Principle:Openai Openai node Job Progress Monitoring
| Knowledge Sources | |
|---|---|
| Domains | Fine_Tuning, Monitoring |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
A principle for monitoring the progress and status of asynchronous fine-tuning jobs through polling, event listing, and checkpoint inspection.
Description
Job Progress Monitoring provides visibility into the fine-tuning process through three mechanisms: (1) job status polling via retrieve(), (2) event stream via listEvents() for detailed progress messages, and (3) checkpoint inspection via checkpoints.list() for training metrics at each saved step.
Jobs transition through states: validating_files → queued → running → succeeded | failed | cancelled. During the running phase, events provide training loss, validation loss, and step progress.
Usage
Use this principle after creating a fine-tuning job to track its progress. Poll the job status periodically, list events for detailed logs, and inspect checkpoints for training metrics.
Theoretical Basis
Monitoring follows a Multi-Source Polling pattern:
// Three complementary information sources:
// 1. Job status (coarse-grained)
job = await jobs.retrieve(jobId)
// job.status: 'running', job.fine_tuned_model: null
// 2. Event log (fine-grained progress)
events = await jobs.listEvents(jobId)
// events: [{message: "Step 100/500, training loss: 0.24"}, ...]
// 3. Checkpoints (training metrics)
checkpoints = await jobs.checkpoints.list(jobId)
// checkpoints: [{step_number: 100, metrics: {training_loss: 0.24}}, ...]