbench Configuration\n\n    Returns:\n        dict: The current Workbench Configuration\n    \"\"\"\n    return super().config()\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.data_source","title":"<code>data_source(data_source_name, database='workbench')</code>","text":"<p>Get the details of a specific Data Source</p> <p>Parameters:</p> Name Type Description Default <code>data_source_name</code> <code>str</code> <p>The name of the Data Source</p> required <code>database</code> <code>str</code> <p>The Glue database. Defaults to 'workbench'.</p> <code>'workbench'</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Data Source (None if not found)</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def data_source(self, data_source_name: str, database: str = \"workbench\") -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Data Source\n\n    Args:\n        data_source_name (str): The name of the Data Source\n        database (str, optional): The Glue database. Defaults to 'workbench'.\n\n    Returns:\n        dict: The details of the Data Source (None if not found)\n    \"\"\"\n    return super().data_source(table_name=data_source_name, database=database)\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.data_sources","title":"<code>data_sources()</code>","text":"<p>Get a summary of the Data Sources deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def data_sources(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n    \"\"\"\n    return super().data_sources()\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.endpoint","title":"<code>endpoint(endpoint_name)</code>","text":"<p>Get the details of a specific Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>The name of the Endpoint</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Endpoint (None if not found)</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Endpoint\n\n    Args:\n        endpoint_name (str): The name of the Endpoint\n\n    Returns:\n        dict: The details of the Endpoint (None if not found)\n    \"\"\"\n    return super().endpoint(endpoint_name=endpoint_name)\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.endpoints","title":"<code>endpoints()</code>","text":"<p>Get a summary of the Endpoints deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Endpoints in the Cloud Platform</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def endpoints(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n    \"\"\"\n    return super().endpoints()\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.etl_jobs","title":"<code>etl_jobs()</code>","text":"<p>Get summary data about Extract, Transform, Load (ETL) Jobs</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def etl_jobs(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n    Returns:\n        pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n    \"\"\"\n    return super().etl_jobs()\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.feature_set","title":"<code>feature_set(feature_set_name)</code>","text":"<p>Get the details of a specific Feature Set</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_name</code> <code>str</code> <p>The name of the Feature Set</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Feature Set (None if not found)</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Feature Set\n\n    Args:\n        feature_set_name (str): The name of the Feature Set\n\n    Returns:\n        dict: The details of the Feature Set (None if not found)\n    \"\"\"\n    return super().feature_set(feature_group_name=feature_set_name)\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.feature_sets","title":"<code>feature_sets(details=False)</code>","text":"<p>Get a summary of the Feature Sets deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n    \"\"\"\n    return super().feature_sets(details=details)\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.glue_job","title":"<code>glue_job(job_name)</code>","text":"<p>Get the details of a specific Glue Job</p> <p>Parameters:</p> Name Type Description Default <code>job_name</code> <code>str</code> <p>The name of the Glue Job</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Glue Job (None if not found)</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def glue_job(self, job_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Glue Job\n\n    Args:\n        job_name (str): The name of the Glue Job\n\n    Returns:\n        dict: The details of the Glue Job (None if not found)\n    \"\"\"\n    return super().glue_job(job_name=job_name)\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.incoming_data","title":"<code>incoming_data()</code>","text":"<p>Get summary data about data in the incoming raw data</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the incoming raw data</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def incoming_data(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about data in the incoming raw data\n\n    Returns:\n        pd.DataFrame: A summary of the incoming raw data\n    \"\"\"\n    return super().incoming_data()\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.model","title":"<code>model(model_name)</code>","text":"<p>Get the details of a specific Model</p> <p>Parameters:</p> Name Type Description Default <code>model_name</code> <code>str</code> <p>The name of the Model</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Model (None if not found)</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def model(self, model_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Model\n\n    Args:\n        model_name (str): The name of the Model\n\n    Returns:\n        dict: The details of the Model (None if not found)\n    \"\"\"\n    return super().model(model_group_name=model_name)\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.models","title":"<code>models(details=False)</code>","text":"<p>Get a summary of the Models deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Models deployed in the Cloud Platform</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def models(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n    \"\"\"\n    return super().models(details=details)\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.pipelines","title":"<code>pipelines()</code>","text":"<p>Get a summary of the ML Pipelines deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Pipelines in the Cloud Platform</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def pipelines(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the ML Pipelines deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Pipelines in the Cloud Platform\n    \"\"\"\n    return super().pipelines()\n</code></pre>"},{"location":"api_classes/meta/#workbench.api.meta.Meta.views","title":"<code>views(database='workbench')</code>","text":"<p>Get a summary of the all the Views, for the given database, in AWS</p> <p>Parameters:</p> Name Type Description Default <code>database</code> <code>str</code> <p>Glue database. Defaults to 'workbench'.</p> <code>'workbench'</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of all the Views, for the given database, in AWS</p> Source code in <code>src/workbench/api/meta.py</code> <pre><code>def views(self, database: str = \"workbench\") -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n    Args:\n        database (str, optional): Glue database. Defaults to 'workbench'.\n\n    Returns:\n        pd.DataFrame: A summary of all the Views, for the given database, in AWS\n    \"\"\"\n    return super().views(database=database)\n</code></pre>"},{"location":"api_classes/meta/#examples","title":"Examples","text":"<p>These example show how to use the <code>Meta()</code> class to pull lists of artifacts from AWS. DataSources, FeatureSets, Models, Endpoints and more. If you're building a web interface plugin, the Meta class is a great place to start.</p> <p>Workbench REPL</p> <p>If you'd like to see exactly what data/details you get back from the <code>Meta()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! Workbench REPL</p> Using Workbench REPL<pre><code>meta = Meta()\nmodel_df = meta.models()\nmodel_df\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\n</code></pre> <p>List the Models in AWS</p> meta_list_models.py<pre><code>from workbench.api import Meta\n\n# Create our Meta Class and get a list of our Models\nmeta = Meta()\nmodel_df = meta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names:\n    pprint(meta.model(name))\n</code></pre> <p>Output</p> <pre><code>Number of Models: 3\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\nwine-classification\nabalone-regression-full\nabalone-regression\n</code></pre> <p>Getting Model Performance Metrics</p> meta_model_metrics.py<pre><code>from workbench.api import Meta\n\n# Create our Meta Class and get a list of our Models\nmeta = Meta()\nmodel_df = meta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names[:5]:\n    model_details = meta.model(name)\n    print(f\"\\n\\nModel: {name}\")\n    performance_metrics = model_details[\"workbench_meta\"][\"workbench_inference_metrics\"]\n    print(f\"\\tPerformance Metrics: {performance_metrics}\")\n</code></pre> <p>Output</p> <pre><code>wine-classification\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/wine-classification\n    Description: Wine Classification Model\n    Tags: wine::classification\n    Performance Metrics:\n        [{'wine_class': 'TypeA', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 12}, {'wine_class': 'TypeB', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 14}, {'wine_class': 'TypeC', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 9}]\n\nabalone-regression\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/abalone-regression\n    Description: Abalone Regression Model\n    Tags: abalone::regression\n    Performance Metrics:\n        [{'MAE': 1.64, 'RMSE': 2.246, 'R2': 0.502, 'MAPE': 16.393, 'MedAE': 1.209, 'NumRows': 834}]\n</code></pre> <p>List the Endpoints in AWS</p> meta_list_endpoints.py<pre><code>from pprint import pprint\nfrom workbench.api import Meta\n\n# Create our Meta Class and get a list of our Endpoints\nmeta = Meta()\nendpoint_df = meta.endpoints()\nprint(f\"Number of Endpoints: {len(endpoint_df)}\")\nprint(endpoint_df)\n\n# Get more details data on the Endpoints\nendpoint_names = endpoint_df[\"Name\"].tolist()\nfor name in endpoint_names:\n    pprint(meta.endpoint(name))\n</code></pre> <p>Output</p> <pre><code>Number of Endpoints: 2\n                      Name   Health            Instance           Created  ...     Status     Variant Capture Samp(%)\n0  wine-classification-end  healthy  Serverless (2GB/5)  2024-03-23 23:09  ...  InService  AllTraffic   False       -\n1   abalone-regression-end  healthy  Serverless (2GB/5)  2024-03-23 21:11  ...  InService  AllTraffic   False       -\n\n[2 rows x 10 columns]\nwine-classification-end\n&lt;lots of details about endpoints&gt;\n</code></pre> <p>Not Finding some particular AWS Data?</p> <p>The Workbench Meta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>"},{"location":"api_classes/model/","title":"Model","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p> <p>Model: Manages AWS Model Package/Group creation and management.</p> <p>Models are automatically set up and provisioned for deployment into AWS. Models can be viewed in the AWS Sagemaker interfaces or in the Workbench Dashboard UI, which provides additional model details and performance metrics</p>"},{"location":"api_classes/model/#workbench.api.model.Model","title":"<code>Model</code>","text":"<p>               Bases: <code>ModelCore</code></p> <p>Model: Workbench Model API Class.</p> Common Usage <pre><code>my_model = Model(name)\nmy_model.details()\nmy_model.to_endpoint()\n</code></pre> Source code in <code>src/workbench/api/model.py</code> <pre><code>class Model(ModelCore):\n    \"\"\"Model: Workbench Model API Class.\n\n    Common Usage:\n        ```python\n        my_model = Model(name)\n        my_model.details()\n        my_model.to_endpoint()\n        ```\n    \"\"\"\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Model Details.\n\n        Returns:\n            dict: A dictionary of details about the Model\n        \"\"\"\n        return super().details(**kwargs)\n\n    def to_endpoint(self, name: str = None, tags: list = None, serverless: bool = True) -&gt; Endpoint:\n        \"\"\"Create an Endpoint from the Model.\n\n        Args:\n            name (str): Set the name for the endpoint. If not specified, an automatic name will be generated\n            tags (list): Set the tags for the endpoint. If not specified automatic tags will be generated.\n            serverless (bool): Set the endpoint to be serverless (default: True)\n\n        Returns:\n            Endpoint: The Endpoint created from the Model\n        \"\"\"\n\n        # Ensure the endpoint_name is valid\n        if name:\n            Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False)\n\n        # If the endpoint_name wasn't given generate it\n        else:\n            name = self.uuid.replace(\"_features\", \"\") + \"-end\"\n            name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n        # Create the Endpoint Tags\n        tags = [name] if tags is None else tags\n\n        # Create an Endpoint from the Model\n        model_to_endpoint = ModelToEndpoint(self.uuid, name, serverless=serverless)\n        model_to_endpoint.set_output_tags(tags)\n        model_to_endpoint.transform()\n\n        # Return the Endpoint\n        return Endpoint(name)\n</code></pre>"},{"location":"api_classes/model/#workbench.api.model.Model.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the Model Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Model</p> Source code in <code>src/workbench/api/model.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Model Details.\n\n    Returns:\n        dict: A dictionary of details about the Model\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"api_classes/model/#workbench.api.model.Model.to_endpoint","title":"<code>to_endpoint(name=None, tags=None, serverless=True)</code>","text":"<p>Create an Endpoint from the Model.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>Set the name for the endpoint. If not specified, an automatic name will be generated</p> <code>None</code> <code>tags</code> <code>list</code> <p>Set the tags for the endpoint. If not specified automatic tags will be generated.</p> <code>None</code> <code>serverless</code> <code>bool</code> <p>Set the endpoint to be serverless (default: True)</p> <code>True</code> <p>Returns:</p> Name Type Description <code>Endpoint</code> <code>Endpoint</code> <p>The Endpoint created from the Model</p> Source code in <code>src/workbench/api/model.py</code> <pre><code>def to_endpoint(self, name: str = None, tags: list = None, serverless: bool = True) -&gt; Endpoint:\n    \"\"\"Create an Endpoint from the Model.\n\n    Args:\n        name (str): Set the name for the endpoint. If not specified, an automatic name will be generated\n        tags (list): Set the tags for the endpoint. If not specified automatic tags will be generated.\n        serverless (bool): Set the endpoint to be serverless (default: True)\n\n    Returns:\n        Endpoint: The Endpoint created from the Model\n    \"\"\"\n\n    # Ensure the endpoint_name is valid\n    if name:\n        Artifact.is_name_valid(name, delimiter=\"-\", lower_case=False)\n\n    # If the endpoint_name wasn't given generate it\n    else:\n        name = self.uuid.replace(\"_features\", \"\") + \"-end\"\n        name = Artifact.generate_valid_name(name, delimiter=\"-\")\n\n    # Create the Endpoint Tags\n    tags = [name] if tags is None else tags\n\n    # Create an Endpoint from the Model\n    model_to_endpoint = ModelToEndpoint(self.uuid, name, serverless=serverless)\n    model_to_endpoint.set_output_tags(tags)\n    model_to_endpoint.transform()\n\n    # Return the Endpoint\n    return Endpoint(name)\n</code></pre>"},{"location":"api_classes/model/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Create a Model from a FeatureSet</p> featureset_to_model.py<pre><code>from workbench.api.feature_set import FeatureSet\nfrom workbench.api.model import ModelType\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"test_features\")\n\n# Create a Model from the FeatureSet\n# Note: ModelTypes can be CLASSIFIER, REGRESSOR (XGBoost is default)\nmy_model = my_features.to_model(name=\"test-model\",\n                                model_type=ModelType.REGRESSOR, \n                                target_column=\"iq_score\")\npprint(my_model.details())\n</code></pre> <p>Output</p> <pre><code>{'approval_status': 'Approved',\n 'content_types': ['text/csv'],\n ...\n 'inference_types': ['ml.t2.medium'],\n 'input': 'test_features',\n 'model_metrics':   metric_name  value\n                0        RMSE  7.924\n                1         MAE  6.554,\n                2          R2  0.604,\n 'regression_predictions':       iq_score  prediction\n                            0   136.519012  139.964460\n                            1   133.616974  130.819950\n                            2   122.495415  124.967834\n                            3   133.279510  121.010284\n                            4   127.881073  113.825005\n    ...\n 'response_types': ['text/csv'],\n 'workbench_tags': ['test-model'],\n 'shapley_values': None,\n 'size': 0.0,\n 'status': 'Completed',\n 'transform_types': ['ml.m5.large'],\n 'uuid': 'test-model',\n 'version': 1}\n</code></pre> <p>Use a specific Scikit-Learn Model</p> <p>featureset_to_knn.py<pre><code>from workbench.api.feature_set import FeatureSet\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"abalone_features\")\n\n# Transform FeatureSet into KNN Regression Model\n# Note: model_class can be any sckit-learn model \n#  \"KNeighborsRegressor\", \"BayesianRidge\",\n#  \"GaussianNB\", \"AdaBoostClassifier\", etc\nmy_model = my_features.to_model(\n    scikit_model_class=\"KNeighborsRegressor\",\n    model_import_str=\"from sklearn.neighbors import KNeighborsRegressor\",\n    target_column=\"class_number_of_rings\",\n    name=\"abalone-knn-reg\",\n    description=\"Abalone KNN Regression\",\n    tags=[\"abalone\", \"knn\"],\n    train_all_data=True,\n)\npprint(my_model.details())\n</code></pre> Another Scikit-Learn Example</p> featureset_to_rfc.py<pre><code>from workbench.api.feature_set import FeatureSet\nfrom pprint import pprint\n\n# Grab a FeatureSet\nmy_features = FeatureSet(\"wine_features\")\n\n# Using a Scikit-Learn Model\n# Note: model_class can be any sckit-learn model (\"KMeans\", \"BayesianRidge\",\n#       \"GaussianNB\", \"AdaBoostClassifier\", \"Ridge, \"Lasso\", \"SVC\", \"SVR\", etc...)\nmy_model = my_features.to_model(\n    scikit_model_class=\"RandomForestClassifier\",\n    model_import_str=\"from sklearn.ensemble import RandomForestClassifier\",\n    target_column=\"wine_class\",\n    name=\"wine-rfc-class\",\n    description=\"Wine RandomForest Classification\",\n    tags=[\"wine\", \"rfc\"]\n)\npprint(my_model.details())\n</code></pre> <p>Create an Endpoint from a Model</p> <p>Endpoint Costs</p> <p>Serverless endpoints are a great option, they have no AWS charges when not running. A realtime endpoint has less latency (no cold start) but AWS charges an hourly fee which can add up quickly!</p> model_to_endpoint.py<pre><code>from workbench.api.model import Model\n\n# Grab the abalone regression Model\nmodel = Model(\"abalone-regression\")\n\n# By default, an Endpoint is serverless, you can\n# make a realtime endpoint with serverless=False\nmodel.to_endpoint(name=\"abalone-regression-end\",\n                  tags=[\"abalone\", \"regression\"],\n                  serverless=True)\n</code></pre> <p>Model Health Check and Metrics</p> model_metrics.py<pre><code>from workbench.api.model import Model\n\n# Grab the abalone-regression Model\nmodel = Model(\"abalone-regression\")\n\n# Perform a health check on the model\n# Note: The health_check() method returns 'issues' if there are any\n#       problems, so if there are no issues, the model is healthy\nhealth_issues = model.health_check()\nif not health_issues:\n    print(\"Model is Healthy\")\nelse:\n    print(\"Model has issues\")\n    print(health_issues)\n\n# Get the model metrics and regression predictions\nprint(model.model_metrics())\nprint(model.regression_predictions())\n</code></pre> <p>Output</p> <pre><code>Model is Healthy\n  metric_name  value\n0        RMSE  2.190\n1         MAE  1.544\n2          R2  0.504\n\n     class_number_of_rings  prediction\n0                        9    8.648378\n1                       11    9.717787\n2                       11   10.933070\n3                       10    9.899738\n4                        9   10.014504\n..                     ...         ...\n495                     10   10.261657\n496                      9   10.788254\n497                     13    7.779886\n498                     12   14.718514\n499                     13   10.637320\n</code></pre>"},{"location":"api_classes/model/#workbench-ui","title":"Workbench UI","text":"<p>Running these few lines of code creates an AWS Model Package Group and an AWS Model Package. These model artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the Workbench Dashboard UI.</p> Workbench Dashboard: Models <p>Not Finding a particular method?</p> <p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: Workbench Core Classes</p>"},{"location":"api_classes/monitor/","title":"Monitor","text":"<p>Monitor Examples</p> <p>Examples of using the Monitor class are listed at the bottom of this page Examples.</p> <p>Monitor: Manages AWS Endpoint Monitor creation and deployment. Endpoints Monitors are set up and provisioned for deployment into AWS. Monitors can be viewed in the AWS Sagemaker interfaces or in the Workbench Dashboard UI, which provides additional monitor details and performance metrics</p>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor","title":"<code>Monitor</code>","text":"<p>               Bases: <code>MonitorCore</code></p> <p>Monitor: Workbench Monitor API Class</p> Common Usage <pre><code>mon = Endpoint(name).get_monitor()  # Pull from endpoint OR\nmon = Monitor(name)                 # Create using Endpoint Name\nmon.summary()\nmon.details()\n\n# One time setup methods\nmon.add_data_capture()\nmon.create_baseline()\nmon.create_monitoring_schedule()\n\n# Pull information from the monitor\nbaseline_df = mon.get_baseline()\nconstraints_df = mon.get_constraints()\nstats_df = mon.get_statistics()\ninput_df, output_df = mon.get_latest_data_capture()\n</code></pre> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>class Monitor(MonitorCore):\n    \"\"\"Monitor: Workbench Monitor API Class\n\n    Common Usage:\n       ```\n       mon = Endpoint(name).get_monitor()  # Pull from endpoint OR\n       mon = Monitor(name)                 # Create using Endpoint Name\n       mon.summary()\n       mon.details()\n\n       # One time setup methods\n       mon.add_data_capture()\n       mon.create_baseline()\n       mon.create_monitoring_schedule()\n\n       # Pull information from the monitor\n       baseline_df = mon.get_baseline()\n       constraints_df = mon.get_constraints()\n       stats_df = mon.get_statistics()\n       input_df, output_df = mon.get_latest_data_capture()\n       ```\n    \"\"\"\n\n    def summary(self) -&gt; dict:\n        \"\"\"Monitor Summary\n\n        Returns:\n            dict: A dictionary of summary information about the Monitor\n        \"\"\"\n        return super().summary()\n\n    def details(self) -&gt; dict:\n        \"\"\"Monitor Details\n\n        Returns:\n            dict: A dictionary of details about the Monitor\n        \"\"\"\n        return super().details()\n\n    def add_data_capture(self, capture_percentage=100):\n        \"\"\"\n        Add data capture configuration for this Monitor/endpoint.\n\n        Args:\n            capture_percentage (int): Percentage of data to capture. Defaults to 100.\n        \"\"\"\n        super().add_data_capture(capture_percentage)\n\n    def create_baseline(self, recreate: bool = False):\n        \"\"\"Code to create a baseline for monitoring\n\n        Args:\n            recreate (bool): If True, recreate the baseline even if it already exists\n\n        Notes:\n            This will create/write three files to the baseline_dir:\n            - baseline.csv\n            - constraints.json\n            - statistics.json\n        \"\"\"\n        super().create_baseline(recreate)\n\n    def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n        \"\"\"\n        Sets up the monitoring schedule for the model endpoint.\n\n        Args:\n            schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n            recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n        \"\"\"\n        super().create_monitoring_schedule(schedule, recreate)\n\n    def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"\n        Get the latest data capture input and output from S3.\n\n        Returns:\n            DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n        \"\"\"\n        return super().get_latest_data_capture()\n\n    def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n        Returns:\n            pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n        \"\"\"\n        return super().get_baseline()\n\n    def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the constraints from the baseline\n\n        Returns:\n           pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n        \"\"\"\n        return super().get_constraints()\n\n    def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the statistics from the baseline\n\n        Returns:\n            pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n        \"\"\"\n        return super().get_statistics()\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.add_data_capture","title":"<code>add_data_capture(capture_percentage=100)</code>","text":"<p>Add data capture configuration for this Monitor/endpoint.</p> <p>Parameters:</p> Name Type Description Default <code>capture_percentage</code> <code>int</code> <p>Percentage of data to capture. Defaults to 100.</p> <code>100</code> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def add_data_capture(self, capture_percentage=100):\n    \"\"\"\n    Add data capture configuration for this Monitor/endpoint.\n\n    Args:\n        capture_percentage (int): Percentage of data to capture. Defaults to 100.\n    \"\"\"\n    super().add_data_capture(capture_percentage)\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.create_baseline","title":"<code>create_baseline(recreate=False)</code>","text":"<p>Code to create a baseline for monitoring</p> <p>Parameters:</p> Name Type Description Default <code>recreate</code> <code>bool</code> <p>If True, recreate the baseline even if it already exists</p> <code>False</code> Notes <p>This will create/write three files to the baseline_dir: - baseline.csv - constraints.json - statistics.json</p> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def create_baseline(self, recreate: bool = False):\n    \"\"\"Code to create a baseline for monitoring\n\n    Args:\n        recreate (bool): If True, recreate the baseline even if it already exists\n\n    Notes:\n        This will create/write three files to the baseline_dir:\n        - baseline.csv\n        - constraints.json\n        - statistics.json\n    \"\"\"\n    super().create_baseline(recreate)\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.create_monitoring_schedule","title":"<code>create_monitoring_schedule(schedule='hourly', recreate=False)</code>","text":"<p>Sets up the monitoring schedule for the model endpoint.</p> <p>Parameters:</p> Name Type Description Default <code>schedule</code> <code>str</code> <p>The schedule for the monitoring job (hourly or daily, defaults to hourly).</p> <code>'hourly'</code> <code>recreate</code> <code>bool</code> <p>If True, recreate the monitoring schedule even if it already exists.</p> <code>False</code> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n    \"\"\"\n    Sets up the monitoring schedule for the model endpoint.\n\n    Args:\n        schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n        recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n    \"\"\"\n    super().create_monitoring_schedule(schedule, recreate)\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.details","title":"<code>details()</code>","text":"<p>Monitor Details</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Monitor</p> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def details(self) -&gt; dict:\n    \"\"\"Monitor Details\n\n    Returns:\n        dict: A dictionary of details about the Monitor\n    \"\"\"\n    return super().details()\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.get_baseline","title":"<code>get_baseline()</code>","text":"<p>Code to get the baseline CSV from the S3 baseline directory</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)</p> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n    Returns:\n        pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n    \"\"\"\n    return super().get_baseline()\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.get_constraints","title":"<code>get_constraints()</code>","text":"<p>Code to get the constraints from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)</p> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the constraints from the baseline\n\n    Returns:\n       pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n    \"\"\"\n    return super().get_constraints()\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.get_latest_data_capture","title":"<code>get_latest_data_capture()</code>","text":"<p>Get the latest data capture input and output from S3.</p> <p>Returns:</p> Name Type Description <code>DataFrame</code> <code>input), DataFrame(output</code> <p>Flattened and processed DataFrames for input and output data.</p> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n    \"\"\"\n    Get the latest data capture input and output from S3.\n\n    Returns:\n        DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n    \"\"\"\n    return super().get_latest_data_capture()\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.get_statistics","title":"<code>get_statistics()</code>","text":"<p>Code to get the statistics from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)</p> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the statistics from the baseline\n\n    Returns:\n        pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n    \"\"\"\n    return super().get_statistics()\n</code></pre>"},{"location":"api_classes/monitor/#workbench.api.monitor.Monitor.summary","title":"<code>summary()</code>","text":"<p>Monitor Summary</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of summary information about the Monitor</p> Source code in <code>src/workbench/api/monitor.py</code> <pre><code>def summary(self) -&gt; dict:\n    \"\"\"Monitor Summary\n\n    Returns:\n        dict: A dictionary of summary information about the Monitor\n    \"\"\"\n    return super().summary()\n</code></pre>"},{"location":"api_classes/monitor/#examples","title":"Examples","text":"<p>Initial Setup of the Endpoint Monitor</p> monitor_setup.py<pre><code>from workbench.api.monitor import Monitor\n\n# Create an Endpoint Monitor Class and perform initial Setup\nendpoint_name = \"abalone-regression-end-rt\"\nmon = Monitor(endpoint_name)\n\n# Add data capture to the endpoint\nmon.add_data_capture(capture_percentage=100)\n\n# Create a baseline for monitoring\nmon.create_baseline()\n\n# Set up the monitoring schedule\nmon.create_monitoring_schedule(schedule=\"hourly\")\n</code></pre> <p>Pulling Information from an Existing Monitor</p> monitor_usage.py<pre><code>from workbench.api.monitor import Monitor\nfrom workbench.api.endpoint import Endpoint\n\n# Construct a Monitor Class in one of Two Ways\nmon = Endpoint(\"abalone-regression-end-rt\").get_monitor()\nmon = Monitor(\"abalone-regression-end-rt\")\n\n# Check the summary and details of the monitoring class\nmon.summary()\nmon.details()\n\n# Check the baseline outputs (baseline, constraints, statistics)\nbase_df = mon.get_baseline()\nbase_df.head()\n\nconstraints_df = mon.get_constraints()\nconstraints_df.head()\n\nstatistics_df = mon.get_statistics()\nstatistics_df.head()\n\n# Get the latest data capture (inputs and outputs)\ninput_df, output_df = mon.get_latest_data_capture()\ninput_df.head()\noutput_df.head()\n</code></pre>"},{"location":"api_classes/monitor/#workbench-ui","title":"Workbench UI","text":"<p>Running these few lines of code creates and deploys an AWS Endpoint Monitor. The Monitor status and outputs can be viewed in the Sagemaker Console interfaces or in the Workbench Dashboard UI. Workbench will use the monitor to track various metrics including Data Quality, Model Bias, etc...</p> Workbench Dashboard: Endpoints <p>Not Finding a particular method?</p> <p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: Workbench Core Classes</p>"},{"location":"api_classes/overview/","title":"Overview","text":"<p>Just Getting Started?</p> <p>You're in the right place, the Workbench API Classes are the best way to get started with Workbench!</p>"},{"location":"api_classes/overview/#welcome-to-the-workbench-api-classes","title":"Welcome to the Workbench API Classes","text":"<p>These classes provide high-level APIs for the Workbench package, they enable your team to build full AWS Machine Learning Pipelines. They handle all the details around updating and managing a complex set of AWS Services. Each class provides an essential component of the overall ML Pipline. Simply combine the classes to build production ready, AWS powered, machine learning pipelines. </p> <ul> <li>DataSource: Manages AWS Data Catalog and Athena</li> <li>FeatureSet: Manages AWS Feature Store and Feature Groups</li> <li>Model: Manages the training and deployment of AWS Model Groups and Packages</li> <li>Endpoint: Manages the deployment and invocations/inference on AWS Endpoints</li> <li>Monitor: Manages the setup and deployment of AWS Endpoint Monitors</li> </ul> <p></p>"},{"location":"api_classes/overview/#example-ml-pipline","title":"Example ML Pipline","text":"full_ml_pipeline.py<pre><code>from workbench.api.data_source import DataSource\nfrom workbench.api.feature_set import FeatureSet\nfrom workbench.api.model import Model, ModelType\nfrom workbench.api.endpoint import Endpoint\n\n# Create the abalone_data DataSource\nds = DataSource(\"s3://workbench-public-data/common/abalone.csv\")\n\n# Now create a FeatureSet\nds.to_features(\"abalone_features\")\n\n# Create the abalone_regression Model\nfs = FeatureSet(\"abalone_features\")\nfs.to_model(\n    name=\"abalone-regression\",\n    model_type=ModelType.REGRESSOR,\n    target_column=\"class_number_of_rings\",\n    tags=[\"abalone\", \"regression\"],\n    description=\"Abalone Regression Model\",\n)\n\n# Create the abalone_regression Endpoint\nmodel = Model(\"abalone-regression\")\nmodel.to_endpoint(name=\"abalone-regression-end\", tags=[\"abalone\", \"regression\"])\n\n# Now we'll run inference on the endpoint\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# Get a DataFrame of data (not used to train) and run predictions\nathena_table = fs.view(\"training\").table\ndf = fs.query(f\"SELECT * FROM {athena_table} where training = FALSE\")\nresults = endpoint.predict(df)\nprint(results[[\"class_number_of_rings\", \"prediction\"]])\n</code></pre> <p>Output</p> <pre><code>Processing...\n     class_number_of_rings  prediction\n0                       12   10.477794\n1                       11    11.11835\n2                       14   13.605763\n3                       12   11.744759\n4                       17    15.55189\n..                     ...         ...\n826                      7    7.981503\n827                     11   11.246113\n828                      9    9.592911\n829                      6    6.129388\n830                      8    7.628252\n</code></pre> <p>Full AWS ML Pipeline Achievement Unlocked!</p> <p>Bing! You just built and deployed a full AWS Machine Learning Pipeline. You can now use the Workbench Dashboard web interface to inspect your AWS artifacts. A comprehensive set of Exploratory Data Analysis techniques and Model Performance Metrics are available for your entire team to review, inspect and interact with.</p> <p></p> <p>Examples</p> <p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p>"},{"location":"api_classes/parameter_store/","title":"Workbench Parameter Storage","text":"<p>Examples</p> <p>Examples of using the Parameter Storage class are listed at the bottom of this page Examples.</p> <p>ParameterStore: Manages Workbench parameters in AWS Systems Manager Parameter Store.</p>"},{"location":"api_classes/parameter_store/#workbench.api.parameter_store.ParameterStore","title":"<code>ParameterStore</code>","text":"<p>ParameterStore: Manages Workbench parameters in AWS Systems Manager Parameter Store.</p> Common Usage <pre><code>params = ParameterStore()\n\n# List Parameters\nparams.list()\n\n['/workbench/abalone_info',\n '/workbench/my_data',\n '/workbench/test',\n '/workbench/pipelines/my_pipeline']\n\n# Add Key\nparams.upsert(\"key\", \"value\")\nvalue = params.get(\"key\")\n\n# Add any data (lists, dictionaries, etc..)\nmy_data = {\"key\": \"value\", \"number\": 4.2, \"list\": [1,2,3]}\nparams.upsert(\"my_data\", my_data)\n\n# Retrieve data\nreturn_value = params.get(\"my_data\")\npprint(return_value)\n\n{'key': 'value', 'list': [1, 2, 3], 'number': 4.2}\n\n# Delete parameters\nparam_store.delete(\"my_data\")\n</code></pre> Source code in <code>src/workbench/api/parameter_store.py</code> <pre><code>class ParameterStore:\n    \"\"\"ParameterStore: Manages Workbench parameters in AWS Systems Manager Parameter Store.\n\n    Common Usage:\n        ```python\n        params = ParameterStore()\n\n        # List Parameters\n        params.list()\n\n        ['/workbench/abalone_info',\n         '/workbench/my_data',\n         '/workbench/test',\n         '/workbench/pipelines/my_pipeline']\n\n        # Add Key\n        params.upsert(\"key\", \"value\")\n        value = params.get(\"key\")\n\n        # Add any data (lists, dictionaries, etc..)\n        my_data = {\"key\": \"value\", \"number\": 4.2, \"list\": [1,2,3]}\n        params.upsert(\"my_data\", my_data)\n\n        # Retrieve data\n        return_value = params.get(\"my_data\")\n        pprint(return_value)\n\n        {'key': 'value', 'list': [1, 2, 3], 'number': 4.2}\n\n        # Delete parameters\n        param_store.delete(\"my_data\")\n        ```\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"ParameterStore Init Method\"\"\"\n        self.log = logging.getLogger(\"workbench\")\n\n        # Initialize a Workbench Session (to assume the Workbench ExecutionRole)\n        self.boto3_session = AWSSession().boto3_session\n\n        # Create a Systems Manager (SSM) client for Parameter Store operations\n        self.ssm_client = self.boto3_session.client(\"ssm\")\n\n    def list(self, prefix: str = None) -&gt; list:\n        \"\"\"List all parameters in the AWS Parameter Store, optionally filtering by a prefix.\n\n        Args:\n            prefix (str, optional): A prefix to filter the parameters by. Defaults to None.\n\n        Returns:\n            list: A list of parameter names and details.\n        \"\"\"\n        try:\n            # Set up parameters for the query\n            params = {\"MaxResults\": 50}\n\n            # If a prefix is provided, add the 'ParameterFilters' for optimization\n            if prefix:\n                params[\"ParameterFilters\"] = [{\"Key\": \"Name\", \"Option\": \"BeginsWith\", \"Values\": [prefix]}]\n\n            # Initialize the list to collect parameter names\n            all_parameters = []\n\n            # Make the initial call to describe parameters\n            response = self.ssm_client.describe_parameters(**params)\n\n            # Aggregate the names from the initial response\n            all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n            # Continue to paginate if there's a NextToken\n            while \"NextToken\" in response:\n                # Update the parameters with the NextToken for subsequent calls\n                params[\"NextToken\"] = response[\"NextToken\"]\n                response = self.ssm_client.describe_parameters(**params)\n\n                # Aggregate the names from the subsequent responses\n                all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n        except Exception as e:\n            self.log.error(f\"Failed to list parameters: {e}\")\n            return []\n\n        # Return the aggregated list of parameter names\n        return all_parameters\n\n    def get(self, name: str, warn: bool = True, decrypt: bool = True) -&gt; Union[str, list, dict, None]:\n        \"\"\"Retrieve a parameter value from the AWS Parameter Store.\n\n        Args:\n            name (str): The name of the parameter to retrieve.\n            warn (bool): Whether to log a warning if the parameter is not found.\n            decrypt (bool): Whether to decrypt secure string parameters.\n\n        Returns:\n            Union[str, list, dict, None]: The value of the parameter or None if not found.\n        \"\"\"\n        try:\n            # Retrieve the parameter from Parameter Store\n            response = self.ssm_client.get_parameter(Name=name, WithDecryption=decrypt)\n            value = response[\"Parameter\"][\"Value\"]\n\n            # Auto-detect and decompress if needed\n            if value.startswith(\"COMPRESSED:\"):\n                # Base64 decode and decompress\n                self.log.important(f\"Decompressing parameter '{name}'...\")\n                compressed_value = base64.b64decode(value[len(\"COMPRESSED:\") :])\n                value = zlib.decompress(compressed_value).decode(\"utf-8\")\n\n            # Attempt to parse the value back to its original type\n            try:\n                parsed_value = json.loads(value)\n                return parsed_value\n            except (json.JSONDecodeError, TypeError):\n                # If parsing fails, return the value as is (assumed to be a simple string)\n                return value\n\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] == \"ParameterNotFound\":\n                if warn:\n                    self.log.warning(f\"Parameter '{name}' not found\")\n            else:\n                self.log.error(f\"Failed to get parameter '{name}': {e}\")\n            return None\n\n    def upsert(self, name: str, value, overwrite: bool = True):\n        \"\"\"Insert or update a parameter in the AWS Parameter Store.\n\n        Args:\n            name (str): The name of the parameter.\n            value (str | list | dict): The value of the parameter.\n            overwrite (bool): Whether to overwrite an existing parameter (default: True)\n        \"\"\"\n        try:\n\n            # Anything that's not a string gets converted to JSON\n            if not isinstance(value, str):\n                value = json.dumps(value)\n\n            # Check size and compress if necessary\n            if len(value) &gt; 4096:\n                self.log.warning(f\"Parameter {name} exceeds 4KB ({len(value)} Bytes)  Compressing...\")\n                compressed_value = zlib.compress(value.encode(\"utf-8\"), level=9)\n                encoded_value = \"COMPRESSED:\" + base64.b64encode(compressed_value).decode(\"utf-8\")\n\n                # Report on the size of the compressed value\n                compressed_size = len(compressed_value)\n                if compressed_size &gt; 4096:\n                    doc_link = \"https://supercowpowers.github.io/workbench/api_classes/df_store\"\n                    self.log.error(f\"Compressed size {compressed_size} bytes, cannot store &gt; 4KB\")\n                    self.log.error(f\"For larger data use the DFStore() class ({doc_link})\")\n                    return\n\n                # Insert or update the compressed parameter in Parameter Store\n                try:\n                    # Insert or update the compressed parameter in Parameter Store\n                    self.ssm_client.put_parameter(Name=name, Value=encoded_value, Type=\"String\", Overwrite=overwrite)\n                    self.log.info(f\"Parameter '{name}' added/updated successfully with compression.\")\n                    return\n                except Exception as e:\n                    self.log.critical(f\"Failed to add/update compressed parameter '{name}': {e}\")\n                    raise\n\n            # Insert or update the parameter normally if under 4KB\n            self.ssm_client.put_parameter(Name=name, Value=value, Type=\"String\", Overwrite=overwrite)\n            self.log.info(f\"Parameter '{name}' added/updated successfully.\")\n\n        except Exception as e:\n            self.log.critical(f\"Failed to add/update parameter '{name}': {e}\")\n            raise\n\n    def delete(self, name: str):\n        \"\"\"Delete a parameter from the AWS Parameter Store.\n\n        Args:\n            name (str): The name of the parameter to delete.\n        \"\"\"\n        try:\n            # Delete the parameter from Parameter Store\n            self.ssm_client.delete_parameter(Name=name)\n            self.log.info(f\"Parameter '{name}' deleted successfully.\")\n        except Exception as e:\n            self.log.error(f\"Failed to delete parameter '{name}': {e}\")\n\n    def __repr__(self):\n        \"\"\"Return a string representation of the ParameterStore object.\"\"\"\n        return \"\\n\".join(self.list())\n</code></pre>"},{"location":"api_classes/parameter_store/#workbench.api.parameter_store.ParameterStore.__init__","title":"<code>__init__()</code>","text":"<p>ParameterStore Init Method</p> Source code in <code>src/workbench/api/parameter_store.py</code> <pre><code>def __init__(self):\n    \"\"\"ParameterStore Init Method\"\"\"\n    self.log = logging.getLogger(\"workbench\")\n\n    # Initialize a Workbench Session (to assume the Workbench ExecutionRole)\n    self.boto3_session = AWSSession().boto3_session\n\n    # Create a Systems Manager (SSM) client for Parameter Store operations\n    self.ssm_client = self.boto3_session.client(\"ssm\")\n</code></pre>"},{"location":"api_classes/parameter_store/#workbench.api.parameter_store.ParameterStore.__repr__","title":"<code>__repr__()</code>","text":"<p>Return a string representation of the ParameterStore object.</p> Source code in <code>src/workbench/api/parameter_store.py</code> <pre><code>def __repr__(self):\n    \"\"\"Return a string representation of the ParameterStore object.\"\"\"\n    return \"\\n\".join(self.list())\n</code></pre>"},{"location":"api_classes/parameter_store/#workbench.api.parameter_store.ParameterStore.delete","title":"<code>delete(name)</code>","text":"<p>Delete a parameter from the AWS Parameter Store.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name of the parameter to delete.</p> required Source code in <code>src/workbench/api/parameter_store.py</code> <pre><code>def delete(self, name: str):\n    \"\"\"Delete a parameter from the AWS Parameter Store.\n\n    Args:\n        name (str): The name of the parameter to delete.\n    \"\"\"\n    try:\n        # Delete the parameter from Parameter Store\n        self.ssm_client.delete_parameter(Name=name)\n        self.log.info(f\"Parameter '{name}' deleted successfully.\")\n    except Exception as e:\n        self.log.error(f\"Failed to delete parameter '{name}': {e}\")\n</code></pre>"},{"location":"api_classes/parameter_store/#workbench.api.parameter_store.ParameterStore.get","title":"<code>get(name, warn=True, decrypt=True)</code>","text":"<p>Retrieve a parameter value from the AWS Parameter Store.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name of the parameter to retrieve.</p> required <code>warn</code> <code>bool</code> <p>Whether to log a warning if the parameter is not found.</p> <code>True</code> <code>decrypt</code> <code>bool</code> <p>Whether to decrypt secure string parameters.</p> <code>True</code> <p>Returns:</p> Type Description <code>Union[str, list, dict, None]</code> <p>Union[str, list, dict, None]: The value of the parameter or None if not found.</p> Source code in <code>src/workbench/api/parameter_store.py</code> <pre><code>def get(self, name: str, warn: bool = True, decrypt: bool = True) -&gt; Union[str, list, dict, None]:\n    \"\"\"Retrieve a parameter value from the AWS Parameter Store.\n\n    Args:\n        name (str): The name of the parameter to retrieve.\n        warn (bool): Whether to log a warning if the parameter is not found.\n        decrypt (bool): Whether to decrypt secure string parameters.\n\n    Returns:\n        Union[str, list, dict, None]: The value of the parameter or None if not found.\n    \"\"\"\n    try:\n        # Retrieve the parameter from Parameter Store\n        response = self.ssm_client.get_parameter(Name=name, WithDecryption=decrypt)\n        value = response[\"Parameter\"][\"Value\"]\n\n        # Auto-detect and decompress if needed\n        if value.startswith(\"COMPRESSED:\"):\n            # Base64 decode and decompress\n            self.log.important(f\"Decompressing parameter '{name}'...\")\n            compressed_value = base64.b64decode(value[len(\"COMPRESSED:\") :])\n            value = zlib.decompress(compressed_value).decode(\"utf-8\")\n\n        # Attempt to parse the value back to its original type\n        try:\n            parsed_value = json.loads(value)\n            return parsed_value\n        except (json.JSONDecodeError, TypeError):\n            # If parsing fails, return the value as is (assumed to be a simple string)\n            return value\n\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] == \"ParameterNotFound\":\n            if warn:\n                self.log.warning(f\"Parameter '{name}' not found\")\n        else:\n            self.log.error(f\"Failed to get parameter '{name}': {e}\")\n        return None\n</code></pre>"},{"location":"api_classes/parameter_store/#workbench.api.parameter_store.ParameterStore.list","title":"<code>list(prefix=None)</code>","text":"<p>List all parameters in the AWS Parameter Store, optionally filtering by a prefix.</p> <p>Parameters:</p> Name Type Description Default <code>prefix</code> <code>str</code> <p>A prefix to filter the parameters by. Defaults to None.</p> <code>None</code> <p>Returns:</p> Name Type Description <code>list</code> <code>list</code> <p>A list of parameter names and details.</p> Source code in <code>src/workbench/api/parameter_store.py</code> <pre><code>def list(self, prefix: str = None) -&gt; list:\n    \"\"\"List all parameters in the AWS Parameter Store, optionally filtering by a prefix.\n\n    Args:\n        prefix (str, optional): A prefix to filter the parameters by. Defaults to None.\n\n    Returns:\n        list: A list of parameter names and details.\n    \"\"\"\n    try:\n        # Set up parameters for the query\n        params = {\"MaxResults\": 50}\n\n        # If a prefix is provided, add the 'ParameterFilters' for optimization\n        if prefix:\n            params[\"ParameterFilters\"] = [{\"Key\": \"Name\", \"Option\": \"BeginsWith\", \"Values\": [prefix]}]\n\n        # Initialize the list to collect parameter names\n        all_parameters = []\n\n        # Make the initial call to describe parameters\n        response = self.ssm_client.describe_parameters(**params)\n\n        # Aggregate the names from the initial response\n        all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n        # Continue to paginate if there's a NextToken\n        while \"NextToken\" in response:\n            # Update the parameters with the NextToken for subsequent calls\n            params[\"NextToken\"] = response[\"NextToken\"]\n            response = self.ssm_client.describe_parameters(**params)\n\n            # Aggregate the names from the subsequent responses\n            all_parameters.extend(param[\"Name\"] for param in response[\"Parameters\"])\n\n    except Exception as e:\n        self.log.error(f\"Failed to list parameters: {e}\")\n        return []\n\n    # Return the aggregated list of parameter names\n    return all_parameters\n</code></pre>"},{"location":"api_classes/parameter_store/#workbench.api.parameter_store.ParameterStore.upsert","title":"<code>upsert(name, value, overwrite=True)</code>","text":"<p>Insert or update a parameter in the AWS Parameter Store.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name of the parameter.</p> required <code>value</code> <code>str | list | dict</code> <p>The value of the parameter.</p> required <code>overwrite</code> <code>bool</code> <p>Whether to overwrite an existing parameter (default: True)</p> <code>True</code> Source code in <code>src/workbench/api/parameter_store.py</code> <pre><code>def upsert(self, name: str, value, overwrite: bool = True):\n    \"\"\"Insert or update a parameter in the AWS Parameter Store.\n\n    Args:\n        name (str): The name of the parameter.\n        value (str | list | dict): The value of the parameter.\n        overwrite (bool): Whether to overwrite an existing parameter (default: True)\n    \"\"\"\n    try:\n\n        # Anything that's not a string gets converted to JSON\n        if not isinstance(value, str):\n            value = json.dumps(value)\n\n        # Check size and compress if necessary\n        if len(value) &gt; 4096:\n            self.log.warning(f\"Parameter {name} exceeds 4KB ({len(value)} Bytes)  Compressing...\")\n            compressed_value = zlib.compress(value.encode(\"utf-8\"), level=9)\n            encoded_value = \"COMPRESSED:\" + base64.b64encode(compressed_value).decode(\"utf-8\")\n\n            # Report on the size of the compressed value\n            compressed_size = len(compressed_value)\n            if compressed_size &gt; 4096:\n                doc_link = \"https://supercowpowers.github.io/workbench/api_classes/df_store\"\n                self.log.error(f\"Compressed size {compressed_size} bytes, cannot store &gt; 4KB\")\n                self.log.error(f\"For larger data use the DFStore() class ({doc_link})\")\n                return\n\n            # Insert or update the compressed parameter in Parameter Store\n            try:\n                # Insert or update the compressed parameter in Parameter Store\n                self.ssm_client.put_parameter(Name=name, Value=encoded_value, Type=\"String\", Overwrite=overwrite)\n                self.log.info(f\"Parameter '{name}' added/updated successfully with compression.\")\n                return\n            except Exception as e:\n                self.log.critical(f\"Failed to add/update compressed parameter '{name}': {e}\")\n                raise\n\n        # Insert or update the parameter normally if under 4KB\n        self.ssm_client.put_parameter(Name=name, Value=value, Type=\"String\", Overwrite=overwrite)\n        self.log.info(f\"Parameter '{name}' added/updated successfully.\")\n\n    except Exception as e:\n        self.log.critical(f\"Failed to add/update parameter '{name}': {e}\")\n        raise\n</code></pre>"},{"location":"api_classes/parameter_store/#bypassing-the-4k-limit","title":"Bypassing the 4k Limit","text":"<p>AWS Parameter Storage has a 4k limit on values, the Workbench class bypasses this limit by detecting large values (strings, data, whatever) and compressing those on the fly. The decompressing is also handled automatically, so for larger data simply use the <code>add()</code> and <code>get()</code> methods and it will all just work.</p>"},{"location":"api_classes/parameter_store/#examples","title":"Examples","text":"<p>These example show how to use the <code>ParameterStore()</code> class to list, add, and get parameters from the AWS Parameter Store Service.</p> <p>Workbench REPL</p> <p>If you'd like to experiment with listing, adding, and getting data with the <code>ParameterStore()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! Workbench REPL</p> Using Workbench REPL<pre><code>params = ParameterStore()\n\n# List Parameters\nparams.list()\n\n['/workbench/abalone_info',\n '/workbench/my_data',\n '/workbench/test',\n '/workbench/pipelines/my_pipeline']\n\n# Add Key\nparams.upsert(\"key\", \"value\")\nvalue = params.get(\"key\")\n\n# Add any data (lists, dictionaries, etc..)\nmy_data = {\"key\": \"value\", \"number\": 4.2, \"list\": [1,2,3]}\nparams.upsert(\"my_data\", my_data)\n\n# Retrieve data\nreturn_value = params.get(\"my_data\")\npprint(return_value)\n\n{'key': 'value', 'list': [1, 2, 3], 'number': 4.2}\n\n# Delete parameters\nparam_store.delete(\"my_data\")\n</code></pre> <p><code>list()</code> not showing ALL parameters?</p> <p>If you want access to ALL the parameters in the parameter store set <code>prefix=None</code> and everything will show up.</p> <pre><code>params = ParameterStore(prefix=None)\nparams.list()\n&lt;all the keys&gt;\n</code></pre>"},{"location":"api_classes/pipelines/","title":"Pipelines","text":"<p>Pipeline Examples</p> <p>Examples of using the Pipeline classes are listed at the bottom of this page Examples.</p> <p>Pipelines store sequences of Workbench transforms. So if you have a nightly ML workflow you can capture that as a Pipeline. Here's an example pipeline:</p> nightly_sol_pipeline_v1.json<pre><code>{\n    \"data_source\": {\n         \"name\": \"nightly_data\",\n         \"tags\": [\"solubility\", \"foo\"],\n         \"s3_input\": \"s3://blah/blah.csv\"\n    },\n    \"feature_set\": {\n          \"name\": \"nightly_features\",\n          \"tags\": [\"blah\", \"blah\"],\n          \"input\": \"nightly_data\"\n          \"schema\": \"mol_descriptors_v1\"\n    },\n    \"model\": {\n          \"name\": \u201cnightly_model\u201d,\n          \"tags\": [\"blah\", \"blah\"],\n          \"features\": [\"col1\", \"col2\"],\n          \"target\": \u201csol\u201d,\n          \"input\": \u201cnightly_features\u201d\n    \"endpoint\": {\n          ...\n}    \n</code></pre> <p>Pipeline: Manages the details around a Workbench Pipeline, including Execution</p>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.log","title":"<code>log = logging.getLogger('workbench')</code>  <code>module-attribute</code>","text":"<p>my_pipeline = Pipeline(\"aqsol_pipeline_v1\") my_pipeline.set_input(\"s3://workbench-public-data/comp_chem/aqsol_public_data.csv\") my_pipeline.execute_partial([\"model\", \"endpoint\"]) exit(0)</p>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline","title":"<code>Pipeline</code>","text":"<p>Pipeline: Workbench Pipeline API Class</p> Common Usage <pre><code>my_pipeline = Pipeline(\"name\")\nmy_pipeline.details()\nmy_pipeline.execute()  # Execute entire pipeline\nmy_pipeline.execute_partial([\"data_source\", \"feature_set\"])\nmy_pipeline.execute_partial([\"model\", \"endpoint\"])\n</code></pre> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>class Pipeline:\n    \"\"\"Pipeline: Workbench Pipeline API Class\n\n    Common Usage:\n        ```python\n        my_pipeline = Pipeline(\"name\")\n        my_pipeline.details()\n        my_pipeline.execute()  # Execute entire pipeline\n        my_pipeline.execute_partial([\"data_source\", \"feature_set\"])\n        my_pipeline.execute_partial([\"model\", \"endpoint\"])\n        ```\n    \"\"\"\n\n    def __init__(self, name: str):\n        \"\"\"Pipeline Init Method\"\"\"\n        self.log = logging.getLogger(\"workbench\")\n        self.uuid = name\n\n        # Spin up a Parameter Store for Pipelines\n        self.prefix = \"/workbench/pipelines\"\n        self.params = ParameterStore()\n        self.pipeline = self.params.get(f\"{self.prefix}/{self.uuid}\")\n\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Pipeline Summary.\n\n        Returns:\n            dict: A dictionary of details about the Pipeline\n        \"\"\"\n        return self.pipeline\n\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Pipeline Details.\n\n        Returns:\n            dict: A dictionary of details about the Pipeline\n        \"\"\"\n        return self.pipeline\n\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the Pipeline Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the Pipeline\n        \"\"\"\n        return {}\n\n    def set_input(self, input: Union[str, pd.DataFrame], artifact: str = \"data_source\"):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n            input (Union[str, pd.DataFrame]): The input for the Pipeline\n            artifact (str): The artifact to set the input for (default: \"data_source\")\n        \"\"\"\n        self.pipeline[artifact][\"input\"] = input\n\n    def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n            id_column (str): The column name of the unique identifier\n            holdout_ids (list[str]): The list of unique identifiers to hold out\n        \"\"\"\n        self.pipeline[\"feature_set\"][\"id_column\"] = id_column\n        self.pipeline[\"feature_set\"][\"holdout_ids\"] = holdout_ids\n\n    def execute(self):\n        \"\"\"Execute the entire Pipeline\n\n        Raises:\n            RunTimeException: If the pipeline execution fails in any way\n        \"\"\"\n        pipeline_executor = PipelineExecutor(self)\n        pipeline_executor.execute()\n\n    def execute_partial(self, subset: list):\n        \"\"\"Execute a partial Pipeline\n\n        Args:\n            subset (list): A subset of the pipeline to execute\n\n        Raises:\n            RunTimeException: If the pipeline execution fails in any way\n        \"\"\"\n        pipeline_executor = PipelineExecutor(self)\n        pipeline_executor.execute_partial(subset)\n\n    def report_settable_fields(self, pipeline: dict = {}, path: str = \"\") -&gt; None:\n        \"\"\"\n        Recursively finds and prints keys with settable fields in a JSON-like dictionary.\n\n        Args:\n        pipeline (dict): pipeline (or sub pipeline) to process.\n        path (str): Current path to the key, used for nested dictionaries.\n        \"\"\"\n        # Grab the entire pipeline if not provided (first call)\n        if not pipeline:\n            self.log.important(f\"Checking Pipeline: {self.uuid}...\")\n            pipeline = self.pipeline\n        for key, value in pipeline.items():\n            if isinstance(value, dict):\n                # Recurse into sub-dictionary\n                self.report_settable_fields(value, path + key + \" -&gt; \")\n            elif isinstance(value, str) and value.startswith(\"&lt;&lt;\") and value.endswith(\"&gt;&gt;\"):\n                # Check if required or optional\n                required = \"[Required]\" if \"required\" in value else \"[Optional]\"\n                self.log.important(f\"{required} Path: {path + key}\")\n\n    def delete(self):\n        \"\"\"Pipeline Deletion\"\"\"\n        self.log.info(f\"Deleting Pipeline: {self.uuid}...\")\n        self.params.delete(f\"{self.prefix}/{self.uuid}\")\n\n    def __repr__(self) -&gt; str:\n        \"\"\"String representation of this pipeline\n\n        Returns:\n            str: String representation of this pipeline\n        \"\"\"\n        # Class name and details\n        class_name = self.__class__.__name__\n        pipeline_details = json.dumps(self.pipeline, indent=4)\n        return f\"{class_name}({pipeline_details})\"\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.__init__","title":"<code>__init__(name)</code>","text":"<p>Pipeline Init Method</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def __init__(self, name: str):\n    \"\"\"Pipeline Init Method\"\"\"\n    self.log = logging.getLogger(\"workbench\")\n    self.uuid = name\n\n    # Spin up a Parameter Store for Pipelines\n    self.prefix = \"/workbench/pipelines\"\n    self.params = ParameterStore()\n    self.pipeline = self.params.get(f\"{self.prefix}/{self.uuid}\")\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.__repr__","title":"<code>__repr__()</code>","text":"<p>String representation of this pipeline</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>String representation of this pipeline</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def __repr__(self) -&gt; str:\n    \"\"\"String representation of this pipeline\n\n    Returns:\n        str: String representation of this pipeline\n    \"\"\"\n    # Class name and details\n    class_name = self.__class__.__name__\n    pipeline_details = json.dumps(self.pipeline, indent=4)\n    return f\"{class_name}({pipeline_details})\"\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.delete","title":"<code>delete()</code>","text":"<p>Pipeline Deletion</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def delete(self):\n    \"\"\"Pipeline Deletion\"\"\"\n    self.log.info(f\"Deleting Pipeline: {self.uuid}...\")\n    self.params.delete(f\"{self.prefix}/{self.uuid}\")\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the Pipeline Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Pipeline</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Pipeline Details.\n\n    Returns:\n        dict: A dictionary of details about the Pipeline\n    \"\"\"\n    return self.pipeline\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.execute","title":"<code>execute()</code>","text":"<p>Execute the entire Pipeline</p> <p>Raises:</p> Type Description <code>RunTimeException</code> <p>If the pipeline execution fails in any way</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def execute(self):\n    \"\"\"Execute the entire Pipeline\n\n    Raises:\n        RunTimeException: If the pipeline execution fails in any way\n    \"\"\"\n    pipeline_executor = PipelineExecutor(self)\n    pipeline_executor.execute()\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.execute_partial","title":"<code>execute_partial(subset)</code>","text":"<p>Execute a partial Pipeline</p> <p>Parameters:</p> Name Type Description Default <code>subset</code> <code>list</code> <p>A subset of the pipeline to execute</p> required <p>Raises:</p> Type Description <code>RunTimeException</code> <p>If the pipeline execution fails in any way</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def execute_partial(self, subset: list):\n    \"\"\"Execute a partial Pipeline\n\n    Args:\n        subset (list): A subset of the pipeline to execute\n\n    Raises:\n        RunTimeException: If the pipeline execution fails in any way\n    \"\"\"\n    pipeline_executor = PipelineExecutor(self)\n    pipeline_executor.execute_partial(subset)\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the Pipeline Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the Pipeline</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Pipeline Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the Pipeline\n    \"\"\"\n    return {}\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.report_settable_fields","title":"<code>report_settable_fields(pipeline={}, path='')</code>","text":"<p>Recursively finds and prints keys with settable fields in a JSON-like dictionary.</p> <p>Args: pipeline (dict): pipeline (or sub pipeline) to process. path (str): Current path to the key, used for nested dictionaries.</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def report_settable_fields(self, pipeline: dict = {}, path: str = \"\") -&gt; None:\n    \"\"\"\n    Recursively finds and prints keys with settable fields in a JSON-like dictionary.\n\n    Args:\n    pipeline (dict): pipeline (or sub pipeline) to process.\n    path (str): Current path to the key, used for nested dictionaries.\n    \"\"\"\n    # Grab the entire pipeline if not provided (first call)\n    if not pipeline:\n        self.log.important(f\"Checking Pipeline: {self.uuid}...\")\n        pipeline = self.pipeline\n    for key, value in pipeline.items():\n        if isinstance(value, dict):\n            # Recurse into sub-dictionary\n            self.report_settable_fields(value, path + key + \" -&gt; \")\n        elif isinstance(value, str) and value.startswith(\"&lt;&lt;\") and value.endswith(\"&gt;&gt;\"):\n            # Check if required or optional\n            required = \"[Required]\" if \"required\" in value else \"[Optional]\"\n            self.log.important(f\"{required} Path: {path + key}\")\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.set_input","title":"<code>set_input(input, artifact='data_source')</code>","text":"<p>Set the input for the Pipeline</p> <p>Parameters:</p> Name Type Description Default <code>input</code> <code>Union[str, DataFrame]</code> <p>The input for the Pipeline</p> required <code>artifact</code> <code>str</code> <p>The artifact to set the input for (default: \"data_source\")</p> <code>'data_source'</code> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def set_input(self, input: Union[str, pd.DataFrame], artifact: str = \"data_source\"):\n    \"\"\"Set the input for the Pipeline\n\n    Args:\n        input (Union[str, pd.DataFrame]): The input for the Pipeline\n        artifact (str): The artifact to set the input for (default: \"data_source\")\n    \"\"\"\n    self.pipeline[artifact][\"input\"] = input\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.set_training_holdouts","title":"<code>set_training_holdouts(id_column, holdout_ids)</code>","text":"<p>Set the input for the Pipeline</p> <p>Parameters:</p> Name Type Description Default <code>id_column</code> <code>str</code> <p>The column name of the unique identifier</p> required <code>holdout_ids</code> <code>list[str]</code> <p>The list of unique identifiers to hold out</p> required Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n    \"\"\"Set the input for the Pipeline\n\n    Args:\n        id_column (str): The column name of the unique identifier\n        holdout_ids (list[str]): The list of unique identifiers to hold out\n    \"\"\"\n    self.pipeline[\"feature_set\"][\"id_column\"] = id_column\n    self.pipeline[\"feature_set\"][\"holdout_ids\"] = holdout_ids\n</code></pre>"},{"location":"api_classes/pipelines/#workbench.api.pipeline.Pipeline.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the Pipeline Summary.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the Pipeline</p> Source code in <code>src/workbench/api/pipeline.py</code> <pre><code>def summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the Pipeline Summary.\n\n    Returns:\n        dict: A dictionary of details about the Pipeline\n    \"\"\"\n    return self.pipeline\n</code></pre>"},{"location":"api_classes/pipelines/#examples","title":"Examples","text":"<p>Make a Pipeline</p> <p>Pipelines are just JSON files (see <code>workbench/examples/pipelines/</code>). You can copy one and make changes to fit your objects/use case, or if you have a set of Workbench artifacts created you can 'backtrack' from the Endpoint and have it create the Pipeline for you.</p> pipeline_manager.py<pre><code>from workbench.api.pipeline_manager import PipelineManager\n\n # Create a PipelineManager\nmy_manager = PipelineManager()\n\n# List the Pipelines\npprint(my_manager.list_pipelines())\n\n# Create a Pipeline from an Endpoint\nabalone_pipeline = my_manager.create_from_endpoint(\"abalone-regression-end\")\n\n# Publish the Pipeline\nmy_manager.publish_pipeline(\"abalone_pipeline_v1\", abalone_pipeline)\n</code></pre> <p>Output</p> <p><pre><code>Listing Pipelines...\n[{'last_modified': datetime.datetime(2024, 4, 16, 21, 10, 6, tzinfo=tzutc()),\n  'name': 'abalone_pipeline_v1',\n  'size': 445}]\n</code></pre> Pipeline Details</p> pipeline_details.py<pre><code>from workbench.api.pipeline import Pipeline\n\n# Retrieve an existing Pipeline\nmy_pipeline = Pipeline(\"abalone_pipeline_v1\")\npprint(my_pipeline.details())\n</code></pre> <p>Output</p> <pre><code>{\n    \"name\": \"abalone_pipeline_v1\",\n    \"s3_path\": \"s3://sandbox/pipelines/abalone_pipeline_v1.json\",\n    \"pipeline\": {\n        \"data_source\": {\n            \"name\": \"abalone_data\",\n            \"tags\": [\n                \"abalone_data\"\n            ],\n            \"input\": \"/Users/briford/work/workbench/data/abalone.csv\"\n        },\n        \"feature_set\": {\n            \"name\": \"abalone_features\",\n            \"tags\": [\n                \"abalone_features\"\n            ],\n            \"input\": \"abalone_data\"\n        },\n        \"model\": {\n            \"name\": \"abalone-regression\",\n            \"tags\": [\n                \"abalone\",\n                \"regression\"\n            ],\n            \"input\": \"abalone_features\"\n        },\n        ...\n    }\n}\n</code></pre> <p>Pipeline Execution</p> <p>Pipeline Execution</p> <p>Executing the Pipeline is obviously the most important reason for creating one. If gives you a reproducible way to capture, inspect, and run the same ML pipeline on different data (nightly).</p> pipeline_execution.py<pre><code>from workbench.api.pipeline import Pipeline\n\n# Retrieve an existing Pipeline\nmy_pipeline = Pipeline(\"abalone_pipeline_v1\")\n\n# Execute the Pipeline\nmy_pipeline.execute()  # Full execution\n\n# Partial executions\nmy_pipeline.execute_partial([\"data_source\", \"feature_set\"])\nmy_pipeline.execute_partial([\"model\", \"endpoint\"])\n</code></pre>"},{"location":"api_classes/pipelines/#pipelines-advanced","title":"Pipelines Advanced","text":"<p>As part of the flexible architecture sometimes DataSources or FeatureSets can be created with a Pandas DataFrame. To support a DataFrame as input to a pipeline we can call the <code>set_input()</code> method to the pipeline object. If you'd like to specify the <code>set_hold_out_ids()</code> you can also provide a list of ids.</p> <pre><code>    def set_input(self, input: Union[str, pd.DataFrame], artifact: str = \"data_source\"):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n            input (Union[str, pd.DataFrame]): The input for the Pipeline\n            artifact (str): The artifact to set the input for (default: \"data_source\")\n        \"\"\"\n        self.pipeline[artifact][\"input\"] = input\n\n    def set_hold_out_ids(self, id_list: list):\n        \"\"\"Set the input for the Pipeline\n\n        Args:\n           id_list (list): The list of hold out ids\n        \"\"\"\n        self.pipeline[\"feature_set\"][\"hold_out_ids\"] = id_list\n</code></pre> <p>Running a pipeline creates and deploys a set of Workbench Artifacts, DataSource, FeatureSet, Model and Endpoint. These artifacts can be viewed in the Sagemaker Console/Notebook interfaces or in the Workbench Dashboard UI.</p> <p>Not Finding a particular method?</p> <p>The Workbench API Classes use the 'Core' Classes Internally, so for an extensive listing of all the methods available please take a deep dive into: Workbench Core Classes</p>"},{"location":"api_classes/views/","title":"Views","text":"<p>View Examples</p> <p>Examples of using the Views classes to extend the functionality of Workbench Artifacts are in the Examples section at the bottom of this page. </p> <p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing Workbench Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p> <p>View: Read from a view (training, display, etc) for DataSources and FeatureSets.</p>"},{"location":"api_classes/views/#workbench.core.views.view.View","title":"<code>View</code>","text":"<p>View: Read from a view (training, display, etc) for DataSources and FeatureSets.</p> Common Usage <pre><code># Grab the Display View for a DataSource\ndisplay_view = ds.view(\"display\")\nprint(display_view.columns)\n\n# Pull a DataFrame for the view\ndf = display_view.pull_dataframe()\n\n# Views also work with FeatureSets\ncomp_view = fs.view(\"computation\")\ncomp_df = comp_view.pull_dataframe()\n\n# Query the view with a custom SQL query\nquery = f\"SELECT * FROM {comp_view.table} WHERE age &gt; 30\"\ndf = comp_view.query(query)\n\n# Delete the view\ncomp_view.delete()\n</code></pre> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>class View:\n    \"\"\"View: Read from a view (training, display, etc) for DataSources and FeatureSets.\n\n    Common Usage:\n        ```python\n\n        # Grab the Display View for a DataSource\n        display_view = ds.view(\"display\")\n        print(display_view.columns)\n\n        # Pull a DataFrame for the view\n        df = display_view.pull_dataframe()\n\n        # Views also work with FeatureSets\n        comp_view = fs.view(\"computation\")\n        comp_df = comp_view.pull_dataframe()\n\n        # Query the view with a custom SQL query\n        query = f\"SELECT * FROM {comp_view.table} WHERE age &gt; 30\"\n        df = comp_view.query(query)\n\n        # Delete the view\n        comp_view.delete()\n        ```\n    \"\"\"\n\n    # Class attributes\n    log = logging.getLogger(\"workbench\")\n    meta = Meta()\n\n    def __init__(self, artifact: Union[DataSource, FeatureSet], view_name: str, **kwargs):\n        \"\"\"View Constructor: Retrieve a View for the given artifact\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): A DataSource or FeatureSet object\n            view_name (str): The name of the view to retrieve (e.g. \"training\")\n        \"\"\"\n\n        # Set the view name\n        self.view_name = view_name\n\n        # Is this a DataSource or a FeatureSet?\n        self.is_feature_set = isinstance(artifact, FeatureSetCore)\n        self.auto_id_column = artifact.id_column if self.is_feature_set else None\n\n        # Get the data_source from the artifact\n        self.artifact_name = artifact.uuid\n        self.data_source = artifact.data_source if self.is_feature_set else artifact\n        self.database = self.data_source.database\n\n        # Construct our base_table_name\n        self.base_table_name = self.data_source.table\n\n        # Check if the view should be auto created\n        self.auto_created = False\n        if kwargs.get(\"auto_create_view\", True) and not self.exists():\n\n            # A direct double check before we auto-create\n            if not self.exists(skip_cache=True):\n                self.log.important(\n                    f\"View {self.view_name} for {self.artifact_name} doesn't exist, attempting to auto-create...\"\n                )\n                self.auto_created = self._auto_create_view()\n\n                # Check for failure of the auto-creation\n                if not self.auto_created:\n                    self.log.error(\n                        f\"View {self.view_name} for {self.artifact_name} doesn't exist and cannot be auto-created...\"\n                    )\n                    self.view_name = self.columns = self.column_types = self.source_table = self.base_table_name = None\n                    return\n\n        # Now fill some details about the view\n        self.columns, self.column_types, self.source_table, self.join_view = view_details(\n            self.table, self.data_source.database, self.data_source.boto3_session\n        )\n\n    def pull_dataframe(self, limit: int = 50000, head: bool = False) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Pull a DataFrame based on the view type\n\n        Args:\n            limit (int): The maximum number of rows to pull (default: 50000)\n            head (bool): Return just the head of the DataFrame (default: False)\n\n        Returns:\n            Union[pd.DataFrame, None]: The DataFrame for the view or None if it doesn't exist\n        \"\"\"\n\n        # Pull the DataFrame\n        if head:\n            limit = 5\n        pull_query = f'SELECT * FROM \"{self.table}\" LIMIT {limit}'\n        df = self.data_source.query(pull_query)\n        return df\n\n    def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Query the view with a custom SQL query\n\n        Args:\n            query (str): The SQL query to execute\n\n        Returns:\n            Union[pd.DataFrame, None]: The DataFrame for the query or None if it doesn't exist\n        \"\"\"\n        return self.data_source.query(query)\n\n    def column_details(self) -&gt; dict:\n        \"\"\"Return a dictionary of the column names and types for this view\n\n        Returns:\n            dict: A dictionary of the column names and types\n        \"\"\"\n        return dict(zip(self.columns, self.column_types))\n\n    @property\n    def table(self) -&gt; str:\n        \"\"\"Construct the view table name for the given view type\n\n        Returns:\n            str: The view table name\n        \"\"\"\n        if self.view_name is None:\n            return None\n        if self.view_name == \"base\":\n            return self.base_table_name\n        return f\"{self.base_table_name}_{self.view_name}\"\n\n    def delete(self):\n        \"\"\"Delete the database view (and supplemental data) if it exists.\"\"\"\n\n        # List any supplemental tables for this data source\n        supplemental_tables = list_supplemental_data_tables(self.base_table_name, self.database)\n        for table in supplemental_tables:\n            if self.view_name in table:\n                self.log.important(f\"Deleting Supplemental Table {table}...\")\n                delete_table(table, self.database, self.data_source.boto3_session)\n\n        # Now drop the view\n        self.log.important(f\"Dropping View {self.table}...\")\n        drop_view_query = f'DROP VIEW \"{self.table}\"'\n\n        # Execute the DROP VIEW query\n        try:\n            self.data_source.execute_statement(drop_view_query, silence_errors=True)\n        except wr.exceptions.QueryFailed as e:\n            if \"View not found\" in str(e):\n                self.log.info(f\"View {self.table} not found, this is fine...\")\n            else:\n                raise\n\n        # We want to do a small sleep so that AWS has time to catch up\n        self.log.info(\"Sleeping for 3 seconds after dropping view to allow AWS to catch up...\")\n        time.sleep(3)\n\n    def exists(self, skip_cache: bool = False) -&gt; bool:\n        \"\"\"Check if the view exists in the database\n\n        Args:\n            skip_cache (bool): Skip the cache and check the database directly (default: False)\n        Returns:\n            bool: True if the view exists, False otherwise.\n        \"\"\"\n        # The BaseView always exists\n        if self.view_name == \"base\":\n            return True\n\n        # If we're skipping the cache, we need to check the database directly\n        if skip_cache:\n            return self._check_database()\n\n        # Use the meta class to see if the view exists\n        views_df = self.meta.views(self.database)\n\n        # Check if we have ANY views\n        if views_df.empty:\n            return False\n\n        # Check if the view exists\n        return self.table in views_df[\"Name\"].values\n\n    def ensure_exists(self):\n        \"\"\"Ensure if the view exists by making a query directly to the database. If it doesn't exist, create it\"\"\"\n\n        # The BaseView always exists\n        if self.view_name == \"base\":\n            return True\n\n        # Check the database directly\n        if not self._check_database():\n            self._auto_create_view()\n\n    def _check_database(self) -&gt; bool:\n        \"\"\"Internal: Check if the view exists in the database\n\n        Returns:\n            bool: True if the view exists, False otherwise\n        \"\"\"\n        # Query to check if the table/view exists\n        check_table_query = f\"\"\"\n        SELECT table_name\n        FROM information_schema.tables\n        WHERE table_schema = '{self.database}' AND table_name = '{self.table}'\n        \"\"\"\n        _df = self.data_source.query(check_table_query)\n        return not _df.empty\n\n    def _auto_create_view(self) -&gt; bool:\n        \"\"\"Internal: Automatically create a view training, display, and computation views\n\n        Returns:\n            bool: True if the view was created, False otherwise\n\n        Raises:\n            ValueError: If the view type is not supported\n        \"\"\"\n        from workbench.core.views import DisplayView, ComputationView, TrainingView\n\n        # First if we're going to auto-create, we need to make sure the data source exists\n        if not self.data_source.exists():\n            self.log.error(f\"Data Source {self.data_source.uuid} does not exist...\")\n            return False\n\n        # DisplayView\n        if self.view_name == \"display\":\n            self.log.important(f\"Auto creating View {self.view_name} for {self.data_source.uuid}...\")\n            DisplayView.create(self.data_source)\n            return True\n\n        # ComputationView\n        if self.view_name == \"computation\":\n            self.log.important(f\"Auto creating View {self.view_name} for {self.data_source.uuid}...\")\n            ComputationView.create(self.data_source)\n            return True\n\n        # TrainingView\n        if self.view_name == \"training\":\n            # We're only going to create training views for FeatureSets\n            if self.is_feature_set:\n                self.log.important(f\"Auto creating View {self.view_name} for {self.data_source.uuid}...\")\n                TrainingView.create(self.data_source, id_column=self.auto_id_column)\n                return True\n            else:\n                self.log.warning(\"Training Views are only supported for FeatureSets...\")\n                return False\n\n        # If we get here, we don't support auto-creating this view\n        self.log.warning(f\"Auto-Create for {self.view_name} not implemented yet...\")\n        return False\n\n    def __repr__(self):\n        \"\"\"Return a string representation of this object\"\"\"\n\n        # Set up various details that we want to print out\n        auto = \"(Auto-Created)\" if self.auto_created else \"\"\n        artifact = \"FeatureSet\" if self.is_feature_set else \"DataSource\"\n\n        info = f'View: \"{self.view_name}\" for {artifact}(\"{self.artifact_name}\")\\n'\n        info += f\"      Database: {self.database}\\n\"\n        info += f\"      Table: {self.table}{auto}\\n\"\n        info += f\"      Source Table: {self.source_table}\\n\"\n        info += f\"      Join View: {self.join_view}\"\n        return info\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.table","title":"<code>table: str</code>  <code>property</code>","text":"<p>Construct the view table name for the given view type</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>The view table name</p>"},{"location":"api_classes/views/#workbench.core.views.view.View.__init__","title":"<code>__init__(artifact, view_name, **kwargs)</code>","text":"<p>View Constructor: Retrieve a View for the given artifact</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>A DataSource or FeatureSet object</p> required <code>view_name</code> <code>str</code> <p>The name of the view to retrieve (e.g. \"training\")</p> required Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def __init__(self, artifact: Union[DataSource, FeatureSet], view_name: str, **kwargs):\n    \"\"\"View Constructor: Retrieve a View for the given artifact\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): A DataSource or FeatureSet object\n        view_name (str): The name of the view to retrieve (e.g. \"training\")\n    \"\"\"\n\n    # Set the view name\n    self.view_name = view_name\n\n    # Is this a DataSource or a FeatureSet?\n    self.is_feature_set = isinstance(artifact, FeatureSetCore)\n    self.auto_id_column = artifact.id_column if self.is_feature_set else None\n\n    # Get the data_source from the artifact\n    self.artifact_name = artifact.uuid\n    self.data_source = artifact.data_source if self.is_feature_set else artifact\n    self.database = self.data_source.database\n\n    # Construct our base_table_name\n    self.base_table_name = self.data_source.table\n\n    # Check if the view should be auto created\n    self.auto_created = False\n    if kwargs.get(\"auto_create_view\", True) and not self.exists():\n\n        # A direct double check before we auto-create\n        if not self.exists(skip_cache=True):\n            self.log.important(\n                f\"View {self.view_name} for {self.artifact_name} doesn't exist, attempting to auto-create...\"\n            )\n            self.auto_created = self._auto_create_view()\n\n            # Check for failure of the auto-creation\n            if not self.auto_created:\n                self.log.error(\n                    f\"View {self.view_name} for {self.artifact_name} doesn't exist and cannot be auto-created...\"\n                )\n                self.view_name = self.columns = self.column_types = self.source_table = self.base_table_name = None\n                return\n\n    # Now fill some details about the view\n    self.columns, self.column_types, self.source_table, self.join_view = view_details(\n        self.table, self.data_source.database, self.data_source.boto3_session\n    )\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.__repr__","title":"<code>__repr__()</code>","text":"<p>Return a string representation of this object</p> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def __repr__(self):\n    \"\"\"Return a string representation of this object\"\"\"\n\n    # Set up various details that we want to print out\n    auto = \"(Auto-Created)\" if self.auto_created else \"\"\n    artifact = \"FeatureSet\" if self.is_feature_set else \"DataSource\"\n\n    info = f'View: \"{self.view_name}\" for {artifact}(\"{self.artifact_name}\")\\n'\n    info += f\"      Database: {self.database}\\n\"\n    info += f\"      Table: {self.table}{auto}\\n\"\n    info += f\"      Source Table: {self.source_table}\\n\"\n    info += f\"      Join View: {self.join_view}\"\n    return info\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.column_details","title":"<code>column_details()</code>","text":"<p>Return a dictionary of the column names and types for this view</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of the column names and types</p> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def column_details(self) -&gt; dict:\n    \"\"\"Return a dictionary of the column names and types for this view\n\n    Returns:\n        dict: A dictionary of the column names and types\n    \"\"\"\n    return dict(zip(self.columns, self.column_types))\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.delete","title":"<code>delete()</code>","text":"<p>Delete the database view (and supplemental data) if it exists.</p> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def delete(self):\n    \"\"\"Delete the database view (and supplemental data) if it exists.\"\"\"\n\n    # List any supplemental tables for this data source\n    supplemental_tables = list_supplemental_data_tables(self.base_table_name, self.database)\n    for table in supplemental_tables:\n        if self.view_name in table:\n            self.log.important(f\"Deleting Supplemental Table {table}...\")\n            delete_table(table, self.database, self.data_source.boto3_session)\n\n    # Now drop the view\n    self.log.important(f\"Dropping View {self.table}...\")\n    drop_view_query = f'DROP VIEW \"{self.table}\"'\n\n    # Execute the DROP VIEW query\n    try:\n        self.data_source.execute_statement(drop_view_query, silence_errors=True)\n    except wr.exceptions.QueryFailed as e:\n        if \"View not found\" in str(e):\n            self.log.info(f\"View {self.table} not found, this is fine...\")\n        else:\n            raise\n\n    # We want to do a small sleep so that AWS has time to catch up\n    self.log.info(\"Sleeping for 3 seconds after dropping view to allow AWS to catch up...\")\n    time.sleep(3)\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.ensure_exists","title":"<code>ensure_exists()</code>","text":"<p>Ensure if the view exists by making a query directly to the database. If it doesn't exist, create it</p> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def ensure_exists(self):\n    \"\"\"Ensure if the view exists by making a query directly to the database. If it doesn't exist, create it\"\"\"\n\n    # The BaseView always exists\n    if self.view_name == \"base\":\n        return True\n\n    # Check the database directly\n    if not self._check_database():\n        self._auto_create_view()\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.exists","title":"<code>exists(skip_cache=False)</code>","text":"<p>Check if the view exists in the database</p> <p>Parameters:</p> Name Type Description Default <code>skip_cache</code> <code>bool</code> <p>Skip the cache and check the database directly (default: False)</p> <code>False</code> <p>Returns:     bool: True if the view exists, False otherwise.</p> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def exists(self, skip_cache: bool = False) -&gt; bool:\n    \"\"\"Check if the view exists in the database\n\n    Args:\n        skip_cache (bool): Skip the cache and check the database directly (default: False)\n    Returns:\n        bool: True if the view exists, False otherwise.\n    \"\"\"\n    # The BaseView always exists\n    if self.view_name == \"base\":\n        return True\n\n    # If we're skipping the cache, we need to check the database directly\n    if skip_cache:\n        return self._check_database()\n\n    # Use the meta class to see if the view exists\n    views_df = self.meta.views(self.database)\n\n    # Check if we have ANY views\n    if views_df.empty:\n        return False\n\n    # Check if the view exists\n    return self.table in views_df[\"Name\"].values\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.pull_dataframe","title":"<code>pull_dataframe(limit=50000, head=False)</code>","text":"<p>Pull a DataFrame based on the view type</p> <p>Parameters:</p> Name Type Description Default <code>limit</code> <code>int</code> <p>The maximum number of rows to pull (default: 50000)</p> <code>50000</code> <code>head</code> <code>bool</code> <p>Return just the head of the DataFrame (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>Union[pd.DataFrame, None]: The DataFrame for the view or None if it doesn't exist</p> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def pull_dataframe(self, limit: int = 50000, head: bool = False) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Pull a DataFrame based on the view type\n\n    Args:\n        limit (int): The maximum number of rows to pull (default: 50000)\n        head (bool): Return just the head of the DataFrame (default: False)\n\n    Returns:\n        Union[pd.DataFrame, None]: The DataFrame for the view or None if it doesn't exist\n    \"\"\"\n\n    # Pull the DataFrame\n    if head:\n        limit = 5\n    pull_query = f'SELECT * FROM \"{self.table}\" LIMIT {limit}'\n    df = self.data_source.query(pull_query)\n    return df\n</code></pre>"},{"location":"api_classes/views/#workbench.core.views.view.View.query","title":"<code>query(query)</code>","text":"<p>Query the view with a custom SQL query</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The SQL query to execute</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>Union[pd.DataFrame, None]: The DataFrame for the query or None if it doesn't exist</p> Source code in <code>src/workbench/core/views/view.py</code> <pre><code>def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Query the view with a custom SQL query\n\n    Args:\n        query (str): The SQL query to execute\n\n    Returns:\n        Union[pd.DataFrame, None]: The DataFrame for the query or None if it doesn't exist\n    \"\"\"\n    return self.data_source.query(query)\n</code></pre>"},{"location":"api_classes/views/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Listing Views</p> views.py<pre><code>from workbench.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\ntest_data = DataSource('test_data')\ntest_data.views()\n[\"display\", \"training\", \"computation\"]\n</code></pre> <p>Getting a Particular View</p> views.py<pre><code>from workbench.api.feature_set import FeatureSet\n\nfs = FeatureSet('test_features')\n\n# Grab the columns for the display view\ndisplay_view = fs.view(\"display\")\ndisplay_view.columns\n['id', 'name', 'height', 'weight', 'salary', ...]\n\n# Pull the dataframe for this view\ndf = display_view.pull_dataframe()\n    id       name     height      weight         salary ...\n0   58  Person 58  71.781227  275.088196  162053.140625  \n</code></pre> <p>View Queries</p> <p>All Workbench Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p> view_query.py<pre><code>from workbench.api.feature_set import FeatureSet\n\n# Grab a FeatureSet View\nfs = FeatureSet(\"abalone_features\")\nt_view = fs.view(\"training\")\n\n# Make some queries using the Athena backend\ndf = t_view(f\"select * from {t_view.table} where height &gt; .3\")\nprint(df.head())\n\ndf = t_view.query(\"select * from abalone_features where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040         0.0050                      2\n</code></pre> <p>Classes to construct View</p> <p>The Workbench Classes used to construct viewss are currently in 'Core'. So you can check out the documentation for those classes here: Workbench View Creators</p> <p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"aws_setup/aws_access_management/","title":"AWS Acesss Management","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>This page gives an overview of how Workbench sets up roles and policies in a granular way that provides 'least priviledge' and also provides a unified framework for AWS access management.</p>"},{"location":"aws_setup/aws_access_management/#conceptual-slide-deck","title":"Conceptual Slide Deck","text":"<p>Workbench AWS Acesss Management</p>"},{"location":"aws_setup/aws_access_management/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Identity Center</li> <li>Users and Groups</li> <li>Permission Sets</li> <li>SSO Command Line/Python Configure</li> </ul>"},{"location":"aws_setup/aws_client_vpn/","title":"Setting Up AWS Client VPN","text":"<p>Follow the steps below to set up and connect using AWS Client VPN.</p>"},{"location":"aws_setup/aws_client_vpn/#step-1-create-a-client-vpn-endpoint-in-aws","title":"Step 1: Create a Client VPN Endpoint in AWS","text":"<ol> <li>Go to the VPC Dashboard in the AWS Management Console.</li> <li>Select Client VPN Endpoints and click Create Client VPN Endpoint.</li> <li>Fill in the required details:</li> <li>Client IPv4 CIDR: Choose an IP range (e.g., <code>10.0.0.0/22</code>) that doesn\u2019t overlap with your VPC CIDR.</li> <li>Server Certificate ARN: Use or create an SSL certificate using AWS Certificate Manager (ACM).</li> <li>Authentication Options: Use either Mutual Authentication (client certificates) or Active Directory (for user-based authentication).</li> <li>Connection Log Options: Optional; you can set up CloudWatch logs.</li> <li>Click Create Client VPN Endpoint.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-2-associate-the-client-vpn-endpoint-with-a-vpc-subnet","title":"Step 2: Associate the Client VPN Endpoint with a VPC Subnet","text":"<ol> <li>Once the endpoint is created, select it and click on Associations.</li> <li>Choose a Target Network Association (a subnet in the VPC where you want VPN clients to access).</li> <li>This allows traffic from the VPN clients to route through your chosen VPC subnet.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-3-authorize-vpn-clients-to-access-the-vpc","title":"Step 3: Authorize VPN Clients to Access the VPC","text":"<ol> <li>Under the Authorization Rules tab, click Add Authorization Rule.</li> <li>For Destination network, specify <code>0.0.0.0/0</code> to allow access to all resources in the VPC.</li> <li>Set Grant access to to <code>Allow access</code> and specify the group you created or allow all users.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-4-download-and-install-aws-vpn-client","title":"Step 4: Download and Install AWS VPN Client","text":"<ol> <li>Download the AWS Client VPN for macOS.</li> <li>Install the client on your Mac.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-5-download-the-client-configuration-file","title":"Step 5: Download the Client Configuration File","text":"<ol> <li>In the AWS Console, go to your Client VPN Endpoint.</li> <li>Click on Download Client Configuration. This file contains the connection details required by the VPN client.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#step-6-import-the-configuration-file-and-connect","title":"Step 6: Import the Configuration File and Connect","text":"<ol> <li>Open the AWS Client VPN app on your Mac.</li> <li>Click File -&gt; Manage Profiles -&gt; Add Profile.</li> <li>Import the configuration file you downloaded.</li> <li>Enter your credentials if required (depending on the authentication method you chose).</li> <li>Click Connect.</li> </ol>"},{"location":"aws_setup/aws_client_vpn/#benefits-of-using-aws-client-vpn","title":"Benefits of Using AWS Client VPN","text":"<ul> <li>Simple Setup: Minimal steps and no need for additional infrastructure.</li> <li>Secure: Uses TLS to secure connections, and you control who has access.</li> <li>Direct Access: Provides direct access to your AWS resources, including the Redis cluster.</li> </ul>"},{"location":"aws_setup/aws_client_vpn/#conclusion","title":"Conclusion","text":"<p>AWS Client VPN is a straightforward, secure, and effective solution for connecting your laptop to an AWS VPC. It requires minimal setup and provides all the security controls you need, making it ideal for a single laptop and user.</p>"},{"location":"aws_setup/aws_setup/","title":"AWS Setup","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"aws_setup/aws_setup/#get-some-information","title":"Get some information","text":"<ul> <li>Go to your AWS Identity Center in the AWS Console</li> <li>On the right side there will be two important pieces of information<ul> <li>Start URL</li> <li>Region </li> </ul> </li> </ul> <p>Write these values down, you'll need them as part of this AWS setup.</p>"},{"location":"aws_setup/aws_setup/#install-aws-cli","title":"Install AWS CLI","text":"<p>AWS CLI Instructions</p>"},{"location":"aws_setup/aws_setup/#running-the-sso-configuration","title":"Running the SSO Configuration","text":"<p>Note: You only need to do this once! Also this will create a NEW profile, so name the profile something like <code>aws_sso</code>.</p> <pre><code>aws configure sso --profile &lt;whatever&gt; (e.g. aws_sso)\nSSO session name (Recommended): sso-session\nSSO start URL []: &lt;the Start URL from info above&gt;\nSSO region []: &lt;the Region from info above&gt;\nSSO registration scopes [sso:account:access]: &lt;just hit return&gt;\n</code></pre> <p>You will get a browser open/redirect at this point and get a list of available accounts.. something like below, just pick the correct account</p> <pre><code>There are 2 AWS accounts available to you.\n&gt; SCP_Sandbox, briford+sandbox@supercowpowers.com (XXXX40646YYY)\n  SCP_Main, briford@supercowpowers.com (XXX576391YYY)\n</code></pre> <p>Now pick the role that you're going to use</p> <pre><code>There are 2 roles available to you.\n&gt; DataScientist\n  AdministratorAccess\n\nCLI default client Region [None]: &lt;same region as above&gt;\nCLI default output format [None]: json\n</code></pre>"},{"location":"aws_setup/aws_setup/#setting-up-some-aliases-for-bashzsh","title":"Setting up some aliases for bash/zsh","text":"<p>Edit your favorite ~/.bashrc ~/.zshrc and add these nice aliases/helper</p> <pre><code># AWS Aliases\nalias aws_sso='export AWS_PROFILE=aws_sso'\n\n# Default AWS Profile\nexport AWS_PROFILE=aws_sso\n</code></pre>"},{"location":"aws_setup/aws_setup/#testing-your-new-aws-profile","title":"Testing your new AWS Profile","text":"<p>Make sure your profile is active/set</p> <p><pre><code>env | grep AWS\nAWS_PROFILE=&lt;aws_sso or whatever&gt;\n</code></pre> Now you can list the S3 buckets in the AWS Account</p> <p><pre><code>aws ls s3\n</code></pre> If you get some message like this...</p> <pre><code>The SSO session associated with this profile has\nexpired or is otherwise invalid. To refresh this SSO\nsession run aws sso login with the corresponding\nprofile.\n</code></pre> <p>This is fine/good, a browser will open up and you can refresh your SSO Token.</p> <p>After that you should get a listing of the S3 buckets without needed to refresh your token.</p> <pre><code>aws s3 ls\n\u276f aws s3 ls\n2023-03-20 20:06:53 aws-athena-query-results-XXXYYY-us-west-2\n2023-03-30 13:22:28 sagemaker-studio-XXXYYY-dbgyvq8ruka\n2023-03-24 22:05:55 sagemaker-us-west-2-XXXYYY\n2023-04-30 13:43:29 scp-workbench-artifacts\n</code></pre>"},{"location":"aws_setup/aws_setup/#back-to-initial-setup","title":"Back to Initial Setup","text":"<p>If you're doing the initial setup of Workbench you should now go back and finish that process: Getting Started</p>"},{"location":"aws_setup/aws_setup/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Identity Center</li> <li>Users and Groups</li> <li>Permission Sets</li> <li>SSO Command Line/Python Configure</li> </ul>"},{"location":"aws_setup/aws_tips_and_tricks/","title":"AWS Tips and Tricks","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>This page tries to give helpful guidance when setting up AWS Accounts, Users, and Groups. In general AWS can be a bit tricky to set up the first time. Feel free to use any material in this guide but we're more than happy to help clients get their AWS Setup ready to go for FREE. Below are some guides for setting up a new AWS account for Workbench and also setting up SSO Users and Groups within AWS.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#new-aws-account-with-aws-organizations-easy","title":"New AWS Account (with AWS Organizations: easy)","text":"<ul> <li>If you already have an AWS Account you can activate the AWS Identity Center/Organization functionality.</li> <li>Now go to AWS Organizations page and hit 'Add an AWS Account' button</li> <li>Add a new User with permissions that allows AWS Stack creation</li> </ul> <p>Email Trick</p> <p>AWS will often not allow the same email to be used for different accounts. If you need a 'new' email just add a plus sign '+' at the end of your existing email (e.g. bob.smith+aws@gmail.com). This email will 'auto forward' to bob.smith@gmail.com.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#new-aws-account-without-aws-organizations-a-bit-harder","title":"New AWS Account (without AWS Organizations: a bit harder)","text":"<ul> <li>Goto: https://aws.amazon.com/free and hit the big button 'Create a Free Account'</li> <li>Enter email and the account name you'd like (anything is fine)</li> <li>You'll get a validation email and go through the rest of the Account setup procedure</li> <li>Add a new User with permissions that allows AWS Stack creation</li> </ul>"},{"location":"aws_setup/aws_tips_and_tricks/#sso-users-and-groups","title":"SSO Users and Groups","text":"<p>AWS SSO (Single Sign-On) is a cloud-based service that allows users to manage access to multiple AWS accounts and business applications using a single set of credentials. It simplifies the authentication process for users and provides centralized management of permissions and access control across various AWS resources. With AWS SSO, users can log in once and access all the applications and accounts they need, streamlining the user experience and increasing productivity. AWS SSO also enables IT administrators to manage access more efficiently by providing a single point of control for managing user access, permissions, and policies, reducing the risk of unauthorized access or security breaches.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#setting-up-sso-users","title":"Setting up SSO Users","text":"<ul> <li>Log in to your AWS account and go to the AWS Identity Center console.</li> <li>Click on the \"Users\" tab and then click on the \"Add user\" button.</li> </ul> <p>The 'Add User' setup is fairly straight forward but here are some screen shots:</p> <p>On the first panel you can fill in the users information.</p> <p></p>"},{"location":"aws_setup/aws_tips_and_tricks/#groups","title":"Groups","text":"<p>On the second panel we suggest that you have at LEAST two groups:</p> <ul> <li>Admin group</li> <li>DataScientists group</li> </ul>"},{"location":"aws_setup/aws_tips_and_tricks/#setting-up-groups","title":"Setting up Groups","text":"<p>This allows you to put most of the users into the DataScientists group that has AWS policies based on their job role. AWS uses 'permission sets' and you assign AWS Policies. This approach makes it easy to give a group of users a set of relevant policies for their tasks. </p> <p>Our standard setup is to have two permission sets with the following policies:</p> <ul> <li>IAM Identity Center --&gt; Permission sets --&gt; DataScientist </li> <li> <p>Add Policy: arn:aws:iam::aws:policy/job-function/DataScientist</p> </li> <li> <p>IAM Identity Center --&gt; Permission sets --&gt; AdministratorAccess </p> </li> <li>Add Policy: arn:aws:iam::aws:policy/job-function/AdministratorAccess</li> </ul> <p>See: Permission Sets for more details and instructions.</p> <p>Another benefit of creating groups is that you can include that group in 'Trust Policy (assume_role)' for the Workbench-ExecutionRole (this gets deployed as part of the Workbench AWS Stack). This means that the management of what Workbench can do/see/read/write is completely done through the Workbench-ExecutionRole.</p>"},{"location":"aws_setup/aws_tips_and_tricks/#back-to-adding-user","title":"Back to Adding User","text":"<p>Okay now that we have our groups set up we can go back to our original goal of adding a user. So here's the second panel with the groups and now we can hit 'Next'</p> <p></p> <p>On the third panel just review the details and hit the 'Add User' button at the bottom. The user will get an email giving them instructions on how to log on to their AWS account.</p> <p></p>"},{"location":"aws_setup/aws_tips_and_tricks/#aws-console","title":"AWS Console","text":"<p>Now when the user logs onto the AWS Console they should see something like this: </p>"},{"location":"aws_setup/aws_tips_and_tricks/#aws-clisso-setup-for-command-linepython-usage","title":"AWS CLI/SSO Setup for Command Line/Python Usage","text":"<p>Please see our AWS Setup</p>"},{"location":"aws_setup/aws_tips_and_tricks/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Identity Center</li> <li>Users and Groups</li> <li>Permission Sets</li> <li>SSO Command Line/Python Configure</li> </ul>"},{"location":"aws_setup/core_stack/","title":"Workbench AWS Stack","text":"<p>Welcome to the Workbench AWS Setup Guide. Workbench is deployed as an AWS Stack following the well architected system practices of AWS. </p> <p>AWS Setup can be a bit complex</p> <p>Setting up Workbench with AWS can be a bit complex, but this only needs to be done ONCE for your entire company. The install uses standard CDK --&gt; AWS Stacks and Workbench tries to make it straight forward. If you have any troubles at all feel free to contact us a workbench@supercowpowers.com or on Discord and we're happy to help you with AWS for FREE.</p>"},{"location":"aws_setup/core_stack/#two-main-options-when-using-workbench","title":"Two main options when using Workbench","text":"<ol> <li>Spin up a new AWS Account for the Workbench Stacks (Make a New Account)</li> <li>Deploy Workbench Stacks into your existing AWS Account</li> </ol> <p>Either of these options are fully supported, but we highly suggest a NEW account as it gives the following benefits:</p> <ul> <li>AWS Data Isolation: Data Scientists will feel empowered to play in the sandbox without impacting production services.</li> <li>AWS Cost Accounting: Monitor and Track all those new ML Pipelines that your team creates with Workbench :)</li> </ul>"},{"location":"aws_setup/core_stack/#setting-up-users-and-groups","title":"Setting up Users and Groups","text":"<p>If your AWS Account already has users and groups set up you can skip this but here's our recommendations on setting up SSO Users and Groups</p>"},{"location":"aws_setup/core_stack/#onboarding-workbench-to-your-aws-account","title":"Onboarding Workbench to your AWS Account","text":"<p>Pulling down the Workbench Repo   <pre><code>git clone https://github.com/SuperCowPowers/workbench.git\n</code></pre></p>"},{"location":"aws_setup/core_stack/#workbench-uses-aws-python-cdk-for-deployments","title":"Workbench uses AWS Python CDK for Deployments","text":"<p>If you don't have AWS CDK already installed you can do these steps:</p> <p>Mac</p> <p><pre><code>brew install node \nnpm install -g aws-cdk\n</code></pre> Linux</p> <p><pre><code>sudo apt install nodejs\nsudo npm install -g aws-cdk\n</code></pre> For more information on Linux installs see Digital Ocean NodeJS</p>"},{"location":"aws_setup/core_stack/#create-an-s3-bucket-for-workbench","title":"Create an S3 Bucket for Workbench","text":"<p>Workbench pushes and pulls data from AWS, it will use this S3 Bucket for storage and processing. You should create a NEW S3 Bucket, we suggest a name like <code>&lt;company-name&gt;-workbench</code></p>"},{"location":"aws_setup/core_stack/#deploying-the-workbench-core-stack","title":"Deploying the Workbench Core Stack","text":"<p>This stack has the <code>Workbench-Execution-Role</code> and an associated role for AWS Glue Jobs.</p> <p>You'll need to set some environmental vars before deploying the stack.</p> <pre><code>export WORKBENCH_BUCKET=name-of-workbench-bucket\nexport WORKBENCH_SSO_GROUP=DataScientist (or whatever)\n</code></pre> <p>Optional ENV Vars</p> <pre><code>export WORKBENCH_ADDITIONAL_BUCKETS=&lt;comma separated list of buckets&gt;\n</code></pre> <p>AWS Stuff</p> <p>Activate your AWS Account that's used for Workbench deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p> <pre><code>cd workbench/aws_setup/workbench_core\npip install -r requirements.txt\ncdk bootstrap\ncdk deploy\n</code></pre>"},{"location":"aws_setup/core_stack/#enable-users-to-assume-workbench-executionrole","title":"Enable Users to Assume Workbench-ExecutionRole","text":"<p>Now that the <code>Workbench-ExecutionRole</code> has been deployed via AWS Stack. These guides walk you through setting up access for both SSO users and IAM users to assume the Workbench-ExecutionRole in your AWS account.</p> <ul> <li>Set up SSO Users</li> <li>Set up IAM Users (not recommend, but contact us we'll help you out)</li> </ul>"},{"location":"aws_setup/core_stack/#aws-account-setup-check","title":"AWS Account Setup Check","text":"<p>After deploying the Workbench Core Stack and setting up users to assume that Role, you can run this test/checking script. If the results ends with <code>INFO AWS Account Clamp: AOK!</code> you're in good shape. If not feel free to contact us on Discord and we'll get it straightened out for you :)</p> <pre><code>pip install workbench (if not already installed)\ncd workbench/aws_setup\npython aws_account_check.py\n&lt;lot of print outs for various checks&gt;\nINFO AWS Account Clamp: AOK!\n</code></pre> <p>Success</p> <p>Congratulations: Workbench is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install workbench</code> and start using the API.</p> <p>If you ran into any issues with this procedure please contact us via Discord or email workbench@supercowpowers.com and the SCP team will provide free setup and support for new Workbench users.</p>"},{"location":"aws_setup/dashboard_stack/","title":"Deploy the Workbench Dashboard Stack","text":"<p>Deploying the Dashboard Stack is reasonably straight forward, it's the same approach as the Core Stack that you've already deployed.</p> <p>Please review the Stack Details section to understand all the AWS components that are included and utilized in the Workbench Dashboard Stack.</p>"},{"location":"aws_setup/dashboard_stack/#deploying-the-dashboard-stack","title":"Deploying the Dashboard Stack","text":"<p>AWS Stuff</p> <p>Activate your AWS Account that's used for Workbench deployment. For this one time install you should use an Admin Account (or an account that had permissions to create/update AWS Stacks)</p> <pre><code>cd workbench/aws_setup/workbench_dashboard_full\nexport WORKBENCH_CONFIG=/full/path/to/config.json\npip install -r requirements.txt\ncdk bootstrap\ncdk deploy\n</code></pre>"},{"location":"aws_setup/dashboard_stack/#stack-details","title":"Stack Details","text":"<p>AWS Questions?</p> <p>There's quite a bit to unpack when deploying an AWS powered Web Service. We're happy to help walk you through the details and options. Contact us anytime for a free consultation.</p> <ul> <li>ECS Fargate</li> <li>Load Balancer</li> <li>2 Availability Zones</li> <li>VPCs / Nat Gateways</li> <li>ElasticCache Cluster (shared Redis Caching)</li> </ul>"},{"location":"aws_setup/dashboard_stack/#aws-stack-benefits","title":"AWS Stack Benefits","text":"<ol> <li>Scalability: Includes an Application Load Balancer and uses ECS with Fargate, and ElasticCache for more robust scaling options.</li> <li>Higher Security: Utilizes security groups for both the ECS tasks, load balancer, plus VPC private subnets for Redis and the utilization of NAT Gateways.</li> </ol> <p>AWS Costs</p> <p>Deploying the Workbench Dashboard does incur some monthly AWS costs. If you're on a tight budget you can deploy the 'lite' version of the Dashboard Stack.</p> <pre><code>cd workbench/aws_setup/workbench_dashboard_lite\nexport WORKBENCH_CONFIG=/full/path/to/config.json\npip install -r requirements.txt\ncdk bootstrap\ncdk deploy\n</code></pre>"},{"location":"aws_setup/domain_cert_setup/","title":"AWS Domain and Certificate Instructions","text":"<p>Need AWS Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>This page tries to give helpful guidance when setting up a new domain and SSL Certificate in your AWS Account.</p>"},{"location":"aws_setup/domain_cert_setup/#new-domain","title":"New Domain","text":"<p>You'll want the Workbench Dashboard to have a domain for your companies internal use. Customers will typically use a domain like <code>&lt;company_name&gt;-ml-dashboard.com</code> but you are free to choose any domain you'd like.</p> <p>Domains are tied to AWS Accounts</p> <p>When you create a new domain in AWS Route 53, that domain is tied to that AWS Account. You can do a cross account setup for domains but it's a bit more tricky. We recommend that each account where Workbench gets deployed owns the domain for that Dashboard.</p>"},{"location":"aws_setup/domain_cert_setup/#multiple-aws-accounts","title":"Multiple AWS Accounts","text":"<p>Many customers will have a dev/stage/prod set of AWS accounts, if that the case then the best practice is to make a domain specific to each account. So for instance:</p> <ul> <li>The AWS Dev Account gets: <code>&lt;company_name&gt;-ml-dashboard-dev.com</code> </li> <li>The AWS Prod Account gets:  <code>&lt;company_name&gt;-ml-dashboard-prod.com</code>.</li> </ul> <p>This means that when you go to that Dashboard it's super obvious which environment your on.</p>"},{"location":"aws_setup/domain_cert_setup/#register-the-domain","title":"Register the Domain","text":"<ul> <li> <p>Open Route 53 Console Route 53 Console</p> </li> <li> <p>Register your New Domain</p> <ul> <li>Click on Registered domains in the left navigation pane.</li> <li>Click on Register Domain.</li> <li>Enter your desired domain name and check for availability.</li> <li>Follow the prompts to complete the domain registration process.</li> <li>After registration, your domain will be listed under Registered domains.</li> </ul> </li> </ul>"},{"location":"aws_setup/domain_cert_setup/#request-a-ssl-certificate-from-acm","title":"Request a SSL Certificate from ACM","text":"<ol> <li> <p>Open ACM Console: AWS Certificate Manager (ACM) Console</p> </li> <li> <p>Request a Certificate:</p> <ul> <li>Click on Request a certificate.</li> <li>Select Request a public certificate and click Next.</li> </ul> </li> <li> <p>Add Domain Names:</p> <ul> <li>Enter the domain name you registered (e.g., <code>yourdomain.com</code>).</li> <li>Add any additional subdomains if needed (e.g., <code>www.yourdomain.com</code>).</li> </ul> </li> <li> <p>Validation Method:</p> <ul> <li>Choose DNS validation (recommended).</li> <li>ACM will provide CNAME records that you need to add to your Route 53 hosted zone.</li> </ul> </li> <li> <p>Add Tags (Optional):</p> <ul> <li>Add any tags if you want to organize your resources.</li> </ul> </li> <li> <p>Review and Request:</p> <ul> <li>Review your request and click Confirm and request.</li> </ul> </li> </ol>"},{"location":"aws_setup/domain_cert_setup/#adding-cname-records-to-route-53","title":"Adding CNAME Records to Route 53","text":"<p>To complete the domain validation process for your SSL/TLS certificate, you need to add the CNAME records provided by AWS Certificate Manager (ACM) to your Route 53 hosted zone. This step ensures that you own the domain and allows ACM to issue the certificate.</p>"},{"location":"aws_setup/domain_cert_setup/#finding-cname-record-names-and-values","title":"Finding CNAME Record Names and Values","text":"<p>You can find the CNAME record names and values in the AWS Certificate Manager (ACM) console:</p> <ol> <li> <p>Open ACM Console: AWS Certificate Manager (ACM) Console</p> </li> <li> <p>Select Your Certificate:</p> <ul> <li>Click on the certificate that is in the Pending Validation state.</li> </ul> </li> <li> <p>View Domains Section:</p> <ul> <li>Under the Domains section, you will see the CNAME record names and values that you need to add to your Route 53 hosted zone.</li> </ul> </li> </ol>"},{"location":"aws_setup/domain_cert_setup/#adding-cname-records-to-domain","title":"Adding CName Records to Domain","text":"<ol> <li> <p>Open Route 53 Console: Route 53 Console</p> </li> <li> <p>Select Your Hosted Zone:</p> <ul> <li>Find and select the hosted zone for your domain (e.g., <code>yourdomain.com</code>).</li> <li>Click on Create record.</li> </ul> </li> <li> <p>Add the First CNAME Record:</p> <ul> <li>For the Record name, enter the name provided by ACM (e.g., <code>_3e8623442477e9eeec.your-domain.com</code>).</li> <li>For the Record type, select <code>CNAME</code>.</li> <li>For the Value, enter the value provided by ACM (e.g., <code>_0908c89646d92.sdgjtdhdhz.acm-validations.aws.</code>) (include the trailing dot).</li> <li>Leave the default settings for TTL.</li> <li>Click on Create records.</li> </ul> </li> <li> <p>Add the Second CNAME Record:</p> <ul> <li>Repeat the process for the second CNAME record.</li> <li>For the Record name, enter the second name provided by ACM (e.g., <code>_75cd9364c643caa.www.your-domain.com</code>).</li> <li>For the Record type, select <code>CNAME</code>.</li> <li>For the Value, enter the second value provided by ACM (e.g., <code>_f72f8cff4fb20f4.sdgjhdhz.acm-validations.aws.</code>)  (include the trailing dot).</li> <li>Leave the default settings for TTL.</li> <li>Click on Create records.</li> </ul> </li> </ol> <p>DNS Propagation and Cert Validation</p> <p>After adding the CNAME records, these DNS records will propagate through the DNS system and ACM will automatically detect the validation records and validate the domain. This process can take a few minutes or up to an hour.</p>"},{"location":"aws_setup/domain_cert_setup/#certificate-states","title":"Certificate States","text":"<p>After requesting a certificate, it will go through the following states:</p> <ul> <li> <p>Pending Validation: The initial state after you request a certificate and before you complete the validation process. ACM is waiting for you to prove domain ownership by adding the CNAME records.</p> </li> <li> <p>Issued: This state indicates that the certificate has been successfully validated and issued. You can now use this certificate with your AWS resources.</p> </li> <li> <p>Validation Timed Out: If you do not complete the validation process within a specified period (usually 72 hours), the certificate request times out and enters this state.</p> </li> <li> <p>Revoked: This state indicates that the certificate has been revoked and is no longer valid.</p> </li> <li> <p>Failed: If the validation process fails for any reason, the certificate enters this state.</p> </li> <li> <p>Inactive: This state indicates that the certificate is not currently in use.</p> </li> </ul> <p>The certificate status should obviously be in the Issued state, if not please contact Workbench Support Team.</p>"},{"location":"aws_setup/domain_cert_setup/#retrieving-the-certificate-arn","title":"Retrieving the Certificate ARN","text":"<ol> <li> <p>Open ACM Console:</p> <ul> <li>Go back to the AWS Certificate Manager (ACM) Console.</li> </ul> </li> <li> <p>Check the Status:</p> <ul> <li>Once the CNAME records are added, ACM will automatically validate the domain.</li> <li>Refresh the ACM console to see the updated status.</li> <li>The status will change to \"Issued\" once validation is complete.</li> </ul> </li> <li> <p>Copy the Certificate ARN:</p> <ul> <li>Click on your issued certificate.</li> <li>Copy the Amazon Resource Name (ARN) from the certificate details.</li> </ul> </li> </ol> <p>You now have the ARN for your certificate, which you can use in your AWS resources such as API Gateway, CloudFront, etc.</p>"},{"location":"aws_setup/domain_cert_setup/#aws-resources","title":"AWS Resources","text":"<ul> <li>AWS Adding or Changing DNS Records</li> <li>AWS Certificate Manager (ACM) Documentation</li> <li>Requesting a Public Certificate</li> <li>Validating Domain Ownership</li> <li>AWS Route 53 Documentation</li> <li>AWS API Gateway Documentation</li> </ul>"},{"location":"aws_setup/full_pipeline/","title":"Testing Full ML Pipeline","text":"<p>Now that the core Workbench AWS Stack has been deployed. Let's test out Workbench by building a full entire AWS ML Pipeline from start to finish. The script <code>build_ml_pipeline.py</code> uses the Workbench API to quickly and easily build an AWS Modeling Pipeline.</p> <p>Taste the Awesome</p> <p>The Workbench \"hello world\" builds a full AWS ML Pipeline. From S3 to deployed model and endpoint. If you have any troubles at all feel free to contact us at workbench email or on Discord and we're happy to help you for FREE.</p> <ul> <li>DataLoader(abalone.csv) --&gt; DataSource</li> <li>DataToFeatureSet Transform --&gt; FeatureSet</li> <li>FeatureSetToModel Transform --&gt; Model</li> <li>ModelToEndpoint Transform --&gt; Endpoint</li> </ul> <p>This script will take a LONG TiME to run, most of the time is waiting on AWS to finalize FeatureGroups, train Models or deploy Endpoints.</p> <p><pre><code>\u276f python build_ml_pipeline.py\n&lt;lot of building ML pipeline outputs&gt;\n</code></pre> After the script completes you will see that it's built out an AWS ML Pipeline and testing artifacts.</p>"},{"location":"aws_setup/full_pipeline/#run-the-workbench-dashboard-local","title":"Run the Workbench Dashboard (Local)","text":"<p>Dashboard AWS Stack</p> <p>Deploying the Dashboard Stack is straight-forward and provides a robust AWS Web Server with Load Balancer, Elastic Container Service, VPC Networks, etc. (see AWS Dashboard Stack)</p> <p>For testing it's nice to run the Dashboard locally, but for longterm use the Workbench Dashboard should be deployed as an AWS Stack. The deployed Stack allows everyone in the company to use, view, and interact with the AWS Machine Learning Artifacts created with Workbench.</p> <p><pre><code>cd workbench/application/aws_dashboard\n./dashboard\n</code></pre> This will open a browser to http://localhost:8000</p> <p> Workbench Dashboard: AWS Pipelines in a Whole New Light! <p>Success</p> <p>Congratulations: Workbench is now deployed to your AWS Account. Deploying the AWS Stack only needs to be done once. Now that this is complete your developers can simply <code>pip install workbench</code> and start using the API.</p> <p>If you ran into any issues with this procedure please contact us via Discord or email workbench@supercowpowers.com and the SCP team will provide free setup and support for new Workbench users.</p>"},{"location":"aws_setup/iam_assume_role/","title":"Setting Up IAM Users to Access Workbench-ExecutionRole","text":"<p>This guide provides step-by-step instructions to configure IAM users to assume the <code>Workbench-ExecutionRole</code> in your AWS account.</p>"},{"location":"aws_setup/iam_assume_role/#prerequisites","title":"Prerequisites","text":"<ul> <li>Administrator permissions to update IAM users and policies.</li> <li>The <code>Workbench-ExecutionRole</code> must already be deployed via the Workbench AWS CDK stack.</li> </ul>"},{"location":"aws_setup/iam_assume_role/#steps-to-update-iam-user-permissions","title":"Steps to Update IAM User Permissions","text":""},{"location":"aws_setup/iam_assume_role/#1-log-in-to-the-aws-management-console","title":"1. Log in to the AWS Management Console","text":"<ul> <li>Navigate to the IAM Console.</li> </ul>"},{"location":"aws_setup/iam_assume_role/#2-select-the-iam-user","title":"2. Select the IAM User","text":"<ol> <li>In the left-hand menu, select Users.</li> <li>Locate and select the IAM user who needs access to the <code>Workbench-ExecutionRole</code>.</li> </ol>"},{"location":"aws_setup/iam_assume_role/#3-attach-an-inline-policy","title":"3. Attach an Inline Policy","text":"<ol> <li>Navigate to the Permissions tab for the IAM user.</li> <li>Click Add inline policy.</li> <li> <p>Select the JSON editor and paste the following policy:</p> <pre><code>{\n    \"Version\": \"2012-10-17\",\n    \"Statement\": [\n        {\n            \"Effect\": \"Allow\",\n            \"Action\": \"sts:AssumeRole\",\n            \"Resource\": \"arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole\"\n        }\n    ]\n}\n</code></pre> <ul> <li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li> </ul> </li> <li> <p>Review and save the policy.</p> </li> </ol>"},{"location":"aws_setup/iam_assume_role/#verifying-access-for-iam-users","title":"Verifying Access for IAM Users","text":"<ol> <li>Log in to the AWS Management Console as the IAM user.</li> <li> <p>Use the following CLI command to test access:</p> <pre><code>aws sts assume-role \\\n    --role-arn arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole \\\n    --role-session-name TestSession\n</code></pre> <ul> <li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li> </ul> </li> <li> <p>If successful, you will receive temporary credentials for the <code>Workbench-ExecutionRole</code>.</p> </li> </ol>"},{"location":"aws_setup/iam_assume_role/#troubleshooting","title":"Troubleshooting","text":""},{"location":"aws_setup/iam_assume_role/#common-issues","title":"Common Issues","text":"<ul> <li>Permission Denied: Ensure the correct inline policy is attached to the IAM user.</li> <li>Role Not Found: Verify that the <code>Workbench-ExecutionRole</code> has been deployed correctly.</li> </ul>"},{"location":"aws_setup/iam_assume_role/#contact-support","title":"Contact Support","text":"<p>If you encounter issues, please contact your AWS administrator or reach out to the Workbench support team.</p>"},{"location":"aws_setup/sso_assume_role/","title":"Setting Up SSO Users to Access Workbench-ExecutionRole","text":"<p>This guide provides step-by-step instructions to configure AWS SSO users to assume the <code>Workbench-ExecutionRole</code> in your AWS account.</p>"},{"location":"aws_setup/sso_assume_role/#prerequisites","title":"Prerequisites","text":"<ul> <li>Access to the management account in your AWS Organization.</li> <li>Administrative permissions to modify AWS SSO permission sets.</li> <li>The <code>Workbench-ExecutionRole</code> must already be deployed via the Workbench AWS CDK stack.</li> </ul>"},{"location":"aws_setup/sso_assume_role/#steps-to-update-sso-permissions","title":"Steps to Update SSO Permissions","text":""},{"location":"aws_setup/sso_assume_role/#1-log-in-to-the-aws-sso-console","title":"1. Log in to the AWS SSO Console","text":"<ul> <li>Login to your 'main' organization AWS Account.</li> <li>Go to the IAM Identity Center</li> </ul>"},{"location":"aws_setup/sso_assume_role/#2-find-the-relevant-permission-set","title":"2. Find the Relevant Permission Set","text":"<ul> <li>In the left menu, select Permission Sets.</li> <li>Locate the permission set used by the group needing access (e.g., <code>DataScientist</code> or another relevant group).</li> </ul>"},{"location":"aws_setup/sso_assume_role/#3-edit-the-permission-set","title":"3. Edit the Permission Set","text":"<ol> <li>Select the permission set</li> <li>Scroll down to Inline policy and click the the Edit button.</li> <li> <p>Add an inline policy with the following content:</p> <pre><code>{\n    \"Version\": \"2012-10-17\",\n    \"Statement\": [\n        {\n            \"Effect\": \"Allow\",\n            \"Action\": \"sts:AssumeRole\",\n            \"Resource\": \"arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole\"\n        }\n    ]\n}\n</code></pre> <ul> <li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li> </ul> </li> </ol>"},{"location":"aws_setup/sso_assume_role/#4-save-changes","title":"4. Save Changes","text":"<ul> <li>Save the updated permission set.</li> <li>AWS SSO will automatically propagate the changes to all users in the associated group.</li> </ul>"},{"location":"aws_setup/sso_assume_role/#verifying-access-for-sso-users","title":"Verifying Access for SSO Users","text":"<ol> <li>Log in to the AWS Management Console as a user in the configured group.</li> <li> <p>Use the following CLI command to test access:</p> <pre><code>aws sts assume-role \\\n    --role-arn arn:aws:iam::&lt;account-id&gt;:role/Workbench-ExecutionRole \\\n    --role-session-name TestSession\n</code></pre> <ul> <li>Replace <code>&lt;account-id&gt;</code> with your AWS account ID.</li> </ul> </li> <li> <p>If successful, you will receive temporary credentials for the <code>Workbench-ExecutionRole</code>.</p> </li> </ol>"},{"location":"aws_setup/sso_assume_role/#troubleshooting","title":"Troubleshooting","text":""},{"location":"aws_setup/sso_assume_role/#common-issues","title":"Common Issues","text":"<ul> <li>Permission Denied: Ensure the correct permission set is updated.</li> <li>Role Not Found: Verify that the <code>Workbench-ExecutionRole</code> has been deployed correctly.</li> </ul>"},{"location":"aws_setup/sso_assume_role/#contact-support","title":"Contact Support","text":"<p>If you encounter issues, please contact your AWS administrator or reach out to the Workbench support team.</p>"},{"location":"blogs/","title":"Workbench Blogs","text":"<p>Just Getting Started?</p> <p>The Workbench Blogs is a great way to see what's possible with Workbench. Also if you're ready to jump in the API Classes will give you details on the Workbench ML Pipeline Classes.</p>"},{"location":"blogs/#latest-blogs","title":"Latest Blogs","text":"<ul> <li>AqSol Residual Analysis: In this Blog we'll look at the popular AqSol compound solubility dataset and walk backward through the ML pipeline by starting with model residuals and backtracking to the features and input data.</li> </ul>"},{"location":"blogs/#stupid-yet-interesting","title":"Stupid yet Interesting","text":"<p>These blogs are odd corner cases that we stumbled into and discovered some interesting aspect of Cloud Platforms or ML Pipelines. </p> <ul> <li>AWS Credentials Refresh Lock We discover that the AWS botocore library has a threading lock when refreshing credentials.</li> </ul>"},{"location":"blogs/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"blogs/aws_credentials_lock/","title":"AWS Credentials Lock","text":"<p>AWS Curious?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"blogs/aws_credentials_lock/#tbd","title":"TBD","text":""},{"location":"blogs/aws_credentials_lock/#threading-lock","title":"Threading Lock","text":"<p>A threading lock object in Python is used to ensure that only one thread can access a specific section of code or resource at a time, preventing race conditions.</p> <p>botocore/credentials.py <pre><code>self._refresh_lock = threading.Lock()\n</code></pre></p>"},{"location":"blogs/aws_credentials_lock/#error","title":"Error","text":"<pre><code>elif self.refresh_needed(self._mandatory_refresh_timeout):\n    # If we're within the mandatory refresh window,\n    # we must block until we get refreshed credentials.\n    with self._refresh_lock:\n        if not self.refresh_needed(self._mandatory_refresh_timeout):\n            return\n        self._protected_refresh(is_mandatory=True)\n</code></pre>"},{"location":"blogs/aws_credentials_lock/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"blogs/eda/","title":"Exploratory Data Analysis","text":"<p>Workbench EDS</p> <p>The Workbench toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components Dashboard.</p> <p>The Workbench framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p> <ul> <li>TBD</li> <li>TBD2</li> </ul> Workbench Exploratory Data Analysis"},{"location":"blogs/eda/#additional-resources","title":"Additional Resources","text":"<ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"blogs/htg/","title":"EDA: High Target Gradients","text":"<p>Workbench EDS</p> <p>The Workbench toolkit a set of plots that show EDA results, it also has a flexible plugin architecture to expand, enhance, or even replace the current set of web components Dashboard.</p> <p>The Workbench framework has a broad range of Exploratory Data Analysis (EDA) functionality. Each time a DataSource or FeatureSet is created that data is run through a full set of EDA techniques:</p> <ul> <li>TBD</li> <li>TBD2</li> </ul> <p>One of the latest EDA techniques we've added is the addition of a concept called High Target Gradients </p> <ol> <li>Definition: For a given data point (x_i) with target value (y_i), and its neighbor (x_j) with target value (y_j), the target gradient (G_{ij}) can be defined as:</li> </ol> <p>[G_{ij} = \\frac{|y_i - y_j|}{d(x_i, x_j)}]</p> <p>where (d(x_i, x_j)) is the distance between (x_i) and (x_j) in the feature space. This equation gives you the rate of change of the target value with respect to the change in features, similar to a slope in a two-dimensional space.</p> <ol> <li>Max Gradient for Each Point: For each data point (x_i), you can compute the maximum target gradient with respect to all its neighbors:</li> </ol> <p>[G_{i}^{max} = \\max_{j \\neq i} G_{ij}]</p> <p>This gives you a scalar value for each point in your training data that represents the maximum rate of change of the target value in its local neighborhood.</p> <ol> <li> <p>Usage: You can use (G_{i}^{max}) to identify and filter areas in the feature space that have high target gradients, which may indicate potential issues with data quality or feature representation.</p> </li> <li> <p>Visualization: Plotting the distribution of (G_{i}^{max}) values or visualizing them in the context of the feature space can help you identify regions or specific points that warrant further investigation.</p> </li> </ol>"},{"location":"blogs/htg/#additional-resources","title":"Additional Resources","text":"<ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"blogs/model_monitoring/","title":"Model Monitoring","text":"<p>Amazon SageMaker Model Monitor currently provides the following types of monitoring:</p> <ul> <li>Monitor Data Quality: Detect drifts in data quality such as deviations from baseline data types.</li> <li>Monitor Model Quality: Monitor drift in model quality metrics, such as accuracy.</li> <li>Monitor Bias Drift for Models in Production: Monitor bias in your model\u2019s predictions.</li> <li>Monitor Feature Attribution Drift for Models in Production: Monitor drift in feature attribution.</li> </ul>"},{"location":"blogs/residual_analysis/","title":"Residual Analysis","text":""},{"location":"blogs/residual_analysis/#residual-analysis","title":"Residual Analysis","text":"<p>Overview and Definition Residual analysis involves examining the differences between observed and predicted values, known as residuals, to assess the performance of a predictive model. It is a critical step in model evaluation as it helps identify patterns of errors, diagnose potential problems, and improve model performance. By understanding where and why a model's predictions deviate from actual values, we can make informed adjustments to the model or the data to enhance accuracy and robustness.</p> <p>Sparse Data Regions The observation is in a part of feature space with little or no nearby training observations, leading to poor generalization in these regions and resulting in high prediction errors.</p> <p>Noisy/Inconsistent Data and Preprocessing Issues The observation is in a part of feature space where the training data is noisy, incorrect, or has high variance in the target variable. Additionally, missing values or incorrect data transformations can introduce errors, leading to unreliable predictions and high residuals.</p> <p>Feature Resolution The current feature set may not fully resolve the compounds, leading to \u2018collisions\u2019 where different compounds are assigned identical features. Such unresolved features can result in different compounds exhibiting the same features, causing high residuals due to unaccounted structural or chemical nuances.</p> <p>Activity Cliffs Structurally similar compounds exhibit significantly different activities, making accurate prediction challenging due to steep changes in activity with minor structural modifications.</p> <p>Feature Engineering Issues Irrelevant or redundant features and poor feature scaling can negatively impact the model's performance and accuracy, resulting in higher residuals.