1. Automated Model Training Workflows with AWS CodeBuild


    If you are looking to automate model training workflows on AWS, you will typically leverage services like AWS CodeBuild, which allow you to build and test code with continuous scaling. You would also likely involve other AWS services such as Amazon S3 for storing data sets and artifacts, and possibly Amazon SageMaker for the model training itself. AWS CodeCommit could be used for storing and versioning your code.

    Here's a high-level overview of what is needed for such a setup:

    1. AWS CodeBuild Project: This resource is where you define the environment for your code to be built and tested. You can specify the build image, compute type, environment variables, and more.

    2. Amazon S3 Bucket: The S3 bucket will be used to store your training data and the trained model artifacts.

    3. AWS CodePipeline: To orchestrate the workflow, you might use AWS CodePipeline, which can automate your release process.

    4. AWS CodeCommit Repository (optional): If you're using AWS CodeCommit as your version control service, this resource will represent your repository.

    5. Amazon SageMaker Training Job (optional): AWS SageMaker can be used to create and manage the training jobs for your machine learning models.

    Now, let's consider a basic Pulumi program that sets up an AWS CodeBuild project. In this program, we will not cover AWS CodeCommit, AWS CodePipeline, Amazon S3, or Amazon SageMaker. These services can further extend the functionality and integrate into your CI/CD pipeline for a complete ML workflow solution.

    Here is a Pulumi program that creates an AWS CodeBuild project:

    import pulumi import pulumi_aws as aws # Create an AWS CodeBuild project for automated model training. model_training_project = aws.codebuild.Project("modelTrainingProject", # This is the name for the CodeBuild project. name="automated-model-training", # Source specifies the repository where the source code is stored. source=aws.codebuild.ProjectSourceArgs( type="GITHUB", # Here you can also use "CODECOMMIT" or the source control of your choice. location="https://github.com/your-repo/model-training.git", # Replace with your repository URL. git_clone_depth=1, # Optional: Integer clone depth. buildspec="buildspec.yml" # Path to the buildspec file. ), # Environment determines the type of build environment. environment=aws.codebuild.ProjectEnvironmentArgs( compute_type="BUILD_GENERAL1_SMALL", # The compute size for the build. image="aws/codebuild/standard:4.0", # The identifier of the Docker image to use. type="LINUX_CONTAINER", # The type of build environment to use for related builds. environment_variables=[ # Environment variables to be passed to the build. aws.codebuild.ProjectEnvironmentEnvironmentVariableArgs( name="S3_BUCKET", value="my-model-data-bucket" # Replace with your Amazon S3 bucket name. ) ] ), # Service role that enables AWS CodeBuild to interact with other AWS services. service_role="arn:aws:iam::123456789012:role/service-role/codebuild-service-role", # Replace with your IAM role ARN. # Artifacts determine how the build output is stored. artifacts=aws.codebuild.ProjectArtifactsArgs( type="NO_ARTIFACTS", # We're not storing build output artifacts here. ), # Additional settings can be specified based on your requirements. ) # Export the CodeBuild project's name. pulumi.export("codebuild_project_name", model_training_project.name) # Export the CodeBuild project's ARN. pulumi.export("codebuild_project_arn", model_training_project.arn)

    In this example, we create a simple AWS CodeBuild project that references an imaginary GitHub repository for your machine learning source code. The buildspec.yml file in this repository should contain the commands to install dependencies, train the model, and possibly upload the trained model to an S3 bucket. Lastly, we export the project name and ARN so that they can be used or referenced later, for instance in a CodePipeline pipeline definition.

    Remember to replace placeholder values like GitHub repository URL, S3 bucket name, and IAM role ARN with your actual information. The environment variables and other settings such as compute_type or image in ProjectEnvironmentArgs would depend on the specific needs of your model training job.

    This is a foundational step towards a more complex CI/CD setup that might involve multiple AWS services and Pulumi resources. The complete CI/CD pipeline for ML might involve more steps, including testing the model, deploying it for inference, and setting up monitoring and alarms. These additional features are beyond the scope of this basic setup but can be built upon with Pulumi resources for a full end-to-end machine learning CI/CD pipeline.