TensorRT C++ API Tutorial

How to use TensorRT C++ API for high performance GPU inference.
A Venice Computer Vision Presentation

Video Presentation . Presentation Slides · Venice Computer Vision

TensorRT C++ Tutorial

This project demonstrates how to use the TensorRT C++ API for high performance GPU inference. It covers how to do the following:

• How to install TensorRT on Ubuntu 20.04
• How to generate a TRT engine file optimized for your GPU
• How to specify a simple optimization profile
• How to read / write data from / into GPU memory
• How to run synchronous inference
• How to work with models with dynamic batch sizes

Getting Started

The following instructions assume you are using Ubuntu 20.04. You will need to supply your own onnx model for this sample code. Ensure to specify a dynamic batch size when exporting the onnx model.

Prerequisites

• sudo apt install build-essential
• sudo apt install python3-pip
• pip3 install cmake
• Download TensorRT from here: https://developer.nvidia.com/nvidia-tensorrt-8x-download
• Extract, and then navigate to the CMakeLists.txt file and replace the TODO with the path to your TensorRT installation

Building the library

• mkdir build && cd build
• cmake ..
• make -j$(nproc)