</p> <p>Model Overfitting or Underfitting Overfitting occurs when the model is too complex and captures noise, while underfitting happens when the model is too simple and misses underlying patterns, both leading to inaccurate predictions.</p>"},{"location":"cached/cached_data_source/","title":"CachedDataSource","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p> <p>CachedDataSource: Caches the method results for Workbench DataSources</p>"},{"location":"cached/cached_data_source/#workbench.cached.cached_data_source.CachedDataSource","title":"<code>CachedDataSource</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>AthenaSource</code></p> <p>CachedDataSource: Caches the method results for Workbench DataSources</p> <p>Note: Cached method values may lag underlying DataSource changes.</p> Common Usage <pre><code>my_data = CachedDataSource(name)\nmy_data.details()\nmy_data.health_check()\nmy_data.workbench_meta()\n</code></pre> Source code in <code>src/workbench/cached/cached_data_source.py</code> <pre><code>class CachedDataSource(CachedArtifactMixin, AthenaSource):\n    \"\"\"CachedDataSource: Caches the method results for Workbench DataSources\n\n    Note: Cached method values may lag underlying DataSource changes.\n\n    Common Usage:\n        ```python\n        my_data = CachedDataSource(name)\n        my_data.details()\n        my_data.health_check()\n        my_data.workbench_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid: str, database: str = \"workbench\"):\n        \"\"\"CachedDataSource Initialization\"\"\"\n        AthenaSource.__init__(self, data_uuid=data_uuid, database=database, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the DataSource Details.\n\n        Returns:\n            dict: A dictionary of details about the DataSource\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the DataSource Details.\n\n        Returns:\n            dict: A dictionary of details about the DataSource\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the DataSource Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the DataSource\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def workbench_meta(self) -&gt; Union[dict, None]:\n        \"\"\"Retrieve the Workbench Metadata for this DataSource.\n\n        Returns:\n            Union[dict, None]: Dictionary of Workbench metadata for this Artifact\n        \"\"\"\n        return super().workbench_meta()\n\n    def smart_sample(self) -&gt; pd.DataFrame:\n        \"\"\"Retrieve the Smart Sample for this DataSource.\n\n        Returns:\n            pd.DataFrame: The Smart Sample DataFrame\n        \"\"\"\n        return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_data_source/#workbench.cached.cached_data_source.CachedDataSource.__init__","title":"<code>__init__(data_uuid, database='workbench')</code>","text":"<p>CachedDataSource Initialization</p> Source code in <code>src/workbench/cached/cached_data_source.py</code> <pre><code>def __init__(self, data_uuid: str, database: str = \"workbench\"):\n    \"\"\"CachedDataSource Initialization\"\"\"\n    AthenaSource.__init__(self, data_uuid=data_uuid, database=database, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_data_source/#workbench.cached.cached_data_source.CachedDataSource.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the DataSource Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the DataSource</p> Source code in <code>src/workbench/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the DataSource Details.\n\n    Returns:\n        dict: A dictionary of details about the DataSource\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_data_source/#workbench.cached.cached_data_source.CachedDataSource.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the DataSource Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the DataSource</p> Source code in <code>src/workbench/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the DataSource Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the DataSource\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_data_source/#workbench.cached.cached_data_source.CachedDataSource.smart_sample","title":"<code>smart_sample()</code>","text":"<p>Retrieve the Smart Sample for this DataSource.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The Smart Sample DataFrame</p> Source code in <code>src/workbench/cached/cached_data_source.py</code> <pre><code>def smart_sample(self) -&gt; pd.DataFrame:\n    \"\"\"Retrieve the Smart Sample for this DataSource.\n\n    Returns:\n        pd.DataFrame: The Smart Sample DataFrame\n    \"\"\"\n    return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_data_source/#workbench.cached.cached_data_source.CachedDataSource.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the DataSource Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the DataSource</p> Source code in <code>src/workbench/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the DataSource Details.\n\n    Returns:\n        dict: A dictionary of details about the DataSource\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_data_source/#workbench.cached.cached_data_source.CachedDataSource.workbench_meta","title":"<code>workbench_meta()</code>","text":"<p>Retrieve the Workbench Metadata for this DataSource.</p> <p>Returns:</p> Type Description <code>Union[dict, None]</code> <p>Union[dict, None]: Dictionary of Workbench metadata for this Artifact</p> Source code in <code>src/workbench/cached/cached_data_source.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef workbench_meta(self) -&gt; Union[dict, None]:\n    \"\"\"Retrieve the Workbench Metadata for this DataSource.\n\n    Returns:\n        Union[dict, None]: Dictionary of Workbench metadata for this Artifact\n    \"\"\"\n    return super().workbench_meta()\n</code></pre>"},{"location":"cached/cached_data_source/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Pull DataSource Details</p> <pre><code>from workbench.cached.cached_data_source import CachedDataSource\n\n# Grab a DataSource\nds = CachedDataSource(\"abalone_data\")\n\n# Show the details\nds.details()\n\n&gt; ds.details()\n\n{'uuid': 'abalone_data',\n 'health_tags': [],\n 'aws_arn': 'arn:aws:glue:x:table/workbench/abalone_data',\n 'size': 0.070272,\n 'created': '2024-11-09T20:42:34.000Z',\n 'modified': '2024-11-10T19:57:52.000Z',\n 'input': 's3://workbench-public-data/common/aBaLone.CSV',\n 'workbench_health_tags': '',\n 'workbench_correlations': {'length': {'diameter': 0.9868115846024996,\n</code></pre>"},{"location":"cached/cached_endpoint/","title":"CachedEndpoint","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p> <p>CachedEndpoint: Caches the method results for Workbench Endpoints</p>"},{"location":"cached/cached_endpoint/#workbench.cached.cached_endpoint.CachedEndpoint","title":"<code>CachedEndpoint</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>EndpointCore</code></p> <p>CachedEndpoint: Caches the method results for Workbench Endpoints</p> <p>Note: Cached method values may lag underlying Endpoint changes.</p> Common Usage <pre><code>my_endpoint = CachedEndpoint(name)\nmy_endpoint.details()\nmy_endpoint.health_check()\nmy_endpoint.workbench_meta()\n</code></pre> Source code in <code>src/workbench/cached/cached_endpoint.py</code> <pre><code>class CachedEndpoint(CachedArtifactMixin, EndpointCore):\n    \"\"\"CachedEndpoint: Caches the method results for Workbench Endpoints\n\n    Note: Cached method values may lag underlying Endpoint changes.\n\n    Common Usage:\n        ```python\n        my_endpoint = CachedEndpoint(name)\n        my_endpoint.details()\n        my_endpoint.health_check()\n        my_endpoint.workbench_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, endpoint_uuid: str):\n        \"\"\"CachedEndpoint Initialization\"\"\"\n        EndpointCore.__init__(self, endpoint_uuid=endpoint_uuid, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedEndpoint Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedEndpoint\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedEndpoint Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedEndpoint\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedEndpoint Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the CachedEndpoint\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def workbench_meta(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n        Returns:\n            str: The Enumerated Model Type\n        \"\"\"\n        return super().workbench_meta()\n\n    @CachedArtifactMixin.cache_result\n    def endpoint_metrics(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Endpoint Metrics\n\n        Returns:\n            str: The Endpoint Metrics\n        \"\"\"\n        return super().endpoint_metrics()\n</code></pre>"},{"location":"cached/cached_endpoint/#workbench.cached.cached_endpoint.CachedEndpoint.__init__","title":"<code>__init__(endpoint_uuid)</code>","text":"<p>CachedEndpoint Initialization</p> Source code in <code>src/workbench/cached/cached_endpoint.py</code> <pre><code>def __init__(self, endpoint_uuid: str):\n    \"\"\"CachedEndpoint Initialization\"\"\"\n    EndpointCore.__init__(self, endpoint_uuid=endpoint_uuid, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_endpoint/#workbench.cached.cached_endpoint.CachedEndpoint.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the CachedEndpoint Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedEndpoint</p> Source code in <code>src/workbench/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedEndpoint Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedEndpoint\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_endpoint/#workbench.cached.cached_endpoint.CachedEndpoint.endpoint_metrics","title":"<code>endpoint_metrics()</code>","text":"<p>Retrieve the Endpoint Metrics</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Endpoint Metrics</p> Source code in <code>src/workbench/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef endpoint_metrics(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Endpoint Metrics\n\n    Returns:\n        str: The Endpoint Metrics\n    \"\"\"\n    return super().endpoint_metrics()\n</code></pre>"},{"location":"cached/cached_endpoint/#workbench.cached.cached_endpoint.CachedEndpoint.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the CachedEndpoint Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the CachedEndpoint</p> Source code in <code>src/workbench/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedEndpoint Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the CachedEndpoint\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_endpoint/#workbench.cached.cached_endpoint.CachedEndpoint.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the CachedEndpoint Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedEndpoint</p> Source code in <code>src/workbench/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedEndpoint Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedEndpoint\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_endpoint/#workbench.cached.cached_endpoint.CachedEndpoint.workbench_meta","title":"<code>workbench_meta()</code>","text":"<p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Enumerated Model Type</p> Source code in <code>src/workbench/cached/cached_endpoint.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef workbench_meta(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n    Returns:\n        str: The Enumerated Model Type\n    \"\"\"\n    return super().workbench_meta()\n</code></pre>"},{"location":"cached/cached_endpoint/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Get Endpoint Details</p> <pre><code>from workbench.cached.cached_endpoint import CachedEndpoint\n\n# Grab an Endpoint\nend = CachedEndpoint(\"abalone-regression\")\n\n# Get the Details\n end.details()\n\n{'uuid': 'abalone-regression-end',\n 'health_tags': [],\n 'status': 'InService',\n 'instance': 'Serverless (2GB/5)',\n 'instance_count': '-',\n 'variant': 'AllTraffic',\n 'model_name': 'abalone-regression',\n 'model_type': 'regressor',\n 'model_metrics':        RMSE     R2    MAPE  MedAE  NumRows\n 1.64  2.246  0.502  16.393  1.209      834,\n 'confusion_matrix': None,\n 'predictions':      class_number_of_rings  prediction    id\n 0                       16   10.516158     7\n 1                        9    9.031365     8\n 2                       10    9.264600    17\n 3                        7    8.578638    18\n 4                       12   10.492446    27\n ..                     ...         ...   ...\n 829                     11   11.915862  4148\n 830                      8    8.210898  4157\n 831                      8    7.693689  4158\n 832                      9    7.542521  4167\n 833                      8    9.060015  4168\n</code></pre>"},{"location":"cached/cached_feature_set/","title":"CachedFeatureSet","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p> <p>CachedFeatureSet: Caches the method results for Workbench FeatureSets</p>"},{"location":"cached/cached_feature_set/#workbench.cached.cached_feature_set.CachedFeatureSet","title":"<code>CachedFeatureSet</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>FeatureSetCore</code></p> <p>CachedFeatureSet: Caches the method results for Workbench FeatureSets</p> <p>Note: Cached method values may lag underlying FeatureSet changes.</p> Common Usage <pre><code>my_features = CachedFeatureSet(name)\nmy_features.details()\nmy_features.health_check()\nmy_features.workbench_meta()\n</code></pre> Source code in <code>src/workbench/cached/cached_feature_set.py</code> <pre><code>class CachedFeatureSet(CachedArtifactMixin, FeatureSetCore):\n    \"\"\"CachedFeatureSet: Caches the method results for Workbench FeatureSets\n\n    Note: Cached method values may lag underlying FeatureSet changes.\n\n    Common Usage:\n        ```python\n        my_features = CachedFeatureSet(name)\n        my_features.details()\n        my_features.health_check()\n        my_features.workbench_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, feature_set_uuid: str, database: str = \"workbench\"):\n        \"\"\"CachedFeatureSet Initialization\"\"\"\n        FeatureSetCore.__init__(self, feature_set_uuid=feature_set_uuid, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the FeatureSet Details.\n\n        Returns:\n            dict: A dictionary of details about the FeatureSet\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the FeatureSet Details.\n\n        Returns:\n            dict: A dictionary of details about the FeatureSet\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the FeatureSet Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the FeatureSet\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def workbench_meta(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Workbench Metadata for this DataSource.\n\n        Returns:\n            Union[dict, None]: Dictionary of Workbench metadata for this Artifact\n        \"\"\"\n        return super().workbench_meta()\n\n    @CachedArtifactMixin.cache_result\n    def smart_sample(self) -&gt; pd.DataFrame:\n        \"\"\"Retrieve the Smart Sample for this FeatureSet.\n\n        Returns:\n            pd.DataFrame: The Smart Sample DataFrame\n        \"\"\"\n        return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_feature_set/#workbench.cached.cached_feature_set.CachedFeatureSet.__init__","title":"<code>__init__(feature_set_uuid, database='workbench')</code>","text":"<p>CachedFeatureSet Initialization</p> Source code in <code>src/workbench/cached/cached_feature_set.py</code> <pre><code>def __init__(self, feature_set_uuid: str, database: str = \"workbench\"):\n    \"\"\"CachedFeatureSet Initialization\"\"\"\n    FeatureSetCore.__init__(self, feature_set_uuid=feature_set_uuid, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_feature_set/#workbench.cached.cached_feature_set.CachedFeatureSet.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the FeatureSet Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the FeatureSet</p> Source code in <code>src/workbench/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the FeatureSet Details.\n\n    Returns:\n        dict: A dictionary of details about the FeatureSet\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_feature_set/#workbench.cached.cached_feature_set.CachedFeatureSet.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the FeatureSet Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the FeatureSet</p> Source code in <code>src/workbench/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the FeatureSet Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the FeatureSet\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_feature_set/#workbench.cached.cached_feature_set.CachedFeatureSet.smart_sample","title":"<code>smart_sample()</code>","text":"<p>Retrieve the Smart Sample for this FeatureSet.</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The Smart Sample DataFrame</p> Source code in <code>src/workbench/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef smart_sample(self) -&gt; pd.DataFrame:\n    \"\"\"Retrieve the Smart Sample for this FeatureSet.\n\n    Returns:\n        pd.DataFrame: The Smart Sample DataFrame\n    \"\"\"\n    return super().smart_sample(recompute=False)\n</code></pre>"},{"location":"cached/cached_feature_set/#workbench.cached.cached_feature_set.CachedFeatureSet.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the FeatureSet Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the FeatureSet</p> Source code in <code>src/workbench/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the FeatureSet Details.\n\n    Returns:\n        dict: A dictionary of details about the FeatureSet\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_feature_set/#workbench.cached.cached_feature_set.CachedFeatureSet.workbench_meta","title":"<code>workbench_meta()</code>","text":"<p>Retrieve the Workbench Metadata for this DataSource.</p> <p>Returns:</p> Type Description <code>Union[str, None]</code> <p>Union[dict, None]: Dictionary of Workbench metadata for this Artifact</p> Source code in <code>src/workbench/cached/cached_feature_set.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef workbench_meta(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Workbench Metadata for this DataSource.\n\n    Returns:\n        Union[dict, None]: Dictionary of Workbench metadata for this Artifact\n    \"\"\"\n    return super().workbench_meta()\n</code></pre>"},{"location":"cached/cached_feature_set/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Pull FeatureSet Details</p> <pre><code>from workbench.cached.cached_feature_set import CachedFeatureSet\n\n# Grab a FeatureSet\nfs = CachedFeatureSet(\"abalone_features\")\n\n# Show the details\nfs.details()\n\n&gt; fs.details()\n\n{'uuid': 'abalone_features',\n 'health_tags': [],\n 'aws_arn': 'arn:aws:glue:x:table/workbench/abalone_data',\n 'size': 0.070272,\n 'created': '2024-11-09T20:42:34.000Z',\n 'modified': '2024-11-10T19:57:52.000Z',\n 'input': 's3://workbench-public-data/common/aBaLone.CSV',\n 'workbench_health_tags': '',\n 'workbench_correlations': {'length': {'diameter': 0.9868115846024996,\n</code></pre>"},{"location":"cached/cached_meta/","title":"CachedMeta","text":"<p>CachedMeta Examples</p> <p>Examples of using the CachedMeta class are listed at the bottom of this page Examples.</p> <p>CachedMeta: A class that provides caching for the Meta() class</p>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta","title":"<code>CachedMeta</code>","text":"<p>               Bases: <code>CloudMeta</code></p> <p>CachedMeta: Singleton class for caching metadata functionality.</p> Common Usage <pre><code>from workbench.cached.cached_meta import CachedMeta\nmeta = CachedMeta()\n\n# Get the AWS Account Info\nmeta.account()\nmeta.config()\n\n# These are 'list' methods\nmeta.etl_jobs()\nmeta.data_sources()\nmeta.feature_sets(details=True/False)\nmeta.models(details=True/False)\nmeta.endpoints()\nmeta.views()\n\n# These are 'describe' methods\nmeta.data_source(\"abalone_data\")\nmeta.feature_set(\"abalone_features\")\nmeta.model(\"abalone-regression\")\nmeta.endpoint(\"abalone-endpoint\")\n</code></pre> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>class CachedMeta(CloudMeta):\n    \"\"\"CachedMeta: Singleton class for caching metadata functionality.\n\n    Common Usage:\n       ```python\n       from workbench.cached.cached_meta import CachedMeta\n       meta = CachedMeta()\n\n       # Get the AWS Account Info\n       meta.account()\n       meta.config()\n\n       # These are 'list' methods\n       meta.etl_jobs()\n       meta.data_sources()\n       meta.feature_sets(details=True/False)\n       meta.models(details=True/False)\n       meta.endpoints()\n       meta.views()\n\n       # These are 'describe' methods\n       meta.data_source(\"abalone_data\")\n       meta.feature_set(\"abalone_features\")\n       meta.model(\"abalone-regression\")\n       meta.endpoint(\"abalone-endpoint\")\n       ```\n    \"\"\"\n\n    _instance = None  # Class attribute to hold the singleton instance\n\n    def __new__(cls, *args, **kwargs):\n        if cls._instance is None:\n            cls._instance = super(CachedMeta, cls).__new__(cls)\n        return cls._instance\n\n    def __init__(self):\n        \"\"\"CachedMeta Initialization\"\"\"\n        if hasattr(self, \"_initialized\") and self._initialized:\n            return  # Prevent reinitialization\n\n        self.log = logging.getLogger(\"workbench\")\n        self.log.important(\"Initializing CachedMeta...\")\n        super().__init__()\n\n        # Create both our Meta Cache and Fresh Cache (tracks if data is stale)\n        self.meta_cache = WorkbenchCache(prefix=\"meta\")\n        self.fresh_cache = WorkbenchCache(prefix=\"meta_fresh\", expire=90)  # 90-second expiration\n\n        # Create a ThreadPoolExecutor for refreshing stale data\n        self.thread_pool = ThreadPoolExecutor(max_workers=5)\n\n        # Mark the instance as initialized\n        self._initialized = True\n\n    def check(self):\n        \"\"\"Check if our underlying caches are working\"\"\"\n        return self.meta_cache.check()\n\n    def list_meta_cache(self):\n        \"\"\"List the current Meta Cache\"\"\"\n        return self.meta_cache.list_keys()\n\n    def clear_meta_cache(self):\n        \"\"\"Clear the current Meta Cache\"\"\"\n        self.meta_cache.clear()\n\n    @cache_result\n    def account(self) -&gt; dict:\n        \"\"\"Cloud Platform Account Info\n\n        Returns:\n            dict: Cloud Platform Account Info\n        \"\"\"\n        return super().account()\n\n    @cache_result\n    def config(self) -&gt; dict:\n        \"\"\"Return the current Workbench Configuration\n\n        Returns:\n            dict: The current Workbench Configuration\n        \"\"\"\n        return super().config()\n\n    @cache_result\n    def incoming_data(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about data in the incoming raw data\n\n        Returns:\n            pd.DataFrame: A summary of the incoming raw data\n        \"\"\"\n        return super().incoming_data()\n\n    @cache_result\n    def etl_jobs(self) -&gt; pd.DataFrame:\n        \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n        Returns:\n            pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n        \"\"\"\n        return super().etl_jobs()\n\n    @cache_result\n    def data_sources(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n        \"\"\"\n        return super().data_sources()\n\n    @cache_result\n    def views(self, database: str = \"workbench\") -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n        Args:\n            database (str, optional): Glue database. Defaults to 'workbench'.\n\n        Returns:\n            pd.DataFrame: A summary of all the Views, for the given database, in AWS\n        \"\"\"\n        return super().views(database=database)\n\n    @cache_result\n    def feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n        \"\"\"\n        return super().feature_sets(details=details)\n\n    @cache_result\n    def models(self, details: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n        Args:\n            details (bool, optional): Include detailed information. Defaults to False.\n\n        Returns:\n            pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n        \"\"\"\n        return super().models(details=details)\n\n    @cache_result\n    def endpoints(self) -&gt; pd.DataFrame:\n        \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n        Returns:\n            pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n        \"\"\"\n        return super().endpoints()\n\n    @cache_result\n    def glue_job(self, job_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Glue Job\n\n        Args:\n            job_name (str): The name of the Glue Job\n\n        Returns:\n            dict: The details of the Glue Job (None if not found)\n        \"\"\"\n        return super().glue_job(job_name=job_name)\n\n    @cache_result\n    def data_source(self, data_source_name: str, database: str = \"workbench\") -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Data Source\n\n        Args:\n            data_source_name (str): The name of the Data Source\n            database (str, optional): The Glue database. Defaults to 'workbench'.\n\n        Returns:\n            dict: The details of the Data Source (None if not found)\n        \"\"\"\n        return super().data_source(data_source_name=data_source_name, database=database)\n\n    @cache_result\n    def feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Feature Set\n\n        Args:\n            feature_set_name (str): The name of the Feature Set\n\n        Returns:\n            dict: The details of the Feature Set (None if not found)\n        \"\"\"\n        return super().feature_set(feature_set_name=feature_set_name)\n\n    @cache_result\n    def model(self, model_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Model\n\n        Args:\n            model_name (str): The name of the Model\n\n        Returns:\n            dict: The details of the Model (None if not found)\n        \"\"\"\n        return super().model(model_name=model_name)\n\n    @cache_result\n    def endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n        \"\"\"Get the details of a specific Endpoint\n\n        Args:\n            endpoint_name (str): The name of the Endpoint\n\n        Returns:\n            dict: The details of the Endpoint (None if not found)\n        \"\"\"\n        return super().endpoint(endpoint_name=endpoint_name)\n\n    def _refresh_data_in_background(self, cache_key, method, *args, **kwargs):\n        \"\"\"Background task to refresh AWS metadata.\"\"\"\n        result = method(self, *args, **kwargs)\n        self.meta_cache.set(cache_key, result)\n        self.log.debug(f\"Updated Metadata for {cache_key}\")\n\n    @staticmethod\n    def _flatten_redis_key(method, *args, **kwargs):\n        \"\"\"Flatten the args and kwargs into a single string\"\"\"\n        arg_str = \"_\".join(str(arg) for arg in args)\n        kwarg_str = \"_\".join(f\"{k}_{v}\" for k, v in sorted(kwargs.items()))\n        return f\"{method.__name__}_{arg_str}_{kwarg_str}\".replace(\" \", \"\").replace(\"'\", \"\")\n\n    def __del__(self):\n        \"\"\"Destructor to shut down the thread pool gracefully.\"\"\"\n        self.close()\n\n    def close(self):\n        \"\"\"Explicitly close the thread pool, if needed.\"\"\"\n        if self.thread_pool:\n            self.log.important(\"Shutting down the ThreadPoolExecutor...\")\n            try:\n                self.thread_pool.shutdown(wait=True)  # Gracefully shutdown\n            except RuntimeError as e:\n                self.log.error(f\"Error during thread pool shutdown: {e}\")\n            finally:\n                self.thread_pool = None\n\n    def __repr__(self):\n        return f\"CachedMeta()\\n\\t{repr(self.meta_cache)}\\n\\t{super().__repr__()}\"\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.__del__","title":"<code>__del__()</code>","text":"<p>Destructor to shut down the thread pool gracefully.</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>def __del__(self):\n    \"\"\"Destructor to shut down the thread pool gracefully.\"\"\"\n    self.close()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.__init__","title":"<code>__init__()</code>","text":"<p>CachedMeta Initialization</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>def __init__(self):\n    \"\"\"CachedMeta Initialization\"\"\"\n    if hasattr(self, \"_initialized\") and self._initialized:\n        return  # Prevent reinitialization\n\n    self.log = logging.getLogger(\"workbench\")\n    self.log.important(\"Initializing CachedMeta...\")\n    super().__init__()\n\n    # Create both our Meta Cache and Fresh Cache (tracks if data is stale)\n    self.meta_cache = WorkbenchCache(prefix=\"meta\")\n    self.fresh_cache = WorkbenchCache(prefix=\"meta_fresh\", expire=90)  # 90-second expiration\n\n    # Create a ThreadPoolExecutor for refreshing stale data\n    self.thread_pool = ThreadPoolExecutor(max_workers=5)\n\n    # Mark the instance as initialized\n    self._initialized = True\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.account","title":"<code>account()</code>","text":"<p>Cloud Platform Account Info</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>Cloud Platform Account Info</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef account(self) -&gt; dict:\n    \"\"\"Cloud Platform Account Info\n\n    Returns:\n        dict: Cloud Platform Account Info\n    \"\"\"\n    return super().account()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.check","title":"<code>check()</code>","text":"<p>Check if our underlying caches are working</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>def check(self):\n    \"\"\"Check if our underlying caches are working\"\"\"\n    return self.meta_cache.check()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.clear_meta_cache","title":"<code>clear_meta_cache()</code>","text":"<p>Clear the current Meta Cache</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>def clear_meta_cache(self):\n    \"\"\"Clear the current Meta Cache\"\"\"\n    self.meta_cache.clear()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.close","title":"<code>close()</code>","text":"<p>Explicitly close the thread pool, if needed.</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>def close(self):\n    \"\"\"Explicitly close the thread pool, if needed.\"\"\"\n    if self.thread_pool:\n        self.log.important(\"Shutting down the ThreadPoolExecutor...\")\n        try:\n            self.thread_pool.shutdown(wait=True)  # Gracefully shutdown\n        except RuntimeError as e:\n            self.log.error(f\"Error during thread pool shutdown: {e}\")\n        finally:\n            self.thread_pool = None\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.config","title":"<code>config()</code>","text":"<p>Return the current Workbench Configuration</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The current Workbench Configuration</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef config(self) -&gt; dict:\n    \"\"\"Return the current Workbench Configuration\n\n    Returns:\n        dict: The current Workbench Configuration\n    \"\"\"\n    return super().config()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.data_source","title":"<code>data_source(data_source_name, database='workbench')</code>","text":"<p>Get the details of a specific Data Source</p> <p>Parameters:</p> Name Type Description Default <code>data_source_name</code> <code>str</code> <p>The name of the Data Source</p> required <code>database</code> <code>str</code> <p>The Glue database. Defaults to 'workbench'.</p> <code>'workbench'</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Data Source (None if not found)</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef data_source(self, data_source_name: str, database: str = \"workbench\") -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Data Source\n\n    Args:\n        data_source_name (str): The name of the Data Source\n        database (str, optional): The Glue database. Defaults to 'workbench'.\n\n    Returns:\n        dict: The details of the Data Source (None if not found)\n    \"\"\"\n    return super().data_source(data_source_name=data_source_name, database=database)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.data_sources","title":"<code>data_sources()</code>","text":"<p>Get a summary of the Data Sources deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef data_sources(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Data Sources deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Data Sources deployed in the Cloud Platform\n    \"\"\"\n    return super().data_sources()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.endpoint","title":"<code>endpoint(endpoint_name)</code>","text":"<p>Get the details of a specific Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>The name of the Endpoint</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Endpoint (None if not found)</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef endpoint(self, endpoint_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Endpoint\n\n    Args:\n        endpoint_name (str): The name of the Endpoint\n\n    Returns:\n        dict: The details of the Endpoint (None if not found)\n    \"\"\"\n    return super().endpoint(endpoint_name=endpoint_name)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.endpoints","title":"<code>endpoints()</code>","text":"<p>Get a summary of the Endpoints deployed in the Cloud Platform</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Endpoints in the Cloud Platform</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef endpoints(self) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Endpoints deployed in the Cloud Platform\n\n    Returns:\n        pd.DataFrame: A summary of the Endpoints in the Cloud Platform\n    \"\"\"\n    return super().endpoints()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.etl_jobs","title":"<code>etl_jobs()</code>","text":"<p>Get summary data about Extract, Transform, Load (ETL) Jobs</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef etl_jobs(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about Extract, Transform, Load (ETL) Jobs\n\n    Returns:\n        pd.DataFrame: A summary of the ETL Jobs deployed in the Cloud Platform\n    \"\"\"\n    return super().etl_jobs()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.feature_set","title":"<code>feature_set(feature_set_name)</code>","text":"<p>Get the details of a specific Feature Set</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_name</code> <code>str</code> <p>The name of the Feature Set</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Feature Set (None if not found)</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef feature_set(self, feature_set_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Feature Set\n\n    Args:\n        feature_set_name (str): The name of the Feature Set\n\n    Returns:\n        dict: The details of the Feature Set (None if not found)\n    \"\"\"\n    return super().feature_set(feature_set_name=feature_set_name)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.feature_sets","title":"<code>feature_sets(details=False)</code>","text":"<p>Get a summary of the Feature Sets deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef feature_sets(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Feature Sets deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Feature Sets deployed in the Cloud Platform\n    \"\"\"\n    return super().feature_sets(details=details)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.glue_job","title":"<code>glue_job(job_name)</code>","text":"<p>Get the details of a specific Glue Job</p> <p>Parameters:</p> Name Type Description Default <code>job_name</code> <code>str</code> <p>The name of the Glue Job</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Glue Job (None if not found)</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef glue_job(self, job_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Glue Job\n\n    Args:\n        job_name (str): The name of the Glue Job\n\n    Returns:\n        dict: The details of the Glue Job (None if not found)\n    \"\"\"\n    return super().glue_job(job_name=job_name)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.incoming_data","title":"<code>incoming_data()</code>","text":"<p>Get summary data about data in the incoming raw data</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the incoming raw data</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef incoming_data(self) -&gt; pd.DataFrame:\n    \"\"\"Get summary data about data in the incoming raw data\n\n    Returns:\n        pd.DataFrame: A summary of the incoming raw data\n    \"\"\"\n    return super().incoming_data()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.list_meta_cache","title":"<code>list_meta_cache()</code>","text":"<p>List the current Meta Cache</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>def list_meta_cache(self):\n    \"\"\"List the current Meta Cache\"\"\"\n    return self.meta_cache.list_keys()\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.model","title":"<code>model(model_name)</code>","text":"<p>Get the details of a specific Model</p> <p>Parameters:</p> Name Type Description Default <code>model_name</code> <code>str</code> <p>The name of the Model</p> required <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the Model (None if not found)</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef model(self, model_name: str) -&gt; Union[dict, None]:\n    \"\"\"Get the details of a specific Model\n\n    Args:\n        model_name (str): The name of the Model\n\n    Returns:\n        dict: The details of the Model (None if not found)\n    \"\"\"\n    return super().model(model_name=model_name)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.models","title":"<code>models(details=False)</code>","text":"<p>Get a summary of the Models deployed in the Cloud Platform</p> <p>Parameters:</p> Name Type Description Default <code>details</code> <code>bool</code> <p>Include detailed information. Defaults to False.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of the Models deployed in the Cloud Platform</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef models(self, details: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the Models deployed in the Cloud Platform\n\n    Args:\n        details (bool, optional): Include detailed information. Defaults to False.\n\n    Returns:\n        pd.DataFrame: A summary of the Models deployed in the Cloud Platform\n    \"\"\"\n    return super().models(details=details)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.CachedMeta.views","title":"<code>views(database='workbench')</code>","text":"<p>Get a summary of the all the Views, for the given database, in AWS</p> <p>Parameters:</p> Name Type Description Default <code>database</code> <code>str</code> <p>Glue database. Defaults to 'workbench'.</p> <code>'workbench'</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A summary of all the Views, for the given database, in AWS</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>@cache_result\ndef views(self, database: str = \"workbench\") -&gt; pd.DataFrame:\n    \"\"\"Get a summary of the all the Views, for the given database, in AWS\n\n    Args:\n        database (str, optional): Glue database. Defaults to 'workbench'.\n\n    Returns:\n        pd.DataFrame: A summary of all the Views, for the given database, in AWS\n    \"\"\"\n    return super().views(database=database)\n</code></pre>"},{"location":"cached/cached_meta/#workbench.cached.cached_meta.cache_result","title":"<code>cache_result(method)</code>","text":"<p>Decorator to cache method results in meta_cache</p> Source code in <code>src/workbench/cached/cached_meta.py</code> <pre><code>def cache_result(method):\n    \"\"\"Decorator to cache method results in meta_cache\"\"\"\n\n    @wraps(method)\n    def wrapper(self, *args, **kwargs):\n        # Create a unique cache key based on the method name and arguments\n        cache_key = CachedMeta._flatten_redis_key(method, *args, **kwargs)\n\n        # Check for fresh data, spawn thread to refresh if stale\n        if WorkbenchCache.refresh_enabled and self.fresh_cache.get(cache_key) is None:\n            self.log.debug(f\"Async: Metadata for {cache_key} refresh thread started...\")\n            self.fresh_cache.set(cache_key, True)  # Mark as refreshed\n\n            # Spawn a thread to refresh data without blocking\n            self.thread_pool.submit(self._refresh_data_in_background, cache_key, method, *args, **kwargs)\n\n        # Return data (fresh or stale) if available\n        cached_value = self.meta_cache.get(cache_key)\n        if cached_value is not None:\n            return cached_value\n\n        # Fall back to calling the method if no cached data found\n        self.log.important(f\"Blocking: Getting Metadata for {cache_key}\")\n        result = method(self, *args, **kwargs)\n        self.meta_cache.set(cache_key, result)\n        return result\n\n    return wrapper\n</code></pre>"},{"location":"cached/cached_meta/#examples","title":"Examples","text":"<p>These example show how to use the <code>CachedMeta()</code> class to pull lists of artifacts from AWS. DataSources, FeatureSets, Models, Endpoints and more. If you're building a web interface plugin, the CachedMeta class is a great place to start.</p> <p>Workbench REPL</p> <p>If you'd like to see exactly what data/details you get back from the <code>CachedMeta()</code> class, you can spin up the Workbench REPL, use the class and test out all the methods. Try it out! Workbench REPL</p> Using Workbench REPL<pre><code>CachedMeta = CachedMeta()\nmodel_df = CachedMeta.models()\nmodel_df\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\n</code></pre> <p>List the Models in AWS</p> <pre><code>from workbench.cached.cached_meta import CachedMeta\n\n# Create our CachedMeta Class and get a list of our Models\nCachedMeta = CachedMeta()\nmodel_df = CachedMeta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names:\n    pprint(CachedMeta.model(name))\n</code></pre> <p>Output</p> <pre><code>Number of Models: 3\n               Model Group   Health Owner  ...             Input     Status                Description\n0      wine-classification  healthy     -  ...     wine_features  Completed  Wine Classification Model\n1  abalone-regression-full  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n2       abalone-regression  healthy     -  ...  abalone_features  Completed   Abalone Regression Model\n\n[3 rows x 10 columns]\nwine-classification\nabalone-regression-full\nabalone-regression\n</code></pre> <p>Getting Model Performance Metrics</p> <pre><code>from workbench.cached.cached_meta import CachedMeta\n\n# Create our CachedMeta Class and get a list of our Models\nCachedMeta = CachedMeta()\nmodel_df = CachedMeta.models()\n\nprint(f\"Number of Models: {len(model_df)}\")\nprint(model_df)\n\n# Get more details data on the Models\nmodel_names = model_df[\"Model Group\"].tolist()\nfor name in model_names[:5]:\n    model_details = CachedMeta.model(name)\n    print(f\"\\n\\nModel: {name}\")\n    performance_metrics = model_details[\"workbench_CachedMeta\"][\"workbench_inference_metrics\"]\n    print(f\"\\tPerformance Metrics: {performance_metrics}\")\n</code></pre> <p>Output</p> <pre><code>wine-classification\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/wine-classification\n    Description: Wine Classification Model\n    Tags: wine::classification\n    Performance Metrics:\n        [{'wine_class': 'TypeA', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 12}, {'wine_class': 'TypeB', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 14}, {'wine_class': 'TypeC', 'precision': 1.0, 'recall': 1.0, 'fscore': 1.0, 'roc_auc': 1.0, 'support': 9}]\n\nabalone-regression\n    ARN: arn:aws:sagemaker:us-west-2:507740646243:model-package-group/abalone-regression\n    Description: Abalone Regression Model\n    Tags: abalone::regression\n    Performance Metrics:\n        [{'MAE': 1.64, 'RMSE': 2.246, 'R2': 0.502, 'MAPE': 16.393, 'MedAE': 1.209, 'NumRows': 834}]\n</code></pre> <p>List the Endpoints in AWS</p> <pre><code>from pprint import pprint\nfrom workbench.cached.cached_meta import CachedMeta\n\n# Create our CachedMeta Class and get a list of our Endpoints\nCachedMeta = CachedMeta()\nendpoint_df = CachedMeta.endpoints()\nprint(f\"Number of Endpoints: {len(endpoint_df)}\")\nprint(endpoint_df)\n\n# Get more details data on the Endpoints\nendpoint_names = endpoint_df[\"Name\"].tolist()\nfor name in endpoint_names:\n    pprint(CachedMeta.endpoint(name))\n</code></pre> <p>Output</p> <pre><code>Number of Endpoints: 2\n                      Name   Health            Instance           Created  ...     Status     Variant Capture Samp(%)\n0  wine-classification-end  healthy  Serverless (2GB/5)  2024-03-23 23:09  ...  InService  AllTraffic   False       -\n1   abalone-regression-end  healthy  Serverless (2GB/5)  2024-03-23 21:11  ...  InService  AllTraffic   False       -\n\n[2 rows x 10 columns]\nwine-classification-end\n&lt;lots of details about endpoints&gt;\n</code></pre> <p>Not Finding some particular AWS Data?</p> <p>The Workbench CachedMeta API Class also has <code>(details=True)</code> arguments, so make sure to check those out.</p>"},{"location":"cached/cached_model/","title":"CachedModel","text":"<p>Model Examples</p> <p>Examples of using the Model Class are in the Examples section at the bottom of this page. AWS Model setup and deployment are quite complicated to do manually but the Workbench Model Class makes it a breeze!</p> <p>CachedModel: Caches the method results for Workbench Models</p>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel","title":"<code>CachedModel</code>","text":"<p>               Bases: <code>CachedArtifactMixin</code>, <code>ModelCore</code></p> <p>CachedModel: Caches the method results for Workbench Models</p> <p>Note: Cached method values may lag underlying Model changes.</p> Common Usage <pre><code>my_model = CachedModel(name)\nmy_model.details()\nmy_model.health_check()\nmy_model.workbench_meta()\n</code></pre> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>class CachedModel(CachedArtifactMixin, ModelCore):\n    \"\"\"CachedModel: Caches the method results for Workbench Models\n\n    Note: Cached method values may lag underlying Model changes.\n\n    Common Usage:\n        ```python\n        my_model = CachedModel(name)\n        my_model.details()\n        my_model.health_check()\n        my_model.workbench_meta()\n        ```\n    \"\"\"\n\n    def __init__(self, uuid: str):\n        \"\"\"CachedModel Initialization\"\"\"\n        ModelCore.__init__(self, model_uuid=uuid, use_cached_meta=True)\n\n    @CachedArtifactMixin.cache_result\n    def summary(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedModel Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedModel\n        \"\"\"\n        return super().summary(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def details(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedModel Details.\n\n        Returns:\n            dict: A dictionary of details about the CachedModel\n        \"\"\"\n        return super().details(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def health_check(self, **kwargs) -&gt; dict:\n        \"\"\"Retrieve the CachedModel Health Check.\n\n        Returns:\n            dict: A dictionary of health check details for the CachedModel\n        \"\"\"\n        return super().health_check(**kwargs)\n\n    @CachedArtifactMixin.cache_result\n    def workbench_meta(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n        Returns:\n            str: The Enumerated Model Type\n        \"\"\"\n        return super().workbench_meta()\n\n    @CachedArtifactMixin.cache_result\n    def get_endpoint_inference_path(self) -&gt; Union[str, None]:\n        \"\"\"Retrieve the Endpoint Inference Path.\n\n        Returns:\n            str: The Endpoint Inference Path\n        \"\"\"\n        return super().get_endpoint_inference_path()\n\n    @CachedArtifactMixin.cache_result\n    def list_inference_runs(self) -&gt; list[str]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Returns:\n            list[str]: List of Inference Runs\n        \"\"\"\n        return super().list_inference_runs()\n\n    @CachedArtifactMixin.cache_result\n    def get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Metrics (might be None)\n        \"\"\"\n        return super().get_inference_metrics(capture_uuid=capture_uuid)\n\n    @CachedArtifactMixin.cache_result\n    def get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n        \"\"\"\n        # Note: This method can generate larger dataframes, so we'll sample if needed\n        df = super().get_inference_predictions(capture_uuid=capture_uuid)\n        if df is not None and len(df) &gt; 5000:\n            self.log.warning(f\"{self.uuid}:{capture_uuid} Sampling Inference Predictions to 5000 rows\")\n            return df.sample(5000)\n        return df\n\n    @CachedArtifactMixin.cache_result\n    def confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the confusion matrix for the model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n        \"\"\"\n        return super().confusion_matrix(capture_uuid=capture_uuid)\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.__init__","title":"<code>__init__(uuid)</code>","text":"<p>CachedModel Initialization</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>def __init__(self, uuid: str):\n    \"\"\"CachedModel Initialization\"\"\"\n    ModelCore.__init__(self, model_uuid=uuid, use_cached_meta=True)\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.confusion_matrix","title":"<code>confusion_matrix(capture_uuid='latest')</code>","text":"<p>Retrieve the confusion matrix for the model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: latest)</p> <code>'latest'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Confusion Matrix (might be None)</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the confusion matrix for the model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n    \"\"\"\n    return super().confusion_matrix(capture_uuid=capture_uuid)\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.details","title":"<code>details(**kwargs)</code>","text":"<p>Retrieve the CachedModel Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedModel</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef details(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedModel Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedModel\n    \"\"\"\n    return super().details(**kwargs)\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.get_endpoint_inference_path","title":"<code>get_endpoint_inference_path()</code>","text":"<p>Retrieve the Endpoint Inference Path.</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Endpoint Inference Path</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef get_endpoint_inference_path(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Endpoint Inference Path.\n\n    Returns:\n        str: The Endpoint Inference Path\n    \"\"\"\n    return super().get_endpoint_inference_path()\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.get_inference_metrics","title":"<code>get_inference_metrics(capture_uuid='latest')</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: latest)</p> <code>'latest'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Captured Metrics (might be None)</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: latest)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Captured Metrics (might be None)\n    \"\"\"\n    return super().get_inference_metrics(capture_uuid=capture_uuid)\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.get_inference_predictions","title":"<code>get_inference_predictions(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: training_holdout)</p> <code>'auto_inference'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Captured Predictions (might be None)</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n    \"\"\"\n    # Note: This method can generate larger dataframes, so we'll sample if needed\n    df = super().get_inference_predictions(capture_uuid=capture_uuid)\n    if df is not None and len(df) &gt; 5000:\n        self.log.warning(f\"{self.uuid}:{capture_uuid} Sampling Inference Predictions to 5000 rows\")\n        return df.sample(5000)\n    return df\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.health_check","title":"<code>health_check(**kwargs)</code>","text":"<p>Retrieve the CachedModel Health Check.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of health check details for the CachedModel</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef health_check(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedModel Health Check.\n\n    Returns:\n        dict: A dictionary of health check details for the CachedModel\n    \"\"\"\n    return super().health_check(**kwargs)\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.list_inference_runs","title":"<code>list_inference_runs()</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of Inference Runs</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef list_inference_runs(self) -&gt; list[str]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Returns:\n        list[str]: List of Inference Runs\n    \"\"\"\n    return super().list_inference_runs()\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.summary","title":"<code>summary(**kwargs)</code>","text":"<p>Retrieve the CachedModel Details.</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about the CachedModel</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef summary(self, **kwargs) -&gt; dict:\n    \"\"\"Retrieve the CachedModel Details.\n\n    Returns:\n        dict: A dictionary of details about the CachedModel\n    \"\"\"\n    return super().summary(**kwargs)\n</code></pre>"},{"location":"cached/cached_model/#workbench.cached.cached_model.CachedModel.workbench_meta","title":"<code>workbench_meta()</code>","text":"<p>Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>The Enumerated Model Type</p> Source code in <code>src/workbench/cached/cached_model.py</code> <pre><code>@CachedArtifactMixin.cache_result\ndef workbench_meta(self) -&gt; Union[str, None]:\n    \"\"\"Retrieve the Enumerated Model Type (REGRESSOR, CLASSIFER, etc).\n\n    Returns:\n        str: The Enumerated Model Type\n    \"\"\"\n    return super().workbench_meta()\n</code></pre>"},{"location":"cached/cached_model/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Pull Inference Run</p> <pre><code>from workbench.cached.cached_model import CachedModel\n\n# Grab a Model\nmodel = CachedModel(\"abalone-regression\")\n\n# List the inference runs\nmodel.list_inference_runs()\n['auto_inference', 'model_training']\n\n# Grab specific inference results\nmodel.get_inference_predictions(\"auto_inference\")\n     class_number_of_rings  prediction    id\n0                       16   10.516158     7\n1                        9    9.031365     8\n..                     ...         ...   ...\n831                      8    7.693689  4158\n832                      9    7.542521  4167\n</code></pre>"},{"location":"cached/overview/","title":"Caching Overview","text":"<p>Caching is Crazy</p> <p>Yes, but it's a necessary evil for Web Interfaces. AWS APIs (boto3, Sagemaker) often takes multiple seconds to respond and will often throttle requests if spammed. So for quicker response and less spamming we're using Cached Classes for any Web Interface work.</p>"},{"location":"cached/overview/#welcome-to-the-workbench-cached-classes","title":"Welcome to the Workbench Cached Classes","text":"<p>These classes provide caching for the for the most used Workbench classes. They transparently handle all the details around retrieving and caching results from the underlying classes.</p> <ul> <li>CachedMeta: Manages lists of Artifacts (get all models, endpoints, etc).</li> <li>CachedDataSource: Caches the method results for Workbench DataSource.</li> <li>CachedFeatureSet: Caches the method results for Workbench FeatureSets.</li> <li>CachedModel: Caches the method results for Workbench Models.</li> <li>CachedEndpoint: Caches the method results for Workbench Endpoints.</li> </ul> <p>Examples</p> <p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p>"},{"location":"cloudwatch/","title":"Workbench CloudWatch","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. As part of this we're including CloudWatch log forwarding/aggregation for any service using the Workbench API (Dashboard, Glue, Lambda, Notebook, Laptop, etc).</p>"},{"location":"cloudwatch/#log-groups-and-streams","title":"Log Groups and Streams","text":"<p>The Workbench logging setup includes the addition of a CloudWatch 'Handler' that forwards all log messages to the <code>WorkbenchLogGroup</code></p> <p>Individual Streams</p> <p>Each process running Workbench will get a unique individual stream.</p> <ul> <li>ecs/DashBoard* (any logs from Workbench Dashboard)</li> <li>glue/* (logs from Glue jobs)</li> <li>lambda/* (logs from Lambda jobs)</li> <li>docker/* (logs from Docker containers)</li> <li>laptop/* (logs from laptop/notebooks)</li> </ul> <p>Since many jobs are run nightly/often, the stream will also have a date on the end... <code>glue/my_job/2024_08_01_17_15</code></p>"},{"location":"cloudwatch/#aws-cloudwatch-made-easy","title":"AWS CloudWatch made Easy","text":"<p>Logs in Easy Mode</p> <p>The Workbench <code>cloud_watch</code> command line tool gives you access to important logs without the hassle. Automatic display of important event and the context around those events.</p> <pre><code>pip install workbench\ncloud_watch\n</code></pre> <p>The <code>cloud_watch</code> script will automatically show the interesting (WARNING and CRITICAL) messages from any source within the last hour. There are lots of options to the script, just use <code>--help</code> to see options and descriptions.</p> <pre><code>cloud_watch --help\n</code></pre> <p>Here are some example options:</p> <pre><code># Show important logs in last 12 hours\ncloud_watch --start-time 720 \n\n# Show a particular stream\ncloud_watch --stream glue/my_job \n\n# Show/search for a message substring\ncloud_watch --search SHAP\n\n# Show a log levels (matching and above)\ncloud_watch --log-level WARNING\ncloud_watch --log-level ERROR\ncloud_watch --log-level CRITICAL\nOR\ncloud_watch --log-level ALL  (for all events)\n\n# Combine flags \ncloud_watch --log-level ERROR --search SHAP\ncloud_watch --log-level ERROR --stream Dashboard\n</code></pre> <p>These options can be used in combination and try out the other options to make the perfect log search :)</p> <p></p>"},{"location":"cloudwatch/#more-information","title":"More Information","text":"<p>Check out our presentation on Workbench CloudWatch</p>"},{"location":"cloudwatch/#access-through-aws-console","title":"Access through AWS Console","text":"<p>Since we're leveraging AWS functionality you can always use the AWS console to look/investigate the logs. In the AWS console go to CloudWatch... Log Groups... WorkbenchLogGroup</p> <p></p>"},{"location":"cloudwatch/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/overview/","title":"Core Classes","text":"<p>Workbench Core Classes</p> <p>These classes interact with many of the Cloud Platform services and are therefore more complex. They provide additional control and refinement over the AWS ML Pipline. For most use cases the API Classes should be used</p> <p>Welcome to the Workbench Core Classes</p> <p>The Core Classes provide low-level APIs for the Workbench package, these classes directly interface with the AWS Sagemaker Pipeline interfaces and have a large number of methods with reasonable complexity.</p> <p>The API Classes have method pass-through so just call the method on the API Class and voil\u00e0 it works the same.</p> <p></p>"},{"location":"core_classes/overview/#artifacts","title":"Artifacts","text":"<ul> <li>AthenaSource: Manages AWS Data Catalog and Athena</li> <li>FeatureSetCore: Manages AWS Feature Store and Feature Groups</li> <li>ModelCore: Manages the training and deployment of AWS Model Groups and Packages</li> <li>EndpointCore: Manages the deployment and invocations/inference on AWS Endpoints</li> </ul>"},{"location":"core_classes/overview/#transforms","title":"Transforms","text":"<p>Transforms are a set of classes that transform one type of <code>Artifact</code> to another type. For instance <code>DataToFeatureSet</code> takes a <code>DataSource</code> artifact and creates a <code>FeatureSet</code> artifact.</p> <ul> <li>DataLoaders Light: Loads various light/smaller data into AWS Data Catalog and Athena</li> <li>DataLoaders Heavy: Loads heavy/larger data (via Glue) into AWS Data Catalog and Athena</li> <li>DataToFeatures: Transforms a DataSource into a FeatureSet (AWS Feature Store/Group)</li> <li>FeaturesToModel: Trains and deploys an AWS Model Package/Group from a FeatureSet</li> <li>ModelToEndpoint: Manages the provisioning and deployment of a Model Endpoint</li> <li>PandasTransforms:Pandas DataFrame transforms and helper methods.</li> </ul>"},{"location":"core_classes/artifacts/artifact/","title":"Artifact","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the any class that inherits from the Artifact Class and voil\u00e0 it works the same.</p> <p>The Workbench Artifact class is a base/abstract class that defines API implemented by all the child classes (DataSource, FeatureSet, Model, Endpoint).</p> <p>Artifact: Abstract Base Class for all Artifact classes in Workbench. Artifacts simply reflect and aggregate one or more AWS Services</p>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact","title":"<code>Artifact</code>","text":"<p>               Bases: <code>ABC</code></p> <p>Artifact: Abstract Base Class for all Artifact classes in Workbench</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>class Artifact(ABC):\n    \"\"\"Artifact: Abstract Base Class for all Artifact classes in Workbench\"\"\"\n\n    # Class-level shared resources\n    log = logging.getLogger(\"workbench\")\n\n    # Config Manager\n    cm = ConfigManager()\n    if not cm.config_okay():\n        log = logging.getLogger(\"workbench\")\n        log.critical(\"Workbench Configuration Incomplete...\")\n        log.critical(\"Run the 'workbench' command and follow the prompts...\")\n        raise FatalConfigError()\n\n    # AWS Account Clamp\n    aws_account_clamp = AWSAccountClamp()\n    boto3_session = aws_account_clamp.boto3_session\n    sm_session = aws_account_clamp.sagemaker_session()\n    sm_client = aws_account_clamp.sagemaker_client()\n    aws_region = aws_account_clamp.region\n\n    # Setup Bucket Paths\n    workbench_bucket = cm.get_config(\"WORKBENCH_BUCKET\")\n    data_sources_s3_path = f\"s3://{workbench_bucket}/data-sources\"\n    feature_sets_s3_path = f\"s3://{workbench_bucket}/feature-sets\"\n    models_s3_path = f\"s3://{workbench_bucket}/models\"\n    endpoints_s3_path = f\"s3://{workbench_bucket}/endpoints\"\n\n    # Delimiter for storing lists in AWS Tags\n    tag_delimiter = \"::\"\n\n    # Grab our Dataframe Storage\n    df_cache = DFStore(path_prefix=\"/workbench/dataframe_cache\")\n\n    def __init__(self, uuid: str, use_cached_meta: bool = False):\n        \"\"\"Initialize the Artifact Base Class\n\n        Args:\n            uuid (str): The UUID of this artifact\n            use_cached_meta (bool): Should we use cached metadata? (default: False)\n        \"\"\"\n        self.uuid = uuid\n        if use_cached_meta:\n            self.log.info(f\"Using Cached Metadata for {self.uuid}\")\n            self.meta = CachedMeta()\n        else:\n            self.meta = CloudMeta()\n\n    def __post_init__(self):\n        \"\"\"Artifact Post Initialization\"\"\"\n\n        # Do I exist? (very metaphysical)\n        if not self.exists():\n            self.log.debug(f\"Artifact {self.uuid} does not exist\")\n            return\n\n        # Conduct a Health Check on this Artifact\n        health_issues = self.health_check()\n        if health_issues:\n            if \"needs_onboard\" in health_issues:\n                self.log.important(f\"Artifact {self.uuid} needs to be onboarded\")\n            elif health_issues == [\"no_activity\"]:\n                self.log.debug(f\"Artifact {self.uuid} has no activity, which is fine\")\n            else:\n                self.log.warning(f\"Health Check Failed {self.uuid}: {health_issues}\")\n            for issue in health_issues:\n                self.add_health_tag(issue)\n        else:\n            self.log.info(f\"Health Check Passed {self.uuid}\")\n\n    @classmethod\n    def is_name_valid(cls, name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; bool:\n        \"\"\"Check if the name adheres to the naming conventions for this Artifact.\n\n        Args:\n            name (str): The name/id to check.\n            delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n            lower_case (bool): Should the name be lowercased? (default: True)\n\n        Returns:\n            bool: True if the name is valid, False otherwise.\n        \"\"\"\n        valid_name = cls.generate_valid_name(name, delimiter=delimiter, lower_case=lower_case)\n        if name != valid_name:\n            cls.log.warning(f\"Artifact name: '{name}' is not valid. Convert it to something like: '{valid_name}'\")\n            return False\n        return True\n\n    @staticmethod\n    def generate_valid_name(name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; str:\n        \"\"\"Only allow letters and the specified delimiter, also lowercase the string.\n\n        Args:\n            name (str): The name/id string to check.\n            delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n            lower_case (bool): Should the name be lowercased? (default: True)\n\n        Returns:\n            str: A generated valid name/id.\n        \"\"\"\n        valid_name = \"\".join(c for c in name if c.isalnum() or c in [\"_\", \"-\"])\n        if lower_case:\n            valid_name = valid_name.lower()\n\n        # Replace with the chosen delimiter\n        return valid_name.replace(\"_\", delimiter).replace(\"-\", delimiter)\n\n    @abstractmethod\n    def exists(self) -&gt; bool:\n        \"\"\"Does the Artifact exist? Can we connect to it?\"\"\"\n        pass\n\n    def workbench_meta(self) -&gt; Union[dict, None]:\n        \"\"\"Get the Workbench specific metadata for this Artifact\n\n        Returns:\n            Union[dict, None]: Dictionary of Workbench metadata for this Artifact\n\n        Note: This functionality will work for FeatureSets, Models, and Endpoints\n              but not for DataSources and Graphs, those classes need to override this method.\n        \"\"\"\n        return self.meta.get_aws_tags(self.arn())\n\n    def expected_meta(self) -&gt; list[str]:\n        \"\"\"Metadata we expect to see for this Artifact when it's ready\n        Returns:\n            list[str]: List of expected metadata keys\n        \"\"\"\n\n        # If an artifact has additional expected metadata override this method\n        return [\"workbench_status\"]\n\n    @abstractmethod\n    def refresh_meta(self):\n        \"\"\"Refresh the Artifact's metadata\"\"\"\n        pass\n\n    def ready(self) -&gt; bool:\n        \"\"\"Is the Artifact ready? Is initial setup complete and expected metadata populated?\"\"\"\n\n        # If anything goes wrong, assume the artifact is not ready\n        try:\n            # Check for the expected metadata\n            expected_meta = self.expected_meta()\n            existing_meta = self.workbench_meta()\n            ready = set(existing_meta.keys()).issuperset(expected_meta)\n            if ready:\n                return True\n            else:\n                self.log.info(\"Artifact is not ready!\")\n                return False\n        except Exception as e:\n            self.log.error(f\"Artifact malformed: {e}\")\n            return False\n\n    @abstractmethod\n    def onboard(self) -&gt; bool:\n        \"\"\"Onboard this Artifact into Workbench\n        Returns:\n            bool: True if the Artifact was successfully onboarded, False otherwise\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def details(self) -&gt; dict:\n        \"\"\"Additional Details about this Artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this artifact in MegaBytes\"\"\"\n        pass\n\n    @abstractmethod\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        pass\n\n    @abstractmethod\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        pass\n\n    @abstractmethod\n    def hash(self) -&gt; str:\n        \"\"\"Return the hash of this artifact, useful for content validation\"\"\"\n        pass\n\n    @abstractmethod\n    def arn(self):\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def aws_url(self):\n        \"\"\"AWS console/web interface for this artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get the full AWS metadata for this artifact\"\"\"\n        pass\n\n    @abstractmethod\n    def delete(self):\n        \"\"\"Delete this artifact including all related AWS objects\"\"\"\n        pass\n\n    def upsert_workbench_meta(self, new_meta: dict):\n        \"\"\"Add Workbench specific metadata to this Artifact\n        Args:\n            new_meta (dict): Dictionary of NEW metadata to add\n        Note:\n            This functionality will work for FeatureSets, Models, and Endpoints\n            but not for DataSources. The DataSource class overrides this method.\n        \"\"\"\n        # Sanity check\n        aws_arn = self.arn()\n        if aws_arn is None:\n            self.log.error(f\"ARN is None for {self.uuid}!\")\n            return\n\n        # Add the new metadata to the existing metadata\n        self.log.info(f\"Adding Tags to {self.uuid}:{str(new_meta)[:50]}...\")\n        aws_tags = dict_to_aws_tags(new_meta)\n        try:\n            self.sm_client.add_tags(ResourceArn=aws_arn, Tags=aws_tags)\n        except Exception as e:\n            self.log.error(f\"Error adding metadata to {aws_arn}: {e}\")\n\n    def remove_workbench_meta(self, key_to_remove: str):\n        \"\"\"Remove Workbench specific metadata from this Artifact\n        Args:\n            key_to_remove (str): The metadata key to remove\n        Note:\n            This functionality will work for FeatureSets, Models, and Endpoints\n            but not for DataSources. The DataSource class overrides this method.\n        \"\"\"\n        aws_arn = self.arn()\n        # Sanity check\n        if aws_arn is None:\n            self.log.error(f\"ARN is None for {self.uuid}!\")\n            return\n        self.log.info(f\"Removing Workbench Metadata {key_to_remove} for Artifact: {aws_arn}...\")\n        sagemaker_delete_tag(aws_arn, self.sm_session, key_to_remove)\n\n    def get_tags(self, tag_type=\"user\") -&gt; list:\n        \"\"\"Get the tags for this artifact\n        Args:\n            tag_type (str): Type of tags to return (user or health)\n        Returns:\n            list[str]: List of tags for this artifact\n        \"\"\"\n        if tag_type == \"user\":\n            user_tags = self.workbench_meta().get(\"workbench_tags\")\n            return user_tags.split(self.tag_delimiter) if user_tags else []\n\n        # Grab our health tags\n        health_tags = self.workbench_meta().get(\"workbench_health_tags\")\n\n        # If we don't have health tags, create the storage and return an empty list\n        if health_tags is None:\n            self.log.important(f\"{self.uuid} creating workbench_health_tags storage...\")\n            self.upsert_workbench_meta({\"workbench_health_tags\": \"\"})\n            return []\n\n        # Otherwise, return the health tags\n        return health_tags.split(self.tag_delimiter) if health_tags else []\n\n    def set_tags(self, tags):\n        self.upsert_workbench_meta({\"workbench_tags\": self.tag_delimiter.join(tags)})\n\n    def add_tag(self, tag, tag_type=\"user\"):\n        \"\"\"Add a tag for this artifact, ensuring no duplicates and maintaining order.\n        Args:\n            tag (str): Tag to add for this artifact\n            tag_type (str): Type of tag to add (user or health)\n        \"\"\"\n        current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n        if tag not in current_tags:\n            current_tags.append(tag)\n            combined_tags = self.tag_delimiter.join(current_tags)\n            if tag_type == \"user\":\n                self.upsert_workbench_meta({\"workbench_tags\": combined_tags})\n            else:\n                self.upsert_workbench_meta({\"workbench_health_tags\": combined_tags})\n\n    def remove_workbench_tag(self, tag, tag_type=\"user\"):\n        \"\"\"Remove a tag from this artifact if it exists.\n        Args:\n            tag (str): Tag to remove from this artifact\n            tag_type (str): Type of tag to remove (user or health)\n        \"\"\"\n        current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n        if tag in current_tags:\n            current_tags.remove(tag)\n            combined_tags = self.tag_delimiter.join(current_tags)\n            if tag_type == \"user\":\n                self.upsert_workbench_meta({\"workbench_tags\": combined_tags})\n            elif tag_type == \"health\":\n                self.upsert_workbench_meta({\"workbench_health_tags\": combined_tags})\n\n    # Syntactic sugar for health tags\n    def get_health_tags(self):\n        return self.get_tags(tag_type=\"health\")\n\n    def set_health_tags(self, tags):\n        self.upsert_workbench_meta({\"workbench_health_tags\": self.tag_delimiter.join(tags)})\n\n    def add_health_tag(self, tag):\n        self.add_tag(tag, tag_type=\"health\")\n\n    def remove_health_tag(self, tag):\n        self.remove_workbench_tag(tag, tag_type=\"health\")\n\n    # Owner of this artifact\n    def get_owner(self) -&gt; str:\n        \"\"\"Get the owner of this artifact\"\"\"\n        return self.workbench_meta().get(\"workbench_owner\", \"unknown\")\n\n    def set_owner(self, owner: str):\n        \"\"\"Set the owner of this artifact\n\n        Args:\n            owner (str): Owner to set for this artifact\n        \"\"\"\n        self.upsert_workbench_meta({\"workbench_owner\": owner})\n\n    def get_input(self) -&gt; str:\n        \"\"\"Get the input data for this artifact\"\"\"\n        return self.workbench_meta().get(\"workbench_input\", \"unknown\")\n\n    def set_input(self, input_data: str):\n        \"\"\"Set the input data for this artifact\n\n        Args:\n            input_data (str): Name of input data for this artifact\n        Note:\n            This breaks the official provenance of the artifact, so use with caution.\n        \"\"\"\n        self.log.important(f\"{self.uuid}: Setting input to {input_data}...\")\n        self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n        self.upsert_workbench_meta({\"workbench_input\": input_data})\n\n    def get_status(self) -&gt; str:\n        \"\"\"Get the status for this artifact\"\"\"\n        return self.workbench_meta().get(\"workbench_status\", \"unknown\")\n\n    def set_status(self, status: str):\n        \"\"\"Set the status for this artifact\n        Args:\n            status (str): Status to set for this artifact\n        \"\"\"\n        self.upsert_workbench_meta({\"workbench_status\": status})\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this artifact\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        health_issues = []\n        if not self.ready():\n            return [\"needs_onboard\"]\n        # FIXME: Revisit AWS URL check\n        # if \"unknown\" in self.aws_url():\n        #    health_issues.append(\"aws_url_unknown\")\n        return health_issues\n\n    def summary(self) -&gt; dict:\n        \"\"\"This is generic summary information for all Artifacts. If you\n        want to get more detailed information, call the details() method\n        which is implemented by the specific Artifact class\"\"\"\n        basic = {\n            \"uuid\": self.uuid,\n            \"health_tags\": self.get_health_tags(),\n            \"aws_arn\": self.arn(),\n            \"size\": self.size(),\n            \"created\": self.created(),\n            \"modified\": self.modified(),\n            \"input\": self.get_input(),\n        }\n        # Combine the workbench metadata with the basic metadata\n        return {**basic, **self.workbench_meta()}\n\n    def __repr__(self) -&gt; str:\n        \"\"\"String representation of this artifact\n\n        Returns:\n            str: String representation of this artifact\n        \"\"\"\n\n        # If the artifact does not exist, return a message\n        if not self.exists():\n            return f\"{self.__class__.__name__}: {self.uuid} does not exist\"\n\n        summary_dict = self.summary()\n        display_keys = [\n            \"aws_arn\",\n            \"health_tags\",\n            \"size\",\n            \"created\",\n            \"modified\",\n            \"input\",\n            \"workbench_status\",\n            \"workbench_tags\",\n        ]\n        summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items() if key in display_keys]\n        summary_str = f\"{self.__class__.__name__}: {self.uuid}\\n\" + \",\\n\".join(summary_items)\n        return summary_str\n\n    def delete_metadata(self, key_to_delete: str):\n        \"\"\"Delete specific metadata from this artifact\n        Args:\n            key_to_delete (str): Metadata key to delete\n        \"\"\"\n\n        aws_arn = self.arn()\n        self.log.important(f\"Deleting Metadata {key_to_delete} for Artifact: {aws_arn}...\")\n\n        # First, fetch all the existing tags\n        response = self.sm_session.list_tags(aws_arn)\n        existing_tags = response.get(\"Tags\", [])\n\n        # Convert existing AWS tags to a dictionary for easy manipulation\n        existing_tags_dict = {item[\"Key\"]: item[\"Value\"] for item in existing_tags}\n\n        # Identify tags to delete\n        tag_list_to_delete = []\n        for key in existing_tags_dict.keys():\n            if key == key_to_delete or key.startswith(f\"{key_to_delete}_chunk_\"):\n                tag_list_to_delete.append(key)\n\n        # Delete the identified tags\n        if tag_list_to_delete:\n            self.sm_client.delete_tags(ResourceArn=aws_arn, TagKeys=tag_list_to_delete)\n        else:\n            self.log.info(f\"No Metadata found: {key_to_delete}...\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.__init__","title":"<code>__init__(uuid, use_cached_meta=False)</code>","text":"<p>Initialize the Artifact Base Class</p> <p>Parameters:</p> Name Type Description Default <code>uuid</code> <code>str</code> <p>The UUID of this artifact</p> required <code>use_cached_meta</code> <code>bool</code> <p>Should we use cached metadata? (default: False)</p> <code>False</code> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def __init__(self, uuid: str, use_cached_meta: bool = False):\n    \"\"\"Initialize the Artifact Base Class\n\n    Args:\n        uuid (str): The UUID of this artifact\n        use_cached_meta (bool): Should we use cached metadata? (default: False)\n    \"\"\"\n    self.uuid = uuid\n    if use_cached_meta:\n        self.log.info(f\"Using Cached Metadata for {self.uuid}\")\n        self.meta = CachedMeta()\n    else:\n        self.meta = CloudMeta()\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.__post_init__","title":"<code>__post_init__()</code>","text":"<p>Artifact Post Initialization</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def __post_init__(self):\n    \"\"\"Artifact Post Initialization\"\"\"\n\n    # Do I exist? (very metaphysical)\n    if not self.exists():\n        self.log.debug(f\"Artifact {self.uuid} does not exist\")\n        return\n\n    # Conduct a Health Check on this Artifact\n    health_issues = self.health_check()\n    if health_issues:\n        if \"needs_onboard\" in health_issues:\n            self.log.important(f\"Artifact {self.uuid} needs to be onboarded\")\n        elif health_issues == [\"no_activity\"]:\n            self.log.debug(f\"Artifact {self.uuid} has no activity, which is fine\")\n        else:\n            self.log.warning(f\"Health Check Failed {self.uuid}: {health_issues}\")\n        for issue in health_issues:\n            self.add_health_tag(issue)\n    else:\n        self.log.info(f\"Health Check Passed {self.uuid}\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.__repr__","title":"<code>__repr__()</code>","text":"<p>String representation of this artifact</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>String representation of this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def __repr__(self) -&gt; str:\n    \"\"\"String representation of this artifact\n\n    Returns:\n        str: String representation of this artifact\n    \"\"\"\n\n    # If the artifact does not exist, return a message\n    if not self.exists():\n        return f\"{self.__class__.__name__}: {self.uuid} does not exist\"\n\n    summary_dict = self.summary()\n    display_keys = [\n        \"aws_arn\",\n        \"health_tags\",\n        \"size\",\n        \"created\",\n        \"modified\",\n        \"input\",\n        \"workbench_status\",\n        \"workbench_tags\",\n    ]\n    summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items() if key in display_keys]\n    summary_str = f\"{self.__class__.__name__}: {self.uuid}\\n\" + \",\\n\".join(summary_items)\n    return summary_str\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.add_tag","title":"<code>add_tag(tag, tag_type='user')</code>","text":"<p>Add a tag for this artifact, ensuring no duplicates and maintaining order. Args:     tag (str): Tag to add for this artifact     tag_type (str): Type of tag to add (user or health)</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def add_tag(self, tag, tag_type=\"user\"):\n    \"\"\"Add a tag for this artifact, ensuring no duplicates and maintaining order.\n    Args:\n        tag (str): Tag to add for this artifact\n        tag_type (str): Type of tag to add (user or health)\n    \"\"\"\n    current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n    if tag not in current_tags:\n        current_tags.append(tag)\n        combined_tags = self.tag_delimiter.join(current_tags)\n        if tag_type == \"user\":\n            self.upsert_workbench_meta({\"workbench_tags\": combined_tags})\n        else:\n            self.upsert_workbench_meta({\"workbench_health_tags\": combined_tags})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.arn","title":"<code>arn()</code>  <code>abstractmethod</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef arn(self):\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.aws_meta","title":"<code>aws_meta()</code>  <code>abstractmethod</code>","text":"<p>Get the full AWS metadata for this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef aws_meta(self) -&gt; dict:\n    \"\"\"Get the full AWS metadata for this artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.aws_url","title":"<code>aws_url()</code>  <code>abstractmethod</code>","text":"<p>AWS console/web interface for this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef aws_url(self):\n    \"\"\"AWS console/web interface for this artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.created","title":"<code>created()</code>  <code>abstractmethod</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.delete","title":"<code>delete()</code>  <code>abstractmethod</code>","text":"<p>Delete this artifact including all related AWS objects</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef delete(self):\n    \"\"\"Delete this artifact including all related AWS objects\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.delete_metadata","title":"<code>delete_metadata(key_to_delete)</code>","text":"<p>Delete specific metadata from this artifact Args:     key_to_delete (str): Metadata key to delete</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def delete_metadata(self, key_to_delete: str):\n    \"\"\"Delete specific metadata from this artifact\n    Args:\n        key_to_delete (str): Metadata key to delete\n    \"\"\"\n\n    aws_arn = self.arn()\n    self.log.important(f\"Deleting Metadata {key_to_delete} for Artifact: {aws_arn}...\")\n\n    # First, fetch all the existing tags\n    response = self.sm_session.list_tags(aws_arn)\n    existing_tags = response.get(\"Tags\", [])\n\n    # Convert existing AWS tags to a dictionary for easy manipulation\n    existing_tags_dict = {item[\"Key\"]: item[\"Value\"] for item in existing_tags}\n\n    # Identify tags to delete\n    tag_list_to_delete = []\n    for key in existing_tags_dict.keys():\n        if key == key_to_delete or key.startswith(f\"{key_to_delete}_chunk_\"):\n            tag_list_to_delete.append(key)\n\n    # Delete the identified tags\n    if tag_list_to_delete:\n        self.sm_client.delete_tags(ResourceArn=aws_arn, TagKeys=tag_list_to_delete)\n    else:\n        self.log.info(f\"No Metadata found: {key_to_delete}...\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.details","title":"<code>details()</code>  <code>abstractmethod</code>","text":"<p>Additional Details about this Artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef details(self) -&gt; dict:\n    \"\"\"Additional Details about this Artifact\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.exists","title":"<code>exists()</code>  <code>abstractmethod</code>","text":"<p>Does the Artifact exist? Can we connect to it?</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef exists(self) -&gt; bool:\n    \"\"\"Does the Artifact exist? Can we connect to it?\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.expected_meta","title":"<code>expected_meta()</code>","text":"<p>Metadata we expect to see for this Artifact when it's ready Returns:     list[str]: List of expected metadata keys</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def expected_meta(self) -&gt; list[str]:\n    \"\"\"Metadata we expect to see for this Artifact when it's ready\n    Returns:\n        list[str]: List of expected metadata keys\n    \"\"\"\n\n    # If an artifact has additional expected metadata override this method\n    return [\"workbench_status\"]\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.generate_valid_name","title":"<code>generate_valid_name(name, delimiter='_', lower_case=True)</code>  <code>staticmethod</code>","text":"<p>Only allow letters and the specified delimiter, also lowercase the string.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name/id string to check.</p> required <code>delimiter</code> <code>str</code> <p>The delimiter to use in the name/id string (default: \"_\")</p> <code>'_'</code> <code>lower_case</code> <code>bool</code> <p>Should the name be lowercased? (default: True)</p> <code>True</code> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>A generated valid name/id.</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@staticmethod\ndef generate_valid_name(name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; str:\n    \"\"\"Only allow letters and the specified delimiter, also lowercase the string.\n\n    Args:\n        name (str): The name/id string to check.\n        delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n        lower_case (bool): Should the name be lowercased? (default: True)\n\n    Returns:\n        str: A generated valid name/id.\n    \"\"\"\n    valid_name = \"\".join(c for c in name if c.isalnum() or c in [\"_\", \"-\"])\n    if lower_case:\n        valid_name = valid_name.lower()\n\n    # Replace with the chosen delimiter\n    return valid_name.replace(\"_\", delimiter).replace(\"-\", delimiter)\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.get_input","title":"<code>get_input()</code>","text":"<p>Get the input data for this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def get_input(self) -&gt; str:\n    \"\"\"Get the input data for this artifact\"\"\"\n    return self.workbench_meta().get(\"workbench_input\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.get_owner","title":"<code>get_owner()</code>","text":"<p>Get the owner of this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def get_owner(self) -&gt; str:\n    \"\"\"Get the owner of this artifact\"\"\"\n    return self.workbench_meta().get(\"workbench_owner\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.get_status","title":"<code>get_status()</code>","text":"<p>Get the status for this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def get_status(self) -&gt; str:\n    \"\"\"Get the status for this artifact\"\"\"\n    return self.workbench_meta().get(\"workbench_status\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.get_tags","title":"<code>get_tags(tag_type='user')</code>","text":"<p>Get the tags for this artifact Args:     tag_type (str): Type of tags to return (user or health) Returns:     list[str]: List of tags for this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def get_tags(self, tag_type=\"user\") -&gt; list:\n    \"\"\"Get the tags for this artifact\n    Args:\n        tag_type (str): Type of tags to return (user or health)\n    Returns:\n        list[str]: List of tags for this artifact\n    \"\"\"\n    if tag_type == \"user\":\n        user_tags = self.workbench_meta().get(\"workbench_tags\")\n        return user_tags.split(self.tag_delimiter) if user_tags else []\n\n    # Grab our health tags\n    health_tags = self.workbench_meta().get(\"workbench_health_tags\")\n\n    # If we don't have health tags, create the storage and return an empty list\n    if health_tags is None:\n        self.log.important(f\"{self.uuid} creating workbench_health_tags storage...\")\n        self.upsert_workbench_meta({\"workbench_health_tags\": \"\"})\n        return []\n\n    # Otherwise, return the health tags\n    return health_tags.split(self.tag_delimiter) if health_tags else []\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.hash","title":"<code>hash()</code>  <code>abstractmethod</code>","text":"<p>Return the hash of this artifact, useful for content validation</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef hash(self) -&gt; str:\n    \"\"\"Return the hash of this artifact, useful for content validation\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this artifact Returns:     list[str]: List of health issues</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this artifact\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    health_issues = []\n    if not self.ready():\n        return [\"needs_onboard\"]\n    # FIXME: Revisit AWS URL check\n    # if \"unknown\" in self.aws_url():\n    #    health_issues.append(\"aws_url_unknown\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.is_name_valid","title":"<code>is_name_valid(name, delimiter='_', lower_case=True)</code>  <code>classmethod</code>","text":"<p>Check if the name adheres to the naming conventions for this Artifact.</p> <p>Parameters:</p> Name Type Description Default <code>name</code> <code>str</code> <p>The name/id to check.</p> required <code>delimiter</code> <code>str</code> <p>The delimiter to use in the name/id string (default: \"_\")</p> <code>'_'</code> <code>lower_case</code> <code>bool</code> <p>Should the name be lowercased? (default: True)</p> <code>True</code> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the name is valid, False otherwise.</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@classmethod\ndef is_name_valid(cls, name: str, delimiter: str = \"_\", lower_case: bool = True) -&gt; bool:\n    \"\"\"Check if the name adheres to the naming conventions for this Artifact.\n\n    Args:\n        name (str): The name/id to check.\n        delimiter (str): The delimiter to use in the name/id string (default: \"_\")\n        lower_case (bool): Should the name be lowercased? (default: True)\n\n    Returns:\n        bool: True if the name is valid, False otherwise.\n    \"\"\"\n    valid_name = cls.generate_valid_name(name, delimiter=delimiter, lower_case=lower_case)\n    if name != valid_name:\n        cls.log.warning(f\"Artifact name: '{name}' is not valid. Convert it to something like: '{valid_name}'\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.modified","title":"<code>modified()</code>  <code>abstractmethod</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.onboard","title":"<code>onboard()</code>  <code>abstractmethod</code>","text":"<p>Onboard this Artifact into Workbench Returns:     bool: True if the Artifact was successfully onboarded, False otherwise</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef onboard(self) -&gt; bool:\n    \"\"\"Onboard this Artifact into Workbench\n    Returns:\n        bool: True if the Artifact was successfully onboarded, False otherwise\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.ready","title":"<code>ready()</code>","text":"<p>Is the Artifact ready? Is initial setup complete and expected metadata populated?</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def ready(self) -&gt; bool:\n    \"\"\"Is the Artifact ready? Is initial setup complete and expected metadata populated?\"\"\"\n\n    # If anything goes wrong, assume the artifact is not ready\n    try:\n        # Check for the expected metadata\n        expected_meta = self.expected_meta()\n        existing_meta = self.workbench_meta()\n        ready = set(existing_meta.keys()).issuperset(expected_meta)\n        if ready:\n            return True\n        else:\n            self.log.info(\"Artifact is not ready!\")\n            return False\n    except Exception as e:\n        self.log.error(f\"Artifact malformed: {e}\")\n        return False\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.refresh_meta","title":"<code>refresh_meta()</code>  <code>abstractmethod</code>","text":"<p>Refresh the Artifact's metadata</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef refresh_meta(self):\n    \"\"\"Refresh the Artifact's metadata\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.remove_workbench_meta","title":"<code>remove_workbench_meta(key_to_remove)</code>","text":"<p>Remove Workbench specific metadata from this Artifact Args:     key_to_remove (str): The metadata key to remove Note:     This functionality will work for FeatureSets, Models, and Endpoints     but not for DataSources. The DataSource class overrides this method.</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def remove_workbench_meta(self, key_to_remove: str):\n    \"\"\"Remove Workbench specific metadata from this Artifact\n    Args:\n        key_to_remove (str): The metadata key to remove\n    Note:\n        This functionality will work for FeatureSets, Models, and Endpoints\n        but not for DataSources. The DataSource class overrides this method.\n    \"\"\"\n    aws_arn = self.arn()\n    # Sanity check\n    if aws_arn is None:\n        self.log.error(f\"ARN is None for {self.uuid}!\")\n        return\n    self.log.info(f\"Removing Workbench Metadata {key_to_remove} for Artifact: {aws_arn}...\")\n    sagemaker_delete_tag(aws_arn, self.sm_session, key_to_remove)\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.remove_workbench_tag","title":"<code>remove_workbench_tag(tag, tag_type='user')</code>","text":"<p>Remove a tag from this artifact if it exists. Args:     tag (str): Tag to remove from this artifact     tag_type (str): Type of tag to remove (user or health)</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def remove_workbench_tag(self, tag, tag_type=\"user\"):\n    \"\"\"Remove a tag from this artifact if it exists.\n    Args:\n        tag (str): Tag to remove from this artifact\n        tag_type (str): Type of tag to remove (user or health)\n    \"\"\"\n    current_tags = self.get_tags(tag_type) if tag_type == \"user\" else self.get_health_tags()\n    if tag in current_tags:\n        current_tags.remove(tag)\n        combined_tags = self.tag_delimiter.join(current_tags)\n        if tag_type == \"user\":\n            self.upsert_workbench_meta({\"workbench_tags\": combined_tags})\n        elif tag_type == \"health\":\n            self.upsert_workbench_meta({\"workbench_health_tags\": combined_tags})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.set_input","title":"<code>set_input(input_data)</code>","text":"<p>Set the input data for this artifact</p> <p>Parameters:</p> Name Type Description Default <code>input_data</code> <code>str</code> <p>Name of input data for this artifact</p> required <p>Note:     This breaks the official provenance of the artifact, so use with caution.</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def set_input(self, input_data: str):\n    \"\"\"Set the input data for this artifact\n\n    Args:\n        input_data (str): Name of input data for this artifact\n    Note:\n        This breaks the official provenance of the artifact, so use with caution.\n    \"\"\"\n    self.log.important(f\"{self.uuid}: Setting input to {input_data}...\")\n    self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n    self.upsert_workbench_meta({\"workbench_input\": input_data})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.set_owner","title":"<code>set_owner(owner)</code>","text":"<p>Set the owner of this artifact</p> <p>Parameters:</p> Name Type Description Default <code>owner</code> <code>str</code> <p>Owner to set for this artifact</p> required Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def set_owner(self, owner: str):\n    \"\"\"Set the owner of this artifact\n\n    Args:\n        owner (str): Owner to set for this artifact\n    \"\"\"\n    self.upsert_workbench_meta({\"workbench_owner\": owner})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.set_status","title":"<code>set_status(status)</code>","text":"<p>Set the status for this artifact Args:     status (str): Status to set for this artifact</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def set_status(self, status: str):\n    \"\"\"Set the status for this artifact\n    Args:\n        status (str): Status to set for this artifact\n    \"\"\"\n    self.upsert_workbench_meta({\"workbench_status\": status})\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.size","title":"<code>size()</code>  <code>abstractmethod</code>","text":"<p>Return the size of this artifact in MegaBytes</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>@abstractmethod\ndef size(self) -&gt; float:\n    \"\"\"Return the size of this artifact in MegaBytes\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.summary","title":"<code>summary()</code>","text":"<p>This is generic summary information for all Artifacts. If you want to get more detailed information, call the details() method which is implemented by the specific Artifact class</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def summary(self) -&gt; dict:\n    \"\"\"This is generic summary information for all Artifacts. If you\n    want to get more detailed information, call the details() method\n    which is implemented by the specific Artifact class\"\"\"\n    basic = {\n        \"uuid\": self.uuid,\n        \"health_tags\": self.get_health_tags(),\n        \"aws_arn\": self.arn(),\n        \"size\": self.size(),\n        \"created\": self.created(),\n        \"modified\": self.modified(),\n        \"input\": self.get_input(),\n    }\n    # Combine the workbench metadata with the basic metadata\n    return {**basic, **self.workbench_meta()}\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.upsert_workbench_meta","title":"<code>upsert_workbench_meta(new_meta)</code>","text":"<p>Add Workbench specific metadata to this Artifact Args:     new_meta (dict): Dictionary of NEW metadata to add Note:     This functionality will work for FeatureSets, Models, and Endpoints     but not for DataSources. The DataSource class overrides this method.</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def upsert_workbench_meta(self, new_meta: dict):\n    \"\"\"Add Workbench specific metadata to this Artifact\n    Args:\n        new_meta (dict): Dictionary of NEW metadata to add\n    Note:\n        This functionality will work for FeatureSets, Models, and Endpoints\n        but not for DataSources. The DataSource class overrides this method.\n    \"\"\"\n    # Sanity check\n    aws_arn = self.arn()\n    if aws_arn is None:\n        self.log.error(f\"ARN is None for {self.uuid}!\")\n        return\n\n    # Add the new metadata to the existing metadata\n    self.log.info(f\"Adding Tags to {self.uuid}:{str(new_meta)[:50]}...\")\n    aws_tags = dict_to_aws_tags(new_meta)\n    try:\n        self.sm_client.add_tags(ResourceArn=aws_arn, Tags=aws_tags)\n    except Exception as e:\n        self.log.error(f\"Error adding metadata to {aws_arn}: {e}\")\n</code></pre>"},{"location":"core_classes/artifacts/artifact/#workbench.core.artifacts.artifact.Artifact.workbench_meta","title":"<code>workbench_meta()</code>","text":"<p>Get the Workbench specific metadata for this Artifact</p> <p>Returns:</p> Type Description <code>Union[dict, None]</code> <p>Union[dict, None]: Dictionary of Workbench metadata for this Artifact</p> This functionality will work for FeatureSets, Models, and Endpoints <p>but not for DataSources and Graphs, those classes need to override this method.</p> Source code in <code>src/workbench/core/artifacts/artifact.py</code> <pre><code>def workbench_meta(self) -&gt; Union[dict, None]:\n    \"\"\"Get the Workbench specific metadata for this Artifact\n\n    Returns:\n        Union[dict, None]: Dictionary of Workbench metadata for this Artifact\n\n    Note: This functionality will work for FeatureSets, Models, and Endpoints\n          but not for DataSources and Graphs, those classes need to override this method.\n    \"\"\"\n    return self.meta.get_aws_tags(self.arn())\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/","title":"AthenaSource","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the DataSource API Class and voil\u00e0 it works the same.</p> <p>AthenaSource: Workbench Data Source accessible through Athena</p>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource","title":"<code>AthenaSource</code>","text":"<p>               Bases: <code>DataSourceAbstract</code></p> <p>AthenaSource: Workbench Data Source accessible through Athena</p> Common Usage <pre><code>my_data = AthenaSource(data_uuid, database=\"workbench\")\nmy_data.summary()\nmy_data.details()\ndf = my_data.query(f\"select * from {data_uuid} limit 5\")\n</code></pre> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>class AthenaSource(DataSourceAbstract):\n    \"\"\"AthenaSource: Workbench Data Source accessible through Athena\n\n    Common Usage:\n        ```python\n        my_data = AthenaSource(data_uuid, database=\"workbench\")\n        my_data.summary()\n        my_data.details()\n        df = my_data.query(f\"select * from {data_uuid} limit 5\")\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid, database=\"workbench\", **kwargs):\n        \"\"\"AthenaSource Initialization\n\n        Args:\n            data_uuid (str): Name of Athena Table\n            database (str): Athena Database Name (default: workbench)\n        \"\"\"\n        # Ensure the data_uuid is a valid name/id\n        self.is_name_valid(data_uuid)\n\n        # Call superclass init\n        super().__init__(data_uuid, database, **kwargs)\n\n        # Grab our metadata from the Meta class\n        self.log.info(f\"Retrieving metadata for: {self.uuid}...\")\n        self.data_source_meta = self.meta.data_source(data_uuid, database=database)\n        if self.data_source_meta is None:\n            self.log.error(f\"Unable to find {database}:{self.table} in Glue Catalogs...\")\n            return\n\n        # Call superclass post init\n        super().__post_init__()\n\n        # All done\n        self.log.debug(f\"AthenaSource Initialized: {database}.{self.table}\")\n\n    def refresh_meta(self):\n        \"\"\"Refresh our internal AWS Broker catalog metadata\"\"\"\n        self.data_source_meta = self.meta.data_source(self.uuid, database=self.database)\n\n    def exists(self) -&gt; bool:\n        \"\"\"Validation Checks for this Data Source\"\"\"\n\n        # Are we able to pull AWS Metadata for this table_name?\"\"\"\n        # Do we have a valid data_source_meta?\n        if getattr(self, \"data_source_meta\", None) is None:\n            self.log.debug(f\"AthenaSource {self.table} not found in Workbench Metadata...\")\n            return False\n        return True\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        # Grab our Workbench Role Manager, get our AWS account id, and region for ARN creation\n        account_id = self.aws_account_clamp.account_id\n        region = self.aws_account_clamp.region\n        arn = f\"arn:aws:glue:{region}:{account_id}:table/{self.database}/{self.table}\"\n        return arn\n\n    def workbench_meta(self) -&gt; dict:\n        \"\"\"Get the Workbench specific metadata for this Artifact\"\"\"\n\n        # Sanity Check if we have invalid AWS Metadata\n        if self.data_source_meta is None:\n            if not self.exists():\n                self.log.error(f\"DataSource {self.uuid} doesn't appear to exist...\")\n            else:\n                self.log.critical(f\"Unable to get AWS Metadata for {self.table}\")\n                self.log.critical(\"Malformed Artifact! Delete this Artifact and recreate it!\")\n            return {}\n\n        # Get the Workbench Metadata from the 'Parameters' section of the DataSource Metadata\n        params = self.data_source_meta.get(\"Parameters\", {})\n        return {key: decode_value(value) for key, value in params.items() if \"workbench\" in key}\n\n    def upsert_workbench_meta(self, new_meta: dict):\n        \"\"\"Add Workbench specific metadata to this Artifact\n\n        Args:\n            new_meta (dict): Dictionary of new metadata to add\n        \"\"\"\n        self.log.important(f\"Upserting Workbench Metadata {self.uuid}:{str(new_meta)[:50]}...\")\n\n        # Give a warning message for keys that don't start with workbench_\n        for key in new_meta.keys():\n            if not key.startswith(\"workbench_\"):\n                self.log.warning(\"Append 'workbench_' to key names to avoid overwriting AWS meta data\")\n\n        # Now convert any non-string values to JSON strings\n        for key, value in new_meta.items():\n            if not isinstance(value, str):\n                new_meta[key] = json.dumps(value, cls=CustomEncoder)\n\n        # Store our updated metadata\n        try:\n            wr.catalog.upsert_table_parameters(\n                parameters=new_meta,\n                database=self.database,\n                table=self.table,\n                boto3_session=self.boto3_session,\n            )\n        except botocore.exceptions.ClientError as e:\n            error_code = e.response[\"Error\"][\"Code\"]\n            if error_code == \"InvalidInputException\":\n                self.log.error(f\"Unable to upsert metadata for {self.table}\")\n                self.log.error(\"Probably because the metadata is too large\")\n                self.log.error(new_meta)\n            elif error_code == \"ConcurrentModificationException\":\n                self.log.warning(\"ConcurrentModificationException... trying again...\")\n                time.sleep(5)\n                wr.catalog.upsert_table_parameters(\n                    parameters=new_meta,\n                    database=self.database,\n                    table=self.table,\n                    boto3_session=self.boto3_session,\n                )\n            else:\n                self.log.critical(f\"Failed to upsert metadata: {e}\")\n                self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n        except Exception as e:\n            self.log.critical(f\"Failed to upsert metadata: {e}\")\n            self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this data in MegaBytes\"\"\"\n        size_in_bytes = sum(wr.s3.size_objects(self.s3_storage_location(), boto3_session=self.boto3_session).values())\n        size_in_mb = size_in_bytes / 1_000_000\n        return size_in_mb\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get the FULL AWS metadata for this artifact\"\"\"\n        return self.data_source_meta\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n        workbench_details = self.workbench_meta().get(\"workbench_details\", {})\n        return workbench_details.get(\"aws_url\", \"unknown\")\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        return self.data_source_meta[\"CreateTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        return self.data_source_meta[\"UpdateTime\"]\n\n    def hash(self) -&gt; str:\n        \"\"\"Get the hash for the set of Parquet files used for this Artifact\"\"\"\n        s3_uri = self.s3_storage_location()\n        return compute_parquet_hash(s3_uri, self.boto3_session)\n\n    def table_hash(self) -&gt; str:\n        \"\"\"Get the table hash for this AthenaSource\"\"\"\n        s3_scratch = f\"s3://{self.workbench_bucket}/temp/athena_output\"\n        return compute_athena_table_hash(self.database, self.table, self.boto3_session, s3_scratch)\n\n    def num_rows(self) -&gt; int:\n        \"\"\"Return the number of rows for this Data Source\"\"\"\n        count_df = self.query(f'select count(*) AS workbench_count from \"{self.database}\".\"{self.table}\"')\n        return count_df[\"workbench_count\"][0] if count_df is not None else 0\n\n    def num_columns(self) -&gt; int:\n        \"\"\"Return the number of columns for this Data Source\"\"\"\n        return len(self.columns)\n\n    @property\n    def columns(self) -&gt; list[str]:\n        \"\"\"Return the column names for this Athena Table\"\"\"\n        return [item[\"Name\"] for item in self.data_source_meta[\"StorageDescriptor\"][\"Columns\"]]\n\n    @property\n    def column_types(self) -&gt; list[str]:\n        \"\"\"Return the column types of the internal AthenaSource\"\"\"\n        return [item[\"Type\"] for item in self.data_source_meta[\"StorageDescriptor\"][\"Columns\"]]\n\n    def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Query the AthenaSource\n\n        Args:\n            query (str): The query to run against the AthenaSource\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n\n        # Call internal class _query method\n        return self.database_query(self.database, query)\n\n    @classmethod\n    def database_query(cls, database: str, query: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Specify the Database and Query the Athena Service\n\n        Args:\n            database (str): The Athena Database to query\n            query (str): The query to run against the AthenaSource\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        cls.log.debug(f\"Executing Query: {query}...\")\n        try:\n            df = wr.athena.read_sql_query(\n                sql=query,\n                database=database,\n                ctas_approach=False,\n                boto3_session=cls.boto3_session,\n            )\n            scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n            if scanned_bytes &gt; 0:\n                cls.log.debug(f\"Athena Query successful (scanned bytes: {scanned_bytes})\")\n            return df\n        except wr.exceptions.QueryFailed as e:\n            cls.log.critical(f\"Failed to execute query: {e}\")\n            return None\n\n    def execute_statement(self, query: str, silence_errors: bool = False):\n        \"\"\"Execute a non-returning SQL statement in Athena with retries.\n\n        Args:\n            query (str): The query to run against the AthenaSource\n            silence_errors (bool): Silence errors (default: False)\n        \"\"\"\n        attempt = 0\n        max_retries = 3\n        retry_delay = 10\n        while attempt &lt; max_retries:\n            try:\n                # Start the query execution\n                query_execution_id = wr.athena.start_query_execution(\n                    sql=query,\n                    database=self.database,\n                    boto3_session=self.boto3_session,\n                )\n                self.log.debug(f\"QueryExecutionId: {query_execution_id}\")\n\n                # Wait for the query to complete\n                wr.athena.wait_query(query_execution_id=query_execution_id, boto3_session=self.boto3_session)\n                self.log.debug(f\"Statement executed successfully: {query_execution_id}\")\n                break  # If successful, exit the retry loop\n            except wr.exceptions.QueryFailed as e:\n                if \"AlreadyExistsException\" in str(e):\n                    self.log.warning(f\"Table already exists: {e} \\nIgnoring...\")\n                    break  # No need to retry for this error\n                elif \"ConcurrentModificationException\" in str(e):\n                    self.log.warning(f\"Concurrent modification detected: {e}\\nRetrying...\")\n                    attempt += 1\n                    if attempt &lt; max_retries:\n                        time.sleep(retry_delay)\n                    else:\n                        if not silence_errors:\n                            self.log.critical(f\"Failed to execute statement after {max_retries} attempts: {e}\")\n                        raise\n                else:\n                    if not silence_errors:\n                        self.log.critical(f\"Failed to execute statement: {e}\")\n                    raise\n\n    def s3_storage_location(self) -&gt; str:\n        \"\"\"Get the S3 Storage Location for this Data Source\"\"\"\n        return self.data_source_meta[\"StorageDescriptor\"][\"Location\"]\n\n    def athena_test_query(self):\n        \"\"\"Validate that Athena Queries are working\"\"\"\n        query = f'select count(*) as workbench_count from \"{self.table}\"'\n        df = wr.athena.read_sql_query(\n            sql=query,\n            database=self.database,\n            ctas_approach=False,\n            boto3_session=self.boto3_session,\n        )\n        scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n        self.log.info(f\"Athena TEST Query successful (scanned bytes: {scanned_bytes})\")\n\n    def descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the descriptive stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of descriptive stats for each column in the form\n                 {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n                  'col2': ...}\n        \"\"\"\n\n        # First check if we have already computed the descriptive stats\n        stat_dict = self.workbench_meta().get(\"workbench_descriptive_stats\")\n        if stat_dict and not recompute:\n            return stat_dict\n\n        # Call the SQL function to compute descriptive stats\n        stat_dict = sql.descriptive_stats(self)\n\n        # Push the descriptive stat data into our DataSource Metadata\n        self.upsert_workbench_meta({\"workbench_descriptive_stats\": stat_dict})\n\n        # Return the descriptive stats\n        return stat_dict\n\n    @cache_dataframe(\"sample\")\n    def sample(self) -&gt; pd.DataFrame:\n        \"\"\"Pull a sample of rows from the DataSource\n\n        Returns:\n            pd.DataFrame: A sample DataFrame for an Athena DataSource\n        \"\"\"\n\n        # Call the SQL function to pull a sample of the rows\n        return sql.sample_rows(self)\n\n    @cache_dataframe(\"outliers\")\n    def outliers(self, scale: float = 1.5, use_stddev=False) -&gt; pd.DataFrame:\n        \"\"\"Compute outliers for all the numeric columns in a DataSource\n\n        Args:\n            scale (float): The scale to use for the IQR (default: 1.5)\n            use_stddev (bool): Use Standard Deviation instead of IQR (default: False)\n\n        Returns:\n            pd.DataFrame: A DataFrame of outliers from this DataSource\n\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n\n        # Compute outliers using the SQL Outliers class\n        sql_outliers = sql.outliers.Outliers()\n        return sql_outliers.compute_outliers(self, scale=scale, use_stddev=use_stddev)\n\n    @cache_dataframe(\"smart_sample\")\n    def smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a smart sample dataframe for this DataSource\n\n        Args:\n            recompute (bool): Recompute the smart sample (default: False)\n\n        Returns:\n            pd.DataFrame: A combined DataFrame of sample data + outliers\n        \"\"\"\n\n        # Compute/recompute the smart sample\n        self.log.important(f\"Computing Smart Sample {self.uuid}...\")\n\n        # Outliers DataFrame\n        outlier_rows = self.outliers()\n\n        # Sample DataFrame\n        sample_rows = self.sample()\n        sample_rows[\"outlier_group\"] = \"sample\"\n\n        # Combine the sample rows with the outlier rows\n        all_rows = pd.concat([outlier_rows, sample_rows]).reset_index(drop=True)\n\n        # Drop duplicates\n        all_except_outlier_group = [col for col in all_rows.columns if col != \"outlier_group\"]\n        all_rows = all_rows.drop_duplicates(subset=all_except_outlier_group, ignore_index=True)\n\n        # Return the smart_sample data\n        return all_rows\n\n    def correlations(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of correlations for each column in this format\n                 {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n                  'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n        \"\"\"\n\n        # First check if we have already computed the correlations\n        correlations_dict = self.workbench_meta().get(\"workbench_correlations\")\n        if correlations_dict and not recompute:\n            return correlations_dict\n\n        # Call the SQL function to compute correlations\n        correlations_dict = sql.correlations(self)\n\n        # Push the correlation data into our DataSource Metadata\n        self.upsert_workbench_meta({\"workbench_correlations\": correlations_dict})\n\n        # Return the correlation data\n        return correlations_dict\n\n    def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Column Stats for all the columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of stats for each column this format\n            NB: String columns will NOT have num_zeros, descriptive_stats or correlation data\n                {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n                 'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100,\n                          'descriptive_stats': {...}, 'correlations': {...}},\n                 ...}\n        \"\"\"\n\n        # First check if we have already computed the column stats\n        columns_stats_dict = self.workbench_meta().get(\"workbench_column_stats\")\n        if columns_stats_dict and not recompute:\n            return columns_stats_dict\n\n        # Call the SQL function to compute column stats\n        column_stats_dict = sql.column_stats(self, recompute=recompute)\n\n        # Push the column stats data into our DataSource Metadata\n        self.upsert_workbench_meta({\"workbench_column_stats\": column_stats_dict})\n\n        # Return the column stats data\n        return column_stats_dict\n\n    def value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the value counts (default: False)\n\n        Returns:\n            dict(dict): A dictionary of value counts for each column in the form\n                 {'col1': {'value_1': 42, 'value_2': 16, 'value_3': 9,...},\n                  'col2': ...}\n        \"\"\"\n\n        # First check if we have already computed the value counts\n        value_counts_dict = self.workbench_meta().get(\"workbench_value_counts\")\n        if value_counts_dict and not recompute:\n            return value_counts_dict\n\n        # Call the SQL function to compute value_counts\n        value_count_dict = sql.value_counts(self)\n\n        # Push the value_count data into our DataSource Metadata\n        self.upsert_workbench_meta({\"workbench_value_counts\": value_count_dict})\n\n        # Return the value_count data\n        return value_count_dict\n\n    def details(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Additional Details about this AthenaSource Artifact\n\n        Args:\n            recompute (bool): Recompute the details (default: False)\n\n        Returns:\n            dict(dict): A dictionary of details about this AthenaSource\n        \"\"\"\n        self.log.info(f\"Computing DataSource Details ({self.uuid})...\")\n\n        # Get the details from the base class\n        details = super().details()\n\n        # Compute additional details\n        details[\"s3_storage_location\"] = self.s3_storage_location()\n        details[\"storage_type\"] = \"athena\"\n\n        # Compute our AWS URL\n        query = f'select * from \"{self.database}.{self.table}\" limit 10'\n        query_exec_id = wr.athena.start_query_execution(\n            sql=query, database=self.database, boto3_session=self.boto3_session\n        )\n        base_url = \"https://console.aws.amazon.com/athena/home\"\n        details[\"aws_url\"] = f\"{base_url}?region={self.aws_region}#query/history/{query_exec_id}\"\n\n        # Push the aws_url data into our DataSource Metadata\n        # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n        # self.upsert_workbench_meta({\"workbench_details\": {\"aws_url\": details[\"aws_url\"]}})\n\n        # Convert any datetime fields to ISO-8601 strings\n        details = convert_all_to_iso8601(details)\n\n        # Add the column stats\n        details[\"column_stats\"] = self.column_stats()\n\n        # Return the details data\n        return details\n\n    def delete(self):\n        \"\"\"Instance Method: Delete the AWS Data Catalog Table and S3 Storage Objects\"\"\"\n\n        # Make sure the AthenaSource exists\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an AthenaSource that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the AthenaSource\n        AthenaSource.managed_delete(self.uuid, database=self.database)\n\n    @classmethod\n    def managed_delete(cls, data_source_name: str, database: str = \"workbench\"):\n        \"\"\"Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects\n\n        Args:\n            data_source_name (str): Name of DataSource (AthenaSource)\n            database (str): Athena Database Name (default: workbench)\n        \"\"\"\n        table = data_source_name  # The table name is the same as the data_source_name\n\n        # Check if the Glue Catalog Table exists\n        if not wr.catalog.does_table_exist(database, table, boto3_session=cls.boto3_session):\n            cls.log.info(f\"DataSource {table} not found in database {database}.\")\n            return\n\n        # Delete any views associated with this AthenaSource\n        cls.delete_views(table, database)\n\n        # Delete S3 Storage Objects (if they exist)\n        try:\n            # Make an AWS Query to get the S3 storage location\n            s3_path = wr.catalog.get_table_location(database, table, boto3_session=cls.boto3_session)\n\n            # Delete Data Catalog Table\n            cls.log.info(f\"Deleting DataCatalog Table: {database}.{table}...\")\n            wr.catalog.delete_table_if_exists(database, table, boto3_session=cls.boto3_session)\n\n            # Make sure we add the trailing slash\n            s3_path = s3_path if s3_path.endswith(\"/\") else f\"{s3_path}/\"\n            cls.log.info(f\"Deleting S3 Storage Objects: {s3_path}...\")\n            wr.s3.delete_objects(s3_path, boto3_session=cls.boto3_session)\n        except Exception as e:\n            cls.log.error(f\"Failure when trying to delete {data_source_name}: {e}\")\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(data_source_name)\n\n    @classmethod\n    def delete_views(cls, table: str, database: str):\n        \"\"\"Delete any views associated with this FeatureSet\n\n        Args:\n            table (str): Name of Athena Table\n            database (str): Athena Database Name\n        \"\"\"\n        from workbench.core.views.view_utils import delete_views_and_supplemental_data\n\n        delete_views_and_supplemental_data(table, database, cls.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.column_types","title":"<code>column_types: list[str]</code>  <code>property</code>","text":"<p>Return the column types of the internal AthenaSource</p>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.columns","title":"<code>columns: list[str]</code>  <code>property</code>","text":"<p>Return the column names for this Athena Table</p>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.__init__","title":"<code>__init__(data_uuid, database='workbench', **kwargs)</code>","text":"<p>AthenaSource Initialization</p> <p>Parameters:</p> Name Type Description Default <code>data_uuid</code> <code>str</code> <p>Name of Athena Table</p> required <code>database</code> <code>str</code> <p>Athena Database Name (default: workbench)</p> <code>'workbench'</code> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def __init__(self, data_uuid, database=\"workbench\", **kwargs):\n    \"\"\"AthenaSource Initialization\n\n    Args:\n        data_uuid (str): Name of Athena Table\n        database (str): Athena Database Name (default: workbench)\n    \"\"\"\n    # Ensure the data_uuid is a valid name/id\n    self.is_name_valid(data_uuid)\n\n    # Call superclass init\n    super().__init__(data_uuid, database, **kwargs)\n\n    # Grab our metadata from the Meta class\n    self.log.info(f\"Retrieving metadata for: {self.uuid}...\")\n    self.data_source_meta = self.meta.data_source(data_uuid, database=database)\n    if self.data_source_meta is None:\n        self.log.error(f\"Unable to find {database}:{self.table} in Glue Catalogs...\")\n        return\n\n    # Call superclass post init\n    super().__post_init__()\n\n    # All done\n    self.log.debug(f\"AthenaSource Initialized: {database}.{self.table}\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    # Grab our Workbench Role Manager, get our AWS account id, and region for ARN creation\n    account_id = self.aws_account_clamp.account_id\n    region = self.aws_account_clamp.region\n    arn = f\"arn:aws:glue:{region}:{account_id}:table/{self.database}/{self.table}\"\n    return arn\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.athena_test_query","title":"<code>athena_test_query()</code>","text":"<p>Validate that Athena Queries are working</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def athena_test_query(self):\n    \"\"\"Validate that Athena Queries are working\"\"\"\n    query = f'select count(*) as workbench_count from \"{self.table}\"'\n    df = wr.athena.read_sql_query(\n        sql=query,\n        database=self.database,\n        ctas_approach=False,\n        boto3_session=self.boto3_session,\n    )\n    scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n    self.log.info(f\"Athena TEST Query successful (scanned bytes: {scanned_bytes})\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get the FULL AWS metadata for this artifact</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get the FULL AWS metadata for this artifact\"\"\"\n    return self.data_source_meta\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying this data source</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n    workbench_details = self.workbench_meta().get(\"workbench_details\", {})\n    return workbench_details.get(\"aws_url\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.column_stats","title":"<code>column_stats(recompute=False)</code>","text":"<p>Compute Column Stats for all the columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the column stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of stats for each column this format</p> <code>NB</code> <code>dict[dict]</code> <p>String columns will NOT have num_zeros, descriptive_stats or correlation data {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},  'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100,           'descriptive_stats': {...}, 'correlations': {...}},  ...}</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Column Stats for all the columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of stats for each column this format\n        NB: String columns will NOT have num_zeros, descriptive_stats or correlation data\n            {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n             'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100,\n                      'descriptive_stats': {...}, 'correlations': {...}},\n             ...}\n    \"\"\"\n\n    # First check if we have already computed the column stats\n    columns_stats_dict = self.workbench_meta().get(\"workbench_column_stats\")\n    if columns_stats_dict and not recompute:\n        return columns_stats_dict\n\n    # Call the SQL function to compute column stats\n    column_stats_dict = sql.column_stats(self, recompute=recompute)\n\n    # Push the column stats data into our DataSource Metadata\n    self.upsert_workbench_meta({\"workbench_column_stats\": column_stats_dict})\n\n    # Return the column stats data\n    return column_stats_dict\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.correlations","title":"<code>correlations(recompute=False)</code>","text":"<p>Compute Correlations for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the column stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of correlations for each column in this format  {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},   'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def correlations(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of correlations for each column in this format\n             {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n              'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n    \"\"\"\n\n    # First check if we have already computed the correlations\n    correlations_dict = self.workbench_meta().get(\"workbench_correlations\")\n    if correlations_dict and not recompute:\n        return correlations_dict\n\n    # Call the SQL function to compute correlations\n    correlations_dict = sql.correlations(self)\n\n    # Push the correlation data into our DataSource Metadata\n    self.upsert_workbench_meta({\"workbench_correlations\": correlations_dict})\n\n    # Return the correlation data\n    return correlations_dict\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    return self.data_source_meta[\"CreateTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.database_query","title":"<code>database_query(database, query)</code>  <code>classmethod</code>","text":"<p>Specify the Database and Query the Athena Service</p> <p>Parameters:</p> Name Type Description Default <code>database</code> <code>str</code> <p>The Athena Database to query</p> required <code>query</code> <code>str</code> <p>The query to run against the AthenaSource</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>@classmethod\ndef database_query(cls, database: str, query: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Specify the Database and Query the Athena Service\n\n    Args:\n        database (str): The Athena Database to query\n        query (str): The query to run against the AthenaSource\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    cls.log.debug(f\"Executing Query: {query}...\")\n    try:\n        df = wr.athena.read_sql_query(\n            sql=query,\n            database=database,\n            ctas_approach=False,\n            boto3_session=cls.boto3_session,\n        )\n        scanned_bytes = df.query_metadata[\"Statistics\"][\"DataScannedInBytes\"]\n        if scanned_bytes &gt; 0:\n            cls.log.debug(f\"Athena Query successful (scanned bytes: {scanned_bytes})\")\n        return df\n    except wr.exceptions.QueryFailed as e:\n        cls.log.critical(f\"Failed to execute query: {e}\")\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.delete","title":"<code>delete()</code>","text":"<p>Instance Method: Delete the AWS Data Catalog Table and S3 Storage Objects</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def delete(self):\n    \"\"\"Instance Method: Delete the AWS Data Catalog Table and S3 Storage Objects\"\"\"\n\n    # Make sure the AthenaSource exists\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an AthenaSource that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the AthenaSource\n    AthenaSource.managed_delete(self.uuid, database=self.database)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.delete_views","title":"<code>delete_views(table, database)</code>  <code>classmethod</code>","text":"<p>Delete any views associated with this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>table</code> <code>str</code> <p>Name of Athena Table</p> required <code>database</code> <code>str</code> <p>Athena Database Name</p> required Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>@classmethod\ndef delete_views(cls, table: str, database: str):\n    \"\"\"Delete any views associated with this FeatureSet\n\n    Args:\n        table (str): Name of Athena Table\n        database (str): Athena Database Name\n    \"\"\"\n    from workbench.core.views.view_utils import delete_views_and_supplemental_data\n\n    delete_views_and_supplemental_data(table, database, cls.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.descriptive_stats","title":"<code>descriptive_stats(recompute=False)</code>","text":"<p>Compute Descriptive Stats for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the descriptive stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of descriptive stats for each column in the form  {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},   'col2': ...}</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the descriptive stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of descriptive stats for each column in the form\n             {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n              'col2': ...}\n    \"\"\"\n\n    # First check if we have already computed the descriptive stats\n    stat_dict = self.workbench_meta().get(\"workbench_descriptive_stats\")\n    if stat_dict and not recompute:\n        return stat_dict\n\n    # Call the SQL function to compute descriptive stats\n    stat_dict = sql.descriptive_stats(self)\n\n    # Push the descriptive stat data into our DataSource Metadata\n    self.upsert_workbench_meta({\"workbench_descriptive_stats\": stat_dict})\n\n    # Return the descriptive stats\n    return stat_dict\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this AthenaSource Artifact</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the details (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about this AthenaSource</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def details(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Additional Details about this AthenaSource Artifact\n\n    Args:\n        recompute (bool): Recompute the details (default: False)\n\n    Returns:\n        dict(dict): A dictionary of details about this AthenaSource\n    \"\"\"\n    self.log.info(f\"Computing DataSource Details ({self.uuid})...\")\n\n    # Get the details from the base class\n    details = super().details()\n\n    # Compute additional details\n    details[\"s3_storage_location\"] = self.s3_storage_location()\n    details[\"storage_type\"] = \"athena\"\n\n    # Compute our AWS URL\n    query = f'select * from \"{self.database}.{self.table}\" limit 10'\n    query_exec_id = wr.athena.start_query_execution(\n        sql=query, database=self.database, boto3_session=self.boto3_session\n    )\n    base_url = \"https://console.aws.amazon.com/athena/home\"\n    details[\"aws_url\"] = f\"{base_url}?region={self.aws_region}#query/history/{query_exec_id}\"\n\n    # Push the aws_url data into our DataSource Metadata\n    # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n    # self.upsert_workbench_meta({\"workbench_details\": {\"aws_url\": details[\"aws_url\"]}})\n\n    # Convert any datetime fields to ISO-8601 strings\n    details = convert_all_to_iso8601(details)\n\n    # Add the column stats\n    details[\"column_stats\"] = self.column_stats()\n\n    # Return the details data\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.execute_statement","title":"<code>execute_statement(query, silence_errors=False)</code>","text":"<p>Execute a non-returning SQL statement in Athena with retries.</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the AthenaSource</p> required <code>silence_errors</code> <code>bool</code> <p>Silence errors (default: False)</p> <code>False</code> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def execute_statement(self, query: str, silence_errors: bool = False):\n    \"\"\"Execute a non-returning SQL statement in Athena with retries.\n\n    Args:\n        query (str): The query to run against the AthenaSource\n        silence_errors (bool): Silence errors (default: False)\n    \"\"\"\n    attempt = 0\n    max_retries = 3\n    retry_delay = 10\n    while attempt &lt; max_retries:\n        try:\n            # Start the query execution\n            query_execution_id = wr.athena.start_query_execution(\n                sql=query,\n                database=self.database,\n                boto3_session=self.boto3_session,\n            )\n            self.log.debug(f\"QueryExecutionId: {query_execution_id}\")\n\n            # Wait for the query to complete\n            wr.athena.wait_query(query_execution_id=query_execution_id, boto3_session=self.boto3_session)\n            self.log.debug(f\"Statement executed successfully: {query_execution_id}\")\n            break  # If successful, exit the retry loop\n        except wr.exceptions.QueryFailed as e:\n            if \"AlreadyExistsException\" in str(e):\n                self.log.warning(f\"Table already exists: {e} \\nIgnoring...\")\n                break  # No need to retry for this error\n            elif \"ConcurrentModificationException\" in str(e):\n                self.log.warning(f\"Concurrent modification detected: {e}\\nRetrying...\")\n                attempt += 1\n                if attempt &lt; max_retries:\n                    time.sleep(retry_delay)\n                else:\n                    if not silence_errors:\n                        self.log.critical(f\"Failed to execute statement after {max_retries} attempts: {e}\")\n                    raise\n            else:\n                if not silence_errors:\n                    self.log.critical(f\"Failed to execute statement: {e}\")\n                raise\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.exists","title":"<code>exists()</code>","text":"<p>Validation Checks for this Data Source</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Validation Checks for this Data Source\"\"\"\n\n    # Are we able to pull AWS Metadata for this table_name?\"\"\"\n    # Do we have a valid data_source_meta?\n    if getattr(self, \"data_source_meta\", None) is None:\n        self.log.debug(f\"AthenaSource {self.table} not found in Workbench Metadata...\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.hash","title":"<code>hash()</code>","text":"<p>Get the hash for the set of Parquet files used for this Artifact</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def hash(self) -&gt; str:\n    \"\"\"Get the hash for the set of Parquet files used for this Artifact\"\"\"\n    s3_uri = self.s3_storage_location()\n    return compute_parquet_hash(s3_uri, self.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.managed_delete","title":"<code>managed_delete(data_source_name, database='workbench')</code>  <code>classmethod</code>","text":"<p>Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects</p> <p>Parameters:</p> Name Type Description Default <code>data_source_name</code> <code>str</code> <p>Name of DataSource (AthenaSource)</p> required <code>database</code> <code>str</code> <p>Athena Database Name (default: workbench)</p> <code>'workbench'</code> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>@classmethod\ndef managed_delete(cls, data_source_name: str, database: str = \"workbench\"):\n    \"\"\"Class Method: Delete the AWS Data Catalog Table and S3 Storage Objects\n\n    Args:\n        data_source_name (str): Name of DataSource (AthenaSource)\n        database (str): Athena Database Name (default: workbench)\n    \"\"\"\n    table = data_source_name  # The table name is the same as the data_source_name\n\n    # Check if the Glue Catalog Table exists\n    if not wr.catalog.does_table_exist(database, table, boto3_session=cls.boto3_session):\n        cls.log.info(f\"DataSource {table} not found in database {database}.\")\n        return\n\n    # Delete any views associated with this AthenaSource\n    cls.delete_views(table, database)\n\n    # Delete S3 Storage Objects (if they exist)\n    try:\n        # Make an AWS Query to get the S3 storage location\n        s3_path = wr.catalog.get_table_location(database, table, boto3_session=cls.boto3_session)\n\n        # Delete Data Catalog Table\n        cls.log.info(f\"Deleting DataCatalog Table: {database}.