1. Isolating Machine Learning Environments with GCP Projects


    To isolate machine learning environments in Google Cloud Platform (GCP), we can utilize GCP Projects as the fundamental unit of organization and isolation. A Project in GCP is the root container for services, APIs, and resources. Each of your environments, such as development, testing, and production, can be represented as a separate GCP project. This way, you maintain separate billing, access control, and settings for each environment, reducing the risk of changes in one environment affecting the others.

    Below is a Pulumi program that will create a new GCP Project specifically for a machine learning environment. We will be using the pulumi_gcp Python package to interact with Google Cloud resources. The program uses the gcp.organizations.Project resource to create a new project, and gcp.projects.IAMMember to set appropriate permissions to a specified member.

    This assumes that you have the necessary permissions to create projects within the organization or folder specified. Also, ensure that the Pulumi CLI and GCP provider are correctly configured with the appropriate credentials.

    import pulumi import pulumi_gcp as gcp # Replace these variables with appropriate values organization_id = "your-organization-id" # The GCP organization ID under which to create the project billing_account_id = "your-billing-account-id" # The Billing Account ID to associate with the project member = "user:your-email@example.com" # The member to add as project editor # Create a new GCP Project for the machine learning environment ml_project = gcp.organizations.Project("ml-env-project", name="ml-environment-project", # The name of the project. org_id=organization_id, # Link the project with your GCP organization. billing_account_id=billing_account_id, # Link the billing account. auto_create_network=True) # Automatically create the default network for the project. # Set a member as an editor for the project project_iam_member = gcp.projects.IAMMember("ml-env-project-editor", project=ml_project.project_id, role="roles/editor", member=member) # Grant the member editor role for the newly created project # Export the project ID and the member's role as stack outputs pulumi.export("project_id", ml_project.project_id) pulumi.export("member_role", project_iam_member.role)

    In the above program:

    • We first set some variable placeholders that you will need to replace with actual values appropriate for your organization and billing structure.
    • We define a GCP Project for our machine learning environment with gcp.organizations.Project.
    • We associate the project with an organization and a billing account, and we decide to create a default network within the project setting auto_create_network to True.
    • We then use gcp.projects.IAMMember to grant an editor role to a specified user for this new project. The role "roles/editor" is a predefined role in GCP that includes permissions for actions that modify state, such as changing existing resources.
    • Lastly, we export the project ID and the member's role so you can easily retrieve these values after the Pulumi program has run.

    With this setup, the machine learning project is isolated, and users and resources can be managed independently from other projects.

    To learn more about Google Cloud Projects and IAM roles, you can refer to the documentation: