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README.md

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-# Examples for TF-TRT
+# Examples for TensorRT in TensorFlow (TF-TRT)
 
-[TF-TRT](https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html)
-(TensorFlow integration with TensorRT) is a part of TensorFlow
+This repository contains a number of different examples
+that show how to use
+[TF-TRT](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/tensorrt).
+TF-TRT is a part of TensorFlow
 that optimizes TensorFlow graphs using
 [TensorRT](https://developer.nvidia.com/tensorrt).
-
-This repository contains a number of different examples
-that show how to use TF-TRT.
+We have used these examples to verify the accuracy and
+performance of TF-TRT. For more information see
+[Verified Models](https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html#verified-models).
 
 ## Examples
 
 * [Image Classification](tftrt/examples/image-classification)
 * [Object Detection](tftrt/examples/object_detection)
 
+
+# Using TensorRT in TensorFlow (TF-TRT)
+
+This module provides necessary bindings and introduces
+`TRTEngineOp` operator that wraps a subgraph in TensorRT.
+This module is under active development.
+
+
+## Installing TF-TRT
+
+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
+
+
+## Installing TensorRT
+
+In order to make use of TF-TRT, you will need a local installation
+of TensorRT from the
+[NVIDIA Developer website](https://developer.nvidia.com/tensorrt).
+Installation instructions for compatibility with TensorFlow are provided on the
+[TensorFlow GPU support](https://www.tensorflow.org/install/gpu) guide.
+
+
+## Documentation
+
+[TF-TRT documentaion](https://docs.nvidia.com/deeplearning/dgx/integrate-tf-trt/index.html)
+gives an overview of the supported functionalities, provides tutorials
+and verified models, explains best practices with troubleshooting guides.
+
+
+## Tests
+
+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.
+
+
+## Compilation
+
+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.
+
+```shell
+bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
+bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/
+```
+
+
 ## License
 
 [Apache License 2.0](LICENSE)