{table}...\")\n        wr.catalog.delete_table_if_exists(database, table, boto3_session=cls.boto3_session)\n\n        # Make sure we add the trailing slash\n        s3_path = s3_path if s3_path.endswith(\"/\") else f\"{s3_path}/\"\n        cls.log.info(f\"Deleting S3 Storage Objects: {s3_path}...\")\n        wr.s3.delete_objects(s3_path, boto3_session=cls.boto3_session)\n    except Exception as e:\n        cls.log.error(f\"Failure when trying to delete {data_source_name}: {e}\")\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(data_source_name)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    return self.data_source_meta[\"UpdateTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.num_columns","title":"<code>num_columns()</code>","text":"<p>Return the number of columns for this Data Source</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def num_columns(self) -&gt; int:\n    \"\"\"Return the number of columns for this Data Source\"\"\"\n    return len(self.columns)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.num_rows","title":"<code>num_rows()</code>","text":"<p>Return the number of rows for this Data Source</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def num_rows(self) -&gt; int:\n    \"\"\"Return the number of rows for this Data Source\"\"\"\n    count_df = self.query(f'select count(*) AS workbench_count from \"{self.database}\".\"{self.table}\"')\n    return count_df[\"workbench_count\"][0] if count_df is not None else 0\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.outliers","title":"<code>outliers(scale=1.5, use_stddev=False)</code>","text":"<p>Compute outliers for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>scale</code> <code>float</code> <p>The scale to use for the IQR (default: 1.5)</p> <code>1.5</code> <code>use_stddev</code> <code>bool</code> <p>Use Standard Deviation instead of IQR (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of outliers from this DataSource</p> Notes <p>Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma) The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>@cache_dataframe(\"outliers\")\ndef outliers(self, scale: float = 1.5, use_stddev=False) -&gt; pd.DataFrame:\n    \"\"\"Compute outliers for all the numeric columns in a DataSource\n\n    Args:\n        scale (float): The scale to use for the IQR (default: 1.5)\n        use_stddev (bool): Use Standard Deviation instead of IQR (default: False)\n\n    Returns:\n        pd.DataFrame: A DataFrame of outliers from this DataSource\n\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n\n    # Compute outliers using the SQL Outliers class\n    sql_outliers = sql.outliers.Outliers()\n    return sql_outliers.compute_outliers(self, scale=scale, use_stddev=use_stddev)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.query","title":"<code>query(query)</code>","text":"<p>Query the AthenaSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the AthenaSource</p> required <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def query(self, query: str) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Query the AthenaSource\n\n    Args:\n        query (str): The query to run against the AthenaSource\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n\n    # Call internal class _query method\n    return self.database_query(self.database, query)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Refresh our internal AWS Broker catalog metadata</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Refresh our internal AWS Broker catalog metadata\"\"\"\n    self.data_source_meta = self.meta.data_source(self.uuid, database=self.database)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.s3_storage_location","title":"<code>s3_storage_location()</code>","text":"<p>Get the S3 Storage Location for this Data Source</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def s3_storage_location(self) -&gt; str:\n    \"\"\"Get the S3 Storage Location for this Data Source\"\"\"\n    return self.data_source_meta[\"StorageDescriptor\"][\"Location\"]\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.sample","title":"<code>sample()</code>","text":"<p>Pull a sample of rows from the DataSource</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A sample DataFrame for an Athena DataSource</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>@cache_dataframe(\"sample\")\ndef sample(self) -&gt; pd.DataFrame:\n    \"\"\"Pull a sample of rows from the DataSource\n\n    Returns:\n        pd.DataFrame: A sample DataFrame for an Athena DataSource\n    \"\"\"\n\n    # Call the SQL function to pull a sample of the rows\n    return sql.sample_rows(self)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.size","title":"<code>size()</code>","text":"<p>Return the size of this data in MegaBytes</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of this data in MegaBytes\"\"\"\n    size_in_bytes = sum(wr.s3.size_objects(self.s3_storage_location(), boto3_session=self.boto3_session).values())\n    size_in_mb = size_in_bytes / 1_000_000\n    return size_in_mb\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.smart_sample","title":"<code>smart_sample(recompute=False)</code>","text":"<p>Get a smart sample dataframe for this DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the smart sample (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A combined DataFrame of sample data + outliers</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>@cache_dataframe(\"smart_sample\")\ndef smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a smart sample dataframe for this DataSource\n\n    Args:\n        recompute (bool): Recompute the smart sample (default: False)\n\n    Returns:\n        pd.DataFrame: A combined DataFrame of sample data + outliers\n    \"\"\"\n\n    # Compute/recompute the smart sample\n    self.log.important(f\"Computing Smart Sample {self.uuid}...\")\n\n    # Outliers DataFrame\n    outlier_rows = self.outliers()\n\n    # Sample DataFrame\n    sample_rows = self.sample()\n    sample_rows[\"outlier_group\"] = \"sample\"\n\n    # Combine the sample rows with the outlier rows\n    all_rows = pd.concat([outlier_rows, sample_rows]).reset_index(drop=True)\n\n    # Drop duplicates\n    all_except_outlier_group = [col for col in all_rows.columns if col != \"outlier_group\"]\n    all_rows = all_rows.drop_duplicates(subset=all_except_outlier_group, ignore_index=True)\n\n    # Return the smart_sample data\n    return all_rows\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.table_hash","title":"<code>table_hash()</code>","text":"<p>Get the table hash for this AthenaSource</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def table_hash(self) -&gt; str:\n    \"\"\"Get the table hash for this AthenaSource\"\"\"\n    s3_scratch = f\"s3://{self.workbench_bucket}/temp/athena_output\"\n    return compute_athena_table_hash(self.database, self.table, self.boto3_session, s3_scratch)\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.upsert_workbench_meta","title":"<code>upsert_workbench_meta(new_meta)</code>","text":"<p>Add Workbench specific metadata to this Artifact</p> <p>Parameters:</p> Name Type Description Default <code>new_meta</code> <code>dict</code> <p>Dictionary of new metadata to add</p> required Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def upsert_workbench_meta(self, new_meta: dict):\n    \"\"\"Add Workbench specific metadata to this Artifact\n\n    Args:\n        new_meta (dict): Dictionary of new metadata to add\n    \"\"\"\n    self.log.important(f\"Upserting Workbench Metadata {self.uuid}:{str(new_meta)[:50]}...\")\n\n    # Give a warning message for keys that don't start with workbench_\n    for key in new_meta.keys():\n        if not key.startswith(\"workbench_\"):\n            self.log.warning(\"Append 'workbench_' to key names to avoid overwriting AWS meta data\")\n\n    # Now convert any non-string values to JSON strings\n    for key, value in new_meta.items():\n        if not isinstance(value, str):\n            new_meta[key] = json.dumps(value, cls=CustomEncoder)\n\n    # Store our updated metadata\n    try:\n        wr.catalog.upsert_table_parameters(\n            parameters=new_meta,\n            database=self.database,\n            table=self.table,\n            boto3_session=self.boto3_session,\n        )\n    except botocore.exceptions.ClientError as e:\n        error_code = e.response[\"Error\"][\"Code\"]\n        if error_code == \"InvalidInputException\":\n            self.log.error(f\"Unable to upsert metadata for {self.table}\")\n            self.log.error(\"Probably because the metadata is too large\")\n            self.log.error(new_meta)\n        elif error_code == \"ConcurrentModificationException\":\n            self.log.warning(\"ConcurrentModificationException... trying again...\")\n            time.sleep(5)\n            wr.catalog.upsert_table_parameters(\n                parameters=new_meta,\n                database=self.database,\n                table=self.table,\n                boto3_session=self.boto3_session,\n            )\n        else:\n            self.log.critical(f\"Failed to upsert metadata: {e}\")\n            self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n    except Exception as e:\n        self.log.critical(f\"Failed to upsert metadata: {e}\")\n        self.log.critical(f\"{self.uuid} is Malformed! Delete this Artifact and recreate it!\")\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.value_counts","title":"<code>value_counts(recompute=False)</code>","text":"<p>Compute 'value_counts' for all the string columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the value counts (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of value counts for each column in the form  {'col1': {'value_1': 42, 'value_2': 16, 'value_3': 9,...},   'col2': ...}</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the value counts (default: False)\n\n    Returns:\n        dict(dict): A dictionary of value counts for each column in the form\n             {'col1': {'value_1': 42, 'value_2': 16, 'value_3': 9,...},\n              'col2': ...}\n    \"\"\"\n\n    # First check if we have already computed the value counts\n    value_counts_dict = self.workbench_meta().get(\"workbench_value_counts\")\n    if value_counts_dict and not recompute:\n        return value_counts_dict\n\n    # Call the SQL function to compute value_counts\n    value_count_dict = sql.value_counts(self)\n\n    # Push the value_count data into our DataSource Metadata\n    self.upsert_workbench_meta({\"workbench_value_counts\": value_count_dict})\n\n    # Return the value_count data\n    return value_count_dict\n</code></pre>"},{"location":"core_classes/artifacts/athena_source/#workbench.core.artifacts.athena_source.AthenaSource.workbench_meta","title":"<code>workbench_meta()</code>","text":"<p>Get the Workbench specific metadata for this Artifact</p> Source code in <code>src/workbench/core/artifacts/athena_source.py</code> <pre><code>def workbench_meta(self) -&gt; dict:\n    \"\"\"Get the Workbench specific metadata for this Artifact\"\"\"\n\n    # Sanity Check if we have invalid AWS Metadata\n    if self.data_source_meta is None:\n        if not self.exists():\n            self.log.error(f\"DataSource {self.uuid} doesn't appear to exist...\")\n        else:\n            self.log.critical(f\"Unable to get AWS Metadata for {self.table}\")\n            self.log.critical(\"Malformed Artifact! Delete this Artifact and recreate it!\")\n        return {}\n\n    # Get the Workbench Metadata from the 'Parameters' section of the DataSource Metadata\n    params = self.data_source_meta.get(\"Parameters\", {})\n    return {key: decode_value(value) for key, value in params.items() if \"workbench\" in key}\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/","title":"DataSource Abstract","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the DataSource API Class and voil\u00e0 it works the same.</p> <p>The DataSource Abstract class is a base/abstract class that defines API implemented by all the child classes (currently just AthenaSource but later RDSSource, FutureThing ).</p> <p>DataSourceAbstract: Abstract Base Class for all data sources (S3: CSV, JSONL, Parquet, RDS, etc)</p>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract","title":"<code>DataSourceAbstract</code>","text":"<p>               Bases: <code>Artifact</code></p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>class DataSourceAbstract(Artifact):\n    def __init__(self, data_uuid: str, database: str = \"workbench\", **kwargs):\n        \"\"\"DataSourceAbstract: Abstract Base Class for all data sources\n        Args:\n            data_uuid(str): The UUID for this Data Source\n            database(str): The database to use for this Data Source (default: workbench)\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(data_uuid, **kwargs)\n\n        # Set up our instance attributes\n        self._database = database\n        self._table_name = data_uuid\n\n    def __post_init__(self):\n        # Call superclass post_init\n        super().__post_init__()\n\n    @deprecated(version=\"0.9\")\n    def get_database(self) -&gt; str:\n        \"\"\"Get the database for this Data Source\"\"\"\n        return self._database\n\n    @property\n    def database(self) -&gt; str:\n        \"\"\"Get the database for this Data Source\"\"\"\n        return self._database\n\n    @property\n    def table(self) -&gt; str:\n        \"\"\"Get the base table name for this Data Source\"\"\"\n        return self._table_name\n\n    @abstractmethod\n    def num_rows(self) -&gt; int:\n        \"\"\"Return the number of rows for this Data Source\"\"\"\n        pass\n\n    @abstractmethod\n    def num_columns(self) -&gt; int:\n        \"\"\"Return the number of columns for this Data Source\"\"\"\n        pass\n\n    @property\n    @abstractmethod\n    def columns(self) -&gt; list[str]:\n        \"\"\"Return the column names for this Data Source\"\"\"\n        pass\n\n    @property\n    @abstractmethod\n    def column_types(self) -&gt; list[str]:\n        \"\"\"Return the column types for this Data Source\"\"\"\n        pass\n\n    def column_details(self) -&gt; dict:\n        \"\"\"Return the column details for this Data Source\n\n        Returns:\n            dict: The column details for this Data Source\n        \"\"\"\n        return dict(zip(self.columns, self.column_types))\n\n    def views(self) -&gt; list[str]:\n        \"\"\"Return the views for this Data Source\"\"\"\n        from workbench.core.views.view_utils import list_views\n\n        return list_views(self)\n\n    def view(self, view_name: str) -&gt; \"View\":\n        \"\"\"Return a DataFrame for a specific view\n        Args:\n            view_name (str): The name of the view to return\n        Returns:\n            pd.DataFrame: A DataFrame for the specified view\n        \"\"\"\n        from workbench.core.views import View\n\n        return View(self, view_name)\n\n    def set_display_columns(self, diplay_columns: list[str]):\n        \"\"\"Set the display columns for this Data Source\n\n        Args:\n            diplay_columns (list[str]): The display columns for this Data Source\n        \"\"\"\n        # Check mismatch of display columns to computation columns\n        c_view = self.view(\"computation\")\n        computation_columns = c_view.columns\n        mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n        if mismatch_columns:\n            self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n        self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n        from workbench.core.views import DisplayView\n\n        # Create a NEW display view\n        DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n\n    def set_computation_columns(self, computation_columns: list[str], recompute_stats: bool = True):\n        \"\"\"Set the computation columns for this Data Source\n\n        Args:\n            computation_columns (list[str]): The computation columns for this Data Source\n            recompute_stats (bool): Recomputes all the stats for this Data Source (default: True)\n        \"\"\"\n        self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n        from workbench.core.views import ComputationView\n\n        # Create a NEW computation view\n        ComputationView.create(self, column_list=computation_columns)\n        if recompute_stats:\n            self.recompute_stats()\n\n    def _create_display_view(self):\n        \"\"\"Internal: Create the Display View for this DataSource\"\"\"\n        from workbench.core.views import View\n\n        View(self, \"display\")\n\n    @abstractmethod\n    def query(self, query: str) -&gt; pd.DataFrame:\n        \"\"\"Query the DataSourceAbstract\n        Args:\n            query(str): The SQL query to execute\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def execute_statement(self, query: str):\n        \"\"\"Execute an SQL statement that doesn't return a result\n        Args:\n            query(str): The SQL statement to execute\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def sample(self) -&gt; pd.DataFrame:\n        \"\"\"Return a sample DataFrame from this DataSourceAbstract\n\n        Returns:\n            pd.DataFrame: A sample DataFrame from this DataSource\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n        Args:\n            recompute (bool): Recompute the descriptive stats (default: False)\n        Returns:\n            dict(dict): A dictionary of descriptive stats for each column in the form\n                 {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n                  'col2': ...}\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def outliers(self, scale: float = 1.5) -&gt; pd.DataFrame:\n        \"\"\"Return a DataFrame of outliers from this DataSource\n\n        Args:\n            scale (float): The scale to use for the IQR (default: 1.5)\n\n        Returns:\n            pd.DataFrame: A DataFrame of outliers from this DataSource\n\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def smart_sample(self) -&gt; pd.DataFrame:\n        \"\"\"Get a SMART sample dataframe from this DataSource\n        Returns:\n            pd.DataFrame: A combined DataFrame of sample data + outliers\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n        Args:\n            recompute (bool): Recompute the value counts (default: False)\n        Returns:\n            dict(dict): A dictionary of value counts for each column in the form\n                 {'col1': {'value_1': X, 'value_2': Y, 'value_3': Z,...},\n                  'col2': ...}\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Column Stats for all the columns in a DataSource\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n        Returns:\n            dict(dict): A dictionary of stats for each column this format\n            NB: String columns will NOT have num_zeros and descriptive stats\n             {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n              'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n              ...}\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def correlations(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n\n        Returns:\n            dict(dict): A dictionary of correlations for each column in this format\n                 {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n                  'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n        \"\"\"\n        pass\n\n    def details(self) -&gt; dict:\n        \"\"\"Additional Details about this DataSourceAbstract Artifact\"\"\"\n        details = self.summary()\n        details[\"num_rows\"] = self.num_rows()\n        details[\"num_columns\"] = self.num_columns()\n        details[\"column_details\"] = self.column_details()\n        return details\n\n    def expected_meta(self) -&gt; list[str]:\n        \"\"\"DataSources have quite a bit of expected Metadata for EDA displays\"\"\"\n\n        # For DataSources, we expect to see the following metadata\n        expected_meta = [\n            # FIXME: Revisit this\n            # \"workbench_details\",\n            \"workbench_descriptive_stats\",\n            \"workbench_value_counts\",\n            \"workbench_correlations\",\n            \"workbench_column_stats\",\n        ]\n        return expected_meta\n\n    def ready(self) -&gt; bool:\n        \"\"\"Is the DataSource ready?\"\"\"\n\n        # Check if the Artifact is ready\n        if not super().ready():\n            return False\n\n        # If we don't have a smart_sample we're probably not ready\n        if not self.df_cache.check(f\"{self.uuid}/smart_sample\"):\n            self.log.warning(f\"DataSource {self.uuid} not ready...\")\n            return False\n\n        # Okay so we have sample, outliers, and smart_sample so we are ready\n        return True\n\n    def onboard(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will onboard the data source (make it ready)\n\n        Returns:\n            bool: True if the DataSource was onboarded successfully\n        \"\"\"\n        self.log.important(f\"Onboarding {self.uuid}...\")\n        self.set_status(\"onboarding\")\n        self.remove_health_tag(\"needs_onboard\")\n\n        # Make sure our display view actually exists\n        self.view(\"display\").ensure_exists()\n\n        # Recompute the stats\n        self.recompute_stats()\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        self.set_status(\"ready\")\n        return True\n\n    def recompute_stats(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will recompute the stats for the data source\n\n        Returns:\n            bool: True if the DataSource stats were recomputed successfully\n        \"\"\"\n        self.log.important(f\"Recomputing Stats {self.uuid}...\")\n\n        # Make sure our computation view actually exists\n        self.view(\"computation\").ensure_exists()\n\n        # Compute the sample, column stats, outliers, and smart_sample\n        self.df_cache.delete(f\"{self.uuid}/sample\")\n        self.sample()\n        self.column_stats(recompute=True)\n        self.refresh_meta()  # Refresh the meta since outliers needs descriptive_stats and value_counts\n        self.df_cache.delete(f\"{self.uuid}/outliers\")\n        self.outliers()\n        self.df_cache.delete(f\"{self.uuid}/smart_sample\")\n        self.smart_sample()\n        return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_types","title":"<code>column_types: list[str]</code>  <code>abstractmethod</code> <code>property</code>","text":"<p>Return the column types for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.columns","title":"<code>columns: list[str]</code>  <code>abstractmethod</code> <code>property</code>","text":"<p>Return the column names for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.database","title":"<code>database: str</code>  <code>property</code>","text":"<p>Get the database for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.table","title":"<code>table: str</code>  <code>property</code>","text":"<p>Get the base table name for this Data Source</p>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.__init__","title":"<code>__init__(data_uuid, database='workbench', **kwargs)</code>","text":"<p>DataSourceAbstract: Abstract Base Class for all data sources Args:     data_uuid(str): The UUID for this Data Source     database(str): The database to use for this Data Source (default: workbench)</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def __init__(self, data_uuid: str, database: str = \"workbench\", **kwargs):\n    \"\"\"DataSourceAbstract: Abstract Base Class for all data sources\n    Args:\n        data_uuid(str): The UUID for this Data Source\n        database(str): The database to use for this Data Source (default: workbench)\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(data_uuid, **kwargs)\n\n    # Set up our instance attributes\n    self._database = database\n    self._table_name = data_uuid\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_details","title":"<code>column_details()</code>","text":"<p>Return the column details for this Data Source</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The column details for this Data Source</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def column_details(self) -&gt; dict:\n    \"\"\"Return the column details for this Data Source\n\n    Returns:\n        dict: The column details for this Data Source\n    \"\"\"\n    return dict(zip(self.columns, self.column_types))\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.column_stats","title":"<code>column_stats(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute Column Stats for all the columns in a DataSource Args:     recompute (bool): Recompute the column stats (default: False) Returns:     dict(dict): A dictionary of stats for each column this format     NB: String columns will NOT have num_zeros and descriptive stats      {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},       'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},       ...}</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Column Stats for all the columns in a DataSource\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n    Returns:\n        dict(dict): A dictionary of stats for each column this format\n        NB: String columns will NOT have num_zeros and descriptive stats\n         {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n          'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n          ...}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.correlations","title":"<code>correlations(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute Correlations for all the numeric columns in a DataSource</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the column stats (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of correlations for each column in this format  {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},   'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef correlations(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Correlations for all the numeric columns in a DataSource\n\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n\n    Returns:\n        dict(dict): A dictionary of correlations for each column in this format\n             {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n              'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.descriptive_stats","title":"<code>descriptive_stats(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute Descriptive Stats for all the numeric columns in a DataSource Args:     recompute (bool): Recompute the descriptive stats (default: False) Returns:     dict(dict): A dictionary of descriptive stats for each column in the form          {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},           'col2': ...}</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef descriptive_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n    Args:\n        recompute (bool): Recompute the descriptive stats (default: False)\n    Returns:\n        dict(dict): A dictionary of descriptive stats for each column in the form\n             {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4},\n              'col2': ...}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.details","title":"<code>details()</code>","text":"<p>Additional Details about this DataSourceAbstract Artifact</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def details(self) -&gt; dict:\n    \"\"\"Additional Details about this DataSourceAbstract Artifact\"\"\"\n    details = self.summary()\n    details[\"num_rows\"] = self.num_rows()\n    details[\"num_columns\"] = self.num_columns()\n    details[\"column_details\"] = self.column_details()\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.execute_statement","title":"<code>execute_statement(query)</code>  <code>abstractmethod</code>","text":"<p>Execute an SQL statement that doesn't return a result Args:     query(str): The SQL statement to execute</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef execute_statement(self, query: str):\n    \"\"\"Execute an SQL statement that doesn't return a result\n    Args:\n        query(str): The SQL statement to execute\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.expected_meta","title":"<code>expected_meta()</code>","text":"<p>DataSources have quite a bit of expected Metadata for EDA displays</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def expected_meta(self) -&gt; list[str]:\n    \"\"\"DataSources have quite a bit of expected Metadata for EDA displays\"\"\"\n\n    # For DataSources, we expect to see the following metadata\n    expected_meta = [\n        # FIXME: Revisit this\n        # \"workbench_details\",\n        \"workbench_descriptive_stats\",\n        \"workbench_value_counts\",\n        \"workbench_correlations\",\n        \"workbench_column_stats\",\n    ]\n    return expected_meta\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.get_database","title":"<code>get_database()</code>","text":"<p>Get the database for this Data Source</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@deprecated(version=\"0.9\")\ndef get_database(self) -&gt; str:\n    \"\"\"Get the database for this Data Source\"\"\"\n    return self._database\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.num_columns","title":"<code>num_columns()</code>  <code>abstractmethod</code>","text":"<p>Return the number of columns for this Data Source</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef num_columns(self) -&gt; int:\n    \"\"\"Return the number of columns for this Data Source\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.num_rows","title":"<code>num_rows()</code>  <code>abstractmethod</code>","text":"<p>Return the number of rows for this Data Source</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef num_rows(self) -&gt; int:\n    \"\"\"Return the number of rows for this Data Source\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.onboard","title":"<code>onboard()</code>","text":"<p>This is a BLOCKING method that will onboard the data source (make it ready)</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the DataSource was onboarded successfully</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def onboard(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will onboard the data source (make it ready)\n\n    Returns:\n        bool: True if the DataSource was onboarded successfully\n    \"\"\"\n    self.log.important(f\"Onboarding {self.uuid}...\")\n    self.set_status(\"onboarding\")\n    self.remove_health_tag(\"needs_onboard\")\n\n    # Make sure our display view actually exists\n    self.view(\"display\").ensure_exists()\n\n    # Recompute the stats\n    self.recompute_stats()\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    self.set_status(\"ready\")\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.outliers","title":"<code>outliers(scale=1.5)</code>  <code>abstractmethod</code>","text":"<p>Return a DataFrame of outliers from this DataSource</p> <p>Parameters:</p> Name Type Description Default <code>scale</code> <code>float</code> <p>The scale to use for the IQR (default: 1.5)</p> <code>1.5</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame of outliers from this DataSource</p> Notes <p>Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef outliers(self, scale: float = 1.5) -&gt; pd.DataFrame:\n    \"\"\"Return a DataFrame of outliers from this DataSource\n\n    Args:\n        scale (float): The scale to use for the IQR (default: 1.5)\n\n    Returns:\n        pd.DataFrame: A DataFrame of outliers from this DataSource\n\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.query","title":"<code>query(query)</code>  <code>abstractmethod</code>","text":"<p>Query the DataSourceAbstract Args:     query(str): The SQL query to execute</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef query(self, query: str) -&gt; pd.DataFrame:\n    \"\"\"Query the DataSourceAbstract\n    Args:\n        query(str): The SQL query to execute\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.ready","title":"<code>ready()</code>","text":"<p>Is the DataSource ready?</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def ready(self) -&gt; bool:\n    \"\"\"Is the DataSource ready?\"\"\"\n\n    # Check if the Artifact is ready\n    if not super().ready():\n        return False\n\n    # If we don't have a smart_sample we're probably not ready\n    if not self.df_cache.check(f\"{self.uuid}/smart_sample\"):\n        self.log.warning(f\"DataSource {self.uuid} not ready...\")\n        return False\n\n    # Okay so we have sample, outliers, and smart_sample so we are ready\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.recompute_stats","title":"<code>recompute_stats()</code>","text":"<p>This is a BLOCKING method that will recompute the stats for the data source</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the DataSource stats were recomputed successfully</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def recompute_stats(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will recompute the stats for the data source\n\n    Returns:\n        bool: True if the DataSource stats were recomputed successfully\n    \"\"\"\n    self.log.important(f\"Recomputing Stats {self.uuid}...\")\n\n    # Make sure our computation view actually exists\n    self.view(\"computation\").ensure_exists()\n\n    # Compute the sample, column stats, outliers, and smart_sample\n    self.df_cache.delete(f\"{self.uuid}/sample\")\n    self.sample()\n    self.column_stats(recompute=True)\n    self.refresh_meta()  # Refresh the meta since outliers needs descriptive_stats and value_counts\n    self.df_cache.delete(f\"{self.uuid}/outliers\")\n    self.outliers()\n    self.df_cache.delete(f\"{self.uuid}/smart_sample\")\n    self.smart_sample()\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.sample","title":"<code>sample()</code>  <code>abstractmethod</code>","text":"<p>Return a sample DataFrame from this DataSourceAbstract</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A sample DataFrame from this DataSource</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef sample(self) -&gt; pd.DataFrame:\n    \"\"\"Return a sample DataFrame from this DataSourceAbstract\n\n    Returns:\n        pd.DataFrame: A sample DataFrame from this DataSource\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.set_computation_columns","title":"<code>set_computation_columns(computation_columns, recompute_stats=True)</code>","text":"<p>Set the computation columns for this Data Source</p> <p>Parameters:</p> Name Type Description Default <code>computation_columns</code> <code>list[str]</code> <p>The computation columns for this Data Source</p> required <code>recompute_stats</code> <code>bool</code> <p>Recomputes all the stats for this Data Source (default: True)</p> <code>True</code> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def set_computation_columns(self, computation_columns: list[str], recompute_stats: bool = True):\n    \"\"\"Set the computation columns for this Data Source\n\n    Args:\n        computation_columns (list[str]): The computation columns for this Data Source\n        recompute_stats (bool): Recomputes all the stats for this Data Source (default: True)\n    \"\"\"\n    self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n    from workbench.core.views import ComputationView\n\n    # Create a NEW computation view\n    ComputationView.create(self, column_list=computation_columns)\n    if recompute_stats:\n        self.recompute_stats()\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.set_display_columns","title":"<code>set_display_columns(diplay_columns)</code>","text":"<p>Set the display columns for this Data Source</p> <p>Parameters:</p> Name Type Description Default <code>diplay_columns</code> <code>list[str]</code> <p>The display columns for this Data Source</p> required Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def set_display_columns(self, diplay_columns: list[str]):\n    \"\"\"Set the display columns for this Data Source\n\n    Args:\n        diplay_columns (list[str]): The display columns for this Data Source\n    \"\"\"\n    # Check mismatch of display columns to computation columns\n    c_view = self.view(\"computation\")\n    computation_columns = c_view.columns\n    mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n    if mismatch_columns:\n        self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n    self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n    from workbench.core.views import DisplayView\n\n    # Create a NEW display view\n    DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.smart_sample","title":"<code>smart_sample()</code>  <code>abstractmethod</code>","text":"<p>Get a SMART sample dataframe from this DataSource Returns:     pd.DataFrame: A combined DataFrame of sample data + outliers</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef smart_sample(self) -&gt; pd.DataFrame:\n    \"\"\"Get a SMART sample dataframe from this DataSource\n    Returns:\n        pd.DataFrame: A combined DataFrame of sample data + outliers\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.value_counts","title":"<code>value_counts(recompute=False)</code>  <code>abstractmethod</code>","text":"<p>Compute 'value_counts' for all the string columns in a DataSource Args:     recompute (bool): Recompute the value counts (default: False) Returns:     dict(dict): A dictionary of value counts for each column in the form          {'col1': {'value_1': X, 'value_2': Y, 'value_3': Z,...},           'col2': ...}</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>@abstractmethod\ndef value_counts(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute 'value_counts' for all the string columns in a DataSource\n    Args:\n        recompute (bool): Recompute the value counts (default: False)\n    Returns:\n        dict(dict): A dictionary of value counts for each column in the form\n             {'col1': {'value_1': X, 'value_2': Y, 'value_3': Z,...},\n              'col2': ...}\n    \"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.view","title":"<code>view(view_name)</code>","text":"<p>Return a DataFrame for a specific view Args:     view_name (str): The name of the view to return Returns:     pd.DataFrame: A DataFrame for the specified view</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def view(self, view_name: str) -&gt; \"View\":\n    \"\"\"Return a DataFrame for a specific view\n    Args:\n        view_name (str): The name of the view to return\n    Returns:\n        pd.DataFrame: A DataFrame for the specified view\n    \"\"\"\n    from workbench.core.views import View\n\n    return View(self, view_name)\n</code></pre>"},{"location":"core_classes/artifacts/data_source_abstract/#workbench.core.artifacts.data_source_abstract.DataSourceAbstract.views","title":"<code>views()</code>","text":"<p>Return the views for this Data Source</p> Source code in <code>src/workbench/core/artifacts/data_source_abstract.py</code> <pre><code>def views(self) -&gt; list[str]:\n    \"\"\"Return the views for this Data Source\"\"\"\n    from workbench.core.views.view_utils import list_views\n\n    return list_views(self)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/","title":"EndpointCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the Endpoint API Class and voil\u00e0 it works the same.</p> <p>EndpointCore: Workbench EndpointCore Class</p>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore","title":"<code>EndpointCore</code>","text":"<p>               Bases: <code>Artifact</code></p> <p>EndpointCore: Workbench EndpointCore Class</p> Common Usage <pre><code>my_endpoint = EndpointCore(endpoint_uuid)\nprediction_df = my_endpoint.predict(test_df)\nmetrics = my_endpoint.regression_metrics(target_column, prediction_df)\nfor metric, value in metrics.items():\n    print(f\"{metric}: {value:0.3f}\")\n</code></pre> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>class EndpointCore(Artifact):\n    \"\"\"EndpointCore: Workbench EndpointCore Class\n\n    Common Usage:\n        ```python\n        my_endpoint = EndpointCore(endpoint_uuid)\n        prediction_df = my_endpoint.predict(test_df)\n        metrics = my_endpoint.regression_metrics(target_column, prediction_df)\n        for metric, value in metrics.items():\n            print(f\"{metric}: {value:0.3f}\")\n        ```\n    \"\"\"\n\n    def __init__(self, endpoint_uuid, **kwargs):\n        \"\"\"EndpointCore Initialization\n\n        Args:\n            endpoint_uuid (str): Name of Endpoint in Workbench\n        \"\"\"\n\n        # Make sure the endpoint_uuid is a valid name\n        self.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call SuperClass Initialization\n        super().__init__(endpoint_uuid, **kwargs)\n\n        # Grab an Cloud Metadata object and pull information for Endpoints\n        self.endpoint_name = endpoint_uuid\n        self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n\n        # Sanity check that we found the endpoint\n        if self.endpoint_meta is None:\n            self.log.important(f\"Could not find endpoint {self.uuid} within current visibility scope\")\n            return\n\n        # Sanity check the Endpoint state\n        if self.endpoint_meta[\"EndpointStatus\"] == \"Failed\":\n            self.log.critical(f\"Endpoint {self.uuid} is in a failed state\")\n            reason = self.endpoint_meta[\"FailureReason\"]\n            self.log.critical(f\"Failure Reason: {reason}\")\n            self.log.critical(\"Please delete this endpoint and re-deploy...\")\n\n        # Set the Inference, Capture, and Monitoring S3 Paths\n        self.endpoint_inference_path = self.endpoints_s3_path + \"/inference/\" + self.uuid\n        self.endpoint_data_capture_path = self.endpoints_s3_path + \"/data_capture/\" + self.uuid\n        self.endpoint_monitoring_path = self.endpoints_s3_path + \"/monitoring/\" + self.uuid\n\n        # Set the Model Name\n        self.model_name = self.get_input()\n\n        # This is for endpoint error handling later\n        self.endpoint_return_columns = None\n\n        # We temporary cache the endpoint metrics\n        self.temp_storage = Cache(prefix=\"temp_storage\", expire=300)  # 5 minutes\n\n        # Call SuperClass Post Initialization\n        super().__post_init__()\n\n        # All done\n        self.log.info(f\"EndpointCore Initialized: {self.endpoint_name}\")\n\n    def refresh_meta(self):\n        \"\"\"Refresh the Artifact's metadata\"\"\"\n        self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n\n    def exists(self) -&gt; bool:\n        \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n        if self.endpoint_meta is None:\n            self.log.debug(f\"Endpoint {self.endpoint_name} not found in AWS Metadata\")\n            return False\n        return True\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this model\n\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        if not self.ready():\n            return [\"needs_onboard\"]\n\n        # Call the base class health check\n        health_issues = super().health_check()\n\n        # Does this endpoint have a config?\n        # Note: This is not an authoritative check, so improve later\n        if self.endpoint_meta.get(\"ProductionVariants\") is None:\n            health_issues.append(\"no_config\")\n\n        # We're going to check for 5xx errors and no activity\n        endpoint_metrics = self.endpoint_metrics()\n\n        # Check if we have metrics\n        if endpoint_metrics is None:\n            health_issues.append(\"unknown_error\")\n            return health_issues\n\n        # Check for 5xx errors\n        num_errors = endpoint_metrics[\"Invocation5XXErrors\"].sum()\n        if num_errors &gt; 5:\n            health_issues.append(\"5xx_errors\")\n        elif num_errors &gt; 0:\n            health_issues.append(\"5xx_errors_min\")\n        else:\n            self.remove_health_tag(\"5xx_errors\")\n            self.remove_health_tag(\"5xx_errors_min\")\n\n        # Check for Endpoint activity\n        num_invocations = endpoint_metrics[\"Invocations\"].sum()\n        if num_invocations == 0:\n            health_issues.append(\"no_activity\")\n        else:\n            self.remove_health_tag(\"no_activity\")\n        return health_issues\n\n    def is_serverless(self) -&gt; bool:\n        \"\"\"Check if the current endpoint is serverless.\n\n        Returns:\n            bool: True if the endpoint is serverless, False otherwise.\n        \"\"\"\n        return \"Serverless\" in self.endpoint_meta[\"InstanceType\"]\n\n    def add_data_capture(self):\n        \"\"\"Add data capture to the endpoint\"\"\"\n        self.get_monitor().add_data_capture()\n\n    def get_monitor(self):\n        \"\"\"Get the MonitorCore class for this endpoint\"\"\"\n        from workbench.core.artifacts.monitor_core import MonitorCore\n\n        return MonitorCore(self.endpoint_name)\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this data in MegaBytes\"\"\"\n        return 0.0\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n        return self.endpoint_meta\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        return self.endpoint_meta[\"EndpointArn\"]\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n        return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        return self.endpoint_meta[\"CreationTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        return self.endpoint_meta[\"LastModifiedTime\"]\n\n    def hash(self) -&gt; Optional[str]:\n        \"\"\"Return the hash for the internal model used by this endpoint\n\n        Returns:\n            Optional[str]: The hash for the internal model used by this endpoint\n        \"\"\"\n        from workbench.utils.endpoint_utils import get_model_data_url  # Avoid circular import\n\n        model_url = get_model_data_url(self.endpoint_config_name(), self.boto3_session)\n        return compute_s3_object_hash(model_url, self.boto3_session)\n\n    def endpoint_metrics(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Return the metrics for this endpoint\n\n        Returns:\n            pd.DataFrame: DataFrame with the metrics for this endpoint (or None if no metrics)\n        \"\"\"\n\n        # Do we have it cached?\n        metrics_key = f\"endpoint:{self.uuid}:endpoint_metrics\"\n        endpoint_metrics = self.temp_storage.get(metrics_key)\n        if endpoint_metrics is not None:\n            return endpoint_metrics\n\n        # We don't have it cached so let's get it from CloudWatch\n        if \"ProductionVariants\" not in self.endpoint_meta:\n            return None\n        self.log.important(\"Updating endpoint metrics...\")\n        variant = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n        endpoint_metrics = EndpointMetrics().get_metrics(self.uuid, variant=variant)\n        self.temp_storage.set(metrics_key, endpoint_metrics)\n        return endpoint_metrics\n\n    def details(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Additional Details about this Endpoint\n        Args:\n            recompute (bool): Recompute the details (default: False)\n        Returns:\n            dict(dict): A dictionary of details about this Endpoint\n        \"\"\"\n\n        # Fill in all the details about this Endpoint\n        details = self.summary()\n\n        # Get details from our AWS Metadata\n        details[\"status\"] = self.endpoint_meta[\"EndpointStatus\"]\n        details[\"instance\"] = self.endpoint_meta[\"InstanceType\"]\n        try:\n            details[\"instance_count\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"CurrentInstanceCount\"] or \"-\"\n        except KeyError:\n            details[\"instance_count\"] = \"-\"\n        if \"ProductionVariants\" in self.endpoint_meta:\n            details[\"variant\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n        else:\n            details[\"variant\"] = \"-\"\n\n        # Add endpoint metrics from CloudWatch\n        details[\"endpoint_metrics\"] = self.endpoint_metrics()\n\n        # Return the details\n        return details\n\n    def onboard(self, interactive: bool = False) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will onboard the Endpoint (make it ready)\n        Args:\n            interactive (bool, optional): If True, will prompt the user for information. (default: False)\n        Returns:\n            bool: True if the Endpoint is successfully onboarded, False otherwise\n        \"\"\"\n\n        # Make sure our input is defined\n        if self.get_input() == \"unknown\":\n            if interactive:\n                input_model = input(\"Input Model?: \")\n            else:\n                self.log.critical(\"Input Model is not defined!\")\n                return False\n        else:\n            input_model = self.get_input()\n\n        # Now that we have the details, let's onboard the Endpoint with args\n        return self.onboard_with_args(input_model)\n\n    def onboard_with_args(self, input_model: str) -&gt; bool:\n        \"\"\"Onboard the Endpoint with the given arguments\n\n        Args:\n            input_model (str): The input model for this endpoint\n        Returns:\n            bool: True if the Endpoint is successfully onboarded, False otherwise\n        \"\"\"\n        # Set the status to onboarding\n        self.set_status(\"onboarding\")\n\n        self.upsert_workbench_meta({\"workbench_input\": input_model})\n        self.model_name = input_model\n\n        # Remove the needs_onboard tag\n        self.remove_health_tag(\"needs_onboard\")\n        self.set_status(\"ready\")\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        return True\n\n    def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the endpoint using FeatureSet data\n\n        Args:\n            capture (bool, optional): Capture the inference results and metrics (default=False)\n        \"\"\"\n\n        # Sanity Check that we have a model\n        model = ModelCore(self.get_input())\n        if not model.exists():\n            self.log.error(\"No model found for this endpoint. Returning empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Now get the FeatureSet and make sure it exists\n        fs = FeatureSetCore(model.get_input())\n        if not fs.exists():\n            self.log.error(\"No FeatureSet found for this endpoint. Returning empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Grab the evaluation data from the FeatureSet\n        table = fs.view(\"training\").table\n        eval_df = fs.query(f'SELECT * FROM \"{table}\" where training = FALSE')\n        capture_uuid = \"auto_inference\" if capture else None\n        return self.inference(eval_df, capture_uuid, id_column=fs.id_column)\n\n    def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n        \"\"\"Run inference and compute performance metrics with optional capture\n\n        Args:\n            eval_df (pd.DataFrame): DataFrame to run predictions on (must have superset of features)\n            capture_uuid (str, optional): UUID of the inference capture (default=None)\n            id_column (str, optional): Name of the ID column (default=None)\n\n        Returns:\n            pd.DataFrame: DataFrame with the inference results\n\n        Note:\n            If capture=True inference/performance metrics are written to S3 Endpoint Inference Folder\n        \"\"\"\n\n        # Run predictions on the evaluation data\n        prediction_df = self._predict(eval_df)\n        if prediction_df.empty:\n            self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n            return prediction_df\n\n        # Get the target column\n        model = ModelCore(self.model_name)\n        target_column = model.target()\n\n        # Sanity Check that the target column is present\n        if target_column and (target_column not in prediction_df.columns):\n            self.log.important(f\"Target Column {target_column} not found in prediction_df!\")\n            self.log.important(\"In order to compute metrics, the target column must be present!\")\n            return prediction_df\n\n        # Compute the standard performance metrics for this model\n        model_type = model.model_type\n        if model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n            prediction_df = self.residuals(target_column, prediction_df)\n            metrics = self.regression_metrics(target_column, prediction_df)\n        elif model_type == ModelType.CLASSIFIER:\n            metrics = self.classification_metrics(target_column, prediction_df)\n        else:\n            # For other model types, we don't compute metrics\n            self.log.important(f\"Model Type: {model_type} doesn't have metrics...\")\n            metrics = pd.DataFrame()\n\n        # Print out the metrics\n        if not metrics.empty:\n            print(f\"Performance Metrics for {self.model_name} on {self.uuid}\")\n            print(metrics.head())\n\n            # Capture the inference results and metrics\n            if capture_uuid is not None:\n                description = capture_uuid.replace(\"_\", \" \").title()\n                self._capture_inference_results(\n                    capture_uuid, prediction_df, target_column, model_type, metrics, description, id_column\n                )\n\n        # Return the prediction DataFrame\n        return prediction_df\n\n    def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n        Args:\n            eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n        Returns:\n            pd.DataFrame: The DataFrame with predictions\n\n        Note:\n            There's no sanity checks or error handling... just FAST Inference!\n        \"\"\"\n        return fast_inference(self.uuid, eval_df, self.sm_session)\n\n    def _predict(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Internal: Run prediction on the given observations in the given DataFrame\n        Args:\n            eval_df (pd.DataFrame): DataFrame to run predictions on (must have superset of features)\n        Returns:\n            pd.DataFrame: Return the DataFrame with additional columns, prediction and any _proba columns\n        \"\"\"\n\n        # Sanity check: Does the DataFrame have 0 rows?\n        if eval_df.empty:\n            self.log.warning(\"Evaluation DataFrame has 0 rows. No predictions to run.\")\n            return pd.DataFrame(columns=eval_df.columns)  # Return empty DataFrame with same structure\n\n        # Sanity check: Does the Model have Features?\n        features = ModelCore(self.model_name).features()\n        if not features:\n            self.log.warning(\"Model does not have features defined, using all columns in the DataFrame\")\n        else:\n            # Sanity check: Does the DataFrame have the required features?\n            df_columns_lower = set(col.lower() for col in eval_df.columns)\n            features_lower = set(feature.lower() for feature in features)\n\n            # Check if the features are a subset of the DataFrame columns (case-insensitive)\n            if not features_lower.issubset(df_columns_lower):\n                missing_features = features_lower - df_columns_lower\n                raise ValueError(f\"DataFrame does not contain required features: {missing_features}\")\n\n        # Create our Endpoint Predictor Class\n        predictor = Predictor(\n            self.endpoint_name,\n            sagemaker_session=self.sm_session,\n            serializer=CSVSerializer(),\n            deserializer=CSVDeserializer(),\n        )\n\n        # Now split up the dataframe into 500 row chunks, send those chunks to our\n        # endpoint (with error handling) and stitch all the chunks back together\n        df_list = []\n        for index in range(0, len(eval_df), 500):\n            self.log.info(\"Processing...\")\n\n            # Compute partial DataFrames, add them to a list, and concatenate at the end\n            partial_df = self._endpoint_error_handling(predictor, eval_df[index : index + 500])\n            df_list.append(partial_df)\n\n        # Concatenate the dataframes\n        combined_df = pd.concat(df_list, ignore_index=True)\n\n        # Convert data to numeric\n        # Note: Since we're using CSV serializers numeric columns often get changed to generic 'object' types\n\n        # Hard Conversion\n        # Note: We explicitly catch exceptions for columns that cannot be converted to numeric\n        converted_df = combined_df.copy()\n        for column in combined_df.columns:\n            try:\n                converted_df[column] = pd.to_numeric(combined_df[column])\n            except ValueError:\n                # If a ValueError is raised, the column cannot be converted to numeric, so we keep it as is\n                pass\n\n        # Soft Conversion\n        # Convert columns to the best possible dtype that supports the pd.NA missing value.\n        converted_df = converted_df.convert_dtypes()\n\n        # Report on any rows that failed\n        failed_rows = converted_df[converted_df[\"prediction\"].isna()]\n        if not failed_rows.empty:\n            self.log.warning(f\"Rows that failed:\\n{failed_rows}\")\n\n        # Convert pd.NA placeholders to pd.NA\n        # Note: CSV serialization converts pd.NA to blank strings, so we have to put in placeholders\n        converted_df.replace(\"__NA__\", pd.NA, inplace=True)\n\n        # Return the Dataframe\n        return converted_df\n\n    def _endpoint_error_handling(self, predictor, feature_df):\n        \"\"\"Internal: Handles errors, retries, and binary search for problematic rows.\"\"\"\n\n        # Convert DataFrame into a CSV buffer\n        csv_buffer = StringIO()\n        feature_df.to_csv(csv_buffer, index=False)\n\n        try:\n            # Send CSV buffer to the predictor and process results\n            results = predictor.predict(csv_buffer.getvalue())\n            results_df = pd.DataFrame.from_records(results[1:], columns=results[0])\n            self.endpoint_return_columns = results_df.columns.tolist()\n            return results_df\n\n        except botocore.exceptions.ClientError as err:\n            error_code = err.response[\"Error\"][\"Code\"]\n\n            if error_code == \"ModelNotReadyException\":\n                self.log.error(f\"Error {error_code}: {err.response.get('Message', 'No message')}\")\n                self.log.error(\"Model not ready. Sleeping and retrying...\")\n                time.sleep(60)\n                return self._endpoint_error_handling(predictor, feature_df)\n\n            elif error_code == \"ModelError\":\n                self.log.warning(\"Model error. Bisecting the DataFrame and retrying...\")\n\n                # Base case: If there is only one row, we can't binary search further\n                if len(feature_df) == 1:\n                    if not self.endpoint_return_columns:\n                        raise\n                    return self._error_df(feature_df, self.endpoint_return_columns)\n\n                # Binary search to find the problematic row(s)\n                mid_point = len(feature_df) // 2\n                first_half = self._endpoint_error_handling(predictor, feature_df.iloc[:mid_point])\n                second_half = self._endpoint_error_handling(predictor, feature_df.iloc[mid_point:])\n                return pd.concat([first_half, second_half], ignore_index=True)\n\n            else:\n                # Unknown ClientError, raise the exception\n                self.log.critical(f\"Unexpected ClientError: {err}\")\n                raise\n\n        except Exception as err:\n            self.log.critical(f\"Unexpected general error: {err}\")\n            raise\n\n    def _error_df(self, df, all_columns):\n        \"\"\"Internal: Method to construct an Error DataFrame (a Pandas DataFrame with one row of NaNs)\"\"\"\n        # Create a new dataframe with all NaNs\n        error_df = pd.DataFrame(dict(zip(all_columns, [[np.NaN]] * len(self.endpoint_return_columns))))\n        # Now set the original values for the incoming dataframe\n        for column in df.columns:\n            error_df[column] = df[column].values\n        return error_df\n\n    def _capture_inference_results(\n        self,\n        capture_uuid: str,\n        pred_results_df: pd.DataFrame,\n        target_column: str,\n        model_type: ModelType,\n        metrics: pd.DataFrame,\n        description: str,\n        id_column: str = None,\n    ):\n        \"\"\"Internal: Capture the inference results and metrics to S3\n\n        Args:\n            capture_uuid (str): UUID of the inference capture\n            pred_results_df (pd.DataFrame): DataFrame with the prediction results\n            target_column (str): Name of the target column\n            model_type (ModelType): Type of the model (e.g. REGRESSOR, CLASSIFIER)\n            metrics (pd.DataFrame): DataFrame with the performance metrics\n            description (str): Description of the inference results\n            id_column (str, optional): Name of the ID column (default=None)\n        \"\"\"\n\n        # Compute a dataframe hash (just use the last 8)\n        data_hash = joblib.hash(pred_results_df)[:8]\n\n        # Metadata for the model inference\n        inference_meta = {\n            \"name\": capture_uuid,\n            \"data_hash\": data_hash,\n            \"num_rows\": len(pred_results_df),\n            \"description\": description,\n        }\n\n        # Create the S3 Path for the Inference Capture\n        inference_capture_path = f\"{self.endpoint_inference_path}/{capture_uuid}\"\n\n        # Write the metadata dictionary and metrics to our S3 Model Inference Folder\n        wr.s3.to_json(\n            pd.DataFrame([inference_meta]),\n            f\"{inference_capture_path}/inference_meta.json\",\n            index=False,\n        )\n        self.log.info(f\"Writing metrics to {inference_capture_path}/inference_metrics.csv\")\n        wr.s3.to_csv(metrics, f\"{inference_capture_path}/inference_metrics.csv\", index=False)\n\n        # Grab the target column, prediction column, any _proba columns, and the ID column (if present)\n        prediction_col = \"prediction\" if \"prediction\" in pred_results_df.columns else \"predictions\"\n        output_columns = [target_column, prediction_col]\n\n        # Add any _proba columns to the output columns\n        output_columns += [col for col in pred_results_df.columns if col.endswith(\"_proba\")]\n\n        # Add any quantile columns to the output columns\n        output_columns += [col for col in pred_results_df.columns if col.startswith(\"q_\") or col.startswith(\"qr_\")]\n\n        # Add the ID column\n        if id_column and id_column in pred_results_df.columns:\n            output_columns.append(id_column)\n\n        # Write the predictions to our S3 Model Inference Folder\n        self.log.info(f\"Writing predictions to {inference_capture_path}/inference_predictions.csv\")\n        subset_df = pred_results_df[output_columns]\n        wr.s3.to_csv(subset_df, f\"{inference_capture_path}/inference_predictions.csv\", index=False)\n\n        # CLASSIFIER: Write the confusion matrix to our S3 Model Inference Folder\n        if model_type == ModelType.CLASSIFIER:\n            conf_mtx = self.generate_confusion_matrix(target_column, pred_results_df)\n            self.log.info(f\"Writing confusion matrix to {inference_capture_path}/inference_cm.csv\")\n            # Note: Unlike other dataframes here, we want to write the index (labels) to the CSV\n            wr.s3.to_csv(conf_mtx, f\"{inference_capture_path}/inference_cm.csv\", index=True)\n\n        # Generate SHAP values for our Prediction Dataframe\n        generate_shap_values(self.endpoint_name, model_type.value, pred_results_df, inference_capture_path)\n\n        # Now recompute the details for our Model\n        self.log.important(f\"Recomputing Details for {self.model_name} to show latest Inference Results...\")\n        model = ModelCore(self.model_name)\n        model._load_inference_metrics(capture_uuid)\n        model.details(recompute=True)\n\n        # Recompute the details so that inference model metrics are updated\n        self.log.important(f\"Recomputing Details for {self.uuid} to show latest Inference Results...\")\n        self.details(recompute=True)\n\n    def regression_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Compute the performance metrics for this Endpoint\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n        Returns:\n            pd.DataFrame: DataFrame with the performance metrics\n        \"\"\"\n\n        # Sanity Check the prediction DataFrame\n        if prediction_df.empty:\n            self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Compute the metrics\n        y_true = prediction_df[target_column]\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        y_pred = prediction_df[prediction_col]\n\n        mae = mean_absolute_error(y_true, y_pred)\n        rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n        r2 = r2_score(y_true, y_pred)\n        # Mean Absolute Percentage Error\n        mape = np.mean(np.where(y_true != 0, np.abs((y_true - y_pred) / y_true), np.abs(y_true - y_pred))) * 100\n        # Median Absolute Error\n        medae = median_absolute_error(y_true, y_pred)\n\n        # Organize and return the metrics\n        metrics = {\n            \"MAE\": round(mae, 3),\n            \"RMSE\": round(rmse, 3),\n            \"R2\": round(r2, 3),\n            \"MAPE\": round(mape, 3),\n            \"MedAE\": round(medae, 3),\n            \"NumRows\": len(prediction_df),\n        }\n        return pd.DataFrame.from_records([metrics])\n\n    def residuals(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Add the residuals to the prediction DataFrame\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n        Returns:\n            pd.DataFrame: DataFrame with two new columns called 'residuals' and 'residuals_abs'\n        \"\"\"\n\n        # Compute the residuals\n        y_true = prediction_df[target_column]\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        y_pred = prediction_df[prediction_col]\n\n        # Check for classification scenario\n        if not pd.api.types.is_numeric_dtype(y_true) or not pd.api.types.is_numeric_dtype(y_pred):\n            self.log.warning(\"Target and Prediction columns are not numeric. Computing 'diffs'...\")\n            prediction_df[\"residuals\"] = (y_true != y_pred).astype(int)\n            prediction_df[\"residuals_abs\"] = prediction_df[\"residuals\"]\n        else:\n            # Compute numeric residuals for regression\n            prediction_df[\"residuals\"] = y_true - y_pred\n            prediction_df[\"residuals_abs\"] = np.abs(prediction_df[\"residuals\"])\n\n        return prediction_df\n\n    @staticmethod\n    def validate_proba_columns(prediction_df: pd.DataFrame, class_labels: list, guessing: bool = False):\n        \"\"\"Ensure probability columns are correctly aligned with class labels\n\n        Args:\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n            class_labels (list): List of class labels\n            guessing (bool, optional): Whether we're guessing the class labels. Defaults to False.\n        \"\"\"\n        proba_columns = [col.replace(\"_proba\", \"\") for col in prediction_df.columns if col.endswith(\"_proba\")]\n\n        if sorted(class_labels) != sorted(proba_columns):\n            if guessing:\n                raise ValueError(f\"_proba columns {proba_columns} != GUESSED class_labels {class_labels}!\")\n            else:\n                raise ValueError(f\"_proba columns {proba_columns} != class_labels {class_labels}!\")\n\n    def classification_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Compute the performance metrics for this Endpoint\n\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n\n        Returns:\n            pd.DataFrame: DataFrame with the performance metrics\n        \"\"\"\n        # Get the class labels from the model\n        class_labels = ModelCore(self.model_name).class_labels()\n        if class_labels is None:\n            self.log.warning(\n                \"Class labels not found in the model. Guessing class labels from the prediction DataFrame.\"\n            )\n            class_labels = prediction_df[target_column].unique().tolist()\n            self.validate_proba_columns(prediction_df, class_labels, guessing=True)\n        else:\n            self.validate_proba_columns(prediction_df, class_labels)\n\n        # Calculate precision, recall, fscore, and support, handling zero division\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        scores = precision_recall_fscore_support(\n            prediction_df[target_column],\n            prediction_df[prediction_col],\n            average=None,\n            labels=class_labels,\n            zero_division=0,\n        )\n\n        # Identify the probability columns and keep them as a Pandas DataFrame\n        proba_columns = [f\"{label}_proba\" for label in class_labels]\n        y_score = prediction_df[proba_columns]\n\n        # One-hot encode the true labels using all class labels (fit with class_labels)\n        encoder = OneHotEncoder(categories=[class_labels], sparse_output=False)\n        y_true = encoder.fit_transform(prediction_df[[target_column]])\n\n        # Calculate ROC AUC per label and handle exceptions for missing classes\n        roc_auc_per_label = []\n        for i, label in enumerate(class_labels):\n            try:\n                roc_auc = roc_auc_score(y_true[:, i], y_score.iloc[:, i])\n            except ValueError as e:\n                self.log.warning(f\"ROC AUC calculation failed for label {label}.\")\n                self.log.warning(f\"{str(e)}\")\n                roc_auc = 0.0\n            roc_auc_per_label.append(roc_auc)\n\n        # Put the scores into a DataFrame\n        score_df = pd.DataFrame(\n            {\n                target_column: class_labels,\n                \"precision\": scores[0],\n                \"recall\": scores[1],\n                \"fscore\": scores[2],\n                \"roc_auc\": roc_auc_per_label,\n                \"support\": scores[3],\n            }\n        )\n\n        # Sort the target labels\n        score_df = score_df.sort_values(by=[target_column], ascending=True)\n        return score_df\n\n    def generate_confusion_matrix(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Compute the confusion matrix for this Endpoint\n        Args:\n            target_column (str): Name of the target column\n            prediction_df (pd.DataFrame): DataFrame with the prediction results\n        Returns:\n            pd.DataFrame: DataFrame with the confusion matrix\n        \"\"\"\n\n        y_true = prediction_df[target_column]\n        prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n        y_pred = prediction_df[prediction_col]\n\n        # Check if our model has class labels, if not we'll use the unique labels in the prediction\n        class_labels = ModelCore(self.model_name).class_labels()\n        if class_labels is None:\n            class_labels = sorted(list(set(y_true) | set(y_pred)))\n\n        # Compute the confusion matrix (sklearn confusion_matrix)\n        conf_mtx = confusion_matrix(y_true, y_pred, labels=class_labels)\n\n        # Create a DataFrame\n        conf_mtx_df = pd.DataFrame(conf_mtx, index=class_labels, columns=class_labels)\n        conf_mtx_df.index.name = \"labels\"\n\n        # Check if our model has class labels. If so make the index and columns ordered\n        model_class_labels = ModelCore(self.model_name).class_labels()\n        if model_class_labels:\n            self.log.important(\"Reordering the confusion matrix based on model class labels...\")\n            conf_mtx_df.index = pd.Categorical(conf_mtx_df.index, categories=model_class_labels, ordered=True)\n            conf_mtx_df.columns = pd.Categorical(conf_mtx_df.columns, categories=model_class_labels, ordered=True)\n            conf_mtx_df = conf_mtx_df.sort_index().sort_index(axis=1)\n        return conf_mtx_df\n\n    def endpoint_config_name(self) -&gt; str:\n        # Grab the Endpoint Config Name from the AWS\n        details = self.sm_client.describe_endpoint(EndpointName=self.endpoint_name)\n        return details[\"EndpointConfigName\"]\n\n    def set_input(self, input: str, force=False):\n        \"\"\"Override: Set the input data for this artifact\n\n        Args:\n            input (str): Name of input for this artifact\n            force (bool, optional): Force the input to be set. Defaults to False.\n        Note:\n            We're going to not allow this to be used for Models\n        \"\"\"\n        if not force:\n            self.log.warning(f\"Endpoint {self.uuid}: Does not allow manual override of the input!\")\n            return\n\n        # Okay we're going to allow this to be set\n        self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n        self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n        self.upsert_workbench_meta({\"workbench_input\": input})\n\n    def delete(self):\n        \"\"\" \"Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint\"\"\"\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the FeatureSet\n        EndpointCore.managed_delete(endpoint_name=self.uuid)\n\n    @classmethod\n    def managed_delete(cls, endpoint_name: str):\n        \"\"\"Delete the Endpoint and associated resources if it exists\"\"\"\n\n        # Check if the endpoint exists\n        try:\n            endpoint_info = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n                cls.log.info(f\"Endpoint {endpoint_name} not found!\")\n                return\n            raise  # Re-raise unexpected errors\n\n        # Delete underlying models (Endpoints store/use models internally)\n        cls.delete_endpoint_models(endpoint_name)\n\n        # Get Endpoint Config Name and delete if exists\n        endpoint_config_name = endpoint_info[\"EndpointConfigName\"]\n        try:\n            cls.log.info(f\"Deleting Endpoint Config {endpoint_config_name}...\")\n            cls.sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n        except ClientError:\n            cls.log.info(f\"Endpoint Config {endpoint_config_name} not found...\")\n\n        # Delete any monitoring schedules associated with the endpoint\n        monitoring_schedules = cls.sm_client.list_monitoring_schedules(EndpointName=endpoint_name)[\n            \"MonitoringScheduleSummaries\"\n        ]\n        for schedule in monitoring_schedules:\n            cls.log.info(f\"Deleting Monitoring Schedule {schedule['MonitoringScheduleName']}...\")\n            cls.sm_client.delete_monitoring_schedule(MonitoringScheduleName=schedule[\"MonitoringScheduleName\"])\n\n        # Delete related S3 artifacts (inference, data capture, monitoring)\n        endpoint_inference_path = cls.endpoints_s3_path + \"/inference/\" + endpoint_name\n        endpoint_data_capture_path = cls.endpoints_s3_path + \"/data_capture/\" + endpoint_name\n        endpoint_monitoring_path = cls.endpoints_s3_path + \"/monitoring/\" + endpoint_name\n        for s3_path in [endpoint_inference_path, endpoint_data_capture_path, endpoint_monitoring_path]:\n            s3_path = f\"{s3_path.rstrip('/')}/\"\n            objects = wr.s3.list_objects(s3_path, boto3_session=cls.boto3_session)\n            if objects:\n                cls.log.info(f\"Deleting S3 Objects at {s3_path}...\")\n                wr.s3.delete_objects(objects, boto3_session=cls.boto3_session)\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(endpoint_name)\n\n        # Delete the endpoint\n        time.sleep(2)  # Allow AWS to catch up\n        try:\n            cls.log.info(f\"Deleting Endpoint {endpoint_name}...\")\n            cls.sm_client.delete_endpoint(EndpointName=endpoint_name)\n        except ClientError as e:\n            cls.log.error(\"Error deleting endpoint.\")\n            raise e\n\n        time.sleep(5)  # Final sleep for AWS to fully register deletions\n\n    @classmethod\n    def delete_endpoint_models(cls, endpoint_name: str):\n        \"\"\"Delete the underlying Model for an Endpoint\n\n        Args:\n            endpoint_name (str): The name of the endpoint to delete\n        \"\"\"\n\n        # Grab the Endpoint Config Name from AWS\n        endpoint_config_name = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)[\"EndpointConfigName\"]\n\n        # Retrieve the Model Names from the Endpoint Config\n        try:\n            endpoint_config = cls.sm_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n        except botocore.exceptions.ClientError:\n            cls.log.info(f\"Endpoint Config {endpoint_config_name} doesn't exist...\")\n            return\n        model_names = [variant[\"ModelName\"] for variant in endpoint_config[\"ProductionVariants\"]]\n        for model_name in model_names:\n            cls.log.info(f\"Deleting Internal Model {model_name}...\")\n            try:\n                cls.sm_client.delete_model(ModelName=model_name)\n            except botocore.exceptions.ClientError as error:\n                error_code = error.response[\"Error\"][\"Code\"]\n                error_message = error.response[\"Error\"][\"Message\"]\n                if error_code == \"ResourceInUse\":\n                    cls.log.warning(f\"Model {model_name} is still in use...\")\n                else:\n                    cls.log.warning(f\"Error: {error_code} - {error_message}\")\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.__init__","title":"<code>__init__(endpoint_uuid, **kwargs)</code>","text":"<p>EndpointCore Initialization</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_uuid</code> <code>str</code> <p>Name of Endpoint in Workbench</p> required Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def __init__(self, endpoint_uuid, **kwargs):\n    \"\"\"EndpointCore Initialization\n\n    Args:\n        endpoint_uuid (str): Name of Endpoint in Workbench\n    \"\"\"\n\n    # Make sure the endpoint_uuid is a valid name\n    self.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call SuperClass Initialization\n    super().__init__(endpoint_uuid, **kwargs)\n\n    # Grab an Cloud Metadata object and pull information for Endpoints\n    self.endpoint_name = endpoint_uuid\n    self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n\n    # Sanity check that we found the endpoint\n    if self.endpoint_meta is None:\n        self.log.important(f\"Could not find endpoint {self.uuid} within current visibility scope\")\n        return\n\n    # Sanity check the Endpoint state\n    if self.endpoint_meta[\"EndpointStatus\"] == \"Failed\":\n        self.log.critical(f\"Endpoint {self.uuid} is in a failed state\")\n        reason = self.endpoint_meta[\"FailureReason\"]\n        self.log.critical(f\"Failure Reason: {reason}\")\n        self.log.critical(\"Please delete this endpoint and re-deploy...\")\n\n    # Set the Inference, Capture, and Monitoring S3 Paths\n    self.endpoint_inference_path = self.endpoints_s3_path + \"/inference/\" + self.uuid\n    self.endpoint_data_capture_path = self.endpoints_s3_path + \"/data_capture/\" + self.uuid\n    self.endpoint_monitoring_path = self.endpoints_s3_path + \"/monitoring/\" + self.uuid\n\n    # Set the Model Name\n    self.model_name = self.get_input()\n\n    # This is for endpoint error handling later\n    self.endpoint_return_columns = None\n\n    # We temporary cache the endpoint metrics\n    self.temp_storage = Cache(prefix=\"temp_storage\", expire=300)  # 5 minutes\n\n    # Call SuperClass Post Initialization\n    super().__post_init__()\n\n    # All done\n    self.log.info(f\"EndpointCore Initialized: {self.endpoint_name}\")\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.add_data_capture","title":"<code>add_data_capture()</code>","text":"<p>Add data capture to the endpoint</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def add_data_capture(self):\n    \"\"\"Add data capture to the endpoint\"\"\"\n    self.get_monitor().add_data_capture()\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    return self.endpoint_meta[\"EndpointArn\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.auto_inference","title":"<code>auto_inference(capture=False)</code>","text":"<p>Run inference on the endpoint using FeatureSet data</p> <p>Parameters:</p> Name Type Description Default <code>capture</code> <code>bool</code> <p>Capture the inference results and metrics (default=False)</p> <code>False</code> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def auto_inference(self, capture: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the endpoint using FeatureSet data\n\n    Args:\n        capture (bool, optional): Capture the inference results and metrics (default=False)\n    \"\"\"\n\n    # Sanity Check that we have a model\n    model = ModelCore(self.get_input())\n    if not model.exists():\n        self.log.error(\"No model found for this endpoint. Returning empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Now get the FeatureSet and make sure it exists\n    fs = FeatureSetCore(model.get_input())\n    if not fs.exists():\n        self.log.error(\"No FeatureSet found for this endpoint. Returning empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Grab the evaluation data from the FeatureSet\n    table = fs.view(\"training\").table\n    eval_df = fs.query(f'SELECT * FROM \"{table}\" where training = FALSE')\n    capture_uuid = \"auto_inference\" if capture else None\n    return self.inference(eval_df, capture_uuid, id_column=fs.id_column)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get ALL the AWS metadata for this artifact</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n    return self.endpoint_meta\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying this data source</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying this data source\"\"\"\n    return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.classification_metrics","title":"<code>classification_metrics(target_column, prediction_df)</code>","text":"<p>Compute the performance metrics for this Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>target_column</code> <code>str</code> <p>Name of the target column</p> required <code>prediction_df</code> <code>DataFrame</code> <p>DataFrame with the prediction results</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: DataFrame with the performance metrics</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def classification_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Compute the performance metrics for this Endpoint\n\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n\n    Returns:\n        pd.DataFrame: DataFrame with the performance metrics\n    \"\"\"\n    # Get the class labels from the model\n    class_labels = ModelCore(self.model_name).class_labels()\n    if class_labels is None:\n        self.log.warning(\n            \"Class labels not found in the model. Guessing class labels from the prediction DataFrame.\"\n        )\n        class_labels = prediction_df[target_column].unique().tolist()\n        self.validate_proba_columns(prediction_df, class_labels, guessing=True)\n    else:\n        self.validate_proba_columns(prediction_df, class_labels)\n\n    # Calculate precision, recall, fscore, and support, handling zero division\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    scores = precision_recall_fscore_support(\n        prediction_df[target_column],\n        prediction_df[prediction_col],\n        average=None,\n        labels=class_labels,\n        zero_division=0,\n    )\n\n    # Identify the probability columns and keep them as a Pandas DataFrame\n    proba_columns = [f\"{label}_proba\" for label in class_labels]\n    y_score = prediction_df[proba_columns]\n\n    # One-hot encode the true labels using all class labels (fit with class_labels)\n    encoder = OneHotEncoder(categories=[class_labels], sparse_output=False)\n    y_true = encoder.fit_transform(prediction_df[[target_column]])\n\n    # Calculate ROC AUC per label and handle exceptions for missing classes\n    roc_auc_per_label = []\n    for i, label in enumerate(class_labels):\n        try:\n            roc_auc = roc_auc_score(y_true[:, i], y_score.iloc[:, i])\n        except ValueError as e:\n            self.log.warning(f\"ROC AUC calculation failed for label {label}.\")\n            self.log.warning(f\"{str(e)}\")\n            roc_auc = 0.0\n        roc_auc_per_label.append(roc_auc)\n\n    # Put the scores into a DataFrame\n    score_df = pd.DataFrame(\n        {\n            target_column: class_labels,\n            \"precision\": scores[0],\n            \"recall\": scores[1],\n            \"fscore\": scores[2],\n            \"roc_auc\": roc_auc_per_label,\n            \"support\": scores[3],\n        }\n    )\n\n    # Sort the target labels\n    score_df = score_df.sort_values(by=[target_column], ascending=True)\n    return score_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    return self.endpoint_meta[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.delete","title":"<code>delete()</code>","text":"<p>\"Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def delete(self):\n    \"\"\" \"Delete an existing Endpoint: Underlying Models, Configuration, and Endpoint\"\"\"\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the FeatureSet\n    EndpointCore.managed_delete(endpoint_name=self.uuid)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.delete_endpoint_models","title":"<code>delete_endpoint_models(endpoint_name)</code>  <code>classmethod</code>","text":"<p>Delete the underlying Model for an Endpoint</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>The name of the endpoint to delete</p> required Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>@classmethod\ndef delete_endpoint_models(cls, endpoint_name: str):\n    \"\"\"Delete the underlying Model for an Endpoint\n\n    Args:\n        endpoint_name (str): The name of the endpoint to delete\n    \"\"\"\n\n    # Grab the Endpoint Config Name from AWS\n    endpoint_config_name = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)[\"EndpointConfigName\"]\n\n    # Retrieve the Model Names from the Endpoint Config\n    try:\n        endpoint_config = cls.sm_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n    except botocore.exceptions.ClientError:\n        cls.log.info(f\"Endpoint Config {endpoint_config_name} doesn't exist...\")\n        return\n    model_names = [variant[\"ModelName\"] for variant in endpoint_config[\"ProductionVariants\"]]\n    for model_name in model_names:\n        cls.log.info(f\"Deleting Internal Model {model_name}...\")\n        try:\n            cls.sm_client.delete_model(ModelName=model_name)\n        except botocore.exceptions.ClientError as error:\n            error_code = error.response[\"Error\"][\"Code\"]\n            error_message = error.response[\"Error\"][\"Message\"]\n            if error_code == \"ResourceInUse\":\n                cls.log.warning(f\"Model {model_name} is still in use...\")\n            else:\n                cls.log.warning(f\"Error: {error_code} - {error_message}\")\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this Endpoint Args:     recompute (bool): Recompute the details (default: False) Returns:     dict(dict): A dictionary of details about this Endpoint</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def details(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Additional Details about this Endpoint\n    Args:\n        recompute (bool): Recompute the details (default: False)\n    Returns:\n        dict(dict): A dictionary of details about this Endpoint\n    \"\"\"\n\n    # Fill in all the details about this Endpoint\n    details = self.summary()\n\n    # Get details from our AWS Metadata\n    details[\"status\"] = self.endpoint_meta[\"EndpointStatus\"]\n    details[\"instance\"] = self.endpoint_meta[\"InstanceType\"]\n    try:\n        details[\"instance_count\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"CurrentInstanceCount\"] or \"-\"\n    except KeyError:\n        details[\"instance_count\"] = \"-\"\n    if \"ProductionVariants\" in self.endpoint_meta:\n        details[\"variant\"] = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n    else:\n        details[\"variant\"] = \"-\"\n\n    # Add endpoint metrics from CloudWatch\n    details[\"endpoint_metrics\"] = self.endpoint_metrics()\n\n    # Return the details\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.endpoint_metrics","title":"<code>endpoint_metrics()</code>","text":"<p>Return the metrics for this endpoint</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame with the metrics for this endpoint (or None if no metrics)</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def endpoint_metrics(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Return the metrics for this endpoint\n\n    Returns:\n        pd.DataFrame: DataFrame with the metrics for this endpoint (or None if no metrics)\n    \"\"\"\n\n    # Do we have it cached?\n    metrics_key = f\"endpoint:{self.uuid}:endpoint_metrics\"\n    endpoint_metrics = self.temp_storage.get(metrics_key)\n    if endpoint_metrics is not None:\n        return endpoint_metrics\n\n    # We don't have it cached so let's get it from CloudWatch\n    if \"ProductionVariants\" not in self.endpoint_meta:\n        return None\n    self.log.important(\"Updating endpoint metrics...\")\n    variant = self.endpoint_meta[\"ProductionVariants\"][0][\"VariantName\"]\n    endpoint_metrics = EndpointMetrics().get_metrics(self.uuid, variant=variant)\n    self.temp_storage.set(metrics_key, endpoint_metrics)\n    return endpoint_metrics\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.exists","title":"<code>exists()</code>","text":"<p>Does the feature_set_name exist in the AWS Metadata?</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n    if self.endpoint_meta is None:\n        self.log.debug(f\"Endpoint {self.endpoint_name} not found in AWS Metadata\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.fast_inference","title":"<code>fast_inference(eval_df)</code>","text":"<p>Run inference on the Endpoint using the provided DataFrame</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>The DataFrame to run predictions on</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with predictions</p> Note <p>There's no sanity checks or error handling... just FAST Inference!</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def fast_inference(self, eval_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Run inference on the Endpoint using the provided DataFrame\n\n    Args:\n        eval_df (pd.DataFrame): The DataFrame to run predictions on\n\n    Returns:\n        pd.DataFrame: The DataFrame with predictions\n\n    Note:\n        There's no sanity checks or error handling... just FAST Inference!\n    \"\"\"\n    return fast_inference(self.uuid, eval_df, self.sm_session)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.generate_confusion_matrix","title":"<code>generate_confusion_matrix(target_column, prediction_df)</code>","text":"<p>Compute the confusion matrix for this Endpoint Args:     target_column (str): Name of the target column     prediction_df (pd.DataFrame): DataFrame with the prediction results Returns:     pd.DataFrame: DataFrame with the confusion matrix</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def generate_confusion_matrix(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Compute the confusion matrix for this Endpoint\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n    Returns:\n        pd.DataFrame: DataFrame with the confusion matrix\n    \"\"\"\n\n    y_true = prediction_df[target_column]\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    y_pred = prediction_df[prediction_col]\n\n    # Check if our model has class labels, if not we'll use the unique labels in the prediction\n    class_labels = ModelCore(self.model_name).class_labels()\n    if class_labels is None:\n        class_labels = sorted(list(set(y_true) | set(y_pred)))\n\n    # Compute the confusion matrix (sklearn confusion_matrix)\n    conf_mtx = confusion_matrix(y_true, y_pred, labels=class_labels)\n\n    # Create a DataFrame\n    conf_mtx_df = pd.DataFrame(conf_mtx, index=class_labels, columns=class_labels)\n    conf_mtx_df.index.name = \"labels\"\n\n    # Check if our model has class labels. If so make the index and columns ordered\n    model_class_labels = ModelCore(self.model_name).class_labels()\n    if model_class_labels:\n        self.log.important(\"Reordering the confusion matrix based on model class labels...\")\n        conf_mtx_df.index = pd.Categorical(conf_mtx_df.index, categories=model_class_labels, ordered=True)\n        conf_mtx_df.columns = pd.Categorical(conf_mtx_df.columns, categories=model_class_labels, ordered=True)\n        conf_mtx_df = conf_mtx_df.sort_index().sort_index(axis=1)\n    return conf_mtx_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.get_monitor","title":"<code>get_monitor()</code>","text":"<p>Get the MonitorCore class for this endpoint</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def get_monitor(self):\n    \"\"\"Get the MonitorCore class for this endpoint\"\"\"\n    from workbench.core.artifacts.monitor_core import MonitorCore\n\n    return MonitorCore(self.endpoint_name)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.hash","title":"<code>hash()</code>","text":"<p>Return the hash for the internal model used by this endpoint</p> <p>Returns:</p> Type Description <code>Optional[str]</code> <p>Optional[str]: The hash for the internal model used by this endpoint</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def hash(self) -&gt; Optional[str]:\n    \"\"\"Return the hash for the internal model used by this endpoint\n\n    Returns:\n        Optional[str]: The hash for the internal model used by this endpoint\n    \"\"\"\n    from workbench.utils.endpoint_utils import get_model_data_url  # Avoid circular import\n\n    model_url = get_model_data_url(self.endpoint_config_name(), self.boto3_session)\n    return compute_s3_object_hash(model_url, self.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of health issues</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this model\n\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    if not self.ready():\n        return [\"needs_onboard\"]\n\n    # Call the base class health check\n    health_issues = super().health_check()\n\n    # Does this endpoint have a config?\n    # Note: This is not an authoritative check, so improve later\n    if self.endpoint_meta.get(\"ProductionVariants\") is None:\n        health_issues.append(\"no_config\")\n\n    # We're going to check for 5xx errors and no activity\n    endpoint_metrics = self.endpoint_metrics()\n\n    # Check if we have metrics\n    if endpoint_metrics is None:\n        health_issues.append(\"unknown_error\")\n        return health_issues\n\n    # Check for 5xx errors\n    num_errors = endpoint_metrics[\"Invocation5XXErrors\"].sum()\n    if num_errors &gt; 5:\n        health_issues.append(\"5xx_errors\")\n    elif num_errors &gt; 0:\n        health_issues.append(\"5xx_errors_min\")\n    else:\n        self.remove_health_tag(\"5xx_errors\")\n        self.remove_health_tag(\"5xx_errors_min\")\n\n    # Check for Endpoint activity\n    num_invocations = endpoint_metrics[\"Invocations\"].sum()\n    if num_invocations == 0:\n        health_issues.append(\"no_activity\")\n    else:\n        self.remove_health_tag(\"no_activity\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.inference","title":"<code>inference(eval_df, capture_uuid=None, id_column=None)</code>","text":"<p>Run inference and compute performance metrics with optional capture</p> <p>Parameters:</p> Name Type Description Default <code>eval_df</code> <code>DataFrame</code> <p>DataFrame to run predictions on (must have superset of features)</p> required <code>capture_uuid</code> <code>str</code> <p>UUID of the inference capture (default=None)</p> <code>None</code> <code>id_column</code> <code>str</code> <p>Name of the ID column (default=None)</p> <code>None</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: DataFrame with the inference results</p> Note <p>If capture=True inference/performance metrics are written to S3 Endpoint Inference Folder</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def inference(self, eval_df: pd.DataFrame, capture_uuid: str = None, id_column: str = None) -&gt; pd.DataFrame:\n    \"\"\"Run inference and compute performance metrics with optional capture\n\n    Args:\n        eval_df (pd.DataFrame): DataFrame to run predictions on (must have superset of features)\n        capture_uuid (str, optional): UUID of the inference capture (default=None)\n        id_column (str, optional): Name of the ID column (default=None)\n\n    Returns:\n        pd.DataFrame: DataFrame with the inference results\n\n    Note:\n        If capture=True inference/performance metrics are written to S3 Endpoint Inference Folder\n    \"\"\"\n\n    # Run predictions on the evaluation data\n    prediction_df = self._predict(eval_df)\n    if prediction_df.empty:\n        self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n        return prediction_df\n\n    # Get the target column\n    model = ModelCore(self.model_name)\n    target_column = model.target()\n\n    # Sanity Check that the target column is present\n    if target_column and (target_column not in prediction_df.columns):\n        self.log.important(f\"Target Column {target_column} not found in prediction_df!\")\n        self.log.important(\"In order to compute metrics, the target column must be present!\")\n        return prediction_df\n\n    # Compute the standard performance metrics for this model\n    model_type = model.model_type\n    if model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n        prediction_df = self.residuals(target_column, prediction_df)\n        metrics = self.regression_metrics(target_column, prediction_df)\n    elif model_type == ModelType.CLASSIFIER:\n        metrics = self.classification_metrics(target_column, prediction_df)\n    else:\n        # For other model types, we don't compute metrics\n        self.log.important(f\"Model Type: {model_type} doesn't have metrics...\")\n        metrics = pd.DataFrame()\n\n    # Print out the metrics\n    if not metrics.empty:\n        print(f\"Performance Metrics for {self.model_name} on {self.uuid}\")\n        print(metrics.head())\n\n        # Capture the inference results and metrics\n        if capture_uuid is not None:\n            description = capture_uuid.replace(\"_\", \" \").title()\n            self._capture_inference_results(\n                capture_uuid, prediction_df, target_column, model_type, metrics, description, id_column\n            )\n\n    # Return the prediction DataFrame\n    return prediction_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.is_serverless","title":"<code>is_serverless()</code>","text":"<p>Check if the current endpoint is serverless.</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the endpoint is serverless, False otherwise.</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def is_serverless(self) -&gt; bool:\n    \"\"\"Check if the current endpoint is serverless.\n\n    Returns:\n        bool: True if the endpoint is serverless, False otherwise.\n    \"\"\"\n    return \"Serverless\" in self.endpoint_meta[\"InstanceType\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.managed_delete","title":"<code>managed_delete(endpoint_name)</code>  <code>classmethod</code>","text":"<p>Delete the Endpoint and associated resources if it exists</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>@classmethod\ndef managed_delete(cls, endpoint_name: str):\n    \"\"\"Delete the Endpoint and associated resources if it exists\"\"\"\n\n    # Check if the endpoint exists\n    try:\n        endpoint_info = cls.sm_client.describe_endpoint(EndpointName=endpoint_name)\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n            cls.log.info(f\"Endpoint {endpoint_name} not found!\")\n            return\n        raise  # Re-raise unexpected errors\n\n    # Delete underlying models (Endpoints store/use models internally)\n    cls.delete_endpoint_models(endpoint_name)\n\n    # Get Endpoint Config Name and delete if exists\n    endpoint_config_name = endpoint_info[\"EndpointConfigName\"]\n    try:\n        cls.log.info(f\"Deleting Endpoint Config {endpoint_config_name}...\")\n        cls.sm_client.delete_endpoint_config(EndpointConfigName=endpoint_config_name)\n    except ClientError:\n        cls.log.info(f\"Endpoint Config {endpoint_config_name} not found...\")\n\n    # Delete any monitoring schedules associated with the endpoint\n    monitoring_schedules = cls.sm_client.list_monitoring_schedules(EndpointName=endpoint_name)[\n        \"MonitoringScheduleSummaries\"\n    ]\n    for schedule in monitoring_schedules:\n        cls.log.info(f\"Deleting Monitoring Schedule {schedule['MonitoringScheduleName']}...\")\n        cls.sm_client.delete_monitoring_schedule(MonitoringScheduleName=schedule[\"MonitoringScheduleName\"])\n\n    # Delete related S3 artifacts (inference, data capture, monitoring)\n    endpoint_inference_path = cls.endpoints_s3_path + \"/inference/\" + endpoint_name\n    endpoint_data_capture_path = cls.endpoints_s3_path + \"/data_capture/\" + endpoint_name\n    endpoint_monitoring_path = cls.endpoints_s3_path + \"/monitoring/\" + endpoint_name\n    for s3_path in [endpoint_inference_path, endpoint_data_capture_path, endpoint_monitoring_path]:\n        s3_path = f\"{s3_path.rstrip('/')}/\"\n        objects = wr.s3.list_objects(s3_path, boto3_session=cls.boto3_session)\n        if objects:\n            cls.log.info(f\"Deleting S3 Objects at {s3_path}...\")\n            wr.s3.delete_objects(objects, boto3_session=cls.boto3_session)\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(endpoint_name)\n\n    # Delete the endpoint\n    time.sleep(2)  # Allow AWS to catch up\n    try:\n        cls.log.info(f\"Deleting Endpoint {endpoint_name}...\")\n        cls.sm_client.delete_endpoint(EndpointName=endpoint_name)\n    except ClientError as e:\n        cls.log.error(\"Error deleting endpoint.\")\n        raise e\n\n    time.sleep(5)  # Final sleep for AWS to fully register deletions\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    return self.endpoint_meta[\"LastModifiedTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.onboard","title":"<code>onboard(interactive=False)</code>","text":"<p>This is a BLOCKING method that will onboard the Endpoint (make it ready) Args:     interactive (bool, optional): If True, will prompt the user for information. (default: False) Returns:     bool: True if the Endpoint is successfully onboarded, False otherwise</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def onboard(self, interactive: bool = False) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will onboard the Endpoint (make it ready)\n    Args:\n        interactive (bool, optional): If True, will prompt the user for information. (default: False)\n    Returns:\n        bool: True if the Endpoint is successfully onboarded, False otherwise\n    \"\"\"\n\n    # Make sure our input is defined\n    if self.get_input() == \"unknown\":\n        if interactive:\n            input_model = input(\"Input Model?: \")\n        else:\n            self.log.critical(\"Input Model is not defined!\")\n            return False\n    else:\n        input_model = self.get_input()\n\n    # Now that we have the details, let's onboard the Endpoint with args\n    return self.onboard_with_args(input_model)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.onboard_with_args","title":"<code>onboard_with_args(input_model)</code>","text":"<p>Onboard the Endpoint with the given arguments</p> <p>Parameters:</p> Name Type Description Default <code>input_model</code> <code>str</code> <p>The input model for this endpoint</p> required <p>Returns:     bool: True if the Endpoint is successfully onboarded, False otherwise</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def onboard_with_args(self, input_model: str) -&gt; bool:\n    \"\"\"Onboard the Endpoint with the given arguments\n\n    Args:\n        input_model (str): The input model for this endpoint\n    Returns:\n        bool: True if the Endpoint is successfully onboarded, False otherwise\n    \"\"\"\n    # Set the status to onboarding\n    self.set_status(\"onboarding\")\n\n    self.upsert_workbench_meta({\"workbench_input\": input_model})\n    self.model_name = input_model\n\n    # Remove the needs_onboard tag\n    self.remove_health_tag(\"needs_onboard\")\n    self.set_status(\"ready\")\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Refresh the Artifact's metadata</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Refresh the Artifact's metadata\"\"\"\n    self.endpoint_meta = self.meta.endpoint(self.endpoint_name)\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.regression_metrics","title":"<code>regression_metrics(target_column, prediction_df)</code>","text":"<p>Compute the performance metrics for this Endpoint Args:     target_column (str): Name of the target column     prediction_df (pd.DataFrame): DataFrame with the prediction results Returns:     pd.DataFrame: DataFrame with the performance metrics</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def regression_metrics(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Compute the performance metrics for this Endpoint\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n    Returns:\n        pd.DataFrame: DataFrame with the performance metrics\n    \"\"\"\n\n    # Sanity Check the prediction DataFrame\n    if prediction_df.empty:\n        self.log.warning(\"No predictions were made. Returning empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Compute the metrics\n    y_true = prediction_df[target_column]\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    y_pred = prediction_df[prediction_col]\n\n    mae = mean_absolute_error(y_true, y_pred)\n    rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n    r2 = r2_score(y_true, y_pred)\n    # Mean Absolute Percentage Error\n    mape = np.mean(np.where(y_true != 0, np.abs((y_true - y_pred) / y_true), np.abs(y_true - y_pred))) * 100\n    # Median Absolute Error\n    medae = median_absolute_error(y_true, y_pred)\n\n    # Organize and return the metrics\n    metrics = {\n        \"MAE\": round(mae, 3),\n        \"RMSE\": round(rmse, 3),\n        \"R2\": round(r2, 3),\n        \"MAPE\": round(mape, 3),\n        \"MedAE\": round(medae, 3),\n        \"NumRows\": len(prediction_df),\n    }\n    return pd.DataFrame.from_records([metrics])\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.residuals","title":"<code>residuals(target_column, prediction_df)</code>","text":"<p>Add the residuals to the prediction DataFrame Args:     target_column (str): Name of the target column     prediction_df (pd.DataFrame): DataFrame with the prediction results Returns:     pd.DataFrame: DataFrame with two new columns called 'residuals' and 'residuals_abs'</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def residuals(self, target_column: str, prediction_df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Add the residuals to the prediction DataFrame\n    Args:\n        target_column (str): Name of the target column\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n    Returns:\n        pd.DataFrame: DataFrame with two new columns called 'residuals' and 'residuals_abs'\n    \"\"\"\n\n    # Compute the residuals\n    y_true = prediction_df[target_column]\n    prediction_col = \"prediction\" if \"prediction\" in prediction_df.columns else \"predictions\"\n    y_pred = prediction_df[prediction_col]\n\n    # Check for classification scenario\n    if not pd.api.types.is_numeric_dtype(y_true) or not pd.api.types.is_numeric_dtype(y_pred):\n        self.log.warning(\"Target and Prediction columns are not numeric. Computing 'diffs'...\")\n        prediction_df[\"residuals\"] = (y_true != y_pred).astype(int)\n        prediction_df[\"residuals_abs\"] = prediction_df[\"residuals\"]\n    else:\n        # Compute numeric residuals for regression\n        prediction_df[\"residuals\"] = y_true - y_pred\n        prediction_df[\"residuals_abs\"] = np.abs(prediction_df[\"residuals\"])\n\n    return prediction_df\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.set_input","title":"<code>set_input(input, force=False)</code>","text":"<p>Override: Set the input data for this artifact</p> <p>Parameters:</p> Name Type Description Default <code>input</code> <code>str</code> <p>Name of input for this artifact</p> required <code>force</code> <code>bool</code> <p>Force the input to be set. Defaults to False.</p> <code>False</code> <p>Note:     We're going to not allow this to be used for Models</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def set_input(self, input: str, force=False):\n    \"\"\"Override: Set the input data for this artifact\n\n    Args:\n        input (str): Name of input for this artifact\n        force (bool, optional): Force the input to be set. Defaults to False.\n    Note:\n        We're going to not allow this to be used for Models\n    \"\"\"\n    if not force:\n        self.log.warning(f\"Endpoint {self.uuid}: Does not allow manual override of the input!\")\n        return\n\n    # Okay we're going to allow this to be set\n    self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n    self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n    self.upsert_workbench_meta({\"workbench_input\": input})\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.size","title":"<code>size()</code>","text":"<p>Return the size of this data in MegaBytes</p> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of this data in MegaBytes\"\"\"\n    return 0.0\n</code></pre>"},{"location":"core_classes/artifacts/endpoint_core/#workbench.core.artifacts.endpoint_core.EndpointCore.validate_proba_columns","title":"<code>validate_proba_columns(prediction_df, class_labels, guessing=False)</code>  <code>staticmethod</code>","text":"<p>Ensure probability columns are correctly aligned with class labels</p> <p>Parameters:</p> Name Type Description Default <code>prediction_df</code> <code>DataFrame</code> <p>DataFrame with the prediction results</p> required <code>class_labels</code> <code>list</code> <p>List of class labels</p> required <code>guessing</code> <code>bool</code> <p>Whether we're guessing the class labels. Defaults to False.</p> <code>False</code> Source code in <code>src/workbench/core/artifacts/endpoint_core.py</code> <pre><code>@staticmethod\ndef validate_proba_columns(prediction_df: pd.DataFrame, class_labels: list, guessing: bool = False):\n    \"\"\"Ensure probability columns are correctly aligned with class labels\n\n    Args:\n        prediction_df (pd.DataFrame): DataFrame with the prediction results\n        class_labels (list): List of class labels\n        guessing (bool, optional): Whether we're guessing the class labels. Defaults to False.\n    \"\"\"\n    proba_columns = [col.replace(\"_proba\", \"\") for col in prediction_df.columns if col.endswith(\"_proba\")]\n\n    if sorted(class_labels) != sorted(proba_columns):\n        if guessing:\n            raise ValueError(f\"_proba columns {proba_columns} != GUESSED class_labels {class_labels}!\")\n        else:\n            raise ValueError(f\"_proba columns {proba_columns} != class_labels {class_labels}!\")\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/","title":"FeatureSetCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the FeatureSet API Class and voil\u00e0 it works the same.</p> <p>FeatureSet: Workbench Feature Set accessible through Athena</p>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore","title":"<code>FeatureSetCore</code>","text":"<p>               Bases: <code>Artifact</code></p> <p>FeatureSetCore: Workbench FeatureSetCore Class</p> Common Usage <pre><code>my_features = FeatureSetCore(feature_uuid)\nmy_features.summary()\nmy_features.details()\n</code></pre> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>class FeatureSetCore(Artifact):\n    \"\"\"FeatureSetCore: Workbench FeatureSetCore Class\n\n    Common Usage:\n        ```python\n        my_features = FeatureSetCore(feature_uuid)\n        my_features.summary()\n        my_features.details()\n        ```\n    \"\"\"\n\n    def __init__(self, feature_set_uuid: str, **kwargs):\n        \"\"\"FeatureSetCore Initialization\n\n        Args:\n            feature_set_uuid (str): Name of Feature Set\n        \"\"\"\n\n        # Make sure the feature_set name is valid\n        self.is_name_valid(feature_set_uuid)\n\n        # Call superclass init\n        super().__init__(feature_set_uuid, **kwargs)\n\n        # Get our FeatureSet metadata\n        self.feature_meta = self.meta.feature_set(self.uuid)\n\n        # Sanity check and then set up our FeatureSet attributes\n        if self.feature_meta is None:\n            self.log.warning(f\"Could not find feature set {self.uuid} within current visibility scope\")\n            self.data_source = None\n            return\n        else:\n            self.id_column = self.feature_meta[\"RecordIdentifierFeatureName\"]\n            self.event_time = self.feature_meta[\"EventTimeFeatureName\"]\n\n            # Pull Athena and S3 Storage information from metadata\n            self.athena_table = self.feature_meta[\"workbench_meta\"][\"athena_table\"]\n            self.athena_database = self.feature_meta[\"workbench_meta\"][\"athena_database\"]\n            self.s3_storage = self.feature_meta[\"workbench_meta\"].get(\"s3_storage\")\n\n            # Create our internal DataSource (hardcoded to Athena for now)\n            self.data_source = AthenaSource(self.athena_table, self.athena_database)\n\n        # Spin up our Feature Store\n        self.feature_store = FeatureStore(self.sm_session)\n\n        # Call superclass post_init\n        super().__post_init__()\n\n        # All done\n        self.log.info(f\"FeatureSet Initialized: {self.uuid}...\")\n\n    @property\n    def table(self) -&gt; str:\n        \"\"\"Get the base table name for this FeatureSet\"\"\"\n        return self.data_source.table\n\n    def refresh_meta(self):\n        \"\"\"Internal: Refresh our internal AWS Feature Store metadata\"\"\"\n        self.log.info(\"Calling refresh_meta() on the underlying DataSource\")\n        self.data_source.refresh_meta()\n\n    def exists(self) -&gt; bool:\n        \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n        if self.feature_meta is None:\n            self.log.debug(f\"FeatureSet {self.uuid} not found in AWS Metadata!\")\n            return False\n        return True\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this model\n\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        # Call the base class health check\n        health_issues = super().health_check()\n\n        # If we have a 'needs_onboard' in the health check then just return\n        if \"needs_onboard\" in health_issues:\n            return health_issues\n\n        # Check our DataSource\n        if not self.data_source.exists():\n            self.log.critical(f\"Data Source check failed for {self.uuid}\")\n            self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n            health_issues.append(\"data_source_missing\")\n        return health_issues\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n        return self.feature_meta\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n        return self.feature_meta[\"FeatureGroupArn\"]\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of the internal DataSource in MegaBytes\"\"\"\n        return self.data_source.size()\n\n    @property\n    def columns(self) -&gt; list[str]:\n        \"\"\"Return the column names of the Feature Set\"\"\"\n        return list(self.column_details().keys())\n\n    @property\n    def column_types(self) -&gt; list[str]:\n        \"\"\"Return the column types of the Feature Set\"\"\"\n        return list(self.column_details().values())\n\n    def column_details(self) -&gt; dict:\n        \"\"\"Return the column details of the Feature Set\n\n        Returns:\n            dict: The column details of the Feature Set\n\n        Notes:\n            We can't call just call self.data_source.column_details() because FeatureSets have different\n            types, so we need to overlay that type information on top of the DataSource type information\n        \"\"\"\n        fs_details = {item[\"FeatureName\"]: item[\"FeatureType\"] for item in self.feature_meta[\"FeatureDefinitions\"]}\n        ds_details = self.data_source.column_details()\n\n        # Overlay the FeatureSet type information on top of the DataSource type information\n        for col, dtype in ds_details.items():\n            ds_details[col] = fs_details.get(col, dtype)\n        return ds_details\n\n    def views(self) -&gt; list[str]:\n        \"\"\"Return the views for this Data Source\"\"\"\n        from workbench.core.views.view_utils import list_views\n\n        return list_views(self.data_source)\n\n    def view(self, view_name: str) -&gt; \"View\":\n        \"\"\"Return a DataFrame for a specific view\n        Args:\n            view_name (str): The name of the view to return\n        Returns:\n            pd.DataFrame: A DataFrame for the specified view\n        \"\"\"\n        from workbench.core.views import View\n\n        return View(self, view_name)\n\n    def set_display_columns(self, diplay_columns: list[str]):\n        \"\"\"Set the display columns for this Data Source\n\n        Args:\n            diplay_columns (list[str]): The display columns for this Data Source\n        \"\"\"\n        # Check mismatch of display columns to computation columns\n        c_view = self.view(\"computation\")\n        computation_columns = c_view.columns\n        mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n        if mismatch_columns:\n            self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n        self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n        from workbench.core.views import DisplayView\n\n        # Create a NEW display view\n        DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n\n    def set_computation_columns(self, computation_columns: list[str], reset_display: bool = True):\n        \"\"\"Set the computation columns for this FeatureSet\n\n        Args:\n            computation_columns (list[str]): The computation columns for this FeatureSet\n            reset_display (bool): Also reset the display columns to match (default: True)\n        \"\"\"\n        self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n        from workbench.core.views import ComputationView\n\n        # Create a NEW computation view\n        ComputationView.create(self, column_list=computation_columns)\n        self.recompute_stats()\n\n        # Reset the display columns to match the computation columns\n        if reset_display:\n            self.set_display_columns(computation_columns)\n\n    def num_columns(self) -&gt; int:\n        \"\"\"Return the number of columns of the Feature Set\"\"\"\n        return len(self.columns)\n\n    def num_rows(self) -&gt; int:\n        \"\"\"Return the number of rows of the internal DataSource\"\"\"\n        return self.data_source.num_rows()\n\n    def query(self, query: str, overwrite: bool = True) -&gt; pd.DataFrame:\n        \"\"\"Query the internal DataSource\n\n        Args:\n            query (str): The query to run against the DataSource\n            overwrite (bool): Overwrite the table name in the query (default: True)\n\n        Returns:\n            pd.DataFrame: The results of the query\n        \"\"\"\n        if overwrite:\n            query = query.replace(\" \" + self.uuid + \" \", \" \" + self.athena_table + \" \")\n        return self.data_source.query(query)\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying the underlying data source\"\"\"\n        workbench_details = self.data_source.workbench_meta().get(\"workbench_details\", {})\n        return workbench_details.get(\"aws_url\", \"unknown\")\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        return self.feature_meta[\"CreationTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        # Note: We can't currently figure out how to this from AWS Metadata\n        return self.feature_meta[\"CreationTime\"]\n\n    def hash(self) -&gt; str:\n        \"\"\"Return the hash for the set of Parquet files for this artifact\"\"\"\n        return self.data_source.hash()\n\n    def table_hash(self) -&gt; str:\n        \"\"\"Return the hash for the Athena table\"\"\"\n        return self.data_source.table_hash()\n\n    def get_data_source(self) -&gt; DataSourceFactory:\n        \"\"\"Return the underlying DataSource object\"\"\"\n        return self.data_source\n\n    def get_feature_store(self) -&gt; FeatureStore:\n        \"\"\"Return the underlying AWS FeatureStore object. This can be useful for more advanced usage\n        with create_dataset() such as Joins and time ranges and a host of other options\n        See: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n        \"\"\"\n        return self.feature_store\n\n    def create_s3_training_data(self) -&gt; str:\n        \"\"\"Create some Training Data (S3 CSV) from a Feature Set using standard options. If you want\n        additional options/features use the get_feature_store() method and see AWS docs for all\n        the details: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n        Returns:\n            str: The full path/file for the CSV file created by Feature Store create_dataset()\n        \"\"\"\n\n        # Set up the S3 Query results path\n        date_time = datetime.now(timezone.utc).strftime(\"%Y-%m-%d_%H:%M:%S\")\n        s3_output_path = self.feature_sets_s3_path + f\"/{self.uuid}/datasets/all_{date_time}\"\n\n        # Make the query\n        table = self.view(\"training\").table\n        query = f'SELECT * FROM \"{table}\"'\n        athena_query = FeatureGroup(name=self.uuid, sagemaker_session=self.sm_session).athena_query()\n        athena_query.run(query, output_location=s3_output_path)\n        athena_query.wait()\n        query_execution = athena_query.get_query_execution()\n\n        # Get the full path to the S3 files with the results\n        full_s3_path = s3_output_path + f\"/{query_execution['QueryExecution']['QueryExecutionId']}.csv\"\n        return full_s3_path\n\n    def get_training_data(self) -&gt; pd.DataFrame:\n        \"\"\"Get the training data for this FeatureSet\n\n        Returns:\n            pd.DataFrame: The training data for this FeatureSet\n        \"\"\"\n        from workbench.core.views.view import View\n\n        return View(self, \"training\").pull_dataframe()\n\n    def snapshot_query(self, table_name: str = None) -&gt; str:\n        \"\"\"An Athena query to get the latest snapshot of features\n\n        Args:\n            table_name (str): The name of the table to query (default: None)\n\n        Returns:\n            str: The Athena query to get the latest snapshot of features\n        \"\"\"\n        # Remove FeatureGroup metadata columns that might have gotten added\n        columns = self.columns\n        filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n        columns = \", \".join(['\"' + x + '\"' for x in columns if x not in filter_columns])\n\n        query = (\n            f\"SELECT {columns} \"\n            f\"    FROM (SELECT *, row_number() OVER (PARTITION BY {self.id_column} \"\n            f\"        ORDER BY {self.event_time} desc, api_invocation_time DESC, write_time DESC) AS row_num \"\n            f'        FROM \"{table_name}\") '\n            \"    WHERE row_num = 1 and NOT is_deleted;\"\n        )\n        return query\n\n    def details(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Additional Details about this FeatureSet Artifact\n\n        Args:\n            recompute (bool): Recompute the details (default: False)\n\n        Returns:\n            dict(dict): A dictionary of details about this FeatureSet\n        \"\"\"\n\n        self.log.info(f\"Computing FeatureSet Details ({self.uuid})...\")\n        details = self.summary()\n        details[\"aws_url\"] = self.aws_url()\n\n        # Store the AWS URL in the Workbench Metadata\n        # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n        # self.upsert_workbench_meta({\"aws_url\": details[\"aws_url\"]})\n\n        # Now get a summary of the underlying DataSource\n        details[\"storage_summary\"] = self.data_source.summary()\n\n        # Number of Columns\n        details[\"num_columns\"] = self.num_columns()\n\n        # Number of Rows\n        details[\"num_rows\"] = self.num_rows()\n\n        # Additional Details\n        details[\"workbench_status\"] = self.get_status()\n        details[\"workbench_input\"] = self.get_input()\n        details[\"workbench_tags\"] = self.tag_delimiter.join(self.get_tags())\n\n        # Underlying Storage Details\n        details[\"storage_type\"] = \"athena\"  # TODO: Add RDS support\n        details[\"storage_uuid\"] = self.data_source.uuid\n\n        # Add the column details and column stats\n        details[\"column_details\"] = self.column_details()\n        details[\"column_stats\"] = self.column_stats()\n\n        # Return the details data\n        return details\n\n    def delete(self):\n        \"\"\"Instance Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\"\"\"\n        # Make sure the AthenaSource exists\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an FeatureSet that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the FeatureSet\n        FeatureSetCore.managed_delete(self.uuid)\n\n    @classmethod\n    def managed_delete(cls, feature_set_name: str):\n        \"\"\"Class Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\n\n        Args:\n            feature_set_name (str): The Name of the FeatureSet to delete\n        \"\"\"\n\n        # See if the FeatureSet exists\n        try:\n            response = cls.sm_client.describe_feature_group(FeatureGroupName=feature_set_name)\n        except cls.sm_client.exceptions.ResourceNotFound:\n            cls.log.info(f\"FeatureSet {feature_set_name} not found!\")\n            return\n\n        # Extract database and table information from the response\n        offline_config = response.get(\"OfflineStoreConfig\", {})\n        database = offline_config.get(\"DataCatalogConfig\", {}).get(\"Database\")\n        offline_table = offline_config.get(\"DataCatalogConfig\", {}).get(\"TableName\")\n        data_source_uuid = offline_table  # Our offline storage IS a DataSource\n\n        # Delete the Feature Group and ensure that it gets deleted\n        cls.log.important(f\"Deleting FeatureSet {feature_set_name}...\")\n        remove_fg = cls.aws_feature_group_delete(feature_set_name)\n        cls.ensure_feature_group_deleted(remove_fg)\n\n        # Delete our underlying DataSource (Data Catalog Table and S3 Storage Objects)\n        AthenaSource.managed_delete(data_source_uuid, database=database)\n\n        # Delete any views associated with this FeatureSet\n        cls.delete_views(offline_table, database)\n\n        # Feature Sets can often have a lot of cruft so delete the entire bucket/prefix\n        s3_delete_path = cls.feature_sets_s3_path + f\"/{feature_set_name}/\"\n        cls.log.info(f\"Deleting All FeatureSet S3 Storage Objects {s3_delete_path}\")\n        wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(feature_set_name)\n\n    @classmethod\n    @aws_throttle\n    def aws_feature_group_delete(cls, feature_set_name):\n        remove_fg = FeatureGroup(name=feature_set_name, sagemaker_session=cls.sm_session)\n        remove_fg.delete()\n        return remove_fg\n\n    @classmethod\n    def ensure_feature_group_deleted(cls, feature_group):\n        status = \"Deleting\"\n        while status == \"Deleting\":\n            cls.log.debug(\"FeatureSet being Deleted...\")\n            try:\n                status = feature_group.describe().get(\"FeatureGroupStatus\")\n            except botocore.exceptions.ClientError as error:\n                # For ResourceNotFound/ValidationException, this is fine, otherwise raise all other exceptions\n                if error.response[\"Error\"][\"Code\"] in [\"ResourceNotFound\", \"ValidationException\"]:\n                    break\n                else:\n                    raise error\n            time.sleep(1)\n        cls.log.info(f\"FeatureSet {feature_group.name} successfully deleted\")\n\n    def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n        \"\"\"Set the hold out ids for the training view for this FeatureSet\n\n        Args:\n            id_column (str): The name of the id column.\n            holdout_ids (list[str]): The list of holdout ids.\n        \"\"\"\n        from workbench.core.views import TrainingView\n\n        # Create a NEW training view\n        self.log.important(f\"Setting Training Holdouts: {len(holdout_ids)} ids...\")\n        TrainingView.create(self, id_column=id_column, holdout_ids=holdout_ids)\n\n    @classmethod\n    def delete_views(cls, table: str, database: str):\n        \"\"\"Delete any views associated with this FeatureSet\n\n        Args:\n            table (str): Name of Athena Table\n            database (str): Athena Database Name\n        \"\"\"\n        from workbench.core.views.view_utils import delete_views_and_supplemental_data\n\n        delete_views_and_supplemental_data(table, database, cls.boto3_session)\n\n    def descriptive_stats(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Get the descriptive stats for the numeric columns of the underlying DataSource\n        Args:\n            recompute (bool): Recompute the descriptive stats (default=False)\n        Returns:\n            dict: A dictionary of descriptive stats for the numeric columns\n        \"\"\"\n        return self.data_source.descriptive_stats(recompute)\n\n    def sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a sample of the data from the underlying DataSource\n        Args:\n            recompute (bool): Recompute the sample (default=False)\n        Returns:\n            pd.DataFrame: A sample of the data from the underlying DataSource\n        \"\"\"\n        return self.data_source.sample(recompute)\n\n    def outliers(self, scale: float = 1.5, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Compute outliers for all the numeric columns in a DataSource\n        Args:\n            scale (float): The scale to use for the IQR (default: 1.5)\n            recompute (bool): Recompute the outliers (default: False)\n        Returns:\n            pd.DataFrame: A DataFrame of outliers from this DataSource\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n        return self.data_source.outliers(scale=scale, recompute=recompute)\n\n    def smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Get a SMART sample dataframe from this FeatureSet\n\n        Args:\n            recompute (bool): Recompute the smart sample (default=False)\n\n        Returns:\n            pd.DataFrame: A combined DataFrame of sample data + outliers\n        \"\"\"\n        return self.data_source.smart_sample(recompute=recompute)\n\n    def anomalies(self) -&gt; pd.DataFrame:\n        \"\"\"Get a set of anomalous data from the underlying DataSource\n        Returns:\n            pd.DataFrame: A dataframe of anomalies from the underlying DataSource\n        \"\"\"\n\n        # FIXME: Mock this for now\n        anom_df = self.sample().copy()\n        anom_df[\"anomaly_score\"] = np.random.rand(anom_df.shape[0])\n        anom_df[\"cluster\"] = np.random.randint(0, 10, anom_df.shape[0])\n        anom_df[\"x\"] = np.random.rand(anom_df.shape[0])\n        anom_df[\"y\"] = np.random.rand(anom_df.shape[0])\n        return anom_df\n\n    def value_counts(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Get the value counts for the string columns of the underlying DataSource\n        Args:\n            recompute (bool): Recompute the value counts (default=False)\n        Returns:\n            dict: A dictionary of value counts for the string columns\n        \"\"\"\n        return self.data_source.value_counts(recompute)\n\n    def correlations(self, recompute: bool = False) -&gt; dict:\n        \"\"\"Get the correlations for the numeric columns of the underlying DataSource\n        Args:\n            recompute (bool): Recompute the value counts (default=False)\n        Returns:\n            dict: A dictionary of correlations for the numeric columns\n        \"\"\"\n        return self.data_source.correlations(recompute)\n\n    def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n        \"\"\"Compute Column Stats for all the columns in the FeatureSets underlying DataSource\n        Args:\n            recompute (bool): Recompute the column stats (default: False)\n        Returns:\n            dict(dict): A dictionary of stats for each column this format\n            NB: String columns will NOT have num_zeros and descriptive_stats\n             {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n              'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n              ...}\n        \"\"\"\n\n        # Grab the column stats from our DataSource\n        ds_column_stats = self.data_source.column_stats(recompute)\n\n        # Map the types from our DataSource to the FeatureSet types\n        fs_type_mapper = self.column_details()\n        for col, details in ds_column_stats.items():\n            details[\"fs_dtype\"] = fs_type_mapper.get(col, \"unknown\")\n\n        return ds_column_stats\n\n    def ready(self) -&gt; bool:\n        \"\"\"Is the FeatureSet ready? Is initial setup complete and expected metadata populated?\n        Note: Since FeatureSet is a composite of DataSource and FeatureGroup, we need to\n           check both to see if the FeatureSet is ready.\"\"\"\n\n        # Check the expected metadata for the FeatureSet\n        expected_meta = self.expected_meta()\n        existing_meta = self.workbench_meta()\n        feature_set_ready = set(existing_meta.keys()).issuperset(expected_meta)\n        if not feature_set_ready:\n            self.log.info(f\"FeatureSet {self.uuid} is not ready!\")\n            return False\n\n        # Okay now call/return the DataSource ready() method\n        return self.data_source.ready()\n\n    def onboard(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will onboard the FeatureSet (make it ready)\"\"\"\n\n        # Set our status to onboarding\n        self.log.important(f\"Onboarding {self.uuid}...\")\n        self.set_status(\"onboarding\")\n        self.remove_health_tag(\"needs_onboard\")\n\n        # Call our underlying DataSource onboard method\n        self.data_source.refresh_meta()\n        if not self.data_source.exists():\n            self.log.critical(f\"Data Source check failed for {self.uuid}\")\n            self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n            return False\n        if not self.data_source.ready():\n            self.data_source.onboard()\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        self.set_status(\"ready\")\n        return True\n\n    def recompute_stats(self) -&gt; bool:\n        \"\"\"This is a BLOCKING method that will recompute the stats for the FeatureSet\"\"\"\n\n        # Call our underlying DataSource recompute stats method\n        self.log.important(f\"Recomputing Stats {self.uuid}...\")\n        self.data_source.recompute_stats()\n        self.details(recompute=True)\n        return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.column_types","title":"<code>column_types: list[str]</code>  <code>property</code>","text":"<p>Return the column types of the Feature Set</p>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.columns","title":"<code>columns: list[str]</code>  <code>property</code>","text":"<p>Return the column names of the Feature Set</p>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.table","title":"<code>table: str</code>  <code>property</code>","text":"<p>Get the base table name for this FeatureSet</p>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.__init__","title":"<code>__init__(feature_set_uuid, **kwargs)</code>","text":"<p>FeatureSetCore Initialization</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_uuid</code> <code>str</code> <p>Name of Feature Set</p> required Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def __init__(self, feature_set_uuid: str, **kwargs):\n    \"\"\"FeatureSetCore Initialization\n\n    Args:\n        feature_set_uuid (str): Name of Feature Set\n    \"\"\"\n\n    # Make sure the feature_set name is valid\n    self.is_name_valid(feature_set_uuid)\n\n    # Call superclass init\n    super().__init__(feature_set_uuid, **kwargs)\n\n    # Get our FeatureSet metadata\n    self.feature_meta = self.meta.feature_set(self.uuid)\n\n    # Sanity check and then set up our FeatureSet attributes\n    if self.feature_meta is None:\n        self.log.warning(f\"Could not find feature set {self.uuid} within current visibility scope\")\n        self.data_source = None\n        return\n    else:\n        self.id_column = self.feature_meta[\"RecordIdentifierFeatureName\"]\n        self.event_time = self.feature_meta[\"EventTimeFeatureName\"]\n\n        # Pull Athena and S3 Storage information from metadata\n        self.athena_table = self.feature_meta[\"workbench_meta\"][\"athena_table\"]\n        self.athena_database = self.feature_meta[\"workbench_meta\"][\"athena_database\"]\n        self.s3_storage = self.feature_meta[\"workbench_meta\"].get(\"s3_storage\")\n\n        # Create our internal DataSource (hardcoded to Athena for now)\n        self.data_source = AthenaSource(self.athena_table, self.athena_database)\n\n    # Spin up our Feature Store\n    self.feature_store = FeatureStore(self.sm_session)\n\n    # Call superclass post_init\n    super().__post_init__()\n\n    # All done\n    self.log.info(f\"FeatureSet Initialized: {self.uuid}...\")\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.anomalies","title":"<code>anomalies()</code>","text":"<p>Get a set of anomalous data from the underlying DataSource Returns:     pd.DataFrame: A dataframe of anomalies from the underlying DataSource</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def anomalies(self) -&gt; pd.DataFrame:\n    \"\"\"Get a set of anomalous data from the underlying DataSource\n    Returns:\n        pd.DataFrame: A dataframe of anomalies from the underlying DataSource\n    \"\"\"\n\n    # FIXME: Mock this for now\n    anom_df = self.sample().copy()\n    anom_df[\"anomaly_score\"] = np.random.rand(anom_df.shape[0])\n    anom_df[\"cluster\"] = np.random.randint(0, 10, anom_df.shape[0])\n    anom_df[\"x\"] = np.random.rand(anom_df.shape[0])\n    anom_df[\"y\"] = np.random.rand(anom_df.shape[0])\n    return anom_df\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for this artifact</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for this artifact\"\"\"\n    return self.feature_meta[\"FeatureGroupArn\"]\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get ALL the AWS metadata for this artifact</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n    return self.feature_meta\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying the underlying data source</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying the underlying data source\"\"\"\n    workbench_details = self.data_source.workbench_meta().get(\"workbench_details\", {})\n    return workbench_details.get(\"aws_url\", \"unknown\")\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.column_details","title":"<code>column_details()</code>","text":"<p>Return the column details of the Feature Set</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The column details of the Feature Set</p> Notes <p>We can't call just call self.data_source.column_details() because FeatureSets have different types, so we need to overlay that type information on top of the DataSource type information</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def column_details(self) -&gt; dict:\n    \"\"\"Return the column details of the Feature Set\n\n    Returns:\n        dict: The column details of the Feature Set\n\n    Notes:\n        We can't call just call self.data_source.column_details() because FeatureSets have different\n        types, so we need to overlay that type information on top of the DataSource type information\n    \"\"\"\n    fs_details = {item[\"FeatureName\"]: item[\"FeatureType\"] for item in self.feature_meta[\"FeatureDefinitions\"]}\n    ds_details = self.data_source.column_details()\n\n    # Overlay the FeatureSet type information on top of the DataSource type information\n    for col, dtype in ds_details.items():\n        ds_details[col] = fs_details.get(col, dtype)\n    return ds_details\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.column_stats","title":"<code>column_stats(recompute=False)</code>","text":"<p>Compute Column Stats for all the columns in the FeatureSets underlying DataSource Args:     recompute (bool): Recompute the column stats (default: False) Returns:     dict(dict): A dictionary of stats for each column this format     NB: String columns will NOT have num_zeros and descriptive_stats      {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},       'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},       ...