The detected CUDA version (11.5) mismatches the version that was used to compile PyTorch (12.2)

Issue Overview

Users are experiencing a mismatch between the installed CUDA version and the version used to compile PyTorch, specifically when attempting to install and run torchvision on the Nvidia Jetson Orin Nano Dev board. The primary symptoms include error messages indicating that the detected CUDA version (11.5) does not match the expected version (12.2) during installation and execution of PyTorch-related libraries. This issue typically arises during the setup phase, particularly when transitioning from one CUDA version to another or when installing libraries that depend on specific CUDA versions. Users have reported inconsistencies in the CUDA version displayed by nvcc and other tools, leading to confusion about which version is actually in use. The problem significantly impacts user experience by preventing successful installation and execution of essential libraries, thereby hindering development workflows.

Possible Causes

• Hardware Incompatibilities: The Jetson Orin Nano may not fully support certain CUDA versions due to hardware limitations.
• Software Bugs or Conflicts: There may be bugs in the PyTorch or torchvision installation scripts that fail to recognize the correct CUDA version.
• Configuration Errors: Incorrect environment variable settings can lead to discrepancies in the reported CUDA version.
• Driver Issues: The installed NVIDIA drivers may not be compatible with the newer CUDA toolkit, causing runtime errors.
• Environmental Factors: Issues such as insufficient power supply or overheating may affect performance and compatibility.
• User Errors or Misconfigurations: Users may inadvertently set incorrect paths or fail to build libraries from source, leading to compatibility issues.

Troubleshooting Steps, Solutions & Fixes

1. Verify Installed Versions:

   • Run nvcc --version to check the currently installed CUDA compiler version.
   • Use nvidia-smi to verify the driver and runtime versions.

2. Set Environment Variables:

   • Ensure that your environment variables point to the correct CUDA installation:

     export LD_LIBRARY_PATH=/usr/local/cuda-12.2/lib64:$LD_LIBRARY_PATH
     export PATH=/usr/local/cuda-12.2/bin:$PATH
     export CUDA_HOME=/usr/local/cuda-12.2
     

   • Add these lines to your ~/.bashrc file for persistence.

3. Build from Source:

   • If using pip fails, build torchvision from source as follows:

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

4. Use Containers:

   • Consider using pre-built containers like l4t-pytorch or torchvision containers from Jetson Containers, which come with compatible libraries pre-installed.

5. Reflash Jetson with New Image:

   • If issues persist, reflashing the Jetson Orin Nano with a fresh image of JetPack (e.g., JetPack 6.0) can resolve underlying configuration issues.

6. Install cuDNN Correctly:

   • Ensure that cuDNN is installed correctly for your specific CUDA version:

     sudo apt-get install libcudnn8-dev
     

   • Verify that libcudnn.so files are present in /usr/local/cuda-12.2/lib64.

7. Check Compatibility Matrix:

   • Always refer to the compatibility matrix for PyTorch and CUDA versions to ensure you are using compatible versions.

8. Run Diagnostic Commands:

   • Use diagnostic commands like deviceQuery and bandwidthTest from the CUDA samples to confirm that your setup is functioning correctly.

9. Documentation and Updates:

   • Regularly check NVIDIA’s documentation for updates on driver and toolkit compatibility, as well as any patches or fixes related to known issues.

10. Community Support:

    • Engage with community forums for additional insights or solutions shared by other users who faced similar challenges.

By following these steps, users can troubleshoot and potentially resolve issues related to CUDA version mismatches on their Nvidia Jetson Orin Nano Dev boards effectively.