TensorRT Inference Performance Issues on Jetson Orin Nano

Issue Overview

Users are experiencing slower-than-expected performance when running PyTorch models on the Jetson Orin Nano. The primary goal is to use TensorRT to speed up inference, but attempts to convert PyTorch models to TensorRT engines have encountered errors. The issue occurs during the model conversion process and affects the ability to run efficient inference for real-time applications, such as processing CAN messages at 100Hz.

Possible Causes

1. Incompatibility between PyTorch model architecture and TensorRT: Some layers or operations in the PyTorch model may not be supported by TensorRT, leading to conversion errors.

2. Version mismatches: Inconsistencies between the installed versions of PyTorch, ONNX, and TensorRT could cause compatibility issues during conversion.

3. CUDA version conflicts: The error mentioning "no kernel image is available for execution on the device" suggests potential CUDA-related problems.

4. Incorrect conversion process: The user may be missing crucial steps or using incorrect parameters during the PyTorch to TensorRT conversion.

5. Hardware limitations: The Jetson Orin Nano’s specific hardware configuration might not support certain operations required by the model.

Troubleshooting Steps, Solutions & Fixes

1. Verify TensorRT compatibility:

   • Check the TensorRT documentation for supported layers and operations.
   • Simplify the model architecture if possible, removing any unsupported layers.

2. Update software versions:

   • Ensure all components (PyTorch, ONNX, TensorRT) are compatible with each other and the Jetson Orin Nano.
   • Consider upgrading to the latest stable versions of each component.

3. CUDA configuration:

   • Verify CUDA installation and compatibility with the Jetson Orin Nano.
   • Check CUDA paths and environment variables.

4. Conversion process:

   • Use the ONNX as an intermediate step for conversion:
     a. Convert PyTorch model to ONNX
     b. Convert ONNX to TensorRT
   • Use trtexec tool to test inference timings with the TensorRT model:

     trtexec --onnx=model.onnx --saveEngine=model.trt
     

5. TensorRT inference in Python:

   • Use the tensorrt Python package to load and run inference with the TensorRT engine.
   • Example code structure:

     import tensorrt as trt
     import numpy as np
     
     def load_engine(engine_path):
         with open(engine_path, "rb") as f, trt.Runtime(trt.Logger(trt.Logger.WARNING)) as runtime:
             return runtime.deserialize_cuda_engine(f.read())
     
     def run_inference(engine, input_data):
         with engine.create_execution_context() as context:
             # Allocate memory for input and output
             # Set input tensor
             # Run inference
             # Get output tensor
         return output
     
     # Load the TensorRT engine
     engine = load_engine("model.trt")
     
     # Prepare input data
     input_data = np.random.random((1, 3, 224, 224)).astype(np.float32)
     
     # Run inference
     output = run_inference(engine, input_data)
     

6. Optimize for real-time performance:

   • Use asynchronous execution in TensorRT for better performance.
   • Implement a producer-consumer pattern to handle CAN message processing and inference separately.

7. Consider C++ implementation:

   • If Python performance is still insufficient, consider implementing the inference loop in C++ for potentially better performance.

8. Monitor system resources:

   • Use tegrastats to monitor CPU, GPU, and memory usage during inference to identify potential bottlenecks.

9. Thermal management:

   • Ensure proper cooling for the Jetson Orin Nano to maintain consistent performance during extended inference sessions.

If issues persist, provide the ONNX model for further debugging and consider reaching out to NVIDIA developer support for Jetson-specific optimizations.