Need Help?
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
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
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.
When using SageWorks for large scale deployments there are a set of AWS Service limits that will need to be increased.
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.
When using Workbench for large scale deployments there are a set of AWS Service limits that will need to be increased.
There are two serverless endpoint quotas that will need to be adjusted.
Unfortunately this one is not adjustable through the AWS Service Quota console and you'll have to initiate an AWS Support ticket.
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
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
Notes and information on how to do the Docker Builds and Push to AWS ECR.
vi Dockerfile -# Install latest Sageworks -RUN pip install --no-cache-dir 'sageworks[ml-tool,chem]'==0.7.0 +# Install latest Workbench +RUN pip install --no-cache-dir 'workbench[ml-tool,chem]'==0.7.0
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 open_source_config.json that's in the directory already.
open_source_config.json
docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_base:v0_7_0_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_base:v0_7_0_amd64 --platform linux/amd64 . Test the Image Locally You have a docker_local_base alias in your ~/.zshrc :) @@ -3323,23 +3323,23 @@ Login to ECR --password-stdin public.ecr.aws
docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_base:v0_7_0_amd64 --platform linux/amd64 .
You have a docker_local_base alias in your ~/.zshrc :)
docker_local_base
~/.zshrc
docker tag sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 +docker tag workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_base:latest +docker push public.ecr.aws/m6i5k1r2/workbench_base:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker push public.ecr.aws/m6i5k1r2/workbench_base:stable Test the ECR Image You have a docker_ecr_base alias in your ~/.zshrc :) 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 @@ - Dashboard Docker Push - SageWorks + Dashboard Docker Push - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -2996,9 +2996,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3160,11 +3160,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3216,9 +3216,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3304,17 +3304,17 @@ Dashboard Docker Build and Push Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR. -Update SageWorks Version +Update Workbench Version cd applications/aws_dashboard vi Dockerfile -# Install Sageworks (changes often) -RUN pip install --no-cache-dir sageworks==0.4.13 <-- change this +# Install Workbench (changes often) +RUN pip install --no-cache-dir workbench==0.4.13 <-- change this Build the Docker Image 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 open_source_config.json that's in the directory already. -docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 . Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page. Test the Image Locally @@ -3325,23 +3325,23 @@ Login to ECR --password-stdin public.ecr.aws Tag/Push the Image to AWS ECR -docker tag sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker tag workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64
docker push public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_base:latest +docker push public.ecr.aws/m6i5k1r2/workbench_base:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker push public.ecr.aws/m6i5k1r2/workbench_base:stable Test the ECR Image You have a docker_ecr_base alias in your ~/.zshrc :) 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 @@ - Dashboard Docker Push - SageWorks + Dashboard Docker Push - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -2996,9 +2996,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3160,11 +3160,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3216,9 +3216,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3304,17 +3304,17 @@ Dashboard Docker Build and Push Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR. -Update SageWorks Version +Update Workbench Version cd applications/aws_dashboard vi Dockerfile -# Install Sageworks (changes often) -RUN pip install --no-cache-dir sageworks==0.4.13 <-- change this +# Install Workbench (changes often) +RUN pip install --no-cache-dir workbench==0.4.13 <-- change this Build the Docker Image 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 open_source_config.json that's in the directory already. -docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 . Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page. Test the Image Locally @@ -3325,23 +3325,23 @@ Login to ECR --password-stdin public.ecr.aws Tag/Push the Image to AWS ECR -docker tag sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker push public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64
docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_base:latest +docker push public.ecr.aws/m6i5k1r2/workbench_base:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker push public.ecr.aws/m6i5k1r2/workbench_base:stable Test the ECR Image You have a docker_ecr_base alias in your ~/.zshrc :) 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 @@ - Dashboard Docker Push - SageWorks + Dashboard Docker Push - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -2996,9 +2996,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3160,11 +3160,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3216,9 +3216,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3304,17 +3304,17 @@ Dashboard Docker Build and Push Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR. -Update SageWorks Version +Update Workbench Version cd applications/aws_dashboard vi Dockerfile -# Install Sageworks (changes often) -RUN pip install --no-cache-dir sageworks==0.4.13 <-- change this +# Install Workbench (changes often) +RUN pip install --no-cache-dir workbench==0.4.13 <-- change this Build the Docker Image 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 open_source_config.json that's in the directory already. -docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 . Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page. Test the Image Locally @@ -3325,23 +3325,23 @@ Login to ECR --password-stdin public.ecr.aws Tag/Push the Image to AWS ECR -docker tag sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:latest
