1. Continuous Integration Pipelines for AI on GitHub Actions


    Continuous Integration (CI) pipelines are an essential part of modern software development, especially for AI and Machine Learning projects where model training and evaluation need to be automated and repeatable. GitHub Actions is a CI/CD platform that allows you to automate your build, test, and deployment workflows right from within your GitHub repository.

    The provided Pulumi registry results give us various GitHub-related resources, that we can use within Pulumi to manage GitHub Actions as part of our infrastructure as code. This is useful for ensuring that the necessary secrets, permissions, and environment variables that your CI pipeline may depend on are managed as code.

    Below is a Python program using Pulumi with the pulumi_github package to create a GitHub Actions secret on a repository. This secret can be used, for example, to store credentials required by a GitHub Actions workflow to deploy an AI model to a cloud provider or another external service.

    In this program, we are setting up a GitHub Actions secret named MODEL_DEPLOY_KEY on a hypothetical repository my-ai-app which belongs to the GitHub user or organization my-github-user. The value of the secret is assumed to be already available in some secure location, and here it is represented by the placeholder your-secret-value.

    import pulumi import pulumi_github as github # Replace 'my-github-user' with your GitHub user or organization name and 'my-ai-app' with your repository name. repo_name = "my-github-user/my-ai-app" # The name of the secret to be added to GitHub Actions secret_name = "MODEL_DEPLOY_KEY" # The value for the secret. This should be the actual secret content, such as an API token or SSH key. # Ensure that this value is kept safe and secure. # NEVER hard-code secrets directly in your Pulumi program. This is simply for illustration purposes. # In a real-world scenario, you would fetch this from a secret store, environment variable, or the Pulumi config system. secret_value = "your-secret-value" # TODO: Replace this with the actual secret value. # Create a GitHub Repository Secret for GitHub Actions. # This secret can be used in your GitHub Actions workflows. # It is encrypted and stored securely by GitHub. github_secret = github.ActionsSecret("model-deploy-key", repository=repo_name, secret_name=secret_name, plaintext_value=secret_value) # Export the name of the secret. Do not export the value, keep it secure! pulumi.export("actions_secret_name", github_secret.secret_name)


    1. We import the required libraries from Pulumi, specifically pulumi for basic Pulumi functionality and pulumi_github for managing GitHub resources.
    2. We define variables for the repository name and the secret name.
    3. We define a secret value variable. In a real-world setup, instead of hardcoding the secret value, you should retrieve it from a secure source at runtime, such as Pulumi's configuration system, environment variables, or a secrets manager.
    4. We create an instance of github.ActionsSecret which represents a GitHub Actions secret. This resource creates a new secret in the specified repository with the provided name and value.
    5. We use pulumi.export to export the name of the secret as a stack output. We don't export the secret's value as it should remain confidential.
    6. Replace my-github-user and my-ai-app with your actual GitHub username/organization and repository respectively.
    7. Replace your-secret-value with the actual secret value when implementing this program.

    This program is a starting point to manage GitHub Actions secrets using Pulumi. In a comprehensive CI setup for AI, you'd also manage permissions, environment variables, workflow files, and more. For each of these, Pulumi provides a corresponding resource type, such as ActionsOrganizationPermissions, ActionsEnvironmentVariable, etc., as seen in the registry search results.