OpenCV with CUDA Error: Device Unavailable on Jetson Orin Nano

Issue Overview

Users of the Nvidia Jetson Orin Nano development board are experiencing issues when attempting to use OpenCV with CUDA support for image processing. Specifically, when calling cv::cuda::setDevice(0) or using the Canny edge detector, an exception is thrown indicating that all CUDA-capable devices are busy or unavailable. This error occurs despite OpenCV being built with CUDA support and the system recognizing the GPU.

The problem appears to be occurring on a system running JetPack 5.1.1 [L4T 35.3.1] with OpenCV 4.9.0-dev installed. The issue persists even after reinstalling different versions of OpenCV (4.4 and 4.9).

Possible Causes

1. Software Conflict: The user mentioned that the problem was related to a combination of their own software with CUDA, suggesting a potential conflict between custom applications and the CUDA runtime.

2. Resource Allocation: The error message indicates that CUDA-capable devices are busy or unavailable, which could be due to other processes holding onto GPU resources.

3. Driver Mismatch: Although not explicitly mentioned, there could be a mismatch between the CUDA version (11.4) and the installed GPU drivers.

4. OpenCV Build Configuration: Despite the build information showing CUDA support, there might be an issue with how OpenCV was compiled or linked against CUDA libraries.

5. System Resource Limitations: The Jetson Orin Nano might have limited resources, causing CUDA initialization to fail under certain conditions.

Troubleshooting Steps, Solutions & Fixes

1. Verify CUDA Installation:

   • Run nvidia-smi to check if the CUDA driver is properly installed and recognized.
   • Use nvcc --version to confirm the CUDA toolkit version matches the one used to build OpenCV.

2. Check for Running CUDA Processes:

   • Use ps aux | grep cuda to see if any other CUDA processes are running and potentially blocking access to the GPU.
   • Terminate unnecessary CUDA processes to free up GPU resources.

3. Rebuild OpenCV:

   • If the issue persists, try rebuilding OpenCV with CUDA support using the following script:

     #!/bin/bash
     # Script to build OpenCV with CUDA support on Jetson Orin Nano
     
     # Install dependencies
     sudo apt-get update
     sudo apt-get install -y build-essential cmake pkg-config
     sudo apt-get install -y libjpeg-dev libtiff5-dev libpng-dev
     sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev libxvidcore-dev libx264-dev libxine2-dev
     sudo apt-get install -y libv4l-dev v4l-utils
     sudo apt-get install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev
     sudo apt-get install -y libgtk2.0-dev
     sudo apt-get install -y mesa-utils libgl1-mesa-dri libgtkgl2.0-dev libgtkglext1-dev
     sudo apt-get install -y libatlas-base-dev gfortran libeigen3-dev
     sudo apt-get install -y python3-dev python3-numpy
     
     # Download and unpack OpenCV sources
     cd ~
     wget -O opencv.zip https://github.com/opencv/opencv/archive/4.9.0.zip
     wget -O opencv_contrib.zip https://github.com/opencv/opencv_contrib/archive/4.9.0.zip
     unzip opencv.zip
     unzip opencv_contrib.zip
     
     # Create build directory and switch to it
     cd opencv-4.9.0
     mkdir build
     cd build
     
     # Configure OpenCV build
     cmake -D CMAKE_BUILD_TYPE=RELEASE \
           -D CMAKE_INSTALL_PREFIX=/usr/local \
           -D WITH_CUDA=ON \
           -D CUDA_ARCH_BIN=8.7 \
           -D CUDA_ARCH_PTX="" \
           -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib-4.9.0/modules \
           -D WITH_GSTREAMER=ON \
           -D WITH_LIBV4L=ON \
           -D BUILD_opencv_python2=OFF \
           -D BUILD_opencv_python3=ON \
           -D BUILD_TESTS=OFF \
           -D BUILD_PERF_TESTS=OFF \
           -D BUILD_EXAMPLES=OFF \
           -D WITH_QT=OFF \
           -D WITH_OPENGL=ON \
           ..
     
     # Build and install OpenCV
     make -j$(nproc)
     sudo make install
     

4. Check System Resources:

   • Use jtop to monitor system resources and ensure there’s enough memory and GPU capacity available.
   • Close unnecessary applications to free up resources.

5. Isolate Custom Software:

   • Create a minimal test program that only uses OpenCV with CUDA to determine if the issue is specific to the custom software.
   • Gradually add components of the custom software to identify which part might be causing the conflict.

6. Update JetPack:

   • Consider updating to the latest JetPack version (e.g., JetPack 6.0 DP) if possible, as it might include fixes for CUDA-related issues.

7. CUDA Samples Test:

   • Run CUDA samples to verify if the GPU is accessible for general CUDA operations:

     cd /usr/local/cuda/samples/1_Utilities/deviceQuery
     sudo make
     ./deviceQuery
     

   • If this fails, it indicates a more fundamental issue with CUDA setup.

8. Environment Variables:

   • Ensure CUDA-related environment variables are correctly set:

     echo 'export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}' >> ~/.bashrc
     echo 'export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}' >> ~/.bashrc
     source ~/.bashrc
     

9. Driver Reinstallation:

   • As a last resort, consider reinstalling the NVIDIA drivers:

     sudo apt-get purge nvidia*
     sudo apt-get update
     sudo apt-get install nvidia-driver-xxx  # Replace xxx with the appropriate version
     

If the issue persists after trying these solutions, it may be necessary to seek support from NVIDIA’s developer forums or submit a bug report to the OpenCV project, providing detailed information about the system configuration and the exact steps to reproduce the problem.