Issues with Nvidia Jetson Orin Nano Dev Board: PyTorch and CUDA Compatibility

Issue Overview

Users of the Nvidia Jetson Orin Nano Dev board, particularly those working with JetPack 6.0, are encountering issues related to CUDA and PyTorch while attempting to set up a YOLOv9 object detection model. The primary symptoms include:

• Driver Version Issues: Running the command nvidia-smi returns "N/A" for the driver version, which raises concerns about the CUDA installation.
• nvcc Command Not Found: Users report receiving an error message stating that the nvcc command is not found, indicating a potential issue with the CUDA installation or path configuration.
• Installation Confusion: Users are uncertain about which versions of PyTorch, TorchAudio, and TorchVision are compatible with CUDA 12.2 and how to install them correctly.

These issues typically occur during the initial setup phase after booting the device and running standard update and installation commands. The frequency of these problems seems common among users attempting similar configurations, significantly impacting their ability to utilize the Jetson Orin Nano for deep learning tasks.

Possible Causes

Several factors could lead to the issues described:

• Hardware Compatibility: The Jetson Orin Nano may have limited support for certain driver versions or CUDA functionalities.

• Software Bugs: There may be bugs in JetPack 6.0 that affect driver detection or CUDA functionality.

• Configuration Errors: Incorrect path settings or environment variables could prevent access to CUDA tools like nvcc.

• Driver Issues: Missing or improperly installed drivers can lead to "N/A" outputs in nvidia-smi.

• User Errors: Missteps during installation or configuration, such as incorrect commands or version mismatches, can exacerbate these problems.

Troubleshooting Steps, Solutions & Fixes

To address the issues with the Nvidia Jetson Orin Nano Dev board, follow these troubleshooting steps:

1. Verify Driver Installation:

   • Check if the NVIDIA drivers are correctly installed by running:

     nvidia-smi
     

   • If it shows "N/A", ensure that you have installed the correct version of JetPack.

2. Fix nvcc Command Not Found:

   • If you encounter nvcc: command not found, add CUDA to your PATH. You can do this by editing your .bashrc file:

     echo 'export PATH=/usr/local/cuda/bin:$PATH' >> ~/.bashrc
     source ~/.bashrc
     

3. Install Compatible PyTorch Packages:

   • For JetPack 6.0, use the following command to install compatible versions of PyTorch, TorchAudio, and TorchVision:

     pip3 install numpy torch==2.3.0 torchvision torchaudio
     

   • If you need a specific version that is not available via pip, consider building from source using instructions provided in relevant documentation.

4. Building from Source (if needed):

   • If you require a different version of PyTorch (e.g., 1.8), follow these steps:

     git clone https://github.com/pytorch/pytorch.git
     cd pytorch
     git checkout v1.8.0  # Change this to your desired version
     python setup.py install
     

5. Refer to Documentation:

   • Check NVIDIA’s official documentation for any updates on driver installations and software compatibility for JetPack 6.0.

6. Prevent Future Issues:

   • Always ensure that you are using packages built specifically for your version of JetPack.
   • Regularly check for updates on NVIDIA’s forums or documentation regarding compatibility issues.

7. Unresolved Aspects:

   • Users have reported inconsistencies when trying to install specific versions of PyTorch; further investigation may be needed into package management for ARM architectures.

By following these steps, users should be able to resolve common issues related to CUDA and PyTorch on their Nvidia Jetson Orin Nano Dev board effectively.