1. Continuous Integration for AI Systems with GitHub Actions


    Continuous Integration (CI) is the practice of automating the integration of code changes from multiple contributors into a single project. It's a key part of modern DevOps practices and helps teams to detect problems early. For AI systems, CI can ensure that models are trained and evaluated consistently as code changes, and that applications depending on machine learning models can be automatically updated with new versions when necessary.

    To implement CI for AI systems with GitHub Actions, you’ll typically define a workflow that performs steps such as setting up the environment, training models, running tests, and, if the tests are successful, possibly deploying the trained model to a production environment.

    GitHub Actions Resources

    Here are some resources we will use from GitHub:

    • github.ActionsSecret: To store sensitive information like cloud credentials or API keys, ensuring they are not exposed in the workflow logs.
    • github.ActionsRepositoryPermissions: Configures the repository permissions for using GitHub Actions, including which actions are allowed to run.

    Here is a basic outline of a Pulumi program which sets up a GitHub Actions workflow in a GitHub repository with secrets that might be needed for deployment or testing purposes, such as cloud provider credentials or other necessary environment variables.

    import pulumi import pulumi_github as github # This example assumes you already have a GitHub repository created. # Replace 'your-username' with your GitHub username and 'your-repo' with your repository name. repository_name = "your-username/your-repo" # Creating a new GitHub Actions secret in your repository # Replace 'CONTINUOUS_INTEGRATION_SECRET_VALUE' with the actual secret value you want to set. ci_secret = github.ActionsSecret("ci-secret", repository=repository_name, secret_name="CONTINUOUS_INTEGRATION_SECRET", plaintext_value="CONTINUOUS_INTEGRATION_SECRET_VALUE" ) # Setting up repository permissions to enable GitHub Actions repository_permissions = github.ActionsRepositoryPermissions("repository-permissions", repository=repository_name, enabled=True, # Enables GitHub Actions for this repository allowed_actions="selected", allowed_actions_config=github.ActionsRepositoryPermissionsAllowedActionsConfigArgs( github_owned_allowed=True, patterns_allowed=["actions/*"] ), ) # Use the `pulumi.export` to output the secret name. This isn't the value, just the name, for verification purposes. pulumi.export("ci_secret_name", ci_secret.secret_name)

    In a Pulumi program like above:

    1. We imported pulumi and the pulumi_github package, which contains classes and methods needed to interact with GitHub Actions.
    2. We defined the repository name where the GitHub Actions would be set up (repository_name).
    3. We created an actions secret for our CI environment using github.ActionsSecret.
    4. We then configured repository permissions to enable GitHub Actions (github.ActionsRepositoryPermissions) and specify what actions are allowed to run. In this case, we are allowing all GitHub-owned actions to run, plus any others that match the patterns specified in patterns_allowed.

    The secret value provided to github.ActionsSecret should be secure and not hardcoded in your Pulumi program; it's only done here for illustration purposes.

    After creating this Pulumi program, you would place your actual CI workflow in a .github/workflows/ directory in your repository. This workflow will reference the secret that we've created using Pulumi and will have steps that might install dependencies, train your AI models, run tests, and deploy if everything is successful.

    Finally, the pulumi.export at the end of the program allows you to output the secret's name, reaffirming that it has been set.

    Deploying the Pulumi Program

    To deploy this Pulumi program:

    1. Install the Pulumi CLI and the necessary programming language runtime (Python in this case) on your local machine.
    2. Log in to the Pulumi service.
    3. Create a new Pulumi project or navigate to an existing one.
    4. Write the above program in your __main__.py file.
    5. Run pulumi up in your terminal from the directory where the Pulumi program resides, which performs the deployment.

    Remember, this is an infrastructure as code (IaC) setup. You'll still need to write the workflow code for GitHub Actions that defines your CI pipeline for your AI Systems, which should be added to .github/workflows/main.yml in your GitHub repository. The actual training, testing, and deployment logic for your AI models will be defined there.