Difference between revisions of "TensorRT"

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Refer to the page [https://elinux.org/TensorRT/YoloV3 TensorRT/YoloV3]<br>
 
Refer to the page [https://elinux.org/TensorRT/YoloV3 TensorRT/YoloV3]<br>
  
 
=== Layer Dump and Analyze ===
 
Refer to the [https://elinux.org/TensorRT/LayerDumpAndAnalyze page]<br>
 
  
 
== Known issue ==
 
== Known issue ==
 
Here we list all the known issue that has been clarified in different TensorRT versions.
 
Here we list all the known issue that has been clarified in different TensorRT versions.

Revision as of 19:59, 31 July 2019

NVIDIA TensorRT™ is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. TensorRT-based applications perform up to 40x faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and finally deploy to hyperscale data centers, embedded, or automotive product platforms.

Introduction

developer.nvidia.com/tensorrt
TensorRT Developer Guide

FAQ

Official FAQ

TensorRT Developer Guide#FAQs


Common FAQ

You can find answers here for some common questions about using TRT.
Refer to the page TensorRT/YoloV3


How to fix some Common Errors

If you met some Errors during using TRT, please find from below page for the answer.
Refer to the page TensorRT/CommonErrorFix


TRT Accuracy FAQ

If your FP16 result or Int8 result is not as expected, below page may help you fix the accuracy issues.
Refer to the page TensorRT/AccuracyIssues


TRT Performance FAQ

If the performance of doing inference with TRT is not as expected, below page may help you to optimize the performance.
Refer to the page TensorRT/PerfIssues


TRT Plugin FAQ

Below page will present some FAQs about TRT Plugin.
Refer to the page TensorRT/PluginFAQ


TRT & YoloV3 FAQ

Refer to the page TensorRT/YoloV3


Known issue

Here we list all the known issue that has been clarified in different TensorRT versions.