1. GCP Folder-Level IAM for Machine Learning Teams


    To manage IAM policies at a folder level for a machine learning team in Google Cloud Platform using Pulumi, you can set up resources that define the IAM roles and permissions for different members of the team. This allows you to control who has access to the resources within the folder and what actions they can perform.

    Let’s walk through the code to accomplish this task. We will use the pulumi_gcp provider, which provides a set of resources to interact with Google Cloud Platform.

    First, we will define a folder in your GCP organization where you want to apply the IAM policies. Then we will assign IAM roles at the folder level to different team members. For instance, we might want to give the machine learning team roles such as 'roles/ml.developer' which contains permissions to run and manage machine learning tasks.

    Below is a Pulumi program in Python that performs these actions:

    1. Create a folder within the GCP organization.
    2. Set IAM policies at the folder level with specific roles for team members.
    import pulumi import pulumi_gcp as gcp # Replace the following variables with your own information organization_id = 'your-organization-id' folder_display_name = 'ml-team-folder' # Create a new GCP folder ml_team_folder = gcp.organizations.Folder("mlTeamFolder", display_name=folder_display_name, parent=f"organizations/{organization_id}") # Define the IAM role for Machine Learning Developer ml_developer_role = "roles/ml.developer" # IAM Member Binding for the Machine Learning Developer Role # Replace `user:{email}` with the actual email addresses of the team members ml_dev_member_binding = gcp.organizations.getIAMPolicy("mlDevMemberBinding", bindings=[{ "role": ml_developer_role, "members": [ "user:alice@example.com", "user:bob@example.com", ], }], resource=ml_team_folder.id) # Set the IAM Policy for the Folder ml_folder_iam_policy = gcp.organizations.IAMPolicy("mlFolderIamPolicy", resource=ml_team_folder.id, policy_data=ml_dev_member_binding.policy_data) # Export the folder id and the applied IAM policy id pulumi.export('mlTeamFolderId', ml_team_folder.id) pulumi.export('mlFolderIamPolicyId', ml_folder_iam_policy.id)

    In the above code:

    • We import required Pulumi modules for Python.
    • We provide the organization ID where you want to create the folder and a display name for the folder.
    • A new folder is created using gcp.organizations.Folder.
    • We define an IAM policy binding, which consists of a role (roles/ml.developer) and a list of members (identified by email) who are assigned this role.
    • Finally, we apply this IAM policy to the created folder using gcp.organizations.IAMPolicy. We also export the folder ID and IAM policy ID so that they can be easily accessed after deployment.

    Before running this program, ensure you have set up Pulumi with GCP access, and replace placeholders with appropriate values. This program will configure your GCP folder-level IAM roles when executed with the Pulumi CLI.