Running ONNX Models with Triton Server on Jetson Orin NX 16GB (JetPack 5.1.1)

Issue Overview

Users are experiencing difficulties in setting up and running ONNX models with Triton Server for GPU inferencing on the Nvidia Jetson Orin NX 16GB device running JetPack 5.1.1. The main challenges include:

• Uncertainty about the correct version of ONNX Runtime and CUDA to use with JetPack 5.1.1
• Confusion regarding the compatibility of existing installation instructions with the current JetPack version
• Concerns about potential system instability or conflicts when updating components
• Lack of clarity on the proper configuration for Triton Server (config.pbtxt)
• Issues with NVCC (NVIDIA CUDA Compiler) not displaying version information

The problem impacts users’ ability to leverage GPU acceleration for inference tasks using ONNX models on their Jetson Orin NX devices.

Possible Causes

1. Version Mismatch: The installed JetPack version (5.1.1) may not be fully compatible with the latest ONNX Runtime and CUDA versions, leading to integration issues.

2. Outdated Documentation: Installation instructions and guides may be tailored for newer JetPack versions (e.g., 6.0), causing confusion and potential misconfigurations.

3. Incomplete CUDA Installation: The absence of NVCC version information suggests a possible issue with the CUDA toolkit installation or environment setup.

4. System Configuration: Incorrect system configurations or missing dependencies could prevent proper functionality of ONNX Runtime or Triton Server.

5. Hardware Limitations: Specific hardware features of the Jetson Orin NX might not be fully supported by older software versions, leading to compatibility issues.

Troubleshooting Steps, Solutions & Fixes

1. Verify JetPack and CUDA Versions:

   • Confirm JetPack version:

     dpkg-query --showformat='${Version}' --show nvidia-jetpack
     

   • Check CUDA version:

     nvcc --version
     

   If NVCC is not responding, ensure CUDA is properly installed and added to the system PATH.

2. Update JetPack (Recommended):

   • Consider upgrading to JetPack 6.0, which offers better support for Triton Server on Jetson devices.
   • Backup your data before upgrading to prevent loss of important information.
   • Follow the official NVIDIA documentation for the upgrade process.

3. Install ONNX Runtime:

   • For JetPack 5.1.1, use the following commands to install ONNX Runtime:

     sudo apt-get update
     sudo apt-get install python3-pip
     pip3 install onnxruntime-gpu
     

   • Verify the installation:

     python3 -c "import onnxruntime as ort; print(ort.__version__)"
     

4. Set Up Triton Server:

   • Download the appropriate Triton Server container for JetPack 5.1.1 from NVIDIA NGC.
   • Run the container with GPU support:

     docker run --gpus all -it --rm -p8000:8000 -p8001:8001 -p8002:8002 -v /path/to/model/repository:/models nvcr.io/nvidia/tritonserver:xx.xx-py3
     

   Replace xx.xx with the appropriate version number.

5. Configure Triton Server:

   • Create a config.pbtxt file in your model repository:

     name: "your_model_name"
     platform: "onnxruntime_onnx"
     max_batch_size: 0
     input [
       {
         name: "input_name"
         data_type: TYPE_FP32
         dims: [ -1, 3, 224, 224 ]
       }
     ]
     output [
       {
         name: "output_name"
         data_type: TYPE_FP32
         dims: [ -1, 1000 ]
       }
     ]
     

   Adjust the input and output configurations according to your specific ONNX model.

6. Test with Perf Analyzer:

   • Install Perf Analyzer:

     sudo apt-get install triton-client
     

   • Run a performance test:

     perf_analyzer -m your_model_name -u localhost:8000 --concurrency-range 1:4
     

7. Troubleshoot CUDA Issues:

   • If NVCC is not responding, try reinstalling CUDA:

     sudo apt-get update
     sudo apt-get install cuda-toolkit-11-4
     

   • Add CUDA to your PATH in ~/.bashrc:

     export PATH=/usr/local/cuda-11.4/bin:$PATH
     export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64:$LD_LIBRARY_PATH
     

   • Source the updated .bashrc:

     source ~/.bashrc
     

8. Monitor System Resources:

   • Use nvidia-smi to monitor GPU usage and memory consumption during inference.
   • If you encounter out-of-memory errors, adjust your model or batch size accordingly.

If issues persist after following these steps, consider reaching out to NVIDIA developer forums or support channels for more specific assistance tailored to your Jetson Orin NX setup and use case.