1. Securely Storing API Keys for ML Model Deployment with GitHub Environment Secrets


    To securely store API keys for your ML model deployment with GitHub Environment Secrets, we'll use the Pulumi GitHub provider to create a GitHub Actions environment and then add a secret to it. This secret will be available to your GitHub Actions workflows which you can use to deploy your ML model.

    The resources we will use are as follows:

    • github.ActionsEnvironment: This represents a GitHub Actions environment where you can define environment-specific secrets and approvals.
    • github.ActionsEnvironmentSecret: This is used to store secrets in the environment you created.

    Here's a step-by-step explanation of the program:

    1. We use the github provider to create a GitHub Actions environment using the ActionsEnvironment resource.
    2. Then we set a secret in that environment using ActionsEnvironmentSecret. The secret could be an API key or any other sensitive data you need to store.

    Here's the Pulumi program to accomplish this:

    import pulumi import pulumi_github as github # Please replace 'your_repo_name' with the name of your GitHub repository # and 'your_github_organization' with your GitHub organization (or username if it's a personal repo). repository_name = 'your_repo_name' github_organization = 'your_github_organization' environment_name = 'production' # Name of the environment, e.g., 'staging', 'production'. api_key_name = 'ML_API_KEY' # The name of the secret you want to store. api_key_value = 'your_api_key' # The actual value of the API key. # Create a GitHub Actions environment. actions_environment = github.ActionsEnvironment("actions-environment", environment=environment_name, repository=repository_name, ) # Securely store an API key in the GitHub Actions environment. actions_environment_secret = github.ActionsEnvironmentSecret("actions-environment-secret", secret_name=api_key_name, plaintext_value=api_key_value, repository=github_organization + '/' + repository_name, environment=environment_name, ) # Export the secret name and environment as outputs. pulumi.export('secret_name', api_key_name) pulumi.export('environment', environment_name)

    Here's an overview of what the program will do:

    • Replace your_repo_name, your_github_organization, environment_name, api_key_name, and api_key_value with your own values.
    • The ActionsEnvironment creates an environment for your repository.
    • The ActionsEnvironmentSecret resource adds a secret to the specified environment. The API key is set as a plaintext value, which GitHub will encrypt.
    • Finally, we export the secret name and environment name. These exports are useful for checking the outputs of your stack and ensuring that resources are named correctly.

    To run the above program, you'll need to install Pulumi, set up your GitHub token with appropriate permissions, and create a new Pulumi Python project. Then copy the provided code into your __main__.py file, making sure to replace the placeholder values with your own data.

    Remember to keep your API key and other sensitive data out of your code files. Do not commit the actual api_key_value to your repository. It's common to use environment variables or a secrets management system to handle such sensitive data.