# Installing from Sources

- 1. Download and Setup
- 2. Requirements
- 3. Build on Ubuntu
- 4. Build on Centos

## Download and Setup

You can download PaddlePaddle from the github source.

    git clone https://github.com/PaddlePaddle/Paddle paddle
    cd paddle

## Requirements

To compile the source code, your computer must be equipped with the following dependencies.

- Compiler: GCC >= 4.8 or Clang >= 3.3 (AppleClang >= 5.1) and gfortran compiler
- CMake: CMake >= 3.0 (at least CMake 3.4 on Mac OS X)
- BLAS: MKL, OpenBlas or ATLAS
- Python: only support Python 2.7

Note: For CUDA 7.0 and CUDA 7.5, GCC 5.0 and up are not supported! For CUDA 8.0, GCC versions later than 5.3 are not supported!

### Options

PaddlePaddle supports some build options.

Optional | Description
--- | ---
WITH_GPU | Compile PaddlePaddle with NVIDIA GPU
WITH_AVX | Compile PaddlePaddle with AVX intrinsics
WITH_DSO | Compile PaddlePaddle with dynamic linked CUDA
WITH_TESTING | Compile PaddlePaddle with unit testing
WITH_SWIG_PY | Compile PaddlePaddle with inference api
WITH_STYLE_CHECK | Compile PaddlePaddle with style check
WITH_PYTHON | Compile PaddlePaddle with python interpreter
WITH_DOUBLE | Compile PaddlePaddle with double precision
WITH_RDMA | Compile PaddlePaddle with RDMA support
WITH_TIMER | Compile PaddlePaddle with stats timer
WITH_PROFILER | Compile PaddlePaddle with GPU profiler
WITH_DOC | Compile PaddlePaddle with documentation
WITH_COVERAGE | Compile PaddlePaddle with code coverage
COVERALLS_UPLOAD | Package code coverage data to coveralls
ON_TRAVIS | Exclude special unit test on Travis CI

Note:

- The GPU version works best with Cuda Toolkit 8.0 and cuDNN v5.
- Other versions like Cuda Toolkit 7.0, 7.5 and cuDNN v3, v4 are also supported.
- To utilize cuDNN v5, Cuda Toolkit 7.5 is prerequisite and vice versa.

As a simple example, consider the following:

1. BLAS Dependencies(optional)

   CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically. To utilize preinstalled BLAS， you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`.

       # specify MKL
       cmake .. -DMKL_ROOT=<mkl_path>
       # or specify OpenBLAS
       cmake .. -DOPENBLAS_ROOT=<openblas_path>

2. Doc Dependencies(optional)

   To generate PaddlePaddle’s documentation, install dependencies and set `-DWITH_DOC=ON` as follows:

       pip install 'sphinx>=1.4.0'
       pip install sphinx_rtd_theme recommonmark

       # install doxygen on Ubuntu
       sudo apt-get install doxygen
       # install doxygen on Mac OS X
       brew install doxygen

       # active docs in cmake
       cmake .. -DWITH_DOC=ON`

## Build on Ubuntu 14.04

### Install Dependencies

- Paddle Dependencies

      # necessary
      sudo apt-get update
      sudo apt-get install -y git curl gcc g++ gfortran make build-essential automake
      sudo apt-get install -y python python-pip python-numpy libpython-dev bison
      sudo pip install 'protobuf==3.1.0.post1'

      # install cmake 3.4
      curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \
          cd cmake-3.4.1 && ./bootstrap && make -j4 && sudo make install && \
          cd .. && rm -rf cmake-3.4.1

- GPU Dependencies (optional)

  To build GPU version, you will need the following installed:

        1. a CUDA-capable GPU
        2. A supported version of Linux with a gcc compiler and toolchain
        3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
        4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)

  The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release.

  After downloading cuDNN library, issue the following commands:

      sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local
      sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

  Then you need to set LD_LIBRARY_PATH, PATH environment variables in ~/.bashrc.

      export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
      export PATH=/usr/local/cuda/bin:$PATH

### Build and Install

As usual, the best option is to create build folder under paddle project directory.

    mkdir build && cd build

Finally, you can build and install PaddlePaddle:

    # you can add build option here, such as:
    cmake .. -DCMAKE_INSTALL_PREFIX=<path to install>
    # please use sudo make install, if you want to install PaddlePaddle into the system
    make -j `nproc` && make install
    # set PaddlePaddle installation path in ~/.bashrc
    export PATH=<path to install>/bin:$PATH
    # install PaddlePaddle Python modules.
    sudo pip install <path to install>/opt/paddle/share/wheels/*.whl

## Build on Centos 7

### Install Dependencies

- CPU Dependencies

      # necessary
      sudo yum update
      sudo yum install -y epel-release
      sudo yum install -y make cmake3 python-devel python-pip gcc-gfortran swig git
      sudo pip install wheel numpy
      sudo pip install 'protobuf>=3.0.0'

- GPU Dependencies (optional)

  To build GPU version, you will need the following installed:

        1. a CUDA-capable GPU
        2. A supported version of Linux with a gcc compiler and toolchain
        3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
        4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn)

  The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release.

  After downloading cuDNN library, issue the following commands:

      sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local
      sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

  Then you need to set LD_LIBRARY_PATH, PATH environment variables in ~/.bashrc.

      export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
      export PATH=/usr/local/cuda/bin:$PATH

### Build and Install

As usual, the best option is to create build folder under paddle project directory.

    mkdir build && cd build

Finally, you can build and install PaddlePaddle:

    # you can add build option here, such as:
    cmake3 .. -DCMAKE_INSTALL_PREFIX=<path to install>
    # please use sudo make install, if you want to install PaddlePaddle into the system
    make -j `nproc` && make install
    # set PaddlePaddle installation path in ~/.bashrc
    export PATH=<path to install>/bin:$PATH
    # install PaddlePaddle Python modules.
    sudo pip install <path to install>/opt/paddle/share/wheels/*.whl