}</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def column_stats(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Compute Column Stats for all the columns in the FeatureSets underlying DataSource\n    Args:\n        recompute (bool): Recompute the column stats (default: False)\n    Returns:\n        dict(dict): A dictionary of stats for each column this format\n        NB: String columns will NOT have num_zeros and descriptive_stats\n         {'col1': {'dtype': 'string', 'unique': 4321, 'nulls': 12},\n          'col2': {'dtype': 'int', 'unique': 4321, 'nulls': 12, 'num_zeros': 100, 'descriptive_stats': {...}},\n          ...}\n    \"\"\"\n\n    # Grab the column stats from our DataSource\n    ds_column_stats = self.data_source.column_stats(recompute)\n\n    # Map the types from our DataSource to the FeatureSet types\n    fs_type_mapper = self.column_details()\n    for col, details in ds_column_stats.items():\n        details[\"fs_dtype\"] = fs_type_mapper.get(col, \"unknown\")\n\n    return ds_column_stats\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.correlations","title":"<code>correlations(recompute=False)</code>","text":"<p>Get the correlations for the numeric columns of the underlying DataSource Args:     recompute (bool): Recompute the value counts (default=False) Returns:     dict: A dictionary of correlations for the numeric columns</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def correlations(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Get the correlations for the numeric columns of the underlying DataSource\n    Args:\n        recompute (bool): Recompute the value counts (default=False)\n    Returns:\n        dict: A dictionary of correlations for the numeric columns\n    \"\"\"\n    return self.data_source.correlations(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.create_s3_training_data","title":"<code>create_s3_training_data()</code>","text":"<p>Create some Training Data (S3 CSV) from a Feature Set using standard options. If you want additional options/features use the get_feature_store() method and see AWS docs for all the details: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html Returns:     str: The full path/file for the CSV file created by Feature Store create_dataset()</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def create_s3_training_data(self) -&gt; str:\n    \"\"\"Create some Training Data (S3 CSV) from a Feature Set using standard options. If you want\n    additional options/features use the get_feature_store() method and see AWS docs for all\n    the details: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n    Returns:\n        str: The full path/file for the CSV file created by Feature Store create_dataset()\n    \"\"\"\n\n    # Set up the S3 Query results path\n    date_time = datetime.now(timezone.utc).strftime(\"%Y-%m-%d_%H:%M:%S\")\n    s3_output_path = self.feature_sets_s3_path + f\"/{self.uuid}/datasets/all_{date_time}\"\n\n    # Make the query\n    table = self.view(\"training\").table\n    query = f'SELECT * FROM \"{table}\"'\n    athena_query = FeatureGroup(name=self.uuid, sagemaker_session=self.sm_session).athena_query()\n    athena_query.run(query, output_location=s3_output_path)\n    athena_query.wait()\n    query_execution = athena_query.get_query_execution()\n\n    # Get the full path to the S3 files with the results\n    full_s3_path = s3_output_path + f\"/{query_execution['QueryExecution']['QueryExecutionId']}.csv\"\n    return full_s3_path\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    return self.feature_meta[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.delete","title":"<code>delete()</code>","text":"<p>Instance Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def delete(self):\n    \"\"\"Instance Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\"\"\"\n    # Make sure the AthenaSource exists\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an FeatureSet that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the FeatureSet\n    FeatureSetCore.managed_delete(self.uuid)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.delete_views","title":"<code>delete_views(table, database)</code>  <code>classmethod</code>","text":"<p>Delete any views associated with this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>table</code> <code>str</code> <p>Name of Athena Table</p> required <code>database</code> <code>str</code> <p>Athena Database Name</p> required Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>@classmethod\ndef delete_views(cls, table: str, database: str):\n    \"\"\"Delete any views associated with this FeatureSet\n\n    Args:\n        table (str): Name of Athena Table\n        database (str): Athena Database Name\n    \"\"\"\n    from workbench.core.views.view_utils import delete_views_and_supplemental_data\n\n    delete_views_and_supplemental_data(table, database, cls.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.descriptive_stats","title":"<code>descriptive_stats(recompute=False)</code>","text":"<p>Get the descriptive stats for the numeric columns of the underlying DataSource Args:     recompute (bool): Recompute the descriptive stats (default=False) Returns:     dict: A dictionary of descriptive stats for the numeric columns</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def descriptive_stats(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Get the descriptive stats for the numeric columns of the underlying DataSource\n    Args:\n        recompute (bool): Recompute the descriptive stats (default=False)\n    Returns:\n        dict: A dictionary of descriptive stats for the numeric columns\n    \"\"\"\n    return self.data_source.descriptive_stats(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this FeatureSet Artifact</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the details (default: False)</p> <code>False</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>A dictionary of details about this FeatureSet</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def details(self, recompute: bool = False) -&gt; dict[dict]:\n    \"\"\"Additional Details about this FeatureSet Artifact\n\n    Args:\n        recompute (bool): Recompute the details (default: False)\n\n    Returns:\n        dict(dict): A dictionary of details about this FeatureSet\n    \"\"\"\n\n    self.log.info(f\"Computing FeatureSet Details ({self.uuid})...\")\n    details = self.summary()\n    details[\"aws_url\"] = self.aws_url()\n\n    # Store the AWS URL in the Workbench Metadata\n    # FIXME: We need to revisit this but doing an upsert just for aws_url is silly\n    # self.upsert_workbench_meta({\"aws_url\": details[\"aws_url\"]})\n\n    # Now get a summary of the underlying DataSource\n    details[\"storage_summary\"] = self.data_source.summary()\n\n    # Number of Columns\n    details[\"num_columns\"] = self.num_columns()\n\n    # Number of Rows\n    details[\"num_rows\"] = self.num_rows()\n\n    # Additional Details\n    details[\"workbench_status\"] = self.get_status()\n    details[\"workbench_input\"] = self.get_input()\n    details[\"workbench_tags\"] = self.tag_delimiter.join(self.get_tags())\n\n    # Underlying Storage Details\n    details[\"storage_type\"] = \"athena\"  # TODO: Add RDS support\n    details[\"storage_uuid\"] = self.data_source.uuid\n\n    # Add the column details and column stats\n    details[\"column_details\"] = self.column_details()\n    details[\"column_stats\"] = self.column_stats()\n\n    # Return the details data\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.exists","title":"<code>exists()</code>","text":"<p>Does the feature_set_name exist in the AWS Metadata?</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Does the feature_set_name exist in the AWS Metadata?\"\"\"\n    if self.feature_meta is None:\n        self.log.debug(f\"FeatureSet {self.uuid} not found in AWS Metadata!\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.get_data_source","title":"<code>get_data_source()</code>","text":"<p>Return the underlying DataSource object</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def get_data_source(self) -&gt; DataSourceFactory:\n    \"\"\"Return the underlying DataSource object\"\"\"\n    return self.data_source\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.get_feature_store","title":"<code>get_feature_store()</code>","text":"<p>Return the underlying AWS FeatureStore object. This can be useful for more advanced usage with create_dataset() such as Joins and time ranges and a host of other options See: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def get_feature_store(self) -&gt; FeatureStore:\n    \"\"\"Return the underlying AWS FeatureStore object. This can be useful for more advanced usage\n    with create_dataset() such as Joins and time ranges and a host of other options\n    See: https://docs.aws.amazon.com/sagemaker/latest/dg/feature-store-create-a-dataset.html\n    \"\"\"\n    return self.feature_store\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.get_training_data","title":"<code>get_training_data()</code>","text":"<p>Get the training data for this FeatureSet</p> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The training data for this FeatureSet</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def get_training_data(self) -&gt; pd.DataFrame:\n    \"\"\"Get the training data for this FeatureSet\n\n    Returns:\n        pd.DataFrame: The training data for this FeatureSet\n    \"\"\"\n    from workbench.core.views.view import View\n\n    return View(self, \"training\").pull_dataframe()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.hash","title":"<code>hash()</code>","text":"<p>Return the hash for the set of Parquet files for this artifact</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def hash(self) -&gt; str:\n    \"\"\"Return the hash for the set of Parquet files for this artifact\"\"\"\n    return self.data_source.hash()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of health issues</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this model\n\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    # Call the base class health check\n    health_issues = super().health_check()\n\n    # If we have a 'needs_onboard' in the health check then just return\n    if \"needs_onboard\" in health_issues:\n        return health_issues\n\n    # Check our DataSource\n    if not self.data_source.exists():\n        self.log.critical(f\"Data Source check failed for {self.uuid}\")\n        self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n        health_issues.append(\"data_source_missing\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.managed_delete","title":"<code>managed_delete(feature_set_name)</code>  <code>classmethod</code>","text":"<p>Class Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects</p> <p>Parameters:</p> Name Type Description Default <code>feature_set_name</code> <code>str</code> <p>The Name of the FeatureSet to delete</p> required Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>@classmethod\ndef managed_delete(cls, feature_set_name: str):\n    \"\"\"Class Method: Delete the Feature Set: Feature Group, Catalog Table, and S3 Storage Objects\n\n    Args:\n        feature_set_name (str): The Name of the FeatureSet to delete\n    \"\"\"\n\n    # See if the FeatureSet exists\n    try:\n        response = cls.sm_client.describe_feature_group(FeatureGroupName=feature_set_name)\n    except cls.sm_client.exceptions.ResourceNotFound:\n        cls.log.info(f\"FeatureSet {feature_set_name} not found!\")\n        return\n\n    # Extract database and table information from the response\n    offline_config = response.get(\"OfflineStoreConfig\", {})\n    database = offline_config.get(\"DataCatalogConfig\", {}).get(\"Database\")\n    offline_table = offline_config.get(\"DataCatalogConfig\", {}).get(\"TableName\")\n    data_source_uuid = offline_table  # Our offline storage IS a DataSource\n\n    # Delete the Feature Group and ensure that it gets deleted\n    cls.log.important(f\"Deleting FeatureSet {feature_set_name}...\")\n    remove_fg = cls.aws_feature_group_delete(feature_set_name)\n    cls.ensure_feature_group_deleted(remove_fg)\n\n    # Delete our underlying DataSource (Data Catalog Table and S3 Storage Objects)\n    AthenaSource.managed_delete(data_source_uuid, database=database)\n\n    # Delete any views associated with this FeatureSet\n    cls.delete_views(offline_table, database)\n\n    # Feature Sets can often have a lot of cruft so delete the entire bucket/prefix\n    s3_delete_path = cls.feature_sets_s3_path + f\"/{feature_set_name}/\"\n    cls.log.info(f\"Deleting All FeatureSet S3 Storage Objects {s3_delete_path}\")\n    wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(feature_set_name)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    # Note: We can't currently figure out how to this from AWS Metadata\n    return self.feature_meta[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.num_columns","title":"<code>num_columns()</code>","text":"<p>Return the number of columns of the Feature Set</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def num_columns(self) -&gt; int:\n    \"\"\"Return the number of columns of the Feature Set\"\"\"\n    return len(self.columns)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.num_rows","title":"<code>num_rows()</code>","text":"<p>Return the number of rows of the internal DataSource</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def num_rows(self) -&gt; int:\n    \"\"\"Return the number of rows of the internal DataSource\"\"\"\n    return self.data_source.num_rows()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.onboard","title":"<code>onboard()</code>","text":"<p>This is a BLOCKING method that will onboard the FeatureSet (make it ready)</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def onboard(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will onboard the FeatureSet (make it ready)\"\"\"\n\n    # Set our status to onboarding\n    self.log.important(f\"Onboarding {self.uuid}...\")\n    self.set_status(\"onboarding\")\n    self.remove_health_tag(\"needs_onboard\")\n\n    # Call our underlying DataSource onboard method\n    self.data_source.refresh_meta()\n    if not self.data_source.exists():\n        self.log.critical(f\"Data Source check failed for {self.uuid}\")\n        self.log.critical(\"Delete this Feature Set and recreate it to fix this issue\")\n        return False\n    if not self.data_source.ready():\n        self.data_source.onboard()\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    self.set_status(\"ready\")\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.outliers","title":"<code>outliers(scale=1.5, recompute=False)</code>","text":"<p>Compute outliers for all the numeric columns in a DataSource Args:     scale (float): The scale to use for the IQR (default: 1.5)     recompute (bool): Recompute the outliers (default: False) Returns:     pd.DataFrame: A DataFrame of outliers from this DataSource Notes:     Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers     The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def outliers(self, scale: float = 1.5, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Compute outliers for all the numeric columns in a DataSource\n    Args:\n        scale (float): The scale to use for the IQR (default: 1.5)\n        recompute (bool): Recompute the outliers (default: False)\n    Returns:\n        pd.DataFrame: A DataFrame of outliers from this DataSource\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) method to compute outliers\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n    return self.data_source.outliers(scale=scale, recompute=recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.query","title":"<code>query(query, overwrite=True)</code>","text":"<p>Query the internal DataSource</p> <p>Parameters:</p> Name Type Description Default <code>query</code> <code>str</code> <p>The query to run against the DataSource</p> required <code>overwrite</code> <code>bool</code> <p>Overwrite the table name in the query (default: True)</p> <code>True</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The results of the query</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def query(self, query: str, overwrite: bool = True) -&gt; pd.DataFrame:\n    \"\"\"Query the internal DataSource\n\n    Args:\n        query (str): The query to run against the DataSource\n        overwrite (bool): Overwrite the table name in the query (default: True)\n\n    Returns:\n        pd.DataFrame: The results of the query\n    \"\"\"\n    if overwrite:\n        query = query.replace(\" \" + self.uuid + \" \", \" \" + self.athena_table + \" \")\n    return self.data_source.query(query)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.ready","title":"<code>ready()</code>","text":"<p>Is the FeatureSet ready? Is initial setup complete and expected metadata populated? Note: Since FeatureSet is a composite of DataSource and FeatureGroup, we need to    check both to see if the FeatureSet is ready.</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def ready(self) -&gt; bool:\n    \"\"\"Is the FeatureSet ready? Is initial setup complete and expected metadata populated?\n    Note: Since FeatureSet is a composite of DataSource and FeatureGroup, we need to\n       check both to see if the FeatureSet is ready.\"\"\"\n\n    # Check the expected metadata for the FeatureSet\n    expected_meta = self.expected_meta()\n    existing_meta = self.workbench_meta()\n    feature_set_ready = set(existing_meta.keys()).issuperset(expected_meta)\n    if not feature_set_ready:\n        self.log.info(f\"FeatureSet {self.uuid} is not ready!\")\n        return False\n\n    # Okay now call/return the DataSource ready() method\n    return self.data_source.ready()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.recompute_stats","title":"<code>recompute_stats()</code>","text":"<p>This is a BLOCKING method that will recompute the stats for the FeatureSet</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def recompute_stats(self) -&gt; bool:\n    \"\"\"This is a BLOCKING method that will recompute the stats for the FeatureSet\"\"\"\n\n    # Call our underlying DataSource recompute stats method\n    self.log.important(f\"Recomputing Stats {self.uuid}...\")\n    self.data_source.recompute_stats()\n    self.details(recompute=True)\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Internal: Refresh our internal AWS Feature Store metadata</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Internal: Refresh our internal AWS Feature Store metadata\"\"\"\n    self.log.info(\"Calling refresh_meta() on the underlying DataSource\")\n    self.data_source.refresh_meta()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.sample","title":"<code>sample(recompute=False)</code>","text":"<p>Get a sample of the data from the underlying DataSource Args:     recompute (bool): Recompute the sample (default=False) Returns:     pd.DataFrame: A sample of the data from the underlying DataSource</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a sample of the data from the underlying DataSource\n    Args:\n        recompute (bool): Recompute the sample (default=False)\n    Returns:\n        pd.DataFrame: A sample of the data from the underlying DataSource\n    \"\"\"\n    return self.data_source.sample(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.set_computation_columns","title":"<code>set_computation_columns(computation_columns, reset_display=True)</code>","text":"<p>Set the computation columns for this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>computation_columns</code> <code>list[str]</code> <p>The computation columns for this FeatureSet</p> required <code>reset_display</code> <code>bool</code> <p>Also reset the display columns to match (default: True)</p> <code>True</code> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def set_computation_columns(self, computation_columns: list[str], reset_display: bool = True):\n    \"\"\"Set the computation columns for this FeatureSet\n\n    Args:\n        computation_columns (list[str]): The computation columns for this FeatureSet\n        reset_display (bool): Also reset the display columns to match (default: True)\n    \"\"\"\n    self.log.important(f\"Setting Computation Columns...{computation_columns}\")\n    from workbench.core.views import ComputationView\n\n    # Create a NEW computation view\n    ComputationView.create(self, column_list=computation_columns)\n    self.recompute_stats()\n\n    # Reset the display columns to match the computation columns\n    if reset_display:\n        self.set_display_columns(computation_columns)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.set_display_columns","title":"<code>set_display_columns(diplay_columns)</code>","text":"<p>Set the display columns for this Data Source</p> <p>Parameters:</p> Name Type Description Default <code>diplay_columns</code> <code>list[str]</code> <p>The display columns for this Data Source</p> required Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def set_display_columns(self, diplay_columns: list[str]):\n    \"\"\"Set the display columns for this Data Source\n\n    Args:\n        diplay_columns (list[str]): The display columns for this Data Source\n    \"\"\"\n    # Check mismatch of display columns to computation columns\n    c_view = self.view(\"computation\")\n    computation_columns = c_view.columns\n    mismatch_columns = [col for col in diplay_columns if col not in computation_columns]\n    if mismatch_columns:\n        self.log.monitor(f\"Display View/Computation mismatch: {mismatch_columns}\")\n\n    self.log.important(f\"Setting Display Columns...{diplay_columns}\")\n    from workbench.core.views import DisplayView\n\n    # Create a NEW display view\n    DisplayView.create(self, source_table=c_view.table, column_list=diplay_columns)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.set_training_holdouts","title":"<code>set_training_holdouts(id_column, holdout_ids)</code>","text":"<p>Set the hold out ids for the training view for this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>id_column</code> <code>str</code> <p>The name of the id column.</p> required <code>holdout_ids</code> <code>list[str]</code> <p>The list of holdout ids.</p> required Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def set_training_holdouts(self, id_column: str, holdout_ids: list[str]):\n    \"\"\"Set the hold out ids for the training view for this FeatureSet\n\n    Args:\n        id_column (str): The name of the id column.\n        holdout_ids (list[str]): The list of holdout ids.\n    \"\"\"\n    from workbench.core.views import TrainingView\n\n    # Create a NEW training view\n    self.log.important(f\"Setting Training Holdouts: {len(holdout_ids)} ids...\")\n    TrainingView.create(self, id_column=id_column, holdout_ids=holdout_ids)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.size","title":"<code>size()</code>","text":"<p>Return the size of the internal DataSource in MegaBytes</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of the internal DataSource in MegaBytes\"\"\"\n    return self.data_source.size()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.smart_sample","title":"<code>smart_sample(recompute=False)</code>","text":"<p>Get a SMART sample dataframe from this FeatureSet</p> <p>Parameters:</p> Name Type Description Default <code>recompute</code> <code>bool</code> <p>Recompute the smart sample (default=False)</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A combined DataFrame of sample data + outliers</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def smart_sample(self, recompute: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Get a SMART sample dataframe from this FeatureSet\n\n    Args:\n        recompute (bool): Recompute the smart sample (default=False)\n\n    Returns:\n        pd.DataFrame: A combined DataFrame of sample data + outliers\n    \"\"\"\n    return self.data_source.smart_sample(recompute=recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.snapshot_query","title":"<code>snapshot_query(table_name=None)</code>","text":"<p>An Athena query to get the latest snapshot of features</p> <p>Parameters:</p> Name Type Description Default <code>table_name</code> <code>str</code> <p>The name of the table to query (default: None)</p> <code>None</code> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>The Athena query to get the latest snapshot of features</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def snapshot_query(self, table_name: str = None) -&gt; str:\n    \"\"\"An Athena query to get the latest snapshot of features\n\n    Args:\n        table_name (str): The name of the table to query (default: None)\n\n    Returns:\n        str: The Athena query to get the latest snapshot of features\n    \"\"\"\n    # Remove FeatureGroup metadata columns that might have gotten added\n    columns = self.columns\n    filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n    columns = \", \".join(['\"' + x + '\"' for x in columns if x not in filter_columns])\n\n    query = (\n        f\"SELECT {columns} \"\n        f\"    FROM (SELECT *, row_number() OVER (PARTITION BY {self.id_column} \"\n        f\"        ORDER BY {self.event_time} desc, api_invocation_time DESC, write_time DESC) AS row_num \"\n        f'        FROM \"{table_name}\") '\n        \"    WHERE row_num = 1 and NOT is_deleted;\"\n    )\n    return query\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.table_hash","title":"<code>table_hash()</code>","text":"<p>Return the hash for the Athena table</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def table_hash(self) -&gt; str:\n    \"\"\"Return the hash for the Athena table\"\"\"\n    return self.data_source.table_hash()\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.value_counts","title":"<code>value_counts(recompute=False)</code>","text":"<p>Get the value counts for the string columns of the underlying DataSource Args:     recompute (bool): Recompute the value counts (default=False) Returns:     dict: A dictionary of value counts for the string columns</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def value_counts(self, recompute: bool = False) -&gt; dict:\n    \"\"\"Get the value counts for the string columns of the underlying DataSource\n    Args:\n        recompute (bool): Recompute the value counts (default=False)\n    Returns:\n        dict: A dictionary of value counts for the string columns\n    \"\"\"\n    return self.data_source.value_counts(recompute)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.view","title":"<code>view(view_name)</code>","text":"<p>Return a DataFrame for a specific view Args:     view_name (str): The name of the view to return Returns:     pd.DataFrame: A DataFrame for the specified view</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def view(self, view_name: str) -&gt; \"View\":\n    \"\"\"Return a DataFrame for a specific view\n    Args:\n        view_name (str): The name of the view to return\n    Returns:\n        pd.DataFrame: A DataFrame for the specified view\n    \"\"\"\n    from workbench.core.views import View\n\n    return View(self, view_name)\n</code></pre>"},{"location":"core_classes/artifacts/feature_set_core/#workbench.core.artifacts.feature_set_core.FeatureSetCore.views","title":"<code>views()</code>","text":"<p>Return the views for this Data Source</p> Source code in <code>src/workbench/core/artifacts/feature_set_core.py</code> <pre><code>def views(self) -&gt; list[str]:\n    \"\"\"Return the views for this Data Source\"\"\"\n    from workbench.core.views.view_utils import list_views\n\n    return list_views(self.data_source)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/","title":"ModelCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the Model API Class and voil\u00e0 it works the same.</p> <p>ModelCore: Workbench ModelCore Class</p>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.InferenceImage","title":"<code>InferenceImage</code>","text":"<p>Class for retrieving locked Scikit-Learn inference images</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>class InferenceImage:\n    \"\"\"Class for retrieving locked Scikit-Learn inference images\"\"\"\n\n    image_uris = {\n        (\"us-east-1\", \"sklearn\", \"1.2.1\"): (\n            \"683313688378.dkr.ecr.us-east-1.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n        (\"us-east-2\", \"sklearn\", \"1.2.1\"): (\n            \"257758044811.dkr.ecr.us-east-2.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n        (\"us-west-1\", \"sklearn\", \"1.2.1\"): (\n            \"746614075791.dkr.ecr.us-west-1.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n        (\"us-west-2\", \"sklearn\", \"1.2.1\"): (\n            \"246618743249.dkr.ecr.us-west-2.amazonaws.com/\"\n            \"sagemaker-scikit-learn@sha256:ed242e33af079f334972acd2a7ddf74d13310d3c9a0ef3a0e9b0429ccc104dcd\"\n        ),\n    }\n\n    @classmethod\n    def get_image_uri(cls, region, framework, version):\n        key = (region, framework, version)\n        if key in cls.image_uris:\n            return cls.image_uris[key]\n        else:\n            raise ValueError(\n                f\"No matching image found for region: {region}, framework: {framework}, version: {version}\"\n            )\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore","title":"<code>ModelCore</code>","text":"<p>               Bases: <code>Artifact</code></p> <p>ModelCore: Workbench ModelCore Class</p> Common Usage <pre><code>my_model = ModelCore(model_uuid)\nmy_model.summary()\nmy_model.details()\n</code></pre> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>class ModelCore(Artifact):\n    \"\"\"ModelCore: Workbench ModelCore Class\n\n    Common Usage:\n        ```python\n        my_model = ModelCore(model_uuid)\n        my_model.summary()\n        my_model.details()\n        ```\n    \"\"\"\n\n    def __init__(self, model_uuid: str, model_type: ModelType = None, **kwargs):\n        \"\"\"ModelCore Initialization\n        Args:\n            model_uuid (str): Name of Model in Workbench.\n            model_type (ModelType, optional): Set this for newly created Models. Defaults to None.\n            **kwargs: Additional keyword arguments\n        \"\"\"\n\n        # Make sure the model name is valid\n        self.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call SuperClass Initialization\n        super().__init__(model_uuid, **kwargs)\n\n        # Initialize our class attributes\n        self.latest_model = None\n        self.model_type = ModelType.UNKNOWN\n        self.model_training_path = None\n        self.endpoint_inference_path = None\n\n        # Grab an Cloud Platform Meta object and pull information for this Model\n        self.model_name = model_uuid\n        self.model_meta = self.meta.model(self.model_name)\n        if self.model_meta is None:\n            self.log.warning(f\"Could not find model {self.model_name} within current visibility scope\")\n            return\n        else:\n            # Is this a model package group without any models?\n            if len(self.model_meta[\"ModelPackageList\"]) == 0:\n                self.log.warning(f\"Model Group {self.model_name} has no Model Packages!\")\n                self.latest_model = None\n                self.add_health_tag(\"model_not_found\")\n                return\n            try:\n                self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n                self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n                self.training_job_name = self._extract_training_job_name()\n                if model_type:\n                    self._set_model_type(model_type)\n                else:\n                    self.model_type = self._get_model_type()\n            except (IndexError, KeyError):\n                self.log.critical(f\"Model {self.model_name} appears to be malformed. Delete and recreate it!\")\n                return\n\n        # Set the Model Training S3 Path\n        self.model_training_path = self.models_s3_path + \"/training/\" + self.model_name\n\n        # Get our Endpoint Inference Path (might be None)\n        self.endpoint_inference_path = self.get_endpoint_inference_path()\n\n        # Call SuperClass Post Initialization\n        super().__post_init__()\n\n        # All done\n        self.log.info(f\"Model Initialized: {self.model_name}\")\n\n    def refresh_meta(self):\n        \"\"\"Refresh the Artifact's metadata\"\"\"\n        self.model_meta = self.meta.model(self.model_name)\n        self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n        self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n        self.training_job_name = self._extract_training_job_name()\n\n    def exists(self) -&gt; bool:\n        \"\"\"Does the model metadata exist in the AWS Metadata?\"\"\"\n        if self.model_meta is None:\n            self.log.info(f\"Model {self.model_name} not found in AWS Metadata!\")\n            return False\n        return True\n\n    def health_check(self) -&gt; list[str]:\n        \"\"\"Perform a health check on this model\n        Returns:\n            list[str]: List of health issues\n        \"\"\"\n        # Call the base class health check\n        health_issues = super().health_check()\n\n        # Check if the model exists\n        if self.latest_model is None:\n            health_issues.append(\"model_not_found\")\n\n        # Model Type\n        if self._get_model_type() == ModelType.UNKNOWN:\n            health_issues.append(\"model_type_unknown\")\n        else:\n            self.remove_health_tag(\"model_type_unknown\")\n\n        # Model Performance Metrics\n        needs_metrics = self.model_type in {ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR, ModelType.CLASSIFIER}\n        if needs_metrics and self.get_inference_metrics() is None:\n            health_issues.append(\"metrics_needed\")\n        else:\n            self.remove_health_tag(\"metrics_needed\")\n\n        # Endpoint\n        if not self.endpoints():\n            health_issues.append(\"no_endpoint\")\n        else:\n            self.remove_health_tag(\"no_endpoint\")\n        return health_issues\n\n    def latest_model_object(self) -&gt; SagemakerModel:\n        \"\"\"Return the latest AWS Sagemaker Model object for this Workbench Model\n\n        Returns:\n           sagemaker.model.Model: AWS Sagemaker Model object\n        \"\"\"\n        return SagemakerModel(\n            model_data=self.model_package_arn(), sagemaker_session=self.sm_session, image_uri=self.container_image()\n        )\n\n    def list_inference_runs(self) -&gt; list[str]:\n        \"\"\"List the inference runs for this model\n\n        Returns:\n            list[str]: List of inference runs\n        \"\"\"\n\n        # Check if we have a model (if not return empty list)\n        if self.latest_model is None:\n            return []\n\n        # Check if we have model training metrics in our metadata\n        have_model_training = True if self.workbench_meta().get(\"workbench_training_metrics\") else False\n\n        # Now grab the list of directories from our inference path\n        inference_runs = []\n        if self.endpoint_inference_path:\n            directories = wr.s3.list_directories(path=self.endpoint_inference_path + \"/\")\n            inference_runs = [urlparse(directory).path.split(\"/\")[-2] for directory in directories]\n\n        # We're going to add the model training to the end of the list\n        if have_model_training:\n            inference_runs.append(\"model_training\")\n        return inference_runs\n\n    def delete_inference_run(self, inference_run_uuid: str):\n        \"\"\"Delete the inference run for this model\n\n        Args:\n            inference_run_uuid (str): UUID of the inference run\n        \"\"\"\n        if inference_run_uuid == \"model_training\":\n            self.log.warning(\"Cannot delete model training data!\")\n            return\n\n        if self.endpoint_inference_path:\n            full_path = f\"{self.endpoint_inference_path}/{inference_run_uuid}\"\n            # Check if there are any objects at the path\n            if wr.s3.list_objects(full_path):\n                wr.s3.delete_objects(path=full_path)\n                self.log.important(f\"Deleted inference run {inference_run_uuid} for {self.model_name}\")\n            else:\n                self.log.warning(f\"Inference run {inference_run_uuid} not found for {self.model_name}!\")\n        else:\n            self.log.warning(f\"No inference data found for {self.model_name}!\")\n\n    def get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the inference performance metrics for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n        Returns:\n            pd.DataFrame: DataFrame of the Model Metrics\n\n        Note:\n            If a capture_uuid isn't specified this will try to return something reasonable\n        \"\"\"\n        # Try to get the auto_capture 'training_holdout' or the training\n        if capture_uuid == \"latest\":\n            metrics_df = self.get_inference_metrics(\"auto_inference\")\n            return metrics_df if metrics_df is not None else self.get_inference_metrics(\"model_training\")\n\n        # Grab the metrics captured during model training (could return None)\n        if capture_uuid == \"model_training\":\n            # Sanity check the workbench metadata\n            if self.workbench_meta() is None:\n                error_msg = f\"Model {self.model_name} has no workbench_meta(). Either onboard() or delete this model!\"\n                self.log.critical(error_msg)\n                raise ValueError(error_msg)\n\n            metrics = self.workbench_meta().get(\"workbench_training_metrics\")\n            return pd.DataFrame.from_dict(metrics) if metrics else None\n\n        else:  # Specific capture_uuid (could return None)\n            s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_metrics.csv\"\n            metrics = pull_s3_data(s3_path, embedded_index=True)\n            if metrics is not None:\n                return metrics\n            else:\n                self.log.warning(f\"Performance metrics {capture_uuid} not found for {self.model_name}!\")\n                return None\n\n    def confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the confusion_matrix for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n        Returns:\n            pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n        \"\"\"\n\n        # Sanity check the workbench metadata\n        if self.workbench_meta() is None:\n            error_msg = f\"Model {self.model_name} has no workbench_meta(). Either onboard() or delete this model!\"\n            self.log.critical(error_msg)\n            raise ValueError(error_msg)\n\n        # Grab the metrics from the Workbench Metadata (try inference first, then training)\n        if capture_uuid == \"latest\":\n            cm = self.confusion_matrix(\"auto_inference\")\n            return cm if cm is not None else self.confusion_matrix(\"model_training\")\n\n        # Grab the confusion matrix captured during model training (could return None)\n        if capture_uuid == \"model_training\":\n            cm = self.workbench_meta().get(\"workbench_training_cm\")\n            return pd.DataFrame.from_dict(cm) if cm else None\n\n        else:  # Specific capture_uuid\n            s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_cm.csv\"\n            cm = pull_s3_data(s3_path, embedded_index=True)\n            if cm is not None:\n                return cm\n            else:\n                self.log.warning(f\"Confusion Matrix {capture_uuid} not found for {self.model_name}!\")\n                return None\n\n    def set_input(self, input: str, force: bool = False):\n        \"\"\"Override: Set the input data for this artifact\n\n        Args:\n            input (str): Name of input for this artifact\n            force (bool, optional): Force the input to be set (default: False)\n        Note:\n            We're going to not allow this to be used for Models\n        \"\"\"\n        if not force:\n            self.log.warning(f\"Model {self.uuid}: Does not allow manual override of the input!\")\n            return\n\n        # Okay we're going to allow this to be set\n        self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n        self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n        self.upsert_workbench_meta({\"workbench_input\": input})\n\n    def size(self) -&gt; float:\n        \"\"\"Return the size of this data in MegaBytes\"\"\"\n        return 0.0\n\n    def aws_meta(self) -&gt; dict:\n        \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n        return self.model_meta\n\n    def arn(self) -&gt; str:\n        \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n        return self.group_arn()\n\n    def group_arn(self) -&gt; Union[str, None]:\n        \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n        return self.model_meta[\"ModelPackageGroupArn\"] if self.model_meta else None\n\n    def model_package_arn(self) -&gt; Union[str, None]:\n        \"\"\"AWS ARN (Amazon Resource Name) for the Latest Model Package (within the Group)\"\"\"\n        if self.latest_model is None:\n            return None\n        return self.latest_model[\"ModelPackageArn\"]\n\n    def container_info(self) -&gt; Union[dict, None]:\n        \"\"\"Container Info for the Latest Model Package\"\"\"\n        return self.latest_model[\"InferenceSpecification\"][\"Containers\"][0] if self.latest_model else None\n\n    def container_image(self) -&gt; str:\n        \"\"\"Container Image for the Latest Model Package\"\"\"\n        return self.container_info()[\"Image\"]\n\n    def aws_url(self):\n        \"\"\"The AWS URL for looking at/querying this model\"\"\"\n        return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n\n    def created(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was created\"\"\"\n        if self.latest_model is None:\n            return \"-\"\n        return self.latest_model[\"CreationTime\"]\n\n    def modified(self) -&gt; datetime:\n        \"\"\"Return the datetime when this artifact was last modified\"\"\"\n        if self.latest_model is None:\n            return \"-\"\n        return self.latest_model[\"CreationTime\"]\n\n    def hash(self) -&gt; Optional[str]:\n        \"\"\"Return the hash for this artifact\n\n        Returns:\n            Optional[str]: The hash for this artifact\n        \"\"\"\n        model_url = self.get_model_data_url()\n        return compute_s3_object_hash(model_url, self.boto3_session)\n\n    def register_endpoint(self, endpoint_name: str):\n        \"\"\"Add this endpoint to the set of registered endpoints for the model\n\n        Args:\n            endpoint_name (str): Name of the endpoint\n        \"\"\"\n        self.log.important(f\"Registering Endpoint {endpoint_name} with Model {self.uuid}...\")\n        registered_endpoints = set(self.workbench_meta().get(\"workbench_registered_endpoints\", []))\n        registered_endpoints.add(endpoint_name)\n        self.upsert_workbench_meta({\"workbench_registered_endpoints\": list(registered_endpoints)})\n\n        # Remove any health tags\n        self.remove_health_tag(\"no_endpoint\")\n\n        # A new endpoint means we need to refresh our inference path\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.endpoint_inference_path = self.get_endpoint_inference_path()\n\n    def remove_endpoint(self, endpoint_name: str):\n        \"\"\"Remove this endpoint from the set of registered endpoints for the model\n\n        Args:\n            endpoint_name (str): Name of the endpoint\n        \"\"\"\n        self.log.important(f\"Removing Endpoint {endpoint_name} from Model {self.uuid}...\")\n        registered_endpoints = set(self.workbench_meta().get(\"workbench_registered_endpoints\", []))\n        registered_endpoints.discard(endpoint_name)\n        self.upsert_workbench_meta({\"workbench_registered_endpoints\": list(registered_endpoints)})\n\n        # If we have NO endpionts, then set a health tags\n        if not registered_endpoints:\n            self.add_health_tag(\"no_endpoint\")\n            self.details(recompute=True)\n\n        # A new endpoint means we need to refresh our inference path\n        time.sleep(2)\n\n    def endpoints(self) -&gt; list[str]:\n        \"\"\"Get the list of registered endpoints for this Model\n\n        Returns:\n            list[str]: List of registered endpoints\n        \"\"\"\n        return self.workbench_meta().get(\"workbench_registered_endpoints\", [])\n\n    def get_endpoint_inference_path(self) -&gt; Union[str, None]:\n        \"\"\"Get the S3 Path for the Inference Data\n\n        Returns:\n            str: S3 Path for the Inference Data (or None if not found)\n        \"\"\"\n\n        # Look for any Registered Endpoints\n        registered_endpoints = self.workbench_meta().get(\"workbench_registered_endpoints\")\n\n        # Note: We may have 0 to N endpoints, so we find the one with the most recent artifacts\n        if registered_endpoints:\n            endpoint_inference_base = self.endpoints_s3_path + \"/inference/\"\n            endpoint_inference_paths = [endpoint_inference_base + e for e in registered_endpoints]\n            inference_path = newest_path(endpoint_inference_paths, self.sm_session)\n            if inference_path is None:\n                self.log.important(f\"No inference data found for {self.model_name}!\")\n                self.log.important(f\"Returning default inference path for {registered_endpoints[0]}...\")\n                self.log.important(f\"{endpoint_inference_paths[0]}\")\n                return endpoint_inference_paths[0]\n            else:\n                return inference_path\n        else:\n            self.log.warning(f\"No registered endpoints found for {self.model_name}!\")\n            return None\n\n    def set_target(self, target_column: str):\n        \"\"\"Set the target for this Model\n\n        Args:\n            target_column (str): Target column for this Model\n        \"\"\"\n        self.upsert_workbench_meta({\"workbench_model_target\": target_column})\n\n    def set_features(self, feature_columns: list[str]):\n        \"\"\"Set the features for this Model\n\n        Args:\n            feature_columns (list[str]): List of feature columns\n        \"\"\"\n        self.upsert_workbench_meta({\"workbench_model_features\": feature_columns})\n\n    def target(self) -&gt; Union[str, None]:\n        \"\"\"Return the target for this Model (if supervised, else None)\n\n        Returns:\n            str: Target column for this Model (if supervised, else None)\n        \"\"\"\n        return self.workbench_meta().get(\"workbench_model_target\")  # Returns None if not found\n\n    def features(self) -&gt; Union[list[str], None]:\n        \"\"\"Return a list of features used for this Model\n\n        Returns:\n            list[str]: List of features used for this Model\n        \"\"\"\n        return self.workbench_meta().get(\"workbench_model_features\")  # Returns None if not found\n\n    def class_labels(self) -&gt; Union[list[str], None]:\n        \"\"\"Return the class labels for this Model (if it's a classifier)\n\n        Returns:\n            list[str]: List of class labels\n        \"\"\"\n        if self.model_type == ModelType.CLASSIFIER:\n            return self.workbench_meta().get(\"class_labels\")  # Returns None if not found\n        else:\n            return None\n\n    def set_class_labels(self, labels: list[str]):\n        \"\"\"Return the class labels for this Model (if it's a classifier)\n\n        Args:\n            labels (list[str]): List of class labels\n        \"\"\"\n        if self.model_type == ModelType.CLASSIFIER:\n            self.upsert_workbench_meta({\"class_labels\": labels})\n        else:\n            self.log.error(f\"Model {self.model_name} is not a classifier!\")\n\n    def details(self, recompute=False) -&gt; dict:\n        \"\"\"Additional Details about this Model\n        Args:\n            recompute (bool, optional): Recompute the details (default: False)\n        Returns:\n            dict: Dictionary of details about this Model\n        \"\"\"\n        self.log.info(\"Computing Model Details...\")\n        details = self.summary()\n        details[\"pipeline\"] = self.get_pipeline()\n        details[\"model_type\"] = self.model_type.value\n        details[\"model_package_group_arn\"] = self.group_arn()\n        details[\"model_package_arn\"] = self.model_package_arn()\n\n        # Sanity check is we have models in the group\n        if self.latest_model is None:\n            self.log.warning(f\"Model Package Group {self.model_name} has no models!\")\n            return details\n\n        # Grab the Model Details\n        details[\"description\"] = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n        details[\"version\"] = self.latest_model[\"ModelPackageVersion\"]\n        details[\"status\"] = self.latest_model[\"ModelPackageStatus\"]\n        details[\"approval_status\"] = self.latest_model.get(\"ModelApprovalStatus\", \"unknown\")\n        details[\"image\"] = self.container_image().split(\"/\")[-1]  # Shorten the image uri\n\n        # Grab the inference and container info\n        inference_spec = self.latest_model[\"InferenceSpecification\"]\n        container_info = self.container_info()\n        details[\"framework\"] = container_info.get(\"Framework\", \"unknown\")\n        details[\"framework_version\"] = container_info.get(\"FrameworkVersion\", \"unknown\")\n        details[\"inference_types\"] = inference_spec[\"SupportedRealtimeInferenceInstanceTypes\"]\n        details[\"transform_types\"] = inference_spec[\"SupportedTransformInstanceTypes\"]\n        details[\"content_types\"] = inference_spec[\"SupportedContentTypes\"]\n        details[\"response_types\"] = inference_spec[\"SupportedResponseMIMETypes\"]\n        details[\"model_metrics\"] = self.get_inference_metrics()\n        if self.model_type == ModelType.CLASSIFIER:\n            details[\"confusion_matrix\"] = self.confusion_matrix()\n            details[\"predictions\"] = None\n        elif self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n            details[\"confusion_matrix\"] = None\n            details[\"predictions\"] = self.get_inference_predictions()\n        else:\n            details[\"confusion_matrix\"] = None\n            details[\"predictions\"] = None\n\n        # Grab the inference metadata\n        details[\"inference_meta\"] = self.get_inference_metadata()\n\n        # Return the details\n        return details\n\n    # Pipeline for this model\n    def get_pipeline(self) -&gt; str:\n        \"\"\"Get the pipeline for this model\"\"\"\n        return self.workbench_meta().get(\"workbench_pipeline\")\n\n    def set_pipeline(self, pipeline: str):\n        \"\"\"Set the pipeline for this model\n\n        Args:\n            pipeline (str): Pipeline that was used to create this model\n        \"\"\"\n        self.upsert_workbench_meta({\"workbench_pipeline\": pipeline})\n\n    def expected_meta(self) -&gt; list[str]:\n        \"\"\"Metadata we expect to see for this Model when it's ready\n        Returns:\n            list[str]: List of expected metadata keys\n        \"\"\"\n        # Our current list of expected metadata, we can add to this as needed\n        return [\"workbench_status\", \"workbench_training_metrics\", \"workbench_training_cm\"]\n\n    def is_model_unknown(self) -&gt; bool:\n        \"\"\"Is the Model Type unknown?\"\"\"\n        return self.model_type == ModelType.UNKNOWN\n\n    def _determine_model_type(self):\n        \"\"\"Internal: Determine the Model Type\"\"\"\n        model_type = input(\"Model Type? (classifier, regressor, quantile_regressor, unsupervised, transformer): \")\n        if model_type == \"classifier\":\n            self._set_model_type(ModelType.CLASSIFIER)\n        elif model_type == \"regressor\":\n            self._set_model_type(ModelType.REGRESSOR)\n        elif model_type == \"quantile_regressor\":\n            self._set_model_type(ModelType.QUANTILE_REGRESSOR)\n        elif model_type == \"unsupervised\":\n            self._set_model_type(ModelType.UNSUPERVISED)\n        elif model_type == \"transformer\":\n            self._set_model_type(ModelType.TRANSFORMER)\n        else:\n            self.log.warning(f\"Unknown Model Type {model_type}!\")\n            self._set_model_type(ModelType.UNKNOWN)\n\n    def onboard(self, ask_everything=False) -&gt; bool:\n        \"\"\"This is an interactive method that will onboard the Model (make it ready)\n\n        Args:\n            ask_everything (bool, optional): Ask for all the details. Defaults to False.\n\n        Returns:\n            bool: True if the Model is successfully onboarded, False otherwise\n        \"\"\"\n        # Set the status to onboarding\n        self.set_status(\"onboarding\")\n\n        # Determine the Model Type\n        while self.is_model_unknown():\n            self._determine_model_type()\n\n        # Is our input data set?\n        if self.get_input() in [\"\", \"unknown\"] or ask_everything:\n            input_data = input(\"Input Data?: \")\n            if input_data not in [\"None\", \"none\", \"\", \"unknown\"]:\n                self.set_input(input_data)\n\n        # Determine the Target Column (can be None)\n        target_column = self.target()\n        if target_column is None or ask_everything:\n            target_column = input(\"Target Column? (for unsupervised/transformer just type None): \")\n            if target_column in [\"None\", \"none\", \"\"]:\n                target_column = None\n\n        # Determine the Feature Columns\n        feature_columns = self.features()\n        if feature_columns is None or ask_everything:\n            feature_columns = input(\"Feature Columns? (use commas): \")\n            feature_columns = [e.strip() for e in feature_columns.split(\",\")]\n            if feature_columns in [[\"None\"], [\"none\"], [\"\"]]:\n                feature_columns = None\n\n        # Registered Endpoints?\n        endpoints = self.endpoints()\n        if not endpoints or ask_everything:\n            endpoints = input(\"Register Endpoints? (use commas for multiple): \")\n            endpoints = [e.strip() for e in endpoints.split(\",\")]\n            if endpoints in [[\"None\"], [\"none\"], [\"\"]]:\n                endpoints = None\n\n        # Model Owner?\n        owner = self.get_owner()\n        if owner in [None, \"unknown\"] or ask_everything:\n            owner = input(\"Model Owner: \")\n            if owner in [\"None\", \"none\", \"\"]:\n                owner = \"unknown\"\n\n        # Model Class Labels (if it's a classifier)\n        if self.model_type == ModelType.CLASSIFIER:\n            class_labels = self.class_labels()\n            if class_labels is None or ask_everything:\n                class_labels = input(\"Class Labels? (use commas): \")\n                class_labels = [e.strip() for e in class_labels.split(\",\")]\n                if class_labels in [[\"None\"], [\"none\"], [\"\"]]:\n                    class_labels = None\n            self.set_class_labels(class_labels)\n\n        # Now that we have all the details, let's onboard the Model with all the args\n        return self.onboard_with_args(self.model_type, target_column, feature_columns, endpoints, owner)\n\n    def onboard_with_args(\n        self,\n        model_type: ModelType,\n        target_column: str = None,\n        feature_list: list = None,\n        endpoints: list = None,\n        owner: str = None,\n    ) -&gt; bool:\n        \"\"\"Onboard the Model with the given arguments\n\n        Args:\n            model_type (ModelType): Model Type\n            target_column (str): Target Column\n            feature_list (list): List of Feature Columns\n            endpoints (list, optional): List of Endpoints. Defaults to None.\n            owner (str, optional): Model Owner. Defaults to None.\n        Returns:\n            bool: True if the Model is successfully onboarded, False otherwise\n        \"\"\"\n        # Set the status to onboarding\n        self.set_status(\"onboarding\")\n\n        # Set All the Details\n        self._set_model_type(model_type)\n        if target_column:\n            self.set_target(target_column)\n        if feature_list:\n            self.set_features(feature_list)\n        if endpoints:\n            for endpoint in endpoints:\n                self.register_endpoint(endpoint)\n        if owner:\n            self.set_owner(owner)\n\n        # Load the training metrics and inference metrics\n        self._load_training_metrics()\n        self._load_inference_metrics()\n\n        # Remove the needs_onboard tag\n        self.remove_health_tag(\"needs_onboard\")\n        self.set_status(\"ready\")\n\n        # Run a health check and refresh the meta\n        time.sleep(2)  # Give the AWS Metadata a chance to update\n        self.health_check()\n        self.refresh_meta()\n        self.details(recompute=True)\n        return True\n\n    def get_model_data_url(self) -&gt; Optional[str]:\n        \"\"\"Retrieve the ModelDataUrl from the model's AWS metadata.\n\n        Returns:\n            Optional[str]: The ModelDataUrl if available, otherwise None.\n        \"\"\"\n        meta = self.aws_meta()\n        try:\n            return meta[\"ModelPackageList\"][0][\"InferenceSpecification\"][\"Containers\"][0][\"ModelDataUrl\"]\n        except (KeyError, IndexError, TypeError):\n            return None\n\n    def delete(self):\n        \"\"\"Delete the Model Packages and the Model Group\"\"\"\n        if not self.exists():\n            self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n        # Call the Class Method to delete the Model Group\n        ModelCore.managed_delete(model_group_name=self.uuid)\n\n    @classmethod\n    def managed_delete(cls, model_group_name: str):\n        \"\"\"Delete the Model Packages, Model Group, and S3 Storage Objects\n\n        Args:\n            model_group_name (str): The name of the Model Group to delete\n        \"\"\"\n        # Check if the model group exists in SageMaker\n        try:\n            cls.sm_client.describe_model_package_group(ModelPackageGroupName=model_group_name)\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n                cls.log.info(f\"Model Group {model_group_name} not found!\")\n                return\n            else:\n                raise  # Re-raise unexpected errors\n\n        # Delete Model Packages within the Model Group\n        try:\n            paginator = cls.sm_client.get_paginator(\"list_model_packages\")\n            for page in paginator.paginate(ModelPackageGroupName=model_group_name):\n                for model_package in page[\"ModelPackageSummaryList\"]:\n                    package_arn = model_package[\"ModelPackageArn\"]\n                    cls.log.info(f\"Deleting Model Package {package_arn}...\")\n                    cls.sm_client.delete_model_package(ModelPackageName=package_arn)\n        except ClientError as e:\n            cls.log.error(f\"Error while deleting model packages: {e}\")\n            raise\n\n        # Delete the Model Package Group\n        cls.log.info(f\"Deleting Model Group {model_group_name}...\")\n        cls.sm_client.delete_model_package_group(ModelPackageGroupName=model_group_name)\n\n        # Delete S3 training artifacts\n        s3_delete_path = f\"{cls.models_s3_path}/training/{model_group_name}/\"\n        cls.log.info(f\"Deleting S3 Objects at {s3_delete_path}...\")\n        wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n        # Delete any dataframes that were stored in the Dataframe Cache\n        cls.log.info(\"Deleting Dataframe Cache...\")\n        cls.df_cache.delete_recursive(model_group_name)\n\n    def _set_model_type(self, model_type: ModelType):\n        \"\"\"Internal: Set the Model Type for this Model\"\"\"\n        self.model_type = model_type\n        self.upsert_workbench_meta({\"workbench_model_type\": self.model_type.value})\n        self.remove_health_tag(\"model_type_unknown\")\n\n    def _get_model_type(self) -&gt; ModelType:\n        \"\"\"Internal: Query the Workbench Metadata to get the model type\n        Returns:\n            ModelType: The ModelType of this Model\n        Notes:\n            This is an internal method that should not be called directly\n            Use the model_type attribute instead\n        \"\"\"\n        model_type = self.workbench_meta().get(\"workbench_model_type\")\n        try:\n            return ModelType(model_type)\n        except ValueError:\n            self.log.warning(f\"Could not determine model type for {self.model_name}!\")\n            return ModelType.UNKNOWN\n\n    def _load_training_metrics(self):\n        \"\"\"Internal: Retrieve the training metrics and Confusion Matrix for this model\n                     and load the data into the Workbench Metadata\n\n        Notes:\n            This may or may not exist based on whether we have access to TrainingJobAnalytics\n        \"\"\"\n        try:\n            df = TrainingJobAnalytics(training_job_name=self.training_job_name).dataframe()\n            if df.empty:\n                self.log.important(f\"No training job metrics found for {self.training_job_name}\")\n                self.upsert_workbench_meta({\"workbench_training_metrics\": None, \"workbench_training_cm\": None})\n                return\n            if self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n                if \"timestamp\" in df.columns:\n                    df = df.drop(columns=[\"timestamp\"])\n\n                # We're going to pivot the DataFrame to get the desired structure\n                reg_metrics_df = df.set_index(\"metric_name\").T\n\n                # Store and return the metrics in the Workbench Metadata\n                self.upsert_workbench_meta(\n                    {\"workbench_training_metrics\": reg_metrics_df.to_dict(), \"workbench_training_cm\": None}\n                )\n                return\n\n        except (KeyError, botocore.exceptions.ClientError):\n            self.log.important(f\"No training job metrics found for {self.training_job_name}\")\n            # Store and return the metrics in the Workbench Metadata\n            self.upsert_workbench_meta({\"workbench_training_metrics\": None, \"workbench_training_cm\": None})\n            return\n\n        # We need additional processing for classification metrics\n        if self.model_type == ModelType.CLASSIFIER:\n            metrics_df, cm_df = self._process_classification_metrics(df)\n\n            # Store and return the metrics in the Workbench Metadata\n            self.upsert_workbench_meta(\n                {\"workbench_training_metrics\": metrics_df.to_dict(), \"workbench_training_cm\": cm_df.to_dict()}\n            )\n\n    def _load_inference_metrics(self, capture_uuid: str = \"auto_inference\"):\n        \"\"\"Internal: Retrieve the inference model metrics for this model\n                     and load the data into the Workbench Metadata\n\n        Args:\n            capture_uuid (str, optional): A specific capture_uuid (default: \"auto_inference\")\n        Notes:\n            This may or may not exist based on whether an Endpoint ran Inference\n        \"\"\"\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_metrics.csv\"\n        inference_metrics = pull_s3_data(s3_path)\n\n        # Store data into the Workbench Metadata\n        metrics_storage = None if inference_metrics is None else inference_metrics.to_dict(\"records\")\n        self.upsert_workbench_meta({\"workbench_inference_metrics\": metrics_storage})\n\n    def get_inference_metadata(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the inference metadata for this model\n\n        Args:\n            capture_uuid (str, optional): A specific capture_uuid (default: \"auto_inference\")\n\n        Returns:\n            dict: Dictionary of the inference metadata (might be None)\n        Notes:\n            Basically when Endpoint inference was run, name of the dataset, the MD5, etc\n        \"\"\"\n        # Sanity check the inference path (which may or may not exist)\n        if self.endpoint_inference_path is None:\n            return None\n\n        # Check for model_training capture_uuid\n        if capture_uuid == \"model_training\":\n            # Create a DataFrame with the training metadata\n            meta_df = pd.DataFrame(\n                [\n                    {\n                        \"name\": \"AWS Training Capture\",\n                        \"data_hash\": \"N/A\",\n                        \"num_rows\": \"-\",\n                        \"description\": \"-\",\n                    }\n                ]\n            )\n            return meta_df\n\n        # Pull the inference metadata\n        try:\n            s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_meta.json\"\n            return wr.s3.read_json(s3_path)\n        except NoFilesFound:\n            self.log.info(f\"Could not find model inference meta at {s3_path}...\")\n            return None\n\n    def get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Retrieve the captured prediction results for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n        \"\"\"\n        self.log.important(f\"Grabbing {capture_uuid} predictions for {self.model_name}...\")\n\n        # Sanity check that the model should have predictions\n        has_predictions = self.model_type in [ModelType.CLASSIFIER, ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]\n        if not has_predictions:\n            self.log.warning(f\"No Predictions for {self.model_name}...\")\n            return None\n\n        # Special case for model_training\n        if capture_uuid == \"model_training\":\n            return self._get_validation_predictions()\n\n        # Construct the S3 path for the Inference Predictions\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_predictions.csv\"\n        return pull_s3_data(s3_path)\n\n    def _get_validation_predictions(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Internal: Retrieve the captured prediction results for this model\n\n        Returns:\n            pd.DataFrame: DataFrame of the Captured Validation Predictions (might be None)\n        \"\"\"\n        # Sanity check the training path (which may or may not exist)\n        if self.model_training_path is None:\n            self.log.warning(f\"No Validation Predictions for {self.model_name}...\")\n            return None\n        self.log.important(f\"Grabbing Validation Predictions for {self.model_name}...\")\n        s3_path = f\"{self.model_training_path}/validation_predictions.csv\"\n        df = pull_s3_data(s3_path)\n        return df\n\n    def _extract_training_job_name(self) -&gt; Union[str, None]:\n        \"\"\"Internal: Extract the training job name from the ModelDataUrl\"\"\"\n        try:\n            model_data_url = self.container_info()[\"ModelDataUrl\"]\n            parsed_url = urllib.parse.urlparse(model_data_url)\n            training_job_name = parsed_url.path.lstrip(\"/\").split(\"/\")[0]\n            return training_job_name\n        except KeyError:\n            self.log.warning(f\"Could not extract training job name from {model_data_url}\")\n            return None\n\n    @staticmethod\n    def _process_classification_metrics(df: pd.DataFrame) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"Internal: Process classification metrics into a more reasonable format\n        Args:\n            df (pd.DataFrame): DataFrame of training metrics\n        Returns:\n            (pd.DataFrame, pd.DataFrame): Tuple of DataFrames. Metrics and confusion matrix\n        \"\"\"\n        # Split into two DataFrames based on 'metric_name'\n        metrics_df = df[df[\"metric_name\"].str.startswith(\"Metrics:\")].copy()\n        cm_df = df[df[\"metric_name\"].str.startswith(\"ConfusionMatrix:\")].copy()\n\n        # Split the 'metric_name' into different parts\n        metrics_df[\"class\"] = metrics_df[\"metric_name\"].str.split(\":\").str[1]\n        metrics_df[\"metric_type\"] = metrics_df[\"metric_name\"].str.split(\":\").str[2]\n\n        # Pivot the DataFrame to get the desired structure\n        metrics_df = metrics_df.pivot(index=\"class\", columns=\"metric_type\", values=\"value\").reset_index()\n        metrics_df = metrics_df.rename_axis(None, axis=1)\n\n        # Now process the confusion matrix\n        cm_df[\"row_class\"] = cm_df[\"metric_name\"].str.split(\":\").str[1]\n        cm_df[\"col_class\"] = cm_df[\"metric_name\"].str.split(\":\").str[2]\n\n        # Pivot the DataFrame to create a form suitable for the heatmap\n        cm_df = cm_df.pivot(index=\"row_class\", columns=\"col_class\", values=\"value\")\n\n        # Convert the values in cm_df to integers\n        cm_df = cm_df.astype(int)\n\n        return metrics_df, cm_df\n\n    def shapley_values(self, capture_uuid: str = \"auto_inference\") -&gt; Union[list[pd.DataFrame], pd.DataFrame, None]:\n        \"\"\"Retrieve the Shapely values for this model\n\n        Args:\n            capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n        Returns:\n            pd.DataFrame: Dataframe(s) of the shapley values or None if not found\n\n        Notes:\n            This may or may not exist based on whether an Endpoint ran Shapley\n        \"\"\"\n\n        # Sanity check the inference path (which may or may not exist)\n        if self.endpoint_inference_path is None:\n            return None\n\n        # Construct the S3 path for the Shapley values\n        shapley_s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}\"\n\n        # Multiple CSV if classifier\n        if self.model_type == ModelType.CLASSIFIER:\n            # CSVs for shap values are indexed by prediction class\n            # Because we don't know how many classes there are, we need to search through\n            # a list of S3 objects in the parent folder\n            s3_paths = wr.s3.list_objects(shapley_s3_path)\n            return [pull_s3_data(f) for f in s3_paths if \"inference_shap_values\" in f]\n\n        # One CSV if regressor\n        if self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n            s3_path = f\"{shapley_s3_path}/inference_shap_values.csv\"\n            return pull_s3_data(s3_path)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.__init__","title":"<code>__init__(model_uuid, model_type=None, **kwargs)</code>","text":"<p>ModelCore Initialization Args:     model_uuid (str): Name of Model in Workbench.     model_type (ModelType, optional): Set this for newly created Models. Defaults to None.     **kwargs: Additional keyword arguments</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def __init__(self, model_uuid: str, model_type: ModelType = None, **kwargs):\n    \"\"\"ModelCore Initialization\n    Args:\n        model_uuid (str): Name of Model in Workbench.\n        model_type (ModelType, optional): Set this for newly created Models. Defaults to None.\n        **kwargs: Additional keyword arguments\n    \"\"\"\n\n    # Make sure the model name is valid\n    self.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call SuperClass Initialization\n    super().__init__(model_uuid, **kwargs)\n\n    # Initialize our class attributes\n    self.latest_model = None\n    self.model_type = ModelType.UNKNOWN\n    self.model_training_path = None\n    self.endpoint_inference_path = None\n\n    # Grab an Cloud Platform Meta object and pull information for this Model\n    self.model_name = model_uuid\n    self.model_meta = self.meta.model(self.model_name)\n    if self.model_meta is None:\n        self.log.warning(f\"Could not find model {self.model_name} within current visibility scope\")\n        return\n    else:\n        # Is this a model package group without any models?\n        if len(self.model_meta[\"ModelPackageList\"]) == 0:\n            self.log.warning(f\"Model Group {self.model_name} has no Model Packages!\")\n            self.latest_model = None\n            self.add_health_tag(\"model_not_found\")\n            return\n        try:\n            self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n            self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n            self.training_job_name = self._extract_training_job_name()\n            if model_type:\n                self._set_model_type(model_type)\n            else:\n                self.model_type = self._get_model_type()\n        except (IndexError, KeyError):\n            self.log.critical(f\"Model {self.model_name} appears to be malformed. Delete and recreate it!\")\n            return\n\n    # Set the Model Training S3 Path\n    self.model_training_path = self.models_s3_path + \"/training/\" + self.model_name\n\n    # Get our Endpoint Inference Path (might be None)\n    self.endpoint_inference_path = self.get_endpoint_inference_path()\n\n    # Call SuperClass Post Initialization\n    super().__post_init__()\n\n    # All done\n    self.log.info(f\"Model Initialized: {self.model_name}\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.arn","title":"<code>arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for the Model Package Group</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def arn(self) -&gt; str:\n    \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n    return self.group_arn()\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.aws_meta","title":"<code>aws_meta()</code>","text":"<p>Get ALL the AWS metadata for this artifact</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def aws_meta(self) -&gt; dict:\n    \"\"\"Get ALL the AWS metadata for this artifact\"\"\"\n    return self.model_meta\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.aws_url","title":"<code>aws_url()</code>","text":"<p>The AWS URL for looking at/querying this model</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def aws_url(self):\n    \"\"\"The AWS URL for looking at/querying this model\"\"\"\n    return f\"https://{self.aws_region}.console.aws.amazon.com/athena/home\"\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.class_labels","title":"<code>class_labels()</code>","text":"<p>Return the class labels for this Model (if it's a classifier)</p> <p>Returns:</p> Type Description <code>Union[list[str], None]</code> <p>list[str]: List of class labels</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def class_labels(self) -&gt; Union[list[str], None]:\n    \"\"\"Return the class labels for this Model (if it's a classifier)\n\n    Returns:\n        list[str]: List of class labels\n    \"\"\"\n    if self.model_type == ModelType.CLASSIFIER:\n        return self.workbench_meta().get(\"class_labels\")  # Returns None if not found\n    else:\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.confusion_matrix","title":"<code>confusion_matrix(capture_uuid='latest')</code>","text":"<p>Retrieve the confusion_matrix for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid or \"training\" (default: \"latest\")</p> <code>'latest'</code> <p>Returns:     pd.DataFrame: DataFrame of the Confusion Matrix (might be None)</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def confusion_matrix(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the confusion_matrix for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n    Returns:\n        pd.DataFrame: DataFrame of the Confusion Matrix (might be None)\n    \"\"\"\n\n    # Sanity check the workbench metadata\n    if self.workbench_meta() is None:\n        error_msg = f\"Model {self.model_name} has no workbench_meta(). Either onboard() or delete this model!\"\n        self.log.critical(error_msg)\n        raise ValueError(error_msg)\n\n    # Grab the metrics from the Workbench Metadata (try inference first, then training)\n    if capture_uuid == \"latest\":\n        cm = self.confusion_matrix(\"auto_inference\")\n        return cm if cm is not None else self.confusion_matrix(\"model_training\")\n\n    # Grab the confusion matrix captured during model training (could return None)\n    if capture_uuid == \"model_training\":\n        cm = self.workbench_meta().get(\"workbench_training_cm\")\n        return pd.DataFrame.from_dict(cm) if cm else None\n\n    else:  # Specific capture_uuid\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_cm.csv\"\n        cm = pull_s3_data(s3_path, embedded_index=True)\n        if cm is not None:\n            return cm\n        else:\n            self.log.warning(f\"Confusion Matrix {capture_uuid} not found for {self.model_name}!\")\n            return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.container_image","title":"<code>container_image()</code>","text":"<p>Container Image for the Latest Model Package</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def container_image(self) -&gt; str:\n    \"\"\"Container Image for the Latest Model Package\"\"\"\n    return self.container_info()[\"Image\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.container_info","title":"<code>container_info()</code>","text":"<p>Container Info for the Latest Model Package</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def container_info(self) -&gt; Union[dict, None]:\n    \"\"\"Container Info for the Latest Model Package\"\"\"\n    return self.latest_model[\"InferenceSpecification\"][\"Containers\"][0] if self.latest_model else None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.created","title":"<code>created()</code>","text":"<p>Return the datetime when this artifact was created</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def created(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was created\"\"\"\n    if self.latest_model is None:\n        return \"-\"\n    return self.latest_model[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.delete","title":"<code>delete()</code>","text":"<p>Delete the Model Packages and the Model Group</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def delete(self):\n    \"\"\"Delete the Model Packages and the Model Group\"\"\"\n    if not self.exists():\n        self.log.warning(f\"Trying to delete an Model that doesn't exist: {self.uuid}\")\n\n    # Call the Class Method to delete the Model Group\n    ModelCore.managed_delete(model_group_name=self.uuid)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.delete_inference_run","title":"<code>delete_inference_run(inference_run_uuid)</code>","text":"<p>Delete the inference run for this model</p> <p>Parameters:</p> Name Type Description Default <code>inference_run_uuid</code> <code>str</code> <p>UUID of the inference run</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def delete_inference_run(self, inference_run_uuid: str):\n    \"\"\"Delete the inference run for this model\n\n    Args:\n        inference_run_uuid (str): UUID of the inference run\n    \"\"\"\n    if inference_run_uuid == \"model_training\":\n        self.log.warning(\"Cannot delete model training data!\")\n        return\n\n    if self.endpoint_inference_path:\n        full_path = f\"{self.endpoint_inference_path}/{inference_run_uuid}\"\n        # Check if there are any objects at the path\n        if wr.s3.list_objects(full_path):\n            wr.s3.delete_objects(path=full_path)\n            self.log.important(f\"Deleted inference run {inference_run_uuid} for {self.model_name}\")\n        else:\n            self.log.warning(f\"Inference run {inference_run_uuid} not found for {self.model_name}!\")\n    else:\n        self.log.warning(f\"No inference data found for {self.model_name}!\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.details","title":"<code>details(recompute=False)</code>","text":"<p>Additional Details about this Model Args:     recompute (bool, optional): Recompute the details (default: False) Returns:     dict: Dictionary of details about this Model</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def details(self, recompute=False) -&gt; dict:\n    \"\"\"Additional Details about this Model\n    Args:\n        recompute (bool, optional): Recompute the details (default: False)\n    Returns:\n        dict: Dictionary of details about this Model\n    \"\"\"\n    self.log.info(\"Computing Model Details...\")\n    details = self.summary()\n    details[\"pipeline\"] = self.get_pipeline()\n    details[\"model_type\"] = self.model_type.value\n    details[\"model_package_group_arn\"] = self.group_arn()\n    details[\"model_package_arn\"] = self.model_package_arn()\n\n    # Sanity check is we have models in the group\n    if self.latest_model is None:\n        self.log.warning(f\"Model Package Group {self.model_name} has no models!\")\n        return details\n\n    # Grab the Model Details\n    details[\"description\"] = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n    details[\"version\"] = self.latest_model[\"ModelPackageVersion\"]\n    details[\"status\"] = self.latest_model[\"ModelPackageStatus\"]\n    details[\"approval_status\"] = self.latest_model.get(\"ModelApprovalStatus\", \"unknown\")\n    details[\"image\"] = self.container_image().split(\"/\")[-1]  # Shorten the image uri\n\n    # Grab the inference and container info\n    inference_spec = self.latest_model[\"InferenceSpecification\"]\n    container_info = self.container_info()\n    details[\"framework\"] = container_info.get(\"Framework\", \"unknown\")\n    details[\"framework_version\"] = container_info.get(\"FrameworkVersion\", \"unknown\")\n    details[\"inference_types\"] = inference_spec[\"SupportedRealtimeInferenceInstanceTypes\"]\n    details[\"transform_types\"] = inference_spec[\"SupportedTransformInstanceTypes\"]\n    details[\"content_types\"] = inference_spec[\"SupportedContentTypes\"]\n    details[\"response_types\"] = inference_spec[\"SupportedResponseMIMETypes\"]\n    details[\"model_metrics\"] = self.get_inference_metrics()\n    if self.model_type == ModelType.CLASSIFIER:\n        details[\"confusion_matrix\"] = self.confusion_matrix()\n        details[\"predictions\"] = None\n    elif self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n        details[\"confusion_matrix\"] = None\n        details[\"predictions\"] = self.get_inference_predictions()\n    else:\n        details[\"confusion_matrix\"] = None\n        details[\"predictions\"] = None\n\n    # Grab the inference metadata\n    details[\"inference_meta\"] = self.get_inference_metadata()\n\n    # Return the details\n    return details\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.endpoints","title":"<code>endpoints()</code>","text":"<p>Get the list of registered endpoints for this Model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of registered endpoints</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def endpoints(self) -&gt; list[str]:\n    \"\"\"Get the list of registered endpoints for this Model\n\n    Returns:\n        list[str]: List of registered endpoints\n    \"\"\"\n    return self.workbench_meta().get(\"workbench_registered_endpoints\", [])\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.exists","title":"<code>exists()</code>","text":"<p>Does the model metadata exist in the AWS Metadata?</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def exists(self) -&gt; bool:\n    \"\"\"Does the model metadata exist in the AWS Metadata?\"\"\"\n    if self.model_meta is None:\n        self.log.info(f\"Model {self.model_name} not found in AWS Metadata!\")\n        return False\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.expected_meta","title":"<code>expected_meta()</code>","text":"<p>Metadata we expect to see for this Model when it's ready Returns:     list[str]: List of expected metadata keys</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def expected_meta(self) -&gt; list[str]:\n    \"\"\"Metadata we expect to see for this Model when it's ready\n    Returns:\n        list[str]: List of expected metadata keys\n    \"\"\"\n    # Our current list of expected metadata, we can add to this as needed\n    return [\"workbench_status\", \"workbench_training_metrics\", \"workbench_training_cm\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.features","title":"<code>features()</code>","text":"<p>Return a list of features used for this Model</p> <p>Returns:</p> Type Description <code>Union[list[str], None]</code> <p>list[str]: List of features used for this Model</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def features(self) -&gt; Union[list[str], None]:\n    \"\"\"Return a list of features used for this Model\n\n    Returns:\n        list[str]: List of features used for this Model\n    \"\"\"\n    return self.workbench_meta().get(\"workbench_model_features\")  # Returns None if not found\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.get_endpoint_inference_path","title":"<code>get_endpoint_inference_path()</code>","text":"<p>Get the S3 Path for the Inference Data</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>S3 Path for the Inference Data (or None if not found)</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def get_endpoint_inference_path(self) -&gt; Union[str, None]:\n    \"\"\"Get the S3 Path for the Inference Data\n\n    Returns:\n        str: S3 Path for the Inference Data (or None if not found)\n    \"\"\"\n\n    # Look for any Registered Endpoints\n    registered_endpoints = self.workbench_meta().get(\"workbench_registered_endpoints\")\n\n    # Note: We may have 0 to N endpoints, so we find the one with the most recent artifacts\n    if registered_endpoints:\n        endpoint_inference_base = self.endpoints_s3_path + \"/inference/\"\n        endpoint_inference_paths = [endpoint_inference_base + e for e in registered_endpoints]\n        inference_path = newest_path(endpoint_inference_paths, self.sm_session)\n        if inference_path is None:\n            self.log.important(f\"No inference data found for {self.model_name}!\")\n            self.log.important(f\"Returning default inference path for {registered_endpoints[0]}...\")\n            self.log.important(f\"{endpoint_inference_paths[0]}\")\n            return endpoint_inference_paths[0]\n        else:\n            return inference_path\n    else:\n        self.log.warning(f\"No registered endpoints found for {self.model_name}!\")\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.get_inference_metadata","title":"<code>get_inference_metadata(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the inference metadata for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>A specific capture_uuid (default: \"auto_inference\")</p> <code>'auto_inference'</code> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[DataFrame, None]</code> <p>Dictionary of the inference metadata (might be None)</p> <p>Notes:     Basically when Endpoint inference was run, name of the dataset, the MD5, etc</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def get_inference_metadata(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the inference metadata for this model\n\n    Args:\n        capture_uuid (str, optional): A specific capture_uuid (default: \"auto_inference\")\n\n    Returns:\n        dict: Dictionary of the inference metadata (might be None)\n    Notes:\n        Basically when Endpoint inference was run, name of the dataset, the MD5, etc\n    \"\"\"\n    # Sanity check the inference path (which may or may not exist)\n    if self.endpoint_inference_path is None:\n        return None\n\n    # Check for model_training capture_uuid\n    if capture_uuid == \"model_training\":\n        # Create a DataFrame with the training metadata\n        meta_df = pd.DataFrame(\n            [\n                {\n                    \"name\": \"AWS Training Capture\",\n                    \"data_hash\": \"N/A\",\n                    \"num_rows\": \"-\",\n                    \"description\": \"-\",\n                }\n            ]\n        )\n        return meta_df\n\n    # Pull the inference metadata\n    try:\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_meta.json\"\n        return wr.s3.read_json(s3_path)\n    except NoFilesFound:\n        self.log.info(f\"Could not find model inference meta at {s3_path}...\")\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.get_inference_metrics","title":"<code>get_inference_metrics(capture_uuid='latest')</code>","text":"<p>Retrieve the inference performance metrics for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid or \"training\" (default: \"latest\")</p> <code>'latest'</code> <p>Returns:     pd.DataFrame: DataFrame of the Model Metrics</p> Note <p>If a capture_uuid isn't specified this will try to return something reasonable</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def get_inference_metrics(self, capture_uuid: str = \"latest\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the inference performance metrics for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid or \"training\" (default: \"latest\")\n    Returns:\n        pd.DataFrame: DataFrame of the Model Metrics\n\n    Note:\n        If a capture_uuid isn't specified this will try to return something reasonable\n    \"\"\"\n    # Try to get the auto_capture 'training_holdout' or the training\n    if capture_uuid == \"latest\":\n        metrics_df = self.get_inference_metrics(\"auto_inference\")\n        return metrics_df if metrics_df is not None else self.get_inference_metrics(\"model_training\")\n\n    # Grab the metrics captured during model training (could return None)\n    if capture_uuid == \"model_training\":\n        # Sanity check the workbench metadata\n        if self.workbench_meta() is None:\n            error_msg = f\"Model {self.model_name} has no workbench_meta(). Either onboard() or delete this model!\"\n            self.log.critical(error_msg)\n            raise ValueError(error_msg)\n\n        metrics = self.workbench_meta().get(\"workbench_training_metrics\")\n        return pd.DataFrame.from_dict(metrics) if metrics else None\n\n    else:  # Specific capture_uuid (could return None)\n        s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_metrics.csv\"\n        metrics = pull_s3_data(s3_path, embedded_index=True)\n        if metrics is not None:\n            return metrics\n        else:\n            self.log.warning(f\"Performance metrics {capture_uuid} not found for {self.model_name}!\")\n            return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.get_inference_predictions","title":"<code>get_inference_predictions(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the captured prediction results for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: training_holdout)</p> <code>'auto_inference'</code> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: DataFrame of the Captured Predictions (might be None)</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def get_inference_predictions(self, capture_uuid: str = \"auto_inference\") -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Retrieve the captured prediction results for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n    Returns:\n        pd.DataFrame: DataFrame of the Captured Predictions (might be None)\n    \"\"\"\n    self.log.important(f\"Grabbing {capture_uuid} predictions for {self.model_name}...\")\n\n    # Sanity check that the model should have predictions\n    has_predictions = self.model_type in [ModelType.CLASSIFIER, ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]\n    if not has_predictions:\n        self.log.warning(f\"No Predictions for {self.model_name}...\")\n        return None\n\n    # Special case for model_training\n    if capture_uuid == \"model_training\":\n        return self._get_validation_predictions()\n\n    # Construct the S3 path for the Inference Predictions\n    s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}/inference_predictions.csv\"\n    return pull_s3_data(s3_path)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.get_model_data_url","title":"<code>get_model_data_url()</code>","text":"<p>Retrieve the ModelDataUrl from the model's AWS metadata.</p> <p>Returns:</p> Type Description <code>Optional[str]</code> <p>Optional[str]: The ModelDataUrl if available, otherwise None.</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def get_model_data_url(self) -&gt; Optional[str]:\n    \"\"\"Retrieve the ModelDataUrl from the model's AWS metadata.\n\n    Returns:\n        Optional[str]: The ModelDataUrl if available, otherwise None.\n    \"\"\"\n    meta = self.aws_meta()\n    try:\n        return meta[\"ModelPackageList\"][0][\"InferenceSpecification\"][\"Containers\"][0][\"ModelDataUrl\"]\n    except (KeyError, IndexError, TypeError):\n        return None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.get_pipeline","title":"<code>get_pipeline()</code>","text":"<p>Get the pipeline for this model</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def get_pipeline(self) -&gt; str:\n    \"\"\"Get the pipeline for this model\"\"\"\n    return self.workbench_meta().get(\"workbench_pipeline\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.group_arn","title":"<code>group_arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for the Model Package Group</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def group_arn(self) -&gt; Union[str, None]:\n    \"\"\"AWS ARN (Amazon Resource Name) for the Model Package Group\"\"\"\n    return self.model_meta[\"ModelPackageGroupArn\"] if self.model_meta else None\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.hash","title":"<code>hash()</code>","text":"<p>Return the hash for this artifact</p> <p>Returns:</p> Type Description <code>Optional[str]</code> <p>Optional[str]: The hash for this artifact</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def hash(self) -&gt; Optional[str]:\n    \"\"\"Return the hash for this artifact\n\n    Returns:\n        Optional[str]: The hash for this artifact\n    \"\"\"\n    model_url = self.get_model_data_url()\n    return compute_s3_object_hash(model_url, self.boto3_session)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.health_check","title":"<code>health_check()</code>","text":"<p>Perform a health check on this model Returns:     list[str]: List of health issues</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def health_check(self) -&gt; list[str]:\n    \"\"\"Perform a health check on this model\n    Returns:\n        list[str]: List of health issues\n    \"\"\"\n    # Call the base class health check\n    health_issues = super().health_check()\n\n    # Check if the model exists\n    if self.latest_model is None:\n        health_issues.append(\"model_not_found\")\n\n    # Model Type\n    if self._get_model_type() == ModelType.UNKNOWN:\n        health_issues.append(\"model_type_unknown\")\n    else:\n        self.remove_health_tag(\"model_type_unknown\")\n\n    # Model Performance Metrics\n    needs_metrics = self.model_type in {ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR, ModelType.CLASSIFIER}\n    if needs_metrics and self.get_inference_metrics() is None:\n        health_issues.append(\"metrics_needed\")\n    else:\n        self.remove_health_tag(\"metrics_needed\")\n\n    # Endpoint\n    if not self.endpoints():\n        health_issues.append(\"no_endpoint\")\n    else:\n        self.remove_health_tag(\"no_endpoint\")\n    return health_issues\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.is_model_unknown","title":"<code>is_model_unknown()</code>","text":"<p>Is the Model Type unknown?</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def is_model_unknown(self) -&gt; bool:\n    \"\"\"Is the Model Type unknown?\"\"\"\n    return self.model_type == ModelType.UNKNOWN\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.latest_model_object","title":"<code>latest_model_object()</code>","text":"<p>Return the latest AWS Sagemaker Model object for this Workbench Model</p> <p>Returns:</p> Type Description <code>Model</code> <p>sagemaker.model.Model: AWS Sagemaker Model object</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def latest_model_object(self) -&gt; SagemakerModel:\n    \"\"\"Return the latest AWS Sagemaker Model object for this Workbench Model\n\n    Returns:\n       sagemaker.model.Model: AWS Sagemaker Model object\n    \"\"\"\n    return SagemakerModel(\n        model_data=self.model_package_arn(), sagemaker_session=self.sm_session, image_uri=self.container_image()\n    )\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.list_inference_runs","title":"<code>list_inference_runs()</code>","text":"<p>List the inference runs for this model</p> <p>Returns:</p> Type Description <code>list[str]</code> <p>list[str]: List of inference runs</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def list_inference_runs(self) -&gt; list[str]:\n    \"\"\"List the inference runs for this model\n\n    Returns:\n        list[str]: List of inference runs\n    \"\"\"\n\n    # Check if we have a model (if not return empty list)\n    if self.latest_model is None:\n        return []\n\n    # Check if we have model training metrics in our metadata\n    have_model_training = True if self.workbench_meta().get(\"workbench_training_metrics\") else False\n\n    # Now grab the list of directories from our inference path\n    inference_runs = []\n    if self.endpoint_inference_path:\n        directories = wr.s3.list_directories(path=self.endpoint_inference_path + \"/\")\n        inference_runs = [urlparse(directory).path.split(\"/\")[-2] for directory in directories]\n\n    # We're going to add the model training to the end of the list\n    if have_model_training:\n        inference_runs.append(\"model_training\")\n    return inference_runs\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.managed_delete","title":"<code>managed_delete(model_group_name)</code>  <code>classmethod</code>","text":"<p>Delete the Model Packages, Model Group, and S3 Storage Objects</p> <p>Parameters:</p> Name Type Description Default <code>model_group_name</code> <code>str</code> <p>The name of the Model Group to delete</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>@classmethod\ndef managed_delete(cls, model_group_name: str):\n    \"\"\"Delete the Model Packages, Model Group, and S3 Storage Objects\n\n    Args:\n        model_group_name (str): The name of the Model Group to delete\n    \"\"\"\n    # Check if the model group exists in SageMaker\n    try:\n        cls.sm_client.describe_model_package_group(ModelPackageGroupName=model_group_name)\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] in [\"ValidationException\", \"ResourceNotFound\"]:\n            cls.log.info(f\"Model Group {model_group_name} not found!\")\n            return\n        else:\n            raise  # Re-raise unexpected errors\n\n    # Delete Model Packages within the Model Group\n    try:\n        paginator = cls.sm_client.get_paginator(\"list_model_packages\")\n        for page in paginator.paginate(ModelPackageGroupName=model_group_name):\n            for model_package in page[\"ModelPackageSummaryList\"]:\n                package_arn = model_package[\"ModelPackageArn\"]\n                cls.log.info(f\"Deleting Model Package {package_arn}...\")\n                cls.sm_client.delete_model_package(ModelPackageName=package_arn)\n    except ClientError as e:\n        cls.log.error(f\"Error while deleting model packages: {e}\")\n        raise\n\n    # Delete the Model Package Group\n    cls.log.info(f\"Deleting Model Group {model_group_name}...\")\n    cls.sm_client.delete_model_package_group(ModelPackageGroupName=model_group_name)\n\n    # Delete S3 training artifacts\n    s3_delete_path = f\"{cls.models_s3_path}/training/{model_group_name}/\"\n    cls.log.info(f\"Deleting S3 Objects at {s3_delete_path}...\")\n    wr.s3.delete_objects(s3_delete_path, boto3_session=cls.boto3_session)\n\n    # Delete any dataframes that were stored in the Dataframe Cache\n    cls.log.info(\"Deleting Dataframe Cache...\")\n    cls.df_cache.delete_recursive(model_group_name)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.model_package_arn","title":"<code>model_package_arn()</code>","text":"<p>AWS ARN (Amazon Resource Name) for the Latest Model Package (within the Group)</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def model_package_arn(self) -&gt; Union[str, None]:\n    \"\"\"AWS ARN (Amazon Resource Name) for the Latest Model Package (within the Group)\"\"\"\n    if self.latest_model is None:\n        return None\n    return self.latest_model[\"ModelPackageArn\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.modified","title":"<code>modified()</code>","text":"<p>Return the datetime when this artifact was last modified</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def modified(self) -&gt; datetime:\n    \"\"\"Return the datetime when this artifact was last modified\"\"\"\n    if self.latest_model is None:\n        return \"-\"\n    return self.latest_model[\"CreationTime\"]\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.onboard","title":"<code>onboard(ask_everything=False)</code>","text":"<p>This is an interactive method that will onboard the Model (make it ready)</p> <p>Parameters:</p> Name Type Description Default <code>ask_everything</code> <code>bool</code> <p>Ask for all the details. Defaults to False.</p> <code>False</code> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if the Model is successfully onboarded, False otherwise</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def onboard(self, ask_everything=False) -&gt; bool:\n    \"\"\"This is an interactive method that will onboard the Model (make it ready)\n\n    Args:\n        ask_everything (bool, optional): Ask for all the details. Defaults to False.\n\n    Returns:\n        bool: True if the Model is successfully onboarded, False otherwise\n    \"\"\"\n    # Set the status to onboarding\n    self.set_status(\"onboarding\")\n\n    # Determine the Model Type\n    while self.is_model_unknown():\n        self._determine_model_type()\n\n    # Is our input data set?\n    if self.get_input() in [\"\", \"unknown\"] or ask_everything:\n        input_data = input(\"Input Data?: \")\n        if input_data not in [\"None\", \"none\", \"\", \"unknown\"]:\n            self.set_input(input_data)\n\n    # Determine the Target Column (can be None)\n    target_column = self.target()\n    if target_column is None or ask_everything:\n        target_column = input(\"Target Column? (for unsupervised/transformer just type None): \")\n        if target_column in [\"None\", \"none\", \"\"]:\n            target_column = None\n\n    # Determine the Feature Columns\n    feature_columns = self.features()\n    if feature_columns is None or ask_everything:\n        feature_columns = input(\"Feature Columns? (use commas): \")\n        feature_columns = [e.strip() for e in feature_columns.split(\",\")]\n        if feature_columns in [[\"None\"], [\"none\"], [\"\"]]:\n            feature_columns = None\n\n    # Registered Endpoints?\n    endpoints = self.endpoints()\n    if not endpoints or ask_everything:\n        endpoints = input(\"Register Endpoints? (use commas for multiple): \")\n        endpoints = [e.strip() for e in endpoints.split(\",\")]\n        if endpoints in [[\"None\"], [\"none\"], [\"\"]]:\n            endpoints = None\n\n    # Model Owner?\n    owner = self.get_owner()\n    if owner in [None, \"unknown\"] or ask_everything:\n        owner = input(\"Model Owner: \")\n        if owner in [\"None\", \"none\", \"\"]:\n            owner = \"unknown\"\n\n    # Model Class Labels (if it's a classifier)\n    if self.model_type == ModelType.CLASSIFIER:\n        class_labels = self.class_labels()\n        if class_labels is None or ask_everything:\n            class_labels = input(\"Class Labels? (use commas): \")\n            class_labels = [e.strip() for e in class_labels.split(\",\")]\n            if class_labels in [[\"None\"], [\"none\"], [\"\"]]:\n                class_labels = None\n        self.set_class_labels(class_labels)\n\n    # Now that we have all the details, let's onboard the Model with all the args\n    return self.onboard_with_args(self.model_type, target_column, feature_columns, endpoints, owner)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.onboard_with_args","title":"<code>onboard_with_args(model_type, target_column=None, feature_list=None, endpoints=None, owner=None)</code>","text":"<p>Onboard the Model with the given arguments</p> <p>Parameters:</p> Name Type Description Default <code>model_type</code> <code>ModelType</code> <p>Model Type</p> required <code>target_column</code> <code>str</code> <p>Target Column</p> <code>None</code> <code>feature_list</code> <code>list</code> <p>List of Feature Columns</p> <code>None</code> <code>endpoints</code> <code>list</code> <p>List of Endpoints. Defaults to None.</p> <code>None</code> <code>owner</code> <code>str</code> <p>Model Owner. Defaults to None.</p> <code>None</code> <p>Returns:     bool: True if the Model is successfully onboarded, False otherwise</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def onboard_with_args(\n    self,\n    model_type: ModelType,\n    target_column: str = None,\n    feature_list: list = None,\n    endpoints: list = None,\n    owner: str = None,\n) -&gt; bool:\n    \"\"\"Onboard the Model with the given arguments\n\n    Args:\n        model_type (ModelType): Model Type\n        target_column (str): Target Column\n        feature_list (list): List of Feature Columns\n        endpoints (list, optional): List of Endpoints. Defaults to None.\n        owner (str, optional): Model Owner. Defaults to None.\n    Returns:\n        bool: True if the Model is successfully onboarded, False otherwise\n    \"\"\"\n    # Set the status to onboarding\n    self.set_status(\"onboarding\")\n\n    # Set All the Details\n    self._set_model_type(model_type)\n    if target_column:\n        self.set_target(target_column)\n    if feature_list:\n        self.set_features(feature_list)\n    if endpoints:\n        for endpoint in endpoints:\n            self.register_endpoint(endpoint)\n    if owner:\n        self.set_owner(owner)\n\n    # Load the training metrics and inference metrics\n    self._load_training_metrics()\n    self._load_inference_metrics()\n\n    # Remove the needs_onboard tag\n    self.remove_health_tag(\"needs_onboard\")\n    self.set_status(\"ready\")\n\n    # Run a health check and refresh the meta\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.health_check()\n    self.refresh_meta()\n    self.details(recompute=True)\n    return True\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.refresh_meta","title":"<code>refresh_meta()</code>","text":"<p>Refresh the Artifact's metadata</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def refresh_meta(self):\n    \"\"\"Refresh the Artifact's metadata\"\"\"\n    self.model_meta = self.meta.model(self.model_name)\n    self.latest_model = self.model_meta[\"ModelPackageList\"][0]\n    self.description = self.latest_model.get(\"ModelPackageDescription\", \"-\")\n    self.training_job_name = self._extract_training_job_name()\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.register_endpoint","title":"<code>register_endpoint(endpoint_name)</code>","text":"<p>Add this endpoint to the set of registered endpoints for the model</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>Name of the endpoint</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def register_endpoint(self, endpoint_name: str):\n    \"\"\"Add this endpoint to the set of registered endpoints for the model\n\n    Args:\n        endpoint_name (str): Name of the endpoint\n    \"\"\"\n    self.log.important(f\"Registering Endpoint {endpoint_name} with Model {self.uuid}...\")\n    registered_endpoints = set(self.workbench_meta().get(\"workbench_registered_endpoints\", []))\n    registered_endpoints.add(endpoint_name)\n    self.upsert_workbench_meta({\"workbench_registered_endpoints\": list(registered_endpoints)})\n\n    # Remove any health tags\n    self.remove_health_tag(\"no_endpoint\")\n\n    # A new endpoint means we need to refresh our inference path\n    time.sleep(2)  # Give the AWS Metadata a chance to update\n    self.endpoint_inference_path = self.get_endpoint_inference_path()\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.remove_endpoint","title":"<code>remove_endpoint(endpoint_name)</code>","text":"<p>Remove this endpoint from the set of registered endpoints for the model</p> <p>Parameters:</p> Name Type Description Default <code>endpoint_name</code> <code>str</code> <p>Name of the endpoint</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def remove_endpoint(self, endpoint_name: str):\n    \"\"\"Remove this endpoint from the set of registered endpoints for the model\n\n    Args:\n        endpoint_name (str): Name of the endpoint\n    \"\"\"\n    self.log.important(f\"Removing Endpoint {endpoint_name} from Model {self.uuid}...\")\n    registered_endpoints = set(self.workbench_meta().get(\"workbench_registered_endpoints\", []))\n    registered_endpoints.discard(endpoint_name)\n    self.upsert_workbench_meta({\"workbench_registered_endpoints\": list(registered_endpoints)})\n\n    # If we have NO endpionts, then set a health tags\n    if not registered_endpoints:\n        self.add_health_tag(\"no_endpoint\")\n        self.details(recompute=True)\n\n    # A new endpoint means we need to refresh our inference path\n    time.sleep(2)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.set_class_labels","title":"<code>set_class_labels(labels)</code>","text":"<p>Return the class labels for this Model (if it's a classifier)</p> <p>Parameters:</p> Name Type Description Default <code>labels</code> <code>list[str]</code> <p>List of class labels</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def set_class_labels(self, labels: list[str]):\n    \"\"\"Return the class labels for this Model (if it's a classifier)\n\n    Args:\n        labels (list[str]): List of class labels\n    \"\"\"\n    if self.model_type == ModelType.CLASSIFIER:\n        self.upsert_workbench_meta({\"class_labels\": labels})\n    else:\n        self.log.error(f\"Model {self.model_name} is not a classifier!\")\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.set_features","title":"<code>set_features(feature_columns)</code>","text":"<p>Set the features for this Model</p> <p>Parameters:</p> Name Type Description Default <code>feature_columns</code> <code>list[str]</code> <p>List of feature columns</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def set_features(self, feature_columns: list[str]):\n    \"\"\"Set the features for this Model\n\n    Args:\n        feature_columns (list[str]): List of feature columns\n    \"\"\"\n    self.upsert_workbench_meta({\"workbench_model_features\": feature_columns})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.set_input","title":"<code>set_input(input, force=False)</code>","text":"<p>Override: Set the input data for this artifact</p> <p>Parameters:</p> Name Type Description Default <code>input</code> <code>str</code> <p>Name of input for this artifact</p> required <code>force</code> <code>bool</code> <p>Force the input to be set (default: False)</p> <code>False</code> <p>Note:     We're going to not allow this to be used for Models</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def set_input(self, input: str, force: bool = False):\n    \"\"\"Override: Set the input data for this artifact\n\n    Args:\n        input (str): Name of input for this artifact\n        force (bool, optional): Force the input to be set (default: False)\n    Note:\n        We're going to not allow this to be used for Models\n    \"\"\"\n    if not force:\n        self.log.warning(f\"Model {self.uuid}: Does not allow manual override of the input!\")\n        return\n\n    # Okay we're going to allow this to be set\n    self.log.important(f\"{self.uuid}: Setting input to {input}...\")\n    self.log.important(\"Be careful with this! It breaks automatic provenance of the artifact!\")\n    self.upsert_workbench_meta({\"workbench_input\": input})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.set_pipeline","title":"<code>set_pipeline(pipeline)</code>","text":"<p>Set the pipeline for this model</p> <p>Parameters:</p> Name Type Description Default <code>pipeline</code> <code>str</code> <p>Pipeline that was used to create this model</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def set_pipeline(self, pipeline: str):\n    \"\"\"Set the pipeline for this model\n\n    Args:\n        pipeline (str): Pipeline that was used to create this model\n    \"\"\"\n    self.upsert_workbench_meta({\"workbench_pipeline\": pipeline})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.set_target","title":"<code>set_target(target_column)</code>","text":"<p>Set the target for this Model</p> <p>Parameters:</p> Name Type Description Default <code>target_column</code> <code>str</code> <p>Target column for this Model</p> required Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def set_target(self, target_column: str):\n    \"\"\"Set the target for this Model\n\n    Args:\n        target_column (str): Target column for this Model\n    \"\"\"\n    self.upsert_workbench_meta({\"workbench_model_target\": target_column})\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.shapley_values","title":"<code>shapley_values(capture_uuid='auto_inference')</code>","text":"<p>Retrieve the Shapely values for this model</p> <p>Parameters:</p> Name Type Description Default <code>capture_uuid</code> <code>str</code> <p>Specific capture_uuid (default: training_holdout)</p> <code>'auto_inference'</code> <p>Returns:</p> Type Description <code>Union[list[DataFrame], DataFrame, None]</code> <p>pd.DataFrame: Dataframe(s) of the shapley values or None if not found</p> Notes <p>This may or may not exist based on whether an Endpoint ran Shapley</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def shapley_values(self, capture_uuid: str = \"auto_inference\") -&gt; Union[list[pd.DataFrame], pd.DataFrame, None]:\n    \"\"\"Retrieve the Shapely values for this model\n\n    Args:\n        capture_uuid (str, optional): Specific capture_uuid (default: training_holdout)\n\n    Returns:\n        pd.DataFrame: Dataframe(s) of the shapley values or None if not found\n\n    Notes:\n        This may or may not exist based on whether an Endpoint ran Shapley\n    \"\"\"\n\n    # Sanity check the inference path (which may or may not exist)\n    if self.endpoint_inference_path is None:\n        return None\n\n    # Construct the S3 path for the Shapley values\n    shapley_s3_path = f\"{self.endpoint_inference_path}/{capture_uuid}\"\n\n    # Multiple CSV if classifier\n    if self.model_type == ModelType.CLASSIFIER:\n        # CSVs for shap values are indexed by prediction class\n        # Because we don't know how many classes there are, we need to search through\n        # a list of S3 objects in the parent folder\n        s3_paths = wr.s3.list_objects(shapley_s3_path)\n        return [pull_s3_data(f) for f in s3_paths if \"inference_shap_values\" in f]\n\n    # One CSV if regressor\n    if self.model_type in [ModelType.REGRESSOR, ModelType.QUANTILE_REGRESSOR]:\n        s3_path = f\"{shapley_s3_path}/inference_shap_values.csv\"\n        return pull_s3_data(s3_path)\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.size","title":"<code>size()</code>","text":"<p>Return the size of this data in MegaBytes</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def size(self) -&gt; float:\n    \"\"\"Return the size of this data in MegaBytes\"\"\"\n    return 0.0\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelCore.target","title":"<code>target()</code>","text":"<p>Return the target for this Model (if supervised, else None)</p> <p>Returns:</p> Name Type Description <code>str</code> <code>Union[str, None]</code> <p>Target column for this Model (if supervised, else None)</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>def target(self) -&gt; Union[str, None]:\n    \"\"\"Return the target for this Model (if supervised, else None)\n\n    Returns:\n        str: Target column for this Model (if supervised, else None)\n    \"\"\"\n    return self.workbench_meta().get(\"workbench_model_target\")  # Returns None if not found\n</code></pre>"},{"location":"core_classes/artifacts/model_core/#workbench.core.artifacts.model_core.ModelType","title":"<code>ModelType</code>","text":"<p>               Bases: <code>Enum</code></p> <p>Enumerated Types for Workbench Model Types</p> Source code in <code>src/workbench/core/artifacts/model_core.py</code> <pre><code>class ModelType(Enum):\n    \"\"\"Enumerated Types for Workbench Model Types\"\"\"\n\n    CLASSIFIER = \"classifier\"\n    REGRESSOR = \"regressor\"\n    CLUSTERER = \"clusterer\"\n    TRANSFORMER = \"transformer\"\n    PROJECTION = \"projection\"\n    UNSUPERVISED = \"unsupervised\"\n    QUANTILE_REGRESSOR = \"quantile_regressor\"\n    DETECTOR = \"detector\"\n    UNKNOWN = \"unknown\"\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/","title":"MonitorCore","text":"<p>API Classes</p> <p>Found a method here you want to use? The API Classes have method pass-through so just call the method on the Monitor API Class and voil\u00e0 it works the same.</p> <p>MonitorCore class for monitoring SageMaker endpoints</p>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore","title":"<code>MonitorCore</code>","text":"Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>class MonitorCore:\n    def __init__(self, endpoint_name, instance_type=\"ml.t3.large\"):\n        \"\"\"ExtractModelArtifact Class\n        Args:\n            endpoint_name (str): Name of the endpoint to set up monitoring for\n            instance_type (str): Instance type to use for monitoring. Defaults to \"ml.t3.large\".\n                                 Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...\n        \"\"\"\n        self.log = logging.getLogger(\"workbench\")\n        self.endpoint_name = endpoint_name\n        self.endpoint = EndpointCore(self.endpoint_name)\n\n        # Initialize Class Attributes\n        self.sagemaker_session = self.endpoint.sm_session\n        self.sagemaker_client = self.endpoint.sm_client\n        self.data_capture_path = self.endpoint.endpoint_data_capture_path\n        self.monitoring_path = self.endpoint.endpoint_monitoring_path\n        self.instance_type = instance_type\n        self.monitoring_schedule_name = f\"{self.endpoint_name}-monitoring-schedule\"\n        self.monitoring_output_path = f\"{self.monitoring_path}/monitoring_reports\"\n        self.baseline_dir = f\"{self.monitoring_path}/baseline\"\n        self.baseline_csv_file = f\"{self.baseline_dir}/baseline.csv\"\n        self.constraints_json_file = f\"{self.baseline_dir}/constraints.json\"\n        self.statistics_json_file = f\"{self.baseline_dir}/statistics.json\"\n\n        # Initialize the DefaultModelMonitor\n        self.workbench_role_arn = AWSAccountClamp().aws_session.get_workbench_execution_role_arn()\n        self.model_monitor = DefaultModelMonitor(role=self.workbench_role_arn, instance_type=self.instance_type)\n\n    def summary(self) -&gt; dict:\n        \"\"\"Return the summary of information about the endpoint monitor\n\n        Returns:\n            dict: Summary of information about the endpoint monitor\n        \"\"\"\n        if self.endpoint.is_serverless():\n            return {\n                \"endpoint_type\": \"serverless\",\n                \"data_capture\": \"not supported\",\n                \"baseline\": \"not supported\",\n                \"monitoring_schedule\": \"not supported\",\n            }\n        else:\n            summary = {\n                \"endpoint_type\": \"realtime\",\n                \"data_capture\": self.is_data_capture_configured(capture_percentage=100),\n                \"baseline\": self.baseline_exists(),\n                \"monitoring_schedule\": self.monitoring_schedule_exists(),\n            }\n            summary.update(self.last_run_details() or {})\n            return summary\n\n    def __repr__(self) -&gt; str:\n        \"\"\"String representation of this MonitorCore object\n\n        Returns:\n            str: String representation of this MonitorCore object\n        \"\"\"\n        summary_dict = self.summary()\n        summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items()]\n        summary_str = f\"{self.__class__.__name__}: {self.endpoint_name}\\n\" + \",\\n\".join(summary_items)\n        return summary_str\n\n    def last_run_details(self) -&gt; Union[dict, None]:\n        \"\"\"Return the details of the last monitoring run for the endpoint\n\n        Returns:\n            dict: The details of the last monitoring run for the endpoint (None if no monitoring schedule)\n        \"\"\"\n        # Check if we have a monitoring schedule\n        if not self.monitoring_schedule_exists():\n            return None\n\n        # Get the details of the last monitoring run\n        schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n            MonitoringScheduleName=self.monitoring_schedule_name\n        )\n        last_run_status = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"MonitoringExecutionStatus\")\n        last_run_time = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"ScheduledTime\")\n        failure_reason = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"FailureReason\")\n        return {\n            \"last_run_status\": last_run_status,\n            \"last_run_time\": str(last_run_time),\n            \"failure_reason\": failure_reason,\n        }\n\n    def details(self) -&gt; dict:\n        \"\"\"Return the details of the monitoring for the endpoint\n\n        Returns:\n            dict: The details of the monitoring for the endpoint\n        \"\"\"\n        # Check if we have data capture\n        if self.is_data_capture_configured(capture_percentage=100):\n            data_capture_path = self.data_capture_path\n        else:\n            data_capture_path = None\n\n        # Check if we have a baseline\n        if self.baseline_exists():\n            baseline_csv_file = self.baseline_csv_file\n            constraints_json_file = self.constraints_json_file\n            statistics_json_file = self.statistics_json_file\n        else:\n            baseline_csv_file = None\n            constraints_json_file = None\n            statistics_json_file = None\n\n        # Check if we have a monitoring schedule\n        if self.monitoring_schedule_exists():\n            schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n                MonitoringScheduleName=self.monitoring_schedule_name\n            )\n\n            # General monitoring details\n            schedule_name = schedule_details.get(\"MonitoringScheduleName\")\n            schedule_status = schedule_details.get(\"MonitoringScheduleStatus\")\n            output_path = self.monitoring_output_path\n            last_run_details = self.last_run_details()\n        else:\n            schedule_name = None\n            schedule_status = \"Not Scheduled\"\n            schedule_details = None\n            output_path = None\n            last_run_details = None\n\n        # General monitoring details\n        general = {\n            \"data_capture_path\": data_capture_path,\n            \"baseline_csv_file\": baseline_csv_file,\n            \"baseline_constraints_json_file\": constraints_json_file,\n            \"baseline_statistics_json_file\": statistics_json_file,\n            \"monitoring_schedule_name\": schedule_name,\n            \"monitoring_output_path\": output_path,\n            \"monitoring_schedule_status\": schedule_status,\n            \"monitoring_schedule_details\": schedule_details,\n        }\n        if last_run_details:\n            general.update(last_run_details)\n        return general\n\n    def add_data_capture(self, capture_percentage=100):\n        \"\"\"\n        Add data capture configuration for the SageMaker endpoint.\n\n        Args:\n            capture_percentage (int): Percentage of data to capture. Defaults to 100.\n        \"\"\"\n\n        # Check if this endpoint is a serverless endpoint\n        if self.endpoint.is_serverless():\n            self.log.warning(\"Data capture is not currently supported for serverless endpoints.\")\n            return\n\n        # Check if the endpoint already has data capture configured\n        if self.is_data_capture_configured(capture_percentage):\n            self.log.important(f\"Data capture {capture_percentage} already configured for {self.endpoint_name}.\")\n            return\n\n        # Get the current endpoint configuration name\n        current_endpoint_config_name = self.endpoint.endpoint_config_name()\n\n        # Log the data capture path\n        self.log.important(f\"Adding Data Capture to {self.endpoint_name} --&gt; {self.data_capture_path}\")\n        self.log.important(\"This normally redeploys the endpoint...\")\n\n        # Setup data capture config\n        data_capture_config = DataCaptureConfig(\n            enable_capture=True,\n            sampling_percentage=capture_percentage,\n            destination_s3_uri=self.data_capture_path,\n            capture_options=[\"Input\", \"Output\"],\n            csv_content_types=[\"text/csv\"],\n        )\n\n        # Create a Predictor instance and update data capture configuration\n        predictor = Predictor(self.endpoint_name, sagemaker_session=self.sagemaker_session)\n        predictor.update_data_capture_config(data_capture_config=data_capture_config)\n\n        # Delete the old endpoint configuration\n        self.log.important(f\"Deleting old endpoint configuration: {current_endpoint_config_name}\")\n        self.sagemaker_client.delete_endpoint_config(EndpointConfigName=current_endpoint_config_name)\n\n    def is_data_capture_configured(self, capture_percentage):\n        \"\"\"\n        Check if data capture is already configured on the endpoint.\n        Args:\n            capture_percentage (int): Expected data capture percentage.\n        Returns:\n            bool: True if data capture is already configured, False otherwise.\n        \"\"\"\n        try:\n            endpoint_config_name = self.endpoint.endpoint_config_name()\n            endpoint_config = self.sagemaker_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n            data_capture_config = endpoint_config.get(\"DataCaptureConfig\", {})\n\n            # Check if data capture is enabled and the percentage matches\n            is_enabled = data_capture_config.get(\"EnableCapture\", False)\n            current_percentage = data_capture_config.get(\"InitialSamplingPercentage\", 0)\n            return is_enabled and current_percentage == capture_percentage\n        except Exception as e:\n            self.log.error(f\"Error checking data capture configuration: {e}\")\n            return False\n\n    def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"\n        Get the latest data capture from S3.\n\n        Returns:\n            DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n        \"\"\"\n        # List files in the specified S3 path\n        files = wr.s3.list_objects(self.data_capture_path)\n\n        if files:\n            print(f\"Found {len(files)} files in {self.data_capture_path}. Reading the most recent file.\")\n\n            # Read the most recent file into a DataFrame\n            df = wr.s3.read_json(path=files[-1], lines=True)  # Reads the last file assuming it's the most recent one\n\n            # Process the captured data and return the input and output DataFrames\n            return self.process_captured_data(df)\n        else:\n            print(f\"No data capture files found in {self.data_capture_path}.\")\n            return None, None\n\n    @staticmethod\n    def process_captured_data(df: pd.DataFrame) -&gt; (pd.DataFrame, pd.DataFrame):\n        \"\"\"\n        Process the captured data DataFrame to extract and flatten the nested data.\n\n        Args:\n            df (DataFrame): DataFrame with captured data.\n\n        Returns:\n            DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n        \"\"\"\n        processed_records = []\n\n        # Phase1: Process the AWS Data Capture format into a flatter DataFrame\n        for _, row in df.iterrows():\n            # Extract data from captureData dictionary\n            capture_data = row[\"captureData\"]\n            input_data = capture_data[\"endpointInput\"]\n            output_data = capture_data[\"endpointOutput\"]\n\n            # Process input and output, both meta and actual data\n            record = {\n                \"input_content_type\": input_data.get(\"observedContentType\"),\n                \"input_encoding\": input_data.get(\"encoding\"),\n                \"input\": input_data.get(\"data\"),\n                \"output_content_type\": output_data.get(\"observedContentType\"),\n                \"output_encoding\": output_data.get(\"encoding\"),\n                \"output\": output_data.get(\"data\"),\n            }\n            processed_records.append(record)\n        processed_df = pd.DataFrame(processed_records)\n\n        # Phase2: Process the input and output 'data' columns into separate DataFrames\n        input_df_list = []\n        output_df_list = []\n        for _, row in processed_df.iterrows():\n            input_df = pd.read_csv(StringIO(row[\"input\"]))\n            input_df_list.append(input_df)\n            output_df = pd.read_csv(StringIO(row[\"output\"]))\n            output_df_list.append(output_df)\n\n        # Return the input and output DataFrames\n        return pd.concat(input_df_list), pd.concat(output_df_list)\n\n    def baseline_exists(self) -&gt; bool:\n        \"\"\"\n        Check if baseline files exist in S3.\n\n        Returns:\n            bool: True if all files exist, False otherwise.\n        \"\"\"\n\n        files = [self.baseline_csv_file, self.constraints_json_file, self.statistics_json_file]\n        return all(wr.s3.does_object_exist(file) for file in files)\n\n    def create_baseline(self, recreate: bool = False):\n        \"\"\"Code to create a baseline for monitoring\n        Args:\n            recreate (bool): If True, recreate the baseline even if it already exists\n        Notes:\n            This will create/write three files to the baseline_dir:\n            - baseline.csv\n            - constraints.json\n            - statistics.json\n        \"\"\"\n        # Check if this endpoint is a serverless endpoint\n        if self.endpoint.is_serverless():\n            self.log.warning(\n                \"You can create a baseline but it can't be used/monitored for serverless endpoints, skipping...\"\n            )\n            return\n\n        if not self.baseline_exists() or recreate:\n            # Create a baseline for monitoring (training data from the FeatureSet)\n            baseline_df = endpoint_utils.fs_training_data(self.endpoint)\n            wr.s3.to_csv(baseline_df, self.baseline_csv_file, index=False)\n\n            self.log.important(f\"Creating baseline files for {self.endpoint_name} --&gt; {self.baseline_dir}\")\n            self.model_monitor.suggest_baseline(\n                baseline_dataset=self.baseline_csv_file,\n                dataset_format=DatasetFormat.csv(header=True),\n                output_s3_uri=self.baseline_dir,\n            )\n        else:\n            self.log.important(f\"Baseline already exists for {self.endpoint_name}\")\n\n    def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n        Returns:\n            pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n        \"\"\"\n        # Read the monitoring data from S3\n        if not wr.s3.does_object_exist(path=self.baseline_csv_file):\n            self.log.warning(\"baseline.csv data does not exist in S3.\")\n            return None\n        else:\n            return wr.s3.read_csv(self.baseline_csv_file)\n\n    def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the constraints from the baseline\n\n        Returns:\n           pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n        \"\"\"\n        return self._get_monitor_json_data(self.constraints_json_file)\n\n    def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Code to get the statistics from the baseline\n\n        Returns:\n            pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n        \"\"\"\n        return self._get_monitor_json_data(self.statistics_json_file)\n\n    def _get_monitor_json_data(self, s3_path: str) -&gt; Union[pd.DataFrame, None]:\n        \"\"\"Internal: Convert the JSON monitoring data into a DataFrame\n        Args:\n            s3_path(str): The S3 path to the monitoring data\n        Returns:\n            pd.DataFrame: Monitoring data in DataFrame form (None if it doesn't exist)\n        \"\"\"\n        # Read the monitoring data from S3\n        if not wr.s3.does_object_exist(path=s3_path):\n            self.log.warning(\"Monitoring data does not exist in S3.\")\n            return None\n        else:\n            raw_json = read_s3_file(s3_path=s3_path)\n            monitoring_data = json.loads(raw_json)\n            monitoring_df = pd.json_normalize(monitoring_data[\"features\"])\n            return monitoring_df\n\n    def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n        \"\"\"\n        Sets up the monitoring schedule for the model endpoint.\n        Args:\n            schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n            recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n        \"\"\"\n        # Check if this endpoint is a serverless endpoint\n        if self.endpoint.is_serverless():\n            self.log.warning(\"Monitoring Schedule is not currently supported for serverless endpoints.\")\n            return\n\n        # Set up the monitoring schedule, name, and output path\n        if schedule == \"daily\":\n            schedule = CronExpressionGenerator.daily()\n        else:\n            schedule = CronExpressionGenerator.hourly()\n\n        # Check if the baseline exists\n        if not self.baseline_exists():\n            self.log.warning(f\"Baseline does not exist for {self.endpoint_name}. Call create_baseline() first...\")\n            return\n\n        # Check if monitoring schedule already exists\n        schedule_exists = self.monitoring_schedule_exists()\n\n        # If the schedule exists, and we don't want to recreate it, return\n        if schedule_exists and not recreate:\n            return\n\n        # If the schedule exists, delete it\n        if schedule_exists:\n            self.log.important(f\"Deleting existing monitoring schedule for {self.endpoint_name}...\")\n            self.sagemaker_client.delete_monitoring_schedule(MonitoringScheduleName=self.monitoring_schedule_name)\n\n        # Set up a NEW monitoring schedule\n        self.model_monitor.create_monitoring_schedule(\n            monitor_schedule_name=self.monitoring_schedule_name,\n            endpoint_input=self.endpoint_name,\n            output_s3_uri=self.monitoring_output_path,\n            statistics=self.statistics_json_file,\n            constraints=self.constraints_json_file,\n            schedule_cron_expression=schedule,\n        )\n        self.log.important(f\"New Monitoring schedule created for {self.endpoint_name}.\")\n\n    def setup_alerts(self):\n        \"\"\"Code to set up alerts based on monitoring results\"\"\"\n        pass\n\n    def monitoring_schedule_exists(self):\n        \"\"\"Code to figure out if a monitoring schedule already exists for this endpoint\"\"\"\n        existing_schedules = self.sagemaker_client.list_monitoring_schedules(MaxResults=100).get(\n            \"MonitoringScheduleSummaries\", []\n        )\n        if any(schedule[\"MonitoringScheduleName\"] == self.monitoring_schedule_name for schedule in existing_schedules):\n            self.log.info(f\"Monitoring schedule already exists for {self.endpoint_name}.\")\n            return True\n        else:\n            self.log.info(f\"Could not find a Monitoring schedule for {self.endpoint_name}.\")\n            return False\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.__init__","title":"<code>__init__(endpoint_name, instance_type='ml.t3.large')</code>","text":"<p>ExtractModelArtifact Class Args:     endpoint_name (str): Name of the endpoint to set up monitoring for     instance_type (str): Instance type to use for monitoring. Defaults to \"ml.t3.large\".                          Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def __init__(self, endpoint_name, instance_type=\"ml.t3.large\"):\n    \"\"\"ExtractModelArtifact Class\n    Args:\n        endpoint_name (str): Name of the endpoint to set up monitoring for\n        instance_type (str): Instance type to use for monitoring. Defaults to \"ml.t3.large\".\n                             Other options: ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ...\n    \"\"\"\n    self.log = logging.getLogger(\"workbench\")\n    self.endpoint_name = endpoint_name\n    self.endpoint = EndpointCore(self.endpoint_name)\n\n    # Initialize Class Attributes\n    self.sagemaker_session = self.endpoint.sm_session\n    self.sagemaker_client = self.endpoint.sm_client\n    self.data_capture_path = self.endpoint.endpoint_data_capture_path\n    self.monitoring_path = self.endpoint.endpoint_monitoring_path\n    self.instance_type = instance_type\n    self.monitoring_schedule_name = f\"{self.endpoint_name}-monitoring-schedule\"\n    self.monitoring_output_path = f\"{self.monitoring_path}/monitoring_reports\"\n    self.baseline_dir = f\"{self.monitoring_path}/baseline\"\n    self.baseline_csv_file = f\"{self.baseline_dir}/baseline.csv\"\n    self.constraints_json_file = f\"{self.baseline_dir}/constraints.json\"\n    self.statistics_json_file = f\"{self.baseline_dir}/statistics.json\"\n\n    # Initialize the DefaultModelMonitor\n    self.workbench_role_arn = AWSAccountClamp().aws_session.get_workbench_execution_role_arn()\n    self.model_monitor = DefaultModelMonitor(role=self.workbench_role_arn, instance_type=self.instance_type)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.__repr__","title":"<code>__repr__()</code>","text":"<p>String representation of this MonitorCore object</p> <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>String representation of this MonitorCore object</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def __repr__(self) -&gt; str:\n    \"\"\"String representation of this MonitorCore object\n\n    Returns:\n        str: String representation of this MonitorCore object\n    \"\"\"\n    summary_dict = self.summary()\n    summary_items = [f\"  {repr(key)}: {repr(value)}\" for key, value in summary_dict.items()]\n    summary_str = f\"{self.__class__.__name__}: {self.endpoint_name}\\n\" + \",\\n\".join(summary_items)\n    return summary_str\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.add_data_capture","title":"<code>add_data_capture(capture_percentage=100)</code>","text":"<p>Add data capture configuration for the SageMaker endpoint.</p> <p>Parameters:</p> Name Type Description Default <code>capture_percentage</code> <code>int</code> <p>Percentage of data to capture. Defaults to 100.</p> <code>100</code> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def add_data_capture(self, capture_percentage=100):\n    \"\"\"\n    Add data capture configuration for the SageMaker endpoint.\n\n    Args:\n        capture_percentage (int): Percentage of data to capture. Defaults to 100.\n    \"\"\"\n\n    # Check if this endpoint is a serverless endpoint\n    if self.endpoint.is_serverless():\n        self.log.warning(\"Data capture is not currently supported for serverless endpoints.\")\n        return\n\n    # Check if the endpoint already has data capture configured\n    if self.is_data_capture_configured(capture_percentage):\n        self.log.important(f\"Data capture {capture_percentage} already configured for {self.endpoint_name}.\")\n        return\n\n    # Get the current endpoint configuration name\n    current_endpoint_config_name = self.endpoint.endpoint_config_name()\n\n    # Log the data capture path\n    self.log.important(f\"Adding Data Capture to {self.endpoint_name} --&gt; {self.data_capture_path}\")\n    self.log.important(\"This normally redeploys the endpoint...\")\n\n    # Setup data capture config\n    data_capture_config = DataCaptureConfig(\n        enable_capture=True,\n        sampling_percentage=capture_percentage,\n        destination_s3_uri=self.data_capture_path,\n        capture_options=[\"Input\", \"Output\"],\n        csv_content_types=[\"text/csv\"],\n    )\n\n    # Create a Predictor instance and update data capture configuration\n    predictor = Predictor(self.endpoint_name, sagemaker_session=self.sagemaker_session)\n    predictor.update_data_capture_config(data_capture_config=data_capture_config)\n\n    # Delete the old endpoint configuration\n    self.log.important(f\"Deleting old endpoint configuration: {current_endpoint_config_name}\")\n    self.sagemaker_client.delete_endpoint_config(EndpointConfigName=current_endpoint_config_name)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.baseline_exists","title":"<code>baseline_exists()</code>","text":"<p>Check if baseline files exist in S3.