How to beginner · 3 min read

How to monitor fine-tuning job

Q: How to monitor fine-tuning job

Use the OpenAI Python SDK's client.fine_tuning.jobs.retrieve() method with the fine-tuning job ID to check the job status and details. Poll this endpoint periodically until the job completes, then use the fine_tuned_model field to run inference with the fine-tuned model.

Quick answer

Use the OpenAI Python SDK's client.fine_tuning.jobs.retrieve() method with the fine-tuning job ID to check the job status and details. Poll this endpoint periodically until the job completes, then use the fine_tuned_model field to run inference with the fine-tuned model.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the latest OpenAI Python SDK and set your API key as an environment variable.

bash

pip install openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

This example shows how to upload a training file, create a fine-tuning job, and poll the job status until completion.

python

import os
import time
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Upload training file
training_file = client.files.create(
    file=open("training.jsonl", "rb"),
    purpose="fine-tune"
)
print(f"Uploaded file ID: {training_file.id}")

# Create fine-tuning job
job = client.fine_tuning.jobs.create(
    training_file=training_file.id,
    model="gpt-4o-mini-2024-07-18"
)
print(f"Created fine-tuning job ID: {job.id}")

# Poll job status until done
while True:
    status = client.fine_tuning.jobs.retrieve(job.id)
    print(f"Job status: {status.status}")
    if status.status in ["succeeded", "failed"]:
        break
    time.sleep(10)  # wait 10 seconds before next check

if status.status == "succeeded":
    print(f"Fine-tuned model: {status.fine_tuned_model}")
else:
    print("Fine-tuning job failed.")

output

Uploaded file ID: file-abc123xyz
Created fine-tuning job ID: ftjob-xyz789abc
Job status: pending
Job status: running
Job status: running
Job status: succeeded
Fine-tuned model: gpt-4o-mini-ft-2026-04-01-xyz

Common variations

You can monitor fine-tuning jobs asynchronously or use different base models. The polling interval can be adjusted based on your needs.

python

import asyncio
import os
from openai import OpenAI

async def monitor_fine_tuning_job(job_id: str):
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    while True:
        status = client.fine_tuning.jobs.retrieve(job_id)
        print(f"Async job status: {status.status}")
        if status.status in ["succeeded", "failed"]:
            return status
        await asyncio.sleep(10)

# Usage example
# asyncio.run(monitor_fine_tuning_job("ftjob-xyz789abc"))

output

Async job status: pending
Async job status: running
Async job status: succeeded

Troubleshooting

If you receive a 404 error when retrieving the job, verify the job ID is correct.
If the job status stays in "pending" for too long, check your training file format and API usage limits.
Use client.files.list() to confirm your training file upload.

✅

Key Takeaways

Use client.fine_tuning.jobs.retrieve() with the job ID to monitor fine-tuning status.
Poll the job status periodically until it reaches "succeeded" or "failed".
After success, use the fine_tuned_model field to run inference with the fine-tuned model.

Verified 2026-04 · gpt-4o-mini-2024-07-18

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