Installing the right torchvision for the torch in Jetson Orin Yolo

Issue Overview

Users are experiencing a RuntimeError when attempting to load custom C++ operations after installing torch and torchvision on the Nvidia Jetson Orin Nano Dev board. The specific error message reads:

RuntimeError: Couldn't load custom C++ ops.

This issue occurs primarily during the setup phase, particularly after installing the libraries to support GPU acceleration for deep learning applications. The problem has been reported consistently by multiple users, indicating a potential compatibility issue between the installed versions of PyTorch and torchvision.

Relevant Specifications

• Hardware: Nvidia Jetson Orin Nano Dev board
• Software: Various versions of JetPack, PyTorch, and torchvision
• Commonly Reported Versions:
  • PyTorch v2.0.0
  • Torchvision v0.14.1

The impact of this issue significantly hampers user experience, preventing successful execution of applications that rely on these libraries for machine learning tasks.

Possible Causes

Several potential causes have been identified for this issue:

• Version Incompatibility: The installed versions of PyTorch and torchvision may not be compatible, leading to runtime errors when attempting to load custom operations.

• Installation Errors: Errors during the compilation of torchvision from source can result in missing or incompatible components.

• Virtual Environment Issues: Users have reported that installations within virtual environments do not work as expected, while installations in the system Python environment function correctly.

• Configuration Errors: Incorrect setup or configuration during installation may lead to runtime issues.

• Dependency Management: The need to build from source for Python versions other than the default provided with JetPack can complicate installations.

Troubleshooting Steps, Solutions & Fixes

To resolve the issue, follow these comprehensive troubleshooting steps:

1. Check Installed Versions:

   • Run the following commands to verify installed versions:

     import torch
     print(torch.__version__)
     
     import torchvision
     print(torchvision.__version__)
     

2. Verify Compatibility:

   • Ensure that you are using compatible versions of PyTorch and torchvision. A known working combination is:
     • PyTorch v2.0.0
     • Torchvision v0.14.1
   • Refer to the PyTorch compatibility matrix for more details.

3. Reinstall torchvision:

   • If versions are incompatible, uninstall and reinstall torchvision to match your PyTorch version:

     pip uninstall torchvision
     pip install torchvision==0.14.1  # Adjust version as necessary
     

4. Use NVIDIA Container:

   • For simplified installation, consider using the NVIDIA NGC container that includes both libraries pre-installed:
     • NVIDIA L4T PyTorch | NVIDIA NGC

5. Test Outside Virtual Environment:

   • If you are using a virtual environment, try running your code in the system Python environment instead.
   • If necessary, create a new virtual environment with access to system site packages:

     python3 -m venv myenv --system-site-packages
     source myenv/bin/activate
     

6. Build from Source (if needed):

   • If you need a specific version of Python that is not supported by pre-built packages, consider building both libraries from source.

7. Monitor Logs for Errors:

   • Check installation logs for any errors during compilation or installation that could indicate underlying issues.

8. Documentation and Updates:

   • Regularly check for updates and documentation on NVIDIA’s official forums and GitHub repositories for any patches or fixes related to your issue.

9. Community Support:

   • Engage with community forums for additional insights or similar experiences from other users facing this issue.

By following these steps, users should be able to diagnose and resolve the compatibility issues related to installing torchvision alongside PyTorch on the Nvidia Jetson Orin Nano Dev board. If problems persist despite these efforts, further investigation may be required into specific environmental factors or configurations unique to individual setups.