AI Model Training Workflow Automation using GitHub Actions

Question

Pulumi · Accepted Answer

To set up an AI Model Training Workflow Automation using GitHub Actions, we will create a GitHub Actions workflow that triggers on a specific event, such as a push to a certain branch. This workflow will include a series of steps that install the necessary dependencies, run the model training script, and optionally, further steps that can push the trained model to a storage of choice or handle other post-processing tasks.

The following Pulumi code programmatically sets up a GitHub repository with GitHub Actions enabled, and configures a workflow file and corresponding secrets needed for the automation. Below are the components we will work with:

1. **GitHub Repository**: This is where your AI model training code resides.
2. **GitHub Actions Workflow**: This is the automated process that will train your AI model.
3. **GitHub Secrets**: These are used to securely store and provide credentials or other sensitive information to the GitHub Actions Workflow.
4. **GitHub Actions Environment Variables**: These can be used to configure environment-specific settings for the workflow.
5. **GitHub Actions Permissions**: Permission settings that define what kind of actions the workflow is allowed to perform.

Let's create the Pulumi program:

```python
import pulumi
import pulumi_github as github

# Configuration
repo_name = "ai-model-training"
workflow_filename = ".github/workflows/model_training.yml"
model_training_script_path = "scripts/train_model.py"

# Create a GitHub repository
repo = github.Repository(repo_name,
    description="Repository for AI model training",
    visibility="private"  # for public repositories, use "public"
)

# Define GitHub Actions Workflow content
workflow_content = f"""
name: Model Training Workflow

on:
  push:
    branches:
      - main  # Trigger the workflow on push to the main branch

jobs:
  train_model:
    runs-on: ubuntu-latest
    steps:
      - name: Check out repository
        uses: actions/checkout@v2

- name: Set up Python
        uses: actions/setup-python@v2
        with:
          python-version: '3.8'

- name: Install dependencies
        run: pip install -r requirements.txt

- name: Train AI model
        run: python {model_training_script_path}
"""

# Add a workflow file to the repository
workflow = github.RepositoryFile("model_training_workflow",
    repository=repo.name,
    file=workflow_filename,
    content=workflow_content,
    opts=pulumi.ResourceOptions(depends_on=[repo])
)

# Add a secret to the repository for authentication with other services if needed
secret = github.ActionsSecret("model-training-secret",
    repository=repo.name,
    secret_name="TRAINING_SECRET",
    plaintext_value="sensitive-information"
)

# Optionally, you can add environment variables specific to the GitHub Actions environment
environment_variable = github.ActionsEnvironmentVariable("model-training-environment-variable",
    repository=repo.name,
    environment="production",
    variable_name="MODEL_ENV",
    value="production"
)

pulumi.export('repository_url', repo.html_url)
pulumi.export('workflow_url', pulumi.Output.concat(repo.html_url, '/actions/workflows/', workflow_filename))
```

Here is a breakdown of the program:

- A new GitHub repository named `ai-model-training` is created. This is where you will store your model code and related files.

- A new GitHub Actions Workflow file is set up under `.github/workflows/model_training.yml`. It specifies the steps required to set up Python, install dependencies, and run the AI model training script located at `scripts/train_model.py`.

- A GitHub Secret `TRAINING_SECRET` is created in the repository. It's set to store sensitive data, which would be 'sensitive-information' in this case.

- An environment variable `MODEL_ENV` is created for the GitHub Actions environment `production`, with the value `production`. This variable can be accessed by the workflow during execution.

- The exported URLs provide easy access to the repository and the workflow file on GitHub.

Keep in mind that the actual content of the `model_training.yml` and the model training script `train_model.py` are placeholders and should be replaced with your actual CI/CD pipeline commands and model training scripts. Moreover, the secret value should be replaced with actual sensitive information you wish to use in your workflow.

This program will create a GitHub repository and set up a workflow file that can be edited to fit the requirements of your AI model training. The repository will initially be private, but its visibility can be changed based on your needs. The workflow can also be customized further by adding more jobs, steps, and triggers. Additionally, the use of GitHub Secrets provides a way to securely make sensitive data available in your Action workflows.