Storing LLM Training Datasets in GitHub Repositories

Question

Pulumi · Accepted Answer

Storing large language model (LLM) training datasets within GitHub repositories can be a practical approach if the datasets are not exceedingly large, as GitHub has file and repository size limitations. Given these constraints, Pulumi's GitHub provider can be used to create and manage GitHub repositories and files programmatically.

To accomplish this, we will use Pulumi to create a new GitHub repository and upload example data files for your LLM training dataset. This can be done by defining resources such as `github.Repository` to create a repository and `github.RepositoryFile` to manage files within the repository.

Before you begin, make sure you have the Pulumi CLI installed and configured for use with GitHub. You'll also need a GitHub personal access token with the appropriate scopes (such as `repo` and `admin:repo_hook`) set in your environment so that Pulumi can authenticate with GitHub APIs on your behalf.

Here's a program that will create a new GitHub repository and add an example file to it:

```python
import pulumi
import pulumi_github as github

# Use your GitHub username or organization here
github_owner = "your-github-username"

# Create a new GitHub repository
repository = github.Repository("training-dataset-repo",
    name="llm-training-datasets",
    description="A repository to store LLM training datasets",
    visibility="private",  # Set to 'public' to make the repository publicly accessible
)

# Add an example LLM training dataset file to the repository
training_data_file = github.RepositoryFile("example-training-data",
    repository=repository.name,
    file="example_dataset.txt",
    content="Example content of the LLM training dataset",
    commit_message="Add example training dataset",
)

# Exporting the repository URL so we can easily access it
pulumi.export("repository_url", repository.html_url)
# Exporting the training data file URL (raw content download URL)
pulumi.export("training_data_file_url", training_data_file.download_url)
```

In the example above, we first declare a GitHub repository with the `github.Repository` resource. We're naming it `llm-training-datasets` and making it a private (visible to authorized users only). You can change the visibility to "public" if you want this repository to be open to everyone.

Next, we create a single example file in this repository using the `github.RepositoryFile` resource. This file is called `example_dataset.txt` and contains a simple string representing the content of your LLM training dataset. The content you provide here should be representative of your actual training data.

Finally, we export the URLs for the repository and the training data file using `pulumi.export`. These exports provide outputs that you can use to easily access the resources created or manage them later with Pulumi or other tools.

Once this Pulumi code is executed using the Pulumi CLI (`pulumi up`), it will create these resources in your GitHub account. You can then clone the repository, add your LLM training datasets, or use the GitHub API to manage these files programmatically.

Do keep in mind that GitHub has file size limits (100MB per file on regular repositories and 2GB per file on Git Large File Storage (LFS)), and there's an overall repository size recommendation of staying below 1GB. For very large data sets, you may need to look for an alternative data storage solution more appropriate for large files, like cloud storage services (AWS S3, Google Cloud Storage, etc.).