NVIDIA Deep Learning TensorRT Documentation - Last updated June 25, 2020 - Send Feedback -


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 7.1.3 product package.
Support Matrix
These support matrices provide a look into the supported platforms, features, and hardware capabilities of the TensorRT 7.1.3 APIs, parsers, and layers.
Installation Guide
This TensorRT 7.1.3 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 documentation for the NVIDIA TensorRT library. The following set of APIs allows developers to import pre-trained models, calibrate their networks using INT8, and build and deploy optimized networks. Networks can be imported directly from NVCaffe, or from other frameworks via the UFF or ONNX formats. 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 7.1.3 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 use that to 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.
Working With TensorRT Samples
This Samples Support Guide provides an overview of all the supported TensorRT 7.2.0 samples included on GitHub and in the product package. The TensorRT samples specifically help in areas such as recommenders, machine translation, character recognition, image classification, and object detection.


Optimizing Performance With TensorRT
This Best Practices Guide covers various performance considerations related to deploying networks using TensorRT 7.1.3. 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.

Optimized Frameworks

Container Release Notes
The TensorRT container is an easy to use container for TensorRT development. The container allows for the TensorRT samples to be built, modified, and executed. These release notes provide a list of key features, packaged software included in the container, software enhancements and improvements, and any known issues for the 20.03 and earlier releases. The TensorRT container is released monthly to provide you with the latest NVIDIA deep learning software libraries and GitHub code contributions that have been sent upstream; which are all tested, tuned, and optimized.


Software License Agreement
This document is the Software License Agreement (SLA) 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.


Documentation Archives
This Archives document provides access to previously released TensorRT documentation versions.