docker push public.ecr.aws/m6i5k1r2/sageworks_base:latest +docker push public.ecr.aws/m6i5k1r2/workbench_base:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker push public.ecr.aws/m6i5k1r2/workbench_base:stable Test the ECR Image You have a docker_ecr_base alias in your ~/.zshrc :) 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 @@ - Dashboard Docker Push - SageWorks + Dashboard Docker Push - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -2996,9 +2996,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3160,11 +3160,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3216,9 +3216,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3304,17 +3304,17 @@ Dashboard Docker Build and Push Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR. -Update SageWorks Version +Update Workbench Version cd applications/aws_dashboard vi Dockerfile -# Install Sageworks (changes often) -RUN pip install --no-cache-dir sageworks==0.4.13 <-- change this +# Install Workbench (changes often) +RUN pip install --no-cache-dir workbench==0.4.13 <-- change this Build the Docker Image 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 open_source_config.json that's in the directory already. -docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 . Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page. Test the Image Locally @@ -3325,23 +3325,23 @@ Login to ECR --password-stdin public.ecr.aws Tag/Push the Image to AWS ECR -docker tag sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker push public.ecr.aws/m6i5k1r2/workbench_base:latest
This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :)
docker tag public.ecr.aws/m6i5k1r2/sageworks_base:v0_7_0_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker push public.ecr.aws/m6i5k1r2/workbench_base:stable Test the ECR Image You have a docker_ecr_base alias in your ~/.zshrc :) 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 @@ - Dashboard Docker Push - SageWorks + Dashboard Docker Push - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -2996,9 +2996,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3160,11 +3160,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3216,9 +3216,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3304,17 +3304,17 @@ Dashboard Docker Build and Push Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR. -Update SageWorks Version +Update Workbench Version cd applications/aws_dashboard vi Dockerfile -# Install Sageworks (changes often) -RUN pip install --no-cache-dir sageworks==0.4.13 <-- change this +# Install Workbench (changes often) +RUN pip install --no-cache-dir workbench==0.4.13 <-- change this Build the Docker Image 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 open_source_config.json that's in the directory already. -docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 . Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page. Test the Image Locally @@ -3325,23 +3325,23 @@ Login to ECR --password-stdin public.ecr.aws Tag/Push the Image to AWS ECR -docker tag sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker tag public.ecr.aws/m6i5k1r2/workbench_base:v0_7_0_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_base:stable
docker push public.ecr.aws/m6i5k1r2/sageworks_base:stable +docker push public.ecr.aws/m6i5k1r2/workbench_base:stable Test the ECR Image You have a docker_ecr_base alias in your ~/.zshrc :) 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 @@ - Dashboard Docker Push - SageWorks + Dashboard Docker Push - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -2996,9 +2996,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3160,11 +3160,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3216,9 +3216,9 @@ - + - Update SageWorks Version + Update Workbench Version @@ -3304,17 +3304,17 @@ Dashboard Docker Build and Push Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR. -Update SageWorks Version +Update Workbench Version cd applications/aws_dashboard vi Dockerfile -# Install Sageworks (changes often) -RUN pip install --no-cache-dir sageworks==0.4.13 <-- change this +# Install Workbench (changes often) +RUN pip install --no-cache-dir workbench==0.4.13 <-- change this Build the Docker Image 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 open_source_config.json that's in the directory already. -docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 . Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page. Test the Image Locally @@ -3325,23 +3325,23 @@ Login to ECR --password-stdin public.ecr.aws Tag/Push the Image to AWS ECR -docker tag sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker push public.ecr.aws/m6i5k1r2/workbench_base:stable
You have a docker_ecr_base alias in your ~/.zshrc :)
docker_ecr_base
Notes and information on how to do the Dashboard Docker Builds and Push to AWS ECR.
cd applications/aws_dashboard vi Dockerfile -# Install Sageworks (changes often) -RUN pip install --no-cache-dir sageworks==0.4.13 <-- change this +# Install Workbench (changes often) +RUN pip install --no-cache-dir workbench==0.4.13 <-- change this
docker build --build-arg SAGEWORKS_CONFIG=open_source_config.json -t \ -sageworks_dashboard:v0_4_13_amd64 --platform linux/amd64 . +docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 . Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page. Test the Image Locally @@ -3325,23 +3325,23 @@ Login to ECR --password-stdin public.ecr.aws
docker build --build-arg WORKBENCH_CONFIG=open_source_config.json -t \ +workbench_dashboard:v0_4_13_amd64 --platform linux/amd64 .
