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Paddle/doc/build/build_from_source.md

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Installing from Sources
==========================
* [1. Download and Setup](#download)
* [2. Requirements](#requirements)
* [3. Build on Ubuntu](#ubuntu)
* [4. Build on Mac OS X](#mac)
## <span id="download">Download and Setup</span>
You can download PaddlePaddle from the [github source](https://github.com/gangliao/Paddle).
```bash
git clone https://github.com/baidu/Paddle paddle
cd paddle
```
## <span id="requirements">Requirements</span>
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.
<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 with GPU mode.</td></tr>
<tr><td class="left">WITH_DOUBLE</td><td class="left">Compile with double precision floating-point, default: single precision.</td></tr>
<tr><td class="left">WITH_GLOG</td><td class="left">Compile with glog. If not found, default: an internal log implementation.</td></tr>
<tr><td class="left">WITH_GFLAGS</td><td class="left">Compile with gflags. If not found, default: an internal flag implementation.</td></tr>
<tr><td class="left">WITH_TESTING</td><td class="left">Compile with gtest for PaddlePaddle's unit testing.</td></tr>
<tr><td class="left">WITH_DOC</td><td class="left"> Compile to generate PaddlePaddle's docs, default: disabled (OFF).</td></tr>
<tr><td class="left">WITH_SWIG_PY</td><td class="left">Compile with python predict API, default: disabled (OFF).</td></tr>
<tr><td class="left">WITH_STYLE_CHECK</td><td class="left">Compile with code style check, default: enabled (ON).</td></tr>
</tbody>
</table>
</html>
**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:
```bash
# 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:
```bash
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`
```
## <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 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:
```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
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**
```bash
cmake .. -DWITH_GPU=OFF
```
- **GPU**
```bash
cmake .. -DWITH_GPU=ON
```
- **GPU with doc and swig**
```bash
cmake .. -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON
```
Finally, you can build PaddlePaddle:
```bash
# 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:$PATH
```
**Note:**
If you set `WITH_SWIG_PY=ON`, related python dependencies also need to be installed.
Otherwise, PaddlePaddle will automatically install python dependencies
at first time when user run paddle commands, such as `paddle version`, `paddle train`.
It may require sudo privileges:
```bash
# you can run
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
# or just run
sudo paddle version
```
## <span id="mac">Building on Mac OS X</span>
### Prerequisites
This guide is based on Mac OS X 10.11 (El Capitan). Note that if you are running an up to date version of OS X,
you will already have Python 2.7.10 and Numpy 1.8 installed.
The best option is to use the package manager homebrew to handle installations and upgrades for you.
To install [homebrew](http://brew.sh/), first open a terminal window (you can find Terminal in the Utilities folder in Applications), and issue the command:
```bash
# install brew
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
# install pip
easy_install pip
```
### Install Dependencies
- **CPU Dependencies**
```bash
# Install fundamental dependents
brew install glog gflags cmake protobuf openblas
# Install google test on Mac OS X
# Download gtest 1.7.0
wget https://github.com/google/googletest/archive/release-1.7.0.tar.gz
tar -xvf googletest-release-1.7.0.tar.gz && cd googletest-release-1.7.0
# Build gtest
mkdir build && cmake ..
make
# Install gtest library
sudo cp -r ../include/gtest /usr/local/include/
sudo cp lib*.a /usr/local/lib
```
- **GPU Dependencies(optional)**
To build GPU version, you will need the following installed:
1. a CUDA-capable GPU
2. Mac OS X 10.11 or later
2. the Clang compiler and toolchain installed using Xcode
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.
1. After downloading cuDNN library, issue the following commands:
```bash
sudo tar -xzf cudnn-7.5-osx-x64-v5.0-ga.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib/libcudnn*
```
2. Then you need to set DYLD\_LIBRARY\_PATH, PATH environment variables in ~/.bashrc.
```bash
export DYLD_LIBRARY_PATH=/usr/local/cuda/lib:$DYLD_LIBRARY_PATH
export PATH=/usr/local/cuda/bin:$PATH
```
### Build and Install
As usual, the best option is to create build folder under paddle project directory.
```bash
mkdir build && cd build
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**
```bash
cmake .. -DWITH_GPU=OFF
```
- **GPU**
```bash
cmake .. -DWITH_GPU=ON
```
- **GPU with doc and swig**
```bash
cmake .. -DWITH_GPU=ON -DWITH_DOC=ON -DWITH_SWIG_PY=ON
```
Finally, you can build PaddlePaddle:
```bash
# you can add build option here, such as:
cmake .. -DWITH_GPU=ON -DCMAKE_INSTALL_PREFIX=<installation path>
# please use sudo make install, if you want to install PaddlePaddle into the system
make -j `sysctl -n hw.ncpu` && make install
# set PaddlePaddle installation path in ~/.bashrc
export PATH=<installation path>/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:
```bash
# you can run
sudo pip install <path to install>/opt/paddle/share/wheels/*.whl
# or just run
sudo paddle version
```
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