TensorFlow examples in C, C++, Go and Python without bazel but with cmake and FindTensorFlow.cmake
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README.md

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 (v1.10.0) and rely on CMake with a custom FindTensorFlow.cmake.

example CI-tested explanation
custom operation Build Status TensorFlow build a custom operation for TensorFLow in C++/CUDA (requires only pip)
inference (C++) Build Status TensorFlow run inference in C++
inference (C) Build Status TensorFlow run inference in C
inference (Go) Build Status TensorFlow run inference in Go
event writer Build Status TensorFlow write event files for TensorBoard in C++
keras cpp-inference example Build Status TensorFlow run a Keras-model in C++
simple example Build Status TensorFlow create and run a TensorFlow graph in C++
resize image example Build Status TensorFlow 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

A simple TensorFlow serving example.

server@host $ cd serving/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
client@host $ python client_rest.py
client@host $ python client_grpc.py

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]