How to build and use Google TensorFlow C++ api

Question

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

If you wish to avoid both building your projects with Bazel and generating a large binary, I have assembled a repository instructing the usage of the TensorFlow C++ library with CMake. You can find it here. The general ideas are as follows:

• Clone the TensorFlow repository.
• Add a build rule to tensorflow/BUILD (the provided ones do not include all of the C++ functionality).
• Build the TensorFlow shared library.
• Install specific versions of Eigen and Protobuf, or add them as external dependencies.
• Configure your CMake project to use the TensorFlow library.

One alternative to using Tensorflow C++ API I found is to use cppflow.

It's a lightweight C++ wrapper around Tensorflow C API. You get very small executables and it links against the libtensorflow.so already compiled file. There are also examples of use and you use CMAKE instead of Bazel.

If you don't mind using CMake, there is also tensorflow_cc project that builds and installs TF C++ API for you, along with convenient CMake targets you can link against. The project README contains an example and Dockerfiles you can easily follow.