Installing from Sources¶

1. Download and Setup
2. Requirements
3. Build on Ubuntu
4. Build on Mac OS X

Download and Setup¶

You can download PaddlePaddle from the github source.

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

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

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:

a CUDA-capable GPU
A supported version of Linux with a gcc compiler and toolchain
NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
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, CUDA_HOME and PATH environment variables in ~/.bashrc.

export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda
export PATH=/usr/local/cuda/bin:$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 -DWITH_DOC=OFF

GPU
```
cmake .. -DWITH_GPU=ON -DWITH_DOC=OFF
```

GPU with doc and swig

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

Finally, you can download source code and build:

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

Building on Mac OS X¶

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, first open a terminal window (you can find Terminal in the Utilities folder in Applications), and issue the command:

# 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

# 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:

a CUDA-capable GPU
Mac OS X 10.11 or later
the Clang compiler and toolchain installed using Xcode
NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads)
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-osx-x64-v5.0-ga.tgz -C /usr/local
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn*

Then you need to set DYLD_LIBRARY_PATH, CUDA_HOME and PATH environment variables in ~/.bashrc.

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.

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 -DWITH_DOC=OFF

GPU
```
cmake .. -DWITH_GPU=ON -DWITH_DOC=OFF
```

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 -DWITH_DOC=OFF -DCMAKE_INSTALL_PREFIX=<installation path>
# 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=<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:

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

Installing from Sources¶

Download and Setup¶

Requirements¶

Dependencies¶

Options¶

Build on Ubuntu 14.04¶

Install Dependencies¶

Build and Install¶

Building on Mac OS X¶

Prerequisites¶

Install Dependencies¶

Build and Install¶

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