1. Uniform Access Control for Distributed AI Training Clusters


    To set up uniform access control for distributed AI training clusters, you will typically be looking at managing permissions and access within the cloud provider's ecosystem where your clusters are deployed. This could involve setting up Identity and Access Management (IAM) roles, policies, and bindings that define who can access what resources within your cloud environment.

    Let's assume you are using Google Cloud Platform (GCP) for your AI training clusters which are orchestrated with Kubernetes Engine (GKE) and leverage Google Cloud AI Platform services for machine learning workloads. To manage access control uniformly across these distributed systems, you would define IAM policies that grant the necessary permissions to the appropriate entities (like users, groups, or service accounts).

    In the code below, I will demonstrate how to use Pulumi to configure IAM policies for a GKE cluster and a Google Cloud AI Platform model. We'll use the gcp.container.Cluster to represent a GKE cluster and gcp.serviceAccount.IAMMember to assign a role to a service account, controlling its access to the GKE cluster. Similarly, we'll use gcp.aiplatform.ModelIamPolicy to set up access control for an AI Platform model to ensure that only authorized entities can interact with your machine learning models.

    Remember to have your GCP credentials configured for Pulumi to authenticate and interact with your GCP resources.

    Here is how you would do it:

    import pulumi import pulumi_gcp as gcp # Create a GCP service account service_account = gcp.serviceaccount.Account("service-account", account_id="my-service-account", display_name="My Service Account") # Grant a GCP IAM role to the service account for GKE cluster access # This example grants 'roles/container.clusterViewer' which allows read-only access # Adjust the role according to your requirements iam_member = gcp.serviceaccount.IAMMember("iam-member", service_account_id=service_account.id, role="roles/container.clusterViewer", member=pulumi.Output.concat("serviceAccount:", service_account.email)) # Assuming you have an existing AI Platform model, you can set an IAM policy # Replace `model_name` with the actual name of your AI model model_iam_policy = gcp.aiplatform.ModelIamPolicy("model-iam-policy", project="your-gcp-project-id", location="us-central1", model="model_name", bindings=[{ "role": "roles/ml.developer", "members": [ pulumi.Output.concat("serviceAccount:", service_account.email), ], }]) # Export the service account email and the model IAM policy ID # These outputs can be used for verification or other purposes pulumi.export("service_account_email", service_account.email) pulumi.export("model_iam_policy_id", model_iam_policy.id)

    In the code above we create a service account which will be used across our distributed AI training clusters, granting it a viewer role to our GKE cluster. We also set an IAM policy for a predefined AI Platform model, specifying the role roles/ml.developer which has permissions to manage AI Platform resources, and adding our service account as a member. This ensures that your AI training clusters and models can only be accessed by entities with the correct permissions.

    Make sure to replace "your-gcp-project-id", "model_name", and other placeholder values with your actual GCP project ID and AI model details. The roles/ml.developer can be replaced with any other role suitable for the operations you intend the service account to perform.

    The example exports the service account email and the model IAM policy ID for further use. These could be the endpoints you use within your application or for logging and auditing purposes.