Walk Through a SageMaker AI MLOps Project Using Third-party Git Repos - Amazon SageMaker AI
Step 1: Set up the GitHub connectionStep 2: Create the ProjectStep 3: Make a Change in the CodeStep 4: Approve the Model(Optional) Step 5: Deploy the Model Version to ProductionStep 6: Clean Up Resources
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Walk Through a SageMaker AI MLOps Project Using Third-party Git Repos

Important

As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see Amazon SageMaker Studio.

This walkthrough uses the template MLOps templates for model building, training, and deployment with third-party Git using CodePipeline to demonstrate how to use MLOps projects to create a CI/CD system to build, train, and deploy models.

Prerequisites

To complete this walkthrough, you need:

Step 1: Set up the GitHub connection

In this step, you connect to your GitHub repositories using an AWS CodeStar connection. The SageMaker AI project uses this connection to access your source code repositories.

To set up the GitHub connection:

  1. Log in to the CodePipeline console at https://console.amazonaws.cn/codepipeline/

  2. Under Settings in the navigation pane, choose Connections.

  3. Choose Create connection.

  4. For Select a provider, select GitHub.

  5. For Connection name, enter a name.

  6. Choose Connect to GitHub.

  7. If the Amazon Connector GitHub app isn’t previously installed, choose Install new app.

    This displays a list of all the GitHub personal accounts and organizations to which you have access.

  8. Choose the account where you want to establish connectivity for use with SageMaker Projects and GitHub repositories.

  9. Choose Configure.

  10. You can optionally select your specific repositories or choose All repositories.

  11. Choose Save. When the app is installed, you’re redirected to the Connect to GitHub page and the installation ID is automatically populated.

  12. Choose Connect.

  13. Add a tag with the key sagemaker and value true to this AWS CodeStar connection.

  14. Copy the connection ARN to save for later. You use the ARN as a parameter in the project creation step.

Step 2: Create the Project

In this step, you create a SageMaker AI MLOps project by using a SageMaker AI-provided project template to build, train, and deploy models.

To create the SageMaker AI MLOps project

  1. Sign in to Studio Classic. For more information, see Amazon SageMaker AI domain overview.

  2. In the Studio Classic sidebar, choose the Home icon ( Black square icon representing a placeholder or empty image. ).

  3. Select Deployments from the menu, and then select Projects.

  4. Choose Create project.

    The Create project tab appears.

  5. For SageMaker AI project templates, choose MLOps template for model building, training, and deployment with third-party Git repositories.

  6. Choose Select project template.

  7. Under ModelBuild CodeRepository Info, provide the following parameters:

    • For URL, enter the URL of your Git repository for the model build code in https://git-url.git format.

    • For Branch, enter the branch to use from your Git repository for pipeline activities.

    • For Full Repository Name, enter the Git repository name in the format of username/repository name or organization/repository name.

    • For Codestar Connection ARN, enter the ARN of the AWS CodeStar connection you created in Step 1.

    • The Sample Code toggle switch lets you choose whether to populate the repository with model build seed code. We can leave it on for this demo.

  8. Under ModelDeploy CodeRepository Info, provide the following parameters:

    • For URL, enter the URL of your Git repository for the model deploy code in https://git-url.git format.

    • For Branch, enter the branch to use from your Git repository for pipeline activities.

    • For Full Repository Name, enter the Git repository name in the format of username/repository name or organization/repository name.

    • For Codestar Connection ARN, enter the ARN of the AWS CodeStar connection you created in Step 1.

    • The Sample Code toggle switch lets you choose whether to populate the repository with model deployment seed code. We can leave it on for this demo.

  9. Choose Create Project.

The project appears in the Projects list with a Status of Created.

Step 3: Make a Change in the Code

Now make a change to the pipeline code that builds the model and commit the change to initiate a new pipeline run. The pipeline run registers a new model version.

To make a code change

  1. In your model build GitHub repo, navigate to the pipelines/abalone folder. Double-click pipeline.py to open the code file.

  2. In the pipeline.py file, find the line that sets the training instance type.

    training_instance_type = ParameterString(
        name="TrainingInstanceType", default_value="ml.m5.xlarge"

    Open the file for editing, change ml.m5.xlarge to ml.m5.large, then commit.

After you commit your code change, the MLOps system initiates a run of the pipeline that creates a new model version. In the next step, you approve the new model version to deploy it to production.

Step 4: Approve the Model

Now you approve the new model version that was created in the previous step to initiate a deployment of the model version to a SageMaker AI endpoint.

To approve the model version

  1. In the Studio Classic sidebar, choose the Home icon ( Black square icon representing a placeholder or empty image. ).

  2. Select Deployments from the menu, and then select Projects.

  3. Find the name of the project you created in the first step and double-click on it to open the project tab for your project.

  4. In the project tab, choose Model groups, then double-click the name of the model group that appears.

    The model group tab appears.

  5. In the model group tab, double-click Version 1. The Version 1 tab opens. Choose Update status.

  6. In the model Update model version status dialog box, in the Status dropdown list, select Approve and then choose Update status.

    Approving the model version causes the MLOps system to deploy the model to staging. To view the endpoint, choose the Endpoints tab on the project tab.

(Optional) Step 5: Deploy the Model Version to Production

Now you can deploy the model version to the production environment.

Note

To complete this step, you need to be an administrator in your Studio Classic domain. If you are not an administrator, skip this step.

To deploy the model version to the production environment

  1. Log in to the CodePipeline console at https://console.amazonaws.cn/codepipeline/

  2. Choose Pipelines, then choose the pipeline with the name sagemaker-projectname-projectid-modeldeploy, where projectname is the name of your project, and projectid is the ID of your project.

  3. In the DeployStaging stage, choose Review.

  4. In the Review dialog box, choose Approve.

    Approving the DeployStaging stage causes the MLOps system to deploy the model to production. To view the endpoint, choose the Endpoints tab on the project tab in Studio Classic.

Step 6: Clean Up Resources

To stop incurring charges, clean up the resources that were created in this walkthrough.

Note

To delete the Amazon CloudFormation stack and the Amazon S3 bucket, you need to be an administrator in Studio Classic. If you are not an administrator, ask your administrator to complete those steps.

  1. In the Studio Classic sidebar, choose the Home icon ( Black square icon representing a placeholder or empty image. ).

  2. Select Deployments from the menu, and then select Projects.

  3. Select the target project from the dropdown list. If you don’t see your project, type the project name and apply the filter to find your project.

  4. Select your project to view its details in the main panel.

  5. Choose Delete from the Actions menu.

  6. Confirm your choice by choosing Delete from the Delete Project window.

    This deletes the Service Catalog provisioned product that the project created. This includes the CodeCommit, CodePipeline, and CodeBuild resources created for the project.

  7. Delete the Amazon CloudFormation stacks that the project created. There are two stacks, one for staging and one for production. The names of the stacks are sagemaker-projectname-project-id-deploy-staging and sagemaker-projectname-project-id-deploy-prod, where projectname is the name of your project, and project-id is the ID of your project.

    For information about how to delete a Amazon CloudFormation stack, see Deleting a stack on the Amazon CloudFormation console in the Amazon CloudFormation User Guide.

  8. Delete the Amazon S3 bucket that the project created. The name of the bucket is sagemaker-project-project-id, where project-id is the ID of your project.