NVIDIA Jetson Xavier - Deep Learning with 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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This TensorRT wiki demonstrates how to use the C++ and Python APIs to implement the most common deep learning layers. Also provides step-by-step instructions with examples 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.

Description

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 focuses 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. And finally, TensorRT C++ and Python APIs can be used to build a model from the ground up. For a more in-depth analysis of each use case refer to the following sections:

1. Using TensorRT integrated with Tensorflow
2. Parsing Tensorflow model for TensorRT
3. Parsing Caffe model for TensorRT
4. Building TensorRT API examples

This GitHub repo has a great collection of Tensorflow models with TensorRT.

Some NVIDIA benchmarks on TX2:

Model	Input Size	Tensorflow on TX2 without TensorRT	Tensorflow on TX2 with TensorRT
inception_v4	299x299	129ms	38.5ms
resnet_v1_50	224x224	55.1ms	12.5ms
resnet_v1_101	224x224	91.0ms	20.6ms
resnet_v1_152	224x224	124ms	28.9ms
mobilenet_v1_1p0_224	224x224	17.3ms	11.1ms