Docker with Custom Plugins: If you're using custom plugins you should visit our Dashboard with Plugins) page.
docker tag sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker tag workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64
docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 Update the 'latest' tag -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64
docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_4_13_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_4_13_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:latest
docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest Update the 'stable' tag This is obviously only when you want to mark a version as stable. Meaning that it seems to 'be good and stable (ish)' :) -docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:latest
docker tag public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_5_4_amd64 \ -public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable -docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker tag public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_5_4_amd64 \ +public.ecr.aws/m6i5k1r2/workbench_dashboard:stable
docker push public.ecr.aws/m6i5k1r2/sageworks_dashboard:stable +docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable Test the ECR Image You have a docker_ecr_dashboard alias in your ~/.zshrc :) 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 @@ - Dashboard with S3 Plugins - SageWorks + Dashboard with S3 Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3139,11 +3139,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3263,9 +3263,9 @@ Deploying S3 Plugins with the Dashboard Need Help? -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 +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 -Notes and information on how to include S3 based plugins with your SageWorks Dashboard. +Notes and information on how to include S3 based plugins with your Workbench Dashboard. 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. 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. Check your Dashboard @@ -3298,10 +3298,10 @@ Restart the ECS Service Getting Cluster and Service Names You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service. The cluster will be something like: -SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker push public.ecr.aws/m6i5k1r2/workbench_dashboard:stable
You have a docker_ecr_dashboard alias in your ~/.zshrc :)
docker_ecr_dashboard
Notes and information on how to include S3 based plugins with your SageWorks Dashboard.
Notes and information on how to include S3 based plugins with your Workbench Dashboard.
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.
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.
Getting Cluster and Service Names
You can go to the AWS Console, Elastic Container Service, find the cluster, click on that and find the service.
The cluster will be something like:
SageworksDashboard-SageworksCluster123456 +WorkbenchDashboard-WorkbenchCluster123456 and the service will be something like: -SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
WorkbenchDashboard-WorkbenchCluster123456
and the service will be something like:
SageworksDashboard-SageworksService789123 +WorkbenchDashboard-WorkbenchService789123 Anyway, find those two things and run this command below (Note: You probably need admin permisions) aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
WorkbenchDashboard-WorkbenchService789123
Anyway, find those two things and run this command below (Note: You probably need admin permisions)
aws ecs update-service --cluster your-cluster-name \ @@ -3312,7 +3312,7 @@ Verify new Plugin changes 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. Questions? -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 +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 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 @@ - + @@ -20,7 +20,7 @@ - Dashboard with Docker Plugins - SageWorks + Dashboard with Docker Plugins - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3108,9 +3108,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3172,11 +3172,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3298,9 +3298,9 @@ - + - Note on SageWorks Configuration + Note on Workbench Configuration @@ -3327,7 +3327,7 @@ Deploying Plugins with the Dashboard -Notes and information on how to include plugins with your SageWorks Dashboard. +Notes and information on how to include plugins with your Workbench Dashboard. ECR: AWS Elastic Container Registry (stores Docker images) ECS: AWS Elastic Container Service (uses Docker images) @@ -3339,15 +3339,15 @@ Install Docker Build the Docker Image 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. -# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
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.
Notes and information on how to include plugins with your SageWorks Dashboard.
Notes and information on how to include plugins with your Workbench Dashboard.
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.
