This module provides necessary bindings and introduces TRT_engine_op operator that wraps a subgraph in TensorRT. This is still a work in progress but should be useable with most common graphs.
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/
After the installation of tensorflow package, TensorRT transformation will be available. An example use can be found in test/test_tftrt.py script
In order to make use of TensorRT integration, you will need a local installation of TensorRT 3.0.4 from the NVIDIA Developer website. Installation instructions for compatibility with TensorFlow are provided on the TensorFlow Installation page.