1. Granting Service Accounts Access to AI Training Data on GCS


    To grant service accounts access to AI training data on Google Cloud Storage (GCS), you will be defining the access policies using Pulumi in conjunction with Google Cloud Platform (GCP) services. This process generally involves the following steps:

    1. Create a GCS Bucket: Define a GCS bucket where your AI training data will be stored.
    2. Upload the AI Training Data: This step is non-Pulumi and would typically involve using gsutil CLI or Google Cloud Console to upload your data to the created GCS bucket.
    3. Create a Service Account: Create a service account that will be used by your AI training application or service.
    4. Grant Access to the Service Account: Assign appropriate IAM roles to the service account that includes permissions to read the data within the GCS bucket.

    Here's a Pulumi program written in Python that accomplishes these steps:

    import pulumi import pulumi_gcp as gcp # Create a GCS Bucket for storing the AI training data training_data_bucket = gcp.storage.Bucket("training-data-bucket", location="us-central1", # You can specify the location according to your requirement uniform_bucket_level_access=True, # Enforces that only IAM policies grant access to the bucket ) # Create a Service Account for the AI training application ai_service_account = gcp.serviceaccount.Account("ai-training-account", account_id="ai-training-account", display_name="AI Training Service Account", ) # Grant the Service Account read access to the GCS Bucket bucket_reader_role = gcp.storage.BucketIAMMember("bucket-reader-role", bucket=training_data_bucket.name, role="roles/storage.objectViewer", member=pulumi.Output.concat("serviceAccount:", ai_service_account.email), ) # Export the bucket URL and service account email so you can use them in your application pulumi.export("bucket_url", training_data_bucket.url) pulumi.export("service_account_email", ai_service_account.email)

    In this program:

    • Firstly, we import the required modules.
    • We then create a GCS bucket called training-data-bucket with a set location and enable uniform_bucket_level_access for consistency in access policies.
    • Next, we create a service account named ai-training-account which the AI application will use to interact with the GCS bucket.
    • We then grant the service account objectViewer access to the GCS bucket using the BucketIAMMember resource. This role provides read-only access to the data in the bucket.
    • Finally, we export the URL of the GCS bucket and the email of the service account. These outputs can later be used to configure your AI training application's access to the data.

    This Pulumi program sets up the necessary infrastructure on GCP for storing AI training data and grants access to a specific service account. Remember to replace "us-central1" with an appropriate location for your data if necessary. You will also need to use additional tools (like gsutil) to upload the actual training data to your GCS bucket since that is out of the scope of what Pulumi does.

    Also, note the use of pulumi.Output.concat when specifying the service account member for the BucketIAMMember resource, which is necessary to properly construct the IAM member string when working with Pulumi's output values.