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 ``` ## 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_GPU	Compile PaddlePaddle with NVIDIA GPU
WITH_AVX	Compile PaddlePaddle with AVX intrinsics
WITH_DSO	Compile PaddlePaddle with dynamic linked CUDA
WITH_TESTING	Compile PaddlePaddle with unit testing
WITH_SWIG_PY	Compile PaddlePaddle with inference api
WITH_STYLE_CHECK	Compile PaddlePaddle with style check
WITH_PYTHON	Compile PaddlePaddle with python interpreter
WITH_DOUBLE	Compile PaddlePaddle with double precision
WITH_RDMA	Compile PaddlePaddle with RDMA support
WITH_TIMER	Compile PaddlePaddle with stats timer
WITH_PROFILER	Compile PaddlePaddle with GPU profiler
WITH_DOC	Compile PaddlePaddle with documentation
ON_COVERALLS	Compile PaddlePaddle with code coverage
COVERALLS_UPLOAD	Package code coverage data to coveralls
ON_TRAVIS	Exclude 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 autotools-dev sudo apt-get install -y python python-pip python-numpy libpython-dev automake 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 ```