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Katherine Wu 12d00c3e34 Serialize concrete function signature using structured_input_signature instead of function inputs. 
This way, if the user calls set_shape on the inputs within the function body, the serialized signature is not affected.

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Change-Id: Ie31b9e2206de57aca4e592bbd43fafbff0d2bda6
2020-10-21 15:25:57 -07:00
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test [TF:TRT] Add support for per cluster maximum batch size. 2020-10-20 16:49:30 -07:00
__init__.py
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README.md Delete extra new line in README 2019-05-31 15:50:17 -07:00
trt_convert_test.py Merge pull request from tfeher:trt_expose_engine_instance_protobuf 2020-09-28 10:04:39 -07:00
trt_convert_windows.py Change TrtConversionParams to a namedtuple subclass. 2020-02-10 14:41:59 -08:00
trt_convert.py Serialize concrete function signature using structured_input_signature instead of function inputs. 2020-10-21 15:25:57 -07:00

README.md

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. Installation instructions for compatibility with TensorFlow are provided on the TensorFlow GPU support guide.

Examples

You can find example scripts for running inference on deep learning models in this repository: https://github.com/tensorflow/tensorrt

We have used these examples to verify the accuracy and performance of TF-TRT. For more information see Verified Models.

Documentation

TF-TRT documentation 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 using 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.

bazel build --config=cuda --config=opt //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/