1. Service Account IAM Roles for Batch Prediction Jobs on GCP.

    Python

    When working with Google Cloud Platform (GCP) and managing permissions for service accounts, particularly for batch prediction jobs, it's important to ensure that the service accounts have the necessary IAM roles assigned to them. This allows the service account to operate the batch prediction job tasks with the appropriate level of security and access to resources.

    To accomplish this with Pulumi in Python, you would typically use the gcp.serviceAccount.IAMMember resource, which attaches an IAM role to a service account, defining which members have which roles.

    Here's a step-by-step Pulumi Python code on how to create a service account and assign it a specific IAM role suitable for batch prediction jobs:

    1. Import necessary modules: Include Pulumi's Google Cloud (GCP) module to interact with GCP resources.
    2. Create a Serivce Account: Define a ServiceAccount which will be used to run batch prediction jobs.
    3. Assign IAM Role to Service Account: Use IAMMember to attach the necessary IAM role to the service account.

    Below is the detailed Pulumi program:

    import pulumi import pulumi_gcp as gcp # Create a new GCP Service Account to run batch prediction jobs service_account = gcp.serviceaccount.Account("batch-prediction-service-account", account_id="batch-prediction-sa", display_name="Batch Prediction Service Account" ) # Define the IAM role for the service account. In this case, it's a custom role tailored for batch prediction, # You might want to replace `roles/ml.engine.batchPredictionJobRunner` with the actual required role for your specific use-case. iam_role = "roles/ml.engine.batchPredictionJobRunner" # Assign the IAM role to the service account, granting the specified role permissions to the service account service_account_iam = gcp.serviceaccount.IAMMember("batch-prediction-service-account-iam", service_account_id=service_account.name, role=iam_role, member=pulumi.Output.concat("serviceAccount:", service_account.email) ) # Export the service account email so you know which account to use in your application or infrastructure pulumi.export("service_account_email", service_account.email)

    In this code:

    • We start by importing the required pulumi and pulumi_gcp modules.
    • We create a ServiceAccount resource using gcp.serviceaccount.Account. The account_id and display_name are parameters that determine the unique ID and the display name of the service account respectively.
    • The IAMMember resource is attached to the service account with the required role for batch prediction jobs. Note that role refers to the IAM role that will be assigned to the member, which in this case is the service account we've just created, denoted by its email.
    • Finally, we export the email of the service account which can be used to reference this service account in other parts of your GCP setup (e.g., when configuring a batch prediction job to use this service account for authentication and permissions handling).

    Always ensure that the IAM role you assign contains just enough permissions to perform the necessary tasks and nothing more, adhering to the principle of least privilege to maximize security.

    For more details on the Pulumi resources used here, refer to their documentation:


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