CUDA Not Available on Jetson Orin Nano Despite Installation

Issue Overview

Users of the NVIDIA Jetson Orin Nano Developer Kit are experiencing difficulties with CUDA functionality despite having installed JetPack 6.0. The specific symptoms include:

• CUDA Version Recognition: Users confirm that CUDA version 12.2.140 is installed, as verified by the command nvcc --version.

• Jtop Output: Running the jtop utility shows CUDA and other libraries like cuDNN and TensorRT are recognized, but OpenCV reports “with CUDA: NO”.

• PyTorch Compatibility: When attempting to utilize CUDA in PyTorch, the command torch.cuda.is_available() returns False, accompanied by an error stating, “Torch not compiled with CUDA enabled”.

• System Specifications:

  • Jetson Orin Nano Developer Kit
  • JetPack Version: 6.0
  • CUDA Version: 12.2.140
  • Python Version: 3.10.12
  • PyTorch Version: 2.4.1
  • TorchVision Version: 0.19.1

The issue arises primarily when users attempt to run GPU-dependent applications, such as YOLO for real-time object detection, significantly impacting performance and usability.

Possible Causes

Potential reasons for the observed issues include:

• Hardware Incompatibilities: The default OpenCV installation may not support CUDA, leading to functionality issues.

• Software Bugs or Conflicts: There may be conflicts between installed software versions (e.g., PyTorch and OpenCV) that hinder CUDA utilization.

• Configuration Errors: Incorrect configurations during installation or setup may prevent proper recognition of CUDA by applications.

• Driver Issues: Outdated or incompatible drivers could lead to CUDA not being recognized by PyTorch.

• Environmental Factors: Power supply issues or overheating could affect system performance and functionality.

• User Errors: Misconfigurations during the installation process could result in incomplete setups.

Troubleshooting Steps, Solutions & Fixes

To address the issue of CUDA not being available on the Jetson Orin Nano, follow these comprehensive troubleshooting steps:

1. Verify Installation:

   • Confirm that JetPack 6.0 is correctly installed.
   • Check that all necessary libraries (CUDA, cuDNN, TensorRT) are present using jtop.

2. Reinstall OpenCV with CUDA Support:

   • The default OpenCV provided with JetPack does not come with CUDA support. To install OpenCV with CUDA, use the following script:

#!/bin/bash

version="4.9.0"
folder="workspace"

set -e

for (( ; ; ))
do
    echo "Do you want to remove the default OpenCV (yes/no)?"
    read rm_old

    if [ "$rm_old" = "yes" ]; then
        echo "** Remove other OpenCV first"
        sudo apt -y purge *libopencv*
        break
    elif [ "$rm_old" = "no" ]; then
        break
    fi
done

# Continue with installation steps...

• This script prompts for the removal of the default OpenCV before proceeding with a custom installation from source.

3. Install PyTorch and TorchVision with CUDA Support:

   • Ensure you are using versions of PyTorch and TorchVision that are compiled with CUDA support.
   • Follow instructions from reliable sources or repositories that provide pre-built binaries for Jetson devices.

4. Check Environment Variables:

   • Ensure that environment variables such as LD_LIBRARY_PATH include paths to your CUDA libraries.

5. Test GPU Functionality:

   • After making changes, test if PyTorch recognizes the GPU by running:

import torch
print(torch.cuda.is_available())

6. Update Drivers and Firmware:

   • Ensure that all drivers are up-to-date via NVIDIA’s official resources.
   • Check for any firmware updates for the Jetson Orin Nano.

7. Consult Documentation and Community Resources:

   • Refer to NVIDIA’s official documentation for troubleshooting guidance.
   • Engage with community forums for additional insights or similar experiences.

8. Best Practices:

   • Always back up configurations before making changes.
   • Regularly check for updates to JetPack and associated libraries.

By following these steps, users should be able to diagnose and potentially resolve issues related to CUDA availability on their Jetson Orin Nano Developer Kit. If problems persist after these troubleshooting steps, further investigation into specific error messages or logs may be necessary.