Support ncf

.gitmodules

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+[submodule "tftrt/examples/third_party/models"]
+	path = tftrt/examples/third_party/models
+	url = https://github.com/tensorflow/models.git
+[submodule "tftrt/examples/third_party/cocoapi"]
+	path = tftrt/examples/third_party/cocoapi
+	url = https://github.com/cocodataset/cocoapi.git
+[submodule "tftrt/examples/third_party/DeepLearningExamples"]
+	path = tftrt/examples/third_party/DeepLearningExamples
+	url = https://github.com/NVIDIA/DeepLearningExamples.git

README.md

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-Coming soon: Examples using [NVIDIA TensorRT](https://developer.nvidia.com/tensorrt) in TensorFlow.
+# Documentation for TensorRT in TensorFlow (TF-TRT)
+
+The documentaion 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
+
+# Examples for TensorRT in TensorFlow (TF-TRT)
+
+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).
+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/tf-trt-user-guide/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/tf-trt-user-guide/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)

setup.py

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+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================
+
+from setuptools import find_packages, setup
+
+setup(
+    name='tftrt',
+    version='0.0',
+    description='NVIDIA TensorRT integration in TensorFlow',
+    author='NVIDIA',
+    packages=find_packages(),
+    install_requires=['tqdm']
+)

tftrt/__init__.py

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+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================

tftrt/examples/__init__.py

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+# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
+#
+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# =============================================================================