1. GitHub as a Repository for Dataset Versioning


    To use GitHub as a repository for dataset versioning with Pulumi, you'll be setting up a GitHub repository where the datasets can be stored, versioned, and managed. The repository will have features like issues for tracking tasks or problems, projects for organizing work, and other GitHub features that can be managed programmatically using Pulumi.

    The Pulumi GitHub provider offers a range of resources that allow you to interact with GitHub in a programmatic way. Using the Pulumi GitHub provider, you can create a repository, manage access permissions, track issues, milestones, and much more—all from code. Managing your dataset versioning this way allows you to have Infrastructure as Code (IaC), which means that your infrastructure (in this case, the GitHub repository and related settings) is defined in code and can be versioned, reviewed, and reliably reproduced.

    Below is a Pulumi program in Python that creates a private GitHub repository suitable for versioning datasets. It initializes the repository with a README and sets up basic configuration including issue labels, and milestones for tracking dataset releases.

    import pulumi import pulumi_github as github # Set up a new private GitHub repository repo = github.Repository("dataset-versioning-repo", description="A repository to version datasets", visibility="private", # Private repository auto_init=True, # Automatically initialize with a README ) # Create a new issue label for dataset review dataset_review_label = github.IssueLabel("dataset-review-label", color="ededed", name="dataset-review", repository=repo.name ) # Create a milestone for the first dataset release v1_0_release = github.Milestone("v1.0-release", title="V1.0 Release", repository=repo.name, description="Tracking milestone for the initial dataset release.", state="open", ) # Export the repository's HTTP clone URL pulumi.export("repo_http_clone_url", repo.http_clone_url)

    Here’s what each Pulumi resource in this program does:

    • github.Repository: This resource creates a new GitHub repository. It sets the repository to be private (visible only to those granted access) and initializes it with a README.md file which is a common practice for repositories.
    • github.IssueLabel: This resource creates a custom label for GitHub issues. The label can be used to tag issues or pull requests that are specifically related to dataset reviews.
    • github.Milestone: This resource sets up a milestone in the repository, which can be used to group together issues and pull requests that correspond to a specific phase or release of a dataset. The milestone here is intended for an initial dataset release.

    In this setup, collaborators can use GitHub's features to manage the datasets:

    • Version Control: Each dataset can be added or updated through commits, with a clear history of changes and the ability to revert if necessary.
    • Issues: For tracking data quality problems, enhancement requests, and tasks.
    • Labels: To categorize and filter issues and pull requests, like the 'dataset-review' label created in the code.
    • Milestones: For tracking progress towards dataset versions or releases, such as the 'V1.0 Release' milestone.

    Running the Program

    To run this Pulumi program:

    1. Ensure Pulumi and Python are installed on your machine.
    2. Configure Pulumi with your GitHub access token.
    3. Create a new directory for your Pulumi project and enter that directory.
    4. Run pulumi new python to create a new Pulumi Python project.
    5. Replace the content of __main__.py with the code provided above.
    6. Run pulumi up to preview and apply the changes.

    After running pulumi up, you will see your new GitHub repository and can start using it for dataset versioning! You can add your datasets to the repository and make use of branches, commits, and tags to manage versions of your datasets systematically.