This repository makes possible the usage of the TensorFlow C++ API from the outside of the TensorFlow source code folders and without the use of the Bazel build system.

This repository contains two CMake projects. The tensorflow_cc project downloads, builds and installs the TensorFlow C++ API into the operating system and the example project demonstrates its simple usage.

If you wish to start using this project right away, fetch a prebuilt image on Docker Hub!

Running the image on CPU:

docker run -it floopcz/tensorflow_cc:ubuntu-shared /bin/bash

If you also want to utilize your NVIDIA GPU, install NVIDIA Docker and run:

docker run --runtime=nvidia -it floopcz/tensorflow_cc:ubuntu-shared-cuda /bin/bash

The list of available images:

To build one of the images yourself, e.g. ubuntu-shared, run:

docker build -t floopcz/tensorflow_cc:ubuntu-shared -f Dockerfiles/ubuntu-shared .

sudo apt-get install build-essential curl git cmake unzip autoconf autogen automake libtool mlocate \
                     zlib1g-dev g++-7 python python3-numpy python3-dev python3-pip python3-wheel wget
sudo updatedb

If you require GPU support on Ubuntu, please also install Bazel, NVIDIA CUDA Toolkit (>=9.2), NVIDIA drivers, cuDNN, and cuda-command-line-tools package. The tensorflow build script will automatically detect CUDA if it is installed in /opt/cuda or /usr/local/cuda directories.

sudo pacman -S base-devel cmake git unzip mlocate python python-numpy wget
sudo updatedb

For GPU support on Arch, also install the following:

sudo pacman -S gcc7 bazel cuda cudnn nvidia

Warning: Newer versions of TensorFlow sometimes fail to build with the latest version of Bazel. You may wish to install an older version of Bazel (e.g., 0.26.1).

git clone https://github.com/FloopCZ/tensorflow_cc.git
cd tensorflow_cc

There are two possible ways to build the TensorFlow C++ library:

1. As a static library (default):
   • Faster to build.
   • Provides only basic functionality, just enough for inferring using an existing network (see contrib/makefile).
   • No GPU support.
2. As a shared library:
   • Requires Bazel.
   • Slower to build.
   • Provides the full TensorFlow C++ API.
   • GPU support.

cd tensorflow_cc
mkdir build && cd build
# for static library only:
cmake ..
# for shared library only (requires Bazel):
# cmake -DTENSORFLOW_STATIC=OFF -DTENSORFLOW_SHARED=ON ..
make && sudo make install

Warning: Optimizations for Intel CPU generation >=ivybridge are enabled by default. If you have a processor that is older than ivybridge generation, you may wish to run export CC_OPT_FLAGS="-march=native" before the build. This command provides the best possible optimizations for your current CPU generation, but it may cause the built library to be incompatible with older generations.

# cleanup bazel build directory
rm -rf ~/.cache
# remove the build folder
cd .. && rm -rf build

// example.cpp

#include <tensorflow/core/platform/env.h>
#include <tensorflow/core/public/session.h>
#include <iostream>
using namespace std;
using namespace tensorflow;

int main()
{
    Session* session;
    Status status = NewSession(SessionOptions(), &session);
    if (!status.ok()) {
        cout << status.ToString() << "\n";
        return 1;
    }
    cout << "Session successfully created.\n";
}

# CMakeLists.txt

find_package(TensorflowCC REQUIRED)
add_executable(example example.cpp)

# Link the static Tensorflow library.
target_link_libraries(example TensorflowCC::Static)

# Altenatively, link the shared Tensorflow library.
# target_link_libraries(example TensorflowCC::Shared)

# For shared library setting, you may also link cuda if it is available.
# find_package(CUDA)
# if(CUDA_FOUND)
#   target_link_libraries(example ${CUDA_LIBRARIES})
# endif()

mkdir build && cd build
cmake .. && make
./example 

If you are still unsure, consult the Dockerfiles for Ubuntu and Arch Linux.