# Pull base sageworks dashboard image with specific tag (pick latest or stable) -FROM public.ecr.aws/m6i5k1r2/sageworks_dashboard:latest +# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins Note: Your plugins directory should looks like this -sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
# Pull base workbench dashboard image with specific tag (pick latest or stable) +FROM public.ecr.aws/m6i5k1r2/workbench_dashboard:latest # Copy the plugin files into the Dashboard plugins dir -COPY ./sageworks_plugins /app/sageworks_plugins -ENV SAGEWORKS_PLUGINS=/app/sageworks_plugins +COPY ./workbench_plugins /app/workbench_plugins +ENV WORKBENCH_PLUGINS=/app/workbench_plugins
Note: Your plugins directory should looks like this
sageworks_plugins/ +workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ... Build it -docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com Tag/Push the Image to AWS ECR -docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
workbench_plugins/ pages/ my_plugin_page.py ... @@ -3359,10 +3359,10 @@ Build the Docker Image ...
docker build -t my_sageworks_with_plugins:v1_0 --platform linux/amd64 . +docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 . Test the Image Locally -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 +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 Login to your ECR Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR. Note: This ECR should be private as your plugins are customized for specific business use cases. @@ -3374,25 +3374,25 @@ Login to your ECR <aws_account_id>.dkr.ecr.us-east-1.amazonaws.com
docker build -t my_workbench_with_plugins:v1_0 --platform linux/amd64 .
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
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
Okay.. so after testing locally you're ready to push the Docker image (with Plugins) to the your ECR.
Note: This ECR should be private as your plugins are customized for specific business use cases.
docker tag my_sageworks_with_plugins:v1_0 \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0 Deploying Plugin Docker Image to AWS Okay now that you have your plugin Docker Image you can deploy to your AWS account: Copy the Dashboard CDK files This is cheesy but just copy all the CDK files into your repo/directory. -cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456", -Note on SageWorks Configuration -All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file. +Note on Workbench Configuration +All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file. 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 @@ - PyPI Release - SageWorks + PyPI Release - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -1817,7 +1817,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1827,7 +1827,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3178,11 +3178,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3364,9 +3364,9 @@ Setup pypirc password = pypi-AgENdG... Tox Background -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 sageworks package) also ran okay. +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 workbench package) also ran okay. Make sure ALL tests pass -$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi Install the TEST PyPI Release -pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
docker tag my_workbench_with_plugins:v1_0 \ +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0
docker push \ -<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/sageworks_with_plugins:v1_0 +<aws_account_id>.dkr.ecr.us-east-1.amazonaws.com/workbench_with_plugins:v1_0
Okay now that you have your plugin Docker Image you can deploy to your AWS account:
Copy the Dashboard CDK files
This is cheesy but just copy all the CDK files into your repo/directory.
cp -r sageworks/aws_setup/sageworks_dashboard_full /my/sageworks/stuff/ +cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/ Change the Docker Image to Deploy Now open up the app.py file and change this line to your Docker Image # When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64" -Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands: -export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456",
cp -r workbench/aws_setup/workbench_dashboard_full /my/workbench/stuff/
Change the Docker Image to Deploy
Now open up the app.py file and change this line to your Docker Image
app.py
# When you want a different docker image change this line -dashboard_image = "public.ecr.aws/m6i5k1r2/sageworks_dashboard:v0_8_3_amd64" +dashboard_image = "public.ecr.aws/m6i5k1r2/workbench_dashboard:v0_8_3_amd64"
Make sure your SAGEWORKS_CONFIG is properly set, and run the following commands:
SAGEWORKS_CONFIG
export SAGEWORKS_CONFIG=/Users/<user_name>/.sageworks/sageworks_config.json +Make sure your WORKBENCH_CONFIG is properly set, and run the following commands: +export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy @@ -3404,8 +3404,8 @@ Deploying Plugin Docker Image to A [+] "Image": "<account>.dkr.ecr.us-east-1/my-plugins:latest_456",
Make sure your WORKBENCH_CONFIG is properly set, and run the following commands:
WORKBENCH_CONFIG
export WORKBENCH_CONFIG=/Users/<user_name>/.workbench/workbench_config.json cdk diff cdk deploy
All Configuration is managed by the CDK Python Script and the SAGEWORKS_CONFIG ENV var. If you want to change things like REDIS_HOST or SAGEWORKS_BUCKET you should do that with a sageworks.config file and then point the SAGEWORKS_CONFIG ENV var to that file.
REDIS_HOST
SAGEWORKS_BUCKET
sageworks.config
All Configuration is managed by the CDK Python Script and the WORKBENCH_CONFIG ENV var. If you want to change things like REDIS_HOST or WORKBENCH_BUCKET you should do that with a workbench.config file and then point the WORKBENCH_CONFIG ENV var to that file.
