Troubleshoot machine learning

After you [set up machine learning with Label Studio] or [create your own machine learning backend] to use with Label Studio, you can troubleshoot any issues you encounter by reviewing the possible causes on this page.

Troubleshoot by reviewing the ML server logs

You can investigate most problems using the server console log. The machine learning backend runs as a separate server from Label Studio, so make sure you check the correct server console logs while troubleshooting. To see more detailed logs, start the ML backend server with the --debug option.

If you’re running an ML backend:

If you’re running an ML backend using Docker Compose:

I launched the ML backend, but it appears as Disconnected after adding it in the Label Studio UI

Your ML backend server might not have started properly.

  1. Check whether the ML backend server is running. Run the following health check:
    curl -X GET http://localhost:9090/health
  2. If the health check doesn’t respond, or you see errors, check the server logs.
  3. If you used Docker Compose to start the ML backend, check for requirements missing from the requirements.txt file used to set up the environment inside Docker.

The ML backend seems to be connected, but after I click “Start Training”, I see “Error. Click here for details.” message

Click the error message to review the traceback. Common errors that you might see include:

My predictions are wrong or I don’t see the model prediction results on the labeling page

Your ML backend might be producing predictions in the wrong format.

The model backend fails to start or run properly

If you see errors about missing packages in the terminal after starting your ML backend server, or in the logs, you might need to specify additional packages in the requirements.txt file for your ML backend.

ML backend is unable to access tasks

Because the ML backend and Label Studio are different services, the assets (images, audio, etc.) that you label must be hosted and be accessible with URLs by the machine learning backend, otherwise it might fail to create predictions.