1. Hierarchical Resource Organization for GCP AI Workloads


    In Google Cloud Platform (GCP), managing complex AI workloads often requires a thoughtful approach to organize resources hierarchically. This could involve setting up various Google Cloud resources such as AI Platform datasets, computing resources, and access policies to ensure efficient management and control over your AI environment.

    To structure these resources hierarchically in a Pulumi program, you could leverage several GCP services and resource types. As an example, we might use AiMetadataStore to manage metadata, google-native.cloudresourcemanager/v1beta1.Project to create a Google Cloud project scope under which these resources will be managed, google-native.workflows/v1beta.Workflow to orchestrate these resources with workflows, and gcp.orgpolicy.Policy to apply organizational policies for resource hierarchy management.

    Below is a program written in Python using Pulumi that outlines the creation and management of a hierarchical resource organization for GCP AI workloads. This program demonstrates the creation of a metadata store for AI datasets, a new project, and a workflow to orchestrate resources, all while applying organizational policies to manage these resources effectively.

    import pulumi import pulumi_gcp as gcp # Create an AI Metadata Store to manage and organize metadata for AI datasets. # More information: https://www.pulumi.com/registry/packages/gcp/api-docs/vertex/aimetadatastore/ ai_metadata_store = gcp.vertex.AiMetadataStore( "my-ai-metadata-store", region="us-central1", # Set the region for the AI Metadata Store project=pulumi.config.require("gcp:project"), description="Metadata store for managing AI dataset metadata" ) # Create a Google Cloud Project which will contain all the resources necessary for AI workloads. # Using the google-native provider which allows us to manage Google Cloud resources. # More information: https://www.pulumi.com/registry/packages/google-native/api-docs/cloudresourcemanager/v1beta1/project/ project = pulumi_google_native.cloudresourcemanager.v1beta1.Project( "my-ai-project", name="my-ai-project", # The name of the project parent=pulumi_google_native.cloudresourcemanager.v1beta1.ProjectArgs( # Define the hierarchy this project will belong to type="folder", id="FOLDER_ID", # Specify the folder ID where this project should reside ), labels={"env": "development"} # Labels for identifying project properties ) # Orchestrate Google Cloud resources with workflows using Workflows. # More information: https://www.pulumi.com/registry/packages/google-native/api-docs/workflows/v1beta/workflow/ workflow = pulumi_google_native.workflows.v1beta.Workflow( "my-ai-workflow", name="my-ai-workflow", # The name of the workflow project=pulumi.config.require("gcp:project"), location="us-central1", # Location where the workflow will execute description="Workflow to orchestrate AI workloads", service_account="SERVICE_ACCOUNT_EMAIL" # Specify the service account e-mail ) # Manage resource hierarchy with GCP Org Policies. # Org Policies help you to configure constraints across your entire resource hierarchy. # More information: https://www.pulumi.com/registry/packages/gcp/api-docs/orgpolicy/policy/ policy = gcp.orgpolicy.Policy( "my-ai-org-policy", name="constraint/compute.disableSerialPortAccess", # Example policy constraint to disable serial port access spec=gcp.orgpolicy.PolicySpecArgs( rules=[gcp.orgpolicy.PolicySpecRuleArgs( enforce=True, )] ), parent=f"projects/{pulumi.config.require('gcp:project')}" # The project resource parent where the policy will be applied ) # Export the project ID and workflow's URL so that they can be easily retrieved and used. pulumi.export("project_id", project.projectId) pulumi.export("workflow_url", workflow.self_link)

    In this program, the following steps are carried out:

    1. We declare an AI Metadata Store using the AiMetadataStore resource to manage AI dataset metadata.
    2. We create a new Google Cloud Project scoped within a specified folder using Project from the google-native package.
    3. We define a workflow with Workflow that will handle the orchestration of AI workload tasks.
    4. We set an organization policy using Policy to administratively enforce rules on the project, such as disabling serial port access for VMs.
    5. Finally, we export the Project ID and the Self Link URL of the workflow so that we can reference them outside of our Pulumi program.

    Each resource is documented with links to their respective Pulumi registry documentation for more detailed information about their specifications and capabilities. Running this program with Pulumi CLI will set up the hierarchical organization desired for managing GCP AI workloads.