Automated Model Deployment Workflows with GitHub Branch Protection

Question

Pulumi · Accepted Answer

To create an automated model deployment workflow with GitHub Branch Protection using Pulumi, we would typically set up a CI/CD pipeline with GitHub Actions or another CI/CD tool that interacts with GitHub. This pipeline would build, test, and deploy our model when changes are merged into a protected branch. Branch protection rules are used to enforce certain workflows, like requiring a code review before merging code changes.

Below, you will find a Pulumi program in Python that sets up branch protection rules for a GitHub repository using the `github.BranchProtection` resource. This ensures that certain conditions are met before code can be merged into the master branch. The conditions we're setting up are:
- **Required status checks**: Ensures specific checks such as continuous integration tests must pass before merging.
- **Required PR reviews**: Enforces that pull requests must have at least one approved review.
- **Include administrators**: Applies these rules even to users with admin permissions.

This resource does not directly implement the deployment of the model; rather, it sets the ground rules to ensure that any code related to the model (or otherwise) undergoes the required scrutiny before being deployed, which is a critical step in automation workflows.

```python
import pulumi
import pulumi_github as github

# Instantiate the GitHub repository we want to protect.
# For this, you would need the repository already created and the Pulumi GitHub provider configured with the necessary credentials.
repo_name = "my-model-deployment-repo"  # Replace with your repository name

# Set up the branch protection for the 'master' branch.
master_branch_protection = github.BranchProtection("master-branch-protection",
    pattern="master",                  # The branch to protect, 'master' in this case.
    repository_id=repo_name,
    enforce_admins=True,               # Enforce rules for admin users as well.
    required_pull_request_reviews=[{   # Require at least one review before merging.
        "dismiss_stale_reviews": True, # Dismiss stale reviews when new commits are pushed.
        "require_code_owner_reviews": True, # Require review from a code owner.
        "required_approving_review_count": 1, # Number of required approving reviews.
    }],
    required_status_checks={           # Require status checks to pass before merging.
        "strict": True,                # Require branches to be up to date before merging.
        "contexts": ["ci/travis-ci"],  # List of required status checks, replace with your CI/CD tool.
    },
    allows_deletions=False,            # Disallow branch deletions.
)

# Export the URL of the protected branch to access it easily later if needed.
pulumi.export('protected_branch_url', pulumi.Output.concat("https://github.com/", repo_name, "/tree/master"))

```

This program must be executed with the Pulumi CLI and with proper access to the GitHub repository.

* `github.BranchProtection`: This is the Pulumi resource that sets up branch protection on GitHub. Consult the [`BranchProtection` documentation](https://www.pulumi.com/registry/packages/github/api-docs/branchprotection/) for more details on the different settings you can configure.
* `pulumi.export`: This outputs the protected branch URL, which is useful to easily access the branch settings or view the branch on GitHub.

This setup is part of the security and robustness aspects of your CI/CD pipelines. After setting these rules, any code that fails the checks or doesn't get reviewed cannot be merged to your master branch, helping prevent faulty or unreviewed code from being deployed. This practice is especially important for model deployment, as models often have significant impacts once they are live.

You would then create a GitHub Actions workflow or configure your CI/CD tool to automatically trigger model testing, build, and deployment processes when code is pushed to the master branch. The workflow can include steps to package the model, run any additional tests, and deploy it to your hosting environment of choice (e.g., AWS Sagemaker, Google AI Platform, Azure ML, etc.).

The CI/CD pipeline configuration is beyond the scope of this Pulumi program and would vary greatly depending on the specifics of your model, preferred CI/CD tooling, hosting environment, and other factors.