TensorFlow-TensorRT (TF-TRT) is an integration of TensorFlow and TensorRT that leverages inference optimization on NVIDIA GPUs within the TensorFlow ecosystem. It provides a simple API that delivers substantial performance gains on NVIDIA GPUs with minimal effort. The documentation on how to accelerate inference in TensorFlow with TensorRT (TF-TRT) is here: https://docs.nvidia.com/deeplearning/dgx/tf-trt-user-guide/index.html
Check out this gentle introduction to TensorFlow TensorRT or watch this quick walkthrough example for more!
This repository contains a number of different examples that show how to use TF-TRT. TF-TRT is a part of TensorFlow that optimizes TensorFlow graphs using TensorRT. We have used these examples to verify the accuracy and performance of TF-TRT. For more information see Verified Models.
This module provides necessary bindings and introduces
TRTEngineOp operator that wraps a subgraph in TensorRT.
This module is under active development.
Currently Tensorflow nightly builds include TF-TRT by default, which means you don't need to install TF-TRT separately. You can pull the latest TF containers from docker hub or install the latest TF pip package to get access to the latest TF-TRT.
If you want to use TF-TRT on NVIDIA Jetson platform, you can find the download links for the relevant Tensorflow pip packages here: https://docs.nvidia.com/deeplearning/dgx/index.html#installing-frameworks-for-jetson
You can also use NVIDIA's Tensorflow container(tested and published monthly).
In order to make use of TF-TRT, you will need a local installation of TensorRT from the NVIDIA Developer website. Installation instructions for compatibility with TensorFlow are provided on the TensorFlow GPU support guide.
TF-TRT documentaion gives an overview of the supported functionalities, provides tutorials and verified models, explains best practices with troubleshooting guides.
TF-TRT includes both Python tests and C++ unit tests.
Most of Python tests are located in the test directory
and they can be executed uring bazel test or directly
with the Python command. Most of the C++ unit tests are
used to test the conversion functions that convert each TF op to
a number of TensorRT layers.
In order to compile the module, you need to have a local TensorRT installation (libnvinfer.so and respective include files). During the configuration step, TensorRT should be enabled and installation path should be set. If installed through package managers (deb,rpm), configure script should find the necessary components from the system automatically. If installed from tar packages, user has to set path to location where the library is installed during configuration.
bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/