How to Convert ONNX to Engine on Jetson Orin Nano Dev Board

Issue Overview

Users are experiencing difficulties converting their ONNX files to TensorRT engine format on the Nvidia Jetson Orin Nano Dev board, specifically when attempting to use a YOLOv4 Tiny network model. The problem arises during the conversion process, where users are uncertain about the appropriate tools and methods to use, particularly concerning compatibility with the Jetson platform.

Symptoms:

• Confusion regarding which conversion tool to use: TAO converter, trtexec, or DeepStream.
• Questions about running trtexec in a container and its compatibility with different CUDA containers.

Context:

• The issue occurs after training the model on an x86 system with an RTX 2070 GPU and attempting to run the conversion on the Jetson Orin using JetPack and DeepStream versions 6.3, CUDA 11.4, and cuDNN 8.6.0.

Frequency:

• This issue appears to be common among users transitioning from x86 systems to Jetson platforms.

Impact:

• Users are unable to utilize their trained models on the Jetson Orin, hindering their development and deployment processes.

Possible Causes

• Hardware Incompatibilities: The Jetson Orin may have specific hardware requirements that differ from those of an x86 system, potentially leading to issues during model conversion.

• Software Bugs or Conflicts: There may be bugs in the JetPack or DeepStream versions being used that affect model conversion functionalities.

• Configuration Errors: Incorrect configurations in the environment or during the setup of tools like TensorRT could lead to failures in model conversion.

• Driver Issues: Outdated or incompatible drivers for CUDA or TensorRT might prevent successful execution of conversion commands.

• User Errors or Misconfigurations: Users may not be familiar with the correct procedures for converting ONNX models on the Jetson platform.

Troubleshooting Steps, Solutions & Fixes

1. Check Model Compatibility with trtexec:

   • Use the following command to check if the ONNX model can work with trtexec:

     /usr/src/tensorrt/bin/trtexec --onnx=[file]
     

2. Determine Conversion Tool:

   • Evaluate which tool to use based on your needs:
     • TAO Converter: Best for models trained using NVIDIA’s TAO toolkit.
     • trtexec: Useful for quick conversions and testing.
     • DeepStream: Integrates with streaming applications but may require additional setup.

3. Run trtexec in a Container:

   • Confirm if you can run trtexec within a container:
     • Ensure that you are using a container compatible with Jetson Orin (e.g., NVIDIA’s CUDA containers).
     • Verify that the container has TensorRT installed.

4. Testing Different Containers:

   • Experiment with different containers:
     • Use TAO containers if your model was trained with TAO.
     • Alternatively, test TensorRT containers for broader compatibility.

5. Update Software Components:

   • Ensure you are using the latest versions of:
     • JetPack
     • DeepStream
     • CUDA
     • cuDNN
   • Check for any available updates or patches that might resolve existing bugs.

6. Consult Documentation:

   • Refer to NVIDIA’s official documentation for detailed guidelines on model conversion specific to your setup:
     • TensorRT documentation
     • DeepStream SDK documentation

7. Best Practices:

   • Always verify your ONNX model before attempting conversion.
   • Maintain backups of your configurations and models.
   • Engage with community forums for shared experiences and solutions.

8. Unresolved Aspects:

   • Further investigation may be needed into specific error messages returned during conversion attempts.
   • Users should report any persistent issues back to forums or NVIDIA support for additional assistance.

By following these troubleshooting steps, users can systematically address their issues related to converting ONNX models into engine format on the Nvidia Jetson Orin Nano Dev board.