build_from_source_en.md

Installing from Sources
==========================

* [1. Download and Setup](#download)
* [2. Requirements](#requirements)
* [3. Build on Ubuntu](#ubuntu)

## <span id="download">Download and Setup</span> 
You can download PaddlePaddle from the [github source](https://github.com/PaddlePaddle/Paddle).

```bash
git clone https://github.com/PaddlePaddle/Paddle paddle
cd paddle
```
## <span id="requirements">Requirements</span>

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)
- **CMake**: version >= 3.0
- **BLAS**: MKL, OpenBlas or ATLAS

**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. 

<html>
<table> 
<thead>
<tr>
<th scope="col" class="left">Optional</th>
<th scope="col" class="left">Description</th>
</tr>
</thead>
<tbody>
<tr><td class="left">WITH_GPU</td><td class="left">Compile PaddlePaddle with NVIDIA GPU</td></tr>
<tr><td class="left">WITH_AVX</td><td class="left">Compile PaddlePaddle with AVX intrinsics</td></tr>
<tr><td class="left">WITH_DSO</td><td class="left">Compile PaddlePaddle with dynamic linked CUDA</td></tr>
<tr><td class="left">WITH_TESTING</td><td class="left">Compile PaddlePaddle with unit testing</td></tr>
<tr><td class="left">WITH_SWIG_PY</td><td class="left">Compile PaddlePaddle with inference api</td></tr>
<tr><td class="left">WITH_STYLE_CHECK</td><td class="left">Compile PaddlePaddle with style check</td></tr>
<tr><td class="left">WITH_PYTHON</td><td class="left">Compile PaddlePaddle with python interpreter</td></tr>
<tr><td class="left">WITH_DOUBLE</td><td class="left">Compile PaddlePaddle with double precision</td></tr>
<tr><td class="left">WITH_RDMA</td><td class="left">Compile PaddlePaddle with RDMA support</td></tr>
<tr><td class="left">WITH_TIMER</td><td class="left">Compile PaddlePaddle with stats timer</td></tr>
<tr><td class="left">WITH_PROFILER</td><td class="left">Compile PaddlePaddle with GPU profiler</td></tr>
<tr><td class="left">WITH_DOC</td><td class="left">Compile PaddlePaddle with documentation</td></tr>
<tr><td class="left">ON_COVERALLS</td><td class="left">Compile PaddlePaddle with code coverage</td></tr>
<tr><td class="left">COVERALLS_UPLOAD</td><td class="left">Package code coverage data to coveralls</td></tr>
<tr><td class="left">ON_TRAVIS</td><td class="left">Exclude special unit test on Travis CI</td></tr>
</tbody>
</table>
</html>

**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)**
  
    Paddle will find BLAS from system's default path. But you can specify MKL, OpenBLAS or ATLAS via `CBLAS_INC_DIR` and `CBLAS_LIBRARIES`.

    ```bash
    cmake .. -DCBLAS_INC_DIR=<cblas_inc> -DCBLAS_LIBRARIES=<cblas_libs>
    ```

2. **Doc Dependencies(optional)**

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

    ```bash
    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`
    ```

## <span id="ubuntu">Build on Ubuntu 14.04</span>

### Install Dependencies

- **CPU Dependencies**

    ```bash
    # necessary
    sudo apt-get update
    sudo apt-get install -y g++ make cmake build-essential libatlas-base-dev python python-pip libpython-dev 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:

    ```bash
    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.

    ```bash
    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.

```bash
mkdir build && cd build
``` 

Finally, you can build and install PaddlePaddle:

```bash
# 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
```