</p> <p>Returns:</p> Name Type Description <code>bool</code> <code>bool</code> <p>True if all files exist, False otherwise.</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def baseline_exists(self) -&gt; bool:\n    \"\"\"\n    Check if baseline files exist in S3.\n\n    Returns:\n        bool: True if all files exist, False otherwise.\n    \"\"\"\n\n    files = [self.baseline_csv_file, self.constraints_json_file, self.statistics_json_file]\n    return all(wr.s3.does_object_exist(file) for file in files)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.create_baseline","title":"<code>create_baseline(recreate=False)</code>","text":"<p>Code to create a baseline for monitoring Args:     recreate (bool): If True, recreate the baseline even if it already exists Notes:     This will create/write three files to the baseline_dir:     - baseline.csv     - constraints.json     - statistics.json</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def create_baseline(self, recreate: bool = False):\n    \"\"\"Code to create a baseline for monitoring\n    Args:\n        recreate (bool): If True, recreate the baseline even if it already exists\n    Notes:\n        This will create/write three files to the baseline_dir:\n        - baseline.csv\n        - constraints.json\n        - statistics.json\n    \"\"\"\n    # Check if this endpoint is a serverless endpoint\n    if self.endpoint.is_serverless():\n        self.log.warning(\n            \"You can create a baseline but it can't be used/monitored for serverless endpoints, skipping...\"\n        )\n        return\n\n    if not self.baseline_exists() or recreate:\n        # Create a baseline for monitoring (training data from the FeatureSet)\n        baseline_df = endpoint_utils.fs_training_data(self.endpoint)\n        wr.s3.to_csv(baseline_df, self.baseline_csv_file, index=False)\n\n        self.log.important(f\"Creating baseline files for {self.endpoint_name} --&gt; {self.baseline_dir}\")\n        self.model_monitor.suggest_baseline(\n            baseline_dataset=self.baseline_csv_file,\n            dataset_format=DatasetFormat.csv(header=True),\n            output_s3_uri=self.baseline_dir,\n        )\n    else:\n        self.log.important(f\"Baseline already exists for {self.endpoint_name}\")\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.create_monitoring_schedule","title":"<code>create_monitoring_schedule(schedule='hourly', recreate=False)</code>","text":"<p>Sets up the monitoring schedule for the model endpoint. Args:     schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).     recreate (bool): If True, recreate the monitoring schedule even if it already exists.</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def create_monitoring_schedule(self, schedule: str = \"hourly\", recreate: bool = False):\n    \"\"\"\n    Sets up the monitoring schedule for the model endpoint.\n    Args:\n        schedule (str): The schedule for the monitoring job (hourly or daily, defaults to hourly).\n        recreate (bool): If True, recreate the monitoring schedule even if it already exists.\n    \"\"\"\n    # Check if this endpoint is a serverless endpoint\n    if self.endpoint.is_serverless():\n        self.log.warning(\"Monitoring Schedule is not currently supported for serverless endpoints.\")\n        return\n\n    # Set up the monitoring schedule, name, and output path\n    if schedule == \"daily\":\n        schedule = CronExpressionGenerator.daily()\n    else:\n        schedule = CronExpressionGenerator.hourly()\n\n    # Check if the baseline exists\n    if not self.baseline_exists():\n        self.log.warning(f\"Baseline does not exist for {self.endpoint_name}. Call create_baseline() first...\")\n        return\n\n    # Check if monitoring schedule already exists\n    schedule_exists = self.monitoring_schedule_exists()\n\n    # If the schedule exists, and we don't want to recreate it, return\n    if schedule_exists and not recreate:\n        return\n\n    # If the schedule exists, delete it\n    if schedule_exists:\n        self.log.important(f\"Deleting existing monitoring schedule for {self.endpoint_name}...\")\n        self.sagemaker_client.delete_monitoring_schedule(MonitoringScheduleName=self.monitoring_schedule_name)\n\n    # Set up a NEW monitoring schedule\n    self.model_monitor.create_monitoring_schedule(\n        monitor_schedule_name=self.monitoring_schedule_name,\n        endpoint_input=self.endpoint_name,\n        output_s3_uri=self.monitoring_output_path,\n        statistics=self.statistics_json_file,\n        constraints=self.constraints_json_file,\n        schedule_cron_expression=schedule,\n    )\n    self.log.important(f\"New Monitoring schedule created for {self.endpoint_name}.\")\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.details","title":"<code>details()</code>","text":"<p>Return the details of the monitoring for the endpoint</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>The details of the monitoring for the endpoint</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def details(self) -&gt; dict:\n    \"\"\"Return the details of the monitoring for the endpoint\n\n    Returns:\n        dict: The details of the monitoring for the endpoint\n    \"\"\"\n    # Check if we have data capture\n    if self.is_data_capture_configured(capture_percentage=100):\n        data_capture_path = self.data_capture_path\n    else:\n        data_capture_path = None\n\n    # Check if we have a baseline\n    if self.baseline_exists():\n        baseline_csv_file = self.baseline_csv_file\n        constraints_json_file = self.constraints_json_file\n        statistics_json_file = self.statistics_json_file\n    else:\n        baseline_csv_file = None\n        constraints_json_file = None\n        statistics_json_file = None\n\n    # Check if we have a monitoring schedule\n    if self.monitoring_schedule_exists():\n        schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n            MonitoringScheduleName=self.monitoring_schedule_name\n        )\n\n        # General monitoring details\n        schedule_name = schedule_details.get(\"MonitoringScheduleName\")\n        schedule_status = schedule_details.get(\"MonitoringScheduleStatus\")\n        output_path = self.monitoring_output_path\n        last_run_details = self.last_run_details()\n    else:\n        schedule_name = None\n        schedule_status = \"Not Scheduled\"\n        schedule_details = None\n        output_path = None\n        last_run_details = None\n\n    # General monitoring details\n    general = {\n        \"data_capture_path\": data_capture_path,\n        \"baseline_csv_file\": baseline_csv_file,\n        \"baseline_constraints_json_file\": constraints_json_file,\n        \"baseline_statistics_json_file\": statistics_json_file,\n        \"monitoring_schedule_name\": schedule_name,\n        \"monitoring_output_path\": output_path,\n        \"monitoring_schedule_status\": schedule_status,\n        \"monitoring_schedule_details\": schedule_details,\n    }\n    if last_run_details:\n        general.update(last_run_details)\n    return general\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.get_baseline","title":"<code>get_baseline()</code>","text":"<p>Code to get the baseline CSV from the S3 baseline directory</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def get_baseline(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the baseline CSV from the S3 baseline directory\n\n    Returns:\n        pd.DataFrame: The baseline CSV as a DataFrame (None if it doesn't exist)\n    \"\"\"\n    # Read the monitoring data from S3\n    if not wr.s3.does_object_exist(path=self.baseline_csv_file):\n        self.log.warning(\"baseline.csv data does not exist in S3.\")\n        return None\n    else:\n        return wr.s3.read_csv(self.baseline_csv_file)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.get_constraints","title":"<code>get_constraints()</code>","text":"<p>Code to get the constraints from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def get_constraints(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the constraints from the baseline\n\n    Returns:\n       pd.DataFrame: The constraints from the baseline (constraints.json) (None if it doesn't exist)\n    \"\"\"\n    return self._get_monitor_json_data(self.constraints_json_file)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.get_latest_data_capture","title":"<code>get_latest_data_capture()</code>","text":"<p>Get the latest data capture from S3.</p> <p>Returns:</p> Name Type Description <code>DataFrame</code> <code>input), DataFrame(output</code> <p>Flattened and processed DataFrames for input and output data.</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def get_latest_data_capture(self) -&gt; (pd.DataFrame, pd.DataFrame):\n    \"\"\"\n    Get the latest data capture from S3.\n\n    Returns:\n        DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n    \"\"\"\n    # List files in the specified S3 path\n    files = wr.s3.list_objects(self.data_capture_path)\n\n    if files:\n        print(f\"Found {len(files)} files in {self.data_capture_path}. Reading the most recent file.\")\n\n        # Read the most recent file into a DataFrame\n        df = wr.s3.read_json(path=files[-1], lines=True)  # Reads the last file assuming it's the most recent one\n\n        # Process the captured data and return the input and output DataFrames\n        return self.process_captured_data(df)\n    else:\n        print(f\"No data capture files found in {self.data_capture_path}.\")\n        return None, None\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.get_statistics","title":"<code>get_statistics()</code>","text":"<p>Code to get the statistics from the baseline</p> <p>Returns:</p> Type Description <code>Union[DataFrame, None]</code> <p>pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def get_statistics(self) -&gt; Union[pd.DataFrame, None]:\n    \"\"\"Code to get the statistics from the baseline\n\n    Returns:\n        pd.DataFrame: The statistics from the baseline (statistics.json) (None if it doesn't exist)\n    \"\"\"\n    return self._get_monitor_json_data(self.statistics_json_file)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.is_data_capture_configured","title":"<code>is_data_capture_configured(capture_percentage)</code>","text":"<p>Check if data capture is already configured on the endpoint. Args:     capture_percentage (int): Expected data capture percentage. Returns:     bool: True if data capture is already configured, False otherwise.</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def is_data_capture_configured(self, capture_percentage):\n    \"\"\"\n    Check if data capture is already configured on the endpoint.\n    Args:\n        capture_percentage (int): Expected data capture percentage.\n    Returns:\n        bool: True if data capture is already configured, False otherwise.\n    \"\"\"\n    try:\n        endpoint_config_name = self.endpoint.endpoint_config_name()\n        endpoint_config = self.sagemaker_client.describe_endpoint_config(EndpointConfigName=endpoint_config_name)\n        data_capture_config = endpoint_config.get(\"DataCaptureConfig\", {})\n\n        # Check if data capture is enabled and the percentage matches\n        is_enabled = data_capture_config.get(\"EnableCapture\", False)\n        current_percentage = data_capture_config.get(\"InitialSamplingPercentage\", 0)\n        return is_enabled and current_percentage == capture_percentage\n    except Exception as e:\n        self.log.error(f\"Error checking data capture configuration: {e}\")\n        return False\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.last_run_details","title":"<code>last_run_details()</code>","text":"<p>Return the details of the last monitoring run for the endpoint</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>Union[dict, None]</code> <p>The details of the last monitoring run for the endpoint (None if no monitoring schedule)</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def last_run_details(self) -&gt; Union[dict, None]:\n    \"\"\"Return the details of the last monitoring run for the endpoint\n\n    Returns:\n        dict: The details of the last monitoring run for the endpoint (None if no monitoring schedule)\n    \"\"\"\n    # Check if we have a monitoring schedule\n    if not self.monitoring_schedule_exists():\n        return None\n\n    # Get the details of the last monitoring run\n    schedule_details = self.sagemaker_client.describe_monitoring_schedule(\n        MonitoringScheduleName=self.monitoring_schedule_name\n    )\n    last_run_status = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"MonitoringExecutionStatus\")\n    last_run_time = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"ScheduledTime\")\n    failure_reason = schedule_details.get(\"LastMonitoringExecutionSummary\", {}).get(\"FailureReason\")\n    return {\n        \"last_run_status\": last_run_status,\n        \"last_run_time\": str(last_run_time),\n        \"failure_reason\": failure_reason,\n    }\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.monitoring_schedule_exists","title":"<code>monitoring_schedule_exists()</code>","text":"<p>Code to figure out if a monitoring schedule already exists for this endpoint</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def monitoring_schedule_exists(self):\n    \"\"\"Code to figure out if a monitoring schedule already exists for this endpoint\"\"\"\n    existing_schedules = self.sagemaker_client.list_monitoring_schedules(MaxResults=100).get(\n        \"MonitoringScheduleSummaries\", []\n    )\n    if any(schedule[\"MonitoringScheduleName\"] == self.monitoring_schedule_name for schedule in existing_schedules):\n        self.log.info(f\"Monitoring schedule already exists for {self.endpoint_name}.\")\n        return True\n    else:\n        self.log.info(f\"Could not find a Monitoring schedule for {self.endpoint_name}.\")\n        return False\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.process_captured_data","title":"<code>process_captured_data(df)</code>  <code>staticmethod</code>","text":"<p>Process the captured data DataFrame to extract and flatten the nested data.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>DataFrame with captured data.</p> required <p>Returns:</p> Name Type Description <code>DataFrame</code> <code>input), DataFrame(output</code> <p>Flattened and processed DataFrames for input and output data.</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>@staticmethod\ndef process_captured_data(df: pd.DataFrame) -&gt; (pd.DataFrame, pd.DataFrame):\n    \"\"\"\n    Process the captured data DataFrame to extract and flatten the nested data.\n\n    Args:\n        df (DataFrame): DataFrame with captured data.\n\n    Returns:\n        DataFrame (input), DataFrame(output): Flattened and processed DataFrames for input and output data.\n    \"\"\"\n    processed_records = []\n\n    # Phase1: Process the AWS Data Capture format into a flatter DataFrame\n    for _, row in df.iterrows():\n        # Extract data from captureData dictionary\n        capture_data = row[\"captureData\"]\n        input_data = capture_data[\"endpointInput\"]\n        output_data = capture_data[\"endpointOutput\"]\n\n        # Process input and output, both meta and actual data\n        record = {\n            \"input_content_type\": input_data.get(\"observedContentType\"),\n            \"input_encoding\": input_data.get(\"encoding\"),\n            \"input\": input_data.get(\"data\"),\n            \"output_content_type\": output_data.get(\"observedContentType\"),\n            \"output_encoding\": output_data.get(\"encoding\"),\n            \"output\": output_data.get(\"data\"),\n        }\n        processed_records.append(record)\n    processed_df = pd.DataFrame(processed_records)\n\n    # Phase2: Process the input and output 'data' columns into separate DataFrames\n    input_df_list = []\n    output_df_list = []\n    for _, row in processed_df.iterrows():\n        input_df = pd.read_csv(StringIO(row[\"input\"]))\n        input_df_list.append(input_df)\n        output_df = pd.read_csv(StringIO(row[\"output\"]))\n        output_df_list.append(output_df)\n\n    # Return the input and output DataFrames\n    return pd.concat(input_df_list), pd.concat(output_df_list)\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.setup_alerts","title":"<code>setup_alerts()</code>","text":"<p>Code to set up alerts based on monitoring results</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def setup_alerts(self):\n    \"\"\"Code to set up alerts based on monitoring results\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/artifacts/monitor_core/#workbench.core.artifacts.monitor_core.MonitorCore.summary","title":"<code>summary()</code>","text":"<p>Return the summary of information about the endpoint monitor</p> <p>Returns:</p> Name Type Description <code>dict</code> <code>dict</code> <p>Summary of information about the endpoint monitor</p> Source code in <code>src/workbench/core/artifacts/monitor_core.py</code> <pre><code>def summary(self) -&gt; dict:\n    \"\"\"Return the summary of information about the endpoint monitor\n\n    Returns:\n        dict: Summary of information about the endpoint monitor\n    \"\"\"\n    if self.endpoint.is_serverless():\n        return {\n            \"endpoint_type\": \"serverless\",\n            \"data_capture\": \"not supported\",\n            \"baseline\": \"not supported\",\n            \"monitoring_schedule\": \"not supported\",\n        }\n    else:\n        summary = {\n            \"endpoint_type\": \"realtime\",\n            \"data_capture\": self.is_data_capture_configured(capture_percentage=100),\n            \"baseline\": self.baseline_exists(),\n            \"monitoring_schedule\": self.monitoring_schedule_exists(),\n        }\n        summary.update(self.last_run_details() or {})\n        return summary\n</code></pre>"},{"location":"core_classes/artifacts/overview/","title":"Workbench Artifacts","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p>"},{"location":"core_classes/artifacts/overview/#welcome-to-the-workbench-core-artifact-classes","title":"Welcome to the Workbench Core Artifact Classes","text":"<p>These classes provide low-level APIs for the Workbench package, they interact more directly with AWS Services and are therefore more complex with a fairly large number of methods. </p> <ul> <li>AthenaSource: Manages AWS Data Catalog and Athena</li> <li>FeatureSetCore: Manages AWS Feature Store and Feature Groups</li> <li>ModelCore: Manages the training and deployment of AWS Model Groups and Packages</li> <li>EndpointCore: Manages the deployment and invocations/inference on AWS Endpoints</li> </ul> <p></p>"},{"location":"core_classes/transforms/data_loaders_heavy/","title":"DataLoaders Heavy","text":"<p>These DataLoader Classes are intended to load larger dataset into AWS. For large data we need to use AWS Glue Jobs/Batch Jobs and in general the process is a bit more complicated and has less features.</p> <p>If you have smaller data please see DataLoaders Light</p> <p>Welcome to the Workbench DataLoaders Heavy Classes</p> <p>These classes provide low-level APIs for loading larger data into AWS services</p> <ul> <li>S3HeavyToDataSource: Loads large data from S3 into a DataSource</li> </ul>"},{"location":"core_classes/transforms/data_loaders_heavy/#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource","title":"<code>S3HeavyToDataSource</code>","text":"Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>class S3HeavyToDataSource:\n    def __init__(self, glue_context: GlueContext, input_uuid: str, output_uuid: str):\n        \"\"\"S3HeavyToDataSource: Class to move HEAVY S3 Files into a Workbench DataSource\n\n        Args:\n            glue_context: GlueContext, AWS Glue Specific wrapper around SparkContext\n            input_uuid (str): The S3 Path to the files to be loaded\n            output_uuid (str): The UUID of the Workbench DataSource to be created\n        \"\"\"\n        self.log = glue_context.get_logger()\n\n        # FIXME: Pull these from Parameter Store or Config\n        self.input_uuid = input_uuid\n        self.output_uuid = output_uuid\n        self.output_meta = {\"workbench_input\": self.input_uuid}\n        workbench_bucket = \"s3://sandbox-workbench-artifacts\"\n        self.data_sources_s3_path = workbench_bucket + \"/data-sources\"\n\n        # Our Spark Context\n        self.glue_context = glue_context\n\n    @staticmethod\n    def resolve_choice_fields(dyf):\n        # Get schema fields\n        schema_fields = dyf.schema().fields\n\n        # Collect choice fields\n        choice_fields = [(field.name, \"cast:long\") for field in schema_fields if field.dataType.typeName() == \"choice\"]\n        print(f\"Choice Fields: {choice_fields}\")\n\n        # If there are choice fields, resolve them\n        if choice_fields:\n            dyf = dyf.resolveChoice(specs=choice_fields)\n\n        return dyf\n\n    def timestamp_conversions(self, dyf: DynamicFrame, time_columns: list = []) -&gt; DynamicFrame:\n        \"\"\"Convert columns in the DynamicFrame to the correct data types\n        Args:\n            dyf (DynamicFrame): The DynamicFrame to convert\n            time_columns (list): A list of column names to convert to timestamp\n        Returns:\n            DynamicFrame: The converted DynamicFrame\n        \"\"\"\n\n        # Convert the timestamp columns to timestamp types\n        spark_df = dyf.toDF()\n        for column in time_columns:\n            spark_df = spark_df.withColumn(column, to_timestamp(col(column)))\n\n        # Convert the Spark DataFrame back to a Glue DynamicFrame and return\n        return DynamicFrame.fromDF(spark_df, self.glue_context, \"output_dyf\")\n\n    @staticmethod\n    def remove_periods_from_columns(dyf: DynamicFrame) -&gt; DynamicFrame:\n        \"\"\"Remove periods from column names in the DynamicFrame\n        Args:\n            dyf (DynamicFrame): The DynamicFrame to convert\n        Returns:\n            DynamicFrame: The converted DynamicFrame\n        \"\"\"\n        # Extract the column names from the schema\n        old_column_names = [field.name for field in dyf.schema().fields]\n\n        # Create a new list of renamed column names\n        new_column_names = [name.replace(\".\", \"_\") for name in old_column_names]\n        print(old_column_names)\n        print(new_column_names)\n\n        # Create a new DynamicFrame with renamed columns\n        for c_old, c_new in zip(old_column_names, new_column_names):\n            dyf = dyf.rename_field(f\"`{c_old}`\", c_new)\n        return dyf\n\n    def transform(\n        self,\n        input_type: str = \"json\",\n        timestamp_columns: list = None,\n        output_format: str = \"parquet\",\n    ):\n        \"\"\"Convert the CSV or JSON data into Parquet Format in the Workbench S3 Bucket, and\n        store the information about the data to the AWS Data Catalog workbench database\n        Args:\n            input_type (str): The type of input files, either 'csv' or 'json'\n            timestamp_columns (list): A list of column names to convert to timestamp\n            output_format (str): The format of the output files, either 'parquet' or 'orc'\n        \"\"\"\n\n        # Add some tags here\n        tags = [\"heavy\"]\n\n        # Create the Output Parquet file S3 Storage Path\n        s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n        # Read JSONL files from S3 and infer schema dynamically\n        self.log.info(f\"Reading JSONL files from {self.input_uuid}...\")\n        input_dyf = self.glue_context.create_dynamic_frame.from_options(\n            connection_type=\"s3\",\n            connection_options={\n                \"paths\": [self.input_uuid],\n                \"recurse\": True,\n                \"gzip\": True,\n            },\n            format=input_type,\n            # format_options={'jsonPath': 'auto'}, Look into this later\n        )\n        self.log.info(\"Incoming DataFrame...\")\n        input_dyf.show(5)\n        input_dyf.printSchema()\n\n        # Resolve Choice fields\n        resolved_dyf = self.resolve_choice_fields(input_dyf)\n\n        # The next couple of lines of code is for un-nesting any nested JSON\n        # Create a Dynamic Frame Collection (dfc)\n        dfc = Relationalize.apply(resolved_dyf, name=\"root\")\n\n        # Aggregate the collection into a single dynamic frame\n        output_dyf = dfc.select(\"root\")\n\n        print(\"Before TimeStamp Conversions\")\n        output_dyf.printSchema()\n\n        # Convert any timestamp columns\n        output_dyf = self.timestamp_conversions(output_dyf, timestamp_columns)\n\n        # Relationalize will put periods in the column names. This will cause\n        # problems later when we try to create a FeatureSet from this DataSource\n        output_dyf = self.remove_periods_from_columns(output_dyf)\n\n        print(\"After TimeStamp Conversions and Removing Periods from column names\")\n        output_dyf.printSchema()\n\n        # Write Parquet files to S3\n        self.log.info(f\"Writing Parquet files to {s3_storage_path}...\")\n        self.glue_context.purge_s3_path(s3_storage_path, {\"retentionPeriod\": 0})\n        self.glue_context.write_dynamic_frame.from_options(\n            frame=output_dyf,\n            connection_type=\"s3\",\n            connection_options={\n                \"path\": s3_storage_path\n                # \"partitionKeys\": [\"year\", \"month\", \"day\"],\n            },\n            format=output_format,\n        )\n\n        # Set up our Workbench metadata (description, tags, etc)\n        description = f\"Workbench data source: {self.output_uuid}\"\n        workbench_meta = {\"workbench_tags\": self.tag_delimiter.join(tags)}\n        for key, value in self.output_meta.items():\n            workbench_meta[key] = value\n\n        # Create a new table in the AWS Data Catalog\n        self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n\n        # Converting the Spark Types to Athena Types\n        def to_athena_type(col):\n            athena_type_map = {\"long\": \"bigint\"}\n            spark_type = col.dataType.typeName()\n            return athena_type_map.get(spark_type, spark_type)\n\n        column_name_types = [{\"Name\": col.name, \"Type\": to_athena_type(col)} for col in output_dyf.schema().fields]\n\n        # Our parameters for the Glue Data Catalog are different for Parquet and ORC\n        if output_format == \"parquet\":\n            glue_input_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat\"\n            glue_output_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat\"\n            serialization_library = \"org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe\"\n        else:\n            glue_input_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n            glue_output_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n            serialization_library = \"org.apache.hadoop.hive.ql.io.orc.OrcSerde\"\n\n        table_input = {\n            \"Name\": self.output_uuid,\n            \"Description\": description,\n            \"Parameters\": workbench_meta,\n            \"TableType\": \"EXTERNAL_TABLE\",\n            \"StorageDescriptor\": {\n                \"Columns\": column_name_types,\n                \"Location\": s3_storage_path,\n                \"InputFormat\": glue_input_format,\n                \"OutputFormat\": glue_output_format,\n                \"Compressed\": True,\n                \"SerdeInfo\": {\n                    \"SerializationLibrary\": serialization_library,\n                },\n            },\n        }\n\n        # Delete the Data Catalog Table if it already exists\n        glue_client = boto3.client(\"glue\")\n        try:\n            glue_client.delete_table(DatabaseName=\"workbench\", Name=self.output_uuid)\n            self.log.info(f\"Deleting Data Catalog Table: {self.output_uuid}...\")\n        except ClientError as e:\n            if e.response[\"Error\"][\"Code\"] != \"EntityNotFoundException\":\n                raise e\n\n        self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n        glue_client.create_table(DatabaseName=\"workbench\", TableInput=table_input)\n\n        # All done!\n        self.log.info(f\"{self.input_uuid} --&gt; {self.output_uuid} complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.__init__","title":"<code>__init__(glue_context, input_uuid, output_uuid)</code>","text":"<p>S3HeavyToDataSource: Class to move HEAVY S3 Files into a Workbench DataSource</p> <p>Parameters:</p> Name Type Description Default <code>glue_context</code> <code>GlueContext</code> <p>GlueContext, AWS Glue Specific wrapper around SparkContext</p> required <code>input_uuid</code> <code>str</code> <p>The S3 Path to the files to be loaded</p> required <code>output_uuid</code> <code>str</code> <p>The UUID of the Workbench DataSource to be created</p> required Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>def __init__(self, glue_context: GlueContext, input_uuid: str, output_uuid: str):\n    \"\"\"S3HeavyToDataSource: Class to move HEAVY S3 Files into a Workbench DataSource\n\n    Args:\n        glue_context: GlueContext, AWS Glue Specific wrapper around SparkContext\n        input_uuid (str): The S3 Path to the files to be loaded\n        output_uuid (str): The UUID of the Workbench DataSource to be created\n    \"\"\"\n    self.log = glue_context.get_logger()\n\n    # FIXME: Pull these from Parameter Store or Config\n    self.input_uuid = input_uuid\n    self.output_uuid = output_uuid\n    self.output_meta = {\"workbench_input\": self.input_uuid}\n    workbench_bucket = \"s3://sandbox-workbench-artifacts\"\n    self.data_sources_s3_path = workbench_bucket + \"/data-sources\"\n\n    # Our Spark Context\n    self.glue_context = glue_context\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.remove_periods_from_columns","title":"<code>remove_periods_from_columns(dyf)</code>  <code>staticmethod</code>","text":"<p>Remove periods from column names in the DynamicFrame Args:     dyf (DynamicFrame): The DynamicFrame to convert Returns:     DynamicFrame: The converted DynamicFrame</p> Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>@staticmethod\ndef remove_periods_from_columns(dyf: DynamicFrame) -&gt; DynamicFrame:\n    \"\"\"Remove periods from column names in the DynamicFrame\n    Args:\n        dyf (DynamicFrame): The DynamicFrame to convert\n    Returns:\n        DynamicFrame: The converted DynamicFrame\n    \"\"\"\n    # Extract the column names from the schema\n    old_column_names = [field.name for field in dyf.schema().fields]\n\n    # Create a new list of renamed column names\n    new_column_names = [name.replace(\".\", \"_\") for name in old_column_names]\n    print(old_column_names)\n    print(new_column_names)\n\n    # Create a new DynamicFrame with renamed columns\n    for c_old, c_new in zip(old_column_names, new_column_names):\n        dyf = dyf.rename_field(f\"`{c_old}`\", c_new)\n    return dyf\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.timestamp_conversions","title":"<code>timestamp_conversions(dyf, time_columns=[])</code>","text":"<p>Convert columns in the DynamicFrame to the correct data types Args:     dyf (DynamicFrame): The DynamicFrame to convert     time_columns (list): A list of column names to convert to timestamp Returns:     DynamicFrame: The converted DynamicFrame</p> Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>def timestamp_conversions(self, dyf: DynamicFrame, time_columns: list = []) -&gt; DynamicFrame:\n    \"\"\"Convert columns in the DynamicFrame to the correct data types\n    Args:\n        dyf (DynamicFrame): The DynamicFrame to convert\n        time_columns (list): A list of column names to convert to timestamp\n    Returns:\n        DynamicFrame: The converted DynamicFrame\n    \"\"\"\n\n    # Convert the timestamp columns to timestamp types\n    spark_df = dyf.toDF()\n    for column in time_columns:\n        spark_df = spark_df.withColumn(column, to_timestamp(col(column)))\n\n    # Convert the Spark DataFrame back to a Glue DynamicFrame and return\n    return DynamicFrame.fromDF(spark_df, self.glue_context, \"output_dyf\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_heavy/#workbench.core.transforms.data_loaders.heavy.S3HeavyToDataSource.transform","title":"<code>transform(input_type='json', timestamp_columns=None, output_format='parquet')</code>","text":"<p>Convert the CSV or JSON data into Parquet Format in the Workbench S3 Bucket, and store the information about the data to the AWS Data Catalog workbench database Args:     input_type (str): The type of input files, either 'csv' or 'json'     timestamp_columns (list): A list of column names to convert to timestamp     output_format (str): The format of the output files, either 'parquet' or 'orc'</p> Source code in <code>src/workbench/core/transforms/data_loaders/heavy/s3_heavy_to_data_source.py</code> <pre><code>def transform(\n    self,\n    input_type: str = \"json\",\n    timestamp_columns: list = None,\n    output_format: str = \"parquet\",\n):\n    \"\"\"Convert the CSV or JSON data into Parquet Format in the Workbench S3 Bucket, and\n    store the information about the data to the AWS Data Catalog workbench database\n    Args:\n        input_type (str): The type of input files, either 'csv' or 'json'\n        timestamp_columns (list): A list of column names to convert to timestamp\n        output_format (str): The format of the output files, either 'parquet' or 'orc'\n    \"\"\"\n\n    # Add some tags here\n    tags = [\"heavy\"]\n\n    # Create the Output Parquet file S3 Storage Path\n    s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n    # Read JSONL files from S3 and infer schema dynamically\n    self.log.info(f\"Reading JSONL files from {self.input_uuid}...\")\n    input_dyf = self.glue_context.create_dynamic_frame.from_options(\n        connection_type=\"s3\",\n        connection_options={\n            \"paths\": [self.input_uuid],\n            \"recurse\": True,\n            \"gzip\": True,\n        },\n        format=input_type,\n        # format_options={'jsonPath': 'auto'}, Look into this later\n    )\n    self.log.info(\"Incoming DataFrame...\")\n    input_dyf.show(5)\n    input_dyf.printSchema()\n\n    # Resolve Choice fields\n    resolved_dyf = self.resolve_choice_fields(input_dyf)\n\n    # The next couple of lines of code is for un-nesting any nested JSON\n    # Create a Dynamic Frame Collection (dfc)\n    dfc = Relationalize.apply(resolved_dyf, name=\"root\")\n\n    # Aggregate the collection into a single dynamic frame\n    output_dyf = dfc.select(\"root\")\n\n    print(\"Before TimeStamp Conversions\")\n    output_dyf.printSchema()\n\n    # Convert any timestamp columns\n    output_dyf = self.timestamp_conversions(output_dyf, timestamp_columns)\n\n    # Relationalize will put periods in the column names. This will cause\n    # problems later when we try to create a FeatureSet from this DataSource\n    output_dyf = self.remove_periods_from_columns(output_dyf)\n\n    print(\"After TimeStamp Conversions and Removing Periods from column names\")\n    output_dyf.printSchema()\n\n    # Write Parquet files to S3\n    self.log.info(f\"Writing Parquet files to {s3_storage_path}...\")\n    self.glue_context.purge_s3_path(s3_storage_path, {\"retentionPeriod\": 0})\n    self.glue_context.write_dynamic_frame.from_options(\n        frame=output_dyf,\n        connection_type=\"s3\",\n        connection_options={\n            \"path\": s3_storage_path\n            # \"partitionKeys\": [\"year\", \"month\", \"day\"],\n        },\n        format=output_format,\n    )\n\n    # Set up our Workbench metadata (description, tags, etc)\n    description = f\"Workbench data source: {self.output_uuid}\"\n    workbench_meta = {\"workbench_tags\": self.tag_delimiter.join(tags)}\n    for key, value in self.output_meta.items():\n        workbench_meta[key] = value\n\n    # Create a new table in the AWS Data Catalog\n    self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n\n    # Converting the Spark Types to Athena Types\n    def to_athena_type(col):\n        athena_type_map = {\"long\": \"bigint\"}\n        spark_type = col.dataType.typeName()\n        return athena_type_map.get(spark_type, spark_type)\n\n    column_name_types = [{\"Name\": col.name, \"Type\": to_athena_type(col)} for col in output_dyf.schema().fields]\n\n    # Our parameters for the Glue Data Catalog are different for Parquet and ORC\n    if output_format == \"parquet\":\n        glue_input_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat\"\n        glue_output_format = \"org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat\"\n        serialization_library = \"org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe\"\n    else:\n        glue_input_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n        glue_output_format = \"org.apache.hadoop.hive.ql.io.orc.OrcInputFormat\"\n        serialization_library = \"org.apache.hadoop.hive.ql.io.orc.OrcSerde\"\n\n    table_input = {\n        \"Name\": self.output_uuid,\n        \"Description\": description,\n        \"Parameters\": workbench_meta,\n        \"TableType\": \"EXTERNAL_TABLE\",\n        \"StorageDescriptor\": {\n            \"Columns\": column_name_types,\n            \"Location\": s3_storage_path,\n            \"InputFormat\": glue_input_format,\n            \"OutputFormat\": glue_output_format,\n            \"Compressed\": True,\n            \"SerdeInfo\": {\n                \"SerializationLibrary\": serialization_library,\n            },\n        },\n    }\n\n    # Delete the Data Catalog Table if it already exists\n    glue_client = boto3.client(\"glue\")\n    try:\n        glue_client.delete_table(DatabaseName=\"workbench\", Name=self.output_uuid)\n        self.log.info(f\"Deleting Data Catalog Table: {self.output_uuid}...\")\n    except ClientError as e:\n        if e.response[\"Error\"][\"Code\"] != \"EntityNotFoundException\":\n            raise e\n\n    self.log.info(f\"Creating Data Catalog Table: {self.output_uuid}...\")\n    glue_client.create_table(DatabaseName=\"workbench\", TableInput=table_input)\n\n    # All done!\n    self.log.info(f\"{self.input_uuid} --&gt; {self.output_uuid} complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/","title":"DataLoaders Light","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>These DataLoader Classes are intended to load smaller dataset into AWS. If you have large data please see DataLoaders Heavy</p> <p>Welcome to the Workbench DataLoaders Light Classes</p> <p>These classes provide low-level APIs for loading smaller data into AWS services</p> <ul> <li>CSVToDataSource: Loads local CSV data into a DataSource</li> <li>JSONToDataSource: Loads local JSON data into a DataSource</li> <li>S3ToDataSourceLight: Loads S3 data into a DataSource</li> </ul>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.CSVToDataSource","title":"<code>CSVToDataSource</code>","text":"<p>               Bases: <code>Transform</code></p> <p>CSVToDataSource: Class to move local CSV Files into a Workbench DataSource</p> Common Usage <pre><code>csv_to_data = CSVToDataSource(csv_file_path, data_uuid)\ncsv_to_data.set_output_tags([\"abalone\", \"csv\", \"whatever\"])\ncsv_to_data.transform()\n</code></pre> Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>class CSVToDataSource(Transform):\n    \"\"\"CSVToDataSource: Class to move local CSV Files into a Workbench DataSource\n\n    Common Usage:\n        ```python\n        csv_to_data = CSVToDataSource(csv_file_path, data_uuid)\n        csv_to_data.set_output_tags([\"abalone\", \"csv\", \"whatever\"])\n        csv_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, csv_file_path: str, data_uuid: str):\n        \"\"\"CSVToDataSource: Class to move local CSV Files into a Workbench DataSource\n\n        Args:\n            csv_file_path (str): The path to the CSV file to be transformed\n            data_uuid (str): The UUID of the Workbench DataSource to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(csv_file_path, data_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.LOCAL_FILE\n        self.output_type = TransformOutput.DATA_SOURCE\n\n    def transform_impl(self, overwrite: bool = True):\n        \"\"\"Convert the local CSV file into Parquet Format in the Workbench Data Sources Bucket, and\n        store the information about the data to the AWS Data Catalog workbench database\n        \"\"\"\n\n        # Report the transformation initiation\n        csv_file = os.path.basename(self.input_uuid)\n        self.log.info(f\"Starting {csv_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n        # Read in the Local CSV as a Pandas DataFrame\n        df = pd.read_csv(self.input_uuid, low_memory=False)\n        df = convert_object_columns(df)\n\n        # Use the Workbench Pandas to Data Source class\n        pandas_to_data = PandasToData(self.output_uuid)\n        pandas_to_data.set_input(df)\n        pandas_to_data.set_output_tags(self.output_tags)\n        pandas_to_data.add_output_meta(self.output_meta)\n        pandas_to_data.transform()\n\n        # Report the transformation results\n        self.log.info(f\"{csv_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform\"\"\"\n        self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.CSVToDataSource.__init__","title":"<code>__init__(csv_file_path, data_uuid)</code>","text":"<p>CSVToDataSource: Class to move local CSV Files into a Workbench DataSource</p> <p>Parameters:</p> Name Type Description Default <code>csv_file_path</code> <code>str</code> <p>The path to the CSV file to be transformed</p> required <code>data_uuid</code> <code>str</code> <p>The UUID of the Workbench DataSource to be created</p> required Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>def __init__(self, csv_file_path: str, data_uuid: str):\n    \"\"\"CSVToDataSource: Class to move local CSV Files into a Workbench DataSource\n\n    Args:\n        csv_file_path (str): The path to the CSV file to be transformed\n        data_uuid (str): The UUID of the Workbench DataSource to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(csv_file_path, data_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.LOCAL_FILE\n    self.output_type = TransformOutput.DATA_SOURCE\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.CSVToDataSource.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform</p> Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform\"\"\"\n    self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.CSVToDataSource.transform_impl","title":"<code>transform_impl(overwrite=True)</code>","text":"<p>Convert the local CSV file into Parquet Format in the Workbench Data Sources Bucket, and store the information about the data to the AWS Data Catalog workbench database</p> Source code in <code>src/workbench/core/transforms/data_loaders/light/csv_to_data_source.py</code> <pre><code>def transform_impl(self, overwrite: bool = True):\n    \"\"\"Convert the local CSV file into Parquet Format in the Workbench Data Sources Bucket, and\n    store the information about the data to the AWS Data Catalog workbench database\n    \"\"\"\n\n    # Report the transformation initiation\n    csv_file = os.path.basename(self.input_uuid)\n    self.log.info(f\"Starting {csv_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n    # Read in the Local CSV as a Pandas DataFrame\n    df = pd.read_csv(self.input_uuid, low_memory=False)\n    df = convert_object_columns(df)\n\n    # Use the Workbench Pandas to Data Source class\n    pandas_to_data = PandasToData(self.output_uuid)\n    pandas_to_data.set_input(df)\n    pandas_to_data.set_output_tags(self.output_tags)\n    pandas_to_data.add_output_meta(self.output_meta)\n    pandas_to_data.transform()\n\n    # Report the transformation results\n    self.log.info(f\"{csv_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.JSONToDataSource","title":"<code>JSONToDataSource</code>","text":"<p>               Bases: <code>Transform</code></p> <p>JSONToDataSource: Class to move local JSON Files into a Workbench DataSource</p> Common Usage <pre><code>json_to_data = JSONToDataSource(json_file_path, data_uuid)\njson_to_data.set_output_tags([\"abalone\", \"json\", \"whatever\"])\njson_to_data.transform()\n</code></pre> Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>class JSONToDataSource(Transform):\n    \"\"\"JSONToDataSource: Class to move local JSON Files into a Workbench DataSource\n\n    Common Usage:\n        ```python\n        json_to_data = JSONToDataSource(json_file_path, data_uuid)\n        json_to_data.set_output_tags([\"abalone\", \"json\", \"whatever\"])\n        json_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, json_file_path: str, data_uuid: str):\n        \"\"\"JSONToDataSource: Class to move local JSON Files into a Workbench DataSource\n\n        Args:\n            json_file_path (str): The path to the JSON file to be transformed\n            data_uuid (str): The UUID of the Workbench DataSource to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(json_file_path, data_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.LOCAL_FILE\n        self.output_type = TransformOutput.DATA_SOURCE\n\n    def transform_impl(self, overwrite: bool = True):\n        \"\"\"Convert the local JSON file into Parquet Format in the Workbench Data Sources Bucket, and\n        store the information about the data to the AWS Data Catalog workbench database\n        \"\"\"\n\n        # Report the transformation initiation\n        json_file = os.path.basename(self.input_uuid)\n        self.log.info(f\"Starting {json_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n        # Read in the Local JSON as a Pandas DataFrame\n        df = pd.read_json(self.input_uuid, lines=True)\n\n        # Use the Workbench Pandas to Data Source class\n        pandas_to_data = PandasToData(self.output_uuid)\n        pandas_to_data.set_input(df)\n        pandas_to_data.set_output_tags(self.output_tags)\n        pandas_to_data.add_output_meta(self.output_meta)\n        pandas_to_data.transform()\n\n        # Report the transformation results\n        self.log.info(f\"{json_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform\"\"\"\n        self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.JSONToDataSource.__init__","title":"<code>__init__(json_file_path, data_uuid)</code>","text":"<p>JSONToDataSource: Class to move local JSON Files into a Workbench DataSource</p> <p>Parameters:</p> Name Type Description Default <code>json_file_path</code> <code>str</code> <p>The path to the JSON file to be transformed</p> required <code>data_uuid</code> <code>str</code> <p>The UUID of the Workbench DataSource to be created</p> required Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>def __init__(self, json_file_path: str, data_uuid: str):\n    \"\"\"JSONToDataSource: Class to move local JSON Files into a Workbench DataSource\n\n    Args:\n        json_file_path (str): The path to the JSON file to be transformed\n        data_uuid (str): The UUID of the Workbench DataSource to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(json_file_path, data_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.LOCAL_FILE\n    self.output_type = TransformOutput.DATA_SOURCE\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.JSONToDataSource.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform</p> Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform\"\"\"\n    self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.JSONToDataSource.transform_impl","title":"<code>transform_impl(overwrite=True)</code>","text":"<p>Convert the local JSON file into Parquet Format in the Workbench Data Sources Bucket, and store the information about the data to the AWS Data Catalog workbench database</p> Source code in <code>src/workbench/core/transforms/data_loaders/light/json_to_data_source.py</code> <pre><code>def transform_impl(self, overwrite: bool = True):\n    \"\"\"Convert the local JSON file into Parquet Format in the Workbench Data Sources Bucket, and\n    store the information about the data to the AWS Data Catalog workbench database\n    \"\"\"\n\n    # Report the transformation initiation\n    json_file = os.path.basename(self.input_uuid)\n    self.log.info(f\"Starting {json_file} --&gt;  DataSource: {self.output_uuid}...\")\n\n    # Read in the Local JSON as a Pandas DataFrame\n    df = pd.read_json(self.input_uuid, lines=True)\n\n    # Use the Workbench Pandas to Data Source class\n    pandas_to_data = PandasToData(self.output_uuid)\n    pandas_to_data.set_input(df)\n    pandas_to_data.set_output_tags(self.output_tags)\n    pandas_to_data.add_output_meta(self.output_meta)\n    pandas_to_data.transform()\n\n    # Report the transformation results\n    self.log.info(f\"{json_file} --&gt;  DataSource: {self.output_uuid} Complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight","title":"<code>S3ToDataSourceLight</code>","text":"<p>               Bases: <code>Transform</code></p> <p>S3ToDataSourceLight: Class to move LIGHT S3 Files into a Workbench DataSource</p> Common Usage <pre><code>s3_to_data = S3ToDataSourceLight(s3_path, data_uuid, datatype=\"csv/json\")\ns3_to_data.set_output_tags([\"abalone\", \"whatever\"])\ns3_to_data.transform()\n</code></pre> Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>class S3ToDataSourceLight(Transform):\n    \"\"\"S3ToDataSourceLight: Class to move LIGHT S3 Files into a Workbench DataSource\n\n    Common Usage:\n        ```python\n        s3_to_data = S3ToDataSourceLight(s3_path, data_uuid, datatype=\"csv/json\")\n        s3_to_data.set_output_tags([\"abalone\", \"whatever\"])\n        s3_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, s3_path: str, data_uuid: str, datatype: str = \"csv\"):\n        \"\"\"S3ToDataSourceLight Initialization\n\n        Args:\n            s3_path (str): The S3 Path to the file to be transformed\n            data_uuid (str): The UUID of the Workbench DataSource to be created\n            datatype (str): The datatype of the file to be transformed (defaults to \"csv\")\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(s3_path, data_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.S3_OBJECT\n        self.output_type = TransformOutput.DATA_SOURCE\n        self.datatype = datatype\n\n    def input_size_mb(self) -&gt; int:\n        \"\"\"Get the size of the input S3 object in MBytes\"\"\"\n        size_in_bytes = wr.s3.size_objects(self.input_uuid, boto3_session=self.boto3_session)[self.input_uuid]\n        size_in_mb = round(size_in_bytes / 1_000_000)\n        return size_in_mb\n\n    def transform_impl(self, overwrite: bool = True):\n        \"\"\"Convert the S3 CSV data into Parquet Format in the Workbench Data Sources Bucket, and\n        store the information about the data to the AWS Data Catalog workbench database\n        \"\"\"\n\n        # Sanity Check for S3 Object size\n        object_megabytes = self.input_size_mb()\n        if object_megabytes &gt; 100:\n            self.log.error(f\"S3 Object too big ({object_megabytes} MBytes): Use the S3ToDataSourceHeavy class!\")\n            return\n\n        # Read in the S3 CSV as a Pandas DataFrame\n        if self.datatype == \"csv\":\n            df = wr.s3.read_csv(self.input_uuid, low_memory=False, boto3_session=self.boto3_session)\n        else:\n            df = wr.s3.read_json(self.input_uuid, lines=True, boto3_session=self.boto3_session)\n\n        # Temporary hack to limit the number of columns in the dataframe\n        if len(df.columns) &gt; 40:\n            self.log.warning(f\"{self.input_uuid} Too Many Columns! Talk to Workbench Support...\")\n\n        # Convert object columns before sending to Workbench Data Source\n        df = convert_object_columns(df)\n\n        # Use the Workbench Pandas to Data Source class\n        pandas_to_data = PandasToData(self.output_uuid)\n        pandas_to_data.set_input(df)\n        pandas_to_data.set_output_tags(self.output_tags)\n        pandas_to_data.add_output_meta(self.output_meta)\n        pandas_to_data.transform()\n\n        # Report the transformation results\n        self.log.info(f\"{self.input_uuid} --&gt;  DataSource: {self.output_uuid} Complete!\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform\"\"\"\n        self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.__init__","title":"<code>__init__(s3_path, data_uuid, datatype='csv')</code>","text":"<p>S3ToDataSourceLight Initialization</p> <p>Parameters:</p> Name Type Description Default <code>s3_path</code> <code>str</code> <p>The S3 Path to the file to be transformed</p> required <code>data_uuid</code> <code>str</code> <p>The UUID of the Workbench DataSource to be created</p> required <code>datatype</code> <code>str</code> <p>The datatype of the file to be transformed (defaults to \"csv\")</p> <code>'csv'</code> Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def __init__(self, s3_path: str, data_uuid: str, datatype: str = \"csv\"):\n    \"\"\"S3ToDataSourceLight Initialization\n\n    Args:\n        s3_path (str): The S3 Path to the file to be transformed\n        data_uuid (str): The UUID of the Workbench DataSource to be created\n        datatype (str): The datatype of the file to be transformed (defaults to \"csv\")\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(s3_path, data_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.S3_OBJECT\n    self.output_type = TransformOutput.DATA_SOURCE\n    self.datatype = datatype\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.input_size_mb","title":"<code>input_size_mb()</code>","text":"<p>Get the size of the input S3 object in MBytes</p> Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def input_size_mb(self) -&gt; int:\n    \"\"\"Get the size of the input S3 object in MBytes\"\"\"\n    size_in_bytes = wr.s3.size_objects(self.input_uuid, boto3_session=self.boto3_session)[self.input_uuid]\n    size_in_mb = round(size_in_bytes / 1_000_000)\n    return size_in_mb\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform</p> Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform\"\"\"\n    self.log.info(\"Post-Transform: S3 to DataSource...\")\n</code></pre>"},{"location":"core_classes/transforms/data_loaders_light/#workbench.core.transforms.data_loaders.light.S3ToDataSourceLight.transform_impl","title":"<code>transform_impl(overwrite=True)</code>","text":"<p>Convert the S3 CSV data into Parquet Format in the Workbench Data Sources Bucket, and store the information about the data to the AWS Data Catalog workbench database</p> Source code in <code>src/workbench/core/transforms/data_loaders/light/s3_to_data_source_light.py</code> <pre><code>def transform_impl(self, overwrite: bool = True):\n    \"\"\"Convert the S3 CSV data into Parquet Format in the Workbench Data Sources Bucket, and\n    store the information about the data to the AWS Data Catalog workbench database\n    \"\"\"\n\n    # Sanity Check for S3 Object size\n    object_megabytes = self.input_size_mb()\n    if object_megabytes &gt; 100:\n        self.log.error(f\"S3 Object too big ({object_megabytes} MBytes): Use the S3ToDataSourceHeavy class!\")\n        return\n\n    # Read in the S3 CSV as a Pandas DataFrame\n    if self.datatype == \"csv\":\n        df = wr.s3.read_csv(self.input_uuid, low_memory=False, boto3_session=self.boto3_session)\n    else:\n        df = wr.s3.read_json(self.input_uuid, lines=True, boto3_session=self.boto3_session)\n\n    # Temporary hack to limit the number of columns in the dataframe\n    if len(df.columns) &gt; 40:\n        self.log.warning(f\"{self.input_uuid} Too Many Columns! Talk to Workbench Support...\")\n\n    # Convert object columns before sending to Workbench Data Source\n    df = convert_object_columns(df)\n\n    # Use the Workbench Pandas to Data Source class\n    pandas_to_data = PandasToData(self.output_uuid)\n    pandas_to_data.set_input(df)\n    pandas_to_data.set_output_tags(self.output_tags)\n    pandas_to_data.add_output_meta(self.output_meta)\n    pandas_to_data.transform()\n\n    # Report the transformation results\n    self.log.info(f\"{self.input_uuid} --&gt;  DataSource: {self.output_uuid} Complete!\")\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/","title":"Data To Features","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas</p> <p>MolecularDescriptors: Compute a Feature Set based on RDKit Descriptors</p> An alternative to using this class is to use the <code>compute_molecular_descriptors</code> function directly. <p>df_features = compute_molecular_descriptors(df) to_features = PandasToFeatures(\"my_feature_set\")    to_features.set_input(df_features, id_column=\"id\")    to_features.set_output_tags([\"blah\", \"whatever\"])    to_features.transform()</p>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight","title":"<code>DataToFeaturesLight</code>","text":"<p>               Bases: <code>Transform</code></p> <p>DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas</p> Common Usage <pre><code>to_features = DataToFeaturesLight(data_uuid, feature_uuid)\nto_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\nto_features.transform(id_column=\"id\"/None, event_time_column=\"date\"/None, query=str/None)\n</code></pre> Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>class DataToFeaturesLight(Transform):\n    \"\"\"DataToFeaturesLight: Base Class for Light DataSource to FeatureSet using Pandas\n\n    Common Usage:\n        ```python\n        to_features = DataToFeaturesLight(data_uuid, feature_uuid)\n        to_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\n        to_features.transform(id_column=\"id\"/None, event_time_column=\"date\"/None, query=str/None)\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid: str, feature_uuid: str):\n        \"\"\"DataToFeaturesLight Initialization\n\n        Args:\n            data_uuid (str): The UUID of the Workbench DataSource to be transformed\n            feature_uuid (str): The UUID of the Workbench FeatureSet to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(data_uuid, feature_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.DATA_SOURCE\n        self.output_type = TransformOutput.FEATURE_SET\n        self.input_df = None\n        self.output_df = None\n\n    def pre_transform(self, query: str = None, **kwargs):\n        \"\"\"Pull the input DataSource into our Input Pandas DataFrame\n        Args:\n            query(str): Optional query to filter the input DataFrame\n        \"\"\"\n\n        # Grab the Input (Data Source)\n        data_to_pandas = DataToPandas(self.input_uuid)\n        data_to_pandas.transform(query=query)\n        self.input_df = data_to_pandas.get_output()\n\n        # Check if there are any columns that are greater than 64 characters\n        for col in self.input_df.columns:\n            if len(col) &gt; 64:\n                raise ValueError(f\"Column name '{col}' &gt; 64 characters. AWS FeatureGroup limits to 64 characters.\")\n\n    def transform_impl(self, **kwargs):\n        \"\"\"Transform the input DataFrame into a Feature Set\"\"\"\n\n        # This is a reference implementation that should be overridden by the subclass\n        self.output_df = self.input_df\n\n    def post_transform(self, id_column, event_time_column=None, one_hot_columns=None, **kwargs):\n        \"\"\"At this point the output DataFrame should be populated, so publish it as a Feature Set\n\n        Args:\n            id_column (str): The ID column (must be specified, use \"auto\" for auto-generated IDs).\n            event_time_column (str, optional): The name of the event time column (default: None).\n            one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n        \"\"\"\n        # Now publish to the output location\n        output_features = PandasToFeatures(self.output_uuid)\n        output_features.set_input(\n            self.output_df, id_column=id_column, event_time_column=event_time_column, one_hot_columns=one_hot_columns\n        )\n        output_features.set_output_tags(self.output_tags)\n        output_features.add_output_meta(self.output_meta)\n        output_features.transform()\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.__init__","title":"<code>__init__(data_uuid, feature_uuid)</code>","text":"<p>DataToFeaturesLight Initialization</p> <p>Parameters:</p> Name Type Description Default <code>data_uuid</code> <code>str</code> <p>The UUID of the Workbench DataSource to be transformed</p> required <code>feature_uuid</code> <code>str</code> <p>The UUID of the Workbench FeatureSet to be created</p> required Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def __init__(self, data_uuid: str, feature_uuid: str):\n    \"\"\"DataToFeaturesLight Initialization\n\n    Args:\n        data_uuid (str): The UUID of the Workbench DataSource to be transformed\n        feature_uuid (str): The UUID of the Workbench FeatureSet to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(data_uuid, feature_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.DATA_SOURCE\n    self.output_type = TransformOutput.FEATURE_SET\n    self.input_df = None\n    self.output_df = None\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.post_transform","title":"<code>post_transform(id_column, event_time_column=None, one_hot_columns=None, **kwargs)</code>","text":"<p>At this point the output DataFrame should be populated, so publish it as a Feature Set</p> <p>Parameters:</p> Name Type Description Default <code>id_column</code> <code>str</code> <p>The ID column (must be specified, use \"auto\" for auto-generated IDs).</p> required <code>event_time_column</code> <code>str</code> <p>The name of the event time column (default: None).</p> <code>None</code> <code>one_hot_columns</code> <code>list</code> <p>The list of columns to one-hot encode (default: None).</p> <code>None</code> Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def post_transform(self, id_column, event_time_column=None, one_hot_columns=None, **kwargs):\n    \"\"\"At this point the output DataFrame should be populated, so publish it as a Feature Set\n\n    Args:\n        id_column (str): The ID column (must be specified, use \"auto\" for auto-generated IDs).\n        event_time_column (str, optional): The name of the event time column (default: None).\n        one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n    \"\"\"\n    # Now publish to the output location\n    output_features = PandasToFeatures(self.output_uuid)\n    output_features.set_input(\n        self.output_df, id_column=id_column, event_time_column=event_time_column, one_hot_columns=one_hot_columns\n    )\n    output_features.set_output_tags(self.output_tags)\n    output_features.add_output_meta(self.output_meta)\n    output_features.transform()\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.pre_transform","title":"<code>pre_transform(query=None, **kwargs)</code>","text":"<p>Pull the input DataSource into our Input Pandas DataFrame Args:     query(str): Optional query to filter the input DataFrame</p> Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def pre_transform(self, query: str = None, **kwargs):\n    \"\"\"Pull the input DataSource into our Input Pandas DataFrame\n    Args:\n        query(str): Optional query to filter the input DataFrame\n    \"\"\"\n\n    # Grab the Input (Data Source)\n    data_to_pandas = DataToPandas(self.input_uuid)\n    data_to_pandas.transform(query=query)\n    self.input_df = data_to_pandas.get_output()\n\n    # Check if there are any columns that are greater than 64 characters\n    for col in self.input_df.columns:\n        if len(col) &gt; 64:\n            raise ValueError(f\"Column name '{col}' &gt; 64 characters. AWS FeatureGroup limits to 64 characters.\")\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.data_to_features_light.DataToFeaturesLight.transform_impl","title":"<code>transform_impl(**kwargs)</code>","text":"<p>Transform the input DataFrame into a Feature Set</p> Source code in <code>src/workbench/core/transforms/data_to_features/light/data_to_features_light.py</code> <pre><code>def transform_impl(self, **kwargs):\n    \"\"\"Transform the input DataFrame into a Feature Set\"\"\"\n\n    # This is a reference implementation that should be overridden by the subclass\n    self.output_df = self.input_df\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors","title":"<code>MolecularDescriptors</code>","text":"<p>               Bases: <code>DataToFeaturesLight</code></p> <p>MolecularDescriptors: Create a FeatureSet (RDKit Descriptors) from a DataSource</p> Common Usage <pre><code>to_features = MolecularDescriptors(data_uuid, feature_uuid)\nto_features.set_output_tags([\"aqsol\", \"whatever\"])\nto_features.transform()\n</code></pre> Source code in <code>src/workbench/core/transforms/data_to_features/light/molecular_descriptors.py</code> <pre><code>class MolecularDescriptors(DataToFeaturesLight):\n    \"\"\"MolecularDescriptors: Create a FeatureSet (RDKit Descriptors) from a DataSource\n\n    Common Usage:\n        ```python\n        to_features = MolecularDescriptors(data_uuid, feature_uuid)\n        to_features.set_output_tags([\"aqsol\", \"whatever\"])\n        to_features.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, data_uuid: str, feature_uuid: str):\n        \"\"\"MolecularDescriptors Initialization\n\n        Args:\n            data_uuid (str): The UUID of the Workbench DataSource to be transformed\n            feature_uuid (str): The UUID of the Workbench FeatureSet to be created\n        \"\"\"\n\n        # Call superclass init\n        super().__init__(data_uuid, feature_uuid)\n\n    def transform_impl(self, **kwargs):\n        \"\"\"Compute a Feature Set based on RDKit Descriptors\"\"\"\n\n        # Compute/add all the Molecular Descriptors\n        self.output_df = compute_molecular_descriptors(self.input_df)\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.__init__","title":"<code>__init__(data_uuid, feature_uuid)</code>","text":"<p>MolecularDescriptors Initialization</p> <p>Parameters:</p> Name Type Description Default <code>data_uuid</code> <code>str</code> <p>The UUID of the Workbench DataSource to be transformed</p> required <code>feature_uuid</code> <code>str</code> <p>The UUID of the Workbench FeatureSet to be created</p> required Source code in <code>src/workbench/core/transforms/data_to_features/light/molecular_descriptors.py</code> <pre><code>def __init__(self, data_uuid: str, feature_uuid: str):\n    \"\"\"MolecularDescriptors Initialization\n\n    Args:\n        data_uuid (str): The UUID of the Workbench DataSource to be transformed\n        feature_uuid (str): The UUID of the Workbench FeatureSet to be created\n    \"\"\"\n\n    # Call superclass init\n    super().__init__(data_uuid, feature_uuid)\n</code></pre>"},{"location":"core_classes/transforms/data_to_features/#workbench.core.transforms.data_to_features.light.molecular_descriptors.MolecularDescriptors.transform_impl","title":"<code>transform_impl(**kwargs)</code>","text":"<p>Compute a Feature Set based on RDKit Descriptors</p> Source code in <code>src/workbench/core/transforms/data_to_features/light/molecular_descriptors.py</code> <pre><code>def transform_impl(self, **kwargs):\n    \"\"\"Compute a Feature Set based on RDKit Descriptors\"\"\"\n\n    # Compute/add all the Molecular Descriptors\n    self.output_df = compute_molecular_descriptors(self.input_df)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/","title":"Features To Model","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>FeaturesToModel: Train/Create a Model from a Feature Set</p>"},{"location":"core_classes/transforms/features_to_model/#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel","title":"<code>FeaturesToModel</code>","text":"<p>               Bases: <code>Transform</code></p> <p>FeaturesToModel: Train/Create a Model from a FeatureSet</p> Common Usage <pre><code>from workbench.core.transforms.features_to_model.features_to_model import FeaturesToModel\nto_model = FeaturesToModel(feature_uuid, model_uuid, model_type=ModelType)\nto_model.set_output_tags([\"abalone\", \"public\", \"whatever\"])\nto_model.transform(target_column=\"class_number_of_rings\",\n                   feature_list=[\"my\", \"best\", \"features\"])\n</code></pre> Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code> <pre><code>class FeaturesToModel(Transform):\n    \"\"\"FeaturesToModel: Train/Create a Model from a FeatureSet\n\n    Common Usage:\n        ```python\n        from workbench.core.transforms.features_to_model.features_to_model import FeaturesToModel\n        to_model = FeaturesToModel(feature_uuid, model_uuid, model_type=ModelType)\n        to_model.set_output_tags([\"abalone\", \"public\", \"whatever\"])\n        to_model.transform(target_column=\"class_number_of_rings\",\n                           feature_list=[\"my\", \"best\", \"features\"])\n        ```\n    \"\"\"\n\n    def __init__(\n        self,\n        feature_uuid: str,\n        model_uuid: str,\n        model_type: ModelType,\n        scikit_model_class=None,\n        model_import_str=None,\n        custom_script=None,\n    ):\n        \"\"\"FeaturesToModel Initialization\n        Args:\n            feature_uuid (str): UUID of the FeatureSet to use as input\n            model_uuid (str): UUID of the Model to create as output\n            model_type (ModelType): ModelType.REGRESSOR or ModelType.CLASSIFIER, etc.\n            scikit_model_class (str, optional): The scikit model (e.g. KNeighborsRegressor) (default None)\n            model_import_str (str, optional): The import string for the model (default None)\n            custom_script (str, optional): Custom script to use for the model (default None)\n        \"\"\"\n\n        # Make sure the model_uuid is a valid name\n        Artifact.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call superclass init\n        super().__init__(feature_uuid, model_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.FEATURE_SET\n        self.output_type = TransformOutput.MODEL\n        self.model_type = model_type\n        self.scikit_model_class = scikit_model_class\n        self.model_import_str = model_import_str\n        self.custom_script = custom_script\n        self.estimator = None\n        self.model_description = None\n        self.model_training_root = self.models_s3_path + \"/training\"\n        self.model_feature_list = None\n        self.target_column = None\n        self.class_labels = None\n\n    def transform_impl(\n        self, target_column: str, description: str = None, feature_list: list = None, train_all_data=False\n    ):\n        \"\"\"Generic Features to Model: Note you should create a new class and inherit from\n        this one to include specific logic for your Feature Set/Model\n        Args:\n            target_column (str): Column name of the target variable\n            description (str): Description of the model (optional)\n            feature_list (list[str]): A list of columns for the features (default None, will try to guess)\n            train_all_data (bool): Train on ALL (100%) of the data (default False)\n        \"\"\"\n        # Delete the existing model (if it exists)\n        self.log.important(\"Trying to delete existing model...\")\n        ModelCore.managed_delete(self.output_uuid)\n\n        # Set our model description\n        self.model_description = description if description is not None else f\"Model created from {self.input_uuid}\"\n\n        # Get our Feature Set and create an S3 CSV Training dataset\n        feature_set = FeatureSetCore(self.input_uuid)\n        s3_training_path = feature_set.create_s3_training_data()\n        self.log.info(f\"Created new training data {s3_training_path}...\")\n\n        # Report the target column\n        self.target_column = target_column\n        self.log.info(f\"Target column: {self.target_column}\")\n\n        # Did they specify a feature list?\n        if feature_list:\n            # AWS Feature Groups will also add these implicit columns, so remove them\n            aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\", \"training\"]\n            feature_list = [c for c in feature_list if c not in aws_cols]\n\n        # If they didn't specify a feature list, try to guess it\n        else:\n            # Try to figure out features with this logic\n            # - Don't include id, event_time, __index_level_0__, or training columns\n            # - Don't include AWS generated columns (e.g. write_time, api_invocation_time, is_deleted)\n            # - Don't include the target columns\n            # - Don't include any columns that are of type string or timestamp\n            # - The rest of the columns are assumed to be features\n            self.log.warning(\"Guessing at the feature list, HIGHLY SUGGESTED to specify an explicit feature list!\")\n            all_columns = feature_set.columns\n            filter_list = [\n                \"id\",\n                \"__index_level_0__\",\n                \"write_time\",\n                \"api_invocation_time\",\n                \"is_deleted\",\n                \"event_time\",\n                \"training\",\n            ] + [self.target_column]\n            feature_list = [c for c in all_columns if c not in filter_list]\n\n            # AWS Feature Store has 3 user column types (String, Integral, Fractional)\n            # and two internal types (Timestamp and Boolean). A Feature List for\n            # modeling can only contain Integral and Fractional types.\n            remove_columns = []\n            column_details = feature_set.column_details()\n            for column_name in feature_list:\n                if column_details[column_name] not in [\"Integral\", \"Fractional\"]:\n                    self.log.warning(\n                        f\"Removing {column_name} from feature list, improper type {column_details[column_name]}\"\n                    )\n                    remove_columns.append(column_name)\n\n            # Remove the columns that are not Integral or Fractional\n            feature_list = [c for c in feature_list if c not in remove_columns]\n\n        # Set the final feature list\n        self.model_feature_list = feature_list\n        self.log.important(f\"Feature List for Modeling: {self.model_feature_list}\")\n\n        # Custom Script\n        if self.custom_script:\n            script_path = self.custom_script\n            self.log.info(\"Custom script path: {script_path}\")\n            # Fixme: We'll need to circle back to this later\n            copy_imports_to_script_dir(script_path, [\"workbench.utils.chem_utils\"])\n\n        # We're using one of the built-in model script templates\n        else:\n            # Set up our parameters for the model script\n            template_params = {\n                \"model_imports\": self.model_import_str,\n                \"model_type\": self.model_type,\n                \"scikit_model_class\": self.scikit_model_class,\n                \"target_column\": self.target_column,\n                \"feature_list\": self.model_feature_list,\n                \"model_metrics_s3_path\": f\"{self.model_training_root}/{self.output_uuid}\",\n                \"train_all_data\": train_all_data,\n            }\n            # Generate our model script\n            script_path = generate_model_script(template_params)\n\n        # Metric Definitions for Regression\n        if self.model_type == ModelType.REGRESSOR or self.model_type == ModelType.QUANTILE_REGRESSOR:\n            metric_definitions = [\n                {\"Name\": \"RMSE\", \"Regex\": \"RMSE: ([0-9.]+)\"},\n                {\"Name\": \"MAE\", \"Regex\": \"MAE: ([0-9.]+)\"},\n                {\"Name\": \"R2\", \"Regex\": \"R2: ([0-9.]+)\"},\n                {\"Name\": \"NumRows\", \"Regex\": \"NumRows: ([0-9]+)\"},\n            ]\n\n        # Metric Definitions for Classification\n        elif self.model_type == ModelType.CLASSIFIER:\n            # We need to get creative with the Classification Metrics\n\n            # Grab all the target column class values (class labels)\n            table = feature_set.data_source.table\n            self.class_labels = feature_set.query(f'select DISTINCT {self.target_column} FROM \"{table}\"')[\n                self.target_column\n            ].to_list()\n\n            # Sanity check on the targets\n            if len(self.class_labels) &gt; 10:\n                msg = f\"Too many target classes ({len(self.class_labels)}) for classification, aborting!\"\n                self.log.critical(msg)\n                raise ValueError(msg)\n\n            # Dynamically create the metric definitions\n            metrics = [\"precision\", \"recall\", \"fscore\"]\n            metric_definitions = []\n            for t in self.class_labels:\n                for m in metrics:\n                    metric_definitions.append({\"Name\": f\"Metrics:{t}:{m}\", \"Regex\": f\"Metrics:{t}:{m} ([0-9.]+)\"})\n\n            # Add the confusion matrix metrics\n            for row in self.class_labels:\n                for col in self.class_labels:\n                    metric_definitions.append(\n                        {\"Name\": f\"ConfusionMatrix:{row}:{col}\", \"Regex\": f\"ConfusionMatrix:{row}:{col} ([0-9.]+)\"}\n                    )\n\n        # If the model type is UNKNOWN, our metric_definitions will be empty\n        else:\n            self.log.important(f\"ModelType is {self.model_type}, skipping metric_definitions...\")\n            metric_definitions = []\n\n        # Take the full script path and extract the entry point and source directory\n        entry_point = str(Path(script_path).name)\n        source_dir = str(Path(script_path).parent)\n\n        # Create a Sagemaker Model with our script\n        image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n        self.estimator = SKLearn(\n            entry_point=entry_point,\n            source_dir=source_dir,\n            role=self.workbench_role_arn,\n            instance_type=\"ml.m5.large\",\n            sagemaker_session=self.sm_session,\n            framework_version=\"1.2-1\",\n            image_uri=image,\n            metric_definitions=metric_definitions,\n        )\n\n        # Training Job Name based on the Model UUID and today's date\n        training_date_time_utc = datetime.now(timezone.utc).strftime(\"%Y-%m-%d-%H-%M\")\n        training_job_name = f\"{self.output_uuid}-{training_date_time_utc}\"\n\n        # Train the estimator\n        self.estimator.fit({\"train\": s3_training_path}, job_name=training_job_name)\n\n        # Now delete the training data\n        self.log.info(f\"Deleting training data {s3_training_path}...\")\n        wr.s3.delete_objects(\n            [s3_training_path, s3_training_path.replace(\".csv\", \".csv.metadata\")],\n            boto3_session=self.boto3_session,\n        )\n\n        # Create Model and officially Register\n        self.log.important(f\"Creating new model {self.output_uuid}...\")\n        self.create_and_register_model()\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Calling onboard() on the Model\"\"\"\n        self.log.info(\"Post-Transform: Calling onboard() on the Model...\")\n\n        # Store the model feature_list and target_column in the workbench_meta\n        output_model = ModelCore(self.output_uuid, model_type=self.model_type)\n        output_model.upsert_workbench_meta({\"workbench_model_features\": self.model_feature_list})\n        output_model.upsert_workbench_meta({\"workbench_model_target\": self.target_column})\n\n        # Store the class labels (if they exist)\n        if self.class_labels:\n            output_model.set_class_labels(self.class_labels)\n\n        # Call the Model onboard method\n        output_model.onboard_with_args(self.model_type, self.target_column, self.model_feature_list)\n\n    def create_and_register_model(self):\n        \"\"\"Create and Register the Model\"\"\"\n\n        # Get the metadata/tags to push into AWS\n        aws_tags = self.get_aws_tags()\n\n        # Create model group (if it doesn't already exist)\n        self.sm_client.create_model_package_group(\n            ModelPackageGroupName=self.output_uuid,\n            ModelPackageGroupDescription=self.model_description,\n            Tags=aws_tags,\n        )\n\n        # Register our model\n        image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n        self.log.important(f\"Registering model {self.output_uuid} with image {image}...\")\n        model = self.estimator.create_model(role=self.workbench_role_arn)\n        model.register(\n            model_package_group_name=self.output_uuid,\n            framework_version=\"1.2.1\",\n            image_uri=image,\n            content_types=[\"text/csv\"],\n            response_types=[\"text/csv\"],\n            inference_instances=[\"ml.t2.medium\"],\n            transform_instances=[\"ml.m5.large\"],\n            approval_status=\"Approved\",\n            description=self.model_description,\n        )\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.__init__","title":"<code>__init__(feature_uuid, model_uuid, model_type, scikit_model_class=None, model_import_str=None, custom_script=None)</code>","text":"<p>FeaturesToModel Initialization Args:     feature_uuid (str): UUID of the FeatureSet to use as input     model_uuid (str): UUID of the Model to create as output     model_type (ModelType): ModelType.REGRESSOR or ModelType.CLASSIFIER, etc.     scikit_model_class (str, optional): The scikit model (e.g. KNeighborsRegressor) (default None)     model_import_str (str, optional): The import string for the model (default None)     custom_script (str, optional): Custom script to use for the model (default None)</p> Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def __init__(\n    self,\n    feature_uuid: str,\n    model_uuid: str,\n    model_type: ModelType,\n    scikit_model_class=None,\n    model_import_str=None,\n    custom_script=None,\n):\n    \"\"\"FeaturesToModel Initialization\n    Args:\n        feature_uuid (str): UUID of the FeatureSet to use as input\n        model_uuid (str): UUID of the Model to create as output\n        model_type (ModelType): ModelType.REGRESSOR or ModelType.CLASSIFIER, etc.\n        scikit_model_class (str, optional): The scikit model (e.g. KNeighborsRegressor) (default None)\n        model_import_str (str, optional): The import string for the model (default None)\n        custom_script (str, optional): Custom script to use for the model (default None)\n    \"\"\"\n\n    # Make sure the model_uuid is a valid name\n    Artifact.is_name_valid(model_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call superclass init\n    super().__init__(feature_uuid, model_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.FEATURE_SET\n    self.output_type = TransformOutput.MODEL\n    self.model_type = model_type\n    self.scikit_model_class = scikit_model_class\n    self.model_import_str = model_import_str\n    self.custom_script = custom_script\n    self.estimator = None\n    self.model_description = None\n    self.model_training_root = self.models_s3_path + \"/training\"\n    self.model_feature_list = None\n    self.target_column = None\n    self.class_labels = None\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.create_and_register_model","title":"<code>create_and_register_model()</code>","text":"<p>Create and Register the Model</p> Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def create_and_register_model(self):\n    \"\"\"Create and Register the Model\"\"\"\n\n    # Get the metadata/tags to push into AWS\n    aws_tags = self.get_aws_tags()\n\n    # Create model group (if it doesn't already exist)\n    self.sm_client.create_model_package_group(\n        ModelPackageGroupName=self.output_uuid,\n        ModelPackageGroupDescription=self.model_description,\n        Tags=aws_tags,\n    )\n\n    # Register our model\n    image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n    self.log.important(f\"Registering model {self.output_uuid} with image {image}...\")\n    model = self.estimator.create_model(role=self.workbench_role_arn)\n    model.register(\n        model_package_group_name=self.output_uuid,\n        framework_version=\"1.2.1\",\n        image_uri=image,\n        content_types=[\"text/csv\"],\n        response_types=[\"text/csv\"],\n        inference_instances=[\"ml.t2.medium\"],\n        transform_instances=[\"ml.m5.large\"],\n        approval_status=\"Approved\",\n        description=self.model_description,\n    )\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Calling onboard() on the Model</p> Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Calling onboard() on the Model\"\"\"\n    self.log.info(\"Post-Transform: Calling onboard() on the Model...\")\n\n    # Store the model feature_list and target_column in the workbench_meta\n    output_model = ModelCore(self.output_uuid, model_type=self.model_type)\n    output_model.upsert_workbench_meta({\"workbench_model_features\": self.model_feature_list})\n    output_model.upsert_workbench_meta({\"workbench_model_target\": self.target_column})\n\n    # Store the class labels (if they exist)\n    if self.class_labels:\n        output_model.set_class_labels(self.class_labels)\n\n    # Call the Model onboard method\n    output_model.onboard_with_args(self.model_type, self.target_column, self.model_feature_list)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#workbench.core.transforms.features_to_model.features_to_model.FeaturesToModel.transform_impl","title":"<code>transform_impl(target_column, description=None, feature_list=None, train_all_data=False)</code>","text":"<p>Generic Features to Model: Note you should create a new class and inherit from this one to include specific logic for your Feature Set/Model Args:     target_column (str): Column name of the target variable     description (str): Description of the model (optional)     feature_list (list[str]): A list of columns for the features (default None, will try to guess)     train_all_data (bool): Train on ALL (100%) of the data (default False)</p> Source code in <code>src/workbench/core/transforms/features_to_model/features_to_model.py</code> <pre><code>def transform_impl(\n    self, target_column: str, description: str = None, feature_list: list = None, train_all_data=False\n):\n    \"\"\"Generic Features to Model: Note you should create a new class and inherit from\n    this one to include specific logic for your Feature Set/Model\n    Args:\n        target_column (str): Column name of the target variable\n        description (str): Description of the model (optional)\n        feature_list (list[str]): A list of columns for the features (default None, will try to guess)\n        train_all_data (bool): Train on ALL (100%) of the data (default False)\n    \"\"\"\n    # Delete the existing model (if it exists)\n    self.log.important(\"Trying to delete existing model...\")\n    ModelCore.managed_delete(self.output_uuid)\n\n    # Set our model description\n    self.model_description = description if description is not None else f\"Model created from {self.input_uuid}\"\n\n    # Get our Feature Set and create an S3 CSV Training dataset\n    feature_set = FeatureSetCore(self.input_uuid)\n    s3_training_path = feature_set.create_s3_training_data()\n    self.log.info(f\"Created new training data {s3_training_path}...\")\n\n    # Report the target column\n    self.target_column = target_column\n    self.log.info(f\"Target column: {self.target_column}\")\n\n    # Did they specify a feature list?\n    if feature_list:\n        # AWS Feature Groups will also add these implicit columns, so remove them\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\", \"training\"]\n        feature_list = [c for c in feature_list if c not in aws_cols]\n\n    # If they didn't specify a feature list, try to guess it\n    else:\n        # Try to figure out features with this logic\n        # - Don't include id, event_time, __index_level_0__, or training columns\n        # - Don't include AWS generated columns (e.g. write_time, api_invocation_time, is_deleted)\n        # - Don't include the target columns\n        # - Don't include any columns that are of type string or timestamp\n        # - The rest of the columns are assumed to be features\n        self.log.warning(\"Guessing at the feature list, HIGHLY SUGGESTED to specify an explicit feature list!\")\n        all_columns = feature_set.columns\n        filter_list = [\n            \"id\",\n            \"__index_level_0__\",\n            \"write_time\",\n            \"api_invocation_time\",\n            \"is_deleted\",\n            \"event_time\",\n            \"training\",\n        ] + [self.target_column]\n        feature_list = [c for c in all_columns if c not in filter_list]\n\n        # AWS Feature Store has 3 user column types (String, Integral, Fractional)\n        # and two internal types (Timestamp and Boolean). A Feature List for\n        # modeling can only contain Integral and Fractional types.\n        remove_columns = []\n        column_details = feature_set.column_details()\n        for column_name in feature_list:\n            if column_details[column_name] not in [\"Integral\", \"Fractional\"]:\n                self.log.warning(\n                    f\"Removing {column_name} from feature list, improper type {column_details[column_name]}\"\n                )\n                remove_columns.append(column_name)\n\n        # Remove the columns that are not Integral or Fractional\n        feature_list = [c for c in feature_list if c not in remove_columns]\n\n    # Set the final feature list\n    self.model_feature_list = feature_list\n    self.log.important(f\"Feature List for Modeling: {self.model_feature_list}\")\n\n    # Custom Script\n    if self.custom_script:\n        script_path = self.custom_script\n        self.log.info(\"Custom script path: {script_path}\")\n        # Fixme: We'll need to circle back to this later\n        copy_imports_to_script_dir(script_path, [\"workbench.utils.chem_utils\"])\n\n    # We're using one of the built-in model script templates\n    else:\n        # Set up our parameters for the model script\n        template_params = {\n            \"model_imports\": self.model_import_str,\n            \"model_type\": self.model_type,\n            \"scikit_model_class\": self.scikit_model_class,\n            \"target_column\": self.target_column,\n            \"feature_list\": self.model_feature_list,\n            \"model_metrics_s3_path\": f\"{self.model_training_root}/{self.output_uuid}\",\n            \"train_all_data\": train_all_data,\n        }\n        # Generate our model script\n        script_path = generate_model_script(template_params)\n\n    # Metric Definitions for Regression\n    if self.model_type == ModelType.REGRESSOR or self.model_type == ModelType.QUANTILE_REGRESSOR:\n        metric_definitions = [\n            {\"Name\": \"RMSE\", \"Regex\": \"RMSE: ([0-9.]+)\"},\n            {\"Name\": \"MAE\", \"Regex\": \"MAE: ([0-9.]+)\"},\n            {\"Name\": \"R2\", \"Regex\": \"R2: ([0-9.]+)\"},\n            {\"Name\": \"NumRows\", \"Regex\": \"NumRows: ([0-9]+)\"},\n        ]\n\n    # Metric Definitions for Classification\n    elif self.model_type == ModelType.CLASSIFIER:\n        # We need to get creative with the Classification Metrics\n\n        # Grab all the target column class values (class labels)\n        table = feature_set.data_source.table\n        self.class_labels = feature_set.query(f'select DISTINCT {self.target_column} FROM \"{table}\"')[\n            self.target_column\n        ].to_list()\n\n        # Sanity check on the targets\n        if len(self.class_labels) &gt; 10:\n            msg = f\"Too many target classes ({len(self.class_labels)}) for classification, aborting!\"\n            self.log.critical(msg)\n            raise ValueError(msg)\n\n        # Dynamically create the metric definitions\n        metrics = [\"precision\", \"recall\", \"fscore\"]\n        metric_definitions = []\n        for t in self.class_labels:\n            for m in metrics:\n                metric_definitions.append({\"Name\": f\"Metrics:{t}:{m}\", \"Regex\": f\"Metrics:{t}:{m} ([0-9.]+)\"})\n\n        # Add the confusion matrix metrics\n        for row in self.class_labels:\n            for col in self.class_labels:\n                metric_definitions.append(\n                    {\"Name\": f\"ConfusionMatrix:{row}:{col}\", \"Regex\": f\"ConfusionMatrix:{row}:{col} ([0-9.]+)\"}\n                )\n\n    # If the model type is UNKNOWN, our metric_definitions will be empty\n    else:\n        self.log.important(f\"ModelType is {self.model_type}, skipping metric_definitions...\")\n        metric_definitions = []\n\n    # Take the full script path and extract the entry point and source directory\n    entry_point = str(Path(script_path).name)\n    source_dir = str(Path(script_path).parent)\n\n    # Create a Sagemaker Model with our script\n    image = InferenceImage.get_image_uri(self.sm_session.boto_region_name, \"sklearn\", \"1.2.1\")\n    self.estimator = SKLearn(\n        entry_point=entry_point,\n        source_dir=source_dir,\n        role=self.workbench_role_arn,\n        instance_type=\"ml.m5.large\",\n        sagemaker_session=self.sm_session,\n        framework_version=\"1.2-1\",\n        image_uri=image,\n        metric_definitions=metric_definitions,\n    )\n\n    # Training Job Name based on the Model UUID and today's date\n    training_date_time_utc = datetime.now(timezone.utc).strftime(\"%Y-%m-%d-%H-%M\")\n    training_job_name = f\"{self.output_uuid}-{training_date_time_utc}\"\n\n    # Train the estimator\n    self.estimator.fit({\"train\": s3_training_path}, job_name=training_job_name)\n\n    # Now delete the training data\n    self.log.info(f\"Deleting training data {s3_training_path}...\")\n    wr.s3.delete_objects(\n        [s3_training_path, s3_training_path.replace(\".csv\", \".csv.metadata\")],\n        boto3_session=self.boto3_session,\n    )\n\n    # Create Model and officially Register\n    self.log.important(f\"Creating new model {self.output_uuid}...\")\n    self.create_and_register_model()\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#supported-models","title":"Supported Models","text":"<p>Currently Workbench supports XGBoost (classifier/regressor), and Scikit Learn models. Those models can be created by just specifying different parameters to the <code>FeaturesToModel</code> class. The main issue with the supported models is they are vanilla versions with default parameters, any customization should be done with Custom Models</p>"},{"location":"core_classes/transforms/features_to_model/#xgboost","title":"XGBoost","text":"<pre><code>from workbench.core.transforms.features_to_model.features_to_model import FeaturesToModel\n\n# XGBoost Regression Model\ninput_uuid = \"abalone_features\"\noutput_uuid = \"abalone-regression\"\nto_model = FeaturesToModel(input_uuid, output_uuid, model_type=ModelType.REGRESSOR)\nto_model.set_output_tags([\"abalone\", \"public\"])\nto_model.transform(target_column=\"class_number_of_rings\", description=\"Abalone Regression\")\n\n# XGBoost Classification Model\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-classification\"\nto_model = FeaturesToModel(input_uuid, output_uuid, ModelType.CLASSIFIER)\nto_model.set_output_tags([\"wine\", \"public\"])\nto_model.transform(target_column=\"wine_class\", description=\"Wine Classification\")\n\n# Quantile Regression Model (Abalone)\ninput_uuid = \"abalone_features\"\noutput_uuid = \"abalone-quantile-reg\"\nto_model = FeaturesToModel(input_uuid, output_uuid, ModelType.QUANTILE_REGRESSOR)\nto_model.set_output_tags([\"abalone\", \"quantiles\"])\nto_model.transform(target_column=\"class_number_of_rings\", description=\"Abalone Quantile Regression\")\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#scikit-learn","title":"Scikit-Learn","text":"<pre><code>from workbench.core.transforms.features_to_model.features_to_model import FeaturesToModel\n\n# Scikit-Learn Kmeans Clustering Model\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-clusters\"\nto_model = FeaturesToModel(\n    input_uuid,\n    output_uuid,\n    scikit_model_class=\"KMeans\",  # Clustering algorithm\n    model_import_str=\"from sklearn.cluster import KMeans\",  # Import statement for KMeans\n    model_type=ModelType.CLUSTERER,\n)\nto_model.set_output_tags([\"wine\", \"clustering\"])\nto_model.transform(target_column=None, description=\"Wine Clustering\", train_all_data=True)\n\n# Scikit-Learn HDBSCAN Clustering Model\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-clusters-hdbscan\"\nto_model = FeaturesToModel(\n    input_uuid,\n    output_uuid,\n    scikit_model_class=\"HDBSCAN\",  # Density-based clustering algorithm\n    model_import_str=\"from sklearn.cluster import HDBSCAN\",\n    model_type=ModelType.CLUSTERER,\n)\nto_model.set_output_tags([\"wine\", \"density-based clustering\"])\nto_model.transform(target_column=None, description=\"Wine Clustering with HDBSCAN\", train_all_data=True)\n\n# Scikit-Learn 2D Projection Model using UMAP\ninput_uuid = \"wine_features\"\noutput_uuid = \"wine-2d-projection\"\nto_model = FeaturesToModel(\n    input_uuid,\n    output_uuid,\n    scikit_model_class=\"UMAP\",\n    model_import_str=\"from umap import UMAP\",\n    model_type=ModelType.PROJECTION,\n)\nto_model.set_output_tags([\"wine\", \"2d-projection\"])\nto_model.transform(target_column=None, description=\"Wine 2D Projection\", train_all_data=True)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#custom-models","title":"Custom Models","text":"<p>For custom models we recommend the following steps:</p> <p>Experimental</p> <p>The Workbench Custom Models are currently in experimental mode so have fun but expect issues. Requires <code>workbench &gt;= 0.8.60</code>. Feel free to submit issues to Workbench Github</p> <ul> <li>Copy the example custom model script into your own directory<ul> <li>See: Custom Model Script</li> </ul> </li> <li>Make a requirements.txt and put into the same directory</li> <li>Train/deploy the ^existing^ example<ul> <li>This is an important step, don't skip it</li> <li>If the existing model script trains/deploys your in great shape for the next step, if it doesn't then now is a good time to debug AWS account/permissions/etc.</li> </ul> </li> <li>Now customize the model script</li> <li>Train/deploy your custom script</li> </ul>"},{"location":"core_classes/transforms/features_to_model/#trainingdeploying-custom-models","title":"Training/Deploying Custom Models","text":"<pre><code>from workbench.api import ModelType\nfrom workbench.core.transforms.features_to_model.features_to_model import FeaturesToModel\n\n# Note this directory should also have a requirements.txt in it\nmy_custom_script = \"/full/path/to/my/directory/my_custom_script.py\"\ninput_uuid = \"wine_features\"    # FeatureSet you want to use\noutput_uuid = \"my-custom-model\" # change to whatever\ntarget_column = \"wine-class\"    # change to whatever\nto_model = FeaturesToModel(input_uuid, output_uuid,\n                           model_type=ModelType.CLASSIFIER, \n                           custom_script=my_custom_script)\nto_model.set_output_tags([\"your\", \"tags\"])\nto_model.transform(target_column=target_column, description=\"Custom Model\")\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#custom-models-create-an-endpointrun-inference","title":"Custom Models: Create an Endpoint/Run Inference","text":"<pre><code>from workbench.api import Model, Endpoint\n\nmodel = Model(\"my-custom-model\")\nend = model.to_endpoint()   # Note: This takes a while\n\n# Now run inference on my custom model :)\nend.auto_inference(capture=True)\n\n# Run inference with my own dataframe\ndf = fs.pull_dataframe()  # Or whatever dataframe\nend.inference(df)\n</code></pre>"},{"location":"core_classes/transforms/features_to_model/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord</p>"},{"location":"core_classes/transforms/model_to_endpoint/","title":"Model to Endpoint","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>ModelToEndpoint: Deploy an Endpoint for a Model</p>"},{"location":"core_classes/transforms/model_to_endpoint/#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint","title":"<code>ModelToEndpoint</code>","text":"<p>               Bases: <code>Transform</code></p> <p>ModelToEndpoint: Deploy an Endpoint for a Model</p> Common Usage <pre><code>to_endpoint = ModelToEndpoint(model_uuid, endpoint_uuid)\nto_endpoint.set_output_tags([\"aqsol\", \"public\", \"whatever\"])\nto_endpoint.transform()\n</code></pre> Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>class ModelToEndpoint(Transform):\n    \"\"\"ModelToEndpoint: Deploy an Endpoint for a Model\n\n    Common Usage:\n        ```python\n        to_endpoint = ModelToEndpoint(model_uuid, endpoint_uuid)\n        to_endpoint.set_output_tags([\"aqsol\", \"public\", \"whatever\"])\n        to_endpoint.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, model_uuid: str, endpoint_uuid: str, serverless: bool = True):\n        \"\"\"ModelToEndpoint Initialization\n        Args:\n            model_uuid(str): The UUID of the input Model\n            endpoint_uuid(str): The UUID of the output Endpoint\n            serverless(bool): Deploy the Endpoint in serverless mode (default: True)\n        \"\"\"\n        # Make sure the endpoint_uuid is a valid name\n        Artifact.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n        # Call superclass init\n        super().__init__(model_uuid, endpoint_uuid)\n\n        # Set up all my instance attributes\n        self.serverless = serverless\n        self.instance_type = \"serverless\" if serverless else \"ml.t2.medium\"\n        self.input_type = TransformInput.MODEL\n        self.output_type = TransformOutput.ENDPOINT\n\n    def transform_impl(self):\n        \"\"\"Deploy an Endpoint for a Model\"\"\"\n\n        # Delete endpoint (if it already exists)\n        EndpointCore.managed_delete(self.output_uuid)\n\n        # Get the Model Package ARN for our input model\n        input_model = ModelCore(self.input_uuid)\n        model_package_arn = input_model.model_package_arn()\n\n        # Deploy the model\n        self._deploy_model(model_package_arn)\n\n        # Add this endpoint to the set of registered endpoints for the model\n        input_model.register_endpoint(self.output_uuid)\n\n        # This ensures that the endpoint is ready for use\n        time.sleep(5)  # We wait for AWS Lag\n        end = EndpointCore(self.output_uuid)\n        self.log.important(f\"Endpoint {end.uuid} is ready for use\")\n\n    def _deploy_model(self, model_package_arn: str):\n        \"\"\"Internal Method: Deploy the Model\n\n        Args:\n            model_package_arn(str): The Model Package ARN used to deploy the Endpoint\n        \"\"\"\n        # Grab the specified Model Package\n        model_package = ModelPackage(\n            role=self.workbench_role_arn,\n            model_package_arn=model_package_arn,\n            sagemaker_session=self.sm_session,\n        )\n\n        # Get the metadata/tags to push into AWS\n        aws_tags = self.get_aws_tags()\n\n        # Is this a serverless deployment?\n        serverless_config = None\n        if self.serverless:\n            serverless_config = ServerlessInferenceConfig(\n                memory_size_in_mb=2048,\n                max_concurrency=5,\n            )\n\n        # Deploy the Endpoint\n        self.log.important(f\"Deploying the Endpoint {self.output_uuid}...\")\n        model_package.deploy(\n            initial_instance_count=1,\n            instance_type=self.instance_type,\n            serverless_inference_config=serverless_config,\n            endpoint_name=self.output_uuid,\n            serializer=CSVSerializer(),\n            deserializer=CSVDeserializer(),\n            tags=aws_tags,\n        )\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Calling onboard() for the Endpoint\"\"\"\n        self.log.info(\"Post-Transform: Calling onboard() for the Endpoint...\")\n\n        # Onboard the Endpoint\n        output_endpoint = EndpointCore(self.output_uuid)\n        output_endpoint.onboard_with_args(input_model=self.input_uuid)\n</code></pre>"},{"location":"core_classes/transforms/model_to_endpoint/#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.__init__","title":"<code>__init__(model_uuid, endpoint_uuid, serverless=True)</code>","text":"<p>ModelToEndpoint Initialization Args:     model_uuid(str): The UUID of the input Model     endpoint_uuid(str): The UUID of the output Endpoint     serverless(bool): Deploy the Endpoint in serverless mode (default: True)</p> Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>def __init__(self, model_uuid: str, endpoint_uuid: str, serverless: bool = True):\n    \"\"\"ModelToEndpoint Initialization\n    Args:\n        model_uuid(str): The UUID of the input Model\n        endpoint_uuid(str): The UUID of the output Endpoint\n        serverless(bool): Deploy the Endpoint in serverless mode (default: True)\n    \"\"\"\n    # Make sure the endpoint_uuid is a valid name\n    Artifact.is_name_valid(endpoint_uuid, delimiter=\"-\", lower_case=False)\n\n    # Call superclass init\n    super().__init__(model_uuid, endpoint_uuid)\n\n    # Set up all my instance attributes\n    self.serverless = serverless\n    self.instance_type = \"serverless\" if serverless else \"ml.t2.medium\"\n    self.input_type = TransformInput.MODEL\n    self.output_type = TransformOutput.ENDPOINT\n</code></pre>"},{"location":"core_classes/transforms/model_to_endpoint/#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Calling onboard() for the Endpoint</p> Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Calling onboard() for the Endpoint\"\"\"\n    self.log.info(\"Post-Transform: Calling onboard() for the Endpoint...\")\n\n    # Onboard the Endpoint\n    output_endpoint = EndpointCore(self.output_uuid)\n    output_endpoint.onboard_with_args(input_model=self.input_uuid)\n</code></pre>"},{"location":"core_classes/transforms/model_to_endpoint/#workbench.core.transforms.model_to_endpoint.model_to_endpoint.ModelToEndpoint.transform_impl","title":"<code>transform_impl()</code>","text":"<p>Deploy an Endpoint for a Model</p> Source code in <code>src/workbench/core/transforms/model_to_endpoint/model_to_endpoint.py</code> <pre><code>def transform_impl(self):\n    \"\"\"Deploy an Endpoint for a Model\"\"\"\n\n    # Delete endpoint (if it already exists)\n    EndpointCore.managed_delete(self.output_uuid)\n\n    # Get the Model Package ARN for our input model\n    input_model = ModelCore(self.input_uuid)\n    model_package_arn = input_model.model_package_arn()\n\n    # Deploy the model\n    self._deploy_model(model_package_arn)\n\n    # Add this endpoint to the set of registered endpoints for the model\n    input_model.register_endpoint(self.output_uuid)\n\n    # This ensures that the endpoint is ready for use\n    time.sleep(5)  # We wait for AWS Lag\n    end = EndpointCore(self.output_uuid)\n    self.log.important(f\"Endpoint {end.uuid} is ready for use\")\n</code></pre>"},{"location":"core_classes/transforms/overview/","title":"Transforms","text":"<p>API Classes</p> <p>For most users the API Classes will provide all the general functionality to create a full AWS ML Pipeline</p> <p>Workbench currently has a large set of Transforms that go from one Artifact type to another (e.g. DataSource to FeatureSet). The Transforms will often have light and heavy versions depending on the scale of data that needs to be transformed.</p>"},{"location":"core_classes/transforms/overview/#transform-details","title":"Transform Details","text":"<ul> <li>DataLoaders Light: Loads various light/smaller data into AWS Data Catalog and Athena</li> <li>DataLoaders Heavy: Loads heavy/larger data (via Glue) into AWS Data Catalog and Athena</li> <li>DataToFeatures: Transforms a DataSource into a FeatureSet (AWS Feature Store/Group)</li> <li>FeaturesToModel: Trains and deploys an AWS Model Package/Group from a FeatureSet</li> <li>ModelToEndpoint: Manages the provisioning and deployment of a Model Endpoint</li> <li>PandasTransforms: Pandas DataFrame transforms and helper methods.</li> </ul>"},{"location":"core_classes/transforms/pandas_transforms/","title":"Pandas Transforms","text":"<p>API Classes</p> <p>The API Classes will often provide helpful methods that give you a DataFrame (data_source.query() for instance), so always check out the API Classes first.</p> <p>These Transforms will give you the ultimate in customization and flexibility when creating AWS Machine Learning Pipelines. Grab a Pandas DataFrame from a DataSource or FeatureSet process in whatever way for your use case and simply create another Workbench DataSource or FeatureSet from the resulting DataFrame.</p> <p>Lots of Options:</p> <p>Not for Large Data</p> <p>Pandas Transforms can't handle large datasets (&gt; 4 GigaBytes). For doing transforma on large data see our Heavy Transforms</p> <ul> <li>S3 --&gt; DF --&gt; DataSource</li> <li>DataSource --&gt; DF --&gt; DataSource</li> <li>DataSoruce --&gt; DF --&gt; FeatureSet</li> <li>Get Creative!</li> </ul> <p>Welcome to the Workbench Pandas Transform Classes</p> <p>These classes provide low-level APIs for using Pandas DataFrames</p> <ul> <li>DataToPandas: Pull a dataframe from a Workbench DataSource</li> <li>FeaturesToPandas: Pull a dataframe from a Workbench FeatureSet</li> <li>PandasToData: Create a Workbench DataSource using a Pandas DataFrame as the source</li> <li>PandasToFeatures: Create a Workbench FeatureSet using a Pandas DataFrame as the source</li> <li>PandasToFeaturesChunked: Create a Workbench FeatureSet using a Chunked/Streaming Pandas DataFrame as the source</li> </ul>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.DataToPandas","title":"<code>DataToPandas</code>","text":"<p>               Bases: <code>Transform</code></p> <p>DataToPandas: Class to transform a Data Source into a Pandas DataFrame</p> Common Usage <pre><code>data_to_df = DataToPandas(data_source_uuid)\ndata_to_df.transform(query=&lt;optional SQL query to filter/process data&gt;)\ndata_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\nmy_df = data_to_df.get_output()\n\nNote: query is the best way to use this class, so use it :)\n</code></pre> Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>class DataToPandas(Transform):\n    \"\"\"DataToPandas: Class to transform a Data Source into a Pandas DataFrame\n\n    Common Usage:\n        ```python\n        data_to_df = DataToPandas(data_source_uuid)\n        data_to_df.transform(query=&lt;optional SQL query to filter/process data&gt;)\n        data_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\n        my_df = data_to_df.get_output()\n\n        Note: query is the best way to use this class, so use it :)\n        ```\n    \"\"\"\n\n    def __init__(self, input_uuid: str):\n        \"\"\"DataToPandas Initialization\"\"\"\n\n        # Call superclass init\n        super().__init__(input_uuid, \"DataFrame\")\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.DATA_SOURCE\n        self.output_type = TransformOutput.PANDAS_DF\n        self.output_df = None\n\n    def transform_impl(self, query: str = None, max_rows=100000):\n        \"\"\"Convert the DataSource into a Pandas DataFrame\n        Args:\n            query(str): The query to run against the DataSource (default: None)\n            max_rows(int): The maximum number of rows to return (default: 100000)\n        \"\"\"\n\n        # Grab the Input (Data Source)\n        input_data = DataSourceFactory(self.input_uuid)\n        if not input_data.exists():\n            self.log.critical(f\"Data Check on {self.input_uuid} failed!\")\n            return\n\n        # If a query is provided, that overrides the queries below\n        if query:\n            self.log.info(f\"Querying {self.input_uuid} with {query}...\")\n            self.output_df = input_data.query(query)\n            return\n\n        # If the data source has more rows than max_rows, do a sample query\n        num_rows = input_data.num_rows()\n        if num_rows &gt; max_rows:\n            percentage = round(max_rows * 100.0 / num_rows)\n            self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n            query = f\"SELECT * FROM {self.input_uuid} TABLESAMPLE BERNOULLI({percentage})\"\n        else:\n            query = f\"SELECT * FROM {self.input_uuid}\"\n\n        # Mark the transform as complete and set the output DataFrame\n        self.output_df = input_data.query(query)\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n        self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n        self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n\n    def get_output(self) -&gt; pd.DataFrame:\n        \"\"\"Get the DataFrame Output from this Transform\"\"\"\n        return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.DataToPandas.__init__","title":"<code>__init__(input_uuid)</code>","text":"<p>DataToPandas Initialization</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def __init__(self, input_uuid: str):\n    \"\"\"DataToPandas Initialization\"\"\"\n\n    # Call superclass init\n    super().__init__(input_uuid, \"DataFrame\")\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.DATA_SOURCE\n    self.output_type = TransformOutput.PANDAS_DF\n    self.output_df = None\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.DataToPandas.get_output","title":"<code>get_output()</code>","text":"<p>Get the DataFrame Output from this Transform</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def get_output(self) -&gt; pd.DataFrame:\n    \"\"\"Get the DataFrame Output from this Transform\"\"\"\n    return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.DataToPandas.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Any checks on the Pandas DataFrame that need to be done</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n    self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n    self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.DataToPandas.transform_impl","title":"<code>transform_impl(query=None, max_rows=100000)</code>","text":"<p>Convert the DataSource into a Pandas DataFrame Args:     query(str): The query to run against the DataSource (default: None)     max_rows(int): The maximum number of rows to return (default: 100000)</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/data_to_pandas.py</code> <pre><code>def transform_impl(self, query: str = None, max_rows=100000):\n    \"\"\"Convert the DataSource into a Pandas DataFrame\n    Args:\n        query(str): The query to run against the DataSource (default: None)\n        max_rows(int): The maximum number of rows to return (default: 100000)\n    \"\"\"\n\n    # Grab the Input (Data Source)\n    input_data = DataSourceFactory(self.input_uuid)\n    if not input_data.exists():\n        self.log.critical(f\"Data Check on {self.input_uuid} failed!\")\n        return\n\n    # If a query is provided, that overrides the queries below\n    if query:\n        self.log.info(f\"Querying {self.input_uuid} with {query}...\")\n        self.output_df = input_data.query(query)\n        return\n\n    # If the data source has more rows than max_rows, do a sample query\n    num_rows = input_data.num_rows()\n    if num_rows &gt; max_rows:\n        percentage = round(max_rows * 100.0 / num_rows)\n        self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n        query = f\"SELECT * FROM {self.input_uuid} TABLESAMPLE BERNOULLI({percentage})\"\n    else:\n        query = f\"SELECT * FROM {self.input_uuid}\"\n\n    # Mark the transform as complete and set the output DataFrame\n    self.output_df = input_data.query(query)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.FeaturesToPandas","title":"<code>FeaturesToPandas</code>","text":"<p>               Bases: <code>Transform</code></p> <p>FeaturesToPandas: Class to transform a FeatureSet into a Pandas DataFrame</p> Common Usage <pre><code>feature_to_df = FeaturesToPandas(feature_set_uuid)\nfeature_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\nmy_df = feature_to_df.get_output()\n</code></pre> Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>class FeaturesToPandas(Transform):\n    \"\"\"FeaturesToPandas: Class to transform a FeatureSet into a Pandas DataFrame\n\n    Common Usage:\n        ```python\n        feature_to_df = FeaturesToPandas(feature_set_uuid)\n        feature_to_df.transform(max_rows=&lt;optional max rows to sample&gt;)\n        my_df = feature_to_df.get_output()\n        ```\n    \"\"\"\n\n    def __init__(self, feature_set_name: str):\n        \"\"\"FeaturesToPandas Initialization\"\"\"\n\n        # Call superclass init\n        super().__init__(input_uuid=feature_set_name, output_uuid=\"DataFrame\")\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.FEATURE_SET\n        self.output_type = TransformOutput.PANDAS_DF\n        self.output_df = None\n        self.transform_run = False\n\n    def transform_impl(self, max_rows=100000):\n        \"\"\"Convert the FeatureSet into a Pandas DataFrame\"\"\"\n\n        # Grab the Input (Feature Set)\n        input_data = FeatureSetCore(self.input_uuid)\n        if not input_data.exists():\n            self.log.critical(f\"Feature Set Check on {self.input_uuid} failed!\")\n            return\n\n        # Grab the table for this Feature Set\n        table = input_data.athena_table\n\n        # Get the list of columns (and subtract metadata columns that might get added)\n        columns = input_data.columns\n        filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n        columns = \", \".join([x for x in columns if x not in filter_columns])\n\n        # Get the number of rows in the Feature Set\n        num_rows = input_data.num_rows()\n\n        # If the data source has more rows than max_rows, do a sample query\n        if num_rows &gt; max_rows:\n            percentage = round(max_rows * 100.0 / num_rows)\n            self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n            query = f'SELECT {columns} FROM \"{table}\" TABLESAMPLE BERNOULLI({percentage})'\n        else:\n            query = f'SELECT {columns} FROM \"{table}\"'\n\n        # Mark the transform as complete and set the output DataFrame\n        self.transform_run = True\n        self.output_df = input_data.query(query)\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n        self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n        self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n\n    def get_output(self) -&gt; pd.DataFrame:\n        \"\"\"Get the DataFrame Output from this Transform\"\"\"\n        if not self.transform_run:\n            self.transform()\n        return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.FeaturesToPandas.__init__","title":"<code>__init__(feature_set_name)</code>","text":"<p>FeaturesToPandas Initialization</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def __init__(self, feature_set_name: str):\n    \"\"\"FeaturesToPandas Initialization\"\"\"\n\n    # Call superclass init\n    super().__init__(input_uuid=feature_set_name, output_uuid=\"DataFrame\")\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.FEATURE_SET\n    self.output_type = TransformOutput.PANDAS_DF\n    self.output_df = None\n    self.transform_run = False\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.FeaturesToPandas.get_output","title":"<code>get_output()</code>","text":"<p>Get the DataFrame Output from this Transform</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def get_output(self) -&gt; pd.DataFrame:\n    \"\"\"Get the DataFrame Output from this Transform\"\"\"\n    if not self.transform_run:\n        self.transform()\n    return self.output_df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.FeaturesToPandas.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Any checks on the Pandas DataFrame that need to be done</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Any checks on the Pandas DataFrame that need to be done\"\"\"\n    self.log.info(\"Post-Transform: Checking Pandas DataFrame...\")\n    self.log.info(f\"DataFrame Shape: {self.output_df.shape}\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.FeaturesToPandas.transform_impl","title":"<code>transform_impl(max_rows=100000)</code>","text":"<p>Convert the FeatureSet into a Pandas DataFrame</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/features_to_pandas.py</code> <pre><code>def transform_impl(self, max_rows=100000):\n    \"\"\"Convert the FeatureSet into a Pandas DataFrame\"\"\"\n\n    # Grab the Input (Feature Set)\n    input_data = FeatureSetCore(self.input_uuid)\n    if not input_data.exists():\n        self.log.critical(f\"Feature Set Check on {self.input_uuid} failed!\")\n        return\n\n    # Grab the table for this Feature Set\n    table = input_data.athena_table\n\n    # Get the list of columns (and subtract metadata columns that might get added)\n    columns = input_data.columns\n    filter_columns = [\"write_time\", \"api_invocation_time\", \"is_deleted\"]\n    columns = \", \".join([x for x in columns if x not in filter_columns])\n\n    # Get the number of rows in the Feature Set\n    num_rows = input_data.num_rows()\n\n    # If the data source has more rows than max_rows, do a sample query\n    if num_rows &gt; max_rows:\n        percentage = round(max_rows * 100.0 / num_rows)\n        self.log.important(f\"DataSource has {num_rows} rows.. sampling down to {max_rows}...\")\n        query = f'SELECT {columns} FROM \"{table}\" TABLESAMPLE BERNOULLI({percentage})'\n    else:\n        query = f'SELECT {columns} FROM \"{table}\"'\n\n    # Mark the transform as complete and set the output DataFrame\n    self.transform_run = True\n    self.output_df = input_data.query(query)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData","title":"<code>PandasToData</code>","text":"<p>               Bases: <code>Transform</code></p> <p>PandasToData: Class to publish a Pandas DataFrame as a DataSource</p> Common Usage <pre><code>df_to_data = PandasToData(output_uuid)\ndf_to_data.set_output_tags([\"test\", \"small\"])\ndf_to_data.set_input(test_df)\ndf_to_data.transform()\n</code></pre> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>class PandasToData(Transform):\n    \"\"\"PandasToData: Class to publish a Pandas DataFrame as a DataSource\n\n    Common Usage:\n        ```python\n        df_to_data = PandasToData(output_uuid)\n        df_to_data.set_output_tags([\"test\", \"small\"])\n        df_to_data.set_input(test_df)\n        df_to_data.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, output_uuid: str, output_format: str = \"parquet\", catalog_db: str = \"workbench\"):\n        \"\"\"PandasToData Initialization\n        Args:\n            output_uuid (str): The UUID of the DataSource to create\n            output_format (str): The file format to store the S3 object data in (default: \"parquet\")\n            catalog_db (str): The AWS Data Catalog Database to use (default: \"workbench\")\n        \"\"\"\n\n        # Make sure the output_uuid is a valid name/id\n        Artifact.is_name_valid(output_uuid)\n\n        # Call superclass init\n        super().__init__(\"DataFrame\", output_uuid, catalog_db)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.PANDAS_DF\n        self.output_type = TransformOutput.DATA_SOURCE\n        self.output_df = None\n\n        # Give a message that Parquet is best in most cases\n        if output_format != \"parquet\":\n            self.log.warning(\"Parquet format works the best in most cases please consider using it\")\n        self.output_format = output_format\n\n    def set_input(self, input_df: pd.DataFrame):\n        \"\"\"Set the DataFrame Input for this Transform\"\"\"\n        self.output_df = input_df.copy()\n\n    def delete_existing(self):\n        # Delete the existing FeatureSet if it exists\n        self.log.info(f\"Deleting the {self.output_uuid} DataSource...\")\n        AthenaSource.managed_delete(self.output_uuid)\n        time.sleep(1)\n\n    def convert_object_to_string(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Try to automatically convert object columns to string columns\"\"\"\n        for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n            try:\n                df[c] = df[c].astype(\"string\")\n                df[c] = df[c].str.replace(\"'\", '\"')  # This is for nested JSON\n            except (ParserError, ValueError, TypeError):\n                self.log.info(f\"Column {c} could not be converted to string...\")\n        return df\n\n    def convert_object_to_datetime(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Try to automatically convert object columns to datetime or string columns\"\"\"\n        for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n            try:\n                df[c] = pd.to_datetime(df[c])\n            except (ParserError, ValueError, TypeError):\n                self.log.debug(f\"Column {c} could not be converted to datetime...\")\n        return df\n\n    @staticmethod\n    def convert_datetime_columns(df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Convert datetime columns to ISO-8601 string\"\"\"\n        datetime_type = [\"datetime\", \"datetime64\", \"datetime64[ns]\", \"datetimetz\"]\n        for c in df.select_dtypes(include=datetime_type).columns:\n            df[c] = df[c].map(datetime_to_iso8601)\n            df[c] = df[c].astype(pd.StringDtype())\n        return df\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Pre-Transform: Delete the existing DataSource if it exists\"\"\"\n        self.delete_existing()\n\n    def transform_impl(self, overwrite: bool = True):\n        \"\"\"Convert the Pandas DataFrame into Parquet Format in the Workbench S3 Bucket, and\n        store the information about the data to the AWS Data Catalog workbench database\n\n        Args:\n            overwrite (bool): Overwrite the existing data in the Workbench S3 Bucket (default: True)\n        \"\"\"\n        self.log.info(f\"DataFrame to Workbench DataSource: {self.output_uuid}...\")\n\n        # Set up our metadata storage\n        workbench_meta = {\"workbench_tags\": self.output_tags}\n        workbench_meta.update(self.output_meta)\n\n        # Create the Output Parquet file S3 Storage Path\n        s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n        # Convert columns names to lowercase, Athena will not work with uppercase column names\n        if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n            for c in self.output_df.columns:\n                if c != c.lower():\n                    self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n            self.output_df.columns = self.output_df.columns.str.lower()\n\n        # Convert Object Columns to String\n        self.output_df = self.convert_object_to_string(self.output_df)\n\n        # Note: Both of these conversions may not be necessary, so we're leaving them commented out\n        \"\"\"\n        # Convert Object Columns to Datetime\n        self.output_df = self.convert_object_to_datetime(self.output_df)\n\n        # Now convert datetime columns to ISO-8601 string\n        # self.output_df = self.convert_datetime_columns(self.output_df)\n        \"\"\"\n\n        # Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format\n        description = f\"Workbench data source: {self.output_uuid}\"\n        glue_table_settings = {\"description\": description, \"parameters\": workbench_meta}\n        if self.output_format == \"parquet\":\n            wr.s3.to_parquet(\n                self.output_df,\n                path=s3_storage_path,\n                dataset=True,\n                mode=\"overwrite\",\n                database=self.data_catalog_db,\n                table=self.output_uuid,\n                filename_prefix=f\"{self.output_uuid}_\",\n                boto3_session=self.boto3_session,\n                partition_cols=None,\n                glue_table_settings=glue_table_settings,\n                sanitize_columns=False,\n            )  # FIXME: Have some logic around partition columns\n\n        # Note: In general Parquet works will for most uses cases. We recommend using Parquet\n        #       You can use JSON_EXTRACT on Parquet string field, and it works great.\n        elif self.output_format == \"jsonl\":\n            self.log.warning(\"We recommend using Parquet format for most use cases\")\n            self.log.warning(\"If you have a use case that requires JSONL please contact Workbench support\")\n            self.log.warning(\"We'd like to understand what functionality JSONL is providing that isn't already\")\n            self.log.warning(\"provided with Parquet and JSON_EXTRACT() for your Athena Queries\")\n            wr.s3.to_json(\n                self.output_df,\n                path=s3_storage_path,\n                orient=\"records\",\n                lines=True,\n                date_format=\"iso\",\n                dataset=True,\n                mode=\"overwrite\",\n                database=self.data_catalog_db,\n                table=self.output_uuid,\n                filename_prefix=f\"{self.output_uuid}_\",\n                boto3_session=self.boto3_session,\n                partition_cols=None,\n                glue_table_settings=glue_table_settings,\n            )\n        else:\n            raise ValueError(f\"Unsupported file format: {self.output_format}\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Calling onboard() fnr the DataSource\"\"\"\n        self.log.info(\"Post-Transform: Calling onboard() for the DataSource...\")\n\n        # Onboard the DataSource\n        output_data_source = DataSourceFactory(self.output_uuid)\n        output_data_source.onboard()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.__init__","title":"<code>__init__(output_uuid, output_format='parquet', catalog_db='workbench')</code>","text":"<p>PandasToData Initialization Args:     output_uuid (str): The UUID of the DataSource to create     output_format (str): The file format to store the S3 object data in (default: \"parquet\")     catalog_db (str): The AWS Data Catalog Database to use (default: \"workbench\")</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def __init__(self, output_uuid: str, output_format: str = \"parquet\", catalog_db: str = \"workbench\"):\n    \"\"\"PandasToData Initialization\n    Args:\n        output_uuid (str): The UUID of the DataSource to create\n        output_format (str): The file format to store the S3 object data in (default: \"parquet\")\n        catalog_db (str): The AWS Data Catalog Database to use (default: \"workbench\")\n    \"\"\"\n\n    # Make sure the output_uuid is a valid name/id\n    Artifact.is_name_valid(output_uuid)\n\n    # Call superclass init\n    super().__init__(\"DataFrame\", output_uuid, catalog_db)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.PANDAS_DF\n    self.output_type = TransformOutput.DATA_SOURCE\n    self.output_df = None\n\n    # Give a message that Parquet is best in most cases\n    if output_format != \"parquet\":\n        self.log.warning(\"Parquet format works the best in most cases please consider using it\")\n    self.output_format = output_format\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.convert_datetime_columns","title":"<code>convert_datetime_columns(df)</code>  <code>staticmethod</code>","text":"<p>Convert datetime columns to ISO-8601 string</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>@staticmethod\ndef convert_datetime_columns(df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Convert datetime columns to ISO-8601 string\"\"\"\n    datetime_type = [\"datetime\", \"datetime64\", \"datetime64[ns]\", \"datetimetz\"]\n    for c in df.select_dtypes(include=datetime_type).columns:\n        df[c] = df[c].map(datetime_to_iso8601)\n        df[c] = df[c].astype(pd.StringDtype())\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.convert_object_to_datetime","title":"<code>convert_object_to_datetime(df)</code>","text":"<p>Try to automatically convert object columns to datetime or string columns</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def convert_object_to_datetime(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Try to automatically convert object columns to datetime or string columns\"\"\"\n    for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n        try:\n            df[c] = pd.to_datetime(df[c])\n        except (ParserError, ValueError, TypeError):\n            self.log.debug(f\"Column {c} could not be converted to datetime...\")\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.convert_object_to_string","title":"<code>convert_object_to_string(df)</code>","text":"<p>Try to automatically convert object columns to string columns</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def convert_object_to_string(self, df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Try to automatically convert object columns to string columns\"\"\"\n    for c in df.columns[df.dtypes == \"object\"]:  # Look at the object columns\n        try:\n            df[c] = df[c].astype(\"string\")\n            df[c] = df[c].str.replace(\"'\", '\"')  # This is for nested JSON\n        except (ParserError, ValueError, TypeError):\n            self.log.info(f\"Column {c} could not be converted to string...\")\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Calling onboard() fnr the DataSource</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Calling onboard() fnr the DataSource\"\"\"\n    self.log.info(\"Post-Transform: Calling onboard() for the DataSource...\")\n\n    # Onboard the DataSource\n    output_data_source = DataSourceFactory(self.output_uuid)\n    output_data_source.onboard()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Pre-Transform: Delete the existing DataSource if it exists</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Pre-Transform: Delete the existing DataSource if it exists\"\"\"\n    self.delete_existing()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.set_input","title":"<code>set_input(input_df)</code>","text":"<p>Set the DataFrame Input for this Transform</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def set_input(self, input_df: pd.DataFrame):\n    \"\"\"Set the DataFrame Input for this Transform\"\"\"\n    self.output_df = input_df.copy()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToData.transform_impl","title":"<code>transform_impl(overwrite=True)</code>","text":"<p>Convert the Pandas DataFrame into Parquet Format in the Workbench S3 Bucket, and store the information about the data to the AWS Data Catalog workbench database</p> <p>Parameters:</p> Name Type Description Default <code>overwrite</code> <code>bool</code> <p>Overwrite the existing data in the Workbench S3 Bucket (default: True)</p> <code>True</code> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_data.py</code> <pre><code>def transform_impl(self, overwrite: bool = True):\n    \"\"\"Convert the Pandas DataFrame into Parquet Format in the Workbench S3 Bucket, and\n    store the information about the data to the AWS Data Catalog workbench database\n\n    Args:\n        overwrite (bool): Overwrite the existing data in the Workbench S3 Bucket (default: True)\n    \"\"\"\n    self.log.info(f\"DataFrame to Workbench DataSource: {self.output_uuid}...\")\n\n    # Set up our metadata storage\n    workbench_meta = {\"workbench_tags\": self.output_tags}\n    workbench_meta.update(self.output_meta)\n\n    # Create the Output Parquet file S3 Storage Path\n    s3_storage_path = f\"{self.data_sources_s3_path}/{self.output_uuid}\"\n\n    # Convert columns names to lowercase, Athena will not work with uppercase column names\n    if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n        for c in self.output_df.columns:\n            if c != c.lower():\n                self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n        self.output_df.columns = self.output_df.columns.str.lower()\n\n    # Convert Object Columns to String\n    self.output_df = self.convert_object_to_string(self.output_df)\n\n    # Note: Both of these conversions may not be necessary, so we're leaving them commented out\n    \"\"\"\n    # Convert Object Columns to Datetime\n    self.output_df = self.convert_object_to_datetime(self.output_df)\n\n    # Now convert datetime columns to ISO-8601 string\n    # self.output_df = self.convert_datetime_columns(self.output_df)\n    \"\"\"\n\n    # Write out the DataFrame to AWS Data Catalog in either Parquet or JSONL format\n    description = f\"Workbench data source: {self.output_uuid}\"\n    glue_table_settings = {\"description\": description, \"parameters\": workbench_meta}\n    if self.output_format == \"parquet\":\n        wr.s3.to_parquet(\n            self.output_df,\n            path=s3_storage_path,\n            dataset=True,\n            mode=\"overwrite\",\n            database=self.data_catalog_db,\n            table=self.output_uuid,\n            filename_prefix=f\"{self.output_uuid}_\",\n            boto3_session=self.boto3_session,\n            partition_cols=None,\n            glue_table_settings=glue_table_settings,\n            sanitize_columns=False,\n        )  # FIXME: Have some logic around partition columns\n\n    # Note: In general Parquet works will for most uses cases. We recommend using Parquet\n    #       You can use JSON_EXTRACT on Parquet string field, and it works great.\n    elif self.output_format == \"jsonl\":\n        self.log.warning(\"We recommend using Parquet format for most use cases\")\n        self.log.warning(\"If you have a use case that requires JSONL please contact Workbench support\")\n        self.log.warning(\"We'd like to understand what functionality JSONL is providing that isn't already\")\n        self.log.warning(\"provided with Parquet and JSON_EXTRACT() for your Athena Queries\")\n        wr.s3.to_json(\n            self.output_df,\n            path=s3_storage_path,\n            orient=\"records\",\n            lines=True,\n            date_format=\"iso\",\n            dataset=True,\n            mode=\"overwrite\",\n            database=self.data_catalog_db,\n            table=self.output_uuid,\n            filename_prefix=f\"{self.output_uuid}_\",\n            boto3_session=self.boto3_session,\n            partition_cols=None,\n            glue_table_settings=glue_table_settings,\n        )\n    else:\n        raise ValueError(f\"Unsupported file format: {self.output_format}\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures","title":"<code>PandasToFeatures</code>","text":"<p>               Bases: <code>Transform</code></p> <p>PandasToFeatures: Class to publish a Pandas DataFrame into a FeatureSet</p> Common Usage <pre><code>to_features = PandasToFeatures(output_uuid)\nto_features.set_output_tags([\"my\", \"awesome\", \"data\"])\nto_features.set_input(df, id_column=\"my_id\")\nto_features.transform()\n</code></pre> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>class PandasToFeatures(Transform):\n    \"\"\"PandasToFeatures: Class to publish a Pandas DataFrame into a FeatureSet\n\n    Common Usage:\n        ```python\n        to_features = PandasToFeatures(output_uuid)\n        to_features.set_output_tags([\"my\", \"awesome\", \"data\"])\n        to_features.set_input(df, id_column=\"my_id\")\n        to_features.transform()\n        ```\n    \"\"\"\n\n    def __init__(self, output_uuid: str):\n        \"\"\"PandasToFeatures Initialization\n\n        Args:\n            output_uuid (str): The UUID of the FeatureSet to create\n        \"\"\"\n\n        # Make sure the output_uuid is a valid name\n        Artifact.is_name_valid(output_uuid)\n\n        # Call superclass init\n        super().__init__(\"DataFrame\", output_uuid)\n\n        # Set up all my instance attributes\n        self.input_type = TransformInput.PANDAS_DF\n        self.output_type = TransformOutput.FEATURE_SET\n        self.id_column = None\n        self.event_time_column = None\n        self.one_hot_columns = []\n        self.categorical_dtypes = {}  # Used for streaming/chunking\n        self.output_df = None\n        self.table_format = TableFormatEnum.ICEBERG\n        self.incoming_hold_out_ids = None\n\n        # These will be set in the transform method\n        self.output_feature_group = None\n        self.output_feature_set = None\n        self.expected_rows = 0\n\n    def set_input(self, input_df: pd.DataFrame, id_column=None, event_time_column=None, one_hot_columns=None):\n        \"\"\"Set the Input DataFrame for this Transform\n\n        Args:\n            input_df (pd.DataFrame): The input DataFrame.\n            id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n            event_time_column (str, optional): The name of the event time column (default: None).