How to build and use Google TensorFlow C++ api

To get started, you should download the source code from Github, by following the instructions here (you'll need Bazel and a recent version of GCC).

The C++ API (and the backend of the system) is in tensorflow/core. Right now, only the C++ Session interface, and the C API are being supported. You can use either of these to execute TensorFlow graphs that have been built using the Python API and serialized to a GraphDef protocol buffer. There is also an experimental feature for building graphs in C++, but this is currently not quite as full-featured as the Python API (e.g. no support for auto-differentiation at present). You can see an example program that builds a small graph in C++ here.

The second part of the C++ API is the API for adding a new OpKernel, which is the class containing implementations of numerical kernels for CPU and GPU. There are numerous examples of how to build these in tensorflow/core/kernels, as well as a tutorial for adding a new op in C++.

To add to @mrry's post, I put together a tutorial that explains how to load a TensorFlow graph with the C++ API. It's very minimal and should help you understand how all of the pieces fit together. Here's the meat of it:

Requirements:

• Bazel installed
• Clone TensorFlow repo

Folder structure:

• tensorflow/tensorflow/|project name|/
• tensorflow/tensorflow/|project name|/|project name|.cc (e.g. https://gist.github.com/jimfleming/4202e529042c401b17b7)
• tensorflow/tensorflow/|project name|/BUILD

BUILD:

cc_binary(
    name = "<project name>",
    srcs = ["<project name>.cc"],
    deps = [
        "//tensorflow/core:tensorflow",
    ]
)

Two caveats for which there are probably workarounds:

• Right now, building things needs to happen within the TensorFlow repo.
• The compiled binary is huge (103MB).

https://medium.com/@jimfleming/loading-a-tensorflow-graph-with-the-c-api-4caaff88463f

If you are thinking into using Tensorflow c++ api on a standalone package you probably will need tensorflow_cc.so ( There is also a c api version tensorflow.so ) to build the c++ version you can use:

bazel build -c opt //tensorflow:libtensorflow_cc.so

Note1: If you want to add intrinsics support you can add this flags as: --copt=-msse4.2 --copt=-mavx

Note2: If you are thinking into using OpenCV on your project as well, there is an issue when using both libs together (tensorflow issue) and you should use --config=monolithic.

After building the library you need to add it to your project. To do that you can include this paths:

tensorflow
tensorflow/bazel-tensorflow/external/eigen_archive
tensorflow/bazel-tensorflow/external/protobuf_archive/src
tensorflow/bazel-genfiles

And link the library to your project:

tensorflow/bazel-bin/tensorflow/libtensorflow_framework.so (unused if you build with --config=monolithic)
tensorflow/bazel-bin/tensorflow/libtensorflow_cc.so

And when you are building your project you should also specify to your compiler that you are going to use c++11 standards.

Side Note: Paths relative to tensorflow version 1.5 (You may need to check if in your version anything changed).

Also this link helped me a lot into finding all this infos: link

First, after installing protobuf and eigen, you'd like to build Tensorflow:

./configure
bazel build //tensorflow:libtensorflow_cc.so

Then Copy the following include headers and dynamic shared library to /usr/local/lib and /usr/local/include:

mkdir /usr/local/include/tf
cp -r bazel-genfiles/ /usr/local/include/tf/
cp -r tensorflow /usr/local/include/tf/
cp -r third_party /usr/local/include/tf/
cp -r bazel-bin/libtensorflow_cc.so /usr/local/lib/

Lastly, compile using an example:

g++ -std=c++11 -o tf_example \
-I/usr/local/include/tf \
-I/usr/local/include/eigen3 \
-g -Wall -D_DEBUG -Wshadow -Wno-sign-compare -w  \
-L/usr/local/lib/libtensorflow_cc \
`pkg-config --cflags --libs protobuf` -ltensorflow_cc tf_example.cpp