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 /bin/bash

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

docker run --gpus all -it floopcz/tensorflow_cc:ubuntu-cuda /bin/bash

The list of available images:

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

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

Install repository requirements:

sudo apt-get install cmake curl g++-7 git python3-dev python3-numpy sudo wget

In order to build the TensorFlow itself, the build procedure also requires Bazel:

curl -fsSL https://bazel.build/bazel-release.pub.gpg | gpg --dearmor > bazel.gpg
sudo mv bazel.gpg /etc/apt/trusted.gpg.d/
echo "deb [arch=amd64] https://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
sudo apt-get update && sudo apt-get install bazel

If you require GPU support on Ubuntu, please also install NVIDIA CUDA Toolkit (>=11.1), NVIDIA drivers, cuDNN, and cuda-command-line-tools package. The build procedure will automatically detect CUDA if it is installed in /opt/cuda or /usr/local/cuda directories.

sudo pacman -S base-devel bazel cmake git python python-numpy wget

For GPU support on Arch, also install the following:

sudo pacman -S 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., 3.7.2).

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

cd tensorflow_cc
mkdir build && cd build
cmake ..
make
sudo make install
sudo ldconfig

Warning: Optimizations for Intel CPU generation >=haswell are enabled by default. If you have a processor that is older than haswell 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.

Warning: In low-memory or many-cpu environments, the bazel scheduler can miss the resource consumption estimates and the build may be terminated by the out-of-memory killer. If that is your case, consider adding resource limit parameters to CMake, e.g., cmake -DLOCAL_RAM_RESOURCES=2048 -DLOCAL_CPU_RESOURCES=4 ..

# 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 Tensorflow library.
target_link_libraries(example TensorflowCC::TensorflowCC)

# 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.