Installing from Sources ========================== * [1. Download and Setup](#download) * [2. Requirements](#requirements) * [3. Build on Ubuntu](#ubuntu) ## Download and Setup You can download PaddlePaddle from the [github source](https://github.com/PaddlePaddle/Paddle). ```bash git clone https://github.com/PaddlePaddle/Paddle paddle cd paddle git submodule update --init --recursive ``` ## 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) - **CMake**: version >= 2.8 - **BLAS**: MKL, OpenBlas or ATLAS - **Protocol Buffers**: version >= 2.4, **Note: 3.x is not supported** - **Python**: only python 2.7 is supported currently **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. To enable it, first you need to install the related libraries.
Optional Description
WITH_GPUCompile with GPU mode.
WITH_DOUBLECompile with double precision floating-point, default: single precision.
WITH_GLOGCompile with glog. If not found, default: an internal log implementation.
WITH_GFLAGSCompile with gflags. If not found, default: an internal flag implementation.
WITH_TESTINGCompile with gtest for PaddlePaddle's unit testing.
WITH_DOC Compile to generate PaddlePaddle's docs, default: disabled (OFF).
WITH_SWIG_PYCompile with python predict API, default: disabled (OFF).
WITH_STYLE_CHECKCompile with code style check, default: enabled (ON).
**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. **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` ``` ## Build on Ubuntu 14.04 ### Install Dependencies - **CPU Dependencies** ```bash # necessary sudo apt-get update sudo apt-get install -y g++ make cmake swig 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 with swig** ```bash cmake .. -DWITH_GPU=OFF -DWITH_SWIG_PY=ON ``` - **GPU with swig** ```bash cmake .. -DWITH_GPU=ON -DWITH_SWIG_PY=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= -DWITH_SWIG_PY=ON # 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=/bin:$PATH ``` 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 /opt/paddle/share/wheels/*.whl # or just run sudo paddle version ```