1. Fine-Tuned Access Control for GCP AI Notebooks


    Google Cloud AI Notebooks is a managed service that offers AI and machine learning (ML) practitioners the convenience of building, training, and deploying models on Google Cloud. Fine-tuned access control to these notebooks is essential to ensure that only authorized individuals can access and manipulate these environments.

    To implement fine-tuned access control for GCP AI Notebooks, you can use IAM (Identity and Access Management) policies to define who has what level of access to the notebooks. This ensures that the users only have the permissions necessary for their role to interact with the AI Notebooks.

    Pulumi allows you to manage these permissions as code, creating a programmable and repeatable way to assign and audit access controls. Below is a program written in Python using Pulumi which demonstrates how to assign a specific IAM role to a GCP AI Notebook instance, thereby fine-tuning access control.

    Let's start by setting up a Pulumi program to manage IAM policies for an AI Notebook instance.

    import pulumi import pulumi_google_native as google_native # This is the name of your GCP project where the AI Notebook instance is located project_name = 'my-gcp-project' # The location where your AI Notebook instance resides location = 'us-west1-b' # The AI Notebook instance name instance_name = 'my-ai-notebook-instance' # The IAM role to be assigned iam_role = 'roles/notebooks.viewer' # The member to whom the IAM role will be assigned. This can be a user, service account, or a group. member = 'user:john.doe@example.com' # Instance IAM Member # Comprehensive information about this resource can be found in the Google Cloud documentation: # https://www.pulumi.com/registry/packages/google-native/api-docs/notebooks/v2/instanceiammember/ notebook_instance_iam_member = google_native.notebooks.v2.InstanceIamMember( "notebookInstanceIamMember", name=f"projects/{project_name}/locations/{location}/instances/{instance_name}", role=iam_role, member=member, ) # Output the assigned IAM role for the AI Notebook instance pulumi.export("notebook_iam_member", notebook_instance_iam_member.member)

    In this program, we imported the pulumi_google_native package to interact with Google Cloud services using Pulumi. We then create an IAM member assignment using the InstanceIamMember class, which represents an IAM policy for a GCP AI Notebook instance resource.

    We specify the name, role, and member arguments for the InstanceIamMember resource. The name is a formatted string telling Pulumi where the AI Notebook instance is located within your GCP project. The role specifies the level of access (in this case, roles/notebooks.viewer which is a predefined role for viewing notebook instances), and member specifies who will receive this role.

    As a final step, we export the assigned IAM member using pulumi.export, which allows us to see the result after the program is deployed.

    To apply this program, you would simply run pulumi up in your terminal within the directory containing this code. Afterward, Pulumi CLI would provision the defined resources on Google Cloud according to the specifications in your program. This will ensure that the specified member has the appropriate permissions to the GCP AI Notebook instance.