NVIDIA Xavier - JetPack 5.0.2 - Components - TensorRT

NVIDIA®Jetson Xavier™
Introduction
SoM Overview Carrier Boards NVIDIA Xavier Developer Kit Connect Tech Rogue Connect Tech Mimic Software Support NVIDIA JetPack
JetPack 5.0.2
Getting Started Installing JetPack Flashing the board with GUI installer JetPack Components Compiling Source Code Flashing the Board from Cmdline Performance Tuning Evaluating Performance Set Values Manually Tuning Power Maximizing Performance
Development in the Board
Getting into the Board Using Serial Console Using SSH Using Ubuntu Login Installing Packages
Video Capture and Display
Description Supported Cameras Imx219 Driver Software Support Using GStreamer Using Libargus
Processors
HD Audio and Video Subystem Video Encoder Video Decoder JPEG Encoder and Decoder Video Image Compositor Audio Processing Engine Deep Learning Accelerator Volta GPU Description Using CUDA Using OpenGL
Interfaces
I2C SD/eMMC Controller USB Controller
GStreamer Pipelines
Capture and Display H264 H265 VP9 MPEG4 Simultaneous Streams Compositor
Deep Learning
TensorRT Using TensorRT integrated with Tensorflow Parsing a Tensorflow model for TensorRT Parsing Caffe model for TensorRT Building TensorRT API examples Deep Learning Accelerator Software Support Deep Learning Tutorials Jetson Inference Jetson Reinforcement
RidgeRun Products
GstPTZR GstCUDA GstColorTransfer GstWebRTC GstRtspSink GstInterpipe GstInference GStreamer Daemon GstShark
Reference Documentation
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TensorRT is a C++ library that facilitates high performance inference on NVIDIA platforms. It is designed to work with the most popular deep learning frameworks, such as TensorFlow, Caffe, PyTorch etc. It focus specifically on running an already trained model, to train the model, other libraries like cuDNN are more suitable. Some frameworks like TensorFlow have integrated TensorRT so that it can be used to accelerate inference within the framework. For other frameworks like Caffe, a parser is provided to generate a model that can be imported on TensorRT. For more information on using this library read our wiki here