Periodic Re-training of Machine Learning Models with GitHub Actions

Question

Pulumi · Accepted Answer

To periodically re-train machine learning models using Pulumi, you can define a GitHub Actions workflow within your GitHub repository. Pulumi does not directly manage the training of machine learning models, but it can help manage the cloud infrastructure required to run your training jobs (such as setting up necessary cloud environments, storage, and compute resources) and integrate with GitHub Actions to automate the process.

Here's a step-by-step guide along with a sample Pulumi program that demonstrates how you would set up GitHub Actions using the `pulumi_github` package to periodically re-train a machine learning model.

1. **Create a GitHub Repository**: Before writing a GitHub Actions workflow, you'll need a GitHub repository that hosts your machine learning code.
2. **Define Secrets and Variables**: For your GitHub Actions workflow, you'll need to define secrets (e.g. cloud provider credentials) and variables (e.g. configuration settings).
3. **Write the Actions Workflow**: You'll write a `.github/workflows/retrain-model.yml` file in your repository defining the necessary steps for your machine learning model training.

Below is a Pulumi program structure that you'll use:

- **GitHub Actions Secret**: This resource allows you to manage secrets within GitHub repositories that can be used by GitHub Actions workflows. Secrets are encrypted environment variables and are crucial for storing sensitive information needed for cloud environments or other services, like passwords or API tokens.

- **GitHub Actions Variable**: It's also possible to define environment variables specifically for use in Actions Workflows.

Let's look at an example Pulumi program in Python which demonstrates how you can manage GitHub Actions secrets required for the re-training of machine learning models. We'll declare two secrets, `CLOUD_PROVIDER_ACCESS_KEY` and `CLOUD_PROVIDER_SECRET_KEY`, which are used to authenticate against your cloud provider.

```python
import pulumi
import pulumi_github as github

# Replace 'your-repo' and 'your-org-or-username' with the targeted GitHub repository and owner.
repo_name = 'your-repo'
owner = 'your-org-or-username'

# GitHub Actions Secret for Access Key.
access_key_secret = github.ActionsSecret("access_key_secret",
    repository=repo_name,
    secret_name="CLOUD_PROVIDER_ACCESS_KEY",
    plaintext_value="your-access-key-value"  # This value should be fetched securely, e.g., from a CI/CD environment variable or a Pulumi config.
)

# GitHub Actions Secret for Secret Key.
secret_key_secret = github.ActionsSecret("secret_key_secret",
    repository=repo_name,
    secret_name="CLOUD_PROVIDER_SECRET_KEY",
    plaintext_value="your-secret-key-value"  # This value should be fetched securely as well.
)

# Here, we export the names of the secrets so they can be verified as outputs of this Pulumi stack.
pulumi.export("access_key_secret_name", access_key_secret.secret_name)
pulumi.export("secret_key_secret_name", secret_key_secret.secret_name)
```

To run the above program:

1. Install the Pulumi CLI and the Pulumi GitHub provider.
2. Set up GitHub authentication for Pulumi to manage resources in your GitHub account.
3. Run `pulumi up` to deploy the program.

This program creates two secrets within your specified repository, which should align with the credentials expected by the model training scripts run in GitHub Actions. When defining your workflow, you'll reference these secrets as needed by your cloud environment for operations like provisioning resources, and when running your machine learning model training jobs.

Additionally, you would define a workflow in `.github/workflows/retrain-model.yml` in your GitHub repository to specify the re-training job. The workflow would reference these secrets to set up the necessary environment for running the training job. It would need to define a schedule using cron-like syntax to periodically trigger re-training.

Bear in mind that actual training operation, the scheduling syntax, and other details of the GitHub Actions workflow are beyond the scope of Pulumi. Pulumi helps with provisioning and managing the underlying cloud infrastructure resources and their associated secrets/variables for the workflow securely.