PyCuda Cuda Context Error with CSI Camera on Jetson Orin Nano

Issue Overview

Users are experiencing a PyCuda Cuda Context error when running object detection inference using a converted YOLOv7 model with TensorRT on a Jetson Orin Nano device. The error occurs specifically when using a CSI camera for inference, but not with a USB camera or on a Jetson Nano with JetPack 4.6.1. The main symptoms include:

• Successful loading of the .trt model and initialization of the Gstreamer pipeline
• Error occurs when starting inference: "cuMemcpyHtoDAsync failed: context is destroyed"
• The issue appears to be related to the Gstreamer pipeline destroying the CUDA context

The problem affects the functionality of object detection applications using CSI cameras on the Jetson Orin Nano.

Possible Causes

1. CUDA context management: The Gstreamer pipeline may be improperly managing or destroying the CUDA context required by PyCuda.

2. Software version incompatibility: There could be conflicts between the versions of CUDA, CuDNN, TensorRT, and PyCuda used on the Jetson Orin Nano.

3. CSI camera driver issues: The problem only occurs with CSI cameras on the Orin Nano, suggesting a potential driver or hardware interaction issue.

4. Resource allocation: The Orin Nano may be handling resource allocation differently than the Jetson Nano, leading to context conflicts.

5. TensorRT implementation: The way TensorRT is integrated with the CSI camera pipeline on the Orin Nano might be causing context management issues.

Troubleshooting Steps, Solutions & Fixes

1. Verify CSI camera functionality:
   Run the following GStreamer command to ensure the camera is working correctly:

   gst-launch-1.0 nvarguscamerasrc ! 'video/x-raw(memory:NVMM), width=1920, height=1080, format=NV12, framerate=30/1' ! fakesink
   

2. Check CUDA context management:

   • Review the code where the CUDA context is created and managed.
   • Ensure that the context is properly initialized before starting the Gstreamer pipeline.
   • Implement proper CUDA context sharing between PyCuda and the Gstreamer pipeline.

3. Update software versions:

   • Verify that all components (CUDA, CuDNN, TensorRT, PyCuda) are compatible with JetPack 5.1.1.
   • Consider upgrading to the latest JetPack version if available, as it may include fixes for known issues.

4. Modify CUDA context handling:

   • Try creating a new CUDA context after the Gstreamer pipeline is initialized.
   • Implement a context manager to ensure the CUDA context remains active throughout the inference process.

5. Isolate the issue:

   • Test the inference pipeline without the CSI camera, using pre-recorded video or image files.
   • If successful, gradually reintroduce the CSI camera components to identify the exact point of failure.

6. Check for memory leaks:

   • Monitor GPU memory usage during the inference process.
   • Ensure all CUDA resources are properly deallocated after use.

7. Implement error handling:

   • Add try-except blocks around CUDA operations to catch and handle context-related errors gracefully.
   • Attempt to recreate the CUDA context if it’s detected as destroyed.

8. Consult NVIDIA documentation:

   • Review the Jetson Orin Nano documentation for any known issues or best practices related to CSI camera usage with CUDA.
   • Check for any errata or updates specific to the Orin Nano that might address this issue.

9. Community solutions:

   • Investigate the solution suggested by user "easybob" in the forum thread, as it was mentioned as potentially helpful.

10. If all else fails:

    • Consider using a USB camera as a temporary workaround if the application allows.
    • Report the issue to NVIDIA support, providing detailed logs and a minimal reproducible example.

Note: As the issue appears to be specific to the Jetson Orin Nano with CSI cameras, it may require further investigation by NVIDIA to provide a definitive solution. Keep an eye on official Jetson forums and documentation for any updates or patches addressing this problem.