FileNotFoundError When Running NanoLLM on Nvidia Jetson Orin Nano Dev Board

Issue Overview

Users are encountering a FileNotFoundError when attempting to run the NanoLLM library within a Docker container on the Nvidia Jetson Orin Nano Dev Board. The error message indicates that a specific directory or file is missing, which is crucial for the model’s operation. The traceback shows that the error occurs during the loading of a model from a specified path:

FileNotFoundError: [Errno 2] No such file or directory: ‘/data/models/huggingface/models–Efficient-Large-Model–VILA1.5-3b/snapshots/42d1dda6807cc521ef27674ca2ae157539d17026/llm’ → ‘/data/models/mlc/dist/models/VILA1.5-3b’

This issue arises during the execution of Python code using the nanollm library, specifically when trying to load a pretrained model. Users have reported that they have already attempted several troubleshooting steps, including deleting and re-downloading the model, restarting the Docker container, and manually copying files into the required directories. However, these efforts have not resolved the problem.

The problem seems to be consistent among users trying to run similar setups with varying degrees of success, indicating that it may be related to either configuration issues or missing dependencies.

Possible Causes

• Missing Model Files: The specified model files may not have been downloaded correctly or are located in an incorrect directory.
• Incorrect Path Configuration: The paths referenced in the code may not align with where the files are actually located within the Docker container.
• Docker Volume Mounting Issues: If the user’s home directory is not correctly mounted into the Docker container, it could lead to missing files.
• Permissions Issues: Insufficient permissions may prevent access to certain directories or files within the Docker environment.
• Docker Run Command Misconfiguration: The command used to start the Docker container may lack necessary volume mounts or environment variables.

Troubleshooting Steps, Solutions & Fixes

1. Verify Model Availability:

   • Check if the model files exist at the specified path within the Docker container:

     ls -l /data/models/huggingface/models--Efficient-Large-Model--VILA1.5-3b/snapshots/42d1dda6807cc521ef27674ca2ae157539d17026
     

   • Ensure that all required files are present.

2. Check Docker Volume Mounts:

   • Confirm that your home directory is correctly mounted into the Docker container. Use a command like:

     sudo docker run -it --volume="/home/ailab:/home/ailab" --user root your_container_name
     

   • Ensure that you include --volume flags for all necessary directories.

3. Manually Create Missing Symlink:

   • If certain directories are missing, you can manually create symbolic links as follows:

     ln -s /data/models/huggingface/models--Efficient-Large-Model--VILA1.5-3b/snapshots/42d1dda6807cc521ef27674ca2ae157539d17026/llm /data/models/mlc/dist/models/VILA1.5-3b
     

4. Modify Docker Run Command:

   • Ensure your Docker run command includes all necessary parameters, such as network settings and volume mounts:

     sudo docker run -it --network host --env="DISPLAY" --volume="/tmp/.X11-unix:/tmp/.X11-unix:rw" -v /home/ailab/Desktop/jetson-containers/data:/data --user root your_container_name
     

5. Check Permissions:

   • Make sure you have appropriate permissions for accessing model directories and files:

     chmod -R 755 /data/models/
     

6. Consult Documentation:

   • Review any relevant documentation for NanoLLM and ensure that all prerequisites and dependencies are installed correctly.

7. Seek Community Support:

   • If issues persist, consider reaching out on forums or communities dedicated to Nvidia Jetson or Docker for further assistance.

8. Best Practices:

   • Regularly update your Docker images and libraries to avoid compatibility issues.
   • Maintain backups of your models and configurations to facilitate easier recovery from errors.

By following these troubleshooting steps, users should be able to resolve the FileNotFoundError and successfully run their NanoLLM applications on the Nvidia Jetson Orin Nano Dev Board.