Storing Large Language Model Training Scripts on Git Branches

Automating Git Branch Management with Infrastructure as Code

To manage and store large language model training scripts in a version-controlled environment, we can leverage Git branches. Git branches enable you to work on new features, fixes, or experiments in isolated environments before integrating them into your main project.

In this context, managing branches via infrastructure as code allows you to automate branch creation and ensure a uniform process across your projects.

For simplicity, let's consider two popular services with Pulumi providers that you can use to manage Git branches programmatically: GitHub and GitLab. The choice between these services depends on where your code is hosted. Below, we’ll discuss two examples using Pulumi for each scenario.

Managing a GitHub Branch

Here, we use the pulumi_github provider to create a new branch in an existing GitHub repository. This is useful if you want to kick off a new set of experiments or start developing a new feature.

import pulumi
import pulumi_github as github

# Initialize the GitHub provider with the target repository owner and name
github_provider = github.Provider("github-provider", owner="your_github_username")

# Define a new branch in your GitHub repository
new_branch = github.Branch("new-branch",
                           repository="your-repo-name",
                           branch="new-feature-branch",
                           source_sha="source_branch_sha", # Replace with the commit SHA to branch from
                           opts=pulumi.ResourceOptions(provider=github_provider))

# Export the created branch name
pulumi.export("branch_name", new_branch.branch)

Managing a GitLab Branch

Here, we will handle a similar task but for a GitLab repository using the pulumi_gitlab provider.

import pulumi
import pulumi_gitlab as gitlab

# Initialize the GitLab provider
gitlab_provider = gitlab.Provider("gitlab-provider")

# Define a new branch in a GitLab repository
new_branch = gitlab.Branch("new-branch",
                           project="your_project_id", # The unique ID or URL-escaped path of the project
                           name="new-feature-branch",
                           ref="master", # The name of the branch you want to create from
                           opts=pulumi.ResourceOptions(provider=gitlab_provider))

# Export the created branch name
pulumi.export("branch_name", new_branch.name)

Why Manage Git Branches with Pulumi?

Automation: Automate the creation of branches for routine operations like preparing environments for new features or custom configurations for training.
Consistency: Ensure that branches are created using standardized naming conventions and sourced from correct points in the codebase.
Integration: Integrate branch creation into larger cloud infrastructure setup for continuous deployment or continuous integration pipelines.

With Pulumi, you can write code in Python, TypeScript, Go, or other programming languages to manage cloud resources. In addition to low-level resources, Pulumi offers higher-level abstractions that you can use to compose and manage cloud infrastructure and applications effectively.

When using this program, ensure your Pulumi CLI configuration is authenticated with your respective Git provider. For GitHub, you can set up a personal access token and use it with the pulumi_github provider to authenticate your requests. With GitLab, you’ll also need to ensure you’re authenticated via a personal access token or another supported method.

Once you've decided on the appropriate version control service and set up the Pulumi environment, you can incorporate these branch management scripts into your workflow. They can be part of a CI/CD pipeline, be triggered by specific events or conditions, or be used for systematic branch creation for model training variant tracking.