Principle:Googleapis Python genai Job Monitoring
| Knowledge Sources | |
|---|---|
| Domains | Fine_Tuning, Operations |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
A polling-based mechanism for tracking the progress and completion status of long-running asynchronous jobs.
Description
Job Monitoring tracks the state of asynchronous operations like fine-tuning jobs. Since tuning jobs run server-side and can take minutes to hours, applications must periodically poll the job status to determine when training completes. The job transitions through states: CREATING (resources being allocated), ACTIVE (training in progress), SUCCEEDED (training complete, model available), FAILED (error occurred), or CANCELLED (user cancelled). Upon success, the tuned model endpoint becomes available for inference.
Usage
After launching a tuning job, implement a polling loop that calls tunings.get at regular intervals. Check the state field for completion. When state is SUCCEEDED, access tuned_model.endpoint for the model identifier. Handle FAILED and CANCELLED states with appropriate error handling.
Theoretical Basis
Job monitoring follows the Polling Pattern for long-running operations:
# Polling pattern (pseudo-code)
import time
job = launch_job()
while job.state not in ("SUCCEEDED", "FAILED", "CANCELLED"):
time.sleep(poll_interval)
job = get_job(job.name)
log(f"State: {job.state}")
if job.state == "SUCCEEDED":
use(job.result)
elif job.state == "FAILED":
handle_error(job.error)