\n            one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n        \"\"\"\n        self.id_column = id_column\n        self.event_time_column = event_time_column\n        self.output_df = input_df.copy()\n        self.one_hot_columns = one_hot_columns or []\n\n        # Now Prepare the DataFrame for its journey into an AWS FeatureGroup\n        self.prep_dataframe()\n\n    def delete_existing(self):\n        # Delete the existing FeatureSet if it exists\n        self.log.info(f\"Deleting the {self.output_uuid} FeatureSet...\")\n        FeatureSetCore.managed_delete(self.output_uuid)\n        time.sleep(1)\n\n    def _ensure_id_column(self):\n        \"\"\"Internal: AWS Feature Store requires an Id field\"\"\"\n        if self.id_column is None:\n            self.log.warning(\"Generating an 'auto_id' column, we highly recommended setting the 'id_column'\")\n            self.output_df[\"auto_id\"] = self.output_df.index\n            self.id_column = \"auto_id\"\n            return\n        if self.id_column not in self.output_df.columns:\n            error_msg = f\"Id column {self.id_column} not found in the DataFrame\"\n            self.log.critical(error_msg)\n            raise ValueError(error_msg)\n\n    def _ensure_event_time(self):\n        \"\"\"Internal: AWS Feature Store requires an event_time field for all data stored\"\"\"\n        if self.event_time_column is None or self.event_time_column not in self.output_df.columns:\n            self.log.info(\"Generating an event_time column before FeatureSet Creation...\")\n            self.event_time_column = \"event_time\"\n            self.output_df[self.event_time_column] = pd.Timestamp(\"now\", tz=\"UTC\")\n\n        # The event_time_column is defined, so we need to make sure it's in ISO-8601 string format\n        # Note: AWS Feature Store only a particular ISO-8601 format not ALL ISO-8601 formats\n        time_column = self.output_df[self.event_time_column]\n\n        # Check if the event_time_column is of type object or string convert it to DateTime\n        if time_column.dtypes == \"object\" or time_column.dtypes.name == \"string\":\n            self.log.info(f\"Converting {self.event_time_column} to DateTime...\")\n            time_column = pd.to_datetime(time_column)\n\n        # Let's make sure it the right type for Feature Store\n        if pd.api.types.is_datetime64_any_dtype(time_column):\n            self.log.info(f\"Converting {self.event_time_column} to ISOFormat Date String before FeatureSet Creation...\")\n\n            # Convert the datetime DType to ISO-8601 string\n            # TableFormat=ICEBERG does not support alternate formats for event_time field, it only supports String type.\n            time_column = time_column.map(datetime_to_iso8601)\n            self.output_df[self.event_time_column] = time_column.astype(\"string\")\n\n    def _convert_objs_to_string(self):\n        \"\"\"Internal: AWS Feature Store doesn't know how to store object dtypes, so convert to String\"\"\"\n        for col in self.output_df:\n            if pd.api.types.is_object_dtype(self.output_df[col].dtype):\n                self.output_df[col] = self.output_df[col].astype(pd.StringDtype())\n\n    def process_column_name(self, column: str, shorten: bool = False) -&gt; str:\n        \"\"\"Call various methods to make sure the column is ready for Feature Store\n        Args:\n            column (str): The column name to process\n            shorten (bool): Should we shorten the column name? (default: False)\n        \"\"\"\n        self.log.debug(f\"Processing column {column}...\")\n\n        # Make sure the column name is valid\n        column = self.sanitize_column_name(column)\n\n        # Make sure the column name isn't too long\n        if shorten:\n            column = self.shorten_column_name(column)\n\n        return column\n\n    def shorten_column_name(self, name, max_length=20):\n        if len(name) &lt;= max_length:\n            return name\n\n        # Start building the new name from the end\n        parts = name.split(\"_\")[::-1]\n        new_name = \"\"\n        for part in parts:\n            if len(new_name) + len(part) + 1 &lt;= max_length:  # +1 for the underscore\n                new_name = f\"{part}_{new_name}\" if new_name else part\n            else:\n                break\n\n        # If new_name is empty, just use the last part of the original name\n        if not new_name:\n            new_name = parts[0]\n\n        self.log.info(f\"Shortening {name} to {new_name}\")\n        return new_name\n\n    def sanitize_column_name(self, name):\n        # Remove all invalid characters\n        sanitized = re.sub(\"[^a-zA-Z0-9-_]\", \"_\", name)\n        sanitized = re.sub(\"_+\", \"_\", sanitized)\n        sanitized = sanitized.strip(\"_\")\n\n        # Log the change if the name was altered\n        if sanitized != name:\n            self.log.info(f\"Sanitizing {name} to {sanitized}\")\n\n        return sanitized\n\n    def one_hot_encode(self, df, one_hot_columns: list) -&gt; pd.DataFrame:\n        \"\"\"One Hot Encoding for Categorical Columns with additional column name management\n\n        Args:\n            df (pd.DataFrame): The DataFrame to process\n            one_hot_columns (list): The list of columns to one-hot encode\n\n        Returns:\n            pd.DataFrame: The DataFrame with one-hot encoded columns\n        \"\"\"\n\n        # Grab the current list of columns\n        current_columns = list(df.columns)\n\n        # Now convert the list of columns into Categorical and then One-Hot Encode\n        self.convert_columns_to_categorical(one_hot_columns)\n        self.log.important(f\"One-Hot encoding columns: {one_hot_columns}\")\n        df = pd.get_dummies(df, columns=one_hot_columns)\n\n        # Compute the new columns generated by get_dummies\n        new_columns = list(set(df.columns) - set(current_columns))\n        self.log.important(f\"New columns generated: {new_columns}\")\n\n        # Convert new columns to int32\n        df[new_columns] = df[new_columns].astype(\"int32\")\n\n        # For the new columns we're going to shorten the names\n        renamed_columns = {col: self.process_column_name(col) for col in new_columns}\n\n        # Rename the columns in the DataFrame\n        df.rename(columns=renamed_columns, inplace=True)\n\n        return df\n\n    # Helper Methods\n    def convert_columns_to_categorical(self, columns: list):\n        \"\"\"Convert column to Categorical type\"\"\"\n        for feature in columns:\n            if feature not in [self.event_time_column, self.id_column]:\n                unique_values = self.output_df[feature].nunique()\n                if 1 &lt; unique_values &lt; 10:\n                    self.log.important(f\"Converting column {feature} to categorical (unique {unique_values})\")\n                    self.output_df[feature] = self.output_df[feature].astype(\"category\")\n                else:\n                    self.log.warning(f\"Column {feature} too many unique values {unique_values} skipping...\")\n\n    def manual_categorical_converter(self):\n        \"\"\"Used for Streaming: Convert object and string types to Categorical\n\n        Note:\n            This method is used for streaming/chunking. You can set the\n            categorical_dtypes attribute to a dictionary of column names and\n            their respective categorical types.\n        \"\"\"\n        for column, cat_d_type in self.categorical_dtypes.items():\n            self.output_df[column] = self.output_df[column].astype(cat_d_type)\n\n    @staticmethod\n    def convert_column_types(df: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Convert the types of the DataFrame to the correct types for the Feature Store\"\"\"\n        for column in list(df.select_dtypes(include=\"bool\").columns):\n            df[column] = df[column].astype(\"int32\")\n        for column in list(df.select_dtypes(include=\"category\").columns):\n            df[column] = df[column].astype(\"str\")\n\n        # Select all columns that are of datetime dtype and convert them to ISO-8601 strings\n        for column in [col for col in df.columns if pd.api.types.is_datetime64_any_dtype(df[col])]:\n            df[column] = df[column].map(datetime_to_iso8601).astype(\"string\")\n\n        \"\"\"FIXME Not sure we need these conversions\n        for column in list(df.select_dtypes(include=\"object\").columns):\n            df[column] = df[column].astype(\"string\")\n        for column in list(df.select_dtypes(include=[pd.Int64Dtype]).columns):\n            df[column] = df[column].astype(\"int64\")\n        for column in list(df.select_dtypes(include=[pd.Float64Dtype]).columns):\n            df[column] = df[column].astype(\"float64\")\n        \"\"\"\n        return df\n\n    def prep_dataframe(self):\n        \"\"\"Prep the DataFrame for Feature Store Creation\"\"\"\n        self.log.info(\"Prep the output_df (cat_convert, convert types, and lowercase columns)...\")\n\n        # Remove any columns generated from AWS\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        self.output_df = self.output_df.drop(columns=aws_cols, errors=\"ignore\")\n\n        # If one-hot columns are provided then one-hot encode them\n        if self.one_hot_columns:\n            self.output_df = self.one_hot_encode(self.output_df, self.one_hot_columns)\n\n        # Convert columns names to lowercase, Athena will not work with uppercase column names\n        if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n            for c in self.output_df.columns:\n                if c != c.lower():\n                    self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n            self.output_df.columns = self.output_df.columns.str.lower()\n\n        # Make sure we have the required id and event_time columns\n        self._ensure_id_column()\n        self._ensure_event_time()\n\n        # Check for a training column (Workbench uses dynamic training columns)\n        if \"training\" in self.output_df.columns:\n            self.log.important(\n                \"\"\"Training column detected: Since FeatureSets are read-only, Workbench creates a training view\n                that can be dynamically changed. We'll use this training column to create a training view.\"\"\"\n            )\n            self.incoming_hold_out_ids = self.output_df[~self.output_df[\"training\"]][self.id_column].tolist()\n            self.output_df = self.output_df.drop(columns=[\"training\"])\n\n        # We need to convert some of our column types to the correct types\n        # Feature Store only supports these data types:\n        # - Integral\n        # - Fractional\n        # - String (timestamp/datetime types need to be converted to string)\n        self.output_df = self.convert_column_types(self.output_df)\n\n    def create_feature_group(self):\n        \"\"\"Create a Feature Group, load our Feature Definitions, and wait for it to be ready\"\"\"\n\n        # Create a Feature Group and load our Feature Definitions\n        my_feature_group = FeatureGroup(name=self.output_uuid, sagemaker_session=self.sm_session)\n        my_feature_group.load_feature_definitions(data_frame=self.output_df)\n\n        # Create the Output S3 Storage Path for this Feature Set\n        s3_storage_path = f\"{self.feature_sets_s3_path}/{self.output_uuid}\"\n\n        # Get the metadata/tags to push into AWS\n        aws_tags = self.get_aws_tags()\n\n        # Create the Feature Group\n        my_feature_group.create(\n            s3_uri=s3_storage_path,\n            record_identifier_name=self.id_column,\n            event_time_feature_name=self.event_time_column,\n            role_arn=self.workbench_role_arn,\n            enable_online_store=True,\n            table_format=self.table_format,\n            tags=aws_tags,\n        )\n\n        # Ensure/wait for the feature group to be created\n        self.ensure_feature_group_created(my_feature_group)\n        return my_feature_group\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Pre-Transform: Delete any existing FeatureSet and Create the Feature Group\"\"\"\n        self.delete_existing()\n        self.output_feature_group = self.create_feature_group()\n\n    def transform_impl(self):\n        \"\"\"Transform Implementation: Ingest the data into the Feature Group\"\"\"\n\n        # Now we actually push the data into the Feature Group (called ingestion)\n        self.log.important(f\"Ingesting rows into Feature Group {self.output_uuid}...\")\n        ingest_manager = self.output_feature_group.ingest(self.output_df, max_workers=8, max_processes=2, wait=False)\n        try:\n            ingest_manager.wait()\n        except IngestionError as exc:\n            self.log.warning(f\"Some rows had an ingesting error: {exc}\")\n\n        # Report on any rows that failed to ingest\n        if ingest_manager.failed_rows:\n            self.log.warning(f\"Number of Failed Rows: {len(ingest_manager.failed_rows)}\")\n\n            # FIXME: This may or may not give us the correct rows\n            # If any index is greater then the number of rows, then the index needs\n            # to be converted to a relative index in our current output_df\n            df_rows = len(self.output_df)\n            relative_indexes = [idx - df_rows if idx &gt;= df_rows else idx for idx in ingest_manager.failed_rows]\n            failed_data = self.output_df.iloc[relative_indexes]\n            for idx, row in failed_data.iterrows():\n                self.log.warning(f\"Failed Row {idx}: {row.to_dict()}\")\n\n        # Keep track of the number of rows we expect to be ingested\n        self.expected_rows += len(self.output_df) - len(ingest_manager.failed_rows)\n        self.log.info(f\"Added rows: {len(self.output_df)}\")\n        self.log.info(f\"Failed rows: {len(ingest_manager.failed_rows)}\")\n        self.log.info(f\"Total rows ingested: {self.expected_rows}\")\n\n        # We often need to wait a bit for AWS to fully register the new Feature Group\n        self.log.important(f\"Waiting for AWS to register the new Feature Group {self.output_uuid}...\")\n        time.sleep(30)\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Populating Offline Storage and onboard()\"\"\"\n        self.log.info(\"Post-Transform: Populating Offline Storage and onboard()...\")\n\n        # Feature Group Ingestion takes a while, so we need to wait for it to finish\n        self.output_feature_set = FeatureSetCore(self.output_uuid)\n        self.log.important(\"Waiting for AWS Feature Group Offline storage to be ready...\")\n        self.log.important(\"This will often take 10-20 minutes...go have coffee or lunch :)\")\n        self.output_feature_set.set_status(\"initializing\")\n        self.wait_for_rows(self.expected_rows)\n\n        # Call the FeatureSet onboard method to compute a bunch of EDA stuff\n        self.output_feature_set.onboard()\n\n        # Set Hold Out Ids (if we got them during creation)\n        if self.incoming_hold_out_ids:\n            self.output_feature_set.set_training_holdouts(self.id_column, self.incoming_hold_out_ids)\n\n    def ensure_feature_group_created(self, feature_group):\n        status = feature_group.describe().get(\"FeatureGroupStatus\")\n        while status == \"Creating\":\n            self.log.debug(\"FeatureSet being Created...\")\n            time.sleep(5)\n            status = feature_group.describe().get(\"FeatureGroupStatus\")\n        if status == \"Created\":\n            self.log.info(f\"FeatureSet {feature_group.name} successfully created\")\n        else:\n            self.log.critical(f\"FeatureSet {feature_group.name} creation failed with status: {status}\")\n\n    def wait_for_rows(self, expected_rows: int):\n        \"\"\"Wait for AWS Feature Group to fully populate the Offline Storage\"\"\"\n        rows = self.output_feature_set.num_rows()\n\n        # Wait for the rows to be populated\n        self.log.info(f\"Waiting for AWS Feature Group {self.output_uuid} Offline Storage...\")\n        max_retry = 20\n        num_retry = 0\n        sleep_time = 30\n        while rows &lt; expected_rows and num_retry &lt; max_retry:\n            num_retry += 1\n            time.sleep(sleep_time)\n            rows = self.output_feature_set.num_rows()\n            self.log.info(f\"Offline Storage {self.output_uuid}: {rows} rows out of {expected_rows}\")\n        if rows == expected_rows:\n            self.log.important(f\"Success: Reached Expected Rows ({rows} rows)...\")\n        else:\n            msg = f\"Did not reach expected rows ({rows}/{expected_rows})...(probably AWS lag)\"\n            self.log.warning(msg)\n            self.log.monitor(msg)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.__init__","title":"<code>__init__(output_uuid)</code>","text":"<p>PandasToFeatures Initialization</p> <p>Parameters:</p> Name Type Description Default <code>output_uuid</code> <code>str</code> <p>The UUID of the FeatureSet to create</p> required Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def __init__(self, output_uuid: str):\n    \"\"\"PandasToFeatures Initialization\n\n    Args:\n        output_uuid (str): The UUID of the FeatureSet to create\n    \"\"\"\n\n    # Make sure the output_uuid is a valid name\n    Artifact.is_name_valid(output_uuid)\n\n    # Call superclass init\n    super().__init__(\"DataFrame\", output_uuid)\n\n    # Set up all my instance attributes\n    self.input_type = TransformInput.PANDAS_DF\n    self.output_type = TransformOutput.FEATURE_SET\n    self.id_column = None\n    self.event_time_column = None\n    self.one_hot_columns = []\n    self.categorical_dtypes = {}  # Used for streaming/chunking\n    self.output_df = None\n    self.table_format = TableFormatEnum.ICEBERG\n    self.incoming_hold_out_ids = None\n\n    # These will be set in the transform method\n    self.output_feature_group = None\n    self.output_feature_set = None\n    self.expected_rows = 0\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.convert_column_types","title":"<code>convert_column_types(df)</code>  <code>staticmethod</code>","text":"<p>Convert the types of the DataFrame to the correct types for the Feature Store</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>@staticmethod\ndef convert_column_types(df: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"Convert the types of the DataFrame to the correct types for the Feature Store\"\"\"\n    for column in list(df.select_dtypes(include=\"bool\").columns):\n        df[column] = df[column].astype(\"int32\")\n    for column in list(df.select_dtypes(include=\"category\").columns):\n        df[column] = df[column].astype(\"str\")\n\n    # Select all columns that are of datetime dtype and convert them to ISO-8601 strings\n    for column in [col for col in df.columns if pd.api.types.is_datetime64_any_dtype(df[col])]:\n        df[column] = df[column].map(datetime_to_iso8601).astype(\"string\")\n\n    \"\"\"FIXME Not sure we need these conversions\n    for column in list(df.select_dtypes(include=\"object\").columns):\n        df[column] = df[column].astype(\"string\")\n    for column in list(df.select_dtypes(include=[pd.Int64Dtype]).columns):\n        df[column] = df[column].astype(\"int64\")\n    for column in list(df.select_dtypes(include=[pd.Float64Dtype]).columns):\n        df[column] = df[column].astype(\"float64\")\n    \"\"\"\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.convert_columns_to_categorical","title":"<code>convert_columns_to_categorical(columns)</code>","text":"<p>Convert column to Categorical type</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def convert_columns_to_categorical(self, columns: list):\n    \"\"\"Convert column to Categorical type\"\"\"\n    for feature in columns:\n        if feature not in [self.event_time_column, self.id_column]:\n            unique_values = self.output_df[feature].nunique()\n            if 1 &lt; unique_values &lt; 10:\n                self.log.important(f\"Converting column {feature} to categorical (unique {unique_values})\")\n                self.output_df[feature] = self.output_df[feature].astype(\"category\")\n            else:\n                self.log.warning(f\"Column {feature} too many unique values {unique_values} skipping...\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.create_feature_group","title":"<code>create_feature_group()</code>","text":"<p>Create a Feature Group, load our Feature Definitions, and wait for it to be ready</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def create_feature_group(self):\n    \"\"\"Create a Feature Group, load our Feature Definitions, and wait for it to be ready\"\"\"\n\n    # Create a Feature Group and load our Feature Definitions\n    my_feature_group = FeatureGroup(name=self.output_uuid, sagemaker_session=self.sm_session)\n    my_feature_group.load_feature_definitions(data_frame=self.output_df)\n\n    # Create the Output S3 Storage Path for this Feature Set\n    s3_storage_path = f\"{self.feature_sets_s3_path}/{self.output_uuid}\"\n\n    # Get the metadata/tags to push into AWS\n    aws_tags = self.get_aws_tags()\n\n    # Create the Feature Group\n    my_feature_group.create(\n        s3_uri=s3_storage_path,\n        record_identifier_name=self.id_column,\n        event_time_feature_name=self.event_time_column,\n        role_arn=self.workbench_role_arn,\n        enable_online_store=True,\n        table_format=self.table_format,\n        tags=aws_tags,\n    )\n\n    # Ensure/wait for the feature group to be created\n    self.ensure_feature_group_created(my_feature_group)\n    return my_feature_group\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.manual_categorical_converter","title":"<code>manual_categorical_converter()</code>","text":"<p>Used for Streaming: Convert object and string types to Categorical</p> Note <p>This method is used for streaming/chunking. You can set the categorical_dtypes attribute to a dictionary of column names and their respective categorical types.</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def manual_categorical_converter(self):\n    \"\"\"Used for Streaming: Convert object and string types to Categorical\n\n    Note:\n        This method is used for streaming/chunking. You can set the\n        categorical_dtypes attribute to a dictionary of column names and\n        their respective categorical types.\n    \"\"\"\n    for column, cat_d_type in self.categorical_dtypes.items():\n        self.output_df[column] = self.output_df[column].astype(cat_d_type)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.one_hot_encode","title":"<code>one_hot_encode(df, one_hot_columns)</code>","text":"<p>One Hot Encoding for Categorical Columns with additional column name management</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The DataFrame to process</p> required <code>one_hot_columns</code> <code>list</code> <p>The list of columns to one-hot encode</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The DataFrame with one-hot encoded columns</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def one_hot_encode(self, df, one_hot_columns: list) -&gt; pd.DataFrame:\n    \"\"\"One Hot Encoding for Categorical Columns with additional column name management\n\n    Args:\n        df (pd.DataFrame): The DataFrame to process\n        one_hot_columns (list): The list of columns to one-hot encode\n\n    Returns:\n        pd.DataFrame: The DataFrame with one-hot encoded columns\n    \"\"\"\n\n    # Grab the current list of columns\n    current_columns = list(df.columns)\n\n    # Now convert the list of columns into Categorical and then One-Hot Encode\n    self.convert_columns_to_categorical(one_hot_columns)\n    self.log.important(f\"One-Hot encoding columns: {one_hot_columns}\")\n    df = pd.get_dummies(df, columns=one_hot_columns)\n\n    # Compute the new columns generated by get_dummies\n    new_columns = list(set(df.columns) - set(current_columns))\n    self.log.important(f\"New columns generated: {new_columns}\")\n\n    # Convert new columns to int32\n    df[new_columns] = df[new_columns].astype(\"int32\")\n\n    # For the new columns we're going to shorten the names\n    renamed_columns = {col: self.process_column_name(col) for col in new_columns}\n\n    # Rename the columns in the DataFrame\n    df.rename(columns=renamed_columns, inplace=True)\n\n    return df\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Populating Offline Storage and onboard()</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Populating Offline Storage and onboard()\"\"\"\n    self.log.info(\"Post-Transform: Populating Offline Storage and onboard()...\")\n\n    # Feature Group Ingestion takes a while, so we need to wait for it to finish\n    self.output_feature_set = FeatureSetCore(self.output_uuid)\n    self.log.important(\"Waiting for AWS Feature Group Offline storage to be ready...\")\n    self.log.important(\"This will often take 10-20 minutes...go have coffee or lunch :)\")\n    self.output_feature_set.set_status(\"initializing\")\n    self.wait_for_rows(self.expected_rows)\n\n    # Call the FeatureSet onboard method to compute a bunch of EDA stuff\n    self.output_feature_set.onboard()\n\n    # Set Hold Out Ids (if we got them during creation)\n    if self.incoming_hold_out_ids:\n        self.output_feature_set.set_training_holdouts(self.id_column, self.incoming_hold_out_ids)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Pre-Transform: Delete any existing FeatureSet and Create the Feature Group</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Pre-Transform: Delete any existing FeatureSet and Create the Feature Group\"\"\"\n    self.delete_existing()\n    self.output_feature_group = self.create_feature_group()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.prep_dataframe","title":"<code>prep_dataframe()</code>","text":"<p>Prep the DataFrame for Feature Store Creation</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def prep_dataframe(self):\n    \"\"\"Prep the DataFrame for Feature Store Creation\"\"\"\n    self.log.info(\"Prep the output_df (cat_convert, convert types, and lowercase columns)...\")\n\n    # Remove any columns generated from AWS\n    aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n    self.output_df = self.output_df.drop(columns=aws_cols, errors=\"ignore\")\n\n    # If one-hot columns are provided then one-hot encode them\n    if self.one_hot_columns:\n        self.output_df = self.one_hot_encode(self.output_df, self.one_hot_columns)\n\n    # Convert columns names to lowercase, Athena will not work with uppercase column names\n    if str(self.output_df.columns) != str(self.output_df.columns.str.lower()):\n        for c in self.output_df.columns:\n            if c != c.lower():\n                self.log.important(f\"Column name {c} converted to lowercase: {c.lower()}\")\n        self.output_df.columns = self.output_df.columns.str.lower()\n\n    # Make sure we have the required id and event_time columns\n    self._ensure_id_column()\n    self._ensure_event_time()\n\n    # Check for a training column (Workbench uses dynamic training columns)\n    if \"training\" in self.output_df.columns:\n        self.log.important(\n            \"\"\"Training column detected: Since FeatureSets are read-only, Workbench creates a training view\n            that can be dynamically changed. We'll use this training column to create a training view.\"\"\"\n        )\n        self.incoming_hold_out_ids = self.output_df[~self.output_df[\"training\"]][self.id_column].tolist()\n        self.output_df = self.output_df.drop(columns=[\"training\"])\n\n    # We need to convert some of our column types to the correct types\n    # Feature Store only supports these data types:\n    # - Integral\n    # - Fractional\n    # - String (timestamp/datetime types need to be converted to string)\n    self.output_df = self.convert_column_types(self.output_df)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.process_column_name","title":"<code>process_column_name(column, shorten=False)</code>","text":"<p>Call various methods to make sure the column is ready for Feature Store Args:     column (str): The column name to process     shorten (bool): Should we shorten the column name? (default: False)</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def process_column_name(self, column: str, shorten: bool = False) -&gt; str:\n    \"\"\"Call various methods to make sure the column is ready for Feature Store\n    Args:\n        column (str): The column name to process\n        shorten (bool): Should we shorten the column name? (default: False)\n    \"\"\"\n    self.log.debug(f\"Processing column {column}...\")\n\n    # Make sure the column name is valid\n    column = self.sanitize_column_name(column)\n\n    # Make sure the column name isn't too long\n    if shorten:\n        column = self.shorten_column_name(column)\n\n    return column\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.set_input","title":"<code>set_input(input_df, id_column=None, event_time_column=None, one_hot_columns=None)</code>","text":"<p>Set the Input DataFrame for this Transform</p> <p>Parameters:</p> Name Type Description Default <code>input_df</code> <code>DataFrame</code> <p>The input DataFrame.</p> required <code>id_column</code> <code>str</code> <p>The ID column (if not specified, an 'auto_id' will be generated).</p> <code>None</code> <code>event_time_column</code> <code>str</code> <p>The name of the event time column (default: None).</p> <code>None</code> <code>one_hot_columns</code> <code>list</code> <p>The list of columns to one-hot encode (default: None).</p> <code>None</code> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def set_input(self, input_df: pd.DataFrame, id_column=None, event_time_column=None, one_hot_columns=None):\n    \"\"\"Set the Input DataFrame for this Transform\n\n    Args:\n        input_df (pd.DataFrame): The input DataFrame.\n        id_column (str, optional): The ID column (if not specified, an 'auto_id' will be generated).\n        event_time_column (str, optional): The name of the event time column (default: None).\n        one_hot_columns (list, optional): The list of columns to one-hot encode (default: None).\n    \"\"\"\n    self.id_column = id_column\n    self.event_time_column = event_time_column\n    self.output_df = input_df.copy()\n    self.one_hot_columns = one_hot_columns or []\n\n    # Now Prepare the DataFrame for its journey into an AWS FeatureGroup\n    self.prep_dataframe()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.transform_impl","title":"<code>transform_impl()</code>","text":"<p>Transform Implementation: Ingest the data into the Feature Group</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def transform_impl(self):\n    \"\"\"Transform Implementation: Ingest the data into the Feature Group\"\"\"\n\n    # Now we actually push the data into the Feature Group (called ingestion)\n    self.log.important(f\"Ingesting rows into Feature Group {self.output_uuid}...\")\n    ingest_manager = self.output_feature_group.ingest(self.output_df, max_workers=8, max_processes=2, wait=False)\n    try:\n        ingest_manager.wait()\n    except IngestionError as exc:\n        self.log.warning(f\"Some rows had an ingesting error: {exc}\")\n\n    # Report on any rows that failed to ingest\n    if ingest_manager.failed_rows:\n        self.log.warning(f\"Number of Failed Rows: {len(ingest_manager.failed_rows)}\")\n\n        # FIXME: This may or may not give us the correct rows\n        # If any index is greater then the number of rows, then the index needs\n        # to be converted to a relative index in our current output_df\n        df_rows = len(self.output_df)\n        relative_indexes = [idx - df_rows if idx &gt;= df_rows else idx for idx in ingest_manager.failed_rows]\n        failed_data = self.output_df.iloc[relative_indexes]\n        for idx, row in failed_data.iterrows():\n            self.log.warning(f\"Failed Row {idx}: {row.to_dict()}\")\n\n    # Keep track of the number of rows we expect to be ingested\n    self.expected_rows += len(self.output_df) - len(ingest_manager.failed_rows)\n    self.log.info(f\"Added rows: {len(self.output_df)}\")\n    self.log.info(f\"Failed rows: {len(ingest_manager.failed_rows)}\")\n    self.log.info(f\"Total rows ingested: {self.expected_rows}\")\n\n    # We often need to wait a bit for AWS to fully register the new Feature Group\n    self.log.important(f\"Waiting for AWS to register the new Feature Group {self.output_uuid}...\")\n    time.sleep(30)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeatures.wait_for_rows","title":"<code>wait_for_rows(expected_rows)</code>","text":"<p>Wait for AWS Feature Group to fully populate the Offline Storage</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features.py</code> <pre><code>def wait_for_rows(self, expected_rows: int):\n    \"\"\"Wait for AWS Feature Group to fully populate the Offline Storage\"\"\"\n    rows = self.output_feature_set.num_rows()\n\n    # Wait for the rows to be populated\n    self.log.info(f\"Waiting for AWS Feature Group {self.output_uuid} Offline Storage...\")\n    max_retry = 20\n    num_retry = 0\n    sleep_time = 30\n    while rows &lt; expected_rows and num_retry &lt; max_retry:\n        num_retry += 1\n        time.sleep(sleep_time)\n        rows = self.output_feature_set.num_rows()\n        self.log.info(f\"Offline Storage {self.output_uuid}: {rows} rows out of {expected_rows}\")\n    if rows == expected_rows:\n        self.log.important(f\"Success: Reached Expected Rows ({rows} rows)...\")\n    else:\n        msg = f\"Did not reach expected rows ({rows}/{expected_rows})...(probably AWS lag)\"\n        self.log.warning(msg)\n        self.log.monitor(msg)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked","title":"<code>PandasToFeaturesChunked</code>","text":"<p>               Bases: <code>Transform</code></p> <p>PandasToFeaturesChunked:  Class to manage a bunch of chunked Pandas DataFrames into a FeatureSet</p> Common Usage <pre><code>to_features = PandasToFeaturesChunked(output_uuid, id_column=\"id\"/None, event_time_column=\"date\"/None)\nto_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\ncat_column_info = {\"sex\": [\"M\", \"F\", \"I\"]}\nto_features.set_categorical_info(cat_column_info)\nto_features.add_chunk(df)\nto_features.add_chunk(df)\n...\nto_features.finalize()\n</code></pre> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>class PandasToFeaturesChunked(Transform):\n    \"\"\"PandasToFeaturesChunked:  Class to manage a bunch of chunked Pandas DataFrames into a FeatureSet\n\n    Common Usage:\n        ```python\n        to_features = PandasToFeaturesChunked(output_uuid, id_column=\"id\"/None, event_time_column=\"date\"/None)\n        to_features.set_output_tags([\"abalone\", \"public\", \"whatever\"])\n        cat_column_info = {\"sex\": [\"M\", \"F\", \"I\"]}\n        to_features.set_categorical_info(cat_column_info)\n        to_features.add_chunk(df)\n        to_features.add_chunk(df)\n        ...\n        to_features.finalize()\n        ```\n    \"\"\"\n\n    def __init__(self, output_uuid: str, id_column=None, event_time_column=None):\n        \"\"\"PandasToFeaturesChunked Initialization\"\"\"\n\n        # Make sure the output_uuid is a valid name\n        Artifact.is_name_valid(output_uuid)\n\n        # Call superclass init\n        super().__init__(\"DataFrame\", output_uuid)\n\n        # Set up all my instance attributes\n        self.id_column = id_column\n        self.event_time_column = event_time_column\n        self.first_chunk = None\n        self.pandas_to_features = PandasToFeatures(output_uuid)\n\n    def set_categorical_info(self, cat_column_info: dict[list[str]]):\n        \"\"\"Set the Categorical Columns\n        Args:\n            cat_column_info (dict[list[str]]): Dictionary of categorical columns and their possible values\n        \"\"\"\n\n        # Create the CategoricalDtypes\n        cat_d_types = {}\n        for col, vals in cat_column_info.items():\n            cat_d_types[col] = CategoricalDtype(categories=vals)\n\n        # Now set the CategoricalDtypes on our underlying PandasToFeatures\n        self.pandas_to_features.categorical_dtypes = cat_d_types\n\n    def add_chunk(self, chunk_df: pd.DataFrame):\n        \"\"\"Add a Chunk of Data to the FeatureSet\"\"\"\n\n        # Is this the first chunk? If so we need to run the pre_transform\n        if self.first_chunk is None:\n            self.log.info(f\"Adding first chunk {chunk_df.shape}...\")\n            self.first_chunk = chunk_df\n            self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n            self.pandas_to_features.pre_transform()\n            self.pandas_to_features.transform_impl()\n        else:\n            self.log.info(f\"Adding chunk {chunk_df.shape}...\")\n            self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n            self.pandas_to_features.transform_impl()\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Pre-Transform: Create the Feature Group with Chunked Data\"\"\"\n\n        # Loading data into a Feature Group takes a while, so set status to loading\n        FeatureSetCore(self.output_uuid).set_status(\"loading\")\n\n    def transform_impl(self):\n        \"\"\"Required implementation of the Transform interface\"\"\"\n        self.log.warning(\"PandasToFeaturesChunked.transform_impl() called.  This is a no-op.\")\n\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform: Any Post Transform Steps\"\"\"\n        self.pandas_to_features.post_transform()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.__init__","title":"<code>__init__(output_uuid, id_column=None, event_time_column=None)</code>","text":"<p>PandasToFeaturesChunked Initialization</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def __init__(self, output_uuid: str, id_column=None, event_time_column=None):\n    \"\"\"PandasToFeaturesChunked Initialization\"\"\"\n\n    # Make sure the output_uuid is a valid name\n    Artifact.is_name_valid(output_uuid)\n\n    # Call superclass init\n    super().__init__(\"DataFrame\", output_uuid)\n\n    # Set up all my instance attributes\n    self.id_column = id_column\n    self.event_time_column = event_time_column\n    self.first_chunk = None\n    self.pandas_to_features = PandasToFeatures(output_uuid)\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.add_chunk","title":"<code>add_chunk(chunk_df)</code>","text":"<p>Add a Chunk of Data to the FeatureSet</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def add_chunk(self, chunk_df: pd.DataFrame):\n    \"\"\"Add a Chunk of Data to the FeatureSet\"\"\"\n\n    # Is this the first chunk? If so we need to run the pre_transform\n    if self.first_chunk is None:\n        self.log.info(f\"Adding first chunk {chunk_df.shape}...\")\n        self.first_chunk = chunk_df\n        self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n        self.pandas_to_features.pre_transform()\n        self.pandas_to_features.transform_impl()\n    else:\n        self.log.info(f\"Adding chunk {chunk_df.shape}...\")\n        self.pandas_to_features.set_input(chunk_df, self.id_column, self.event_time_column)\n        self.pandas_to_features.transform_impl()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.post_transform","title":"<code>post_transform(**kwargs)</code>","text":"<p>Post-Transform: Any Post Transform Steps</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def post_transform(self, **kwargs):\n    \"\"\"Post-Transform: Any Post Transform Steps\"\"\"\n    self.pandas_to_features.post_transform()\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Pre-Transform: Create the Feature Group with Chunked Data</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Pre-Transform: Create the Feature Group with Chunked Data\"\"\"\n\n    # Loading data into a Feature Group takes a while, so set status to loading\n    FeatureSetCore(self.output_uuid).set_status(\"loading\")\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.set_categorical_info","title":"<code>set_categorical_info(cat_column_info)</code>","text":"<p>Set the Categorical Columns Args:     cat_column_info (dict[list[str]]): Dictionary of categorical columns and their possible values</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def set_categorical_info(self, cat_column_info: dict[list[str]]):\n    \"\"\"Set the Categorical Columns\n    Args:\n        cat_column_info (dict[list[str]]): Dictionary of categorical columns and their possible values\n    \"\"\"\n\n    # Create the CategoricalDtypes\n    cat_d_types = {}\n    for col, vals in cat_column_info.items():\n        cat_d_types[col] = CategoricalDtype(categories=vals)\n\n    # Now set the CategoricalDtypes on our underlying PandasToFeatures\n    self.pandas_to_features.categorical_dtypes = cat_d_types\n</code></pre>"},{"location":"core_classes/transforms/pandas_transforms/#workbench.core.transforms.pandas_transforms.PandasToFeaturesChunked.transform_impl","title":"<code>transform_impl()</code>","text":"<p>Required implementation of the Transform interface</p> Source code in <code>src/workbench/core/transforms/pandas_transforms/pandas_to_features_chunked.py</code> <pre><code>def transform_impl(self):\n    \"\"\"Required implementation of the Transform interface\"\"\"\n    self.log.warning(\"PandasToFeaturesChunked.transform_impl() called.  This is a no-op.\")\n</code></pre>"},{"location":"core_classes/transforms/transform/","title":"Transform","text":"<p>API Classes</p> <p>The API Classes will use Transforms internally. So model.to_endpoint() uses the ModelToEndpoint() transform. If you need more control over the Transform you can use the Core Classes directly.</p> <p>The Workbench Transform class is a base/abstract class that defines API implemented by all the child classes (DataLoaders, DataSourceToFeatureSet, ModelToEndpoint, etc).</p> <p>Transform: Base Class for all transforms within Workbench Inherited Classes must implement the abstract transform_impl() method</p>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform","title":"<code>Transform</code>","text":"<p>               Bases: <code>ABC</code></p> <p>Transform: Abstract Base Class for all transforms within Workbench. Inherited Classes must implement the abstract transform_impl() method</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>class Transform(ABC):\n    \"\"\"Transform: Abstract Base Class for all transforms within Workbench. Inherited Classes\n    must implement the abstract transform_impl() method\"\"\"\n\n    def __init__(self, input_uuid: str, output_uuid: str, catalog_db: str = \"workbench\"):\n        \"\"\"Transform Initialization\n\n        Args:\n            input_uuid (str): The UUID of the Input Artifact\n            output_uuid (str): The UUID of the Output Artifact\n            catalog_db (str): The AWS Data Catalog Database to use (default: \"workbench\")\n        \"\"\"\n\n        self.log = logging.getLogger(\"workbench\")\n        self.input_type = None\n        self.output_type = None\n        self.output_tags = \"\"\n        self.input_uuid = str(input_uuid)  # Occasionally we get a pathlib.Path object\n        self.output_uuid = str(output_uuid)  # Occasionally we get a pathlib.Path object\n        self.output_meta = {\"workbench_input\": self.input_uuid}\n        self.data_catalog_db = catalog_db\n\n        # Grab our Workbench Bucket\n        cm = ConfigManager()\n        if not cm.config_okay():\n            self.log.error(\"Workbench Configuration Incomplete...\")\n            self.log.error(\"Run the 'workbench' command and follow the prompts...\")\n            raise FatalConfigError()\n        self.workbench_bucket = cm.get_config(\"WORKBENCH_BUCKET\")\n        self.data_sources_s3_path = \"s3://\" + self.workbench_bucket + \"/data-sources\"\n        self.feature_sets_s3_path = \"s3://\" + self.workbench_bucket + \"/feature-sets\"\n        self.models_s3_path = \"s3://\" + self.workbench_bucket + \"/models\"\n        self.endpoints_sets_s3_path = \"s3://\" + self.workbench_bucket + \"/endpoints\"\n\n        # Grab a Workbench Role ARN, Boto3, SageMaker Session, and SageMaker Client\n        self.aws_account_clamp = AWSAccountClamp()\n        self.workbench_role_arn = self.aws_account_clamp.aws_session.get_workbench_execution_role_arn()\n        self.boto3_session = self.aws_account_clamp.boto3_session\n        self.sm_session = self.aws_account_clamp.sagemaker_session()\n        self.sm_client = self.aws_account_clamp.sagemaker_client()\n\n        # Delimiter for storing lists in AWS Tags\n        self.tag_delimiter = \"::\"\n\n    @abstractmethod\n    def transform_impl(self, **kwargs):\n        \"\"\"Abstract Method: Implement the Transformation from Input to Output\"\"\"\n        pass\n\n    def pre_transform(self, **kwargs):\n        \"\"\"Perform any Pre-Transform operations\"\"\"\n        self.log.debug(\"Pre-Transform...\")\n\n    @abstractmethod\n    def post_transform(self, **kwargs):\n        \"\"\"Post-Transform ensures that the output Artifact is ready for use\"\"\"\n        pass\n\n    def set_output_tags(self, tags: Union[list, str]):\n        \"\"\"Set the tags that will be associated with the output object\n        Args:\n            tags (Union[list, str]): The list of tags or a '::' separated string of tags\"\"\"\n        if isinstance(tags, list):\n            self.output_tags = self.tag_delimiter.join(tags)\n        else:\n            self.output_tags = tags\n\n    def add_output_meta(self, meta: dict):\n        \"\"\"Add additional metadata that will be associated with the output artifact\n        Args:\n            meta (dict): A dictionary of metadata\"\"\"\n        self.output_meta = self.output_meta | meta\n\n    @staticmethod\n    def convert_to_aws_tags(metadata: dict):\n        \"\"\"Convert a dictionary to the AWS tag format (list of dicts)\n        [ {Key: key_name, Value: value}, {..}, ...]\"\"\"\n        return [{\"Key\": key, \"Value\": value} for key, value in metadata.items()]\n\n    def get_aws_tags(self):\n        \"\"\"Get the metadata/tags and convert them into AWS Tag Format\"\"\"\n        # Set up our metadata storage\n        workbench_meta = {\"workbench_tags\": self.output_tags}\n        for key, value in self.output_meta.items():\n            workbench_meta[key] = value\n        aws_tags = self.convert_to_aws_tags(workbench_meta)\n        return aws_tags\n\n    @final\n    def transform(self, **kwargs):\n        \"\"\"Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations\"\"\"\n        self.pre_transform(**kwargs)\n        self.transform_impl(**kwargs)\n        self.post_transform(**kwargs)\n\n    def input_type(self) -&gt; TransformInput:\n        \"\"\"What Input Type does this Transform Consume\"\"\"\n        return self.input_type\n\n    def output_type(self) -&gt; TransformOutput:\n        \"\"\"What Output Type does this Transform Produce\"\"\"\n        return self.output_type\n\n    def set_input_uuid(self, input_uuid: str):\n        \"\"\"Set the Input UUID (Name) for this Transform\"\"\"\n        self.input_uuid = input_uuid\n\n    def set_output_uuid(self, output_uuid: str):\n        \"\"\"Set the Output UUID (Name) for this Transform\"\"\"\n        self.output_uuid = output_uuid\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.__init__","title":"<code>__init__(input_uuid, output_uuid, catalog_db='workbench')</code>","text":"<p>Transform Initialization</p> <p>Parameters:</p> Name Type Description Default <code>input_uuid</code> <code>str</code> <p>The UUID of the Input Artifact</p> required <code>output_uuid</code> <code>str</code> <p>The UUID of the Output Artifact</p> required <code>catalog_db</code> <code>str</code> <p>The AWS Data Catalog Database to use (default: \"workbench\")</p> <code>'workbench'</code> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def __init__(self, input_uuid: str, output_uuid: str, catalog_db: str = \"workbench\"):\n    \"\"\"Transform Initialization\n\n    Args:\n        input_uuid (str): The UUID of the Input Artifact\n        output_uuid (str): The UUID of the Output Artifact\n        catalog_db (str): The AWS Data Catalog Database to use (default: \"workbench\")\n    \"\"\"\n\n    self.log = logging.getLogger(\"workbench\")\n    self.input_type = None\n    self.output_type = None\n    self.output_tags = \"\"\n    self.input_uuid = str(input_uuid)  # Occasionally we get a pathlib.Path object\n    self.output_uuid = str(output_uuid)  # Occasionally we get a pathlib.Path object\n    self.output_meta = {\"workbench_input\": self.input_uuid}\n    self.data_catalog_db = catalog_db\n\n    # Grab our Workbench Bucket\n    cm = ConfigManager()\n    if not cm.config_okay():\n        self.log.error(\"Workbench Configuration Incomplete...\")\n        self.log.error(\"Run the 'workbench' command and follow the prompts...\")\n        raise FatalConfigError()\n    self.workbench_bucket = cm.get_config(\"WORKBENCH_BUCKET\")\n    self.data_sources_s3_path = \"s3://\" + self.workbench_bucket + \"/data-sources\"\n    self.feature_sets_s3_path = \"s3://\" + self.workbench_bucket + \"/feature-sets\"\n    self.models_s3_path = \"s3://\" + self.workbench_bucket + \"/models\"\n    self.endpoints_sets_s3_path = \"s3://\" + self.workbench_bucket + \"/endpoints\"\n\n    # Grab a Workbench Role ARN, Boto3, SageMaker Session, and SageMaker Client\n    self.aws_account_clamp = AWSAccountClamp()\n    self.workbench_role_arn = self.aws_account_clamp.aws_session.get_workbench_execution_role_arn()\n    self.boto3_session = self.aws_account_clamp.boto3_session\n    self.sm_session = self.aws_account_clamp.sagemaker_session()\n    self.sm_client = self.aws_account_clamp.sagemaker_client()\n\n    # Delimiter for storing lists in AWS Tags\n    self.tag_delimiter = \"::\"\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.add_output_meta","title":"<code>add_output_meta(meta)</code>","text":"<p>Add additional metadata that will be associated with the output artifact Args:     meta (dict): A dictionary of metadata</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def add_output_meta(self, meta: dict):\n    \"\"\"Add additional metadata that will be associated with the output artifact\n    Args:\n        meta (dict): A dictionary of metadata\"\"\"\n    self.output_meta = self.output_meta | meta\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.convert_to_aws_tags","title":"<code>convert_to_aws_tags(metadata)</code>  <code>staticmethod</code>","text":"<p>Convert a dictionary to the AWS tag format (list of dicts) [ {Key: key_name, Value: value}, {..}, ...]</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>@staticmethod\ndef convert_to_aws_tags(metadata: dict):\n    \"\"\"Convert a dictionary to the AWS tag format (list of dicts)\n    [ {Key: key_name, Value: value}, {..}, ...]\"\"\"\n    return [{\"Key\": key, \"Value\": value} for key, value in metadata.items()]\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.get_aws_tags","title":"<code>get_aws_tags()</code>","text":"<p>Get the metadata/tags and convert them into AWS Tag Format</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def get_aws_tags(self):\n    \"\"\"Get the metadata/tags and convert them into AWS Tag Format\"\"\"\n    # Set up our metadata storage\n    workbench_meta = {\"workbench_tags\": self.output_tags}\n    for key, value in self.output_meta.items():\n        workbench_meta[key] = value\n    aws_tags = self.convert_to_aws_tags(workbench_meta)\n    return aws_tags\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.input_type","title":"<code>input_type()</code>","text":"<p>What Input Type does this Transform Consume</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def input_type(self) -&gt; TransformInput:\n    \"\"\"What Input Type does this Transform Consume\"\"\"\n    return self.input_type\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.output_type","title":"<code>output_type()</code>","text":"<p>What Output Type does this Transform Produce</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def output_type(self) -&gt; TransformOutput:\n    \"\"\"What Output Type does this Transform Produce\"\"\"\n    return self.output_type\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.post_transform","title":"<code>post_transform(**kwargs)</code>  <code>abstractmethod</code>","text":"<p>Post-Transform ensures that the output Artifact is ready for use</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>@abstractmethod\ndef post_transform(self, **kwargs):\n    \"\"\"Post-Transform ensures that the output Artifact is ready for use\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.pre_transform","title":"<code>pre_transform(**kwargs)</code>","text":"<p>Perform any Pre-Transform operations</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def pre_transform(self, **kwargs):\n    \"\"\"Perform any Pre-Transform operations\"\"\"\n    self.log.debug(\"Pre-Transform...\")\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.set_input_uuid","title":"<code>set_input_uuid(input_uuid)</code>","text":"<p>Set the Input UUID (Name) for this Transform</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def set_input_uuid(self, input_uuid: str):\n    \"\"\"Set the Input UUID (Name) for this Transform\"\"\"\n    self.input_uuid = input_uuid\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.set_output_tags","title":"<code>set_output_tags(tags)</code>","text":"<p>Set the tags that will be associated with the output object Args:     tags (Union[list, str]): The list of tags or a '::' separated string of tags</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def set_output_tags(self, tags: Union[list, str]):\n    \"\"\"Set the tags that will be associated with the output object\n    Args:\n        tags (Union[list, str]): The list of tags or a '::' separated string of tags\"\"\"\n    if isinstance(tags, list):\n        self.output_tags = self.tag_delimiter.join(tags)\n    else:\n        self.output_tags = tags\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.set_output_uuid","title":"<code>set_output_uuid(output_uuid)</code>","text":"<p>Set the Output UUID (Name) for this Transform</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>def set_output_uuid(self, output_uuid: str):\n    \"\"\"Set the Output UUID (Name) for this Transform\"\"\"\n    self.output_uuid = output_uuid\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.transform","title":"<code>transform(**kwargs)</code>","text":"<p>Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>@final\ndef transform(self, **kwargs):\n    \"\"\"Perform the Transformation from Input to Output with pre_transform() and post_transform() invocations\"\"\"\n    self.pre_transform(**kwargs)\n    self.transform_impl(**kwargs)\n    self.post_transform(**kwargs)\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.Transform.transform_impl","title":"<code>transform_impl(**kwargs)</code>  <code>abstractmethod</code>","text":"<p>Abstract Method: Implement the Transformation from Input to Output</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>@abstractmethod\ndef transform_impl(self, **kwargs):\n    \"\"\"Abstract Method: Implement the Transformation from Input to Output\"\"\"\n    pass\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.TransformInput","title":"<code>TransformInput</code>","text":"<p>               Bases: <code>Enum</code></p> <p>Enumerated Types for Workbench Transform Inputs</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>class TransformInput(Enum):\n    \"\"\"Enumerated Types for Workbench Transform Inputs\"\"\"\n\n    LOCAL_FILE = auto()\n    PANDAS_DF = auto()\n    SPARK_DF = auto()\n    S3_OBJECT = auto()\n    DATA_SOURCE = auto()\n    FEATURE_SET = auto()\n    MODEL = auto()\n</code></pre>"},{"location":"core_classes/transforms/transform/#workbench.core.transforms.transform.TransformOutput","title":"<code>TransformOutput</code>","text":"<p>               Bases: <code>Enum</code></p> <p>Enumerated Types for Workbench Transform Outputs</p> Source code in <code>src/workbench/core/transforms/transform.py</code> <pre><code>class TransformOutput(Enum):\n    \"\"\"Enumerated Types for Workbench Transform Outputs\"\"\"\n\n    PANDAS_DF = auto()\n    SPARK_DF = auto()\n    S3_OBJECT = auto()\n    DATA_SOURCE = auto()\n    FEATURE_SET = auto()\n    MODEL = auto()\n    ENDPOINT = auto()\n</code></pre>"},{"location":"core_classes/views/computation_view/","title":"Computation View","text":"<p>Experimental</p> <p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>Note: This class can be automatically invoked from DataSource/FeatureSet <code>set_computation_columns()</code> DataSource or FeatureSet. If you need more control then you can use this class directly.</p> <p>ComputationView Class: Create a View with a subset of columns for display purposes</p>"},{"location":"core_classes/views/computation_view/#workbench.core.views.computation_view.ComputationView","title":"<code>ComputationView</code>","text":"<p>               Bases: <code>ColumnSubsetView</code></p> <p>ComputationView Class: Create a View with a subset of columns for computation purposes</p> Common Usage <pre><code># Create a default ComputationView\nfs = FeatureSet(\"test_features\")\ncomp_view = ComputationView.create(fs)\ndf = comp_view.pull_dataframe()\n\n# Create a ComputationView with a specific set of columns\ncomp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n</code></pre> Source code in <code>src/workbench/core/views/computation_view.py</code> <pre><code>class ComputationView(ColumnSubsetView):\n    \"\"\"ComputationView Class: Create a View with a subset of columns for computation purposes\n\n    Common Usage:\n        ```python\n        # Create a default ComputationView\n        fs = FeatureSet(\"test_features\")\n        comp_view = ComputationView.create(fs)\n        df = comp_view.pull_dataframe()\n\n        # Create a ComputationView with a specific set of columns\n        comp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        artifact: Union[DataSource, FeatureSet],\n        source_table: str = None,\n        column_list: Union[list[str], None] = None,\n        column_limit: int = 30,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a ComputationView instance.\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None\n            column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n            column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n        return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/computation_view/#workbench.core.views.computation_view.ComputationView.create","title":"<code>create(artifact, source_table=None, column_list=None, column_limit=30)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a ComputationView instance.</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>The DataSource or FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None</p> <code>None</code> <code>column_list</code> <code>Union[list[str], None]</code> <p>A list of columns to include. Defaults to None.</p> <code>None</code> <code>column_limit</code> <code>int</code> <p>The max number of columns to include. Defaults to 30.</p> <code>30</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/workbench/core/views/computation_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    artifact: Union[DataSource, FeatureSet],\n    source_table: str = None,\n    column_list: Union[list[str], None] = None,\n    column_limit: int = 30,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a ComputationView instance.\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None\n        column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n        column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n    return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/computation_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/display_view/","title":"Display View","text":"<p>Experimental</p> <p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>Note: This class will be used in the future to fine tune what columns get displayed. For now just use the DataSource/FeatureSet <code>set_computation_columns()</code> DataSource or FeatureSet</p> <p>DisplayView Class: Create a View with a subset of columns for display purposes</p>"},{"location":"core_classes/views/display_view/#workbench.core.views.display_view.DisplayView","title":"<code>DisplayView</code>","text":"<p>               Bases: <code>ColumnSubsetView</code></p> <p>DisplayView Class: Create a View with a subset of columns for display purposes</p> Common Usage <pre><code># Create a default DisplayView\nfs = FeatureSet(\"test_features\")\ndisplay_view = DisplayView.create(fs)\ndf = display_view.pull_dataframe()\n\n# Create a DisplayView with a specific set of columns\ndisplay_view = DisplayView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = display_view.query(f\"SELECT * FROM {display_view.table} where awesome = 'yes'\")\n</code></pre> Source code in <code>src/workbench/core/views/display_view.py</code> <pre><code>class DisplayView(ColumnSubsetView):\n    \"\"\"DisplayView Class: Create a View with a subset of columns for display purposes\n\n    Common Usage:\n        ```python\n        # Create a default DisplayView\n        fs = FeatureSet(\"test_features\")\n        display_view = DisplayView.create(fs)\n        df = display_view.pull_dataframe()\n\n        # Create a DisplayView with a specific set of columns\n        display_view = DisplayView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = display_view.query(f\"SELECT * FROM {display_view.table} where awesome = 'yes'\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        artifact: Union[DataSource, FeatureSet],\n        source_table: str = None,\n        column_list: Union[list[str], None] = None,\n        column_limit: int = 30,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a DisplayView instance.\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None\n            column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n            column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Use the create logic directly from ColumnSubsetView with the \"display\" view name\n        return ColumnSubsetView.create(\"display\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/display_view/#workbench.core.views.display_view.DisplayView.create","title":"<code>create(artifact, source_table=None, column_list=None, column_limit=30)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a DisplayView instance.</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>The DataSource or FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None</p> <code>None</code> <code>column_list</code> <code>Union[list[str], None]</code> <p>A list of columns to include. Defaults to None.</p> <code>None</code> <code>column_limit</code> <code>int</code> <p>The max number of columns to include. Defaults to 30.</p> <code>30</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/workbench/core/views/display_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    artifact: Union[DataSource, FeatureSet],\n    source_table: str = None,\n    column_list: Union[list[str], None] = None,\n    column_limit: int = 30,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a DisplayView instance.\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None\n        column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n        column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Use the create logic directly from ColumnSubsetView with the \"display\" view name\n    return ColumnSubsetView.create(\"display\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"core_classes/views/display_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/mdq_view/","title":"ModelDataQuality View","text":"<p>Experimental</p> <p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>MDQView Class: A View that computes various endpoint data quality metrics</p>"},{"location":"core_classes/views/mdq_view/#workbench.core.views.mdq_view.MDQView","title":"<code>MDQView</code>","text":"<p>MDQView Class: A View that computes various endpoint data quality metrics</p> Common Usage <pre><code># Grab a FeatureSet and an Endpoint\nfs = FeatureSet(\"abalone_features\")\nendpoint = Endpoint(\"abalone-regression-end\")\n\n# Create a ModelDataQuality View\nmdq_view = MDQView.create(fs, endpoint=endpoint, id_column=\"id\")\nmy_df = mdq_view.pull_dataframe(head=True)\n\n# Query the view\ndf = mdq_view.query(f\"SELECT * FROM {mdq_view.table} where residuals &gt; 0.5\")\n</code></pre> Source code in <code>src/workbench/core/views/mdq_view.py</code> <pre><code>class MDQView:\n    \"\"\"MDQView Class: A View that computes various endpoint data quality metrics\n\n    Common Usage:\n        ```python\n        # Grab a FeatureSet and an Endpoint\n        fs = FeatureSet(\"abalone_features\")\n        endpoint = Endpoint(\"abalone-regression-end\")\n\n        # Create a ModelDataQuality View\n        mdq_view = MDQView.create(fs, endpoint=endpoint, id_column=\"id\")\n        my_df = mdq_view.pull_dataframe(head=True)\n\n        # Query the view\n        df = mdq_view.query(f\"SELECT * FROM {mdq_view.table} where residuals &gt; 0.5\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        fs: FeatureSet,\n        endpoint: Endpoint,\n        id_column: str,\n        use_reference_model: bool = False,\n    ) -&gt; Union[View, None]:\n        \"\"\"Create a Model Data Quality View with metrics\n\n        Args:\n            fs (FeatureSet): The FeatureSet object\n            endpoint (Endpoint): The Endpoint object to use for the target and features\n            id_column (str): The name of the id column (must be defined for join logic)\n            use_reference_model (bool): Use the reference model for inference (default: False)\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed)\n        \"\"\"\n        # Log view creation\n        fs.log.important(\"Creating Model Data Quality View...\")\n\n        # Get the target and feature columns from the endpoints model input\n        model_input = Model(endpoint.get_input())\n        target = model_input.target()\n        features = model_input.features()\n\n        # Pull in data from the source table\n        df = fs.data_source.query(f\"SELECT * FROM {fs.data_source.uuid}\")\n\n        # Check if the target and features are available in the data source\n        missing_columns = [col for col in [target] + features if col not in df.columns]\n        if missing_columns:\n            fs.log.error(f\"Missing columns in data source: {missing_columns}\")\n            return None\n\n        # Check if the target is categorical\n        categorical_target = not pd.api.types.is_numeric_dtype(df[target])\n\n        # Compute row tags with RowTagger\n        row_tagger = RowTagger(\n            df,\n            features=features,\n            id_column=id_column,\n            target_column=target,\n            within_dist=0.25,\n            min_target_diff=1.0,\n            outlier_df=fs.data_source.outliers(),\n            categorical_target=categorical_target,\n        )\n        mdq_df = row_tagger.tag_rows()\n\n        # Rename and compute data quality scores based on tags\n        mdq_df.rename(columns={\"tags\": \"data_quality_tags\"}, inplace=True)\n\n        # We're going to compute a data_quality score based on the tags.\n        mdq_df[\"data_quality\"] = mdq_df[\"data_quality_tags\"].apply(cls.calculate_data_quality)\n\n        # Compute residuals using ResidualsCalculator\n        if use_reference_model:\n            residuals_calculator = ResidualsCalculator()\n        else:\n            residuals_calculator = ResidualsCalculator(endpoint=endpoint)\n        residuals_df = residuals_calculator.fit_transform(df[features], df[target])\n\n        # Add id_column to the residuals dataframe and merge with mdq_df\n        residuals_df[id_column] = df[id_column]\n\n        # Drop overlapping columns in mdq_df (except for the id_column) to avoid _x and _y suffixes\n        overlap_columns = [col for col in residuals_df.columns if col in mdq_df.columns and col != id_column]\n        mdq_df = mdq_df.drop(columns=overlap_columns)\n\n        # Merge the DataFrames, with the id_column as the join key\n        mdq_df = mdq_df.merge(residuals_df, on=id_column, how=\"left\")\n\n        # Delegate view creation to PandasToView\n        view_name = \"mdq_ref\" if use_reference_model else \"mdq\"\n        return PandasToView.create(view_name, fs, df=mdq_df, id_column=id_column)\n\n    @staticmethod\n    def calculate_data_quality(tags):\n        score = 1.0  # Start with the default score\n        if \"coincident\" in tags:\n            score -= 1.0\n        if \"htg\" in tags:\n            score -= 0.5\n        if \"outlier\" in tags:\n            score -= 0.25\n        score = max(0.0, score)\n        return score\n</code></pre>"},{"location":"core_classes/views/mdq_view/#workbench.core.views.mdq_view.MDQView.create","title":"<code>create(fs, endpoint, id_column, use_reference_model=False)</code>  <code>classmethod</code>","text":"<p>Create a Model Data Quality View with metrics</p> <p>Parameters:</p> Name Type Description Default <code>fs</code> <code>FeatureSet</code> <p>The FeatureSet object</p> required <code>endpoint</code> <code>Endpoint</code> <p>The Endpoint object to use for the target and features</p> required <code>id_column</code> <code>str</code> <p>The name of the id column (must be defined for join logic)</p> required <code>use_reference_model</code> <code>bool</code> <p>Use the reference model for inference (default: False)</p> <code>False</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed)</p> Source code in <code>src/workbench/core/views/mdq_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    fs: FeatureSet,\n    endpoint: Endpoint,\n    id_column: str,\n    use_reference_model: bool = False,\n) -&gt; Union[View, None]:\n    \"\"\"Create a Model Data Quality View with metrics\n\n    Args:\n        fs (FeatureSet): The FeatureSet object\n        endpoint (Endpoint): The Endpoint object to use for the target and features\n        id_column (str): The name of the id column (must be defined for join logic)\n        use_reference_model (bool): Use the reference model for inference (default: False)\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed)\n    \"\"\"\n    # Log view creation\n    fs.log.important(\"Creating Model Data Quality View...\")\n\n    # Get the target and feature columns from the endpoints model input\n    model_input = Model(endpoint.get_input())\n    target = model_input.target()\n    features = model_input.features()\n\n    # Pull in data from the source table\n    df = fs.data_source.query(f\"SELECT * FROM {fs.data_source.uuid}\")\n\n    # Check if the target and features are available in the data source\n    missing_columns = [col for col in [target] + features if col not in df.columns]\n    if missing_columns:\n        fs.log.error(f\"Missing columns in data source: {missing_columns}\")\n        return None\n\n    # Check if the target is categorical\n    categorical_target = not pd.api.types.is_numeric_dtype(df[target])\n\n    # Compute row tags with RowTagger\n    row_tagger = RowTagger(\n        df,\n        features=features,\n        id_column=id_column,\n        target_column=target,\n        within_dist=0.25,\n        min_target_diff=1.0,\n        outlier_df=fs.data_source.outliers(),\n        categorical_target=categorical_target,\n    )\n    mdq_df = row_tagger.tag_rows()\n\n    # Rename and compute data quality scores based on tags\n    mdq_df.rename(columns={\"tags\": \"data_quality_tags\"}, inplace=True)\n\n    # We're going to compute a data_quality score based on the tags.\n    mdq_df[\"data_quality\"] = mdq_df[\"data_quality_tags\"].apply(cls.calculate_data_quality)\n\n    # Compute residuals using ResidualsCalculator\n    if use_reference_model:\n        residuals_calculator = ResidualsCalculator()\n    else:\n        residuals_calculator = ResidualsCalculator(endpoint=endpoint)\n    residuals_df = residuals_calculator.fit_transform(df[features], df[target])\n\n    # Add id_column to the residuals dataframe and merge with mdq_df\n    residuals_df[id_column] = df[id_column]\n\n    # Drop overlapping columns in mdq_df (except for the id_column) to avoid _x and _y suffixes\n    overlap_columns = [col for col in residuals_df.columns if col in mdq_df.columns and col != id_column]\n    mdq_df = mdq_df.drop(columns=overlap_columns)\n\n    # Merge the DataFrames, with the id_column as the join key\n    mdq_df = mdq_df.merge(residuals_df, on=id_column, how=\"left\")\n\n    # Delegate view creation to PandasToView\n    view_name = \"mdq_ref\" if use_reference_model else \"mdq\"\n    return PandasToView.create(view_name, fs, df=mdq_df, id_column=id_column)\n</code></pre>"},{"location":"core_classes/views/mdq_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/overview/","title":"Views","text":"<p>View Examples</p> <p>Examples of using the Views classes to extend the functionality of Workbench Artifacts are in the Examples section at the bottom of this page. </p> <p>Views are a powerful way to filter and agument your DataSources and FeatureSets. With Views you can subset columns, rows, and even add data to existing Workbench Artifacts. If you want to compute outliers, runs some statistics or engineer some new features, Views are an easy way to change, modify, and add to DataSources and FeatureSets.</p> <p>If you're looking to read and pull data from a view please see the Views documentation.</p>"},{"location":"core_classes/views/overview/#view-constructor-classes","title":"View Constructor Classes","text":"<p>These classes provide APIs for creating Views for DataSources and FeatureSets.</p> <ul> <li>DisplayView: The Display View is leveraged by the web views/components and allows fine tuning of the UI for the Workbench Dashboard.</li> <li>ComputationView: The Computation View controls which columns have descriptive stats, outliers, and correlation calculations. Typically the computation view is a superset of the display view.</li> <li>TrainingView: The Training View will add a 'training' column to the data for model training, validation, and testing. Each row will have a 1 or 0 indicated whether is was used in the model training.</li> <li>MDQView: The Model Data Quality View computes various data quality metrics that include modeling inference results.</li> </ul>"},{"location":"core_classes/views/overview/#examples","title":"Examples","text":"<p>All of the Workbench Examples are in the Workbench Repository under the <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p> <p>Listing Views</p> views.py<pre><code>from workbench.api.data_source import DataSource\n\n# Convert the Data Source to a Feature Set\ntest_data = DataSource('test_data')\ntest_data.views()\n[\"display\", \"training\", \"computation\"]\n</code></pre> <p>Getting a Particular View</p> views.py<pre><code>from workbench.api.feature_set import FeatureSet\n\nfs = FeatureSet('test_features')\n\n# Grab the columns for the display view\ndisplay_view = fs.view(\"display\")\ndisplay_view.columns\n['id', 'name', 'height', 'weight', 'salary', ...]\n\n# Pull the dataframe for this view\ndf = display_view.pull_dataframe()\n    id       name     height      weight         salary ...\n0   58  Person 58  71.781227  275.088196  162053.140625  \n</code></pre> <p>View Queries</p> <p>All Workbench Views are stored in AWS Athena, so any query that you can make with Athena is accessible through the View Query API.</p> view_query.py<pre><code>from workbench.api.feature_set import FeatureSet\n\n# Grab a FeatureSet View\nfs = FeatureSet(\"abalone_features\")\nt_view = fs.view(\"training\")\n\n# Make some queries using the Athena backend\ndf = t_view(f\"select * from {t_view.table} where height &gt; .3\")\nprint(df.head())\n\ndf = t_view.query(\"select * from abalone_features where class_number_of_rings &lt; 3\")\nprint(df.head())\n</code></pre> <p>Output</p> <pre><code>  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   M   0.705     0.565   0.515         2.210          1.1075          0.4865        0.5120                     10\n1   F   0.455     0.355   1.130         0.594          0.3320          0.1160        0.1335                      8\n\n  sex  length  diameter  height  whole_weight  shucked_weight  viscera_weight  shell_weight  class_number_of_rings\n0   I   0.075     0.055   0.010         0.002          0.0010          0.0005        0.0015                      1\n1   I   0.150     0.100   0.025         0.015          0.0045          0.0040         0.0050                      2\n</code></pre> <p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"core_classes/views/training_view/","title":"Training View","text":"<p>Experimental</p> <p>The Workbench View classes are currently in experimental mode so have fun but expect issues and API changes going forward.</p> <p>TrainingView Class: A View with an additional training column that marks holdout ids</p>"},{"location":"core_classes/views/training_view/#workbench.core.views.training_view.TrainingView","title":"<code>TrainingView</code>","text":"<p>               Bases: <code>CreateView</code></p> <p>TrainingView Class: A View with an additional training column that marks holdout ids</p> Common Usage <pre><code># Create a default TrainingView\nfs = FeatureSet(\"test_features\")\ntraining_view = TrainingView.create(fs)\ndf = training_view.pull_dataframe()\n\n# Create a TrainingView with a specific set of columns\ntraining_view = TrainingView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = training_view.query(f\"SELECT * FROM {training_view.table} where training = TRUE\")\n</code></pre> Source code in <code>src/workbench/core/views/training_view.py</code> <pre><code>class TrainingView(CreateView):\n    \"\"\"TrainingView Class: A View with an additional training column that marks holdout ids\n\n    Common Usage:\n        ```python\n        # Create a default TrainingView\n        fs = FeatureSet(\"test_features\")\n        training_view = TrainingView.create(fs)\n        df = training_view.pull_dataframe()\n\n        # Create a TrainingView with a specific set of columns\n        training_view = TrainingView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = training_view.query(f\"SELECT * FROM {training_view.table} where training = TRUE\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        feature_set: FeatureSet,\n        source_table: str = None,\n        id_column: str = None,\n        holdout_ids: Union[list[str], list[int], None] = None,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a TrainingView instance.\n\n        Args:\n            feature_set (FeatureSet): A FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None.\n            id_column (str, optional): The name of the id column. Defaults to None.\n            holdout_ids (Union[list[str], list[int], None], optional): A list of holdout ids. Defaults to None.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Instantiate the TrainingView with \"training\" as the view name\n        instance = cls(\"training\", feature_set, source_table)\n\n        # Drop any columns generated from AWS\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        source_table_columns = get_column_list(instance.data_source, instance.source_table)\n        column_list = [col for col in source_table_columns if col not in aws_cols]\n\n        # Sanity check on the id column\n        if not id_column:\n            instance.log.important(\"No id column specified, we'll try the auto_id_column ..\")\n            if not instance.auto_id_column:\n                instance.log.error(\"No id column specified and no auto_id_column found, aborting ..\")\n                return None\n            else:\n                if instance.auto_id_column not in column_list:\n                    instance.log.error(\n                        f\"Auto id column {instance.auto_id_column} not found in column list, aborting ..\"\n                    )\n                    return None\n                else:\n                    id_column = instance.auto_id_column\n\n        # If we don't have holdout ids, create a default training view\n        if not holdout_ids:\n            instance._default_training_view(instance.data_source, id_column)\n            return View(instance.data_source, instance.view_name, auto_create_view=False)\n\n        # Format the list of holdout ids for SQL IN clause\n        if holdout_ids and all(isinstance(id, str) for id in holdout_ids):\n            formatted_holdout_ids = \", \".join(f\"'{id}'\" for id in holdout_ids)\n        else:\n            formatted_holdout_ids = \", \".join(map(str, holdout_ids))\n\n        # Enclose each column name in double quotes\n        sql_columns = \", \".join([f'\"{column}\"' for column in column_list])\n\n        # Construct the CREATE VIEW query\n        create_view_query = f\"\"\"\n        CREATE OR REPLACE VIEW {instance.table} AS\n        SELECT {sql_columns}, CASE\n            WHEN {id_column} IN ({formatted_holdout_ids}) THEN False\n            ELSE True\n        END AS training\n        FROM {instance.source_table}\n        \"\"\"\n\n        # Execute the CREATE VIEW query\n        instance.data_source.execute_statement(create_view_query)\n\n        # Return the View\n        return View(instance.data_source, instance.view_name, auto_create_view=False)\n\n    # This is an internal method that's used to create a default training view\n    def _default_training_view(self, data_source: DataSource, id_column: str):\n        \"\"\"Create a default view in Athena that assigns roughly 80% of the data to training\n\n        Args:\n            data_source (DataSource): The Workbench DataSource object\n            id_column (str): The name of the id column\n        \"\"\"\n        self.log.important(f\"Creating default Training View {self.table}...\")\n\n        # Drop any columns generated from AWS\n        aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n        column_list = [col for col in data_source.columns if col not in aws_cols]\n\n        # Enclose each column name in double quotes\n        sql_columns = \", \".join([f'\"{column}\"' for column in column_list])\n\n        # Construct the CREATE VIEW query with a simple modulo operation for the 80/20 split\n        create_view_query = f\"\"\"\n        CREATE OR REPLACE VIEW \"{self.table}\" AS\n        SELECT {sql_columns}, CASE\n            WHEN MOD(ROW_NUMBER() OVER (ORDER BY {id_column}), 10) &lt; 8 THEN True  -- Assign 80% to training\n            ELSE False  -- Assign roughly 20% to validation/test\n        END AS training\n        FROM {self.base_table_name}\n        \"\"\"\n\n        # Execute the CREATE VIEW query\n        data_source.execute_statement(create_view_query)\n</code></pre>"},{"location":"core_classes/views/training_view/#workbench.core.views.training_view.TrainingView.create","title":"<code>create(feature_set, source_table=None, id_column=None, holdout_ids=None)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a TrainingView instance.</p> <p>Parameters:</p> Name Type Description Default <code>feature_set</code> <code>FeatureSet</code> <p>A FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None.</p> <code>None</code> <code>id_column</code> <code>str</code> <p>The name of the id column. Defaults to None.</p> <code>None</code> <code>holdout_ids</code> <code>Union[list[str], list[int], None]</code> <p>A list of holdout ids. Defaults to None.</p> <code>None</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/workbench/core/views/training_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    feature_set: FeatureSet,\n    source_table: str = None,\n    id_column: str = None,\n    holdout_ids: Union[list[str], list[int], None] = None,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a TrainingView instance.\n\n    Args:\n        feature_set (FeatureSet): A FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None.\n        id_column (str, optional): The name of the id column. Defaults to None.\n        holdout_ids (Union[list[str], list[int], None], optional): A list of holdout ids. Defaults to None.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Instantiate the TrainingView with \"training\" as the view name\n    instance = cls(\"training\", feature_set, source_table)\n\n    # Drop any columns generated from AWS\n    aws_cols = [\"write_time\", \"api_invocation_time\", \"is_deleted\", \"event_time\"]\n    source_table_columns = get_column_list(instance.data_source, instance.source_table)\n    column_list = [col for col in source_table_columns if col not in aws_cols]\n\n    # Sanity check on the id column\n    if not id_column:\n        instance.log.important(\"No id column specified, we'll try the auto_id_column ..\")\n        if not instance.auto_id_column:\n            instance.log.error(\"No id column specified and no auto_id_column found, aborting ..\")\n            return None\n        else:\n            if instance.auto_id_column not in column_list:\n                instance.log.error(\n                    f\"Auto id column {instance.auto_id_column} not found in column list, aborting ..\"\n                )\n                return None\n            else:\n                id_column = instance.auto_id_column\n\n    # If we don't have holdout ids, create a default training view\n    if not holdout_ids:\n        instance._default_training_view(instance.data_source, id_column)\n        return View(instance.data_source, instance.view_name, auto_create_view=False)\n\n    # Format the list of holdout ids for SQL IN clause\n    if holdout_ids and all(isinstance(id, str) for id in holdout_ids):\n        formatted_holdout_ids = \", \".join(f\"'{id}'\" for id in holdout_ids)\n    else:\n        formatted_holdout_ids = \", \".join(map(str, holdout_ids))\n\n    # Enclose each column name in double quotes\n    sql_columns = \", \".join([f'\"{column}\"' for column in column_list])\n\n    # Construct the CREATE VIEW query\n    create_view_query = f\"\"\"\n    CREATE OR REPLACE VIEW {instance.table} AS\n    SELECT {sql_columns}, CASE\n        WHEN {id_column} IN ({formatted_holdout_ids}) THEN False\n        ELSE True\n    END AS training\n    FROM {instance.source_table}\n    \"\"\"\n\n    # Execute the CREATE VIEW query\n    instance.data_source.execute_statement(create_view_query)\n\n    # Return the View\n    return View(instance.data_source, instance.view_name, auto_create_view=False)\n</code></pre>"},{"location":"core_classes/views/training_view/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord</p>"},{"location":"data_algorithms/overview/","title":"Data Algorithms","text":"<p>Data Algorithms</p> <p>WIP: These classes are currently actively being developed and are subject to change in both API and functionality over time. They provide a set of data algorithms for various types of data storage. We currently have subdirectorys for:</p> <ul> <li>DataFrames: A set of algorithms that consume/return a Panda dataFrame.</li> <li>Graphs: Algorithms for node/edge graphs, specifically focused on NetworkX graphs.</li> <li>Spark: A set of algorithms that consume/return a Spark dataFrame.</li> <li> <p>SQL: SQL queries that provide a wide range of functionality:</p> <ul> <li>Outliers</li> <li>Descriptive Stats</li> <li>Correlations</li> <li>and More</li> </ul> </li> </ul> <p>Welcome to the Workbench Data Algorithms</p> <p>Docs TBD</p>"},{"location":"data_algorithms/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/dataframes/overview/","title":"Pandas Dataframe Algorithms","text":"<p>Pandas Dataframes</p> <p>Pandas dataframes are obviously not going to scale as well as our Spark and SQL Algorithms, but for 'moderate' sized data these algorithms provide some nice functionality.</p> <p>Pandas Dataframe Algorithms</p> <p>Workbench has a growing set of algorithms and data processing tools for Pandas Dataframes. In general these algorithm will take a dataframe as input and give you back a dataframe with additional columns.</p> <p>FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.</p> <p>DimensionalityReduction: Perform Dimensionality Reduction on a DataFrame</p>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity","title":"<code>FeatureSpaceProximity</code>","text":"Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>class FeatureSpaceProximity:\n    def __init__(self, df: pd.DataFrame, features: list, id_column: str, target: str = None, neighbors: int = 10):\n        \"\"\"FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.\n\n        Args:\n            df: Pandas DataFrame\n            features: List of feature column names\n            id_column: Name of the ID column\n            target: Optional name of the target column to include target-based functionality (default: None)\n            neighbors: Number of neighbors to use in the KNN model (default: 10)\n        \"\"\"\n        self.log = logging.getLogger(\"workbench\")\n        self.df = df\n        self.features = features\n        self.id_column = id_column\n        self.target = target\n        self.knn_neighbors = neighbors\n\n        # Standardize the feature values and build the KNN model\n        self.log.info(\"Building KNN model for FeatureSpaceProximity...\")\n        self.scaler = StandardScaler().fit(df[features])\n        scaled_features = self.scaler.transform(df[features])\n        self.knn_model = NearestNeighbors(n_neighbors=neighbors, algorithm=\"auto\").fit(scaled_features)\n\n        # Compute Z-Scores or Consistency Scores for the target values\n        if self.target and is_numeric_dtype(self.df[self.target]):\n            self.log.info(\"Computing Z-Scores for target values...\")\n            self.target_z_scores()\n        else:\n            self.log.info(\"Computing target consistency scores...\")\n            self.target_consistency()\n\n        # Now compute the outlier scores\n        self.log.info(\"Computing outlier scores...\")\n        self.outliers()\n\n    @classmethod\n    def from_model(cls, model) -&gt; \"FeatureSpaceProximity\":\n        \"\"\"Create a FeatureSpaceProximity instance from a Workbench model object.\n\n        Args:\n            model (Model): A Workbench model object.\n\n        Returns:\n            FeatureSpaceProximity: A new instance of the FeatureSpaceProximity class.\n        \"\"\"\n        from workbench.api import FeatureSet\n\n        # Extract necessary attributes from the Workbench model\n        fs = FeatureSet(model.get_input())\n        features = model.features()\n        target = model.target()\n\n        # Retrieve the training DataFrame from the feature set\n        df = fs.view(\"training\").pull_dataframe()\n\n        # Create and return a new instance of FeatureSpaceProximity\n        return cls(df=df, features=features, id_column=fs.id_column, target=target)\n\n    def neighbors(self, query_id: Union[str, int], radius: float = None, include_self: bool = True) -&gt; pd.DataFrame:\n        \"\"\"Return neighbors of the given query ID, either by fixed neighbors or within a radius.\n\n        Args:\n            query_id (Union[str, int]): The ID of the query point.\n            radius (float): Optional radius within which neighbors are to be searched, else use fixed neighbors.\n            include_self (bool): Whether to include the query ID itself in the neighbor results.\n\n        Returns:\n            pd.DataFrame: Filtered DataFrame that includes the query ID, its neighbors, and optionally target values.\n        \"\"\"\n        if query_id not in self.df[self.id_column].values:\n            self.log.warning(f\"Query ID '{query_id}' not found in the DataFrame. Returning an empty DataFrame.\")\n            return pd.DataFrame()\n\n        # Get a single-row DataFrame for the query ID\n        query_df = self.df[self.df[self.id_column] == query_id]\n\n        # Use the neighbors_bulk method with the appropriate radius\n        neighbors_info_df = self.neighbors_bulk(query_df, radius=radius, include_self=include_self)\n\n        # Extract the neighbor IDs and distances from the results\n        neighbor_ids = neighbors_info_df[\"neighbor_ids\"].iloc[0]\n        neighbor_distances = neighbors_info_df[\"neighbor_distances\"].iloc[0]\n\n        # Sort neighbors by distance (ascending order)\n        sorted_neighbors = sorted(zip(neighbor_ids, neighbor_distances), key=lambda x: x[1])\n        sorted_ids, sorted_distances = zip(*sorted_neighbors)\n\n        # Filter the internal DataFrame to include only the sorted neighbors\n        neighbors_df = self.df[self.df[self.id_column].isin(sorted_ids)]\n        neighbors_df = neighbors_df.set_index(self.id_column).reindex(sorted_ids).reset_index()\n        neighbors_df[\"knn_distance\"] = sorted_distances\n        return neighbors_df\n\n    def neighbors_bulk(self, query_df: pd.DataFrame, radius: float = None, include_self: bool = False) -&gt; pd.DataFrame:\n        \"\"\"Return neighbors for each row in the given query dataframe, either by fixed neighbors or within a radius.\n\n        Args:\n            query_df: Pandas DataFrame with the same features as the training data.\n            radius: Optional radius within which neighbors are to be searched, else use fixed neighbors.\n            include_self: Boolean indicating whether to include the query ID in the neighbor results.\n\n        Returns:\n            pd.DataFrame: DataFrame with query ID, neighbor IDs, neighbor targets, and neighbor distances.\n        \"\"\"\n        # Scale the query data using the same scaler as the training data\n        query_scaled = self.scaler.transform(query_df[self.features])\n\n        # Retrieve neighbors based on radius or standard neighbors\n        if radius is not None:\n            distances, indices = self.knn_model.radius_neighbors(query_scaled, radius=radius)\n        else:\n            distances, indices = self.knn_model.kneighbors(query_scaled)\n\n        # Collect neighbor information (IDs, target values, and distances)\n        query_ids = query_df[self.id_column].values\n        neighbor_ids = [[self.df.iloc[idx][self.id_column] for idx in index_list] for index_list in indices]\n        neighbor_targets = (\n            [\n                [self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0] for neighbor in index_list]\n                for index_list in neighbor_ids\n            ]\n            if self.target\n            else None\n        )\n        neighbor_distances = [list(dist_list) for dist_list in distances]\n\n        # Automatically remove the query ID itself from the neighbor results if include_self is False\n        for i, query_id in enumerate(query_ids):\n            if query_id in neighbor_ids[i] and not include_self:\n                idx_to_remove = neighbor_ids[i].index(query_id)\n                neighbor_ids[i].pop(idx_to_remove)\n                neighbor_distances[i].pop(idx_to_remove)\n                if neighbor_targets:\n                    neighbor_targets[i].pop(idx_to_remove)\n\n            # Sort neighbors by distance (ascending order)\n            sorted_neighbors = sorted(zip(neighbor_ids[i], neighbor_distances[i]), key=lambda x: x[1])\n            neighbor_ids[i], neighbor_distances[i] = list(zip(*sorted_neighbors)) if sorted_neighbors else ([], [])\n            if neighbor_targets:\n                neighbor_targets[i] = [\n                    self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0]\n                    for neighbor in neighbor_ids[i]\n                ]\n\n        # Create and return a results DataFrame with the updated neighbor information\n        result_df = pd.DataFrame(\n            {\n                \"query_id\": query_ids,\n                \"neighbor_ids\": neighbor_ids,\n                \"neighbor_distances\": neighbor_distances,\n            }\n        )\n\n        if neighbor_targets:\n            result_df[\"neighbor_targets\"] = neighbor_targets\n\n        return result_df\n\n    def outliers(self) -&gt; None:\n        \"\"\"Compute a unified 'outlier' score based on either 'target_z' or 'target_consistency'.\"\"\"\n        if \"target_z\" in self.df.columns:\n            # Normalize Z-Scores to a 0-1 range\n            self.df[\"outlier\"] = (self.df[\"target_z\"].abs() / (self.df[\"target_z\"].abs().max() + 1e-6)).clip(0, 1)\n\n        elif \"target_consistency\" in self.df.columns:\n            # Calculate outlier score as 1 - consistency\n            self.df[\"outlier\"] = 1 - self.df[\"target_consistency\"]\n\n        else:\n            self.log.warning(\"No 'target_z' or 'target_consistency' column found to compute outlier scores.\")\n\n    def target_z_scores(self) -&gt; None:\n        \"\"\"Compute Z-Scores for NUMERIC target values.\"\"\"\n        if not self.target:\n            self.log.warning(\"No target column defined for Z-Score computation.\")\n            return\n\n        # Get the neighbors and distances for each internal observation\n        distances, indices = self.knn_model.kneighbors()\n\n        # Retrieve all neighbor target values in a single operation\n        neighbor_targets = self.df[self.target].values[indices]  # Shape will be (n_samples, n_neighbors)\n\n        # Compute the mean and std along the neighbors axis (axis=1)\n        neighbor_means = neighbor_targets.mean(axis=1)\n        neighbor_stds = neighbor_targets.std(axis=1, ddof=0)\n\n        # Vectorized Z-score calculation\n        current_targets = self.df[self.target].values\n        z_scores = np.where(neighbor_stds == 0, 0.0, (current_targets - neighbor_means) / neighbor_stds)\n\n        # Assign the computed Z-Scores back to the DataFrame\n        self.df[\"target_z\"] = z_scores\n\n    def target_consistency(self) -&gt; None:\n        \"\"\"Compute a Neighborhood Consistency Score for CATEGORICAL targets.\"\"\"\n        if not self.target:\n            self.log.warning(\"No target column defined for neighborhood consistency computation.\")\n            return\n\n        # Get the neighbors and distances for each internal observation (already excludes the query)\n        distances, indices = self.knn_model.kneighbors()\n\n        # Calculate the Neighborhood Consistency Score for each observation\n        consistency_scores = []\n        for idx, idx_list in enumerate(indices):\n            query_target = self.df.iloc[idx][self.target]  # Get current observation's target value\n\n            # Get the neighbors' target values\n            neighbor_targets = self.df.iloc[idx_list][self.target]\n\n            # Calculate the proportion of neighbors that have the same category as the query observation\n            consistency_score = (neighbor_targets == query_target).mean()\n            consistency_scores.append(consistency_score)\n\n        # Add the 'target_consistency' column to the internal dataframe\n        self.df[\"target_consistency\"] = consistency_scores\n\n    def get_neighbor_indices_and_distances(self):\n        \"\"\"Retrieve neighbor indices and distances for all points in the dataset.\"\"\"\n        distances, indices = self.knn_model.kneighbors()\n        return indices, distances\n\n    def target_summary(self, query_id: Union[str, int]) -&gt; pd.DataFrame:\n        \"\"\"WIP: Provide a summary of target values in the neighborhood of the given query ID\"\"\"\n        neighbors_df = self.neighbors(query_id, include_self=False)\n        if self.target and not neighbors_df.empty:\n            summary_stats = neighbors_df[self.target].describe()\n            return pd.DataFrame(summary_stats).transpose()\n        else:\n            self.log.warning(f\"No target values found for neighbors of Query ID '{query_id}'.\")\n            return pd.DataFrame()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.__init__","title":"<code>__init__(df, features, id_column, target=None, neighbors=10)</code>","text":"<p>FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>Pandas DataFrame</p> required <code>features</code> <code>list</code> <p>List of feature column names</p> required <code>id_column</code> <code>str</code> <p>Name of the ID column</p> required <code>target</code> <code>str</code> <p>Optional name of the target column to include target-based functionality (default: None)</p> <code>None</code> <code>neighbors</code> <code>int</code> <p>Number of neighbors to use in the KNN model (default: 10)</p> <code>10</code> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def __init__(self, df: pd.DataFrame, features: list, id_column: str, target: str = None, neighbors: int = 10):\n    \"\"\"FeatureSpaceProximity: A class for neighbor lookups using KNN with optional target information.\n\n    Args:\n        df: Pandas DataFrame\n        features: List of feature column names\n        id_column: Name of the ID column\n        target: Optional name of the target column to include target-based functionality (default: None)\n        neighbors: Number of neighbors to use in the KNN model (default: 10)\n    \"\"\"\n    self.log = logging.getLogger(\"workbench\")\n    self.df = df\n    self.features = features\n    self.id_column = id_column\n    self.target = target\n    self.knn_neighbors = neighbors\n\n    # Standardize the feature values and build the KNN model\n    self.log.info(\"Building KNN model for FeatureSpaceProximity...\")\n    self.scaler = StandardScaler().fit(df[features])\n    scaled_features = self.scaler.transform(df[features])\n    self.knn_model = NearestNeighbors(n_neighbors=neighbors, algorithm=\"auto\").fit(scaled_features)\n\n    # Compute Z-Scores or Consistency Scores for the target values\n    if self.target and is_numeric_dtype(self.df[self.target]):\n        self.log.info(\"Computing Z-Scores for target values...\")\n        self.target_z_scores()\n    else:\n        self.log.info(\"Computing target consistency scores...\")\n        self.target_consistency()\n\n    # Now compute the outlier scores\n    self.log.info(\"Computing outlier scores...\")\n    self.outliers()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.from_model","title":"<code>from_model(model)</code>  <code>classmethod</code>","text":"<p>Create a FeatureSpaceProximity instance from a Workbench model object.</p> <p>Parameters:</p> Name Type Description Default <code>model</code> <code>Model</code> <p>A Workbench model object.</p> required <p>Returns:</p> Name Type Description <code>FeatureSpaceProximity</code> <code>FeatureSpaceProximity</code> <p>A new instance of the FeatureSpaceProximity class.</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>@classmethod\ndef from_model(cls, model) -&gt; \"FeatureSpaceProximity\":\n    \"\"\"Create a FeatureSpaceProximity instance from a Workbench model object.\n\n    Args:\n        model (Model): A Workbench model object.\n\n    Returns:\n        FeatureSpaceProximity: A new instance of the FeatureSpaceProximity class.\n    \"\"\"\n    from workbench.api import FeatureSet\n\n    # Extract necessary attributes from the Workbench model\n    fs = FeatureSet(model.get_input())\n    features = model.features()\n    target = model.target()\n\n    # Retrieve the training DataFrame from the feature set\n    df = fs.view(\"training\").pull_dataframe()\n\n    # Create and return a new instance of FeatureSpaceProximity\n    return cls(df=df, features=features, id_column=fs.id_column, target=target)\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.get_neighbor_indices_and_distances","title":"<code>get_neighbor_indices_and_distances()</code>","text":"<p>Retrieve neighbor indices and distances for all points in the dataset.</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def get_neighbor_indices_and_distances(self):\n    \"\"\"Retrieve neighbor indices and distances for all points in the dataset.\"\"\"\n    distances, indices = self.knn_model.kneighbors()\n    return indices, distances\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors","title":"<code>neighbors(query_id, radius=None, include_self=True)</code>","text":"<p>Return neighbors of the given query ID, either by fixed neighbors or within a radius.</p> <p>Parameters:</p> Name Type Description Default <code>query_id</code> <code>Union[str, int]</code> <p>The ID of the query point.</p> required <code>radius</code> <code>float</code> <p>Optional radius within which neighbors are to be searched, else use fixed neighbors.</p> <code>None</code> <code>include_self</code> <code>bool</code> <p>Whether to include the query ID itself in the neighbor results.</p> <code>True</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: Filtered DataFrame that includes the query ID, its neighbors, and optionally target values.</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def neighbors(self, query_id: Union[str, int], radius: float = None, include_self: bool = True) -&gt; pd.DataFrame:\n    \"\"\"Return neighbors of the given query ID, either by fixed neighbors or within a radius.\n\n    Args:\n        query_id (Union[str, int]): The ID of the query point.\n        radius (float): Optional radius within which neighbors are to be searched, else use fixed neighbors.\n        include_self (bool): Whether to include the query ID itself in the neighbor results.\n\n    Returns:\n        pd.DataFrame: Filtered DataFrame that includes the query ID, its neighbors, and optionally target values.\n    \"\"\"\n    if query_id not in self.df[self.id_column].values:\n        self.log.warning(f\"Query ID '{query_id}' not found in the DataFrame. Returning an empty DataFrame.\")\n        return pd.DataFrame()\n\n    # Get a single-row DataFrame for the query ID\n    query_df = self.df[self.df[self.id_column] == query_id]\n\n    # Use the neighbors_bulk method with the appropriate radius\n    neighbors_info_df = self.neighbors_bulk(query_df, radius=radius, include_self=include_self)\n\n    # Extract the neighbor IDs and distances from the results\n    neighbor_ids = neighbors_info_df[\"neighbor_ids\"].iloc[0]\n    neighbor_distances = neighbors_info_df[\"neighbor_distances\"].iloc[0]\n\n    # Sort neighbors by distance (ascending order)\n    sorted_neighbors = sorted(zip(neighbor_ids, neighbor_distances), key=lambda x: x[1])\n    sorted_ids, sorted_distances = zip(*sorted_neighbors)\n\n    # Filter the internal DataFrame to include only the sorted neighbors\n    neighbors_df = self.df[self.df[self.id_column].isin(sorted_ids)]\n    neighbors_df = neighbors_df.set_index(self.id_column).reindex(sorted_ids).reset_index()\n    neighbors_df[\"knn_distance\"] = sorted_distances\n    return neighbors_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.neighbors_bulk","title":"<code>neighbors_bulk(query_df, radius=None, include_self=False)</code>","text":"<p>Return neighbors for each row in the given query dataframe, either by fixed neighbors or within a radius.</p> <p>Parameters:</p> Name Type Description Default <code>query_df</code> <code>DataFrame</code> <p>Pandas DataFrame with the same features as the training data.</p> required <code>radius</code> <code>float</code> <p>Optional radius within which neighbors are to be searched, else use fixed neighbors.</p> <code>None</code> <code>include_self</code> <code>bool</code> <p>Boolean indicating whether to include the query ID in the neighbor results.</p> <code>False</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: DataFrame with query ID, neighbor IDs, neighbor targets, and neighbor distances.</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def neighbors_bulk(self, query_df: pd.DataFrame, radius: float = None, include_self: bool = False) -&gt; pd.DataFrame:\n    \"\"\"Return neighbors for each row in the given query dataframe, either by fixed neighbors or within a radius.\n\n    Args:\n        query_df: Pandas DataFrame with the same features as the training data.\n        radius: Optional radius within which neighbors are to be searched, else use fixed neighbors.\n        include_self: Boolean indicating whether to include the query ID in the neighbor results.\n\n    Returns:\n        pd.DataFrame: DataFrame with query ID, neighbor IDs, neighbor targets, and neighbor distances.\n    \"\"\"\n    # Scale the query data using the same scaler as the training data\n    query_scaled = self.scaler.transform(query_df[self.features])\n\n    # Retrieve neighbors based on radius or standard neighbors\n    if radius is not None:\n        distances, indices = self.knn_model.radius_neighbors(query_scaled, radius=radius)\n    else:\n        distances, indices = self.knn_model.kneighbors(query_scaled)\n\n    # Collect neighbor information (IDs, target values, and distances)\n    query_ids = query_df[self.id_column].values\n    neighbor_ids = [[self.df.iloc[idx][self.id_column] for idx in index_list] for index_list in indices]\n    neighbor_targets = (\n        [\n            [self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0] for neighbor in index_list]\n            for index_list in neighbor_ids\n        ]\n        if self.target\n        else None\n    )\n    neighbor_distances = [list(dist_list) for dist_list in distances]\n\n    # Automatically remove the query ID itself from the neighbor results if include_self is False\n    for i, query_id in enumerate(query_ids):\n        if query_id in neighbor_ids[i] and not include_self:\n            idx_to_remove = neighbor_ids[i].index(query_id)\n            neighbor_ids[i].pop(idx_to_remove)\n            neighbor_distances[i].pop(idx_to_remove)\n            if neighbor_targets:\n                neighbor_targets[i].pop(idx_to_remove)\n\n        # Sort neighbors by distance (ascending order)\n        sorted_neighbors = sorted(zip(neighbor_ids[i], neighbor_distances[i]), key=lambda x: x[1])\n        neighbor_ids[i], neighbor_distances[i] = list(zip(*sorted_neighbors)) if sorted_neighbors else ([], [])\n        if neighbor_targets:\n            neighbor_targets[i] = [\n                self.df.loc[self.df[self.id_column] == neighbor, self.target].values[0]\n                for neighbor in neighbor_ids[i]\n            ]\n\n    # Create and return a results DataFrame with the updated neighbor information\n    result_df = pd.DataFrame(\n        {\n            \"query_id\": query_ids,\n            \"neighbor_ids\": neighbor_ids,\n            \"neighbor_distances\": neighbor_distances,\n        }\n    )\n\n    if neighbor_targets:\n        result_df[\"neighbor_targets\"] = neighbor_targets\n\n    return result_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.outliers","title":"<code>outliers()</code>","text":"<p>Compute a unified 'outlier' score based on either 'target_z' or 'target_consistency'.</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def outliers(self) -&gt; None:\n    \"\"\"Compute a unified 'outlier' score based on either 'target_z' or 'target_consistency'.\"\"\"\n    if \"target_z\" in self.df.columns:\n        # Normalize Z-Scores to a 0-1 range\n        self.df[\"outlier\"] = (self.df[\"target_z\"].abs() / (self.df[\"target_z\"].abs().max() + 1e-6)).clip(0, 1)\n\n    elif \"target_consistency\" in self.df.columns:\n        # Calculate outlier score as 1 - consistency\n        self.df[\"outlier\"] = 1 - self.df[\"target_consistency\"]\n\n    else:\n        self.log.warning(\"No 'target_z' or 'target_consistency' column found to compute outlier scores.\")\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_consistency","title":"<code>target_consistency()</code>","text":"<p>Compute a Neighborhood Consistency Score for CATEGORICAL targets.</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def target_consistency(self) -&gt; None:\n    \"\"\"Compute a Neighborhood Consistency Score for CATEGORICAL targets.\"\"\"\n    if not self.target:\n        self.log.warning(\"No target column defined for neighborhood consistency computation.\")\n        return\n\n    # Get the neighbors and distances for each internal observation (already excludes the query)\n    distances, indices = self.knn_model.kneighbors()\n\n    # Calculate the Neighborhood Consistency Score for each observation\n    consistency_scores = []\n    for idx, idx_list in enumerate(indices):\n        query_target = self.df.iloc[idx][self.target]  # Get current observation's target value\n\n        # Get the neighbors' target values\n        neighbor_targets = self.df.iloc[idx_list][self.target]\n\n        # Calculate the proportion of neighbors that have the same category as the query observation\n        consistency_score = (neighbor_targets == query_target).mean()\n        consistency_scores.append(consistency_score)\n\n    # Add the 'target_consistency' column to the internal dataframe\n    self.df[\"target_consistency\"] = consistency_scores\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_summary","title":"<code>target_summary(query_id)</code>","text":"<p>WIP: Provide a summary of target values in the neighborhood of the given query ID</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def target_summary(self, query_id: Union[str, int]) -&gt; pd.DataFrame:\n    \"\"\"WIP: Provide a summary of target values in the neighborhood of the given query ID\"\"\"\n    neighbors_df = self.neighbors(query_id, include_self=False)\n    if self.target and not neighbors_df.empty:\n        summary_stats = neighbors_df[self.target].describe()\n        return pd.DataFrame(summary_stats).transpose()\n    else:\n        self.log.warning(f\"No target values found for neighbors of Query ID '{query_id}'.\")\n        return pd.DataFrame()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.feature_space_proximity.FeatureSpaceProximity.target_z_scores","title":"<code>target_z_scores()</code>","text":"<p>Compute Z-Scores for NUMERIC target values.</p> Source code in <code>src/workbench/algorithms/dataframe/feature_space_proximity.py</code> <pre><code>def target_z_scores(self) -&gt; None:\n    \"\"\"Compute Z-Scores for NUMERIC target values.\"\"\"\n    if not self.target:\n        self.log.warning(\"No target column defined for Z-Score computation.\")\n        return\n\n    # Get the neighbors and distances for each internal observation\n    distances, indices = self.knn_model.kneighbors()\n\n    # Retrieve all neighbor target values in a single operation\n    neighbor_targets = self.df[self.target].values[indices]  # Shape will be (n_samples, n_neighbors)\n\n    # Compute the mean and std along the neighbors axis (axis=1)\n    neighbor_means = neighbor_targets.mean(axis=1)\n    neighbor_stds = neighbor_targets.std(axis=1, ddof=0)\n\n    # Vectorized Z-score calculation\n    current_targets = self.df[self.target].values\n    z_scores = np.where(neighbor_stds == 0, 0.0, (current_targets - neighbor_means) / neighbor_stds)\n\n    # Assign the computed Z-Scores back to the DataFrame\n    self.df[\"target_z\"] = z_scores\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator","title":"<code>ResidualsCalculator</code>","text":"<p>               Bases: <code>BaseEstimator</code>, <code>TransformerMixin</code></p> <p>A custom transformer for calculating residuals using cross-validation or an endpoint.</p> <p>This transformer performs K-Fold cross-validation (if no endpoint is provided), or it uses the endpoint to generate predictions and compute residuals. It adds 'prediction', 'residuals', 'residuals_abs', 'prediction_100', 'residuals_100', and 'residuals_100_abs' columns to the input DataFrame.</p> <p>Attributes:</p> Name Type Description <code>model_class</code> <code>Union[RegressorMixin, XGBRegressor]</code> <p>The machine learning model class used for predictions.</p> <code>n_splits</code> <code>int</code> <p>Number of splits for cross-validation.</p> <code>random_state</code> <code>int</code> <p>Random state for reproducibility.</p> <code>endpoint</code> <code>Optional</code> <p>The Workbench endpoint object for running inference, if provided.</p> Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code> <pre><code>class ResidualsCalculator(BaseEstimator, TransformerMixin):\n    \"\"\"\n    A custom transformer for calculating residuals using cross-validation or an endpoint.\n\n    This transformer performs K-Fold cross-validation (if no endpoint is provided), or it uses the endpoint\n    to generate predictions and compute residuals. It adds 'prediction', 'residuals', 'residuals_abs',\n    'prediction_100', 'residuals_100', and 'residuals_100_abs' columns to the input DataFrame.\n\n    Attributes:\n        model_class (Union[RegressorMixin, XGBRegressor]): The machine learning model class used for predictions.\n        n_splits (int): Number of splits for cross-validation.\n        random_state (int): Random state for reproducibility.\n        endpoint (Optional): The Workbench endpoint object for running inference, if provided.\n    \"\"\"\n\n    def __init__(\n        self,\n        endpoint: Optional[object] = None,\n        reference_model_class: Union[RegressorMixin, XGBRegressor] = XGBRegressor,\n    ):\n        \"\"\"\n        Initializes the ResidualsCalculator with the specified parameters.\n\n        Args:\n            endpoint (Optional): A Workbench endpoint object to run inference, if available.\n            reference_model_class (Union[RegressorMixin, XGBRegressor]): The reference model class for predictions.\n        \"\"\"\n        self.n_splits = 5\n        self.random_state = 42\n        self.reference_model_class = reference_model_class  # Store the class, instantiate the model later\n        self.reference_model = None  # Lazy model initialization\n        self.endpoint = endpoint  # Use this endpoint for inference if provided\n        self.X = None\n        self.y = None\n        super().__init__()\n\n    def fit(self, X: pd.DataFrame, y: pd.Series) -&gt; BaseEstimator:\n        \"\"\"\n        Fits the model. If no endpoint is provided, fitting involves storing the input data\n        and initializing a reference model.\n\n        Args:\n            X (pd.DataFrame): The input features.\n            y (pd.Series): The target variable.\n\n        Returns:\n            self: Returns an instance of self.\n        \"\"\"\n        self.X = X\n        self.y = y\n\n        if self.endpoint is None:\n            # Only initialize the reference model if no endpoint is provided\n            self.reference_model = self.reference_model_class()\n        return self\n\n    def transform(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"\n        Transforms the input DataFrame by adding 'prediction', 'residuals', 'residuals_abs',\n        'prediction_100', 'residuals_100', and 'residuals_100_abs' columns.\n\n        Args:\n            X (pd.DataFrame): The input features.\n\n        Returns:\n            pd.DataFrame: The transformed DataFrame with additional columns.\n        \"\"\"\n        check_is_fitted(self, [\"X\", \"y\"])  # Ensure fit has been called\n\n        if self.endpoint:\n            # If an endpoint is provided, run inference on the full data\n            result_df = self._run_inference_via_endpoint(X)\n        else:\n            # If no endpoint, perform cross-validation and full model fitting\n            result_df = self._run_cross_validation(X)\n\n        return result_df\n\n    def _run_cross_validation(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"\n        Handles the cross-validation process when no endpoint is provided.\n\n        Args:\n            X (pd.DataFrame): The input features.\n\n        Returns:\n            pd.DataFrame: DataFrame with predictions and residuals from cross-validation and full model fit.\n        \"\"\"\n        kf = KFold(n_splits=self.n_splits, shuffle=True, random_state=self.random_state)\n\n        # Initialize pandas Series to store predictions and residuals, aligned by index\n        predictions = pd.Series(index=self.y.index, dtype=np.float64)\n        residuals = pd.Series(index=self.y.index, dtype=np.float64)\n        residuals_abs = pd.Series(index=self.y.index, dtype=np.float64)\n\n        # Perform cross-validation and collect predictions and residuals\n        for train_index, test_index in kf.split(self.X):\n            X_train, X_test = self.X.iloc[train_index], self.X.iloc[test_index]\n            y_train, y_test = self.y.iloc[train_index], self.y.iloc[test_index]\n\n            # Fit the model on the training data\n            self.reference_model.fit(X_train, y_train)\n\n            # Predict on the test data\n            y_pred = self.reference_model.predict(X_test)\n\n            # Compute residuals and absolute residuals\n            residuals_fold = y_test - y_pred\n            residuals_abs_fold = np.abs(residuals_fold)\n\n            # Place the predictions and residuals in the correct positions based on index\n            predictions.iloc[test_index] = y_pred\n            residuals.iloc[test_index] = residuals_fold\n            residuals_abs.iloc[test_index] = residuals_abs_fold\n\n        # Train on all data and compute residuals for 100% training\n        self.reference_model.fit(self.X, self.y)\n        y_pred_100 = self.reference_model.predict(self.X)\n        residuals_100 = self.y - y_pred_100\n        residuals_100_abs = np.abs(residuals_100)\n\n        # Create a copy of the provided DataFrame and add the new columns\n        result_df = X.copy()\n        result_df[\"prediction\"] = predictions\n        result_df[\"residuals\"] = residuals\n        result_df[\"residuals_abs\"] = residuals_abs\n        result_df[\"prediction_100\"] = y_pred_100\n        result_df[\"residuals_100\"] = residuals_100\n        result_df[\"residuals_100_abs\"] = residuals_100_abs\n        result_df[self.y.name] = self.y  # Add the target column back\n\n        return result_df\n\n    def _run_inference_via_endpoint(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"\n        Handles the inference process when an endpoint is provided.\n\n        Args:\n            X (pd.DataFrame): The input features.\n\n        Returns:\n            pd.DataFrame: DataFrame with predictions and residuals from the endpoint.\n        \"\"\"\n        # Run inference on all data using the endpoint (include the target column)\n        X = X.copy()\n        X.loc[:, self.y.name] = self.y\n        results_df = self.endpoint.inference(X)\n        predictions = results_df[\"prediction\"]\n\n        # Compute residuals and residuals_abs based on the endpoint's predictions\n        residuals = self.y - predictions\n        residuals_abs = np.abs(residuals)\n\n        # To maintain consistency, populate both 'prediction' and 'prediction_100' with the same values\n        result_df = X.copy()\n        result_df[\"prediction\"] = predictions\n        result_df[\"residuals\"] = residuals\n        result_df[\"residuals_abs\"] = residuals_abs\n        result_df[\"prediction_100\"] = predictions\n        result_df[\"residuals_100\"] = residuals\n        result_df[\"residuals_100_abs\"] = residuals_abs\n\n        return result_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.__init__","title":"<code>__init__(endpoint=None, reference_model_class=XGBRegressor)</code>","text":"<p>Initializes the ResidualsCalculator with the specified parameters.</p> <p>Parameters:</p> Name Type Description Default <code>endpoint</code> <code>Optional</code> <p>A Workbench endpoint object to run inference, if available.</p> <code>None</code> <code>reference_model_class</code> <code>Union[RegressorMixin, XGBRegressor]</code> <p>The reference model class for predictions.</p> <code>XGBRegressor</code> Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code> <pre><code>def __init__(\n    self,\n    endpoint: Optional[object] = None,\n    reference_model_class: Union[RegressorMixin, XGBRegressor] = XGBRegressor,\n):\n    \"\"\"\n    Initializes the ResidualsCalculator with the specified parameters.\n\n    Args:\n        endpoint (Optional): A Workbench endpoint object to run inference, if available.\n        reference_model_class (Union[RegressorMixin, XGBRegressor]): The reference model class for predictions.\n    \"\"\"\n    self.n_splits = 5\n    self.random_state = 42\n    self.reference_model_class = reference_model_class  # Store the class, instantiate the model later\n    self.reference_model = None  # Lazy model initialization\n    self.endpoint = endpoint  # Use this endpoint for inference if provided\n    self.X = None\n    self.y = None\n    super().__init__()\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.fit","title":"<code>fit(X, y)</code>","text":"<p>Fits the model. If no endpoint is provided, fitting involves storing the input data and initializing a reference model.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>DataFrame</code> <p>The input features.</p> required <code>y</code> <code>Series</code> <p>The target variable.</p> required <p>Returns:</p> Name Type Description <code>self</code> <code>BaseEstimator</code> <p>Returns an instance of self.</p> Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code> <pre><code>def fit(self, X: pd.DataFrame, y: pd.Series) -&gt; BaseEstimator:\n    \"\"\"\n    Fits the model. If no endpoint is provided, fitting involves storing the input data\n    and initializing a reference model.\n\n    Args:\n        X (pd.DataFrame): The input features.\n        y (pd.Series): The target variable.\n\n    Returns:\n        self: Returns an instance of self.\n    \"\"\"\n    self.X = X\n    self.y = y\n\n    if self.endpoint is None:\n        # Only initialize the reference model if no endpoint is provided\n        self.reference_model = self.reference_model_class()\n    return self\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.residuals_calculator.ResidualsCalculator.transform","title":"<code>transform(X)</code>","text":"<p>Transforms the input DataFrame by adding 'prediction', 'residuals', 'residuals_abs', 'prediction_100', 'residuals_100', and 'residuals_100_abs' columns.</p> <p>Parameters:</p> Name Type Description Default <code>X</code> <code>DataFrame</code> <p>The input features.</p> required <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: The transformed DataFrame with additional columns.</p> Source code in <code>src/workbench/algorithms/dataframe/residuals_calculator.py</code> <pre><code>def transform(self, X: pd.DataFrame) -&gt; pd.DataFrame:\n    \"\"\"\n    Transforms the input DataFrame by adding 'prediction', 'residuals', 'residuals_abs',\n    'prediction_100', 'residuals_100', and 'residuals_100_abs' columns.\n\n    Args:\n        X (pd.DataFrame): The input features.\n\n    Returns:\n        pd.DataFrame: The transformed DataFrame with additional columns.\n    \"\"\"\n    check_is_fitted(self, [\"X\", \"y\"])  # Ensure fit has been called\n\n    if self.endpoint:\n        # If an endpoint is provided, run inference on the full data\n        result_df = self._run_inference_via_endpoint(X)\n    else:\n        # If no endpoint, perform cross-validation and full model fitting\n        result_df = self._run_cross_validation(X)\n\n    return result_df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction","title":"<code>DimensionalityReduction</code>","text":"Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>class DimensionalityReduction:\n    def __init__(self):\n        \"\"\"DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame\"\"\"\n        self.log = logging.getLogger(\"workbench\")\n        self.projection_model = None\n        self.features = None\n\n    def fit_transform(self, df: pd.DataFrame, features: list = None, projection: str = \"TSNE\") -&gt; pd.DataFrame:\n        \"\"\"Fit and Transform the DataFrame\n        Args:\n            df: Pandas DataFrame\n            features: List of feature column names (default: None)\n            projection: The projection model to use (TSNE, MDS or PCA, default: PCA)\n        Returns:\n            Pandas DataFrame with new columns x and y\n        \"\"\"\n\n        # If no features are given, indentify all numeric columns\n        if features is None:\n            features = [x for x in df.select_dtypes(include=\"number\").columns.tolist() if not x.endswith(\"id\")]\n            # Also drop group_count if it exists\n            features = [x for x in features if x != \"group_count\"]\n            self.log.info(\"No features given, auto identifying numeric columns...\")\n            self.log.info(f\"{features}\")\n        self.features = features\n\n        # Sanity checks\n        if not all(column in df.columns for column in self.features):\n            self.log.critical(\"Some features are missing in the DataFrame\")\n            return df\n        if len(self.features) &lt; 2:\n            self.log.critical(\"At least two features are required\")\n            return df\n        if df.empty:\n            self.log.critical(\"DataFrame is empty\")\n            return df\n\n        # Most projection models will fail if there are any NaNs in the data\n        # So we'll fill NaNs with the mean value for that column\n        for col in df[self.features].columns:\n            df[col].fillna(df[col].mean(), inplace=True)\n\n        # Normalize the features\n        scaler = StandardScaler()\n        normalized_data = scaler.fit_transform(df[self.features])\n        df[self.features] = normalized_data\n\n        # Project the multidimensional features onto an x,y plane\n        self.log.info(\"Projecting features onto an x,y plane...\")\n\n        # Perform the projection\n        if projection == \"TSNE\":\n            # Perplexity is a hyperparameter that controls the number of neighbors used to compute the manifold\n            # The number of neighbors should be less than the number of samples\n            perplexity = min(40, len(df) - 1)\n            self.log.info(f\"Perplexity: {perplexity}\")\n            self.projection_model = TSNE(perplexity=perplexity)\n        elif projection == \"MDS\":\n            self.projection_model = MDS(n_components=2, random_state=0)\n        elif projection == \"PCA\":\n            self.projection_model = PCA(n_components=2)\n\n        # Fit the projection model\n        # Hack PCA + TSNE to work together\n        projection = self.projection_model.fit_transform(df[self.features])\n\n        # Put the projection results back into the given DataFrame\n        df[\"x\"] = projection[:, 0]  # Projection X Column\n        df[\"y\"] = projection[:, 1]  # Projection Y Column\n\n        # Jitter the data to resolve coincident points\n        # df = self.resolve_coincident_points(df)\n\n        # Return the DataFrame with the new columns\n        return df\n\n    @staticmethod\n    def resolve_coincident_points(df: pd.DataFrame):\n        \"\"\"Resolve coincident points in a DataFrame\n        Args:\n            df(pd.DataFrame): The DataFrame to resolve coincident points in\n        Returns:\n            pd.DataFrame: The DataFrame with resolved coincident points\n        \"\"\"\n        # Adding Jitter to the projection\n        x_scale = (df[\"x\"].max() - df[\"x\"].min()) * 0.1\n        y_scale = (df[\"y\"].max() - df[\"y\"].min()) * 0.1\n        df[\"x\"] += np.random.normal(-x_scale, +x_scale, len(df))\n        df[\"y\"] += np.random.normal(-y_scale, +y_scale, len(df))\n        return df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.__init__","title":"<code>__init__()</code>","text":"<p>DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame</p> Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>def __init__(self):\n    \"\"\"DimensionalityReduction:  Perform Dimensionality Reduction on a DataFrame\"\"\"\n    self.log = logging.getLogger(\"workbench\")\n    self.projection_model = None\n    self.features = None\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.fit_transform","title":"<code>fit_transform(df, features=None, projection='TSNE')</code>","text":"<p>Fit and Transform the DataFrame Args:     df: Pandas DataFrame     features: List of feature column names (default: None)     projection: The projection model to use (TSNE, MDS or PCA, default: PCA) Returns:     Pandas DataFrame with new columns x and y</p> Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>def fit_transform(self, df: pd.DataFrame, features: list = None, projection: str = \"TSNE\") -&gt; pd.DataFrame:\n    \"\"\"Fit and Transform the DataFrame\n    Args:\n        df: Pandas DataFrame\n        features: List of feature column names (default: None)\n        projection: The projection model to use (TSNE, MDS or PCA, default: PCA)\n    Returns:\n        Pandas DataFrame with new columns x and y\n    \"\"\"\n\n    # If no features are given, indentify all numeric columns\n    if features is None:\n        features = [x for x in df.select_dtypes(include=\"number\").columns.tolist() if not x.endswith(\"id\")]\n        # Also drop group_count if it exists\n        features = [x for x in features if x != \"group_count\"]\n        self.log.info(\"No features given, auto identifying numeric columns...\")\n        self.log.info(f\"{features}\")\n    self.features = features\n\n    # Sanity checks\n    if not all(column in df.columns for column in self.features):\n        self.log.critical(\"Some features are missing in the DataFrame\")\n        return df\n    if len(self.features) &lt; 2:\n        self.log.critical(\"At least two features are required\")\n        return df\n    if df.empty:\n        self.log.critical(\"DataFrame is empty\")\n        return df\n\n    # Most projection models will fail if there are any NaNs in the data\n    # So we'll fill NaNs with the mean value for that column\n    for col in df[self.features].columns:\n        df[col].fillna(df[col].mean(), inplace=True)\n\n    # Normalize the features\n    scaler = StandardScaler()\n    normalized_data = scaler.fit_transform(df[self.features])\n    df[self.features] = normalized_data\n\n    # Project the multidimensional features onto an x,y plane\n    self.log.info(\"Projecting features onto an x,y plane...\")\n\n    # Perform the projection\n    if projection == \"TSNE\":\n        # Perplexity is a hyperparameter that controls the number of neighbors used to compute the manifold\n        # The number of neighbors should be less than the number of samples\n        perplexity = min(40, len(df) - 1)\n        self.log.info(f\"Perplexity: {perplexity}\")\n        self.projection_model = TSNE(perplexity=perplexity)\n    elif projection == \"MDS\":\n        self.projection_model = MDS(n_components=2, random_state=0)\n    elif projection == \"PCA\":\n        self.projection_model = PCA(n_components=2)\n\n    # Fit the projection model\n    # Hack PCA + TSNE to work together\n    projection = self.projection_model.fit_transform(df[self.features])\n\n    # Put the projection results back into the given DataFrame\n    df[\"x\"] = projection[:, 0]  # Projection X Column\n    df[\"y\"] = projection[:, 1]  # Projection Y Column\n\n    # Jitter the data to resolve coincident points\n    # df = self.resolve_coincident_points(df)\n\n    # Return the DataFrame with the new columns\n    return df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.dimensionality_reduction.DimensionalityReduction.resolve_coincident_points","title":"<code>resolve_coincident_points(df)</code>  <code>staticmethod</code>","text":"<p>Resolve coincident points in a DataFrame Args:     df(pd.DataFrame): The DataFrame to resolve coincident points in Returns:     pd.DataFrame: The DataFrame with resolved coincident points</p> Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>@staticmethod\ndef resolve_coincident_points(df: pd.DataFrame):\n    \"\"\"Resolve coincident points in a DataFrame\n    Args:\n        df(pd.DataFrame): The DataFrame to resolve coincident points in\n    Returns:\n        pd.DataFrame: The DataFrame with resolved coincident points\n    \"\"\"\n    # Adding Jitter to the projection\n    x_scale = (df[\"x\"].max() - df[\"x\"].min()) * 0.1\n    y_scale = (df[\"y\"].max() - df[\"y\"].min()) * 0.1\n    df[\"x\"] += np.random.normal(-x_scale, +x_scale, len(df))\n    df[\"y\"] += np.random.normal(-y_scale, +y_scale, len(df))\n    return df\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#workbench.algorithms.dataframe.dimensionality_reduction.test","title":"<code>test()</code>","text":"<p>Test for the Dimensionality Reduction Class</p> Source code in <code>src/workbench/algorithms/dataframe/dimensionality_reduction.py</code> <pre><code>def test():\n    \"\"\"Test for the Dimensionality Reduction Class\"\"\"\n    # Set some pandas options\n    pd.set_option(\"display.max_columns\", None)\n    pd.set_option(\"display.width\", 1000)\n\n    # Make some fake data\n    data = {\n        \"ID\": [\n            \"id_0\",\n            \"id_0\",\n            \"id_2\",\n            \"id_3\",\n            \"id_4\",\n            \"id_5\",\n            \"id_6\",\n            \"id_7\",\n            \"id_8\",\n            \"id_9\",\n        ],\n        \"feat1\": [1.0, 1.0, 1.1, 3.0, 4.0, 1.0, 1.0, 1.1, 3.0, 4.0],\n        \"feat2\": [1.0, 1.0, 1.1, 3.0, 4.0, 1.0, 1.0, 1.1, 3.0, 4.0],\n        \"feat3\": [0.1, 0.1, 0.2, 1.6, 2.5, 0.1, 0.1, 0.2, 1.6, 2.5],\n        \"price\": [31, 60, 62, 40, 20, 31, 61, 60, 40, 20],\n    }\n    data_df = pd.DataFrame(data)\n    features = [\"feat1\", \"feat2\", \"feat3\"]\n\n    # Create the class and run the dimensionality reduction\n    projection = DimensionalityReduction()\n    new_df = projection.fit_transform(data_df, features=features, projection=\"TSNE\")\n\n    # Check that the x and y columns were added\n    assert \"x\" in new_df.columns\n    assert \"y\" in new_df.columns\n\n    # Output the DataFrame\n    print(new_df)\n</code></pre>"},{"location":"data_algorithms/dataframes/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/graphs/overview/","title":"Graph Algorithms","text":"<p>Graph Algorithms</p> <p>WIP: These classes are currently actively being developed and are subject to change in both API and functionality over time.</p> <ul> <li>TBD: TBD</li> </ul> <p>Graph Algorithms</p> <p>Docs TBD</p>"},{"location":"data_algorithms/graphs/overview/#workbench.algorithms.graph.light.proximity_graph.ProximityGraph","title":"<code>ProximityGraph</code>","text":"<p>Build a proximity graph of the nearest neighbors based on feature space.</p> Source code in <code>src/workbench/algorithms/graph/light/proximity_graph.py</code> <pre><code>class ProximityGraph:\n    \"\"\"\n    Build a proximity graph of the nearest neighbors based on feature space.\n    \"\"\"\n\n    def __init__(self, n_neighbors: int = 5):\n        \"\"\"Initialize the ProximityGraph with the specified parameters.\n\n        Args:\n            n_neighbors (int): Number of neighbors to consider (default: 5)\n        \"\"\"\n        self.n_neighbors = n_neighbors\n        self.nx_graph = nx.Graph()\n\n    def build_graph(\n        self,\n        df: pd.DataFrame,\n        features: list,\n        id_column: str,\n        target: str,\n        store_features=True,\n    ) -&gt; nx.Graph:\n        \"\"\"\n        Processes the input DataFrame and builds a proximity graph.\n\n        Args:\n            df (pd.DataFrame): The input DataFrame containing feature columns.\n            features (list): List of feature column names to be used for building the proximity graph.\n            id_column (str): Name of the ID column in the DataFrame.\n            target (str): Name of the target column in the DataFrame.\n            store_features (bool): Whether to store the features as node attributes (default: True).\n\n        Returns:\n            nx.Graph: The proximity graph as a NetworkX graph.\n        \"\"\"\n        # Drop NaNs from the DataFrame using the provided utility\n        df = drop_nans(df)\n\n        # Initialize FeatureSpaceProximity with the input DataFrame and the specified features\n        knn_spider = FeatureSpaceProximity(\n            df,\n            features=features,\n            id_column=id_column,\n            target=target,\n            neighbors=self.n_neighbors,\n        )\n\n        # Use FeatureSpaceProximity to get all neighbor indices and distances\n        indices, distances = knn_spider.get_neighbor_indices_and_distances()\n\n        # Compute max distance for scaling (to [0, 1])\n        max_distance = distances.max()\n\n        # Initialize an empty graph\n        self.nx_graph = nx.Graph()\n\n        # Use the ID column for node IDs instead of relying on the DataFrame index\n        node_ids = df[id_column].values\n\n        # Add nodes with their features as attributes using the ID column\n        for node_id in node_ids:\n            if store_features:\n                self.nx_graph.add_node(\n                    node_id, **df[df[id_column] == node_id].iloc[0].to_dict()\n                )  # Use .iloc[0] for correct node attributes\n            else:\n                self.nx_graph.add_node(node_id)\n\n        # Add edges with weights based on inverse distance\n        for i, neighbors in enumerate(indices):\n            one_edge_added = False\n            for j, neighbor_idx in enumerate(neighbors):\n                if i != neighbor_idx:\n                    # Compute the weight of the edge (inverse of distance)\n                    weight = 1.0 - (distances[i][j] / max_distance)  # Scale to [0, 1]\n\n                    # Map back to the ID column instead of the DataFrame index\n                    src_node = node_ids[i]\n                    dst_node = node_ids[neighbor_idx]\n\n                    # Add the edge to the graph (if the weight is greater than 0.1)\n                    if weight &gt; 0.1 or not one_edge_added:\n                        self.nx_graph.add_edge(src_node, dst_node, weight=weight)\n                        one_edge_added = True\n\n        # Return the NetworkX graph\n        return self.nx_graph\n\n    def get_neighborhood(self, node_id: Union[str, int], radius: int = 1) -&gt; nx.Graph:\n        \"\"\"\n        Get a subgraph containing nodes within a given number of hops around a specific node.