Getting Started

Quick Start Guide
This TensorRT 8.0.3 Quick Start Guide is a starting point for developers who want to try out TensorRT SDK; specifically, this document demonstrates how to quickly construct an application to run inference on a TensorRT engine.
Release Notes
NVIDIA TensorRT is a C++ library that facilitates high performance inference on NVIDIA GPUs. It is designed to work in connection with deep learning frameworks that are commonly used for training. TensorRT focuses specifically on running an already trained network quickly and efficiently on a GPU for the purpose of generating a result; also known as inferencing. These release notes describe the key features, software enhancements and improvements, and known issues for the TensorRT 8.0.3 product package.
Support Matrix
These support matrices provide a look into the supported platforms, features, and hardware capabilities of the TensorRT 8.0.3 APIs, parsers, and layers.
Installation Guide
This TensorRT 8.0.3 Installation Guide provides the installation requirements, a list of what is included in the TensorRT package, and step-by-step instructions for installing TensorRT.

Inference Library

API Reference
This is the API Reference documentation for the NVIDIA TensorRT library. The following set of APIs allows developers to import pre-trained models, calibrate networks for INT8, and build and deploy optimized networks with TensorRT. Networks can be imported from ONNX. They may also be created programmatically using the C++ or Python API by instantiating individual layers and setting parameters and weights directly.
Developer Guide
This TensorRT Developer Guide demonstrates how to use the C++ and Python APIs for implementing the most common deep learning layers. It shows how you can take an existing model built with a deep learning framework and build a TensorRT engine using the provided parsers. The Developer Guide also provides step-by-step instructions for common user tasks such as creating a TensorRT network definition, invoking the TensorRT builder, serializing and deserializing, and how to feed the engine with data and perform inference; all while using either the C++ or Python API.
Sample Support Guide
This Samples Support Guide provides an overview of all the supported TensorRT 8.0.3 samples included on GitHub and in the product package. The TensorRT samples specifically help in areas such as recommenders, machine comprehension, character recognition, image classification, and object detection.


Best Practices For TensorRT Performance
This Best Practices Guide covers various performance considerations related to deploying networks using TensorRT 8.0.3. These sections assume that you have a model that is working at an appropriate level of accuracy and that you are able to successfully use TensorRT to do inference for your model.


ONNX GraphSurgeon API Reference
ONNX GraphSurgeon provides a convenient way to create and modify ONNX models.
Polygraphy API Reference
Polygraphy is a toolkit designed to assist in running and debugging deep learning models in various frameworks.
PyTorch-Quantization Toolkit User Guide
PyTorch-Quantization is a toolkit for training and evaluating PyTorch models with simulated quantization. Quantization can be added to the model automatically, or manually, allowing the model to be tuned for accuracy and performance. The quantized model can be exported to ONNX and imported to an upcoming version of TensorRT.


This document is the LICENSE AGREEMENT FOR NVIDIA SOFTWARE DEVELOPMENT KITS for NVIDIA TensorRT. This document contains specific license terms and conditions for NVIDIA TensorRT. By accepting this agreement, you agree to comply with all the terms and conditions applicable to the specific product(s) included herein.