Model Version Control with GitHub Branches.

Question

Pulumi · Accepted Answer

Version control is a critical aspect of software development, especially when multiple people are working on the same project. It allows you to track changes, revert to previous states, and manage different versions of the codebase. GitHub is one of the most popular platforms that provide version control using Git.

In the context of using Pulumi with GitHub for model version control, you would typically be managing the infrastructure where your models are deployed, tested, and served. This could involve setting up environments for different branches in a GitHub repository, where each branch represents a different version or stage of development of your machine learning models.

Here's how you can use Pulumi to achieve model version control with branches in a GitHub repository:

1. **GitHub Branches** - Create and manage branches in GitHub to represent different versions or stages of your models.
2. **Infrastructure Per Branch** - Use Pulumi to provision and manage unique infrastructure setups per branch, e.g., separate compute instances for testing different versions of a model.
3. **Branch Protection** - Enforce certain workflows or requirements before merging changes into main branches, such as passing CI/CD pipelines or peer reviews.

Below is a Pulumi program in Python that demonstrates how to create a GitHub branch and set up branch protection rules. This program manages these resources using the `pulumi_github` provider.

```python
import pulumi
import pulumi_github as github

# Configuration
repo_name = "my-model-repo"  # Name of the GitHub repository
branch_name = "model-v1"     # Branch name for a version of the model
source_sha = "main"          # The commit SHA or branch name where the new branch will branch off

# Create a new GitHub branch for a specific version of the model
model_branch = github.Branch("model-branch",
    repository=repo_name,
    branch=branch_name,
    source_sha=source_sha
)

# Set up branch protection to enforce certain policies on the new branch
branch_protection = github.BranchProtection("branch-protection",
    repository_id=model_branch.repository,
    pattern=branch_name,  # The branch pattern to apply the protection to
    # The following settings specify the protection rules; these can be adjusted as needed
    enforce_admins=True,
    allows_deletions=False,
    required_status_checks=github.BranchProtectionRequiredStatusCheckArgs(
        strict=True,
        contexts=["ci/travis"]
    ),
    required_pull_request_reviews=github.BranchProtectionRequiredPullRequestReviewsArgs(
        required_approving_review_count=1
    ),
    restrictions=github.BranchProtectionRestrictionsArgs(
        users=["username"],  # Replace with actual GitHub usernames
        teams=["team-name"]  # Replace with actual GitHub team names
    )
)

# Export the branch protection ID
pulumi.export("branch_protection_id", branch_protection.id)
```

In this program, we first define a GitHub repository and a new branch name. Using the `github.Branch` class, we create a new branch off the main branch for one version of the model. Then, we apply branch protection rules to this branch using `github.BranchProtection`. These rules include:

- Enforcing admin regulations.
- Preventing the branch from being deleted.
- Requiring specific status checks to pass before merging, such as CI/CD pipeline checks.
- Requiring a certain number of reviews on pull requests.
- Restricting who can push to the branch by specifying allowed users and teams.

By adjusting the protection rules, you can tailor the workflow to your project's needs, ensuring that your model versions are managed correctly and safely. Once set, these rules help maintain a rigorous development and deployment process, which is particularly important for machine learning models where the accuracy and reliability of the output are paramount.

After running this Pulumi program, the defined branch with its protection rules will be created in your GitHub repository. You can manage multiple branches for different versions of your models following similar steps, enabling robust version control for your projects.