1. Managing Permissions for ML Dataset Access in GCP.


    Managing permissions for machine learning datasets in Google Cloud Platform can involve several components, primarily Google Cloud Storage for dataset storage, and Google Cloud IAM for managing access controls.

    To manage permissions, you'll typically use IAM policies or IAM roles. Here, I'll guide you through creating a Pulumi program in Python that sets up a GCP IAM policy for a machine learning dataset. We will use two primary resources:

    1. google-native:ml/v1:ModelIamPolicy: This resource allows us to manage access control policies for Google Cloud machine learning models, which might be closely tied to datasets in many ML workflows.

    2. gcp:bigquery/dataset: BigQuery datasets are often used to store and query the data used by machine learning models, and you'll want to manage access to these datasets as well.

    Now, let's create a program that sets IAM permissions for a machine learning model and a BigQuery dataset in GCP. This program assumes you have already created a machine learning model and a BigQuery dataset.

    import pulumi import pulumi_gcp as gcp import pulumi_google_native.ml.v1 as google_ml # Replace these variables with your actual model and dataset details project = 'my-gcp-project' model_name = 'projects/{}/models/{}'.format(project, 'my-model') dataset_id = 'my_dataset' # IAM policy for the machine learning model # Model is referenced by its project and model name model_iam_policy = google_ml.ModelIamPolicy("modelIamPolicy", resource_name=model_name, bindings=[{ "role": "roles/ml.developer", # Example role "members": [ "user:user1@example.com", "serviceAccount:my-service-account@{}.iam.gserviceaccount.com".format(project), ], }]) # IAM policy for the BigQuery dataset # Dataset details are set for the respective project and id dataset_iam_policy = gcp.bigquery.DatasetIamPolicy("datasetIamPolicy", dataset_id=dataset_id, project=project, bindings=[ gcp.bigquery.DatasetIamPolicyBindingArgs( role="roles/bigquery.dataEditor", # Example role members=[ "user:user1@example.com", ], ), ]) pulumi.export('ml_model_iam_policy', model_iam_policy) pulumi.export('bigquery_dataset_iam_policy', dataset_iam_policy)

    Let's go through it step by step:

    • First, we import the necessary Pulumi components. We include both pulumi_gcp and pulumi_google_native for working with GCP and Google Machine Learning resources.

    • We define the project, model, and dataset variables. Note that you need to replace 'my-gcp-project', 'my-model', and 'my_dataset' with your actual project ID, model name, and dataset ID. The model_name needs to be in the format projects/{project}/models/{model_id}.

    • The ModelIamPolicy and DatasetIamPolicy resources are created to manage the IAM policies for the model and dataset respectively. In each policy, we define a list of bindings, specifying roles and members. The role is the type of access we're granting, and members are the identities we're granting this access to. This could be user emails, service account emails, or another type of member identifier.

    • The roles/ml.developer role allows designated users to manage machine learning models and jobs, while the roles/bigquery.dataEditor allows users to manage and modify data in BigQuery datasets.

    • Finally, pulumi.export statements make the IAM policy URLs accessible after deployment. This way, you can check the policies or use them in another program.

    Note that roles and members used in the bindings lists should be replaced with actual roles needed for your use case and the correct identities that should have these roles.

    Please ensure that your Pulumi CLI is configured correctly with GCP credentials and that you've selected an appropriate stack before running this program. After these setup steps are completed, you can run this program using the Pulumi CLI commands pulumi up to deploy the changes and pulumi destroy to clean up resources.