Skip to content

Latest commit

 

History

History
167 lines (130 loc) · 5.67 KB

README.md

File metadata and controls

167 lines (130 loc) · 5.67 KB
Raw

tensorflow_cc

Build Status TF version

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 --gpus all -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

Set up Python 3 to be the default Python:

update-alternatives --install /usr/bin/python python /usr/bin/python3 1

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.

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., 5.1.1).

Warning: If your program uses Protobuf and you encounter linkage or other problems, you can try -DINSTALL_PROTOBUF=ON option to install a Protobuf version matching the version bundled with TensorFlow. Our Dockerfiles are already built with the right version of Protobuf, so you may want to try your program in the Dockerfile first.

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
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 ..

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.