1. Training Data Versioning with Git Branches


    Using Pulumi to manage infrastructure related to versioning training data with Git branches can be a common practice in machine learning workflows. This allows you to keep track of different versions of your datasets and models over time.

    For this example, I'll demonstrate how you can utilize Pulumi with GitHub to create a new Git branch that could represent a version of your training data. The concept is that each branch can represent a state of the data at a particular point in time. Every time you have a new version of your data, you can create a new branch from the main branch, which signifies the most stable or production version of your dataset. By doing this, you enable data scientists and machine learning engineers to experiment with different datasets while maintaining an easy way to revert to or compare with previous data states.

    Let's write a Pulumi program to create a new Git branch in a GitHub repository:

    import pulumi import pulumi_github as github # Configuration variables for the GitHub repository and the new branch name repo_name = "data-repository" # Name of the GitHub repository new_branch_name = "data-version-123" # The name of the new branch to create base_branch_name = "main" # The name of the base branch # Fetch the specified repository from GitHub repo = github.Repository("repo", name=repo_name ) # Get the latest commit from the base branch to use it as a reference for the new branch base_branch_ref = github.get_branch( repository=repo.name, branch=base_branch_name ) # Create a new branch by referring to the latest commit of the base branch new_branch = github.Branch("new-branch", repository=repo.name, branch=new_branch_name, source_sha=base_branch_ref.commit_sha ) # Export the URL of the repository to access it later pulumi.export("repository_url", repo.html_url) # Export the name of the new branch pulumi.export("new_branch_name", new_branch.branch)

    Here's what each part of this Pulumi program does:

    1. We import the pulumi and pulumi_github modules, which contain the classes and functions we'll use.
    2. We define configuration variables for the repository name, the new branch name, and the base branch (usually main).
    3. We fetch the existing GitHub repository using the github.Repository class. This repository is where your training data and related code might live.
    4. We use the github.get_branch() function to get the latest commit from the base branch, which we'll use as a starting point for the new branch.
    5. We create a new branch in the repository using the github.Branch resource, referencing the SHA of the latest commit from the base branch.
    6. We export the repository's URL and the new branch name using pulumi.export() so you can easily access them after the Pulumi program runs.

    By running this Pulumi program, you will have a new branch in your GitHub repository, ready to be used to version a new set of training data. Remember to replace data-repository and data-version-123 with your actual GitHub repository name and the desired branch name for your training data.