WORKBENCH_BUCKET
workbench.config
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 sageworks package) also ran okay.
sageworks
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 workbench package) also ran okay.
workbench
$ cd sageworks +$ cd workbench $ tox If ALL the test above pass... @@ -3382,7 +3382,7 @@ Create the TEST PyPI Release twine upload dist/* -r testpypi
$ cd workbench $ tox
If ALL the test above pass...
pip install --index-url https://test.pypi.org/simple sageworks +pip install --index-url https://test.pypi.org/simple workbench Create the REAL PyPI Release twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
pip install --index-url https://test.pypi.org/simple workbench
twine upload dist/* -r 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 @@ - SageWorks Docker for Lambdas - SageWorks + Workbench Docker for Lambdas - Workbench @@ -72,7 +72,7 @@ - + Skip to content @@ -88,7 +88,7 @@ - + @@ -102,13 +102,13 @@ - SageWorks + Workbench - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -209,13 +209,13 @@ - + - SageWorks + Workbench @@ -291,7 +291,7 @@ - SageWorks REPL + Workbench REPL @@ -1815,7 +1815,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1825,7 +1825,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3061,7 +3061,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3072,7 +3072,7 @@ - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3356,8 +3356,8 @@ -SageWorks Docker Image for Lambdas -Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks. +Workbench Docker Image for Lambdas +Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench. 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. Creating a Private ECR You only need to do this if you don't already have a private ECR. @@ -3365,21 +3365,21 @@ AWS Console to create Private ECR Open the Amazon ECR console. Choose "Create repository". -For "Repository name", enter sageworks_base. +For "Repository name", enter workbench_base. Ensure "Private" is selected. Choose "Create repository". Command Line to create Private ECR Create the ECR repository using the AWS CLI: -aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
Using the SageWorks Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with SageWorks.
Using the Workbench Docker Image for AWS Lambda Jobs allows your Lambda Jobs to use and create AWS ML Pipeline Artifacts with Workbench.
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.
You only need to do this if you don't already have a private ECR.
sageworks_base
workbench_base
Create the ECR repository using the AWS CLI:
aws ecr create-repository --repository-name sageworks_base --region <region> +aws ecr create-repository --repository-name workbench_base --region <region> Pulling Docker Image into Private ECR -Note: You'll only need to do this when you want to update the SageWorks Docker image -Pull the SageWorks Public ECR Image -docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
aws ecr create-repository --repository-name workbench_base --region <region>
Note: You'll only need to do this when you want to update the SageWorks Docker image
Pull the SageWorks Public ECR Image
docker pull public.ecr.aws/m6i5k1r2/sageworks_base:latest +Note: You'll only need to do this when you want to update the Workbench Docker image +Pull the Workbench Public ECR Image +docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest Tag the image for your private ECR -docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag> Using the Docker Image for your Lambdas -Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs. +Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs. AWS Console Open the AWS Lambda console. 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 @@ - DataSource - SageWorks + DataSource - Workbench @@ -90,7 +90,7 @@ - + @@ -104,7 +104,7 @@ - SageWorks + Workbench @@ -211,13 +211,13 @@ - + - SageWorks + Workbench @@ -293,7 +293,7 @@ - SageWorks REPL + Workbench REPL @@ -434,7 +434,7 @@ - + data_source @@ -443,7 +443,7 @@ - + DataSource @@ -453,7 +453,7 @@ - + __init__ @@ -462,7 +462,7 @@ - + details @@ -471,7 +471,7 @@ - + pull_dataframe @@ -480,7 +480,7 @@ - + query @@ -489,7 +489,7 @@ - + to_features @@ -512,9 +512,9 @@ - + - SageWorks UI + Workbench UI @@ -1946,7 +1946,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -1956,7 +1956,7 @@ - SageWorks Enterprise + Workbench Enterprise @@ -3175,11 +3175,11 @@ - + - SageWorks Docker for Lambdas + Workbench Docker for Lambdas @@ -3231,7 +3231,7 @@ - + data_source @@ -3240,7 +3240,7 @@ - + DataSource @@ -3250,7 +3250,7 @@ - + __init__ @@ -3259,7 +3259,7 @@ - + details @@ -3268,7 +3268,7 @@ - + pull_dataframe @@ -3277,7 +3277,7 @@ - + query @@ -3286,7 +3286,7 @@ - + to_features @@ -3309,9 +3309,9 @@ - + - SageWorks UI + Workbench UI @@ -3343,14 +3343,14 @@ DataSource - + 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. +can be viewed and explored within the Workbench Dashboard UI. @@ -3372,7 +3372,7 @@ DataSource - + DataSource @@ -3381,10 +3381,10 @@ - Bases: AthenaSource + Bases: AthenaSource - DataSource: SageWorks DataSource API Class + DataSource: Workbench DataSource API Class @@ -3400,7 +3400,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
Note: You'll only need to do this when you want to update the Workbench Docker image
Pull the Workbench Public ECR Image
docker pull public.ecr.aws/m6i5k1r2/workbench_base:latest
Tag the image for your private ECR
docker tag public.ecr.aws/m6i5k1r2/sageworks_base:latest \ +docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest Push the image to your private ECR @@ -3389,7 +3389,7 @@ Pulling Docker Image into Private docker push <account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:<tag>
docker tag public.ecr.aws/m6i5k1r2/workbench_base:latest \ <your-account-id>.dkr.ecr.<region>.amazonaws.com/<private-repo>:latest
Push the image to your private ECR
Okay, now that you have the SageWorks Docker image in your private ECR, here's how you use that image for your Lambda jobs.
Okay, now that you have the Workbench Docker image in your private ECR, here's how you use that image for your Lambda jobs.
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.
DataSource
- Bases: AthenaSource
AthenaSource
DataSource: SageWorks DataSource API Class
DataSource: Workbench DataSource API Class
src/sageworks/api/data_source.py
src/workbench/api/data_source.py
22 23 24 @@ -3553,7 +3553,7 @@ 171 172 173
class DataSource(AthenaSource): - """DataSource: SageWorks DataSource API Class + """DataSource: Workbench DataSource API Class Common Usage: ```python @@ -3722,7 +3722,7 @@ - + __init__(source, name=None, tags=None, **kwargs) @@ -3796,7 +3796,7 @@
__init__(source, name=None, tags=None, **kwargs)
33 34 35 @@ -3867,7 +3867,7 @@ - + details(**kwargs) @@ -3901,7 +3901,7 @@
details(**kwargs)
65 66 67 @@ -3924,7 +3924,7 @@ - + pull_dataframe(include_aws_columns=False) @@ -3994,7 +3994,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
pull_dataframe(include_aws_columns=False)
84 85 86 @@ -4051,7 +4051,7 @@ - + query(query) @@ -4116,7 +4116,7 @@
query(query)
73 74 75 @@ -4145,7 +4145,7 @@ - + to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None) @@ -4262,7 +4262,7 @@ FeatureSet - Union[FeatureSet, None] + Union[FeatureSet, None] @@ -4274,7 +4274,7 @@ - Source code in src/sageworks/api/data_source.py + Source code in src/workbench/api/data_source.py 109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
to_features(name, id_column=None, tags=None, event_time_column=None, one_hot_columns=None)
FeatureSet
Union[FeatureSet, None]
109 110 111 @@ -4378,20 +4378,20 @@ Examples -All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples +All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples Create a DataSource from an S3 Path or File Path -datasource_from_s3.pyfrom sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
All of the SageWorks Examples are in the Sageworks Repository under the examples/ directory. For a full code listing of any example please visit our SageWorks Examples
examples/
All of the Workbench Examples are in the Workbench Repository under the examples/ directory. For a full code listing of any example please visit our Workbench Examples
Create a DataSource from an S3 Path or File Path
from sageworks.api.data_source import DataSource +datasource_from_s3.pyfrom workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details()) Create a DataSource from a Pandas Dataframe -datasource_from_df.pyfrom sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py
from workbench.api.data_source import DataSource # Create a DataSource from an S3 Path (or a local file) -source_path = "s3://sageworks-public-data/common/abalone.csv" +source_path = "s3://workbench-public-data/common/abalone.csv" # source_path = "/full/path/to/local/file.csv" my_data = DataSource(source_path) print(my_data.details())
Create a DataSource from a Pandas Dataframe
from sageworks.utils.test_data_generator import TestDataGenerator -from sageworks.api.data_source import DataSource +datasource_from_df.py