1. Isolating Compute Resources for AI Training in GCP Folders


    To isolate compute resources for AI training in GCP using folders, you can employ a combination of GCP's Resource Policies and IAM (Identity and Access Management) Policies. Folders in GCP allow you to hierarchically organize resources, which helps in managing permissions and structuring your cloud environment to reflect your organization.

    Here's a program that demonstrates how to set up a folder on GCP, apply resource policies to manage how compute resources like Google Compute Engine instances are used, and enforce IAM policies for access control:

    Program Explanation

    1. Create a Folder: We start by creating a GCP folder. This will serve as the container for all our compute resources that we want to isolate for AI training.
    2. Resource Policy: We create a resource policy attached to compute resources. This can be used to define various aspects like schedules for VM instance creation and deletion, snapshot policies, etc.
    3. IAM Policies: We set IAM policies at the folder level, which will govern who has access to the resources within this folder. This can be as granular as needed, assigning specific roles to individuals or groups for this collection of resources.

    Let's go through the code step-by-step.

    Pulumi Program for Isolating Compute Resources in a GCP Folder

    import pulumi import pulumi_gcp as gcp # Replace 'your-project-id' and 'your-folder-name' with your GCP project ID and desired folder name. project_id = 'your-project-id' folder_name = 'your-folder-name' # Create a GCP Folder under the organization. ai_folder = gcp.resourcemanager.Folder(folder_name, parent=f"organizations/{project_id}", display_name="AI Training Resources") # Define a compute resource policy. resource_policy = gcp.compute.ResourcePolicy("ai-training-policy", region="us-central1", snapshot_schedule_policy=gcp.compute.ResourcePolicySnapshotSchedulePolicyArgs( schedule=gcp.compute.ResourcePolicySnapshotSchedulePolicyScheduleArgs( daily_schedule=gcp.compute.ResourcePolicySnapshotSchedulePolicyScheduleDailyScheduleArgs( days_in_cycle=1, start_time="04:00" ) ), retention_policy=gcp.compute.ResourcePolicySnapshotSchedulePolicyRetentionPolicyArgs( max_retention_days=7, on_source_disk_delete="KEEP_AUTO_SNAPSHOTS" ) ), project=project_id) # Replace 'your-role-id' with the appropriate role ID for IAM (e.g., roles/compute.viewer). # Replace 'user:example-user@example.com' with the user's email. role_id = "your-role-id" member = "user:example-user@example.com" # Applying an IAM policy on the folder level. iam_policy = gcp.folder.IAMPolicy("ai-folder-iam", folder=ai_folder.id, policy_data=pulumi.Output.all(ai_folder.id).apply(lambda folder_id: gcp.iam.get_policy( project=project_id, folder=folder_id ).then(lambda policy: gcp.iam.Policy( bindings=[ gcp.iam.PolicyBinding( role=role_id, members=[member] ) ] ) )) ) # Exporting the folder ID and resource policy ID. pulumi.export("folder_id", ai_folder.id) pulumi.export("resource_policy_id", resource_policy.id)

    In this code, we specify the project ID, folder name, IAM role, and a user(email) to apply the policy. We first create a GCP folder situated under an organization, which requires replacing 'your-project-id' with your actual GCP project ID. The IAM policy is then generated with a specified role and a member, replacing 'your-role-id' and 'user:example-user@example.com' with actual values suitable for your setup.

    The resource policy ai-training-policy is established to manage snapshot schedules, dictating when snapshots are taken and how long they're retained. Adjust 'us-central1' to the appropriate region as necessary.

    Once this Pulumi program is run, it will create a folder and apply the defined resource and IAM policies to it. The folder ID and resource policy ID will be exported as output, making them available for reference in the Pulumi Console.

    Be sure to set up your environment with proper GCP credentials and Pulumi installed to run this code. This program should be placed in a __main__.py file within a Pulumi project directory. Run pulumi up in the terminal to execute the code and apply the changes to your GCP environment.

    Remember, this is a starting point, and depending on your precise use case and security requirements, you may need to add more fine-tuned policies and resource configurations.