This guide provides an overview of how to integrate ONNX models with TensorRT for optimized inference performance. TensorRT is a deep learning inference optimizer and runtime library for C++ and Python, developed by NVIDIA.

Key Features

  • High Performance: ONNX-TensorRT provides significant speedup for inference tasks compared to traditional methods.
  • Easy Integration: Simple steps to convert ONNX models to TensorRT engines.
  • Cross-Platform: Supports various platforms including NVIDIA GPUs and CPUs.

Getting Started

To get started with ONNX-TensorRT, follow these steps:

  1. Install ONNX: Download and install ONNX.
  2. Install TensorRT: Download and install TensorRT.
  3. Convert ONNX Model: Use the ONNX-TensorRT Python API to convert your ONNX model to a TensorRT engine.
import tensorrt as trt

# Load the ONNX model
onnx_model = trt.OnnxParser(trt.Logger(), "path/to/your/model.onnx")

# Build the TensorRT engine
engine = trt.Builder(trt.Logger()).build_engine(onnx_model.parse())

# Save the engine to a file
with open("path/to/your/engine", "wb") as f:
    f.write(engine.serialize())

Performance Optimization

ONNX-TensorRT provides several optimization techniques to improve inference performance:

  • Layer Fusion: Combines multiple layers into a single layer to reduce memory usage and improve speed.
  • Kernel Fusion: Combines multiple operations into a single kernel to reduce latency.
  • Precision Tuning: Adjusts the precision of the model to reduce memory usage and improve speed.

Troubleshooting

If you encounter any issues while integrating ONNX-TensorRT, refer to the TensorRT troubleshooting guide.

Learn More

For more information on ONNX-TensorRT, visit the following resources:

TensorRT Architecture