1. Controlling Access to AI Platform Resources via IAM Bindings

    Python

    Controlling access to AI platform resources is fundamental for managing security and permissions in cloud environments. In the context of Google Cloud Platform (GCP), this involves setting IAM (Identity and Access Management) policies that define who has what type of access to the resources.

    To accomplish this with Pulumi, we will define IAM bindings for a specific AI Platform resource. In this example, we'll use a Job IAM Binding, which controls access to a Machine Learning (ML) job resource. We will grant a member (like a user, service account, or group) a specific role on the ML job resource.

    Here is a detailed explanation of how the code works:

    • We start by importing the necessary libraries — namely pulumi and pulumi_gcp, which contain the necessary components for working with resources in GCP.
    • We create an instance of the JobIAMBinding class from the google-native.ml.v1 namespace. This Pulumi resource is responsible for applying an IAM policy to a ML job.
    • The JobIAMBinding requires specific properties: jobId, which is the identifier of the job; role, defining the level of access; and members, which are the identities receiving the role on the resource.
    • We then export the job_id chosen for easy reference.

    Below is the Pulumi program that accomplishes this:

    import pulumi import pulumi_google_native.ml.v1 as ml # Assuming that a machine learning job already exists and we have its ID, # replace '<your-job-id>' with your actual ML job ID. job_id = '<your-job-id>' # IAM binding for the ML Job job_iam_binding = ml.JobIamBinding("jobIamBinding", name=job_id, role="roles/editor", # The role you want to assign, e.g., viewer, editor, or a custom role members=[ "user:example-user@gmail.com", # You can add more member identifiers as needed. ]) # Export the job ID for easy reference. pulumi.export("job_id", job_id)

    In the role field, you can specify various predefined roles like roles/ml.developer, roles/editor, or any other suitable role as per your requirement. The members field takes an array of identities that you want to give access to, which can be user accounts, service accounts, or groups, designated with the appropriate prefix, like user:, serviceAccount:, or group:.

    For more information on the various roles and permissions that you can grant, you might want to look into the GCP IAM documentation. This will provide a comprehensive list of roles along with the permissions each role contains.

    Remember to replace <your-job-id> with the actual ID of the ML job you wish to control access for. If you're working with a different AI resource on GCP (like a dataset or a model), you'll want to look for the specific IAM binding resource in Pulumi that matches your resource type.

    Always check the actual permissions required for your use case and ensure you follow the principle of least privilege, granting only the permissions necessary for the task.


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