AWS CodePipeline for Orchestrating Machine Learning Workflows

Question

Pulumi · Accepted Answer

AWS CodePipeline is a continuous integration and continuous delivery (CI/CD) service which you can use to automate your machine learning (ML) workflow. One common use case for CodePipeline in ML is to automate the steps of the machine learning process, including data gathering, processing, model training, and deployment. You might use AWS CodeCommit for version control of your ML code and data sets, AWS CodeBuild for building and testing the code or models, and AWS SageMaker for training and deploying ML models.

To orchestrate ML workflows on AWS, you'd likely use services such as AWS SageMaker for model training and deployment, and AWS Lambda for custom actions in your pipeline. You’d set up a CodePipeline that uses these services to automatically process data, update models, run tests, and deploy to production.

The following Pulumi program outlines how you can set up such an ML workflow using CodePipeline. The program includes the following key components:

- AWS CodeCommit Repository: To store the ML model's code and associated data.
- AWS CodeBuild Project: To run unit tests or scripts for data preprocessing or other purposes.
- AWS CodePipeline: To orchestrate the workflow of pulling the code from CodeCommit, processing with CodeBuild, and triggering a deployment action, which can be integrated with SageMaker.

```python
import pulumi
import pulumi_aws as aws

# Define the AWS CodeCommit repository where your ML code and data will reside.
code_repo = aws.codecommit.Repository("ml_code_repo",
    repository_name="MLRepository",
    description="Repository for ML source code and data sets")

# Define the AWS CodeBuild project for running build/test jobs or any data processing needed.
code_build = aws.codebuild.Project("ml_code_build",
    name="MLBuildProject",
    service_role="arn:aws:iam::123456789012:role/service-role/codebuild-role",
    source=aws.codebuild.ProjectSourceArgs(
        type="CODECOMMIT",
        location=code_repo.clone_url_http
    ),
    environment=aws.codebuild.ProjectEnvironmentArgs(
        compute_type="BUILD_GENERAL1_SMALL",
        image="aws/codebuild/standard:4.0",  # Replace with an image suited for your ML workload
        type="LINUX_CONTAINER"
    ),
    artifacts=aws.codebuild.ProjectArtifactsArgs(
        type="NO_ARTIFACTS"
    )
)

# Define the CodePipeline to orchestrate the ML workflow.
ml_pipeline = aws.codepipeline.Pipeline("ml_pipeline",
    name="MLModelPipeline",
    role_arn="arn:aws:iam::123456789012:role/service-role/codepipeline-role",
    stages=[
        aws.codepipeline.PipelineStageArgs(
            name="Source",
            actions=[
                aws.codepipeline.PipelineStageActionArgs(
                    name="SourceAction",
                    category="Source",
                    owner="AWS",
                    provider="CodeCommit",
                    version="1",
                    output_artifacts=["sourceOutput"],
                    configuration={
                        "RepositoryName": code_repo.name,
                        "BranchName": "master",
                    }
                )
            ]
        ),
        aws.codepipeline.PipelineStageArgs(
            name="Build",
            actions=[
                aws.codepipeline.PipelineStageActionArgs(
                    name="BuildAction",
                    category="Build",
                    owner="AWS",
                    provider="CodeBuild",
                    input_artifacts=["sourceOutput"],
                    output_artifacts=["buildOutput"],
                    version="1",
                    configuration={
                        "ProjectName": code_build.name,
                    }
                )
            ]
        ),
        # Additional stages, such as a deploy stage to update SageMaker model, go here.
    ]
)

pulumi.export("code_commit_repo_url", code_repo.clone_url_http)
```

In this program:

1. We start by creating an AWS CodeCommit repository named `MLRepository` which will be used to store the ML code and datasets. You would push your ML project code and data to this repo.
2. Next, we create an AWS CodeBuild project called `MLBuildProject` using a standard compute type to perform tasks like running tests and data processing. The source location for CodeBuild is the previously created `MLRepository`.
3. We then set up a CodePipeline with the name `MLModelPipeline`. It has a `Source` stage to pull code from the `MLRepository` and a `Build` stage using the `MLBuildProject` to perform actions on the source code.
4. This pipeline can be extended to include additional stages according to your ML workflow requirement. For example, you could add a `Deploy` stage to train a model using SageMaker or deploy an updated version of the model.

Remember to replace the `service_role` ARNs with the actual ARNs of the IAM roles that have the required permissions to run CodeBuild projects and CodePipeline.

Be sure to check the [AWS CodePipeline documentation](https://www.pulumi.com/docs/reference/pkg/aws/codepipeline/pipeline/) and the documentation for [AWS CodeBuild](https://www.pulumi.com/docs/reference/pkg/aws/codebuild/project/) and [AWS CodeCommit](https://www.pulumi.com/docs/reference/pkg/aws/codecommit/repository/) for further details and customization of pipelines, builds, and repositories.