\n\n        Args:\n            node_id: The ID of the node to center the neighborhood around.\n            radius: The number of hops to consider around the node (default: 1).\n\n        Returns:\n            nx.Graph: A subgraph containing the specified neighborhood.\n        \"\"\"\n        # Use NetworkX's ego_graph to extract the neighborhood within the given radius\n        if node_id in self.nx_graph:\n            return nx.ego_graph(self.nx_graph, node_id, radius=radius)\n        else:\n            raise ValueError(f\"Node ID '{node_id}' not found in the graph.\")\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#workbench.algorithms.graph.light.proximity_graph.ProximityGraph.__init__","title":"<code>__init__(n_neighbors=5)</code>","text":"<p>Initialize the ProximityGraph with the specified parameters.</p> <p>Parameters:</p> Name Type Description Default <code>n_neighbors</code> <code>int</code> <p>Number of neighbors to consider (default: 5)</p> <code>5</code> Source code in <code>src/workbench/algorithms/graph/light/proximity_graph.py</code> <pre><code>def __init__(self, n_neighbors: int = 5):\n    \"\"\"Initialize the ProximityGraph with the specified parameters.\n\n    Args:\n        n_neighbors (int): Number of neighbors to consider (default: 5)\n    \"\"\"\n    self.n_neighbors = n_neighbors\n    self.nx_graph = nx.Graph()\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#workbench.algorithms.graph.light.proximity_graph.ProximityGraph.build_graph","title":"<code>build_graph(df, features, id_column, target, store_features=True)</code>","text":"<p>Processes the input DataFrame and builds a proximity graph.</p> <p>Parameters:</p> Name Type Description Default <code>df</code> <code>DataFrame</code> <p>The input DataFrame containing feature columns.</p> required <code>features</code> <code>list</code> <p>List of feature column names to be used for building the proximity graph.</p> required <code>id_column</code> <code>str</code> <p>Name of the ID column in the DataFrame.</p> required <code>target</code> <code>str</code> <p>Name of the target column in the DataFrame.</p> required <code>store_features</code> <code>bool</code> <p>Whether to store the features as node attributes (default: True).</p> <code>True</code> <p>Returns:</p> Type Description <code>Graph</code> <p>nx.Graph: The proximity graph as a NetworkX graph.</p> Source code in <code>src/workbench/algorithms/graph/light/proximity_graph.py</code> <pre><code>def build_graph(\n    self,\n    df: pd.DataFrame,\n    features: list,\n    id_column: str,\n    target: str,\n    store_features=True,\n) -&gt; nx.Graph:\n    \"\"\"\n    Processes the input DataFrame and builds a proximity graph.\n\n    Args:\n        df (pd.DataFrame): The input DataFrame containing feature columns.\n        features (list): List of feature column names to be used for building the proximity graph.\n        id_column (str): Name of the ID column in the DataFrame.\n        target (str): Name of the target column in the DataFrame.\n        store_features (bool): Whether to store the features as node attributes (default: True).\n\n    Returns:\n        nx.Graph: The proximity graph as a NetworkX graph.\n    \"\"\"\n    # Drop NaNs from the DataFrame using the provided utility\n    df = drop_nans(df)\n\n    # Initialize FeatureSpaceProximity with the input DataFrame and the specified features\n    knn_spider = FeatureSpaceProximity(\n        df,\n        features=features,\n        id_column=id_column,\n        target=target,\n        neighbors=self.n_neighbors,\n    )\n\n    # Use FeatureSpaceProximity to get all neighbor indices and distances\n    indices, distances = knn_spider.get_neighbor_indices_and_distances()\n\n    # Compute max distance for scaling (to [0, 1])\n    max_distance = distances.max()\n\n    # Initialize an empty graph\n    self.nx_graph = nx.Graph()\n\n    # Use the ID column for node IDs instead of relying on the DataFrame index\n    node_ids = df[id_column].values\n\n    # Add nodes with their features as attributes using the ID column\n    for node_id in node_ids:\n        if store_features:\n            self.nx_graph.add_node(\n                node_id, **df[df[id_column] == node_id].iloc[0].to_dict()\n            )  # Use .iloc[0] for correct node attributes\n        else:\n            self.nx_graph.add_node(node_id)\n\n    # Add edges with weights based on inverse distance\n    for i, neighbors in enumerate(indices):\n        one_edge_added = False\n        for j, neighbor_idx in enumerate(neighbors):\n            if i != neighbor_idx:\n                # Compute the weight of the edge (inverse of distance)\n                weight = 1.0 - (distances[i][j] / max_distance)  # Scale to [0, 1]\n\n                # Map back to the ID column instead of the DataFrame index\n                src_node = node_ids[i]\n                dst_node = node_ids[neighbor_idx]\n\n                # Add the edge to the graph (if the weight is greater than 0.1)\n                if weight &gt; 0.1 or not one_edge_added:\n                    self.nx_graph.add_edge(src_node, dst_node, weight=weight)\n                    one_edge_added = True\n\n    # Return the NetworkX graph\n    return self.nx_graph\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#workbench.algorithms.graph.light.proximity_graph.ProximityGraph.get_neighborhood","title":"<code>get_neighborhood(node_id, radius=1)</code>","text":"<p>Get a subgraph containing nodes within a given number of hops around a specific node.</p> <p>Parameters:</p> Name Type Description Default <code>node_id</code> <code>Union[str, int]</code> <p>The ID of the node to center the neighborhood around.</p> required <code>radius</code> <code>int</code> <p>The number of hops to consider around the node (default: 1).</p> <code>1</code> <p>Returns:</p> Type Description <code>Graph</code> <p>nx.Graph: A subgraph containing the specified neighborhood.</p> Source code in <code>src/workbench/algorithms/graph/light/proximity_graph.py</code> <pre><code>def get_neighborhood(self, node_id: Union[str, int], radius: int = 1) -&gt; nx.Graph:\n    \"\"\"\n    Get a subgraph containing nodes within a given number of hops around a specific node.\n\n    Args:\n        node_id: The ID of the node to center the neighborhood around.\n        radius: The number of hops to consider around the node (default: 1).\n\n    Returns:\n        nx.Graph: A subgraph containing the specified neighborhood.\n    \"\"\"\n    # Use NetworkX's ego_graph to extract the neighborhood within the given radius\n    if node_id in self.nx_graph:\n        return nx.ego_graph(self.nx_graph, node_id, radius=radius)\n    else:\n        raise ValueError(f\"Node ID '{node_id}' not found in the graph.\")\n</code></pre>"},{"location":"data_algorithms/graphs/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/spark/overview/","title":"Graph Algorithms","text":"<p>Graph Algorithms</p> <p>WIP: These classes are currently actively being developed and are subject to change in both API and functionality over time.</p> <ul> <li>TBD: TBD</li> </ul> <p>Graph Algorithms</p> <p>Docs TBD</p> <p>ComputationView Class: Create a View with a subset of columns for display purposes</p>"},{"location":"data_algorithms/spark/overview/#workbench.core.views.computation_view.ComputationView","title":"<code>ComputationView</code>","text":"<p>               Bases: <code>ColumnSubsetView</code></p> <p>ComputationView Class: Create a View with a subset of columns for computation purposes</p> Common Usage <pre><code># Create a default ComputationView\nfs = FeatureSet(\"test_features\")\ncomp_view = ComputationView.create(fs)\ndf = comp_view.pull_dataframe()\n\n# Create a ComputationView with a specific set of columns\ncomp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n# Query the view\ndf = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n</code></pre> Source code in <code>src/workbench/core/views/computation_view.py</code> <pre><code>class ComputationView(ColumnSubsetView):\n    \"\"\"ComputationView Class: Create a View with a subset of columns for computation purposes\n\n    Common Usage:\n        ```python\n        # Create a default ComputationView\n        fs = FeatureSet(\"test_features\")\n        comp_view = ComputationView.create(fs)\n        df = comp_view.pull_dataframe()\n\n        # Create a ComputationView with a specific set of columns\n        comp_view = ComputationView.create(fs, column_list=[\"my_col1\", \"my_col2\"])\n\n        # Query the view\n        df = comp_view.query(f\"SELECT * FROM {comp_view.table} where prediction &gt; 0.5\")\n        ```\n    \"\"\"\n\n    @classmethod\n    def create(\n        cls,\n        artifact: Union[DataSource, FeatureSet],\n        source_table: str = None,\n        column_list: Union[list[str], None] = None,\n        column_limit: int = 30,\n    ) -&gt; Union[View, None]:\n        \"\"\"Factory method to create and return a ComputationView instance.\n\n        Args:\n            artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n            source_table (str, optional): The table/view to create the view from. Defaults to None\n            column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n            column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n        Returns:\n            Union[View, None]: The created View object (or None if failed to create the view)\n        \"\"\"\n        # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n        return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"data_algorithms/spark/overview/#workbench.core.views.computation_view.ComputationView.create","title":"<code>create(artifact, source_table=None, column_list=None, column_limit=30)</code>  <code>classmethod</code>","text":"<p>Factory method to create and return a ComputationView instance.</p> <p>Parameters:</p> Name Type Description Default <code>artifact</code> <code>Union[DataSource, FeatureSet]</code> <p>The DataSource or FeatureSet object</p> required <code>source_table</code> <code>str</code> <p>The table/view to create the view from. Defaults to None</p> <code>None</code> <code>column_list</code> <code>Union[list[str], None]</code> <p>A list of columns to include. Defaults to None.</p> <code>None</code> <code>column_limit</code> <code>int</code> <p>The max number of columns to include. Defaults to 30.</p> <code>30</code> <p>Returns:</p> Type Description <code>Union[View, None]</code> <p>Union[View, None]: The created View object (or None if failed to create the view)</p> Source code in <code>src/workbench/core/views/computation_view.py</code> <pre><code>@classmethod\ndef create(\n    cls,\n    artifact: Union[DataSource, FeatureSet],\n    source_table: str = None,\n    column_list: Union[list[str], None] = None,\n    column_limit: int = 30,\n) -&gt; Union[View, None]:\n    \"\"\"Factory method to create and return a ComputationView instance.\n\n    Args:\n        artifact (Union[DataSource, FeatureSet]): The DataSource or FeatureSet object\n        source_table (str, optional): The table/view to create the view from. Defaults to None\n        column_list (Union[list[str], None], optional): A list of columns to include. Defaults to None.\n        column_limit (int, optional): The max number of columns to include. Defaults to 30.\n\n    Returns:\n        Union[View, None]: The created View object (or None if failed to create the view)\n    \"\"\"\n    # Use the create logic directly from ColumnSubsetView with the \"computation\" view name\n    return ColumnSubsetView.create(\"computation\", artifact, source_table, column_list, column_limit)\n</code></pre>"},{"location":"data_algorithms/spark/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"data_algorithms/sql/overview/","title":"SQL Algorithms","text":"<p>SQL Algorithms</p> <p>One of the main benefit of SQL Algorithms is that the 'heavy lifting' is all done on the SQL Database, so if you have large datassets this is the place for you.</p> <ul> <li> <p>SQL: SQL queries that provide a wide range of functionality:</p> <ul> <li>Outliers</li> <li>Descriptive Stats</li> <li>Correlations</li> <li>and More Oulliers</li> </ul> </li> </ul> <p>SQL based Outliers: Compute outliers for all the columns in a DataSource using SQL</p> <p>SQL based Descriptive Stats: Compute Descriptive Stats for all the numeric columns in a DataSource using SQL</p> <p>SQL based Correlations: Compute Correlations for all the numeric columns in a DataSource using SQL</p>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.outliers.Outliers","title":"<code>Outliers</code>","text":"<p>Outliers: Class to compute outliers for all the columns in a DataSource using SQL</p> Source code in <code>src/workbench/algorithms/sql/outliers.py</code> <pre><code>class Outliers:\n    \"\"\"Outliers: Class to compute outliers for all the columns in a DataSource using SQL\"\"\"\n\n    def __init__(self):\n        \"\"\"SQLOutliers Initialization\"\"\"\n        self.outlier_group = \"unknown\"\n\n    def compute_outliers(\n        self, data_source: DataSourceAbstract, scale: float = 1.5, use_stddev: bool = False\n    ) -&gt; pd.DataFrame:\n        \"\"\"Compute outliers for all the numeric columns in a DataSource\n        Args:\n            data_source(DataSource): The DataSource that we're computing outliers on\n            scale (float): The scale to use for either the IQR or stddev outlier calculation (default: 1.5)\n            use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False)\n        Returns:\n            pd.DataFrame: A DataFrame of outliers for this DataSource\n        Notes:\n            Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n            The scale parameter can be adjusted to change the IQR multiplier\n        \"\"\"\n\n        # Note: If use_stddev is True, then the scale parameter needs to be adjusted\n        if use_stddev and scale == 1.5:  # If the default scale is used, adjust it\n            scale = 2.5\n\n        # Compute the numeric outliers\n        outlier_df = self._numeric_outliers(data_source, scale, use_stddev)\n\n        # If there are no outliers, return a DataFrame with the computation columns but no rows\n        if outlier_df is None:\n            columns = data_source.view(\"computation\").columns\n            return pd.DataFrame(columns=columns + [\"outlier_group\"])\n\n        # Get the top N outliers for each outlier group\n        outlier_df = self.get_top_n_outliers(outlier_df)\n\n        # Make sure the dataframe isn't too big, if it's too big sample it down\n        if len(outlier_df) &gt; 300:\n            log.important(f\"Outliers DataFrame is too large {len(outlier_df)}, sampling down to 300 rows\")\n            outlier_df = outlier_df.sample(300)\n\n        # Sort by outlier_group and reset the index\n        outlier_df = outlier_df.sort_values(\"outlier_group\").reset_index(drop=True)\n\n        # Shorten any long string values\n        outlier_df = shorten_values(outlier_df)\n        return outlier_df\n\n    def _numeric_outliers(self, data_source: DataSourceAbstract, scale: float, use_stddev=False) -&gt; pd.DataFrame:\n        \"\"\"Internal method to compute outliers for all numeric columns\n        Args:\n            data_source(DataSource): The DataSource that we're computing outliers on\n            scale (float): The scale to use for the IQR outlier calculation\n            use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False)\n        Returns:\n            pd.DataFrame: A DataFrame of all the outliers combined\n        \"\"\"\n\n        # Grab the column stats and descriptive stats for this DataSource\n        column_stats = data_source.column_stats()\n        descriptive_stats = data_source.descriptive_stats()\n\n        # If there are no numeric columns, return None\n        if not descriptive_stats:\n            log.warning(\"No numeric columns found in the current computation view of the DataSource\")\n            log.warning(\"If the data source was created from a DataFrame, ensure that the DataFrame was properly typed\")\n            log.warning(\"Recommendation: Properly type the DataFrame and recreate the Workbench artifact\")\n            return None\n\n        # Get the column names and types from the DataSource\n        column_details = data_source.view(\"computation\").column_details()\n\n        # For every column in the data_source that is numeric get the outliers\n        # This loop computes the columns, lower bounds, and upper bounds for the SQL query\n        log.info(\"Computing Outliers for numeric columns...\")\n        numeric = [\"tinyint\", \"smallint\", \"int\", \"bigint\", \"float\", \"double\", \"decimal\"]\n        columns = []\n        lower_bounds = []\n        upper_bounds = []\n        for column, data_type in column_details.items():\n            if data_type in numeric:\n                # Skip columns that just have one value (or are all nans)\n                if column_stats[column][\"unique\"] &lt;= 1:\n                    log.info(f\"Skipping unary column {column} with value {descriptive_stats[column]['min']}\")\n                    continue\n\n                # Skip columns that are 'binary' columns\n                if column_stats[column][\"unique\"] == 2:\n                    log.info(f\"Skipping binary column {column}\")\n                    continue\n\n                # Do they want to use the stddev instead of IQR?\n                if use_stddev:\n                    mean = descriptive_stats[column][\"mean\"]\n                    stddev = descriptive_stats[column][\"stddev\"]\n                    lower_bound = mean - (stddev * scale)\n                    upper_bound = mean + (stddev * scale)\n\n                # Compute the IQR for this column\n                else:\n                    iqr = descriptive_stats[column][\"q3\"] - descriptive_stats[column][\"q1\"]\n                    lower_bound = descriptive_stats[column][\"q1\"] - (iqr * scale)\n                    upper_bound = descriptive_stats[column][\"q3\"] + (iqr * scale)\n\n                # Add the column, lower bound, and upper bound to the lists\n                columns.append(column)\n                lower_bounds.append(lower_bound)\n                upper_bounds.append(upper_bound)\n\n        # Compute the SQL query\n        query = self._multi_column_outlier_query(data_source, columns, lower_bounds, upper_bounds)\n        outlier_df = data_source.query(query)\n\n        # Label the outlier groups\n        outlier_df = self._label_outlier_groups(outlier_df, columns, lower_bounds, upper_bounds)\n        return outlier_df\n\n    @staticmethod\n    def _multi_column_outlier_query(\n        data_source: DataSourceAbstract, columns: list, lower_bounds: list, upper_bounds: list\n    ) -&gt; str:\n        \"\"\"Internal method to compute outliers for multiple columns\n        Args:\n            data_source(DataSource): The DataSource that we're computing outliers on\n            columns(list): The columns to compute outliers on\n            lower_bounds(list): The lower bounds for outliers\n            upper_bounds(list): The upper bounds for outliers\n        Returns:\n            str: A SQL query to compute outliers for multiple columns\n        \"\"\"\n        # Grab the DataSource computation table name\n        table = data_source.view(\"computation\").table\n\n        # Get the column names and types from the DataSource\n        column_details = data_source.view(\"computation\").column_details()\n        sql_columns = \", \".join([f'\"{col}\"' for col in column_details.keys()])\n\n        query = f'SELECT {sql_columns} FROM \"{table}\" WHERE '\n        for col, lb, ub in zip(columns, lower_bounds, upper_bounds):\n            query += f\"({col} &lt; {lb} OR {col} &gt; {ub}) OR \"\n        query = query[:-4]\n\n        # Add a limit just in case\n        query += \" LIMIT 5000\"\n        return query\n\n    @staticmethod\n    def _label_outlier_groups(\n        outlier_df: pd.DataFrame, columns: list, lower_bounds: list, upper_bounds: list\n    ) -&gt; pd.DataFrame:\n        \"\"\"Internal method to label outliers by group.\n        Args:\n            outlier_df(pd.DataFrame): The DataFrame of outliers\n            columns(list): The columns for which to compute outliers\n            lower_bounds(list): The lower bounds for each column\n            upper_bounds(list): The upper bounds for each column\n        Returns:\n            pd.DataFrame: A DataFrame with an added 'outlier_group' column, indicating the type of outlier.\n        \"\"\"\n\n        column_outlier_dfs = []\n        for col, lb, ub in zip(columns, lower_bounds, upper_bounds):\n            mask_low = outlier_df[col] &lt; lb\n            mask_high = outlier_df[col] &gt; ub\n\n            low_df = outlier_df[mask_low].copy()\n            low_df[\"outlier_group\"] = f\"{col}_low\"\n\n            high_df = outlier_df[mask_high].copy()\n            high_df[\"outlier_group\"] = f\"{col}_high\"\n\n            column_outlier_dfs.extend([low_df, high_df])\n\n        # If there are no outliers, return the original DataFrame with an empty 'outlier_group' column\n        if not column_outlier_dfs:\n            log.critical(\"No outliers found in the data source.. probably something is wrong\")\n            return outlier_df.assign(outlier_group=\"\")\n\n        # Concatenate the DataFrames and return\n        return pd.concat(column_outlier_dfs, ignore_index=True)\n\n    @staticmethod\n    def get_top_n_outliers(outlier_df: pd.DataFrame, n: int = 10) -&gt; pd.DataFrame:\n        \"\"\"Function to retrieve the top N highest and lowest outliers for each outlier group.\n\n        Args:\n            outlier_df (pd.DataFrame): The DataFrame of outliers with 'outlier_group' column\n            n (int): Number of top outliers to retrieve for each group, defaults to 10\n\n        Returns:\n            pd.DataFrame: A DataFrame containing the top N outliers for each outlier group\n        \"\"\"\n\n        def get_extreme_values(group: pd.DataFrame) -&gt; pd.DataFrame:\n            \"\"\"Helper function to get the top N extreme values from a group.\"\"\"\n            col, extreme_type = group.name.rsplit(\"_\", 1)\n            if extreme_type == \"low\":\n                return group.nsmallest(n, col)\n            else:\n                return group.nlargest(n, col)\n\n        # Group by 'outlier_group' and apply the helper function, explicitly selecting columns\n        top_outliers = outlier_df.groupby(\"outlier_group\", group_keys=False).apply(\n            get_extreme_values, include_groups=True\n        )\n        return top_outliers.reset_index(drop=True)\n</code></pre>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.outliers.Outliers.__init__","title":"<code>__init__()</code>","text":"<p>SQLOutliers Initialization</p> Source code in <code>src/workbench/algorithms/sql/outliers.py</code> <pre><code>def __init__(self):\n    \"\"\"SQLOutliers Initialization\"\"\"\n    self.outlier_group = \"unknown\"\n</code></pre>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.outliers.Outliers.compute_outliers","title":"<code>compute_outliers(data_source, scale=1.5, use_stddev=False)</code>","text":"<p>Compute outliers for all the numeric columns in a DataSource Args:     data_source(DataSource): The DataSource that we're computing outliers on     scale (float): The scale to use for either the IQR or stddev outlier calculation (default: 1.5)     use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False) Returns:     pd.DataFrame: A DataFrame of outliers for this DataSource Notes:     Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)     The scale parameter can be adjusted to change the IQR multiplier</p> Source code in <code>src/workbench/algorithms/sql/outliers.py</code> <pre><code>def compute_outliers(\n    self, data_source: DataSourceAbstract, scale: float = 1.5, use_stddev: bool = False\n) -&gt; pd.DataFrame:\n    \"\"\"Compute outliers for all the numeric columns in a DataSource\n    Args:\n        data_source(DataSource): The DataSource that we're computing outliers on\n        scale (float): The scale to use for either the IQR or stddev outlier calculation (default: 1.5)\n        use_stddev (bool): Option to use the standard deviation for the outlier calculation (default: False)\n    Returns:\n        pd.DataFrame: A DataFrame of outliers for this DataSource\n    Notes:\n        Uses the IQR * 1.5 (~= 2.5 Sigma) (use 1.7 for ~= 3 Sigma)\n        The scale parameter can be adjusted to change the IQR multiplier\n    \"\"\"\n\n    # Note: If use_stddev is True, then the scale parameter needs to be adjusted\n    if use_stddev and scale == 1.5:  # If the default scale is used, adjust it\n        scale = 2.5\n\n    # Compute the numeric outliers\n    outlier_df = self._numeric_outliers(data_source, scale, use_stddev)\n\n    # If there are no outliers, return a DataFrame with the computation columns but no rows\n    if outlier_df is None:\n        columns = data_source.view(\"computation\").columns\n        return pd.DataFrame(columns=columns + [\"outlier_group\"])\n\n    # Get the top N outliers for each outlier group\n    outlier_df = self.get_top_n_outliers(outlier_df)\n\n    # Make sure the dataframe isn't too big, if it's too big sample it down\n    if len(outlier_df) &gt; 300:\n        log.important(f\"Outliers DataFrame is too large {len(outlier_df)}, sampling down to 300 rows\")\n        outlier_df = outlier_df.sample(300)\n\n    # Sort by outlier_group and reset the index\n    outlier_df = outlier_df.sort_values(\"outlier_group\").reset_index(drop=True)\n\n    # Shorten any long string values\n    outlier_df = shorten_values(outlier_df)\n    return outlier_df\n</code></pre>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.outliers.Outliers.get_top_n_outliers","title":"<code>get_top_n_outliers(outlier_df, n=10)</code>  <code>staticmethod</code>","text":"<p>Function to retrieve the top N highest and lowest outliers for each outlier group.</p> <p>Parameters:</p> Name Type Description Default <code>outlier_df</code> <code>DataFrame</code> <p>The DataFrame of outliers with 'outlier_group' column</p> required <code>n</code> <code>int</code> <p>Number of top outliers to retrieve for each group, defaults to 10</p> <code>10</code> <p>Returns:</p> Type Description <code>DataFrame</code> <p>pd.DataFrame: A DataFrame containing the top N outliers for each outlier group</p> Source code in <code>src/workbench/algorithms/sql/outliers.py</code> <pre><code>@staticmethod\ndef get_top_n_outliers(outlier_df: pd.DataFrame, n: int = 10) -&gt; pd.DataFrame:\n    \"\"\"Function to retrieve the top N highest and lowest outliers for each outlier group.\n\n    Args:\n        outlier_df (pd.DataFrame): The DataFrame of outliers with 'outlier_group' column\n        n (int): Number of top outliers to retrieve for each group, defaults to 10\n\n    Returns:\n        pd.DataFrame: A DataFrame containing the top N outliers for each outlier group\n    \"\"\"\n\n    def get_extreme_values(group: pd.DataFrame) -&gt; pd.DataFrame:\n        \"\"\"Helper function to get the top N extreme values from a group.\"\"\"\n        col, extreme_type = group.name.rsplit(\"_\", 1)\n        if extreme_type == \"low\":\n            return group.nsmallest(n, col)\n        else:\n            return group.nlargest(n, col)\n\n    # Group by 'outlier_group' and apply the helper function, explicitly selecting columns\n    top_outliers = outlier_df.groupby(\"outlier_group\", group_keys=False).apply(\n        get_extreme_values, include_groups=True\n    )\n    return top_outliers.reset_index(drop=True)\n</code></pre>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.descriptive_stats.descriptive_stats","title":"<code>descriptive_stats(data_source)</code>","text":"<p>Compute Descriptive Stats for all the numeric columns in a DataSource Args:     data_source(DataSource): The DataSource that we're computing descriptive stats on Returns:     dict(dict): A dictionary of descriptive stats for each column in this format          {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4, 'mean': 2.5, 'stddev': 1.5},           'col2': ...}</p> Source code in <code>src/workbench/algorithms/sql/descriptive_stats.py</code> <pre><code>def descriptive_stats(data_source: DataSourceAbstract) -&gt; dict[dict]:\n    \"\"\"Compute Descriptive Stats for all the numeric columns in a DataSource\n    Args:\n        data_source(DataSource): The DataSource that we're computing descriptive stats on\n    Returns:\n        dict(dict): A dictionary of descriptive stats for each column in this format\n             {'col1': {'min': 0, 'q1': 1, 'median': 2, 'q3': 3, 'max': 4, 'mean': 2.5, 'stddev': 1.5},\n              'col2': ...}\n    \"\"\"\n    # Grab the DataSource computation view table name\n    table = data_source.view(\"computation\").table\n\n    # Figure out which columns are numeric\n    num_type = [\"double\", \"float\", \"int\", \"bigint\", \"smallint\", \"tinyint\"]\n    details = data_source.view(\"computation\").column_details()\n    numeric = [column for column, data_type in details.items() if data_type in num_type]\n\n    # Sanity Check for numeric columns\n    if len(numeric) == 0:\n        log.warning(\"No numeric columns found in the current computation view of the DataSource\")\n        log.warning(\"If the data source was created from a DataFrame, ensure that the DataFrame was properly typed\")\n        log.warning(\"Recommendation: Properly type the DataFrame and recreate the Workbench artifact\")\n        return {}\n\n    # Build the query\n    query = descriptive_stats_query(numeric, table)\n\n    # Run the query\n    log.debug(query)\n    result_df = data_source.query(query)\n\n    # Process the results\n    # Note: The result_df is a DataFrame with a single row and a column for each stat metric\n    stats_dict = result_df.to_dict(orient=\"index\")[0]\n\n    # Convert the dictionary to a nested dictionary\n    # Note: The keys are in the format col1__col2\n    nested_descriptive_stats = defaultdict(dict)\n    for key, value in stats_dict.items():\n        col1, col2 = key.split(\"___\")\n        nested_descriptive_stats[col1][col2] = value\n\n    # Return the nested dictionary\n    return dict(nested_descriptive_stats)\n</code></pre>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.descriptive_stats.descriptive_stats_query","title":"<code>descriptive_stats_query(columns, table_name)</code>","text":"<p>Build a query to compute the descriptive stats for all columns in a table Args:     columns(list(str)): The columns to compute descriptive stats on     table_name(str): The table to compute descriptive stats on Returns:     str: The SQL query to compute descriptive stats</p> Source code in <code>src/workbench/algorithms/sql/descriptive_stats.py</code> <pre><code>def descriptive_stats_query(columns: list[str], table_name: str) -&gt; str:\n    \"\"\"Build a query to compute the descriptive stats for all columns in a table\n    Args:\n        columns(list(str)): The columns to compute descriptive stats on\n        table_name(str): The table to compute descriptive stats on\n    Returns:\n        str: The SQL query to compute descriptive stats\n    \"\"\"\n    query = f'SELECT &lt;&lt;column_descriptive_stats&gt;&gt; FROM \"{table_name}\"'\n    column_descriptive_stats = \"\"\n    for c in columns:\n        column_descriptive_stats += (\n            f'min(\"{c}\") AS \"{c}___min\", '\n            f'approx_percentile(\"{c}\", 0.25) AS \"{c}___q1\", '\n            f'approx_percentile(\"{c}\", 0.5) AS \"{c}___median\", '\n            f'approx_percentile(\"{c}\", 0.75) AS \"{c}___q3\", '\n            f'max(\"{c}\") AS \"{c}___max\", '\n            f'avg(\"{c}\") AS \"{c}___mean\", '\n            f'stddev(\"{c}\") AS \"{c}___stddev\", '\n        )\n    query = query.replace(\"&lt;&lt;column_descriptive_stats&gt;&gt;\", column_descriptive_stats[:-2])\n\n    # Return the query\n    return query\n</code></pre>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.correlations.correlation_query","title":"<code>correlation_query(columns, table_name)</code>","text":"<p>Build a query to compute the correlations between columns in a table</p> <p>Parameters:</p> Name Type Description Default <code>columns</code> <code>list(str</code> <p>The columns to compute correlations on</p> required <code>table_name</code> <code>str</code> <p>The table to compute correlations on</p> required <p>Returns:</p> Name Type Description <code>str</code> <code>str</code> <p>The SQL query to compute correlations</p> Pearson correlation coefficient ranges from -1 to 1: <p>+1 indicates a perfect positive linear relationship. -1 indicates a perfect negative linear relationship.  0 indicates no linear relationship.</p> Source code in <code>src/workbench/algorithms/sql/correlations.py</code> <pre><code>def correlation_query(columns: list[str], table_name: str) -&gt; str:\n    \"\"\"Build a query to compute the correlations between columns in a table\n\n    Args:\n        columns (list(str)): The columns to compute correlations on\n        table_name (str): The table to compute correlations on\n\n    Returns:\n        str: The SQL query to compute correlations\n\n    Notes: Pearson correlation coefficient ranges from -1 to 1:\n           +1 indicates a perfect positive linear relationship.\n           -1 indicates a perfect negative linear relationship.\n            0 indicates no linear relationship.\n    \"\"\"\n    query = f'SELECT &lt;&lt;cross_correlations&gt;&gt; FROM \"{table_name}\"'\n    cross_correlations = \"\"\n    for c in columns:\n        for d in columns:\n            if c != d:\n                cross_correlations += f'corr(\"{c}\", \"{d}\") AS \"{c}__{d}\", '\n    query = query.replace(\"&lt;&lt;cross_correlations&gt;&gt;\", cross_correlations[:-2])\n\n    # Return the query\n    return query\n</code></pre>"},{"location":"data_algorithms/sql/overview/#workbench.algorithms.sql.correlations.correlations","title":"<code>correlations(data_source)</code>","text":"<p>Compute Correlations for all the numeric columns in a DataSource Args:     data_source(DataSource): The DataSource that we're computing correlations on Returns:     dict(dict): A dictionary of correlations for each column in this format          {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},           'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}</p> Source code in <code>src/workbench/algorithms/sql/correlations.py</code> <pre><code>def correlations(data_source: DataSourceAbstract) -&gt; dict[dict]:\n    \"\"\"Compute Correlations for all the numeric columns in a DataSource\n    Args:\n        data_source(DataSource): The DataSource that we're computing correlations on\n    Returns:\n        dict(dict): A dictionary of correlations for each column in this format\n             {'col1': {'col2': 0.5, 'col3': 0.9, 'col4': 0.4, ...},\n              'col2': {'col1': 0.5, 'col3': 0.8, 'col4': 0.3, ...}}\n    \"\"\"\n    data_source.log.info(\"Computing Correlations for numeric columns...\")\n\n    # Figure out which columns are numeric\n    num_type = [\"double\", \"float\", \"int\", \"bigint\", \"smallint\", \"tinyint\"]\n    details = data_source.view(\"computation\").column_details()\n\n    # Get the numeric columns\n    numeric = [column for column, data_type in details.items() if data_type in num_type]\n\n    # If we have at least two numeric columns, compute the correlations\n    if len(numeric) &lt; 2:\n        return {}\n\n    # Grab the DataSource computation table name\n    table = data_source.view(\"computation\").table\n\n    # Build the query\n    query = correlation_query(numeric, table)\n\n    # Run the query\n    log.debug(query)\n    result_df = data_source.query(query)\n\n    # Drop any columns that have NaNs\n    result_df = result_df.dropna(axis=1)\n\n    # Process the results\n    # Note: The result_df is a DataFrame with a single row and a column for each pairwise correlation\n    correlation_dict = result_df.to_dict(orient=\"index\")[0]\n\n    # Convert the dictionary to a nested dictionary\n    # Note: The keys are in the format col1__col2\n    nested_corr = defaultdict(dict)\n    for key, value in correlation_dict.items():\n        col1, col2 = key.split(\"__\")\n        nested_corr[col1][col2] = value\n\n    # Sort the nested dictionaries\n    sorted_dict = {}\n    for key, sub_dict in nested_corr.items():\n        sorted_dict[key] = {k: v for k, v in sorted(sub_dict.items(), key=lambda item: item[1], reverse=True)}\n    return sorted_dict\n</code></pre>"},{"location":"data_algorithms/sql/overview/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"enterprise/","title":"Workbench Enterprise","text":"<p>The Workbench API and User Interfaces cover a broad set of AWS Machine Learning services and provide easy to use abstractions and visualizations of your AWS ML data. We offer a wide range of options to best fit your companies needs.</p> Accelerate ML Pipeline development with an Enterprise License! Free Enterprise: Lite Enterprise: Standard Enterprise: Pro Python API \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 Workbench REPL \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 Dashboard \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 AWS Onboarding \u2796 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 Dashboard Plugins \u2796 \ud83d\udfe2 \ud83d\udfe2 \ud83d\udfe2 Custom Pages \u2796 \u2796 \ud83d\udfe2 \ud83d\udfe2 Themes \u2796 \u2796 \ud83d\udfe2 \ud83d\udfe2 ML Pipelines \u2796 \u2796 \u2796 \ud83d\udfe2 Project Branding \u2796 \u2796 \u2796 \ud83d\udfe2 Prioritized Feature Requests \u2796 \u2796 \u2796 \ud83d\udfe2 Pricing \u2796 $1500* $3000* $4000* <p>*USD per month, includes AWS setup, support, and training: Everything needed to accelerate your AWS ML Development team. Interested in Data Science/Engineering consulting? We have top notch Consultants with a depth and breadth of AWS ML/DS/Engineering expertise.</p>"},{"location":"enterprise/#try-workbench","title":"Try Workbench","text":"<p>We encourage new users to try out the free version, first. We offer support in our Discord channel and our Documentation has instructions for how to get started with Workbench. So try it out and when you're ready to accelerate your AWS ML Adventure with an Enterprise licence contact us at Workbench Sales</p>"},{"location":"enterprise/#data-engineeringscience-consulting","title":"Data Engineering/Science Consulting","text":"<p>Alongside our Workbench Enterprise offerings, we provide comprehensive consulting services and domain expertise through our Partnerships. We specialize in AWS Machine Learning Systems and our extended team of Data Scientists and Engineers, have Masters and Ph.D. degrees in Computer Science, Chemistry, and Pharmacology. We also have a parntership with Nomic Networks to support our Network Security Clients.</p> <p>Using AWS and Workbench, our experts are equipped to deliver tailored solutions that are focused on your project needs and deliverables. For more information please touch base and we'll set up a free initial consultation Workbench Consulting</p>"},{"location":"enterprise/#contact-us","title":"Contact Us","text":"<p>Contact us on our Discord channel, we're happy to answer any questions that you might have about Workbench and accelerating your AWS ML Pipelines. You can also send us email at Workbench Info or  Workbench Sales</p>"},{"location":"enterprise/private_saas/","title":"Benefits of a Private SaaS Architecture","text":""},{"location":"enterprise/private_saas/#self-hosted-vs-private-saas-vs-public-saas","title":"Self Hosted vs Private SaaS vs Public SaaS?","text":"<p>At the top level your team/project is making a decision about how they are going to build, expand, support, and maintain a machine learning pipeline.</p> <p>Conceptual ML Pipeline</p> <pre><code>Data \u2b95 Features \u2b95 Models \u2b95 Deployment (end-user application)\n</code></pre> <p>Concrete/Real World Example</p> <pre><code>S3 \u2b95 Glue Job \u2b95 Data Catalog \u2b95 FeatureGroups \u2b95 Models \u2b95 Endpoints \u2b95 App\n</code></pre> <p>When building out a framework to support ML Pipelines there are three main options:</p> <ul> <li>Self Hosted</li> <li>Private SaaS</li> <li>Public SaaS</li> </ul> <p>The other choice, that we're not going to cover here, is whether you use AWS, Azure, GCP, or something else. Workbench is architected and powered by a broad and rich set of AWS ML Pipeline services. We believe that AWS provides the best set of functionality and APIs for flexible, real world ML architectures.</p> <p></p>"},{"location":"enterprise/private_saas/#resources","title":"Resources","text":"<p>See our full presentation on the Workbench Private SaaS Architecture</p>"},{"location":"enterprise/project_branding/","title":"Project Branding","text":"<p>The Workbench Dashboard can be customized extensively. Using Workbench Project Branding allows you to change page headers, titles, and logos to match your project. All user interfaces will reflect your project name and company logos. </p>"},{"location":"enterprise/project_branding/#contact-us","title":"Contact Us","text":"<p>Contact us on our Discord channel, we're happy to answer any questions that you might have about Workbench and accelerating your AWS ML Pipelines. You can also send us email at Workbench Info or  Workbench Sales.</p>"},{"location":"enterprise/themes/","title":"Workbench Themes","text":"<p>The Workbench Dashboard can be customized extensively. Using Workbench Themes allows you to customize the User Interfaces to suit your preferences, including completely customized color palettes and fonts. We offer a set of default 'dark' and 'light' themes, but we'll also customize the theme to match your company's color palette and logos.</p>"},{"location":"enterprise/themes/#contact-us","title":"Contact Us","text":"<p>Contact us on our Discord channel, we're happy to answer any questions that you might have about Workbench and accelerating your AWS ML Pipelines. You can also send us email at Workbench Info or  Workbench Sales.</p>"},{"location":"getting_started/","title":"Getting Started","text":"<p>For the initial setup of Workbench we'll be using the Workbench REPL. When you start <code>workbench</code> it will recognize that it needs to complete the initial configuration and will guide you through that process.</p> <p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"getting_started/#initial-setupconfig","title":"Initial Setup/Config","text":"<p>Notes: Workbench uses your existing AWS account/profile/SSO. So if you don't already have an AWS Profile or SSO Setup you'll need to do that first AWS Setup</p> <p>Okay so after you've completed your AWS Setup you can now install Workbench.</p> <p><pre><code>&gt; pip install workbench\n&gt; workbench &lt;-- This starts the REPL\n\nWelcome to Workbench!\nLooks like this is your first time using Workbench...\nLet's get you set up...\nAWS_PROFILE: my_aws_profile\nWORKBENCH_BUCKET: my-company-workbench\n[optional] REDIS_HOST(localhost): my-redis.cache.amazon (or leave blank)\n[optional] REDIS_PORT(6379):\n[optional] REDIS_PASSWORD():\n[optional] WORKBENCH_API_KEY(open_source): my_api_key (or leave blank)\n</code></pre> That's It: You're now all set. This configuration only needs to be ONCE :)</p>"},{"location":"getting_started/#data-scientistsengineers","title":"Data Scientists/Engineers","text":"<ul> <li>Workbench REPL: Workbench REPL</li> <li>Using Workbench for ML Pipelines: Workbench API Classes</li> <li>SCP Workbench Github: Github Repo</li> </ul>"},{"location":"getting_started/#aws-administrators","title":"AWS Administrators","text":"<p>For companies that are setting up Workbench on an internal AWS Account: Company AWS Setup</p>"},{"location":"getting_started/#additional-resources","title":"Additional Resources","text":"<ul> <li>Workbench Core Classes: Core Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"glue/","title":"AWS Glue Jobs","text":"<p>AWS Glue Simplified</p> <p>AWS Glue Jobs are a great way to automate ETL and data processing. Workbench takes all the hassle out of creating and debugging Glue Jobs. Follow this guide and empower your Glue Jobs with Workbench!</p> <p>Workbench make creating, testing, and debugging of AWS Glue Jobs easy. The exact same Workbench API Classes are used in your Glue Jobs. Also since Workbench manages the roles for both API and Glue Jobs you'll be able to test new Glue Jobs locally and minimizes surprises when deploying your Glue Job.</p>"},{"location":"glue/#glue-job-setup","title":"Glue Job Setup","text":"<p>Setting up a AWS Glue Job that uses Workbench is straight forward. Workbench can be 'installed' on AWS Glue via the <code>--additional-python-modules</code> parameter and then you can use the Workbench API just like normal. </p> <p></p> <p>Here are the settings and a screen shot to guide you. There are several ways to set up and run Glue Jobs, with either the Workbench-ExecutionRole or using the WorkbenchAPIPolicy. Please feel free to contact Workbench support if you need any help with setting up Glue Jobs.</p> <ul> <li>IAM Role: Workbench-ExecutionRole</li> <li>Type: Spark</li> <li>Glue Version: Glue 4.0</li> <li>Worker Type: G.1X</li> <li>Number of Workers: 2</li> <li>Job Parameters</li> <li>--additional-python-modules: workbench&gt;=0.4.6</li> <li>--workbench-bucket: &lt;your workbench bucket&gt;</li> </ul> <p>Glue IAM Role Details</p> <p>If your Glue Jobs already use an existing IAM Role then you can add the <code>WorkbenchAPIPolicy</code> to that Role to enable the Glue Job to perform Workbench API Tasks.</p>"},{"location":"glue/#workbench-glue-example","title":"Workbench Glue Example","text":"<p>Anyone familiar with a typical Glue Job should be pleasantly surpised by how simple the example below is. Also Workbench allows you to test Glue Jobs locally using the same code that you use for script and Notebooks (see Glue Testing)</p> <p>Glue Job Arguments</p> <p>AWS Glue Jobs take arguments in the form of Job Parameters (see screenshot above). There's a Workbench utility function <code>get_resolved_options</code> that turns these Job Parameters into a nice dictionary for ease of use.</p> examples/glue_hello_world.py<pre><code>import sys\n\n# Workbench Imports\nfrom workbench.api.data_source import DataSource\nfrom workbench.utils.config_manager import ConfigManager\nfrom workbench.utils.glue_utils import get_resolved_options\n\n# Convert Glue Job Args to a Dictionary\nglue_args = get_resolved_options(sys.argv)\n\n# Set the WORKBENCH_BUCKET for the ConfigManager\ncm = ConfigManager()\ncm.set_config(\"WORKBENCH_BUCKET\", glue_args[\"workbench-bucket\"])\n\n# Create a new Data Source from an S3 Path\nsource_path = \"s3://workbench-public-data/common/abalone.csv\"\nmy_data = DataSource(source_path, name=\"abalone_glue_test\")\n</code></pre>"},{"location":"glue/#glue-example-2","title":"Glue Example 2","text":"<p>This example takes two 'Job Parameters'</p> <ul> <li>--workbench-bucket : &lt;your workbench bucket&gt;</li> <li>--input-s3-path : &lt;your S3 input path&gt;</li> </ul> <p>The example will convert all CSV files in an S3 bucket/prefix and load them up as DataSources in Workbench.</p> examples/glue_load_s3_bucket.py<pre><code>import sys\n\n# Workbench Imports\nfrom workbench.api.data_source import DataSource\nfrom workbench.utils.config_manager import ConfigManager\nfrom workbench.utils.glue_utils import get_resolved_options, list_s3_files\n\n# Convert Glue Job Args to a Dictionary\nglue_args = get_resolved_options(sys.argv)\n\n# Set the WORKBENCH_BUCKET for the ConfigManager\ncm = ConfigManager()\ncm.set_config(\"WORKBENCH_BUCKET\", glue_args[\"workbench-bucket\"])\n\n# List all the CSV files in the given S3 Path\ninput_s3_path = glue_args[\"input-s3-path\"]\nfor input_file in list_s3_files(input_s3_path):\n\n    # Note: If we don't specify a name, one will be 'auto-generated'\n    my_data = DataSource(input_file, name=None)\n</code></pre>"},{"location":"glue/#exception-log-forwarding","title":"Exception Log Forwarding","text":"<p>When a Glue Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use Workbench log forwarding the exception/stack will be forwarded to CloudWatch.</p> <p><pre><code>from workbench.utils.workbench_logging import exception_log_forward\n\nwith exception_log_forward():\n   &lt;my glue code&gt;\n   ...\n   &lt;exception happens&gt;\n   &lt;more of my code&gt;\n</code></pre> The <code>exception_log_forward</code> sets up a context manager that will trap exceptions and forward the exception/stack to CloudWatch for diagnosis. </p>"},{"location":"glue/#glue-job-local-testing","title":"Glue Job Local Testing","text":"<p>Glue Power without the Pain. Workbench manages the AWS Execution Role, so local API and Glue Jobs will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Glue Jobs a breeze.</p> <pre><code>export WORKBENCH_CONFIG=&lt;your config&gt;  # Only if not already set up\npython my_glue_job.py --workbench-bucket &lt;your bucket&gt;\n</code></pre>"},{"location":"glue/#additional-resources","title":"Additional Resources","text":"<ul> <li>Workbench Glue Jobs: Workbench Glue</li> <li>Setting up Workbench on your AWS Account: AWS Setup</li> <li>Using Workbench for ML Pipelines: Workbench API Classes</li> </ul> <ul> <li>Workbench Core Classes: Core Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"lambda_layer/","title":"AWS Lambda Layer","text":"<p>Workbench Lambda Layers</p> <p>AWS Lambda Jobs are a great way to spin up data processing jobs. Follow this guide and empower AWS Lambda with Workbench!</p> <p>Workbench makes creating, testing, and debugging of AWS Lambda Functions easy. The exact same Workbench API Classes are used in your AWS Lambda Functions. Also since Workbench manages the access policies you'll be able to test new Lambda Jobs locally and minimizes surprises when deploying.</p> <p>Work In Progress</p> <p>The Workbench Lambda Layers are a great way to use Workbench but they are still in 'beta' mode so please let us know if you have any issues.</p>"},{"location":"lambda_layer/#lambda-job-setup","title":"Lambda Job Setup","text":"<p>Setting up a AWS Lambda Job that uses Workbench is straight forward. Workbench can be 'installed' using a Lambda Layer and then you can use the Workbench API just like normal.</p> <p>Here are the ARNs for the current Workbench Lambda Layers, please note they are specified with region and Python version in the name, so if your lambda is us-east-1, python 3.12, pick this ARN with those values in it.</p>"},{"location":"lambda_layer/#python-312-if-you-need-another-versionregion-let-us-know","title":"Python 3.12 (if you need another version/region let us know)","text":"<p>us-east-1</p> <ul> <li>arn:aws:lambda:us-east-1:507740646243:layer:workbench_lambda_layer-us-east-1-python312:7</li> </ul> <p>us-west-2</p> <ul> <li>arn:aws:lambda:us-west-2:507740646243:layer:workbench_lambda_layer-us-west-2-python312:8</li> </ul> <p>Note: If you're using lambdas on a different region or with a different Python version, just let us know and we'll publish some additional layers.</p> <p></p> <p>At the bottom of the Lambda page there's an 'Add Layer' button. You can click that button and specify the layer using the ARN above. Also in the 'General Configuration' set these parameters:</p> <ul> <li>Timeout: 5 Minutes</li> <li>Memory: 4096</li> <li>Ephemeral storage: 2048</li> </ul> <p>Set the WORKBENCH_BUCKET ENV Workbench will need to know what bucket to work out of, so go into the Configuration...Environment Variables... and add one for the Workbench bucket that your are using for AWS Account (dev, prod, etc). </p> <p>Lambda Role Details</p> <p>If your Lambda Function already use an existing IAM Role then you can add the Workbench policies to that Role to enable the Lambda Job to perform Workbench API Tasks. See Workbench Access Controls</p>"},{"location":"lambda_layer/#workbench-lambda-example","title":"Workbench Lambda Example","text":"<p>Here's a simple example of using Workbench in your Lambda Function. </p> <p>Workbench Layer is Compressed</p> <p>The Workbench Lambda Layer is compressed (to fit all the awesome). This means that the <code>load_lambda_layer()</code> method must be called before using any other Workbench imports, see the example below. If you do not do this you'll probably get a <code>No module named 'numpy'</code> error or something like that.</p> examples/lambda_hello_world.py<pre><code>import json\nfrom pprint import pprint\nfrom workbench.utils.lambda_utils import load_lambda_layer\n\n# Load/Decompress the Workbench Lambda Layer\nload_lambda_layer()\n\n# After 'load_lambda_layer()' we can use other Workbench imports\nfrom workbench.api import Meta\nfrom workbench.api import Model \n\ndef lambda_handler(event, context):\n\n    # Create our Meta Class and get a list of our Models\n    meta = Meta()\n    models = meta.models()\n\n    print(f\"Number of Models: {len(models)}\")\n    print(models)\n\n    # Onboard a model\n    model = Model(\"abalone-regression\")\n    pprint(model.details())\n\n    # Return success\n    return {\n        'statusCode': 200,\n        'body': { \"incoming_event\": event}\n    }\n</code></pre>"},{"location":"lambda_layer/#exception-log-forwarding","title":"Exception Log Forwarding","text":"<p>When a Lambda Job crashes (has an exception), the AWS console will show you the last line of the exception, this is mostly useless. If you use Workbench log forwarding the exception/stack will be forwarded to CloudWatch.</p> <p><pre><code>from workbench.utils.workbench_logging import exception_log_forward\n\nwith exception_log_forward():\n   &lt;my lambda code&gt;\n   ...\n   &lt;exception happens&gt;\n   &lt;more of my code&gt;\n</code></pre> The <code>exception_log_forward</code> sets up a context manager that will trap exceptions and forward the exception/stack to CloudWatch for diagnosis. </p>"},{"location":"lambda_layer/#lambda-function-local-testing","title":"Lambda Function Local Testing","text":"<p>Lambda Power without the Pain. Workbench manages the AWS Execution Role/Policies, so local API and Lambda Functions will have the same permissions/access. Also using the same Code as your notebooks or scripts makes creating and testing Lambda Functions a breeze.</p> <pre><code>python my_lambda_function.py --workbench-bucket &lt;your bucket&gt;\n</code></pre>"},{"location":"lambda_layer/#additional-resources","title":"Additional Resources","text":"<ul> <li>Workbench Access Management: Workbench Access Management</li> <li>Setting up Workbench on your AWS Account: AWS Setup</li> </ul> <ul> <li> <p>Using Workbench for ML Pipelines: Workbench API Classes</p> </li> <li> <p>Consulting Available: SuperCowPowers LLC</p> </li> </ul>"},{"location":"misc/faq/","title":"Workbench: FAQ","text":"<p>Artifact and Column Naming?</p> <p>You might have noticed that Workbench has some unintuitive constraints when naming Artifacts and restrictions on column names. All of these restrictions come from AWS. Workbench uses Glue, Athena, Feature Store, Models and Endpoints, each of these services have their own constraints, Workbench simply 'reflects' those contraints.</p>"},{"location":"misc/faq/#naming-underscores-dashes-and-lower-case","title":"Naming: Underscores, Dashes, and Lower Case","text":"<p>Data Sources and Feature Sets must adhere to AWS restrictions on table names and columns names (here is a snippet from the AWS documentation)</p> <p>Database, table, and column names</p> <p>When you create schema in AWS Glue to query in Athena, consider the following:</p> <p>A database name cannot be longer than 255 characters. A table name cannot be longer than 255 characters. A column name cannot be longer than 255 characters.</p> <p>The only acceptable characters for database names, table names, and column names are lowercase letters, numbers, and the underscore character.</p> <p>For more info see: Glue Best Practices</p>"},{"location":"misc/faq/#datasourcefeatureset-use-_-and-modelendpoint-use-","title":"DataSource/FeatureSet use '_'  and Model/Endpoint use '-'","text":"<p>You may notice that DataSource and FeatureSet uuid/name examples have underscores but the model and endpoints have dashes. Yes, it\u2019s super annoying to have one convention for DataSources and FeatureSets and another for Models and Endpoints but this is an AWS restriction and not something that Workbench can control.</p> <p>DataSources and FeatureSet: Underscores. You cannot use a dash because both classes use Athena for Storage and Athena tables names cannot have a dash.</p> <p>Models and Endpoints: Dashes. You cannot use an underscores because AWS imposes a restriction on the naming.</p>"},{"location":"misc/faq/#additional-information-on-the-lower-case-issue","title":"Additional information on the lower case issue","text":"<p>We\u2019ve tried to create a glue table with Mixed Case column names and haven\u2019t had any luck. We\u2019ve bypassed wrangler and used the boto3 low level calls directly. In all cases when it shows up in the Glue Table the columns have always been converted to lower case. We've also tried uses the Athena DDL directly, that also doesn't work. Here's the relevant AWS documentation and the two scripts that reproduce the issue.</p> <p>AWS Docs</p> <ul> <li>Athena Naming Restrictions</li> <li>Glue Best Practices</li> </ul> <p>Scripts to Reproduce</p> <ul> <li>scripts/athena_ddl_mixed_case.py</li> <li>scripts/glue_mixed_case.py</li> </ul>"},{"location":"misc/general_info/","title":"General info","text":""},{"location":"misc/general_info/#general-info","title":"General Info","text":""},{"location":"misc/general_info/#workbench-the-scientists-workbench-powered-by-aws-for-scalability-flexibility-and-security","title":"Workbench: The scientist's workbench powered by AWS\u00ae for scalability, flexibility, and security.","text":"<p>Workbench is a medium granularity framework that manages and aggregates AWS\u00ae Services into classes and concepts. When you use Workbench you think about DataSources, FeatureSets, Models, and Endpoints. Underneath the hood those classes handle all the details around updating and managing a complex set of AWS Services. All the power and none of the pain so that your team can Do Science Faster!</p>"},{"location":"misc/general_info/#workbench-documentation","title":"Workbench Documentation","text":"<p>See our Python API and AWS documentation here: Workbench Documentation</p>"},{"location":"misc/general_info/#full-workbench-overview","title":"Full Workbench OverView","text":"<p>Workbench Architected FrameWork</p>"},{"location":"misc/general_info/#why-workbench","title":"Why Workbench?","text":"<ul> <li>The AWS SageMaker\u00ae ecosystem is awesome but has a large number of services with significant complexity</li> <li>Workbench provides rapid prototyping through easy to use classes and transforms</li> <li>Workbench provides visibility and transparency into AWS SageMaker\u00ae Pipelines<ul> <li>What S3 data sources are getting pulled?</li> <li>What Features Store/Group is the Model Using?</li> <li>What's the Provenance of a Model in Model Registry?</li> <li>What SageMaker Endpoints are associated with this model?</li> </ul> </li> </ul>"},{"location":"misc/general_info/#single-pane-of-glass","title":"Single Pane of Glass","text":"<p>Visibility into the AWS Services that underpin the Workbench Classes. We can see that Workbench automatically tags and tracks the inputs of all artifacts providing 'data provenance' for all steps in the AWS modeling pipeline.</p> <p>Image TBD</p> <p> Clearly illustrated: Workbench provides intuitive and transparent visibility into the full pipeline of your AWS Sagemaker Deployments.</p>"},{"location":"misc/general_info/#getting-started","title":"Getting Started","text":"<ul> <li>Workbench Overview Slides that cover and illustrate the Workbench Modeling Pipeline.</li> <li>Workbench Docs/Wiki Our general documentation for getting started with Workbench.</li> <li>Workbench AWS Onboarding Deploy the Workbench Stack to your AWS Account. </li> <li>Notebook: Start to Finish AWS ML Pipeline Building an AWS\u00ae ML Pipeline from start to finish.</li> <li>Video: Coding with Workbench Informal coding + chatting while building a full ML pipeline.</li> <li>Join our Discord for questions and advice on using Workbench within your organization.</li> </ul>"},{"location":"misc/general_info/#workbench-zen","title":"Workbench Zen","text":"<ul> <li>The AWS SageMaker\u00ae set of services is vast and complex.</li> <li>Workbench Classes encapsulate, organize, and manage sets of AWS\u00ae Services.</li> <li>Heavy transforms typically use AWS Athena or Apache Spark (AWS Glue/EMR Serverless).</li> <li>Light transforms will typically use Pandas.</li> <li>Heavy and Light transforms both update AWS Artifacts (collections of AWS Services).</li> <li>Quick prototypes are typically built with the light path and then flipped to the heavy path as the system matures and usage grows.</li> </ul>"},{"location":"misc/general_info/#classes-and-concepts","title":"Classes and Concepts","text":"<p>The Workbench Classes are organized to work in concert with AWS Services. For more details on the current classes and class hierarchies see Workbench Classes and Concepts.</p>"},{"location":"misc/general_info/#contributions","title":"Contributions","text":"<p>If you'd like to contribute to the Workbench project, you're more than welcome. All contributions will fall under the existing project license. If you are interested in contributing or have questions please feel free to contact us at workbench@supercowpowers.com.</p>"},{"location":"misc/general_info/#workbench-alpha-testers-wanted","title":"Workbench Alpha Testers Wanted","text":"<p>Our experienced team can provide development and consulting services to help you effectively use Amazon\u2019s Machine Learning services within your organization.</p> <p>The popularity of cloud based Machine Learning services is booming. The problem many companies face is how that capability gets effectively used and harnessed to drive real business decisions and provide concrete value for their organization.</p> <p>Using Workbench will minimize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a Workbench Alpha Tester, contact us at workbench@supercowpowers.com.</p> <p>\u00ae Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates.</p>"},{"location":"misc/scp_consulting/","title":"Scp consulting","text":""},{"location":"misc/scp_consulting/#consulting","title":"Consulting","text":""},{"location":"misc/scp_consulting/#workbench-scp-consulting-awesome","title":"Workbench + SCP Consulting = Awesome","text":"<p>Our experienced team can provide development and consulting services to help you effectively use Amazon\u2019s Machine Learning services within your organization.</p> <p>The popularity of cloud based Machine Learning services is booming. The problem many companies face is how that capability gets effectively used and harnessed to drive real business decisions and provide concrete value for their organization.</p> <p>Using Workbench will minimizize the time and manpower needed to incorporate AWS ML into your organization. If your company would like to be a Workbench Alpha Tester, contact us at workbench@supercowpowers.com.</p>"},{"location":"misc/scp_consulting/#typical-engagements","title":"Typical Engagements","text":"<p>Workbench clients typically want a tailored web_interface that helps to drive business decisions and provides value for their organization.</p> <ul> <li>Workbench components provide a set of classes and transforms the will dramatically reduce time and increase productivity when building AWS ML Systems.</li> <li>Workbench enables rapid prototyping via it's light paths and provides AWS production workflows on large scale data through it's heavy paths.</li> <li> <p>Rapid Prototyping is typically done via these steps.</p> </li> <li> <p>Quick Construction of Web Interface (tailored)</p> </li> <li>Custom Components (tailored)</li> <li>Demo/Review the Application with the Client</li> <li>Get Feedback/Changes/Improvements</li> <li> <p>Goto Step 1</p> </li> <li> <p>When the client is happy/excited about the ProtoType we then bolt down the system, test the heavy paths, review AWS access, security and ensure 'least privileged' roles and policies.</p> </li> </ul> <p>Contact us for a free initial consultation on how we can accelerate the use of AWS ML at your company workbench@supercowpowers.com.</p>"},{"location":"misc/workbench_classes_concepts/","title":"Workbench Classes and Concepts","text":"<p>A flexible, rapid, and customizable AWS\u00ae ML Sandbox. Here's some of the classes and concepts we use in the Workbench system:</p> <p></p> <ul> <li>Artifacts</li> <li>DataLoader</li> <li>DataSource</li> <li>FeatureSet</li> <li>Model</li> <li> <p>Endpoint</p> </li> <li> <p>Transforms</p> </li> <li>DataSource to DataSource<ul> <li>Heavy <ul> <li>AWS Glue Jobs</li> <li>AWS EMR Serverless</li> </ul> </li> <li>Light<ul> <li>Local/Laptop</li> <li>Lambdas</li> <li>StepFunctions</li> </ul> </li> </ul> </li> <li>DataSource to FeatureSet<ul> <li>Heavy/Light (see above breakout)</li> </ul> </li> <li>FeatureSet to FeatureSet<ul> <li>Heavy/Light (see above breakout)</li> </ul> </li> <li>FeatureSet to Model</li> <li>Model to Endpoint</li> </ul>"},{"location":"plugins/","title":"OverView","text":"<p>Workbench Plugins</p> <p>The Workbench toolkit provides a flexible plugin architecture to expand, enhance, or even replace the Dashboard. Make custom UI components, views, and entire pages with the plugin classes described here.</p> <p>The Workbench Plugin system allows clients to customize how their AWS Machine Learning Pipeline is displayed, analyzed, and visualized. Our easy to use Python API enables developers to make new Dash/Plotly components, data views, and entirely new web pages focused on business use cases.</p>"},{"location":"plugins/#concept-docs","title":"Concept Docs","text":"<p>Many classes in Workbench need additional high-level material that covers class design and illustrates class usage. Here's the Concept Docs for Plugins:</p> <ul> <li>Plugin Concepts: Read First!</li> <li>How to Write a Plugin </li> <li>Plugin Pages</li> <li>Plugins Advanced</li> </ul>"},{"location":"plugins/#make-a-plugin","title":"Make a plugin","text":"<p>Each plugin class inherits from the Workbench PluginInterface class and needs to set two attributes and implement two methods. These requirements are set so that each Plugin will conform to the Workbench infrastructure; if the required attributes and methods aren\u2019t included in the class definition, errors will be raised during tests and at runtime.</p> <p>Note: For full code see Model Plugin Example</p> <pre><code>class ModelPlugin(PluginInterface):\n    \"\"\"MyModelPlugin Component\"\"\"\n\n    \"\"\"Initialize this Plugin Component \"\"\"\n    auto_load_page = PluginPage.MODEL\n    plugin_input_type = PluginInputType.MODEL\n\n    def create_component(self, component_id: str) -&gt; dcc.Graph:\n        \"\"\"Create the container for this component\n        Args:\n            component_id (str): The ID of the web component\n        Returns:\n            dcc.Graph: The EndpointTurbo Component\n        \"\"\"\n        self.component_id = component_id\n        self.container = dcc.Graph(id=component_id, ...)\n\n        # Fill in plugin properties\n        self.properties = [(self.component_id, \"figure\")]\n\n        # Return the container\n        return self.container\n\n    def update_properties(self, model: Model, **kwargs) -&gt; list:\n        \"\"\"Update the properties for the plugin.\n\n        Args:\n            model (Model): An instantiated Model object\n            **kwargs: Additional keyword arguments\n\n        Returns:\n            list: A list of the updated property values\n        \"\"\"\n\n        # Create a pie chart with the endpoint name as the title\n        pie_figure = go.Figure(data=..., ...)\n\n        # Return the updated property values for the plugin\n        return [pie_figure]\n</code></pre>"},{"location":"plugins/#required-attributes","title":"Required Attributes","text":"<p>The class variable plugin_page determines what type of plugin the MyPlugin class is. This variable is inspected during plugin loading at runtime in order to load the plugin to the correct artifact page in the Workbench dashboard. The PluginPage class can be DATA_SOURCE, FEATURE_SET, MODEL, or ENDPOINT.</p>"},{"location":"plugins/#s3-bucket-plugins-work-in-progress","title":"S3 Bucket Plugins (Work in Progress)","text":"<p>Note: This functionality is coming soon</p> <p>Offers the most flexibility and fast prototyping. Simple set your config/env for  blah to an S3 Path and Workbench will load the plugins from S3 directly.</p> <p>Helpful Tip</p> <p>You can copy files from your local system up to S3 with this handy AWS CLI call</p> <pre><code> aws s3 cp . s3://my-workbench/workbench_plugins \\\n --recursive --exclude \"*\" --include \"*.py\"\n</code></pre>"},{"location":"plugins/#additional-resources","title":"Additional Resources","text":"<p>Need help with plugins? Want to develop a customized application tailored to your business needs?</p> <ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"presentations/","title":"Workbench Presentations","text":"<p>The Workbench framework makes AWS\u00ae both easier to use and more powerful. Workbench handles all the details around updating and managing a complex set of AWS Services. With a simple-to-use Python API and a beautiful set of web interfaces, Workbench makes creating AWS ML pipelines a snap.</p> <p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"presentations/#workbench-presentations_1","title":"Workbench Presentations","text":"<ul> <li>Workbench Overview</li> <li>Private SaaS Architecture</li> <li>Python API</li> <li>Plugins Overview</li> <li>Plugins: Getting Started</li> <li>Plugins: Pages</li> <li>Plugins: Advanced</li> <li>Exploratory Data Analysis</li> <li>Architected ML Framework</li> <li>AWS Access Management</li> <li>Workbench Config</li> <li>Workbench REPL</li> </ul>"},{"location":"presentations/#workbench-python-api-docs","title":"Workbench Python API Docs","text":"<p>The Workbench API documentation Workbench API covers our in-depth Python API and contains code examples. The code examples are provided in the Github repo <code>examples/</code> directory. For a full code listing of any example please visit our Workbench Examples</p>"},{"location":"presentations/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p></p> <p>\u00ae Amazon Web Services, AWS, the Powered by AWS logo, are trademarks of Amazon.com, Inc. or its affiliates</p>"},{"location":"release_notes/0_8_50/","title":"Release 0.8.50","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.46</code> to <code>0.8.50</code></p>"},{"location":"release_notes/0_8_50/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_50/#featureset-id_column-lock-in","title":"FeatureSet: id_column lock in","text":"<p>We're going to lock in id_columns when FeatureSets are created, AWS FeatureGroup requires an id column, so this is the best place to do it, see API Changes below.</p>"},{"location":"release_notes/0_8_50/#featureset-robust-handling-of-training-column","title":"FeatureSet: Robust handling of training column","text":"<p>In the past we haven't supported giving a training column as input data. FeatureSets are read-only, so locking in the training rows is 'suboptimal'. In general you might want to use the FeatureSet for several models with different training/hold_out sets. Now if a FeatureSet detects a training column it will give the follow message:</p> <pre><code>Training column detected: Since FeatureSets are read only, Workbench \ncreates training views that can be dynamically changed. We'll use \nthis training column to create a training view.\n</code></pre>"},{"location":"release_notes/0_8_50/#endpoint-auto_inference","title":"Endpoint: auto_inference()","text":"<p>We're changing the internal logic for the <code>auto_inference()</code> method to include the id_column in it's output.</p>"},{"location":"release_notes/0_8_50/#api-changes","title":"API Changes","text":"<p>FeatureSet</p> <p>When creating a FeatureSet the <code>id_column</code> is now a required argument.</p> <pre><code>ds = DataSource(\"test_data\")\nfs = ds.to_features(\"test_features\", id_column=\"my_id\") &lt;-- Required\n</code></pre> <p><pre><code>to_features = PandasToFeatures(\"my_feature_set\")\nto_features.set_input(df_features, id_column=\"my_id\")  &lt;-- Required\nto_features.set_output_tags([\"blah\", \"whatever\"])\nto_features.transform()\n</code></pre> If you're data doesn't have a id column you can specify \"auto\"</p> <pre><code>to_features = PandasToFeatures(\"my_feature_set\")\nto_features.set_input(df_features, id_column=\"auto\")  &lt;-- Auto Id (index)\n</code></pre> <p>For more details see: FeatureSet Class</p> <p>The new Meta() API will be used inside of the Artifact classes (see Internal Changes...Artifacts... below)</p>"},{"location":"release_notes/0_8_50/#improvements","title":"Improvements","text":"<p>DFStore</p> <p>Robust handling of slashes, so now it will 'just work' with various upserts and gets:</p> <pre><code>```\n# These all give you /ml/shap_value dataframe\ndf_store.get(\"/ml/shap_values\")\ndf_store.get(\"ml/shap_values\")\ndf_store.get(\"//ml/shap_values\")\n```\n</code></pre>"},{"location":"release_notes/0_8_50/#internal-changes","title":"Internal Changes","text":"<p>There's a whole new directory structure that helps isolate Cloud Platform specific funcitonality.</p> <pre><code>- workbench/src\n     - core/cloud_platform\n        - aws\n        - azure\n        - gcp\n</code></pre> <ul> <li>The <code>DFStore</code> now uses <code>AWSDFStore</code> as its concrete implementation class.</li> <li>Both <code>CachedMeta</code> and <code>AWSAccountClamp</code> have had a revamp of their singleton logic.</li> </ul>"},{"location":"release_notes/0_8_50/#internal-caching","title":"Internal Caching","text":"<p>So as part of our v0.9.0 Roadmap we're continuing to revamp caching. We're experimenting with CachedMeta Class inside the Artifact classes. Caching continues to be challenging for the framework, it's an absolute must for Web Inferface/UI performance and then it needs to get out of the way for batch jobs and the concurrent building of ML pipelines.</p>"},{"location":"release_notes/0_8_50/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.46 --&gt; v0.8.50 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/0_8_50/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_55/","title":"Release 0.8.55","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.50</code> to <code>0.8.55</code></p>"},{"location":"release_notes/0_8_55/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_55/#featureset-training-column-values","title":"FeatureSet: training column values","text":"<p>We're got a good suggestion from one of our beta customers to change the training column to use True/False values instead of 1/0. Having boolean values make semantic sense and make filtering easier and more intuitive.</p>"},{"location":"release_notes/0_8_55/#api-changes","title":"API Changes","text":"<p>FeatureSet Queries</p> <p>Since the training column now contains True/False, any code that you have where you're doing a query against the training view.</p> <pre><code>fs.query(f'SELECT * FROM \"{table}\" where training = 1')\n&lt;changed to&gt;\nfs.query(f'SELECT * FROM \"{table}\" where training = TRUE')\n\nfs.query(f'SELECT * FROM \"{table}\" where training = 0')\n&lt;changed to&gt;\nfs.query(f'SELECT * FROM \"{table}\" where training = FALSE')\n</code></pre> <p>Also dataframe filtering is easier now, so if you have a call to filter the dataframe that also needs to change.</p> <p><pre><code>df_train = all_df[all_df[\"training\"] == 1].copy()\n&lt;changed to&gt;\ndf_train = all_df[all_df[\"training\"]].copy()\n\ndf_val = all_df[all_df[\"training\"] == 0].copy()\n&lt;changed to&gt;\ndf_val = all_df[~all_df[\"training\"]].copy()\n</code></pre> For more details see: Training View</p> <p>Model Instantiation</p> <p>We got a request to reduce the time for Model() object instantiation. So we created a new <code>CachedModel()</code> class that is much faster to instantiate.</p> <p><pre><code>%time Model(\"abalone-regression\")\nCPU times: user 227 ms, sys: 19.5 ms, total: 246 ms\nWall time: 2.97 s\n\n%time CachedModel(\"abalone-regression\")\nCPU times: user 8.83 ms, sys: 2.64 ms, total: 11.5 ms\nWall time: 22.7 ms\n</code></pre> For more details see: CachedModel</p>"},{"location":"release_notes/0_8_55/#improvements","title":"Improvements","text":"<p>Workbench REPL Onboarding</p> <p>At some point the onboarding with Workbench REPL got broken and wasn't properly responding when the user didn't have a complete AWS/Workbench setup.</p>"},{"location":"release_notes/0_8_55/#internal-changes","title":"Internal Changes","text":"<p>The decorator for the CachedMeta class did not work properly in Python 3.9 so had to be slightly refactored.</p>"},{"location":"release_notes/0_8_55/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.50 --&gt; v0.8.55 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/0_8_55/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_58/","title":"Release 0.8.58","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.55</code> to <code>0.8.58</code></p>"},{"location":"release_notes/0_8_58/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_58/#caching-refactoring","title":"Caching Refactoring","text":"<p>We've created a new set of Cached Classes:</p> <ul> <li>CachedDataSource</li> <li>CachedFeatureSet</li> <li>CachedModel</li> <li>CacheEndpoint</li> </ul> <p>As part of this there's now a <code>workbench/cached</code> directory that housed these classes and the <code>CachedMeta</code> class.</p>"},{"location":"release_notes/0_8_58/#api-changes","title":"API Changes","text":"<p>Meta Imports Yes, this changed AGAIN :)</p> <pre><code>from workbench.meta import Meta\n&lt;change to&gt;\nfrom workbench.api import Meta\n</code></pre> <p>CachedModel Import <pre><code>from workbench.api import CachedModel\n&lt;change to&gt;\nfrom workbench.cached.cached_model import CachedModel\n</code></pre> For more details see: CachedModel</p>"},{"location":"release_notes/0_8_58/#improvements","title":"Improvements","text":"<p>Dashboard Responsiveness</p> <p>The whole point of these Cached Classes is to improve Dashboard/Web Interface responsiveness. The Dashboard uses both the CachedMeta and Cached(Artifact) classes to make both overview and drilldowns faster.</p> <p>Supporting a Client Use Case There was a use case where a set of plugin pages needed to iterate over all the models to gather and aggregate information. We've supported that use case with a new decorator that avoids overloading AWS/Throttling issues.</p> <p>Internal The Dashboard now refreshes all data every 90 seconds, so if you don't see you're new model on the dashboard... just wait longer. :)</p>"},{"location":"release_notes/0_8_58/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.55 --&gt; v0.8.58 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call a nonsense meeting?       .... Moo-larkey</p>"},{"location":"release_notes/0_8_58/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_60/","title":"Release 0.8.60","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.58</code> to <code>0.8.60</code></p>"},{"location":"release_notes/0_8_60/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_60/#custom-models","title":"Custom Models","text":"<p>We've now exposed additional functionality and API around adding your own custom models. The new custom model support is documented on the Features to Models page.  </p>"},{"location":"release_notes/0_8_60/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/0_8_60/#notes","title":"Notes","text":"<p>Custom models introduce models that don't have model metrics or inference runs, so you'll see a lot of log messages complaining about not finding metrics or inference results, please just ignore those, we'll put in additional logic to address those cases.</p>"},{"location":"release_notes/0_8_60/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.58 --&gt; v0.8.60 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call a nonsense meeting?       .... Moo-larkey</p>"},{"location":"release_notes/0_8_60/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_71/","title":"Release 0.8.71","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.60</code> to <code>0.8.71</code></p>"},{"location":"release_notes/0_8_71/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_71/#dash-thread-safety","title":"Dash Thread Safety","text":"<p>We learned that thread safety is good when using plugin classes. We had a model plugin class that was setting an attribute in one callback and then using that attribute in another callback, this mostly worked until it didn't. Anyway so the Inference Run dropdown box on the Models page now actually works correctly.</p>"},{"location":"release_notes/0_8_71/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/0_8_71/#internal-changes","title":"Internal Changes","text":"<p>When using PandasToFeatures it will overwrite FeatureSets if you give the same name. This behavior is expected. The issue was that it was super eager about doing that and would do it during class initiation, so we've moved that logic to when <code>transform()</code> is called.</p> <pre><code># Create a Feature Set from a DataFrame\ndf_to_features = PandasToFeatures(\"test_features\")\ndf_to_features.set_input(data_df, id_column=\"id\", one_hot_columns=[\"food\"])\ndf_to_features.set_output_tags([\"test\", \"small\"])\ndf_to_features.transform()      &lt;--- Overwrite happens here\n</code></pre>"},{"location":"release_notes/0_8_71/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.60 --&gt; v0.8.71 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call that feeling like you\u2019ve done this before?               Deja-moo</p>"},{"location":"release_notes/0_8_71/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_74/","title":"Release 0.8.74","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.71</code> to <code>0.8.74</code></p>"},{"location":"release_notes/0_8_74/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_74/#s3-based-plugins","title":"S3 based Plugins","text":"<p>We've added the ability to grab Dashboard plugins directly from an S3 bucket. Please see Dashboard S3 Plugin</p>"},{"location":"release_notes/0_8_74/#artifact-hashes","title":"Artifact Hashes","text":"<p>For content verification purposes we've added a <code>hash()</code> method to all of the Workbench Artifact classes (DataSource, FeatureSet, Model, Endpoint, Graph, etc). Also for DataSources and FeatureSets there is a <code>table_hash()</code> method that will compute a total hash of all data in the Athena table.</p> <pre><code>ds = DataSource(\"abalone_data\")\n\nds.modified()\nOut[2]: datetime.datetime(2024, 11, 17, 19, 45, 58, tzinfo=tzlocal())\n\nds.hash()\nOut[3]: '67a9ebb495af573604794aa9c31eded8'\n\nds.table_hash()\nOut[4]: '622f5ddba9d4cad2cf642d1ea5555de9'\n\nfs = FeatureSet(\"test_features\")\n\nfs.hash()\nOut[5]: '1571eee207b72f14bd5065d6c4acdaaf'\n\n# Note: Model/Endpoint hashes will backtrack to model.tar.gz and can be used for validation\nmodel = Model(\"abalone-regression\")\nend = Endpoint(\"abalone-regression-end\")\n\nmodel.get_model_data_url()\nOut[6]: 's3://sagemaker-us-west-2-507740646243/abalone-regression-2024-11-18-03-09/output/model.tar.gz'\n\nmodel.hash()\nOut[7]: '00def9381366cdd062413d0b395ba70c'\n\n# Verify endpoint is using expected model\nend.hash()\nOut[7]: '00def9381366cdd062413d0b395ba70c'\n\n# Realtime endpoint created from the same model\nend = Endpoint(\"abalone-regression-end-rt\")\nend.hash()\nOut[8]: '00def9381366cdd062413d0b395ba70c'\n</code></pre> <p>Note: You will get a performance warning when running hash() or table_hash() as these operations compute actual content hashes on the underlying data.</p>"},{"location":"release_notes/0_8_74/#custom-model-scripts","title":"Custom Model Scripts","text":"<p>We're expanding our custom model script to include some additional chemical informatics capabilities. In addition to our molecular descriptors and Morgan fingerprints we also have RDKIT canonicalization and tautomerization.</p>"},{"location":"release_notes/0_8_74/#api-changes","title":"API Changes","text":"<p>FeatureSet</p> <p>When creating a FeatureSet the <code>id_column</code> is now NOT required. The old \"auto\" functionality is gone, if you don't specify an <code>id_column</code> and id column named <code>auto_id</code> will be automatically generated.</p> <pre><code>ds = DataSource(\"test_data\")\nfs = ds.to_features(\"test_features\", id_column=\"auto\")\n&lt;replaced by&gt;\nfs = ds.to_features(\"test_features\")\n\n# We do recommend that you specify an id :)\nfs = ds.to_features(\"test_features\", id_column=\"my_id\")\n</code></pre> <ul> <li> <p><code>get_database()</code> has a deprecation warning, it's replaced with just the <code>database</code> property.</p> <pre><code>ds.get_database()\n&lt;replaced by&gt;\nds.database\n</code></pre> </li> <li> <p>Added the <code>hash()</code> method to Artifacts (see above).</p> </li> <li>Added <code>table_hash()</code> method to DataSources and FeatuerSet (see above).</li> </ul>"},{"location":"release_notes/0_8_74/#internal-changes","title":"Internal Changes","text":"<p>There was a small refactor of the cache decorator. We fixed a case where if we blocked on getting a value we also spun up a background thread to get it. This chance will not affect existing code or APIs.</p>"},{"location":"release_notes/0_8_74/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.71 --&gt; v0.8.74 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call that feeling like you\u2019ve done this before?               Deja-moo</p>"},{"location":"release_notes/0_8_74/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/0_8_78/","title":"Release 0.8.78","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.74</code> to <code>0.8.78</code></p>"},{"location":"release_notes/0_8_78/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/0_8_78/#s3-based-plugins","title":"S3 based Plugins","text":"<p>We've added the ability to grab Dashboard plugins directly from an S3 bucket. Please see Dashboard S3 Plugin</p>"},{"location":"release_notes/0_8_78/#tbd-stuff","title":"TBD Stuff","text":""},{"location":"release_notes/0_8_78/#api-changes","title":"API Changes","text":""},{"location":"release_notes/0_8_78/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.74 --&gt; v0.8.78 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call that feeling like you\u2019ve done this before?               Deja-moo</p>"},{"location":"release_notes/0_8_78/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_7_8/","title":"Release 0.7.8","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on [Discord](https://discord.gg/WHAJuz8sw8</p> <p>Since we've recently introduced a View() class for DataSources and FeatureSets we needed to rename a few classes/modules.</p>"},{"location":"release_notes/archived/0_7_8/#featuresets","title":"FeatureSets","text":"<p>For setting holdout ids we've changed/combined to just one method <code>set_training_holdouts()</code>, so if you're using <code>create_training_view()</code> or <code>set_holdout_ids()</code> you can now just use the unified method <code>set_training_holdouts()</code>.</p> <p>There's also a change to getting the training view table method.</p> <pre><code>old: fs.get_training_view_table(create=False)\nnew: fs.get_training_view_table(), does not need the create=False\n</code></pre>"},{"location":"release_notes/archived/0_7_8/#models","title":"Models","text":"<pre><code>inference_predictions() --&gt; get_inference_predictions()\n</code></pre>"},{"location":"release_notes/archived/0_7_8/#webplugins","title":"Web/Plugins","text":"<p>We've changed the Web/UI View class to 'WebView'. So anywhere where you used to have view just replace with web_view</p> <p><pre><code>from workbench.views.artifacts_view import ArtifactsView\n</code></pre> is now</p> <pre><code>from workbench.web_interface.page_views.all_artifacts import AllArtifacts\n</code></pre>"},{"location":"release_notes/archived/0_7_8/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_11/","title":"Release 0.8.11","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover all the changes from <code>0.8.8</code> to <code>0.8.11</code></p>"},{"location":"release_notes/archived/0_8_11/#general","title":"General","text":"<p>The AWSAccountClamp had too many responsibilities so that class has been split up into two classes and a set of utilities:</p> <ul> <li>AWSAccountClamp</li> <li>AWSSession</li> <li>utils/execution_environment.py</li> </ul>"},{"location":"release_notes/archived/0_8_11/#api-changes","title":"API Changes","text":"<p>For all/most of these API changes they include both DataSources and FeatureSets. We're using a FeatureSet (fs) in the examples below but also applies to DataSoources.</p> <ul> <li> <p>Column Names/Table Names</p> <pre><code>fs.column_names() -&gt; fs.columns\nfs.get_table_name() -&gt; fs.table_name\n</code></pre> </li> <li> <p>Display/Training/Computation Views</p> <p>In general methods for FS/DS are now part of the View API, here's a change list:</p> <pre><code>fs.get_display_view() -&gt; fs.view(\"display\")\nfs.get_training_view() -&gt; fs.view(\"training\")\nfs.get_display_columns() -&gt; fs.view(\"display\").columns\nfs.get_computation_columns() -&gt; fs.view(\"computation\").columns\nfs.get_training_view_table() -&gt; fs.view(\"training\").table_name\nfs.get_training_data(self) -&gt; fs.view(\"training\").pull_dataframe()\n</code></pre> <p>Some FS/DS methods have also been removed</p> <p><code>num_display_columns() -&gt; gone    num_computation_columns() -&gt; gone</code></p> </li> <li> <p>Views: Methods that we're Keeping</p> <p>We're keeping the methods below since they handle some underlying mechanics and serve as nice convenience methods. </p> <pre><code>ds/fs.set_display_columns()\nds/fs.set_computation_columns()\n</code></pre> </li> <li> <p>AWSAccountClamp</p> <pre><code>AWSAccountClamp().boto_session() --&gt; AWSAccountClamp().boto3_session\n</code></pre> </li> <li> <p>All Classes</p> <p>If the class previously had a <code>boto_session</code> attribute that has been renamed to <code>boto3_session</code></p> </li> </ul>"},{"location":"release_notes/archived/0_8_11/#glue-job-fixes","title":"Glue Job Fixes","text":"<p>For <code>workbench==0.8.8</code> you needed to be careful about when/where you set your config/ENV vars. With <code>&gt;=0.8.9</code> you can now use the typical setup like this:</p> <pre><code>```\nfrom workbench.utils.config_manager import ConfigManager\n\n# Set the Workbench Config\ncm = ConfigManager()\ncm.set_config(\"WORKBENCH_BUCKET\", args_dict[\"workbench-bucket\"])\ncm.set_config(\"REDIS_HOST\", args_dict[\"redis-host\"])\n```\n</code></pre>"},{"location":"release_notes/archived/0_8_11/#robust-modelnotreadyexception-handling","title":"Robust ModelNotReadyException Handling","text":"<p>AWS will 'deep freeze' Serverless Endpoints and if that endpoint hasn't been used for a while it can sometimes take a long time to come up and be ready for inference. Workbench now properly manages this AWS error condition, it will report the issue, wait 60 seconds, and try again 5 times before raising the exception.</p> <pre><code>(endpoint_core.py:502) ERROR Endpoint model not ready\n(endpoint_core.py:503) ERROR Waiting and Retrying...\n...\nAfter a while, inference will run successfully :)\n</code></pre>"},{"location":"release_notes/archived/0_8_11/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_20/","title":"Release 0.8.20","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.11</code> to <code>0.8.20</code></p>"},{"location":"release_notes/archived/0_8_20/#general","title":"General","text":"<p>The <code>cloud_watch</code> AWS log aggregator, is now officially awesome. It provides a fairly sophisticated way of both doing broad scanning and deep dives on individual streams. Please see our Cloud Watch documentation.</p> <p>The View classes have finished their refactoring. The 'read' class <code>View()</code> can be constructed either directly or with the <code>ds/fs.view(\"display\")</code> methods. See Views for more details. There also a set of classes for constructing views, please see View Overview</p>"},{"location":"release_notes/archived/0_8_20/#api-changes","title":"API Changes","text":"<ul> <li> <p>Table Name attribute</p> <p>The <code>table_name</code> attribute/property has been replaced with just <code>table</code></p> <pre><code>ds.table_name -&gt; ds.table\nfs.table_name -&gt; fs.table\nview.table_name -&gt; view.table\n</code></pre> </li> <li> <p>Endpoint Confusion Matrix</p> <p>The <code>endpoint</code> class had a method called <code>confusion_matrix()</code> this has been renamed to the more descriptive <code>generate_confusion_matrix()</code>. Note: The model method, of the same name, has NOT changed.</p> <pre><code>end.confusion_matrix() -&gt; end.generate_confusion_matrix()\nmodel.confusion_matrix() == no change\n</code></pre> </li> </ul>"},{"location":"release_notes/archived/0_8_20/#bug-fixes","title":"Bug Fixes","text":"<p>Fixed: There was a corner case where if you had the following sequence:</p> <ul> <li>New FeatureSet without a training view</li> <li>Created a traiing view (like with <code>set_training_holdouts()</code> </li> <li>Immediately query/pull from the FS training view</li> </ul> <p>The corner case was a race-condition where the FeatureSet would not 'know' that a training view was already there and would create a default training view.</p>"},{"location":"release_notes/archived/0_8_20/#improvements","title":"Improvements","text":"<p>The log messages that you receive on a plugin validation failure should now be more distinguishable and more informative. They will look like this and in some cases even tell you the line to look at.</p> <pre><code>ERROR Plugin 'MyPlugin' failed validation:\nERROR    File: ../workbench_plugins/components/my_plugin.py\nERROR    Class: MyPlugin\nERROR    Details: my_plugin.py (line 35): Incorrect return type for update_properties (expected list, got Figure)\n</code></pre>"},{"location":"release_notes/archived/0_8_20/#internal-api-changes","title":"Internal API Changes","text":"<p>In theory these API should not affect end user of the Workbench API but are documented here for completeness.</p> <ul> <li>Artifact Name Validation</li> </ul> <p>The internal method used by Artifact subclasses has changed names from <code>ensure_valid_name</code> to <code>is_name_valid</code>, we've also introduced an optional argument to turn on/off lowercase enforcement, this will be used later when we support uppercase for models, endpoints, and graphs.</p>"},{"location":"release_notes/archived/0_8_20/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_22/","title":"Release 0.8.22","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.20</code> to <code>0.8.22</code></p>"},{"location":"release_notes/archived/0_8_22/#general","title":"General","text":"<p>Mostly bug fixes and minor API changes.</p>"},{"location":"release_notes/archived/0_8_22/#api-changes","title":"API Changes","text":"<ul> <li> <p>Removing <code>target_column</code> arg when creating FeatureSets</p> <p>When creating a FeatureSet via DataSource or Pandas Dataframe there was an optional argument for the <code>target_column</code> after some discussion we decided to remove this argument. In general <code>FeatureSets</code> are often used to create multiple models with different targets, so it doesn't make sense to specify a <code>target</code> at the FeatureSet level.</p> <p>Changed for both <code>DataSource.to_features()</code> and the <code>PandasToFeatures()</code> classes.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_22/#minor-bug-fixes","title":"Minor Bug Fixes","text":"<p>Fixed: The SHAP computation was occasionally complaining about the additivity check so we flipped that flag to False</p> <pre><code>shap_vals = explainer.shap_values(X_pred, check_additivity=False)\n</code></pre>"},{"location":"release_notes/archived/0_8_22/#improvements","title":"Improvements","text":"<p>The optional requirements for <code>[UI]</code> now include matplotlib since it will probably be useful in the future.</p>"},{"location":"release_notes/archived/0_8_22/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_23/","title":"Release 0.8.23","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.22</code> to <code>0.8.23</code></p>"},{"location":"release_notes/archived/0_8_23/#general","title":"General","text":"<p>Mostly bug fixes and minor API changes.</p>"},{"location":"release_notes/archived/0_8_23/#api-changes","title":"API Changes","text":"<ul> <li> <p>Removing <code>auto_one_hot</code> arg from <code>PandasToFeatures</code> and <code>DataSource.to_features()</code></p> <p>When creating a <code>PandasToFeatures</code> object or using <code>DataSource.to_features()</code> there was an optional argument <code>auto_one_hot</code>. This would try to automatically convert object/string columns to be one-hot encoded. In general this was only useful for 'toy' datasets but for more complex data we need to specify exactly which columns we want converted.</p> </li> <li> <p>Adding optional <code>one_hot_columns</code> arg to <code>PandasToFeatures.set_input()</code> and <code>DataSource.to_features()</code> </p> <p>When calling either of these FeatureSet creation methods you can now add an option arg <code>one_hot_columns</code> as a list of columns that you would like to be one-hot encoded.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_23/#minor-bug-fixes","title":"Minor Bug Fixes","text":"<p>Our pandas dependency was outdated and causing an issue with an <code>include_groups</code> arg when outlier groups were computed. We've changed the requirements:</p> <p><pre><code>pandas&gt;=2.1.2\nto\npandas&gt;=2.2.1\n</code></pre> We also have a ticket for the logic change so that we avoid the deprecation warning.</p>"},{"location":"release_notes/archived/0_8_23/#improvements","title":"Improvements","text":"<p>The time to <code>ingest</code> new rows into a FeatureSet can take a LONG time. Calling the FeatureGroup AWS API and waiting on the results is what takes all the time.</p> <p>There will hopefully be a series of optimizations around this process, the first one is simply increasing the number of workers/processes for the ingestion manager class.</p> <pre><code>feature_group.ingest(.., max_processes=8)\n(has been changed to)\nfeature_group.ingest(..., max_processes=16, num_workers=4)\n</code></pre>"},{"location":"release_notes/archived/0_8_23/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_27/","title":"Release 0.8.27","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.23</code> to <code>0.8.27</code></p>"},{"location":"release_notes/archived/0_8_27/#general","title":"General","text":"<ul> <li>Plugin Launcher: A flexible way to test plugins</li> <li>FeatureSpaceProximity (FSP): A Class for investigating feature space, neighbors, distances, etc.</li> <li>ProximityGraph: A class to construct NetworkX Graphs. Uses the FSP class to pull neighbors, contruct edges via 'distances', etc.</li> <li>Case</li> </ul>"},{"location":"release_notes/archived/0_8_27/#api-changes","title":"API Changes","text":"<ul> <li> <p>KNNSpider() --&gt; FeatureSpaceProximity()</p> <p>If you were previously using the <code>KNNSpider</code> that class has been replaced with <code>FeatureSpaceProximity</code>. The API is also a bit different please see the documentation on the FeatureSpaceProximity Class.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_27/#minor-improvements","title":"Minor Improvements","text":"<p>The model scripts used in deployed AWS Endpoints are now case-insensitive. In general this should make the use of the endpoints a bit more flexible for End-User Applications to hit the endpoints with less pre-processing of their column names.</p> <p>CloudWatch default buffers have been increased to 60 seconds as we appears to have been hitting some AWS limits with running 10 concurrent glue jobs.</p>"},{"location":"release_notes/archived/0_8_27/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_29/","title":"Release 0.8.29","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.27</code> to <code>0.8.29</code></p>"},{"location":"release_notes/archived/0_8_29/#general","title":"General","text":"<p>Locking AWS Model Training Image: AWS will randomly update the images associated with training and model registration. In particular the SKLearn Estimator has been updated into a non-working state for our use cases. So for both training and registration we're now explicitly specifying the image that we want to use.</p> <pre><code> self.estimator = SKLearn(\n     ...\n     framework_version=\"1.2-1\",\n     image_uri=image,  # New\n )\n</code></pre>"},{"location":"release_notes/archived/0_8_29/#api-changes","title":"API Changes","text":"<ul> <li> <p>delete() --&gt; class.delete(uuid)</p> <p>We've changed the API for deleting artifacts in AWS (DataSource, FeatureSet, etc). This is part of our efforts to minimize race-conditions when objects are deleted. </p> <pre><code>my_model = Model(\"xyz\")  # Creating object\nmy_model.delete()        # just to delete\n\n&lt;Now just one line&gt;\nModel.delete(\"xyz\")      # Delete\n</code></pre> </li> </ul>"},{"location":"release_notes/archived/0_8_29/#minor-improvements","title":"Minor Improvements","text":"<p>Bulk Delete: Added a Bulk Delete utility</p> <pre><code>from workbench.utils.bulk_utils import bulk_delete\n\ndelete_list = [(\"DataSource\", \"abc\"), (\"FeatureSet\", \"abc_features\")]\nbulk_delete(delete_list)\n</code></pre>"},{"location":"release_notes/archived/0_8_29/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_33/","title":"Release 0.8.33","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.29</code> to <code>0.8.33</code></p>"},{"location":"release_notes/archived/0_8_33/#general","title":"General","text":"<p>Replaced WatchTower Code: Had lots of issues with WatchTower on Glue/Lambda, the use of forks/threads was overkill for our logging needs, so simply replaced the code with boto3 <code>put_log_events()</code> calls and some simple token handling and buffering.</p>"},{"location":"release_notes/archived/0_8_33/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_33/#improvementsfixes","title":"Improvements/Fixes","text":"<p>DataSource from DataFrame: When creating a DataSource from a Pandas Dataframe, the internal <code>transform()</code> was not deleting the existing DataSource (if it existed).</p> <p>ROCAUC on subset of classes: When running inference on input data that only had a subset of the classification labels (e.g. rows only had \"low\" and \"medium\" when model was trained on \"low\", \"medium\", \"high\"). The input to ROCAUC needed to be adjusted so that ROCAUC doesn't crash. When this case happens we're returning proper defaults based on scikit learn docs.</p>"},{"location":"release_notes/archived/0_8_33/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_35/","title":"Release 0.8.35","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.33</code> to <code>0.8.35</code></p>"},{"location":"release_notes/archived/0_8_35/#general","title":"General","text":"<p>Workbench REPL: The REPL now has a workaround for the current iPython embedded shell namespace scoping issue. See: iPython Embedded Shell Scoping Issue. So this pretty much means the REPL is 110% more awesome now!</p>"},{"location":"release_notes/archived/0_8_35/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_35/#improvementsfixes","title":"Improvements/Fixes","text":"<p>AWS Service Broker: The AWS service broker was dramatic when it pulls meta data for something that just got deleted (or partially deleted), it was throwing CRITICAL log messages. We've refined the AWS error handling so that it's more granular about the error_codes for Validation or ResourceNotFound exceptions those are reduced to WARNINGS.</p> <p>ROCAUC modifications: Version <code>0.8.33</code> put in quite a few changes, for <code>0.8.35</code> we've also added logic to both validate and ensure proper order of the probability columns with the class labels.</p>"},{"location":"release_notes/archived/0_8_35/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.33 --&gt; v0.8.35 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What do you call a cow with no legs?     ........Ground beef.</p>"},{"location":"release_notes/archived/0_8_35/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_36/","title":"Release 0.8.36","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.35</code> to <code>0.8.36</code></p>"},{"location":"release_notes/archived/0_8_36/#general","title":"General","text":"<p>Fast Inference: The current inference method for endpoints provides error handling, metrics calculations and capture mechanics. There are use cases where the inference needs to happen as fast as possible without all the additional features. So we've added a <code>fast_inference()</code> method that streamlines the calls to the endpoint.</p> <pre><code>end = Endpoint(\"my_endpoint\")\nend.inference(df)  # Metrics, Capture, Error Handling\nWall time: 5.07 s\n\nend.fast_inference(df)  # No frills, but Fast!\nWall time: 308 ms\n</code></pre>"},{"location":"release_notes/archived/0_8_36/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_36/#improvementsfixes","title":"Improvements/Fixes","text":"<p>Version Update Check: Added functionality that checks the current Workbench version against the latest released and gives a log message for update available.</p> <p>ROCAUC modifications: Functionality now includes 'per label' rocauc calculation along with label order and alignment from previous versions.</p>"},{"location":"release_notes/archived/0_8_36/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.35 --&gt; v0.8.36 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>What\u2019s a cow\u2019s best subject in school?     ......Cow-culus.</p>"},{"location":"release_notes/archived/0_8_36/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_39/","title":"Release 0.8.39","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.36</code> to <code>0.8.39</code></p>"},{"location":"release_notes/archived/0_8_39/#general","title":"General","text":"<p>Just a small set of error handling and bug fixes.</p>"},{"location":"release_notes/archived/0_8_39/#api-changes","title":"API Changes","text":"<p>None</p>"},{"location":"release_notes/archived/0_8_39/#improvementsfixes","title":"Improvements/Fixes","text":"<p>Scatter Plot: Fixed a corner case where the hoover columns included AWS generated fields.</p> <p>Athena Queries: Put in additional error handling and retries when looking for and querying Athena/Glue Catalogs. These changes affect both DataSource and Features (which have DataSources internally for offline storage).</p> <p>FeatureSet Creation: Put in additional error handling and retries when pulling AWS meta data for FeatureSets (and internal DataSources).</p>"},{"location":"release_notes/archived/0_8_39/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.36 --&gt; v0.8.39 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/archived/0_8_39/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_42/","title":"Release 0.8.42","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.39</code> to <code>0.8.42</code></p>"},{"location":"release_notes/archived/0_8_42/#general","title":"General","text":"<p>Artifact deletion got a substantial overhaul. The 4 main classes received internal code changes for how they get deleted. Specifically deletion is now handled via a class method that allows an artifact to be delteed without instantiating an object. The API for deletion is actually more flexible now, please see API Changes below.</p>"},{"location":"release_notes/archived/0_8_42/#api-changes","title":"API Changes","text":"<p>Artifact Deletion</p> <p>The API for Artifact deletion is more flexible, if you already have an instantiated object, you can simply call <code>delete()</code> on it. If you're deleting an object in bulk/batch mode, you can call the class method <code>managed_delete()</code>, see code example below.</p> <p><pre><code>fs = FeatureSet(\"my_fs\")\nfs.delete()                        # Used for notebooks, scripts, etc.. \nOR\nFeatureSet.managed_delete(\"my_fs\") # Bulk/batch/internal use\n\n&lt;Same API for DataSources, Models, and Endpoints&gt;\n</code></pre> Note: Internally these use the same functionality, the dual API is simply for ease-of-use.</p>"},{"location":"release_notes/archived/0_8_42/#improvementsfixes","title":"Improvements/Fixes","text":"<p>Race Conditions</p> <p>In theory, the changes to a class based delete will reduce race conditions where an object would try to create itself (just to be deleted) and the AWS Service Broker was encountering partially created (or partially deleted objects) and would barf error messages.</p> <p>Slightly Better Throttling Logic</p> <p>The AWS Throttles have been 'tuned' a bit to back off a bit faster and also not retry the list_tags request when the ARN isn't found.</p>"},{"location":"release_notes/archived/0_8_42/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.39 --&gt; v0.8.42 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/archived/0_8_42/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_46/","title":"Release 0.8.46","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p> <p>Note: These release notes cover the changes from <code>0.8.42</code> to <code>0.8.46</code></p>"},{"location":"release_notes/archived/0_8_46/#general","title":"General","text":"<p>This release is an incremental release as part of the road map for <code>v.0.9.0</code>. Please see the full details of the planned changes here: v0.9.0 Roadmap. </p>"},{"location":"release_notes/archived/0_8_46/#new-deprecation-warnings","title":"New Deprecation Warnings","text":"<p>We're starting to put in deprecation warning as we streamline classes and APIs. If you're using a class or method that's going to be deprecated you'll see a log message like this:</p> <pre><code>my_class = SomeOldClass()\nWARNING SomeOldClass is deprecated and will be removed in version 0.9.\n</code></pre> <p>In general these warning messages will be annoying but they will help us smoothly transistion and streamline our Classes and APIs.</p>"},{"location":"release_notes/archived/0_8_46/#deprecations","title":"Deprecations","text":"<ul> <li>AWSServiceBroker: Replaced by the Meta() class</li> </ul>"},{"location":"release_notes/archived/0_8_46/#api-changes","title":"API Changes","text":"<p>Meta()</p> <p>The new <code>Meta()</code> class will provide API that aligns with the AWS <code>list</code> and <code>describe</code> API. We'll have functionality for listing objects (models, feature sets, etc) and then functionality around the details for a named artifact.</p> <pre><code>meta = Meta()\nmodels_list = meta.models()  # List API\nend_list = meta.endpoints()  # List API\n\nfs_dict = meta.feature_set(\"my_fs\") # Describe API\nmodel_dict = meta.model(\"my_model\") # Describe API\n</code></pre> <p>For more details see: Meta Class</p> <p>The new Meta() API will be used inside of the Artifact classes (see Internal Changes...Artifacts... below)</p> <p>Artifact Classes</p> <p>The artifact classes (DataSource, FeatureSet, Model, Endpoint) have had some old arguments removed.</p> <pre><code>DataSource(force_refresh=True)  -&gt; Gone (remove it)\nFeatureSet(force_refresh=True)  -&gt; Gone (remove it)\nModel(force_refresh=True)       -&gt; Gone (remove it)\nModel(legacy=True)              -&gt; Gone (remove it)\n</code></pre>"},{"location":"release_notes/archived/0_8_46/#improvements","title":"Improvements","text":"<p>Scalability</p> <p>The changes to caching and the Meta() class should allow better horizontal scaling, we'll flex out the stress tests for upcoming releases before <code>0.9.0</code>.</p> <p>Table Names starting with Numbers</p> <p>Some of the Athena queries didn't properly escape the tables names and if you created a DataSource/FeatureSet with a name that started with a number the query would fail. Fixed now. :)</p>"},{"location":"release_notes/archived/0_8_46/#internal-changes","title":"Internal Changes","text":"<p>Meta()</p> <p><code>Meta()</code> doesn't do any caching now. If you want to use Caching as part of your meta data retrieval use the <code>CachedMeta()</code> class.</p> <p>Artifacts</p> <p>We're got rid of most (soon all) caching for individual Artifacts, if you're constructing an artifact object, you probably want detailed information that's 'up to date' and waiting a bit is probably fine. Note: We'll still make these instantiations as fast as we can, removing the caching logic will as least simplify the implementations.</p>"},{"location":"release_notes/archived/0_8_46/#specific-code-changes","title":"Specific Code Changes","text":"<p>Code Diff v0.8.42 --&gt; v0.8.46 </p> <p>Who doesn't like looking at code! Also +3 points for getting down this far! Here's a cow joke as a reward:</p> <p>That feeling like you\u2019ve done this before?       .... Deja-moo</p>"},{"location":"release_notes/archived/0_8_46/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_6/","title":"Release 0.8.6","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines. We've also fixed various corner cases mostly around 'half constructed' AWS artifacts (models/endpoints).</p>"},{"location":"release_notes/archived/0_8_6/#additional-functionality","title":"Additional Functionality","text":"<ul> <li>View Support (phase 1)</li> <li>CloudWatch (phase 1)</li> <li>Exception Log Forwarding Exception Handling</li> <li>Lambda Layers Workbench Lambda Layers</li> <li>Better Docs for Deploying Plugins Deploying Plugins</li> </ul>"},{"location":"release_notes/archived/0_8_6/#issues-addressed","title":"Issues Addressed","text":"<ul> <li> <p>Model to Endpoint under AWS Throttle</p> <p>A corner case where the <code>to_endpoint()</code> method would fail when not 'knowing' the model input. This happened when AWS was throttling responses and the <code>get_input()</code> of the Endpoint returned <code>unknown</code> which caused a <code>NoneType</code> error when using the 'unknown' model.</p> </li> <li> <p>Empty Model Package Groups</p> <p>There are cases where customers might construct a Model Package Group (MPG) container and not put any Model Packages in that Group. Workbench has assumed that all MPGs would have at least one model package. The current 'support' for empty MPGs treats it as an error condition but the API tries to accommodate the condition and will properly display the model group. The group will indicate that it's 'empty' and provides an alert health icons.</p> </li> </ul>"},{"location":"release_notes/archived/0_8_6/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"release_notes/archived/0_8_8/","title":"Release 0.8.8","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>"},{"location":"release_notes/archived/0_8_8/#additional-functionality","title":"Additional Functionality","text":"<ul> <li>View Support (Views)</li> <li>CloudWatch (CloudWatch Docs)</li> </ul>"},{"location":"release_notes/archived/0_8_8/#api-changes","title":"API Changes","text":"<ul> <li> <p>Auto Inference name change</p> <p>When auto_inference is run on an endpoint the name of that inference run is currently <code>training_holdout</code>. That is too close to <code>model_training</code> and is confusing. So we're going to change the name to <code>auto_inference</code> which is way more explanatory and intuitive.</p> <p>Porting plugins: There should really not be any hard coding for <code>training_holdout</code>, plugins should just call <code>list_inference_runs()</code> (see below) and use the first one on the list.</p> </li> <li> <p><code>list_inference_runs()</code></p> <p>The <code>list_inference_runs()</code> method on Models has been improved. It now handles error states better (no model, no model training data) and will return 'model_training' LAST on the list, this should improve UX for plugin components.</p> </li> </ul> <p>Examples</p> <pre><code> model = Model(\"abalone-regression\")\n model.list_inference_runs()\n Out[1]: ['auto_inference', 'model_training']\n\n model = Model(\"wine-classification\")\n model.list_inference_runs()\n Out[2]: ['auto_inference', 'training_holdout', 'model_training']\n\n model = Model(\"aqsol-mol-regression\")\n model.list_inference_runs()\n Out[3]: ['training_holdout', 'model_training']\n\n model = Model(\"empty-model-group\")\n model.list_inference_runs()\n Out[4]: []\n</code></pre>"},{"location":"release_notes/archived/0_8_8/#glue-job-changes","title":"Glue Job Changes","text":"<p>We're spinning up the CloudWatch Handler much earlier now, so if you're setting config like this:</p> <pre><code>from workbench.utils.config_manager import ConfigManager\n\n# Set the Workbench Config\ncm = ConfigManager()\ncm.set_config(\"WORKBENCH_BUCKET\", args_dict[\"workbench-bucket\"])\ncm.set_config(\"REDIS_HOST\", args_dict[\"redis-host\"])\n</code></pre> <p>Just switch out that code for this code. Note: these need to be set before importing workbench</p> <pre><code># Set these ENV vars for Workbench \nos.environ['WORKBENCH_BUCKET'] = args_dict[\"workbench-bucket\"]\nos.environ[\"REDIS_HOST\"] = args_dict[\"redis-host\"]\n</code></pre>"},{"location":"release_notes/archived/0_8_8/#misc","title":"Misc","text":"<p>Confusion Matrix support for 'ordinal' labels</p> <p>Pandas has an \u2018ordinal\u2019 type, so the confusion matrix method <code>endpoint.confusion_matrix()</code> now checks the label column to see if it\u2019s ordinal and uses that order, if not just it will alphabetically sort. </p> <p>Note: This change may not affect your UI experience. Confusion matricies are saved in the Workbench/S3 meta data storage, so a bunch of stuff upstream will also need to happen. FeatureSet object/api for setting the label order, recreation of the model/endpoint and confustion matrix, etc. In general this is a forwarding looking change that will be useful later. :)</p>"},{"location":"release_notes/archived/0_8_8/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"repl/","title":"Workbench REPL","text":"<p>Visibility and Control</p> <p>The Workbench REPL provides AWS ML Pipeline visibility just like the Workbench Dashboard but also provides control over the creation, modification, and deletion of artifacts through the Python API.</p> <p>The Workbench REPL is a customized iPython shell. It provides tailored functionality for easy interaction with Workbench objects and since it's based on iPython developers will feel right at home using autocomplete, history, help, etc. Both easy and powerful, the Workbench REPL puts control of AWS ML Pipelines at your fingertips.</p>"},{"location":"repl/#installation","title":"Installation","text":"<p><code>pip install workbench</code></p>"},{"location":"repl/#usage","title":"Usage","text":"<p>Just type <code>workbench</code> at the command line and the Workbench shell will spin up and provide a command view of your AWS Machine Learning Pipelines.</p> <p>At startup the Workbench shell, will connect to your AWS Account and create a summary of the Machine Learning artifacts currently residing on the account.</p> <p></p> <p>Available Commands:</p> <ul> <li>status</li> <li>config</li> <li>incoming_data</li> <li>glue_jobs</li> <li>data_sources</li> <li>feature_sets</li> <li>models</li> <li>endpoints</li> <li>and more...</li> </ul> <p>All of the API Classes are auto-loaded, so drilling down on an individual artifact is easy. The same Python API is provided so if you want additional info on a model, for instance, simply create a model object and use any of the documented API methods.</p> <pre><code>m = Model(\"abalone-regression\")\nm.details()\n&lt;shows info about the model&gt;\n</code></pre>"},{"location":"repl/#additional-resources","title":"Additional Resources","text":"<ul> <li>Setting up Workbench on your AWS Account: AWS Setup</li> <li>Using Workbench for ML Pipelines: Workbench API Classes</li> </ul> <ul> <li>Workbench Core Classes: Core Classes</li> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"road_maps/0_9_0/","title":"Road Map v0.9.0","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>"},{"location":"road_maps/0_9_0/#general","title":"General","text":"<p>Streamlining</p> <p>We've learned a lot from our beta testers!</p> <p>One of the important lessons is not to 'over manage' AWS. We want to provide useful, granular Classes and APIs. Getting out of the way is just as important as providing functionality. So streamlining will be a big part of our <code>0.9.0</code> roadmap.</p> <p>Horizontal Scaling</p> <p>The framwork is currently struggling with 10 parallel ML pipelines being run concurrently. When running simultaneous pipelines we're seeing AWS service/query contention, throttling, and the occasional 'partial state' that we get back from AWS.</p> <p>Our plan for <code>0.9.0</code> is to have formalized horizontal stress testing that tests everything from 4 to 32 concurrent ML pipelines. Even though 32 may not seem like much, AWS has various quotas and limits that we'll be hitting, so 32 is a good goal for <code>0.9.0</code>. Obviously once we get to 32 we'll look forward to an architecture that will support 100's of concurrent pipelines.</p> <ul> <li>Feature Processor Quotas</li> <li>Sagemaker Endpoint Quotas</li> </ul> <p>Full Artifact Load or None</p> <p>For the Workbench\u2019 DataSource, FeatureSet, Model, and Endpoint classes the new functionality will ensure that objects are only instantiated when all required data is fully available, returning None if the artifact ID is invalid or if the object is only partially constructed in AWS. </p> <p>By preventing partially constructed objects, this approach reduces runtime errors when accessing incomplete attributes and simplifies error handling for clients, enhancing robustness and reliability. We are looking at Pydantic for formally capturing schema and types (see Pydantic).</p> <p>Onboarding</p> <p>We'll have to balance the 'full artifact or None' with the need to <code>onboard()</code> artifacts created outside of Workbench. We'll probably have a class method for onboarding, something like:</p> <pre><code>my_model = Model.onboard(\"some_random_model\")\nif my_model is None:\n    &lt;handle failure to onboard&gt;\n</code></pre> <p>Caching</p> <p>Caching needs a substantial overhaul. Right now Workbench over uses caching. We baked it into our AWSServiceBroker and that gets used by everything.</p> <p>Caching only really makes sense when we can't wait for AWS to respond to requests. The Dashboard and Web Interfaces are the only use case where responsiveness is important. Other use cases like nightly batch processing, scripts or notebooks, will work totally fine waiting for AWS responses.</p> <p>Class/API Reductions</p> <p>The organic growth of Workbench was based on user feedback and testing, that organic growth has led to an abundance of Classes and API calls. We'll be identifying classes and methods that are 'cruft' from some development push and will be deprecating those.</p>"},{"location":"road_maps/0_9_0/#deprecation-warnings","title":"Deprecation Warnings","text":"<p>We're starting to put in deprecation warning as we streamline classes and APIs. If you're using a class or method that's going to be deprecated you'll see a log message like this:</p> <pre><code>broker = AWSServiceBroker()\nWARNING AWSServiceBroker is deprecated and will be removed in version 0.9.\n</code></pre> <p>If you're using a class that's NOT going to be deprecated but currently uses/relies on one that is you'll still get a warning that you can ignore (developers will take care of it).</p> <pre><code># This class is NOT deprecated but an internal class is\nmeta = Meta() \nWARNING AWSServiceBroker is deprecated and will be removed in version 0.9.\n</code></pre> <p>In general these warning messages will be annoying but they will help us smoothly transistion and streamline our Classes and APIs.</p>"},{"location":"road_maps/0_9_0/#deprecations","title":"Deprecations","text":"<ul> <li>AWSServiceBroker: Replaced by the Meta() class</li> <li>Other Stuff</li> </ul>"},{"location":"road_maps/0_9_0/#api-changes","title":"API Changes","text":"<p>Meta()</p> <p>The new <code>Meta()</code> class will provide API that aligns with the AWS <code>list</code> and <code>describe</code> API. We'll have functionality for listing objects (models, feature sets, etc) and then functionality around the details for a named artifact.</p> <pre><code>meta = Meta()\nmodels_list = meta.models()  # List API\nend_list = meta.endpoints()  # List API\n\nfs_dict = meta.feature_set(\"my_fs\") # Describe API\nmodel_dict = meta.model(\"my_model\") # Describe API\n</code></pre> <p>The new Meta() API will be used inside of the Artifact classes (see Internal Changes...Artifacts... below)</p>"},{"location":"road_maps/0_9_0/#improvementsfixes","title":"Improvements/Fixes","text":"<p>FeatureSet</p> <p>When running concurrent ML pipelines we occasion get a partially constructed FeatureSet, FeatureSets will now 'wait and fail' if they detect partially constructed data (like offline storage not being ready).</p>"},{"location":"road_maps/0_9_0/#internal-changes","title":"Internal Changes","text":"<p>Meta()</p> <p>We're going to make a bunch of changes to <code>Meta()</code> specifically around more granular (LESS) caching. Also there will be an <code>AWSMeta()</code> subclass that manages the AWS specific API calls. We'll also put stubs in for <code>AzureMeta()</code> and <code>GCPMeta()</code>, cause hey we might have a client who really wants that flexibility.</p> <p>The new Meta class will also include API that's more aligned to the AWS <code>list</code> and <code>describe</code> interfacts. Allowing both broad and deep queries of the Machine Learning Artifacts within AWS.</p> <p>Artifacts</p> <p>We're getting rid of caching for individual Artifacts, if you're constructing an artifact object, you probably want detailed information that's 'up to date' and waiting a bit is probably fine. Note: We'll still make these instantiations as fast as we can, removing the caching logic will as least simplify the implementations.</p>"},{"location":"road_maps/0_9_0/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"road_maps/0_9_5/","title":"Road Map v0.9.5","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>The Workbench framework continues to flex to support different real world use cases when operating a set of production machine learning pipelines.</p>"},{"location":"road_maps/0_9_5/#general","title":"General","text":"<p>ML Pipelines</p> <p>We've learned a lot from our beta testers!</p> <p>One of the important lessons is that when you make it easier to build ML Pipelines the users are going to build lots of pipelines.</p> <p>For the creation, monitoring, and deployment of 50-100 of pipelines, we need to focus on the consoldation of artifacts into <code>Pipelines</code>. </p> <p>Pipelines are DAGs</p> <p>The use of Directed Acyclic Graphs for the storage and management of ML Pipelines will provide a good abstraction. Real world ML Pipelines will often branch multiple times, 1 DataSource may become 2 FeatureSets might become 3 Models/Endpoints. </p> <p>New Pipeline Dashboard Top Page</p> <p>The current main page shows all the individual artifacts, as we scale up to 100's models we need 2 additional levels of aggregation:</p> <ul> <li>Pipeline Groups<ul> <li>Group_1<ul> <li>Pipeline_1 (DS, FS, Model, Endpoint)</li> <li>Pipeline_2 (DS, FS, Model, Endpoint)</li> </ul> </li> <li>Group_2<ul> <li>Pipeline_3 (DS, FS...)</li> <li>Pipeline_4 (DS, FS...)</li> </ul> </li> </ul> </li> </ul> <p>New Pipeline Details Page</p> <p>When a pipeline is clicked on the top page, a Pipeline details page comes up for that specific pipeline. This page will give all relevant information about the pipeline, including model performance, monitoring, and endpoint status.</p> <p>Awesome image TBD</p>"},{"location":"road_maps/0_9_5/#versioned-artifacts","title":"Versioned Artifacts","text":"<p>Our beta customers have requested versioning for artifacts, so we support versioning for both Model and FeatureSets. Endpoints and DataSources typically do not need versioning, so we may wait on the versioning support for those artifact until a later version.</p>"},{"location":"road_maps/0_9_5/#questions","title":"Questions?","text":"<p>The SuperCowPowers team is happy to answer any questions you may have about AWS and Workbench. Please contact us at workbench@supercowpowers.com or on chat us up on Discord </p>"},{"location":"themes/","title":"Theme Support for Workbench","text":"<p>Need Help?</p> <p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at workbench@supercowpowers.com or on chat us up on Discord </p> <p>Workbench interfaces are going to have full support for theming and branding to enable customizations of components, and pages. The theming support will include all of the default pages/components but will also support full theming for plugin components and pages Workbench Plugins</p>"},{"location":"themes/#summary-of-theming-mechanisms","title":"Summary of Theming Mechanisms","text":""},{"location":"themes/#1-css-files","title":"1. CSS Files","text":"<p>CSS affects global styling of the entire application, including:</p> <ul> <li>Font styles and sizes.</li> <li>Layout styles (e.g., margins, padding, spacing).</li> <li>Background and text colors for non-Bootstrap or custom elements.</li> <li>Borders and shadows.</li> <li>Interactive element styling (e.g., hover effects, link colors).</li> <li>Dash custom components.</li> </ul>"},{"location":"themes/#2-plotly-figure-templates","title":"2. Plotly Figure Templates","text":"<p>Plotly templates control figure-specific styling for Plotly charts and graphs:</p> <ul> <li>Axis styling (e.g., line colors, grid lines, and ticks).</li> <li>Color scales for heatmaps, bar charts, and scatter plots.*</li> <li>Marker and line styles.</li> <li>Background colors for the figure and plot areas.</li> <li>Font styles inside the figures.</li> </ul>"},{"location":"themes/#3-dbc-components-lightdark","title":"3. DBC Components Light/Dark","text":"<p>The <code>data-bs-theme=light/dark</code> html attribute affects Bootstrap-specific components, primarily from Dash Bootstrap Components (DBC):</p> <ul> <li>Buttons.</li> <li>Tables.</li> <li>Forms (e.g., input boxes, checkboxes, radio buttons).</li> <li>Cards, modals, and alerts.</li> </ul>"},{"location":"themes/#key-differences","title":"Key Differences","text":"Aspect CSS Files Plotly Templates DBC Dark/Light Scope Global app styling Plotly figures DBC Components Level of Control Full Range Partial/Full* Just Light and Dark Dynamic Switching? Not easily (reloading needed) Can update dynamically Supports dynamic switching <ul> <li>Some figure parameters will automatically work, but for stuff like colorscales the component code needs to 'pull' that from the template meta data when it updates it's figure.</li> </ul>"},{"location":"themes/#additional-resources","title":"Additional Resources","text":"<p>Need help with plugins? Want to develop a customized application tailored to your business needs?</p> <ul> <li>Consulting Available: SuperCowPowers LLC</li> </ul>"},{"location":"themes/details/","title":"Understanding Bootstrap and DBC Theme Hooks in Dash","text":""},{"location":"themes/details/#overview","title":"Overview","text":"<p>Bootstrap and Dash Bootstrap Components (DBC) provide mechanisms to control themes and styling through CSS classes, CSS variables, and data attributes. These hooks enable consistent theming across applications.</p>"},{"location":"themes/details/#theme-hooks-overview","title":"Theme Hooks Overview","text":""},{"location":"themes/details/#1-css-classes","title":"1. CSS Classes","text":"<ul> <li>Description: Bootstrap provides pre-defined classes like <code>container</code>, <code>row</code>, <code>col</code>, <code>btn</code>, etc., for responsive layouts and component styling.</li> <li>Usage: DBC components automatically apply many of these classes, and you can also customize them using the <code>className</code> property.</li> </ul>"},{"location":"themes/details/#2-css-variables","title":"2. CSS Variables","text":"<ul> <li>Description: Bootstrap uses CSS variables (e.g., <code>--bs-body-bg</code>, <code>--bs-primary</code>) to allow flexible theming and styling.</li> <li>How They Work: These variables dynamically adjust based on the theme (e.g., light or dark) or can be customized via custom stylesheets.</li> <li>Common Variables:</li> <li><code>--bs-body-bg</code>: Background color.</li> <li><code>--bs-body-color</code>: Text color.</li> <li><code>--bs-border-color</code>: Border color.</li> </ul>"},{"location":"themes/details/#3-data-attributes","title":"3. Data Attributes","text":"<ul> <li>Description: The <code>data-bs-theme</code> attribute enables quick switching between light and dark themes.</li> <li>Usage: Setting <code>data-bs-theme=\"light\"</code> or <code>data-bs-theme=\"dark\"</code> on a root container (e.g., <code>&lt;body&gt;</code> or a main <code>html.Div</code>) dynamically adjusts Bootstrap variables globally.</li> </ul>"},{"location":"themes/details/#how-these-hooks-work-together","title":"How These Hooks Work Together","text":"<ol> <li>Class Names:</li> <li>Used in DBC components to apply Bootstrap styling.</li> <li> <p>Affects layout, grid, and overall component behavior.</p> </li> <li> <p>CSS Variables:</p> </li> <li>Allow fine-grained control over the visual appearance based on the <code>data-bs-theme</code>.</li> <li> <p>Ensure consistent styling for text, backgrounds, borders, etc.</p> </li> <li> <p>Data Attributes:</p> </li> <li>Act as a high-level toggle for light/dark theming, affecting all components globally.</li> <li>Automatically updates the values of Bootstrap CSS variables.</li> </ol>"},{"location":"themes/details/#notes-on-redundancy","title":"Notes on Redundancy","text":"<ul> <li>When using <code>data-bs-theme</code>, explicitly setting styles (e.g., <code>backgroundColor</code>) might be redundant since the Bootstrap CSS variables (e.g., <code>--bs-body-bg</code>) automatically adjust based on the selected theme.</li> <li>DBC components like <code>dbc.Container</code> do not automatically set <code>data-bs-theme</code> or specific class names, so you may need to explicitly define these for global theming.</li> </ul>"},{"location":"themes/details/#conclusion","title":"Conclusion","text":"<ul> <li>Use data attributes (<code>data-bs-theme</code>) for global light/dark switching.</li> <li>Leverage CSS variables (<code>--bs-*</code>) for consistent theming across components.</li> <li>Apply CSS classes (<code>className</code>) for additional customization or integration with custom styles.</li> </ul>"},{"location":"themes/details/#theming-strategy-for-workbench-dashboard","title":"Theming Strategy for Workbench Dashboard","text":""},{"location":"themes/details/#phase-1-application-start-up-theming","title":"Phase 1: Application Start-Up Theming","text":"<p>This focuses on setting a polished, cohesive interface at startup. It ensures:</p> <ol> <li>CSS and Plotly Templates at Start-Up:</li> <li>The current system handles global styles via CSS (including Flask-served <code>custom.css</code>) and Plotly templates (via <code>pio.templates.default</code>) during app initialization.</li> <li>Highly Customizable Interface:</li> <li>By leveraging both custom CSS and Plotly templates, you can style everything to match branding and preferences.</li> </ol>"},{"location":"themes/details/#why-phase-1-works-well","title":"Why Phase 1 Works Well","text":"<ul> <li>Plotly Templates: Figures are automatically styled if <code>pio.templates.default</code> is set before app initialization.</li> <li>CSS: Dynamically affects the entire app without additional callbacks.</li> <li>Scalability: Suitable for large apps since styles and templates are applied globally.</li> </ul>"},{"location":"themes/details/#phase-2-dynamic-theme-switching","title":"Phase 2: Dynamic Theme Switching","text":"<p>Dynamic theme switching allows users to toggle between themes (e.g., light and dark) at runtime. This is more complex, and splitting it into its own phase will reduce the complexity of each Phase.</p>"},{"location":"themes/details/#challenges-with-dynamic-switching","title":"Challenges with Dynamic Switching","text":"<ol> <li>Encapsulation Barriers:</li> <li>A centralized callback for theme switching needs to know about every figure or component in the app, which is not scalable.</li> <li>Dynamic Content/Plugins:</li> <li>We have plugin pages, views, and components. A Typical app has 20 figures dynamically created, adding complexity to centralized callbacks.</li> </ol>"},{"location":"themes/details/#proposed-solution-set_theme-method","title":"Proposed Solution: <code>set_theme()</code> Method","text":"<p>Introduce a <code>set_theme()</code> method in each component to manage its own updates:</p> <ul> <li>Figures: Update their <code>layout.template</code> or regenerate themselves.</li> <li>HTML/CSS Components: Dynamically update styles or toggle CSS classes.</li> </ul>"},{"location":"themes/details/#advantages-of-set_theme","title":"Advantages of <code>set_theme()</code>","text":"<ol> <li>Encapsulation:</li> <li>Each component knows how to update itself based on the theme.</li> <li>Simple Callback:</li> <li>The theme switch callback iterates through all registered components and calls their <code>set_theme()</code> method.</li> <li>Scalable:</li> <li>Suitable for multi-page apps with many dynamic components.</li> </ol>"},{"location":"themes/details/#how-the-two-phases-work-together","title":"How the Two Phases Work Together","text":""},{"location":"themes/details/#phase-1","title":"Phase 1:","text":"<ul> <li>Focus: Global theming at app start-up.</li> <li>What Works:</li> <li>Plotly templates are applied to all figures by default.</li> <li>CSS styles are applied across the app.</li> </ul>"},{"location":"themes/details/#phase-2","title":"Phase 2:","text":"<ul> <li>Focus: Extend components to dynamically update their theme.</li> <li>What to Add:</li> <li>Extend components with a <code>set_theme(theme_name: str)</code> method.</li> <li>The callback triggers <code>set_theme()</code> for each registered component.</li> </ul>"},{"location":"themes/details/#next-steps","title":"Next Steps","text":"<ul> <li>Phase 1: Finalize CSS and Plotly template integration for start-up theming.</li> <li>Phase 2: Begin implementing a <code>set_theme()</code> system for individual components.</li> </ul>"},{"location":"themes/details/#storage","title":"Storage","text":""},{"location":"themes/details/#theme-support-in-workbench","title":"Theme Support in Workbench","text":"<p>For a Dash/Plotly web applications there are two main concepts that fall under the general umbrella of 'theme support'.</p>"},{"location":"themes/details/#css-for-high-level-theming","title":"CSS for High-Level Theming","text":"<ul> <li>Purpose: Styles the overall web interface (e.g., layout, buttons, dropdowns, fonts, background colors).</li> <li>Scope: Applies to Dash components (e.g., <code>dcc.Graph</code>, <code>dbc.Button</code>) and layout elements.</li> <li>Implementation:</li> <li>Use a CSS file or a framework like Bootstrap.</li> <li>Dynamically switch themes (e.g., light/dark) using CSS class changes.</li> </ul>"},{"location":"themes/details/#templates-for-plotly-figures","title":"Templates for Plotly Figures","text":"<ul> <li>Purpose: Styles Plotly figures (e.g., background, gridlines, colorscales, font styles) to match the app's theme.</li> <li>Scope: Only affects Plotly-generated visuals like charts, graphs, and heatmaps.</li> <li>Implementation:</li> <li>Use predefined templates (e.g., <code>darkly</code>, <code>mintly</code>) or create custom JSON templates.</li> <li>Apply globally or on a per-figure basis.</li> <li>Great Resource:     dash-bootstrap-templates</li> </ul>"},{"location":"themes/details/#how-they-complement-each-other","title":"How They Complement Each Other","text":"<ol> <li> <p>CSS handles the web app\u2019s overall look:</p> <p>Example: A \"dark mode\" CSS class changes the app's background, text color, and component styles.</p> </li> <li> <p>Templates handle Plotly-specific figure styling:</p> <p>Example: A \"dark mode\" Plotly template ensures charts have dark backgrounds, white gridlines, and matching font colors.</p> </li> </ol>"}]}
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
index 13d8081c4cbe9591694b4a05c5feb44a1aa3a596..21080bb5fc5c6a438c1d5a2e7a780740782752e8 100644
GIT binary patch
delta 13
Ucmb=gXP58h;9y`)o5)@P02e$1I{*Lx

