1. AI Model Training Workflow Automation using GitHub Actions


    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:

    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.