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 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.

I get a validation error when adding the ML backend

If you get a validation error when adding the ML backend URL to your Label Studio project, check the following:

• Is the labeling interface set up with a valid configuration?
• Is the machine learning backend running? 
  Run the following health check: curl -X GET http://localhost:9090/health
• Is your machine learning backend available from your Label Studio instance? It must be available to the instance running Label Studio.

If you're running Label Studio in Docker, you must run the machine learning backend inside the same Docker container, or otherwise make it available to the Docker container running Label Studio.

You can use the docker exec command to run commands inside the Docker container, or use docker exec -it <container_id> /bin/sh to start a shell in the context of the container.

See the docker exec documentation.

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:

• Production training logs are located in my_backend/logs/rq.log
• Production runtime logs are located in my_backend/logs/uwsgi.log
  In development mode, training logs appear in the web browser console.

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

• Training logs are located in logs/rq.log
• Main process and inference logs are located in logs/uwsgi.log

The ML backend seems to be connected, but I see “Error. Click here for details.” message when I start training

After you click Start Training, you see a message that says “Error. Click here for details.”

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

• Insufficient number of annotations completed for training to begin.
• Memory issues on the server.

If you can’t resolve the traceback issues by yourself, open a ticket or contact us through Slack.