1. Organizing Large-Scale AI Workflows with GCP Folder IAM Structures


    Organizing large-scale AI workflows in Google Cloud Platform (GCP) often involves setting up a hierarchy of resources that reflect your organization's structure and access control policies. Using Pulumi to manage this infrastructure as code allows you to define these resources in a structured, repeatable, and version-controlled manner.

    In GCP, folders are a way to group projects and other folders that share common IAM policies. By using folders, you can control access to the resources by setting folder-level IAM policies, and any projects within that folder automatically inherit the policies.

    To help you get started, I will provide you with a Python program that uses Pulumi to set up a simple folder structure for organizing AI workflows with IAM controls. The components we will use include:

    1. Folders: Folders are organizational entities that can contain projects, other folders, or a combination of both. They enable you to implement the hierarchy and structure of your GCP resources.

    2. Folder IAM Policies: These policies set the permissions on the folders which will be inherited by all resources within the folder. This is critical for managing access control at scale.

    3. Custom IAM Roles: Sometimes, predefined IAM roles do not fit your specific security requirements. Custom IAM roles allow you to specify a set of permissions that are tailored to your needs.

    Let's create a Pulumi program that implements these concepts:

    import pulumi import pulumi_gcp as gcp # Create a GCP folder to organize resources for the AI workflows. ai_workflows_folder = gcp.organizations.Folder("aiWorkflowsFolder", display_name="AI-Workflows", parent="FOLDER_ID", # Replace with your parent folder or organization ID. opts=pulumi.ResourceOptions()) pulumi.export("ai_workflows_folder_name", ai_workflows_folder.display_name) # Assign a custom role to the folder to allow for the management of AI workflows. # The role permits viewing and managing AI Platform resources. custom_ai_role = gcp.organizations.IAMCustomRole("customAIRole", permissions=[ "aiplatform.datasets.get", "aiplatform.datasets.list", "aiplatform.models.create", "aiplatform.models.delete", "aiplatform.models.get", "aiplatform.models.list", # Add other permissions relevant to the workflows. ], role_id="CustomAIRole", title="Custom AI Manager", stage="GA", opts=pulumi.ResourceOptions()) pulumi.export("custom_ai_role_id", custom_ai_role.role_id) # Bind the custom role to a service account at the folder level. This service # account will be used to run the AI workflows. ai_service_account = gcp.serviceaccount.Account("aiServiceAccount", account_id="ai-workflow-manager", display_name="AI Workflow Manager", opts=pulumi.ResourceOptions()) ai_folder_binding = gcp.folder.IAMBinding("aiFolderIAMBinding", folder=ai_workflows_folder.name, members=[f"serviceAccount:{ai_service_account.email}"], role=custom_ai_role.name, opts=pulumi.ResourceOptions(parent=ai_workflows_folder)) pulumi.export("ai_service_account_email", ai_service_account.email) # Additionally, we might want to set up specific IAM policies on projects under this folder. # Here, we define a new project under the AI workflows folder with a specific IAM policy allowing # the AI workflow manager service account to manage storage buckets within the project. ai_project = gcp.organizations.Project("aiProject", folder_id=ai_workflows_folder.name, name="ai-project", opts=pulumi.ResourceOptions(parent=ai_workflows_folder)) pulumi.export("ai_project_id", ai_project.project_id) storage_admin_policy = gcp.projects.IAMPolicy("storageAdminPolicy", project=ai_project.project_id, policy_data=ai_service_account.email.apply( lambda email: f""" {{ "bindings": [ {{ "role": "roles/storage.admin", "members": ["serviceAccount:{email}"] }} ] }} """), opts=pulumi.ResourceOptions(parent=ai_project)) pulumi.export("storage_admin_policy_role", storage_admin_policy.policy_data)

    In this program:

    • We define an organizational Folder that will house all the AI workflows.
    • We create a Custom IAM role for AI-related tasks, with a list of permissions needed for managing AI Platform resources.
    • A ServiceAccount is created for the management of AI workflows.
    • We attach the custom role to the service account at the folder level using IAMBinding.
    • We create a new Project under the AI workflows folder.
    • An IAMPolicy for the project is applied to provide the AI workflow manager service account admin access to storage buckets within the project.

    Replace "FOLDER_ID" with your actual parent folder or organization ID to reference the right hierarchy in your organization.

    Please note that the properties like parent, role_id, and folder_id should be based on your specific GCP organization hierarchy and naming conventions.

    The use of pulumi.export allows you to output the generated resource names, IDs, or email addresses for further use or reference.