delta 13
Ucmb=gXP58h;9y8fp2%JS02u)Tl>h($

diff --git a/themes/details/index.html b/themes/details/index.html
index 6c935d0b3..98bf47be3 100644
--- a/themes/details/index.html
+++ b/themes/details/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Support Phases (WIP) - SageWorks</title>
+        <title>Support Phases (WIP) - Workbench</title>
       
     
     
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href="../.." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href="../.." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href="../.." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1937,7 +1937,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1947,7 +1947,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3166,11 +3166,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3372,7 +3372,7 @@ <h2 id="conclusion">Conclusion</h2>
 <li>Leverage <strong>CSS variables</strong> (<code>--bs-*</code>) for consistent theming across components.</li>
 <li>Apply <strong>CSS classes</strong> (<code>className</code>) for additional customization or integration with custom styles.</li>
 </ul>
-<h1 id="theming-strategy-for-sageworks-dashboard">Theming Strategy for SageWorks Dashboard</h1>
+<h1 id="theming-strategy-for-workbench-dashboard">Theming Strategy for Workbench Dashboard</h1>
 <h2 id="phase-1-application-start-up-theming"><strong>Phase 1: Application Start-Up Theming</strong></h2>
 <p>This focuses on setting a polished, cohesive interface at startup. It ensures:</p>
 <ol>
@@ -3435,7 +3435,7 @@ <h2 id="next-steps"><strong>Next Steps</strong></h2>
 <p><br><br><br>
 <br><br><br></p>
 <h2 id="storage">Storage</h2>
-<h3 id="theme-support-in-sageworks">Theme Support in SageWorks</h3>
+<h3 id="theme-support-in-workbench">Theme Support in Workbench</h3>
 <p>For a Dash/Plotly web applications there are two main concepts that fall under the general umbrella of 'theme support'.</p>
 <h3 id="css-for-high-level-theming">CSS for High-Level Theming</h3>
 <ul>
diff --git a/themes/index.html b/themes/index.html
index 89d335085..270863dc5 100644
--- a/themes/index.html
+++ b/themes/index.html
@@ -20,7 +20,7 @@
     
     
       
-        <title>Overview - SageWorks</title>
+        <title>Overview - Workbench</title>
       
     
     
@@ -74,7 +74,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#theme-support-for-sageworks" class="md-skip">
+        <a href="#theme-support-for-workbench" class="md-skip">
           Skip to content
         </a>
       
@@ -90,7 +90,7 @@
 
 <header class="md-header md-header--shadow" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
-    <a href=".." title="SageWorks" class="md-header__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-header__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
@@ -104,7 +104,7 @@
       <div class="md-header__ellipsis">
         <div class="md-header__topic">
           <span class="md-ellipsis">
-            SageWorks
+            Workbench
           </span>
         </div>
         <div class="md-header__topic" data-md-component="header-topic">
@@ -211,13 +211,13 @@
 
 <nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
   <label class="md-nav__title" for="__drawer">
-    <a href=".." title="SageWorks" class="md-nav__button md-logo" aria-label="SageWorks" data-md-component="logo">
+    <a href=".." title="Workbench" class="md-nav__button md-logo" aria-label="Workbench" data-md-component="logo">
       
   
   <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M12 8a3 3 0 0 0 3-3 3 3 0 0 0-3-3 3 3 0 0 0-3 3 3 3 0 0 0 3 3m0 3.54C9.64 9.35 6.5 8 3 8v11c3.5 0 6.64 1.35 9 3.54 2.36-2.19 5.5-3.54 9-3.54V8c-3.5 0-6.64 1.35-9 3.54"/></svg>
 
     </a>
-    SageWorks
+    Workbench
   </label>
   
   <ul class="md-nav__list" data-md-scrollfix>
@@ -293,7 +293,7 @@
         
   
   <span class="md-ellipsis">
-    SageWorks REPL
+    Workbench REPL
   </span>
   
 
@@ -1829,7 +1829,7 @@
             
   
   <span class="md-ellipsis">
-    SageWorks Enterprise
+    Workbench Enterprise
   </span>
   
 
@@ -1839,7 +1839,7 @@
         <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_12_label" aria-expanded="false">
           <label class="md-nav__title" for="__nav_12">
             <span class="md-nav__icon md-icon"></span>
-            SageWorks Enterprise
+            Workbench Enterprise
           </label>
           <ul class="md-nav__list" data-md-scrollfix>
             
@@ -3058,11 +3058,11 @@
   
   
     <li class="md-nav__item">
-      <a href="../admin/sageworks_docker_for_lambdas/" class="md-nav__link">
+      <a href="../admin/workbench_docker_for_lambdas/" class="md-nav__link">
         
   
   <span class="md-ellipsis">
-    SageWorks Docker for Lambdas
+    Workbench Docker for Lambdas
   </span>
   
 
@@ -3120,12 +3120,12 @@
                   
 
 
-<h1 id="theme-support-for-sageworks">Theme Support for SageWorks</h1>
+<h1 id="theme-support-for-workbench">Theme Support for Workbench</h1>
 <div class="admonition tip inline end">
 <p class="admonition-title">Need Help?</p>
-<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and SageWorks. So please contact us at <a href="mailto:sageworks@supercowpowers.com">sageworks@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
+<p>The SuperCowPowers team is happy to give any assistance needed when setting up AWS and Workbench. So please contact us at <a href="mailto:workbench@supercowpowers.com">workbench@supercowpowers.com</a> or on chat us up on <a href="https://discord.gg/WHAJuz8sw8">Discord</a> </p>
 </div>
-<p>SageWorks interfaces are going to have full support for theming and branding to enable customizations of components, and pages. The theming support will include all of the default pages/components but will also support full theming for plugin components and pages <a href="../plugins/">SageWorks Plugins</a></p>
+<p>Workbench interfaces are going to have full support for theming and branding to enable customizations of components, and pages. The theming support will include all of the default pages/components but will also support full theming for plugin components and pages <a href="../plugins/">Workbench Plugins</a></p>
 <h1 id="summary-of-theming-mechanisms">Summary of Theming Mechanisms</h1>
 <h2 id="1-css-files">1. CSS Files</h2>
 <p>CSS affects <strong>global styling</strong> of the entire application, including:</p>