- Get started
- Create user accounts
Install and Upgrade
- Install and upgrade
- Database setup
- Get data
- Import pre-annotated data
- Sync data from external storage
Manage Data & Projects
- Set up your labeling project
- Label and annotate data
- Export results
Machine Learning Setup
- Set up machine learning
- Write your own ML backend
- ML Examples and Tutorials
- Troubleshoot machine learning
- Frontend library
- Frontend reference
- Backend API
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.
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
If you’re running an ML backend:
- Production training logs are located in
- Production runtime logs are located in
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
- Main process and inference logs are located in
Your ML backend server might not have started properly.
- Check whether the ML backend server is running. Run the following health check:
curl -X GET http://localhost:9090/health
- If the health check doesn’t respond, or you see errors, check the server logs.
- If you used Docker Compose to start the ML backend, check for requirements missing from the
requirements.txtfile 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:
- Insufficient number of annotations completed for training to begin.
- Memory issues on the server.
If you can’t resolve the traceback issues by yourself, contact us on Slack.
Your ML backend might be producing predictions in the wrong format.
- Check to see whether the ML backend predictions format follows the same structure as predictions in imported pre-annotations.
- Confirm that your project’s label configuration matches the output produced by your ML backend. For example, use the Choices tag to create a class of predictions for text. See more Label Studio tags.
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.
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.