Installing TorchVision 0.15.1 on Jetson Orin Nano: Compilation and Import Issues

Issue Overview

Users are experiencing difficulties installing and importing TorchVision 0.15.1 on the Nvidia Jetson Orin Nano 8GB development board. The main symptoms include:

• Compilation errors when installing TorchVision from source
• Inability to find the installed TorchVision package in the expected directory
• Failure to import TorchVision in Python environments
• Runtime errors when attempting to use TorchVision with YOLOv5

This issue occurs in the context of setting up a deep learning environment on the Jetson Orin Nano, specifically when trying to use TorchVision alongside PyTorch 2.0.0+nv23.05.

Possible Causes

1. Incompatible pre-built packages: The pre-built TorchVision packages from PyPI are not compatible with the Jetson Orin Nano’s ARM64 architecture.

2. Missing dependencies: The compilation process indicates missing or incorrectly configured dependencies, such as Ninja and FFmpeg.

3. Compiler warnings and errors: The installation process generates multiple warnings, which may indicate potential compatibility issues or bugs in the source code.

4. Incorrect installation location: The compiled package is not being installed in the expected directory, leading to import failures.

5. Python version conflicts: The presence of multiple Python versions (2.7, 3.8, 3.9) on the system may cause confusion in the installation and import process.

6. JetPack version compatibility: The specific JetPack version (possibly 5.1.1) may not be fully compatible with the desired TorchVision version.

Troubleshooting Steps, Solutions & Fixes

1. Install from source using NVIDIA’s recommended method:
   Follow the instructions provided in the NVIDIA Developer Forums to build TorchVision from source. This method is specifically tailored for Jetson devices:

   sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev
   git clone --branch v0.15.1 https://github.com/pytorch/vision torchvision
   cd torchvision
   export BUILD_VERSION=0.15.1
   python3 setup.py install --user
   cd ../
   

2. Verify CUDA and PyTorch installation:
   Ensure that CUDA and PyTorch are correctly installed and recognized:

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

   This should return True and the correct PyTorch version (2.0.0+nv23.05).

3. Check for conflicting Python versions:
   Use which python3 and python3 --version to verify that you’re using the correct Python installation (3.8 in this case).

4. Manually add TorchVision to Python path:
   If the installation succeeds but importing fails, try adding the installation directory to your Python path:

   import sys
   sys.path.append('/path/to/torchvision-egg-directory')
   import torchvision
   

5. Clean existing installations:
   Before reinstalling, remove any existing TorchVision installations:

   sudo pip3 uninstall torchvision
   sudo rm -rf /usr/local/lib/python3.8/dist-packages/torchvision*
   

6. Install additional dependencies:
   Ensure all necessary dependencies are installed:

   sudo apt-get update
   sudo apt-get install ninja-build libopenmpi-dev libjpeg-dev zlib1g-dev
   

7. Use a virtual environment:
   Despite the manufacturer’s advice, consider using a virtual environment to isolate the installation:

   python3 -m venv torchvision_env
   source torchvision_env/bin/activate
   

   Then proceed with the installation steps within this environment.

8. Check for JetPack updates:
   Verify if there are any available updates for JetPack that might resolve compatibility issues:

   sudo apt update
   sudo apt upgrade
   

9. Monitor system resources:
   During compilation, monitor system resources to ensure there’s enough memory and storage:

   htop
   df -h
   

10. Compile with verbose output:
    If issues persist, compile with verbose output for more detailed error messages:

    python3 setup.py install --user --verbose
    

If these steps do not resolve the issue, consider reaching out to NVIDIA’s developer support or the PyTorch community forums for more specific assistance tailored to the Jetson Orin Nano platform.