Storing Training Data for Deep Learning on GitHub

Question

Pulumi · Accepted Answer

To store training data for deep learning on GitHub, you would typically create a repository where you can organize and version-control your datasets alongside your codebase. GitHub repositories are perfect for collaborating with others, tracking changes over time, and easily sharing your work.

Below is a Python program using Pulumi to create a new GitHub repository. We will use the `pulumi_github` package to interact with GitHub. You'll need to have a GitHub account and provide an authentication token with appropriate permissions to Pulumi so it can create resources on your behalf.

In this program, we will:

1. **Import the required libraries**: `pulumi` and `pulumi_github`.
2. **Set up a new GitHub repository**: We'll create a new repository for storing your training data. We'll make the repository private to ensure that your data isn't publicly accessible.
3. **Configure repository details**: Add a description, and enable issues and projects for tracking tasks and features related to managing the training data.

Here's a program that accomplishes these steps:

```python
import pulumi
import pulumi_github as github

# Initialize a new private GitHub repository to store training data.
training_data_repo = github.Repository("training_data_repo",
    # Replace 'your-repo-name' with a name for your repository.
    name="your-repo-name",
    description="Repository for storing training data for deep learning models.",
    visibility="private",  # Set the repository visibility to private.
    # Enable issues and project boards, useful for tracking updates or changes.
    has_issues=True,
    has_projects=True,
    # It's a common practice to initialize the repo with a README.
    auto_init=True,
    # Specify a license template if applicable. For example, "mit" for MIT license.
    license_template="mit",
    # Assign topics to improve discoverability of the repository.
    topics=["deep-learning", "training-data", "machine-learning"],
)

# Export the URL to the repository so it can be accessed easily after deployment.
pulumi.export('training_data_repo_url', training_data_repo.html_url)
```

To execute this Pulumi program:

1. **Install Pulumi and the GitHub package**:

```bash
pip install pulumi pulumi_github
```

2. **Set up Pulumi**:
Ensure you've installed and set up the Pulumi CLI and logged in to the pulumi.com service.

3. **Set up GitHub authentication**:
You'll need to generate a GitHub personal access token with the `repo` and `admin:org` scopes and set it as an environment variable named `GITHUB_TOKEN` or configure it in the Pulumi stack configuration.

4. **Run the Pulumi program**:

Navigate to the directory containing the program, and use the following Pulumi CLI commands:

```bash
pulumi stack init dev  # Initialize a new stack named 'dev'
pulumi up              # Preview and deploy the changes
```

Pulumi will prompt you to review the changes before applying them. Once you approve, it will create a new private repository in your GitHub account with the specified configuration.

Remember, data stored in GitHub is subject to GitHub's data policies, and you should be cautious about the size and nature of the data you store in Git repositories, as significant data size may impact repository performance and cloning times. For large scale datasets, consider using Git Large File Storage (LFS) or an alternative data storage solution more suitable for large files.