Intermittent cuDNN Initialization Error on Nvidia Jetson Orin Nano

Issue Overview

Users of the Nvidia Jetson Orin Nano development board with JetPack 5.1.2 have reported intermittent issues when running TensorFlow operations that require cuDNN. The problem manifests as an initialization error, specifically a "CUDNN_STATUS_NOT_INITIALIZED" error, which prevents the execution of certain TensorFlow operations. This issue occurs despite the system reporting the correct versions of CUDA (11.4) and cuDNN (8.6) installed.

The error message indicates that the DNN library cannot be found or initialized, which is crucial for running deep learning models on the GPU. Interestingly, basic TensorFlow operations that don’t require cuDNN seem to work fine, with the GPU being correctly detected and utilized.

Possible Causes

1. Inconsistent System State: The issue’s intermittent nature suggests that the system’s state may not be consistent across reboots or power cycles. This could be due to incomplete initialization of the CUDA or cuDNN libraries during boot.

2. Resource Contention: There might be a race condition or resource contention issue during the initialization of the CUDA and cuDNN libraries, especially if multiple processes are trying to access these resources simultaneously.

3. Driver Mismatch: Although the user upgraded CUDA to version 11.8, there could be a mismatch between the installed drivers and the CUDA/cuDNN versions, leading to initialization failures.

4. Power Management Issues: The Jetson Orin Nano’s power management features might be interfering with the proper initialization of the GPU and its associated libraries.

5. Incomplete or Corrupted Installation: The cuDNN or CUDA installation might be incomplete or corrupted, causing intermittent failures in library initialization.

Troubleshooting Steps, Solutions & Fixes

1. Reboot the Device:
   As the issue resolved itself after a reboot, always try a full system restart when encountering this error.

2. Verify CUDA and cuDNN Versions:
   Double-check the installed versions of CUDA and cuDNN using the following commands:

   nvcc --version
   cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
   

3. Reinstall TensorFlow and CUDA Toolkit:
   If the issue persists, try reinstalling the TensorFlow package and CUDA Toolkit:

   sudo apt-get update
   sudo apt-get install --reinstall cuda-toolkit-11-8
   pip3 uninstall tensorflow
   pip3 install --no-cache-dir tensorflow==2.12.0+nv23.6
   

4. Check System Logs:
   Examine system logs for any errors related to CUDA or GPU initialization:

   sudo dmesg | grep -i cuda
   sudo journalctl -b | grep -i cuda
   

5. Verify GPU Detection:
   Ensure that the GPU is properly detected by the system:

   import tensorflow as tf
   print(tf.config.list_physical_devices('GPU'))
   

6. Set Environment Variables:
   Set the following environment variables before running your TensorFlow script:

   export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
   export CUDA_HOME=/usr/local/cuda
   

7. Check Power Mode:
   Ensure that the Jetson is not in a low-power mode that might affect GPU initialization:

   sudo nvpmodel -q
   

   If necessary, set a higher power mode:

   sudo nvpmodel -m 0
   

8. Update JetPack:
   Consider updating to the latest version of JetPack if available, as it might include fixes for known issues:

   sudo apt-get update
   sudo apt-get upgrade
   

9. Preload cuDNN Library:
   Try preloading the cuDNN library before running your TensorFlow script:

   LD_PRELOAD=/usr/lib/aarch64-linux-gnu/libcudnn.so.8 python3 your_script.py
   

10. Gradual Model Complexity:
    When testing, start with simpler models and gradually increase complexity to isolate where the issue occurs. The provided script can serve as a good starting point for testing.

If the issue persists after trying these solutions, consider reaching out to Nvidia’s developer forums or support channels for further assistance, as there might be a specific issue with the Jetson Orin Nano that requires official intervention.