Segmentation Fault Core Dumped Error in Tkinter GUI on Jetson Orin Nano

Issue Overview

Users are experiencing a segmentation fault core dumped error when attempting to run a Python Tkinter GUI executable on the Jetson Orin Nano 8GB custom board from Avermedia. The issue arises when the application, which utilizes YOLOv5 models, is executed. Symptoms include the application crashing immediately or after an initial successful launch. This problem has been reported inconsistently across different boards, indicating that while the same executable works on some devices, it fails on others. The error manifests during runtime rather than during setup, and users have noted that reflashing the OS and reinstalling Jetpack libraries often resolves the issue temporarily. However, the underlying cause remains unclear.

Possible Causes

• Hardware Incompatibilities or Defects: Variations in hardware configurations between different boards may lead to inconsistent behavior.
• Software Bugs or Conflicts: Issues may arise from conflicts between libraries, particularly if TensorFlow is inadvertently imported.
• Configuration Errors: Incorrect settings in the environment or dependencies not aligned with the application requirements can trigger segmentation faults.
• Driver Issues: Outdated or incompatible drivers might lead to memory allocation errors.
• Environmental Factors: Power supply inconsistencies or overheating could impact performance and stability.
• User Errors or Misconfigurations: Improper installation of libraries or incorrect import order in the code can result in runtime errors.

Troubleshooting Steps, Solutions & Fixes

1. Check Dependencies:

   • Ensure all required libraries are installed correctly. Use:

     pip list
     

   • Verify that no conflicting versions of libraries like TensorFlow and PyTorch are present.

2. Reinstall PyTorch:

   • If using a CPU version of PyTorch, switch to a GPU-compatible version for Jetson:

     sudo apt-get install python3-pytorch
     

3. Review Import Order:

   • Change the import order in your script to prioritize cv2 over other libraries:

     import cv2
     import base64
     # Other imports...
     

4. Run Diagnostic Commands:

   • Check system logs for errors:

     sudo dmesg | grep segfault
     

   • This can help identify if there are memory allocation issues.

5. Use LD_PRELOAD Command:

   • If encountering memory allocation issues, try preloading specific libraries:

     export LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libGLdispatch.so
     

6. Reflash OS and Reinstall Libraries:

   • If all else fails, reflashing the OS and reinstalling Jetpack libraries can resolve persistent issues.

7. Code Review:

   • Share your source code within community forums for peer review to identify potential coding issues.

8. Monitor Memory Usage:

   • Use tools like htop to monitor memory consumption while running the application to identify any spikes leading to crashes.

9. Seek Community Support:

   • Engage with NVIDIA developer forums for additional insights from users who may have faced similar issues.

10. Recommended Approach:

    • Several users reported success after removing TensorFlow and PyQt5 folders from the internal directory used by PyInstaller during executable creation. This suggests that these libraries may be causing unintended imports.

By following these troubleshooting steps and solutions, users should be able to diagnose and potentially resolve the segmentation fault issues encountered with Tkinter GUI applications on the Jetson Orin Nano platform.