Automated Code Merge Checks for AI Model Updates

Question

Pulumi · Accepted Answer

In order to implement automated code merge checks for AI Model updates within the realm of Pulumi's infrastructure as code offerings, we can leverage the resources provided for version control, continuous integration, and continuous deployment. One such provider is Azure, and specifically, we'll be using the Azure DevOps services which include repositories, pipelines, and policies for branch protection.

Below, I'll provide a Pulumi program written in Python that showcases how you can use Azure DevOps resources to enforce a code merge check in your project. This automated check could be used as a part of your AI model update process to ensure that certain conditions are met before code is merged. For example, it may run automated tests or validations on the model.

First, we'll use the `azuredevops.BranchPolicyStatusCheck` resource to create a merge check policy on a given branch. This policy requires that a certain status check passes before a pull request can be merged to that branch.

Here is a detailed Pulumi Python program that sets up a status check branch policy on a repository within Azure DevOps:

```python
import pulumi
import pulumi_azuredevops as azuredevops

# Set up a new Azure DevOps Project.
project = azuredevops.Project("example-project",
                              description="Example Project")

# Set up a new Azure DevOps Git Repository within the above project.
repo = azuredevops.GitRepository("example-repo",
                                 project_id=project.id,
                                 initialization=azuredevops.GitRepositoryInitializationArgs(
                                     init_type="Clean"
                                 ))

# Branch Policy Configuration for requiring status checks before merging.
branch_policy = azuredevops.BranchPolicyStatusCheck("example-policy-status-check",
                                                    project_id=project.id,
                                                    repository_id=repo.id,
                                                    enabled=True,
                                                    blocking=True,
                                                    settings=azuredevops.BranchPolicyStatusCheckSettingsArgs(
                                                        scopes=[azuredevops.BranchPolicyStatusCheckSettingsScopeArgs(
                                                            repository_id=repo.id,
                                                            repository_ref="refs/heads/master",  # Applying this policy to the master branch.
                                                            match_type="Exact"
                                                        )],
                                                        status_name="model_validation_status",  # The name of the status check to look for.
                                                        status_genre="model_validation"  # The genre of the status.
                                                    ))

# Export the Project URL so we can access it easily after deployment.
pulumi.export("project_url", project.url)
# Export the Repository URL so we can clone it and begin working on our AI models.
pulumi.export("repo_url", repo.web_url)
```

In the program above, we're doing the following:

1. We create a new Azure DevOps project called "example-project".
2. Within the project, we create a new Git repository "example-repo".
3. We then apply a branch policy to the `master` branch of the newly created repository, requiring that a status check with the name "model_validation_status" and genre "model_validation" is passing before allowing merges.

In a real-world scenario, this status check could be tied to your CI/CD pipeline that runs tests against your AI model's code and updates the status check based on the pipeline's outcome.

- The `pulumi.export` lines output the URLs for the created project and repository, which can be useful for accessing them through a web interface after deployment.

Remember to replace `"model_validation_status"` and `"model_validation"` with the actual name and genre of the status checks that your CI/CD pipeline uses.

To use this Pulumi program:

1. Have the Pulumi CLI installed and your Azure credentials configured.
2. Create a new Pulumi program directory and install the necessary Python packages (`pulumi` and `pulumi_azuredevops`).
3. Save the above program to a file (e.g., `__main__.py`).
4. Run `pulumi up` to deploy the resources. The CLI will guide you through the process and show you the changes before they are applied.