1. Environment Variables for AI Workloads


    When deploying AI workloads using Pulumi, you might want to set environment variables for your application or services. These environment variables might be used to adjust runtime behavior, set access credentials, or influence the application's configuration without changing the code.

    In a Pulumi program, environment variables can be set at various levels depending on where your AI workload is running:

    1. Cloud Functions: If your AI workload runs as a function in a cloud provider like AWS Lambda, Google Cloud Functions, or Azure Functions, you would set environment variables in the function's configuration.

    2. Containers: If your AI workload runs in containers orchestrated by services like Kubernetes, Amazon ECS, or Google Cloud Run, you would set environment variables in the container definition or deployment configuration.

    3. Virtual Machines: If running on virtual machines, you could use cloud-init scripts or configuration management tools that Pulumi can invoke to set environment variables at the VM level.

    4. PaaS: If you use a Platform as a Service (PaaS) like Heroku or Vercel, you have the option to configure environment variables through the PaaS provider’s console or via Pulumi code.

    Below, I will show you a Pulumi program that sets environment variables for a Google Cloud Function. This is merely an illustration. If you're deploying to another environment, the process will differ slightly.

    In this example, we'll use the google-native.cloudfunctions/v2.Function resource to deploy a Google Cloud Function with specific environment variables. This resource is part of the pulumi_google_native package, which interacts with Google Cloud resources.

    Here’s a simple Pulumi program to deploy a Google Cloud Function with environment variables:

    import pulumi import pulumi_google_native as google_native # Replace these variables with your specific values project_id = 'your-gcp-project-id' location = 'us-central1' # Or any other GCP region # Define the Google Cloud Function function = google_native.cloudfunctions.v2.Function( "my-ai-function", args=google_native.cloudfunctions.v2.FunctionArgs( project=project_id, location=location, function=google_native.cloudfunctions.v2.FunctionFunctionArgs( name='my-ai-function', description='A function for AI workload with env vars', build_config=google_native.cloudfunctions.v2.FunctionBuildConfigArgs( entry_point='main', # The function within your code to execute runtime='python38', # Runtime environment for the function source=google_native.cloudfunctions.v2.FunctionSourceArgs( storage_source=google_native.cloudfunctions.v2.FunctionStorageSourceArgs( bucket='your-source-code-bucket-name', object='path/to/your/deployment/zip/or/tarball', ) ), environment_variables={ 'AI_MODEL_NAME': 'my-special-model', # Environment variable for the function } ), service_config=google_native.cloudfunctions.v2.FunctionServiceConfigArgs( available_memory='256MB' # Allocate memory for the function ) ) ) ) # Export the function's URL pulumi.export('function_url', function.https_trigger_url)

    In the build_config block, we've added an environment_variables dictionary that contains the key-value pair for our environment variable.

    Important points to consider:

    • project_id: Replace this with your GCP project id where the cloud function will be deployed.
    • location: The region where the Google Cloud Function will be deployed. It should be one of the available regions for the Google Cloud Functions service.
    • The source for the function code is specified to be in a GCP storage bucket your-source-code-bucket-name. Ensure that the correct path to a zip or a tarball containing the function code is provided.
    • Replace entry_point, runtime, and any other specific configurations with appropriate values according to your workload.

    This Pulumi program will create a Google Cloud Function with the specified environment variable set, which can then be accessed within your function code. Remember to replace all placeholders with actual values that match your environment and requirements.

    You can adapt this example to other environments by using the appropriate Pulumi resources for those services. If you let me know more specifics about your AI workload deployment target, I can provide a more targeted example.