PyTorch Installation Issues on Jetson Orin Nano with JetPack 6.0DP

Issue Overview

Users are experiencing difficulties installing PyTorch on the Nvidia Jetson Orin Nano development board running JetPack 6.0DP. The main problems include:

• Outdated installation instructions in the official NVIDIA documentation
• Unavailability of specific package versions for Ubuntu 22.04
• Uncertainty about the necessity of using specific versions for dependencies

These issues are causing confusion during the setup process and potentially preventing users from successfully installing PyTorch on their Jetson devices.

Possible Causes

1. Outdated Documentation: The official NVIDIA documentation has not been updated to reflect the latest JetPack and Ubuntu versions, leading to confusion about required packages and installation steps.

2. Operating System Mismatch: The installation guide may be referencing packages specific to older Ubuntu versions, which are not available in Ubuntu 22.04.

3. Dependency Version Conflicts: The specified versions of dependencies like numpy and scipy may not be compatible with the latest PyTorch or JetPack versions.

4. Unnecessary Version Specifications: Some of the version requirements in the installation instructions may be overly specific and unnecessary for the current JetPack environment.

Troubleshooting Steps, Solutions & Fixes

1. Simplified Installation Process:
   Instead of following the complex installation instructions, try a simplified approach:

   pip3 install xyz.whl
   

   Replace "xyz.whl" with the appropriate PyTorch wheel file for JetPack 6.0DP.

2. Use Latest Package Versions:
   For the compiler and related packages, use the latest versions available in Ubuntu 22.04 repositories:

   sudo apt-get install g++ gcc clang lld gfortran
   

   This eliminates the need for specific version suffixes like "-8".

3. Flexible Dependency Installation:
   When installing dependencies, avoid specifying exact versions unless absolutely necessary:

   python3 -m pip install aiohttp numpy scipy
   

   This allows pip to install the most compatible versions for your system.

4. Use Pre-built Containers:
   For a more streamlined setup, consider using pre-built containers that include PyTorch and other machine learning tools:

   • Visit the jetson-containers GitHub repository
   • Follow the instructions to set up and use the containers with PyTorch, torchvision, and other related packages.

5. Check for Updated Documentation:
   Regularly check the NVIDIA Developer website for updated documentation on PyTorch installation for Jetson devices. The community often provides more up-to-date information than official docs during rapid development cycles.

6. Community Support:
   If issues persist, engage with the NVIDIA Developer community forums or the Jetson community on platforms like GitHub. These communities often have the most current information and workarounds for recent software releases.

By following these steps, users should be able to successfully install PyTorch on their Jetson Orin Nano devices running JetPack 6.0DP. Remember that as this is a Developer Preview (DP) release, some instability or inconsistencies may be expected, and solutions may evolve rapidly.