PyTorch Runtime Issue on Jetson Orin Nano

Issue Overview

Users are experiencing runtime errors when attempting to run PyTorch on the Jetson Orin Nano, specifically related to compatibility issues between installed versions of PyTorch and TorchVision. The primary symptoms include:

• Installation Conflicts: During the installation of PyTorch, users encounter dependency conflicts, indicating that installed versions of torchaudio and torchvision require a different version of torch than what is being installed. For example, torchaudio 2.2.2 and torchvision 0.17.2 require torch==2.2.2, but the installed version is torch 2.3.0a0.
• Runtime Errors: After installation, when users attempt to import TorchVision, they receive errors such as RuntimeError: operator torchvision::nms does not exist, suggesting that there is a mismatch in the compiled versions of PyTorch and TorchVision.
• Context: The issue arises during the setup and execution of deep learning applications using PyTorch on the Jetson Orin Nano platform, particularly after following installation guides from NVIDIA.

Possible Causes

Several potential causes for this issue have been identified:

• Version Incompatibility: The installed versions of PyTorch, TorchVision, and Torchaudio may not be compatible with each other due to differing CUDA versions or release cycles.
• Installation Method: Using pip to install packages without ensuring version compatibility can lead to unresolved dependencies.
• CUDA Version Mismatch: Different CUDA versions for PyTorch (11.4) and TorchVision (11.8) can cause runtime errors.
• User Environment Issues: Conflicts may arise from previously installed packages or configurations in the user’s Python environment.

Troubleshooting Steps, Solutions & Fixes

To resolve the runtime issue with PyTorch on the Jetson Orin Nano, follow these comprehensive troubleshooting steps:

1. Check Installed Versions:

   • Run the following commands to check currently installed versions of PyTorch and related libraries:

     python3 -c 'import torch; print(torch.__version__)'
     python3 -c 'import torchvision; print(torchvision.__version__)'
     python3 -c 'import torchaudio; print(torchaudio.__version__)'
     

2. Uninstall Conflicting Packages:

   • If there are version conflicts, uninstall the incompatible packages:

     pip3 uninstall torch torchvision torchaudio
     

3. Install Compatible Versions:

   • Install a specific version of PyTorch that matches your JetPack version (e.g., 2.3.0) along with compatible versions of TorchVision and Torchaudio:

     pip3 install --no-cache https://developer.download.nvidia.cn/compute/redist/jp/v60dp/pytorch/torch-2.3.0a0+6ddf5cf85e.nv24.04-cp310-cp310-linux_aarch64.whl
     pip3 install torchvision==<compatible_version>
     pip3 install torchaudio==<compatible_version>
     

   • Use prebuilt packages from NVIDIA when available.

4. Verify CUDA Compatibility:

   • Ensure that the CUDA version used by your installed PyTorch matches that of your JetPack installation (check using nvcc --version).

5. Test Installation:

   • After installation, run a simple test script to verify that CUDA is available:

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

6. Use Prebuilt Packages:

   • If issues persist, consider using prebuilt packages for TorchVision and Torchaudio specifically designed for Jetson devices.

7. Check Environment Variables:

   • Ensure that your environment variables are correctly set for CUDA paths in .bashrc or /etc/profile.

8. Best Practices for Future Installations:

   • Always check compatibility between different libraries before installation.
   • Use virtual environments to isolate dependencies.

9. Documentation and Resources:

   • Refer to NVIDIA’s official documentation for installing PyTorch on Jetson platforms for up-to-date instructions.
   • Links to relevant documentation or driver updates can be found in NVIDIA’s forums or GitHub discussions.

By following these steps, users should be able to resolve the runtime issues encountered with PyTorch on the Jetson Orin Nano effectively.