Installation Issue with torchvision with Jetpack 5.14

Issue Overview

Users are experiencing installation issues with torchvision while using the Jetson Orin Nano development board with JetPack 5.14. The primary symptoms include build errors during the installation process of torchvision, particularly when attempting to compile specific kernel files.

The context of the problem arises during the setup phase after successfully flashing JetPack 5.14 and attempting to install PyTorch and torchvision. Users have noted that they had to use PyTorch version compatible with JetPack 5.12, as there is no version available for 5.14. The error log indicates problems related to the compilation of CUDA-related files, specifically deform_conv2d_kernel.cpp.

The issue appears to be consistent among users who attempt to build torchvision under similar conditions, impacting their ability to utilize functionalities dependent on the library, thus hindering development efforts.

Possible Causes

• Hardware Incompatibilities or Defects: If there are discrepancies between the Jetson Orin Nano’s hardware capabilities and the software requirements, it may lead to compilation errors.

• Software Bugs or Conflicts: Using a version of PyTorch that is not fully compatible with torchvision or JetPack could result in build failures.

• Configuration Errors: Incorrect configuration settings during the installation process can cause issues when compiling libraries that rely on specific paths or dependencies.

• Driver Issues: Outdated or incompatible CUDA drivers may prevent successful compilation of CUDA-dependent components in torchvision.

• Environmental Factors: Insufficient memory or processing power during the build process could lead to incomplete builds or crashes.

• User Errors or Misconfigurations: Incorrect commands or paths specified during installation may lead to failures in locating necessary files for compilation.

Troubleshooting Steps, Solutions & Fixes

1. Verify Environment Setup

   • Ensure that you are using the correct versions of PyTorch and torchvision compatible with your JetPack version.
   • Check that your CUDA toolkit is properly installed and matches the requirements for your PyTorch version.

2. Gather System Information

   • Run the following command to check your CUDA version:

     nvcc --version
     

   • Verify installed PyTorch and torchvision versions:

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

3. Review Build Logs

   • If you encounter errors, capture and review the complete output log for detailed error messages.
   • Share this log in forums for community assistance if needed.

4. Isolate the Issue

   • Test building torchvision without any modifications first.
   • Attempt using a different version of PyTorch (e.g., reverting back to 5.12) to see if it resolves the issue.

5. Install Dependencies

   • Ensure all required dependencies are installed:

     sudo apt-get install python3-dev python3-pip libjpeg-dev zlib1g-dev
     

6. Update Drivers and Libraries

   • Check for any available updates for your JetPack installation, including CUDA drivers.
   • Update pip and setuptools:

     pip install --upgrade pip setuptools
     

7. Build from Source

   • If issues persist, consider cloning the torchvision repository and building from source:

     git clone https://github.com/pytorch/vision.git
     cd vision
     python3 setup.py install
     

8. Recommended Approach

   • Many users have reported success by ensuring compatibility between all versions (PyTorch, torchvision, JetPack) before proceeding with installations.

9. Documentation and Resources

   • Refer to official NVIDIA documentation for specific instructions on setting up Jetson devices.
   • Look for any firmware upgrades that may address compatibility issues.

10. Best Practices

    • Regularly check for updates on PyTorch and torchvision compatibility with JetPack versions.
    • Maintain a backup of working configurations to quickly restore functionality if issues arise in future installations.

Unresolved aspects include specific error codes or messages that might require deeper investigation into library dependencies or environmental settings unique to individual setups. Further community input may also be beneficial in resolving these issues effectively.