# Installing from Sources

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

## Download and Setup

You can download PaddlePaddle from the github source.

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

## Requirements

To compile the source code, your computer must be equipped with GCC >=4.6 or Clang compiler.

### Dependencies

- CMake: version >= 2.8
- BLAS: MKL, OpenBlas or ATLAS
- protobuf: version >= 2.4, Note: 3.x is not supported
- python: only python 2.7 is supported currently

### Options

PaddlePaddle supports some build options. To enable it, first you need to install the related libraries.

Optional | Description
--- | ---
WITH_GPU | Compile with GPU mode.
WITH_DOUBLE | Compile with double precision floating-point, default: single precision.
WITH_GLOG | Compile with glog. If not found, default: an internal log implementation.
WITH_GFLAGS | Compile with gflags. If not found, default: an internal flag implementation.
WITH_TESTING | Compile with gtest for PaddlePaddle's unit testing.
WITH_DOC | Compile to generate PaddlePaddle's docs, default: disabled (OFF).
WITH_SWIG_PY | Compile with python predict API, default: disabled (OFF).
WITH_STYLE_CHECK | Compile with code style check, default: enabled (ON).

Note:

- The GPU version works best with Cuda Toolkit 7.5 and cuDNN v5.
- Other versions like Cuda Toolkit 6.5, 7.0, 8.0 and cuDNN v2, 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. Python Dependencies(optional)

   To compile PaddlePaddle with python predict API, make sure swig installed and set `-DWITH_SWIG_PY=ON` as follows:

       # install swig on ubuntu
       sudo apt-get install swig
       # install swig on Mac OS X
       brew install swig

       # active swig in cmake
       cmake .. -DWITH_SWIG_PY=ON

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 breathe 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

- CPU Dependencies

      # necessary
      sudo apt-get update
      sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python python-pip libpython-dev m4 libprotobuf-dev protobuf-compiler python-protobuf python-numpy git
      # optional
      sudo apt-get install libgoogle-glog-dev
      sudo apt-get install libgflags-dev
      sudo apt-get install libgtest-dev
      sudo pip install wheel
      pushd /usr/src/gtest
      cmake .
      make
      sudo cp *.a /usr/lib
      popd

- 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
    cmake ..

CMake first check PaddlePaddle’s dependencies in system default path. After installing some optional libraries, corresponding build option will be set automatically (for instance, glog, gtest and gflags). If still not found, you can manually set it based on CMake error information from your screen.

As a simple example, consider the following:

- Only CPU

      cmake  .. -DWITH_GPU=OFF

- GPU

      cmake .. -DWITH_GPU=ON

- GPU with doc and swig

      cmake .. -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON

Finally, you can build PaddlePaddle:

    # you can add build option here, such as:
    cmake .. -DWITH_GPU=ON -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

Note:

If you set `WITH_SWIG_PY=ON`, related python dependencies also need to be installed. Otherwise, PaddlePaddle will automatically install python dependencies at first time when user run paddle commands, such as `paddle version`, `paddle train`. It may require sudo privileges:

    # you can run
    sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
    # or just run
    sudo paddle version