Continuous Training Pipelines for ML Models with AWS CodePipeline

Question

Pulumi · Accepted Answer

To create continuous training pipelines for ML models with AWS CodePipeline, you'll need several AWS services to work together. AWS CodePipeline allows you to automate your software release process, in this case, for continuously training and deploying machine learning models. AWS CodeBuild can be used to run your training scripts or ML workflows. You may also use AWS CodeCommit as a source repository to store your ML code, and potentially AWS S3 to store training data and model artifacts.

Below you can find a Pulumi program that sets up a simple continuous training pipeline for ML models using AWS CodePipeline. The program also includes AWS CodeCommit for source control and AWS CodeBuild for running the training jobs.

The example assumes you have your machine learning model code and a buildspec.yml file that tells CodeBuild how to run your training job.

```python
import pulumi
import pulumi_aws as aws

# Creating a new CodeCommit repository for storing the ML model code
ml_repo = aws.codecommit.Repository("mlRepo",
    repository_name="ml-model-repo",
    description="Repository for ML model code and training scripts")

# Define the build project for AWS CodeBuild to run the training job
ml_build_project = aws.codebuild.Project("mlBuildProject",
    name="ml-model-training",
    description="Build project for training ML models",
    artifacts=aws.codebuild.ProjectArtifactsArgs(
        type="NO_ARTIFACTS", # No artifacts, only training job
    ),
    environment=aws.codebuild.ProjectEnvironmentArgs(
        compute_type="BUILD_GENERAL1_SMALL", # Choose an instance type for the training job
        image="aws/codebuild/standard:5.0",
        type="LINUX_CONTAINER",
        environment_variables=[
            aws.codebuild.ProjectEnvironmentEnvironmentVariableArgs(
                name="S3_BUCKET",
                value="my-bucket-for-training-data-and-models", # Replace with your S3 bucket name
            ),
        ],
    ),
    service_role=aws.iam.Role("codebuildRole",
        assume_role_policy=json.dumps({
            "Version": "2012-10-17",
            "Statement": [{
                "Action": "sts:AssumeRole",
                "Effect": "Allow",
                "Principal": {"Service": "codebuild.amazonaws.com"},
            }]
        })
    ).arn,
    source=aws.codebuild.ProjectSourceArgs(
        type="CODECOMMIT",
        location=ml_repo.clone_url_http,
    ))

# Define the CodePipeline to manage the workflow
ml_pipeline = aws.codepipeline.Pipeline("mlPipeline",
    name="ml-model-training-pipeline",
    role_arn=aws.iam.Role("codepipelineRole",
        assume_role_policy=json.dumps({
            "Version": "2012-10-17",
            "Statement": [{
                "Action": "sts:AssumeRole",
                "Effect": "Allow",
                "Principal": {"Service": "codepipeline.amazonaws.com"},
            }]
        })
    ).arn,
    stages=[
        aws.codepipeline.PipelineStageArgs(
            name="Source",
            actions=[
                aws.codepipeline.PipelineStageActionArgs(
                    name="Source",
                    category="Source",
                    owner="AWS",
                    provider="CodeCommit",
                    version="1",
                    output_artifacts=["sourceOutput"],
                    configuration={
                        "RepositoryName": ml_repo.repository_name,
                        "BranchName": "main", # Replace with your branch name if necessary
                    },
                ),
            ],
        ),
        aws.codepipeline.PipelineStageArgs(
            name="Build",
            actions=[
                aws.codepipeline.PipelineStageActionArgs(
                    name="Build",
                    category="Build",
                    owner="AWS",
                    provider="CodeBuild",
                    input_artifacts=["sourceOutput"],
                    version="1",
                    configuration={
                        "ProjectName": ml_build_project.name,
                    },
                ),
            ],
        ),
        # Add deploy stage if needed
    ])

# Export the repository clone URL so you can easily access it if needed
pulumi.export('ml_repo_clone_url_http', ml_repo.clone_url_http)
```

Let's walk through what this code does:

1. The `aws.codecommit.Repository` creates a new AWS CodeCommit repository to store your source code.

2. The `aws.codebuild.Project` defines the project in AWS CodeBuild that will be used for running the training jobs. Here, you can customize the `compute_type` (the instance type used for computation), `image` (the docker image used), and `environment_variables`.

3. The `aws.codepipeline.Pipeline` sets up the pipeline that orchestrates the workflow. It has a source stage connected to the CodeCommit repository and a build stage connected to the CodeBuild project.

4. The CodeBuild project will use a build specification (`buildspec.yml`) which you'll need to create in your CodeCommit repository. This file defines the commands and settings used by CodeBuild to run the training job.

5. In the pipeline definition, `output_artifacts` and `input_artifacts` are used for passing information between stages.

You should replace placeholder values like `"my-bucket-for-training-data-and-models"` with your actual S3 bucket name where you plan to store your training data and the resulting models.

This is a simplified example, showing how you can get started with AWS services for continuous training of ML models. For a real-world application, you might want to add more stages, such as for testing or deployment, handle more complex workflows or deal with multiple environments.