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TensorFlow CMake/C++ Collection

Looking at the official docs: What do you see? The usual fare? Now, guess what: This is a bazel-free zone. We use CMake here!

This collection contains reliable and dead-simple examples to use TensorFlow in C, C++, Go and Python: load a pre-trained model or compile a custom operation with or without CUDA. All builds are tested against the most recent stable TensorFlow version and rely on CMake with a custom FindTensorFlow.cmake. This cmake file includes common work arounds for bugs in specific TF versions.

TensorFlow Status
1.14.0 Build Status TensorFlow
1.13.1 Build Status TensorFlow
1.12.0 Build Status TensorFlow
1.11.0 Build Status TensorFlow
1.10.0 Build Status TensorFlow
1.9.0 Build Status TensorFlow

The repository contains the following examples.

Example Explanation
custom operation build a custom operation for TensorFLow in C++/CUDA (requires only pip)
inference (C++) run inference in C++
inference (C) run inference in C
inference (Go) run inference in Go
event writer write event files for TensorBoard in C++
keras cpp-inference example run a Keras-model in C++
simple example create and run a TensorFlow graph in C++
resize image example resize an image in TensorFlow with/without OpenCV

Custom Operation

This example illustrates the process of creating a custom operation using C++/CUDA and CMake. It is not intended to show an implementation obtaining peak-performance. Instead, it is just a boilerplate-template.

user@host $ pip install tensorflow-gpu --user # solely the pip package is needed
user@host $ cd custom_op/user_ops
user@host $ cmake .
user@host $ make
user@host $ python test_matrix_add.py
user@host $ cd ..
user@host $ python example.py

TensorFlow Graph within C++

This example illustrates the process of loading an image (using OpenCV or TensorFlow), resizing the image saving the image as a JPG or PNG (using OpenCV or TensorFlow).

user@host $ cd examples/resize
user@host $ export TENSORFLOW_BUILD_DIR=...
user@host $ export TENSORFLOW_SOURCE_DIR=...
user@host $ cmake .
user@host $ make

TensorFlow-Serving

There are two examples demonstrating the handling of TensorFlow-Serving: using a vector input and using an encoded image input.

server@host $ CHOOSE=basic # or image
server@host $ cd serving/${CHOOSE}/training
server@host $ python create.py # create some model
server@host $ cd serving/server/
server@host $ ./run.sh # start server

# some some queries

client@host $ cd client/bash
client@host $ ./client.sh
client@host $ cd client/python
# for the basic-example
client@host $ python client_rest.py
client@host $ python client_grpc.py
# for the image-example
client@host $ python client_rest.py /path/to/img.[png,jpg]
client@host $ python client_grpc.py /path/to/img.[png,jpg]

Inference

Create a model in Python, save the graph to disk and load it in C/C+/Go/Python to perform inference. As these examples are based on the TensorFlow C-API they require the libtensorflow_cc.so library which is not shipped in the pip-package (tensorfow-gpu). Hence, you will need to build TensorFlow from source beforehand, e.g.,

user@host $ ls ${TENSORFLOW_SOURCE_DIR}

ACKNOWLEDGMENTS     bazel-genfiles      configure          pip
ADOPTERS.md         bazel-out           configure.py       py.pynano
ANDROID_NDK_HOME    bazel-tensorflow    configure.py.bkp   README.md
...
user@host $ cd ${TENSORFLOW_SOURCE_DIR}
user@host $  ./configure
user@host $  # ... or whatever options you used here
user@host $ bazel build -c opt --copt=-mfpmath=both --copt=-msse4.2 --config=cuda //tensorflow:libtensorflow.so
user@host $ bazel build -c opt --copt=-mfpmath=both --copt=-msse4.2 --config=cuda //tensorflow:libtensorflow_cc.so

user@host $ export TENSORFLOW_BUILD_DIR=/tensorflow_dist
user@host $ mkdir ${TENSORFLOW_BUILD_DIR}
user@host $ cp ${TENSORFLOW_SOURCE_DIR}/bazel-bin/tensorflow/*.so ${TENSORFLOW_BUILD_DIR}/
user@host $ cp ${TENSORFLOW_SOURCE_DIR}/bazel-genfiles/tensorflow/cc/ops/*.h ${TENSORFLOW_BUILD_DIR}/includes/tensorflow/cc/ops/

1. Save Model

We just run a very basic model

x = tf.placeholder(tf.float32, shape=[1, 2], name='input')
output = tf.identity(tf.layers.dense(x, 1), name='output')

Therefore, save the model like you regularly do. This is done in example.py besides some outputs

user@host $ python example.py

[<tf.Variable 'dense/kernel:0' shape=(2, 1) dtype=float32_ref>, <tf.Variable 'dense/bias:0' shape=(1,) dtype=float32_ref>]
input            [[1. 1.]]
output           [[2.1909506]]
dense/kernel:0   [[0.9070684]
 [1.2838823]]
dense/bias:0     [0.]

2. Run Inference

Python

user@host $ python python/inference.py

[<tf.Variable 'dense/kernel:0' shape=(2, 1) dtype=float32_ref>, <tf.Variable 'dense/bias:0' shape=(1,) dtype=float32_ref>]
input            [[1. 1.]]
output           [[2.1909506]]
dense/kernel:0   [[0.9070684]
 [1.2838823]]
dense/bias:0     [0.]

C++

user@host $ cd cc
user@host $ cmake .
user@host $ make
user@host $ cd ..
user@host $ ./cc/inference_cc

input           Tensor<type: float shape: [1,2] values: [1 1]>
output          Tensor<type: float shape: [1,1] values: [2.19095063]>
dense/kernel:0  Tensor<type: float shape: [2,1] values: [0.907068372][1.28388226]>
dense/bias:0    Tensor<type: float shape: [1] values: 0>

C

user@host $ cd c
user@host $ cmake .
user@host $ make
user@host $ cd ..
user@host $ ./c/inference_c

2.190951

Go

user@host $ go get github.com/tensorflow/tensorflow/tensorflow/go
user@host $ cd go
user@host $ ./build.sh
user@host $ cd ../
user@host $ ./inference_go

input           [[1 1]]
output          [[2.1909506]]
dense/kernel:0  [[0.9070684] [1.2838823]]
dense/bias:0    [0]