tensorflow_cc
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.
Docker
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 --runtime=nvidia -it floopcz/tensorflow_cc:ubuntu-cuda /bin/bash
The list of available images:
Image name | Description |
---|---|
floopcz/tensorflow_cc:ubuntu |
Ubuntu build of tensorflow_cc |
floopcz/tensorflow_cc:ubuntu-cuda |
Ubuntu build of tensorflow_cc + NVIDIA CUDA |
floopcz/tensorflow_cc:archlinux |
Arch Linux build of tensorflow_cc |
floopcz/tensorflow_cc:archlinux-cuda |
Arch Linux build of tensorflow_cc + NVIDIA CUDA |
To build one of the images yourself, e.g. ubuntu
, run:
docker build -t floopcz/tensorflow_cc:ubuntu -f Dockerfiles/ubuntu .
Installation
1) Install requirements
Ubuntu 18.04:
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 https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -
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 (>=10.1), 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.
Arch Linux:
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., 2.0.0).
2) Clone this repository
git clone https://github.com/FloopCZ/tensorflow_cc.git
cd tensorflow_cc
3) Build and install the library
cd tensorflow_cc
mkdir build && cd build
cmake ..
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.
4) (Optional) Free disk space
# cleanup bazel build directory
rm -rf ~/.cache
# remove the build folder
cd .. && rm -rf build
Usage
1) Write your C++ code:
// 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";
}
2) Link TensorflowCC to your program using CMake
# 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()
3) Build and run your program
mkdir build && cd build
cmake .. && make
./example
If you are still unsure, consult the Dockerfiles for Ubuntu and Arch Linux.