PyTorch Installation Issues on Nvidia Jetson Orin Nano Dev Board

Issue Overview

Users are encountering problems when attempting to install and run PyTorch and torchvision on the Nvidia Jetson Orin Nano Dev board. The specific symptoms include:

• Error Message: A runtime error indicating a mismatch in CUDA versions between PyTorch and torchvision:

  RuntimeError: Detected that PyTorch and torchvision were compiled with different CUDA versions. PyTorch has CUDA Version=11.4 and torchvision has CUDA Version=11.8.
  

• Context: This issue arises during the installation of PyTorch version 2.0.0+nv23.05 and torchvision version 0.15.1, particularly after following Nvidia’s installation guide for Jetson devices.
• Hardware/Software Specifications:
  • JetPack version: 5.1.1
  • CUDA version: Initially installed was 11.8, but users are advised to revert to 11.4.
• Frequency: Multiple users have reported similar issues, indicating a common problem.
• Impact: This issue prevents users from utilizing GPU acceleration for deep learning tasks, significantly hindering performance and usability.

Possible Causes

Several potential causes for this issue have been identified:

• CUDA Version Mismatch: The installed versions of PyTorch and torchvision require different CUDA versions, leading to compatibility issues.
• Incorrect Environment Paths: Users may have multiple paths set for different CUDA versions, causing confusion during library imports.
• Installation Method: Using pip instead of building from source may lead to mismatches in the expected CUDA versions.
• User Configuration Errors: Incorrectly configured environment variables (e.g., CUDA_HOME, LD_LIBRARY_PATH) can lead to runtime errors.

Troubleshooting Steps, Solutions & Fixes

Here are comprehensive steps to diagnose and resolve the issue:

1. Check Installed Versions:

   • Verify the currently installed versions of PyTorch and torchvision:

     python3 -c "import torch; print(torch.__version__); print(torch.version.cuda)"
     python3 -c "import torchvision; print(torchvision.__version__)"
     

2. Match CUDA Versions:

   • Ensure that both libraries are compiled with the same CUDA version. If you have upgraded to CUDA 11.8, consider downgrading torchvision to match PyTorch’s version (which may require rebuilding from source):

     # Change symlink back to CUDA 11.4
     sudo ln -sf /usr/local/cuda-11.4 /usr/local/cuda
     

3. Reinstall Libraries:

   • Uninstall existing installations of PyTorch and torchvision:

     pip uninstall torch torchvision
     

   • Reinstall matching versions using pip:

     pip install torch==2.0.0+cu114 torchvision==0.15.1+cu114 --extra-index-url https://download.pytorch.org/whl/cu114
     

4. Use Pre-built Containers:

   • For a hassle-free setup, consider using the NVIDIA L4T PyTorch container which comes pre-installed with compatible versions of PyTorch and torchvision:

     docker run --runtime nvidia --gpus all nvcr.io/nvidia/l4t-pytorch:rxx.x-py3
     

5. Adjust Environment Variables:

   • Modify your environment variables to ensure they point to the correct CUDA installation:

     export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64:$LD_LIBRARY_PATH
     export PATH=/usr/local/cuda-11.4/bin:$PATH
     export CUDA_HOME=/usr/local/cuda-11.4
     

6. Building from Source (if necessary):

   • If issues persist, consider building torchvision from source to ensure compatibility with the installed version of PyTorch.

7. Best Practices for Future Installations:

   • Always check compatibility matrices provided by both NVIDIA and PyTorch before installation.
   • Use virtual environments (e.g., conda) to isolate dependencies and avoid conflicts.

8. Documentation & Resources:

   • Refer to the official NVIDIA documentation for installation guides.
   • Check the PyTorch installation page for specific commands tailored to your setup.

Unresolved Aspects

While many users have successfully resolved their issues by following these steps, some aspects remain unclear, such as specific configurations that may differ based on individual setups or additional dependencies that might be required for certain projects (e.g., YOLO-based applications). Further investigation may be needed for unique cases or advanced configurations not covered in this document.