1. Continuous Integration for ML with Databricks Repos


    Continuous Integration (CI) is a software development practice where developers frequently merge their code changes into a central repository, after which automated builds and tests are run. Integrating CI with Machine Learning (ML) can involve using Databricks Repos, which is a feature provided by Databricks that allows you to associate Git repositories with Databricks workspaces and run notebooks and jobs based on the contents of the repositories.

    Below, I'll write a Pulumi program in Python that sets up a Databricks Repo, which can be used as part of your CI pipeline for ML. This will involve creating a Databricks Repo resource that is tied to your Git repository.

    The databricks.Repo resource creates a link between your Databricks workspace and your Git repository containing the ML code you want to integrate with CI. This allows you to clone, pull, and push to and from your Git provider directly within the Databricks workspace. By doing so, you can manage your notebooks, experiment with models, and track experiments directly inside your Databricks environment, while keeping your changes version-controlled in Git.

    To use the databricks.Repo resource, you would typically need the Git URL, path to store the repo within Databricks, branch to checkout, and commit hash to be checked out in the Databricks workspace. For this program, we'll assume you already have a Git provider like GitHub or GitLab, and a branch with the necessary ML code set up.

    Here is how you would set it up in Pulumi using the Python SDK:

    import pulumi import pulumi_databricks as databricks # Replace the values of these variables with your actual data. git_url = "https://github.com/your-user/your-ml-repo.git" # Git repository URL with your ML code branch_name = "main" # Branch you want to sync with Databricks repo_path = "/Repos/your-username/your-repo" # Path within Databricks to clone your repo into # Creating a Databricks Repo resource to tie in the Git repository with Databricks. ml_repo = databricks.Repo("ml-repo", url=git_url, path=repo_path, branch=branch_name, # For demonstration, we omit the commitHash for the latest commit on branch, # but you could specify a particular commit here. git_provider="github" # Replace with your Git provider ('github' or 'gitlab') ) # Exporting the Databricks Repo URL so you can easily access it in the Databricks workspace. pulumi.export('databricks_repo_url', ml_repo.url)

    This program initializes a Pulumi stack in Python and uses the pulumi-databricks provider to create a Repo resource. The ml-repo resource is created with properties such as the repository URL, the location within Databricks to clone the repository, the branch name to check out, and the Git provider (GitHub or GitLab).

    The pulumi.export line at the end is not strictly necessary for the setup to work, but it can provide a convenient output that you could use to access your Databricks Repo URL outside of the Pulumi application.

    Please note that to run this Pulumi program, you will need the Pulumi CLI installed and configured, as well as the pulumi_databricks provider. You would also need permissions to manage resources within Databricks and access to the Git repository that contains your ML code.