Installing from Sources ========================== * [1. Download and Setup](#download) * [2. Requirements](#requirements) * [3. Build on Ubuntu](#ubuntu) * [4. Build on Centos](#centos) ## 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 ``` ## 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) and gfortran compiler - **CMake**: CMake >= 3.0 (at least CMake 3.4 on Mac OS X) - **BLAS**: MKL, OpenBlas or ATLAS - **Python**: only support Python 2.7 **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.
Optional Description
WITH_GPUCompile PaddlePaddle with NVIDIA GPU
WITH_AVXCompile PaddlePaddle with AVX intrinsics
WITH_DSOCompile PaddlePaddle with dynamic linked CUDA
WITH_TESTINGCompile PaddlePaddle with unit testing
WITH_SWIG_PYCompile PaddlePaddle with inference api
WITH_STYLE_CHECKCompile PaddlePaddle with style check
WITH_PYTHONCompile PaddlePaddle with python interpreter
WITH_DOUBLECompile PaddlePaddle with double precision
WITH_RDMACompile PaddlePaddle with RDMA support
WITH_TIMERCompile PaddlePaddle with stats timer
WITH_PROFILERCompile PaddlePaddle with GPU profiler
WITH_DOCCompile PaddlePaddle with documentation
WITH_COVERAGECompile PaddlePaddle with code coverage
COVERALLS_UPLOADPackage code coverage data to coveralls
ON_TRAVISExclude special unit test on Travis CI
**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)** CMake will search BLAS libraries from system. If not found, OpenBLAS will be downloaded, built and installed automatically. To utilize preinstalled BLAS, you can simply specify MKL, OpenBLAS or ATLAS via `MKL_ROOT`, `OPENBLAS_ROOT` or `ATLAS_ROOT`. ```bash # specify MKL cmake .. -DMKL_ROOT= # or specify OpenBLAS cmake .. -DOPENBLAS_ROOT= ``` 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` ``` ## Build on Ubuntu 14.04 ### Install Dependencies - **Paddle Dependencies** ```bash # necessary sudo apt-get update sudo apt-get install -y git curl gcc g++ gfortran make build-essential automake sudo apt-get install -y python python-pip python-numpy libpython-dev bison sudo pip install 'protobuf==3.1.0.post1' # install cmake 3.4 curl -sSL https://cmake.org/files/v3.4/cmake-3.4.1.tar.gz | tar -xz && \ cd cmake-3.4.1 && ./bootstrap && make -j4 && sudo make install && \ cd .. && rm -rf cmake-3.4.1 ``` - **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= # 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:$PATH # install PaddlePaddle Python modules. sudo pip install /opt/paddle/share/wheels/*.whl ``` ## Build on Centos 7 ### Install Dependencies - **CPU Dependencies** ```bash # necessary sudo yum update sudo yum install -y epel-release sudo yum install -y make cmake3 python-devel python-pip gcc-gfortran swig git sudo pip install wheel numpy sudo pip install 'protobuf>=3.0.0' ``` - **GPU Dependencies (optional)** To build GPU version, you will need the following installed: 1. a CUDA-capable GPU 2. A supported version of Linux with a gcc compiler and toolchain 3. NVIDIA CUDA Toolkit (available at http://developer.nvidia.com/cuda-downloads) 4. NVIDIA cuDNN Library (availabel at https://developer.nvidia.com/cudnn) The CUDA development environment relies on tight integration with the host development environment, including the host compiler and C runtime libraries, and is therefore only supported on distribution versions that have been qualified for this CUDA Toolkit release. After downloading cuDNN library, issue the following commands: ```bash sudo tar -xzf cudnn-7.5-linux-x64-v5.1.tgz -C /usr/local sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* ``` Then you need to set LD\_LIBRARY\_PATH, PATH environment variables in ~/.bashrc. ```bash export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH export PATH=/usr/local/cuda/bin:$PATH ``` ### Build and Install As usual, the best option is to create build folder under paddle project directory. ```bash mkdir build && cd build ``` Finally, you can build and install PaddlePaddle: ```bash # you can add build option here, such as: cmake3 .. -DCMAKE_INSTALL_PREFIX= # please use sudo make install, if you want to install PaddlePaddle into the system make -j `nproc` && make install # set PaddlePaddle installation path in ~/.bashrc export PATH=/bin:$PATH # install PaddlePaddle Python modules. sudo pip install /opt/paddle/share/wheels/*.whl ```