1. Storing LLM Training Scripts in GCP Source Repositories


    Storing large language model (LLM) training scripts in Google Cloud Source Repositories allows you to manage and version your training code just like you would with any software development project. Google Cloud Source Repositories provide a fully-featured, scalable, private Git repository hosted on Google Cloud.

    Below is a Pulumi program in Python that sets up a new Source Repository on Google Cloud to store LLM training scripts. The program does the following:

    1. Imports the necessary module for Google Cloud (pulumi_gcp) to interact with GCP resources.
    2. Creates a new source repository named llm-training-scripts.
    3. Optionally sets access control with IAM policies to define who can read or write to the repository if needed.

    Here's the Pulumi Python program for creating a Google Cloud Source Repository:

    import pulumi import pulumi_gcp as gcp # Create a new Google Cloud Source Repository to store LLM training scripts source_repo = gcp.sourcerepo.Repository("llm-training-scripts", name="llm-training-scripts") # Export the URL of the created repository pulumi.export("repository_url", source_repo.url) # (Optional) Define IAM policy for the Source Repository # Here, we are assuming that you want to give a specific user the role of a writer to your repository # Replace '[USER_EMAIL]' with the email of the user repo_iam_member = gcp.sourcerepo.RepositoryIamMember("repo-iam-member", repository=source_repo.name, role="roles/source.writer", member="user:[USER_EMAIL]") # Here, we export the IAM member email to be visible in the Pulumi stack output # This is optional but helps in tracking access if managed through Pulumi pulumi.export("iam_member_email", repo_iam_member.member)

    The above code sets up a basic framework to start using the repository. However, in a real-world scenario, you would also want to automate committing your LLM training scripts into this repository and possibly even set up CI/CD pipelines using Google Cloud Build.

    To use this Pulumi program:

    1. First, Install Pulumi and set it up for GCP.
    2. Save this code to a file named __main__.py.
    3. Run pulumi up from the same directory to create the resources.

    The pulumi.export("repository_url", source_repo.url) line makes the repository's URL accessible after deployment, which you can use to clone and interact with your repo.

    Remember to replace '[USER_EMAIL]' with the actual email address of the user you want to give access to. This step is optional, if you omit this, the repository will still be created, but you'll need to set up IAM permissions through the GCP console or via other means.

    After the repository is set up, you can use git to clone the repository and manage your LLM training scripts as you would with any Git repository.

    The resources used in this program are: