Once your newly created notebook instance (“SageMaker
notebook”) shows as InService, open the instance in Jupyter
-Lab. From there, we will select the pre-built pytorch environment
-(conda_pytorch3_p310) to start our first .ipynb notebook (“Jupyter
-notebook”). This will save us the trouble of having to install pytorch
-on this instance / notebook evnironment later. You can name your Jupyter
-notebook something along the lines of,
+Lab. From there, we will select the standard python3 environment
+(conda_python3) to start our first .ipynb notebook (“Jupyter notebook”).
+You can name your Jupyter notebook something along the lines of,
Interacting-with-S3.ipynb.
To begin each SageMaker notebook, it’s important to set up an AWS -environment that will allow seamless access to the necessary cloud -resources. Here’s what we’ll do to get started:
+We can use the standard conda_python3 environment since we aren’t +doing any training/tuning just yet. #### Set up AWS environment To begin +each SageMaker notebook, it’s important to set up an AWS environment +that will allow seamless access to the necessary cloud resources. Here’s +what we’ll do to get started:
Define the Role: We’ll use
get_execution_role() to retrieve the IAM role associated
with the SageMaker instance. This role specifies the permissions needed
diff --git a/aio.html b/aio.html
index 1c7865b..1c566c8 100644
--- a/aio.html
+++ b/aio.html
@@ -1260,19 +1260,15 @@
To begin each SageMaker notebook, it’s important to set up an AWS -environment that will allow seamless access to the necessary cloud -resources. Here’s what we’ll do to get started:
+We can use the standard conda_python3 environment since we aren’t +doing any training/tuning just yet. #### Set up AWS environment To begin +each SageMaker notebook, it’s important to set up an AWS environment +that will allow seamless access to the necessary cloud resources. Here’s +what we’ll do to get started:
Define the Role: We’ll use
get_execution_role() to retrieve the IAM role associated
diff --git a/instructor/Accessing-S3-via-SageMaker-notebooks.html b/instructor/Accessing-S3-via-SageMaker-notebooks.html
index af7153b..00bd4fc 100644
--- a/instructor/Accessing-S3-via-SageMaker-notebooks.html
+++ b/instructor/Accessing-S3-via-SageMaker-notebooks.html
@@ -379,18 +379,15 @@
Once your newly created notebook instance (“SageMaker
notebook”) shows as InService, open the instance in Jupyter
-Lab. From there, we will select the pre-built pytorch environment
-(conda_pytorch3_p310) to start our first .ipynb notebook (“Jupyter
-notebook”). This will save us the trouble of having to install pytorch
-on this instance / notebook evnironment later. You can name your Jupyter
-notebook something along the lines of,
+Lab. From there, we will select the standard python3 environment
+(conda_python3) to start our first .ipynb notebook (“Jupyter notebook”).
+You can name your Jupyter notebook something along the lines of,
Interacting-with-S3.ipynb.
To begin each SageMaker notebook, it’s important to set up an AWS -environment that will allow seamless access to the necessary cloud -resources. Here’s what we’ll do to get started:
+We can use the standard conda_python3 environment since we aren’t +doing any training/tuning just yet. #### Set up AWS environment To begin +each SageMaker notebook, it’s important to set up an AWS environment +that will allow seamless access to the necessary cloud resources. Here’s +what we’ll do to get started:
Define the Role: We’ll use
get_execution_role() to retrieve the IAM role associated
with the SageMaker instance. This role specifies the permissions needed
diff --git a/instructor/aio.html b/instructor/aio.html
index 1d96d0e..e3318b7 100644
--- a/instructor/aio.html
+++ b/instructor/aio.html
@@ -1266,19 +1266,15 @@
To begin each SageMaker notebook, it’s important to set up an AWS -environment that will allow seamless access to the necessary cloud -resources. Here’s what we’ll do to get started:
+We can use the standard conda_python3 environment since we aren’t +doing any training/tuning just yet. #### Set up AWS environment To begin +each SageMaker notebook, it’s important to set up an AWS environment +that will allow seamless access to the necessary cloud resources. Here’s +what we’ll do to get started:
Define the Role: We’ll use
get_execution_role() to retrieve the IAM role associated
diff --git a/md5sum.txt b/md5sum.txt
index 0cbcf7f..6434be0 100644
--- a/md5sum.txt
+++ b/md5sum.txt
@@ -7,7 +7,7 @@
"episodes/SageMaker-overview.md" "d04359991728bd3e4cd21a1d0a96e98d" "site/built/SageMaker-overview.md" "2024-11-05"
"episodes/Data-storage-setting-up-S3.md" "e051fb55d0f69de00887058f1f46852d" "site/built/Data-storage-setting-up-S3.md" "2024-11-05"
"episodes/SageMaker-notebooks-as-controllers.md" "7b44f533d49559aa691b8ab2574b4e81" "site/built/SageMaker-notebooks-as-controllers.md" "2024-11-06"
-"episodes/Accessing-S3-via-SageMaker-notebooks.md" "146f3d9e9698f2a38f3599a75af6f717" "site/built/Accessing-S3-via-SageMaker-notebooks.md" "2024-11-06"
+"episodes/Accessing-S3-via-SageMaker-notebooks.md" "fe9561e747c000e7ed75cb0820a8d00e" "site/built/Accessing-S3-via-SageMaker-notebooks.md" "2024-11-06"
"episodes/Interacting-with-code-repo.md" "e0151f0fc4f6d3628647769f17643390" "site/built/Interacting-with-code-repo.md" "2024-11-06"
"episodes/Training-models-in-SageMaker-notebooks.md" "2378750690016fdbdf1cb83881903e67" "site/built/Training-models-in-SageMaker-notebooks.md" "2024-11-03"
"instructors/instructor-notes.md" "cae72b6712578d74a49fea7513099f8c" "site/built/instructor-notes.md" "2023-03-16"
diff --git a/pkgdown.yml b/pkgdown.yml
index 52a7efe..64c4667 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -2,4 +2,4 @@ pandoc: 3.1.11
pkgdown: 2.1.1
pkgdown_sha: ~
articles: {}
-last_built: 2024-11-06T19:21Z
+last_built: 2024-11-06T19:44Z