Add GCP Billing Report Labels to Airflow's BatchPredictionJobHook

Integrating Google Cloud Platform (GCP) billing labels with Airflow's BatchPredictionJobHook can enhance the way you manage and track your machine learning job costs. This tutorial will guide you through the process of adding billing labels to your Airflow batch prediction jobs, enabling you to efficiently filter and analyze costs in GCP Billing.

Key Takeaways

• Learn to add billing labels to Airflow BatchPredictionJobHook jobs.
• Understand the significance of labels in GCP Billing reports.
• Follow a practical step-by-step guide with code examples.
• Troubleshoot common issues related to label visibility.
• Utilize labels to categorize and manage ML job costs.

Google Cloud's billing labels are key-value pairs that help in organizing and categorizing resources for cost management. In this tutorial, we will explore how to include these labels when using Airflow's BatchPredictionJobHook to submit batch prediction jobs. This integration is crucial for teams looking to optimize resource utilization and billing visibility in GCP.

Prerequisites

• Familiarity with Apache Airflow and its DAG creation.
• Access to a GCP project with billing enabled.
• Installed and configured Airflow with Google Cloud provider.
• Basic understanding of Python programming (Python 3.8+ recommended).

Step 1: Set Up Your Airflow Environment

Ensure that Airflow is installed and configured with the necessary Google Cloud provider packages. This setup is essential for creating and managing batch prediction jobs.

pip install apache-airflow-providers-google

Configure your Google Cloud connection in Airflow by adding a new connection through the Airflow UI, ensuring that the service account has the necessary permissions.

Step 2: Define Your Batch Prediction Job

Create a new Python file to define your DAG and include the BatchPredictionJobHook. This step involves specifying the parameters for your batch prediction job, including the model, input data, and labels.

from airflow import DAG
from airflow.providers.google.cloud.hooks.vertex_ai import BatchPredictionJobHook
from datetime import datetime

default_args = {
    'start_date': datetime(2026, 1, 1),
}

def create_batch_prediction_job():
    hook = BatchPredictionJobHook(gcp_conn_id='my_gcp_connection')
    job = hook.create_batch_prediction_job(
        project_id='my-gcp-project',
        region='us-central1',
        model_name='projects/my-gcp-project/models/my-model',
        job_display_name='my_batch_prediction_job',
        instances_format='jsonl',
        gcs_source_uri='gs://my-bucket/input-data.jsonl',
        gcs_destination_uri_prefix='gs://my-bucket/output/',
        labels={
            'environment': 'production',
            'team': 'data-science'
        }
    )
    print(f"Batch prediction job created: {job}")

dag = DAG('batch_prediction_dag', default_args=default_args, schedule_interval=None)

The labels defined will assist in categorizing the job for better billing analysis.

Step 3: Deploy and Trigger the DAG

Deploy your DAG by placing the Python file in your Airflow DAGs directory. Once Airflow detects the new DAG, trigger it from the Airflow UI to start the batch prediction job.

Monitor the job execution in the Airflow UI and check the logs for any errors or issues.

After the job completion, verify the output in the specified GCS bucket.

Step 4: Verify Label Visibility in GCP Billing

Navigate to the GCP Billing section and select the report view. Use the filter feature to check if your labels are appearing under the dimensions option. It may take up to 24 hours for the labels to sync and appear in billing reports.

Common Errors/Troubleshooting

• Labels Not Visible: Ensure that your job's labels are correctly defined and that the billing account is linked to the project.
• Permission Issues: Verify that the service account used by Airflow has appropriate permissions to submit batch prediction jobs and access billing data.
• Job Failures: Check Airflow logs for detailed error messages and ensure the input data format matches the expected schema.

Frequently Asked Questions

Why are my labels not appearing in the billing report?

Labels may take up to 24 hours to appear. Ensure that labels are correctly applied and that the billing account is active.

What permissions are required for the service account?

The service account needs roles such as 'Vertex AI User' and 'Billing Account Viewer' to manage jobs and access billing data.

Can I update labels after job submission?

No, labels must be set during job creation. To change labels, you need to submit a new job with the updated labels.

Frequently Asked Questions

Why are my labels not appearing in the billing report?

Labels may take up to 24 hours to appear. Ensure that labels are correctly applied and that the billing account is active.

What permissions are required for the service account?

The service account needs roles such as 'Vertex AI User' and 'Billing Account Viewer' to manage jobs and access billing data.

Can I update labels after job submission?

No, labels must be set during job creation. To change labels, you need to submit a new job with the updated labels.