1. Automatic Model Retraining Pipelines via GitHub Webhooks


    Automatic model retraining pipelines are a critical component of a robust machine learning (ML) system. They ensure that ML models stay up-to-date with the latest data and continue to perform well over time.

    To set up an automatic model retraining pipeline using GitHub webhooks, the general idea is to create a pipeline that gets triggered every time there's a push to a specific branch, or when a pull request is merged into the master branch of a GitHub repository. This trigger can then initiate a series of tasks like data pre-processing, model training, evaluation, and possibly redeployment of the model if the performance is satisfactory.

    In this setup, we'll use Pulumi to configure a GitHub webhook and an AWS CodePipeline that listens to this webhook for changes to the repository. This continuous integration (CI) pipeline will leverage AWS services such as AWS CodeBuild for building and testing the code, and AWS Sagemaker for training and evaluating the model.

    Here's a step-by-step Pulumi program in Python to set it up:

    1. Create a GitHub Repository webhook using the pulumi_github provider.
    2. Define an AWS CodePipeline using the pulumi_aws provider which will watch for webhook events.
    3. Setup AWS CodeBuild projects within the CodePipeline for the necessary build and test phases.
    4. Configure AWS SageMaker to train and evaluate the model.

    Now let's write the program:

    import pulumi import pulumi_github as github import pulumi_aws as aws # Step 1: Creating a GitHub repository webhook. # Replace `myrepo` with the name of your repository and `myorg/myrepo` with the organization and repository name. # The `secret` is the value that GitHub uses to ensure the webhook sent to the URL is from GitHub. You should generate this secret and keep it safe. github_webhook = github.RepositoryWebhook("example-webhook", repository="myrepo", configuration=github.RepositoryWebhookConfigurationArgs( url="https://webhooks.example.com/hook", content_type="json", secret="mysecret" ), events=["push", "pull_request"], active=True) # Step 2: Define an AWS CodePipeline that gets triggered by the webhook. # The details of pipeline stages like source, build, and deploy would be defined here. pipeline = aws.codepipeline.Pipeline("example-pipeline", role_arn="<IAM Role ARN>", # IAM role must have relevant permissions for CodePipeline. artifact_store=aws.codepipeline.PipelineArtifactStoreArgs( location="<S3 Bucket Name>", # Specify the S3 bucket for storing artifacts. type="S3" ), stages=[ # Define your stages here. ]) # Step 3: Setup AWS CodeBuild projects for building and testing within the stages of the CodePipeline. # Example of a CodeBuild project setup which would be part of a stage in the CodePipeline above. codebuild_project = aws.codebuild.Project("example-build", artifacts=aws.codebuild.ProjectArtifactsArgs(type="CODEPIPELINE"), environment=aws.codebuild.ProjectEnvironmentArgs( compute_type="BUILD_GENERAL1_SMALL", image="aws/codebuild/standard:4.0", type="LINUX_CONTAINER", ), service_role="<IAM Role ARN>", # This IAM role should have permissions for CodeBuild operations. source=aws.codebuild.ProjectSourceArgs( type="CODEPIPELINE" )) # Step 4: Configure AWS SageMaker for model training. # Assuming you have a training script and necessary permissions, you could configure a training job for retraining. # Remember to replace placeholders with actual values suitable for your setup. training_job = aws.sagemaker.TrainingJob("example-training-job", role_arn="<IAM Role ARN for Sagemaker>", # Specify the IAM role for SageMaker. algorithm_specification=aws.sagemaker.TrainingJobAlgorithmSpecificationArgs( training_image="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:1", training_input_mode="File"), input_data_config=[ # Configuration for input data aws.sagemaker.TrainingJobInputDataConfigArgs( channel_name="train", data_source=aws.sagemaker.TrainingJobInputDataSourceArgs( s3_data_source=aws.sagemaker.TrainingJobInputS3DataSourceArgs( s3_data_type="S3Prefix", s3_location="s3://bucket/path/to/training/data", s3_uri="s3://<Bucket Name>/<Training Data Path>" ) ) ) ], output_data_config=aws.sagemaker.TrainingJobOutputDataConfigArgs( s3_output_location="s3://<Bucket Name>/<Output Data Path>" ), resource_config=aws.sagemaker.TrainingJobResourceConfigArgs( instance_count=1, instance_type="ml.c4.xlarge", volume_size_in_gb=10 )) # Export the webhook URL from the GitHub webhook so it can be used as a payload URL. pulumi.export("webhook_url", github_webhook.configuration.apply(lambda c: c.url))

    Keep in mind that for this script to work, you would need to set up the appropriate IAM roles with the necessary permissions for each of the AWS services involved, such as CodePipeline, CodeBuild, and SageMaker. The actual names of the buckets, IAM roles, and other AWS entities depend on your setup.

    Also, ensure you have the Pulumi CLI and AWS CLI configured properly on your system before running this program, and the webhook secret should be generated and managed securely.