1. Storing Training Datasets for LLMs on GitHub Repos


    Storing large training datasets for Language Learning Models (LLMs) on GitHub repositories involves a few steps. GitHub repositories are a great choice for version control and collaboration on code, but they are not designed to store large datasets, especially due to their limitations on file size and repository size.

    Typically, for large datasets, you would use Git LFS (Large File Storage) which allows you to track large files in your repository. However, for simplicity, I am providing a Pulumi program that creates a GitHub repository that could be used for smaller datasets, code related to LLMs, or potentially could be configured with Git LFS (or similar tool) for larger data.

    This program uses the pulumi_github provider to create a new GitHub repository. It also sets up a README file in the repository with basic information. In a real-world situation, you'd further configure the repository settings, add collaborators, and manage permissions as needed.

    Here is a Pulumi program in Python that accomplishes these basic tasks:

    import pulumi import pulumi_github as github # Replace 'your-github-username' with your actual GitHub username, and # 'llm-dataset-repo' with your desired repository name. repo_name = "llm-dataset-repo" github_user = "your-github-username" # Create a new GitHub repository. repo = github.Repository(repo_name, description="Repository to store training datasets for LLMs", visibility="public", # Can be 'public' or 'private'. auto_init=True, # Automatically initialize the repository with an initial commit. gitignore_template="Python", # Assuming Python-related content; change as needed. license_template="mit") # Choose an appropriate license. # Add a README file to the repository. readme_file = github.RepositoryFile("README", repository=repo.name, file="README.md", content="# Training Datasets for LLMs\nThis repository contains training datasets for Language Learning Models.", branch="main") # Assumes the default branch is 'main'. # Export the URL of the newly created repository. pulumi.export('repository_url', repo.html_url)

    Let's break down what's happening in the program:

    • We import the required packages: Pulumi itself and the Pulumi provider for GitHub.
    • We define our repository name and the GitHub username.
    • We create a GitHub repository using github.Repository where the repository name, description, visibility, auto-initialization preference, .gitignore template, and license template are defined.
    • We add a README file to the repository with initial content that describes the repository's purpose. This is done using github.RepositoryFile.
    • Finally, we export the URL of the repository, which can be used to access the repository on the web once the Pulumi program is applied and the resources are created.

    When you run this Pulumi program, it will communicate with the GitHub API to create these resources in your GitHub account. Remember, if you're managing large datasets, you will need to consider using Git LFS or storing your datasets in another service more suited to large files, like Amazon S3 or Google Cloud Storage, and then linking to those files from your GitHub repository.