Principle:Openai Openai python Fine Tuning Job Monitoring

Knowledge Sources	OpenAI Fine-tuning Guide openai-python
Domains	Fine_Tuning, Monitoring
Last Updated	2026-02-15 00:00 GMT

Overview

A polling-based observation pattern for tracking fine-tuning job progress through status checks and event logs.

Description

Job monitoring tracks the lifecycle of a fine-tuning job from file validation through training to completion or failure. It provides job status polling (retrieve), event log streaming (list_events), and job listing with pagination (list). The status field progresses through: validating_files, queued, running, succeeded, failed, or cancelled.

Usage

Use this principle after creating a fine-tuning job to track its progress. Poll the job status periodically, check events for training metrics, and retrieve the final model name upon completion.

Theoretical Basis

# Monitoring loop
while True:
    job = retrieve(job_id)
    if job.status == "succeeded":
        model_name = job.fine_tuned_model
        break
    elif job.status == "failed":
        handle_failure(job.error)
        break
    events = list_events(job_id)
    for event in events:
        log(event.message)  # Training loss, metrics, etc.
    sleep(interval)

Related Pages

Implemented By

• Implementation:Openai_Openai_python_Fine_Tuning_Jobs_Retrieve