1. Service Accounts for Access Control in AI Platform Jobs


    In order to create and manage service accounts for access control in AI platform jobs using Pulumi, you'll typically use the Google Cloud Platform (GCP) provider for Pulumi. Service accounts in GCP provide an identity for instances or other services that can be granted specific permissions and used to authenticate applications.

    Below I will guide you through the creation of a basic Pulumi program in Python that sets up a service account with the appropriate roles for managing AI platform jobs. It will demonstrate how to create a service account, assign roles to it, and use it in AI platform jobs.

    Here's what we'll do:

    1. Create a GCP Service Account: A GCP service account is an identity that allows your service running on GCP, such as AI Platform jobs, to authenticate and authorize itself to other Google Cloud services.

    2. Assign Roles to the Service Account: Roles in GCP define what operations a service account can perform. For AI Platform jobs, you would typically need roles that allow for training and deploying machine learning models, such as roles/ml.developer.

    3. Use the Service Account with AI Platform Jobs: When you submit a job to AI Platform, you can specify the service account to be used for that particular job. This allows for fine-grained control over permissions and resource access.

    Here is the program that accomplishes the above steps:

    import pulumi import pulumi_gcp as gcp # Replace these variables with desired values project = 'my-gcp-project' # Your GCP Project ID service_account_name = 'my-ai-service-account' # Name of the service account to create # Create a GCP Service Account for the AI Platform Jobs service_account = gcp.serviceaccount.Account(f"{service_account_name}-sa", account_id=service_account_name, display_name="Service Account for AI Platform Jobs", project=project) # Assign ML roles to the Service Account # This role allows the service account to manage jobs and models in AI Platform service_account_iam_binding = gcp.projects.IAMBinding("sa-ml-developer-binding", members=[f"serviceAccount:{service_account.email}"], role="roles/ml.developer", project=project) # Export the service account email so it can be used to authenticate jobs pulumi.export('service_account_email', service_account.email)

    Here's a breakdown of what each part of the script does:

    • We import the necessary modules for Pulumi and specify the Google Cloud Platform module.

    • We create a service account with the gcp.serviceaccount.Account resource. We must provide an account ID and a display name. Optionally, we can also specify the project ID.

    • Next, we give the newly created service account the roles/ml.developer role using gcp.projects.IAMBinding. This role is essential for managing AI Platform jobs and interacting with other GCP resources necessary for machine learning tasks.

    • Lastly, we export the service account email which is useful for configuring AI platform jobs to use this service account.

    Please ensure that your GCP project name is correctly set, and that you've already configured your Pulumi environment to authenticate with GCP. This program is a basic example that creates a service account, assigns it a role for AI tasks, and exports its email for further use.

    After running this Pulumi program, you will have a service account that can be used in AI Platform jobs for authentication and authorization. You can refer to the specific GCP documentation for AI Platform jobs to learn more about using service accounts with different AI workloads.