1. Version Control for Machine Learning Models with GitHub


    Version control for machine learning models is a crucial aspect of the ML lifecycle, as it allows you to track, version, share, and collaborate on machine learning models and associated code. GitHub, a widely-used platform for version control, can be used for versioning machine learning models through repositories. To automate the setup of such a system on GitHub, you can use the pulumi_github package to programmatically create repositories, manage permissions, and set up projects and other resources.

    Below is a Pulumi Python program that demonstrates how to use the GitHub provider to create a new repository with some features that can be useful for version controlling machine learning models:

    1. A new GitHub repository is created with initial README, license, and .gitignore.
    2. Issues and projects are enabled for tracking tasks and enhancements.
    3. A GitHub action workflow is set up for Continuous Integration (CI) which can be expanded to include steps to lint, test, and build your machine learning code.
    4. Webhooks can be integrated to trigger external builds or deployments when a push to the repository happens.

    Let's walk through the program.

    import pulumi import pulumi_github as github # Create a new GitHub repository to store machine learning models ml_repository = github.Repository("ml_models_repository", # A description for the repository description="Repository for versioning machine learning models", # Set the visibility to public or private visibility="private", # Initialize the repository with an initial commit including a README auto_init=True, # Template for the initial README.md content template=github.RepositoryTemplateArgs( owner="github", repository="hello-world", include_all_branches=False, ), # Enable Issues for tracking and collaboration has_issues=True, # Set the default branch default_branch="main", ) # Enable GitHub Pages for static site hosting directly from the repository github_pages = github.RepositoryPages("ml_models_repository_pages", repository=ml_repository.name, source=github.RepositoryPagesSourceArgs( branch="main", path="/docs", ), opts=pulumi.ResourceOptions(depends_on=[ml_repository]), ) # Create a GitHub Action workflow for CI github_workflow = github.ActionSecret("ml_models_workflow", secret_name="GH_ACTION_WORKFLOW", repository=ml_repository.name, plaintext_value="Your_Secret_Value_Here", opts=pulumi.ResourceOptions(depends_on=[ml_repository]), ) # Create a webhook for triggering external actions on push events webhook = github.RepositoryWebhook("ml_models_webhook", repository=ml_repository.name, configuration=github.RepositoryWebhookConfigurationArgs( url="https://example.com/webhook", content_type="json", ), events=["push"], active=True, opts=pulumi.ResourceOptions(depends_on=[ml_repository]), ) # Export the repository URL pulumi.export("repository_url", ml_repository.html_url) # Export the GitHub Pages URL pulumi.export("pages_url", github_pages.url)

    This program sets up a GitHub repository that can be used for ML model version control. It exports the repository URL and the GitHub Pages URL, so you can access them after deployment. You would need to replace "Your_Secret_Value_Here" with a real secret value for any CI workflows.

    Remember that this is a basic setup. You would need to push your machine learning code, datasets (if not too large and not private), model version files (like .pkl or .h5), and potentially setup more sophisticated CI/CD pipelines to fully utilize version control with GitHub for your ML projects.

    This program does not include instructions for managing branches, merges, pull requests, and releases, which are also crucial parts of version control in a collaborative environment. However, these tasks are primarily carried by developers through Git command line or GitHub UI, and GitHub Actions can be configured to integrate with Pulumi for infrastructure updates if required.