No NVIDIA GPU available or detected on Nvidia Jetson Orin Nano

Issue Overview

Users are experiencing an issue where the NVIDIA Jetson Orin Nano is unable to detect its GPU. Symptoms include the output from PyTorch indicating that CUDA is not available, and logs from the jtop utility confirming that no NVIDIA GPU is detected. This problem arose after a sudden reboot of the device while running an intensive Python script. Other Orin Nano devices rebooted without encountering this specific issue, suggesting a potential hardware or software malfunction unique to this unit. The device is running JetPack version 5.1.1, and users reported difficulties executing TensorRT inference scripts due to the GPU not being recognized.

Possible Causes

1. Hardware Incompatibilities or Defects: The sudden reboot may have caused a hardware malfunction, leading to the GPU being unresponsive.
2. Software Bugs or Conflicts: Issues with the installed version of JetPack or other software components could prevent proper GPU detection.
3. Configuration Errors: Incorrect settings in the system configuration may hinder GPU functionality.
4. Driver Issues: The necessary drivers for GPU operation may not be correctly installed or configured.
5. Environmental Factors: Power supply issues or overheating could impact GPU performance and detection.
6. User Errors or Misconfigurations: Incorrect installation procedures for software packages like PyTorch or TensorRT may lead to detection failures.

Troubleshooting Steps, Solutions & Fixes

1. Check Device Status:

   • Run sudo jtop to check the status of the device and confirm if the GPU is listed as unavailable.
   • If jtop fails to start, check service status with:

     sudo systemctl status jtop.service
     

2. Run Device Query Sample:

   • To verify GPU functionality, execute the following commands:

     git clone https://github.com/NVIDIA/cuda-samples.git
     cd cuda-samples/Samples/1_Utilities/deviceQuery
     make
     ./deviceQuery
     

   • This will help determine if the CUDA-capable device is detected.

3. Reboot Device:

   • Attempt a soft reboot using:

     sudo reboot
     

   • After rebooting, recheck GPU detection with deviceQuery.

4. User Permissions:

   • Ensure that your user account has permission to access GPU resources by adding it to the video group:

     sudo usermod -a -G video <username>
     

   • Log out and back in for changes to take effect.

5. Check Kernel Modules:

   • Verify if NVIDIA kernel modules are loaded properly:

     find /lib/modules/$(uname -r) -type f -name 'nvidia*.ko*'
     

   • If modules are missing or corrupted, consider reinstalling drivers.

6. Reinstall PyTorch with CUDA Support:

   • If PyTorch was installed without CUDA support, reinstall it using the prebuilt binaries from NVIDIA’s repository:

     export TORCH_INSTALL=https://developer.download.nvidia.cn/compute/redist/jp/v511/pytorch/torch-2.0.0+nv23.05-cp38-cp38-linux_aarch64.whl
     pip install --no-cache $TORCH_INSTALL
     

7. Kernel Upgrade:

   • If issues persist, consider upgrading JetPack to a newer version (if possible) to ensure compatibility with current software packages.

8. Check for Software Updates:

   • Run updates on existing packages and fix broken installations using:

     sudo apt update && sudo apt upgrade
     sudo apt install --fix-broken
     

9. Contact Support:

   • If all troubleshooting steps fail and the device remains under warranty, consider contacting NVIDIA support for further assistance or potential RMA processes.

10. Document Findings:

    • Keep a log of all commands executed and their outputs to assist in further troubleshooting or when seeking support.

This document serves as a comprehensive guide for users facing issues with GPU detection on their Nvidia Jetson Orin Nano devices after unexpected reboots or operational failures.