1. Gatekeeper Policy Management for ML Workflow Authorization


    To manage Gatekeeper Policy for ML Workflow Authorization on Google Cloud, you'll be working with Identity and Access Management (IAM) policies associated with different Google Cloud resources involved in the Machine Learning (ML) workflows, such as models, jobs, or workflow templates. These IAM policies control who has what type of access to these resources.

    In this context, you can apply IAM policies at various levels:

    • Model Level: You might set IAM policies on individual ML models to control access to them.
    • Job Level: Similarly, you can control who can run or manage ML jobs.
    • Workflow Templates: Manage who can create, update, or use workflow templates.

    Using Pulumi with the Google Native provider, you can define these IAM policies as infrastructure-as-code. This can be valuable for automating and tracking changes to access control in a reproducible manner.

    Below is a Python program using Pulumi's Google Native provider that demonstrates how you might manage IAM policies for an ML model:

    import pulumi import pulumi_google_native.ml.v1 as ml # Model IAM Binding # This resource represents an IAM policy for an ML model, where you can specify the role and the members who should have that role. # Replace 'model_name', 'role', and 'members' with the actual name of the ML model, the IAM role, and the members who should possess this role. model_iam_binding = ml.ModelIamBinding("model-iam-binding", name="projects/[PROJECT]/locations/[LOCATION]/models/[MODEL_NAME]", role="roles/ml.editor", members=[ "user:alice@example.com", "serviceAccount:my-ml-service-account@project.iam.gserviceaccount.com", ], ) # Export the model IAM policy id, which can be used to reference this IAM policy binding in other parts of your infrastructure or in other Pulumi programs. pulumi.export("model_iam_binding_id", model_iam_binding.id)

    In this program:

    • ml.ModelIamBinding: This resource is used to create an IAM policy binding for an ML Model on Google Cloud. You specify the fully qualified name of the model resource in the name parameter, the role that defines the permissions in the role parameter, and a list of members to whom this role should be granted in the members parameter.

    • name: The fully qualified name of the ML model resource, which includes the project ID, the location, and the model name.

    • role: The role that you are binding the members to. This should reflect the level of access you want the members to have. Roles such as roles/ml.editor and roles/ml.viewer are common for granting edit and view permissions, respectively.

    • members: A list of members who are granted the role on the model. Members can be user accounts, service accounts, or other types of Google Cloud identities.

    • pulumi.export: This command is used to output the id of the IAM policy binding, which you can then use outside of Pulumi for reference or use in other tools.

    This program uses Pulumi's Google Native provider, which allows you to manage Google Cloud resources using their native syntax and schema. Before running this program, you need to have the prerequisites sorted out:

    • A Pulumi account configured with the correct access to interact with Google Cloud resources.
    • The Google Cloud project and credentials set up to allow deployment.
    • The Pulumi CLI installed and configured on the machine from which you are deploying.

    To deploy this program:

    1. Save the code to a file named main.py.
    2. Run pulumi stack init to create a new stack for your project.
    3. Run pulumi up to create the resources specified in your program.

    This is a fundamental example, and in practice, you'd be managing more complex IAM configurations involving various roles and resources. With Pulumi, you can define the full gamut of IAM policies that Google Cloud supports and tie them directly to the infrastructure resources they relate to.