Need Assistance with Software Versions for JetPack 5.1.1 on Orin Nano

Issue Overview

Users are seeking help regarding software version compatibility on the Nvidia Jetson Orin Nano after experiencing issues with running a training script (train.py). The user has provided a list of installed software versions and is unsure if any of these versions are problematic, which could be causing the script to fail. The main concern is whether the installed versions of Python, CUDA, TensorRT, and other libraries are compatible with JetPack 5.1.1.

Specific Symptoms

• The train.py script no longer works after changes to the environment.
• Users receive an error traceback related to NumPy during execution.
• The installed software versions include:
  • Release: 5.10.104-tegra
  • Python: 3.8.10
  • CUDA: 11.4.315
  • cuDNN: 8.6.0.166
  • TensorRT: 8.5.2.2
  • L4T: 35.3.1
  • JetPack: 5.1.1
  • NumPy version: 1.24.3

Context

The issue arises during the development phase of machine learning applications where users are trying to utilize PyTorch and other libraries on the Jetson platform. Users are uncertain about the compatibility of their current software stack and how it affects their ability to run training scripts.

Frequency of Issue

This issue may not be widespread but indicates a potential problem with library compatibility that can affect users working with machine learning frameworks on the Jetson platform.

Impact on User Experience

The inability to run essential scripts can severely limit users’ productivity and progress in their projects, especially in machine learning and AI applications.

Possible Causes

1. Incompatible Library Versions: The installed versions of libraries such as NumPy may not be compatible with other components like TensorFlow or PyTorch.

   • Explanation: If libraries have dependencies that require specific versions, mismatches can lead to runtime errors.

2. Python Version Issues: The version of Python being used (3.8.10) may not align with the requirements of certain packages.

   • Explanation: Some libraries may have dropped support for older Python versions or require newer features available only in later releases.

3. CUDA Compatibility: The version of CUDA (11.4) may not be compatible with the versions of TensorRT or PyTorch being used.

   • Explanation: Each version of PyTorch and TensorRT is typically built against specific CUDA versions; incompatibilities can lead to installation or runtime failures.

4. Installation Procedure Errors: Users may not have followed the correct installation steps for setting up their environment.

   • Explanation: Deviations from recommended installation procedures can result in missing dependencies or misconfigured environments.

Troubleshooting Steps, Solutions & Fixes

1. Check Compatibility Matrix:

   • Refer to Nvidia’s documentation for a compatibility matrix that outlines which versions of CUDA, TensorRT, and PyTorch work together:
     • Nvidia Developer Documentation

2. Reinstall Compatible Versions:

   • If necessary, reinstall compatible versions of libraries:

     pip install numpy==1.22
     

3. Verify Installed Packages:

   • List currently installed packages and check for conflicts:

     pip list | grep torch
     pip list | grep numpy
     

4. Check Error Traceback:

   • Analyze the error traceback provided during execution to identify specific library issues or conflicts.

5. Consult Community Forums:

   • Engage with forums such as Nvidia Developer Forums or Stack Overflow to find similar issues faced by other users and potential solutions.

6. Test with a Clean Environment:

   • Create a new virtual environment to isolate dependencies and test if the script runs successfully there:

     python3 -m venv myenv
     source myenv/bin/activate
     pip install numpy torch torchvision torchaudio
     

7. Monitor System Logs:

   • Check system logs for any additional error messages that could provide more context about what is failing during execution:

     dmesg | grep -i 'error'
     

8. Consult Documentation for Installation Instructions:

   • Review relevant documentation from Nvidia regarding installation practices for JetPack and associated libraries.

9. Unresolved Aspects:

   • Further investigation may be needed regarding specific compatibility issues between different library versions.
   • Users should continue sharing their experiences in forums to build a collective understanding of these issues.

By following these troubleshooting steps, users should be able to effectively diagnose and resolve issues related to software version compatibility on their Nvidia Jetson Orin Nano devices running JetPack 5.1.1.