Build from Sources¶
How To Build¶
PaddlePaddle mainly uses CMake and GCC, G++ as compile tools. We recommend you to use our pre-built Docker image to run the build to avoid installing dependencies by yourself. We have several build environment Docker images here .
If you choose not to use Docker image for your build, you need to install the below Compile Dependencies before run the build.
git clone https://github.com/PaddlePaddle/Paddle.git cd Paddle # run the following command to build a CPU-Only binaries if you are using docker docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x /paddle/paddle/scripts/docker/build.sh # else run these commands mkdir build cd build cmake -DWITH_GPU=OFF -DWITH_TESTING=OFF .. make
When the compile finishes, you can get the output whl package under build/python/dist, then you can choose to install the whl on local machine or copy it to the target machine.
pip install build/python/dist/*.whl
If you wish to run the tests, you may follow the below steps:
When using Docker, set
WITH_TESTING=ON will run test immediately after the build.
WITH_GPU=ON Can also run tests on GPU.
docker run -it -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=ON" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 bash -x paddle/paddle/scripts/docker/build.sh
If you don’t use Docker, just run ctest will start the tests:
mkdir build cd build cmake -DWITH_GPU=OFF -DWITH_TESTING=ON .. make ctest # run a single test like test_mul_op ctest -R test_mul_op
PaddlePaddle need the following dependencies when compiling, other dependencies will be downloaded automatically.
|GCC||4.8.2||Recommend devtools2 for CentOS|
Build options include whether build binaries for CPU or GPU, which BLAS library to use etc. You may pass these settings when running cmake. For detailed cmake tutorial please refer to here 。
Bool Type Options¶
You can add
-D argument to pass such options, like:
cmake .. -DWITH_GPU=OFF
|WITH_GPU||Build with GPU support||ON|
|WITH_C_API||Build only CAPI||OFF|
|WITH_DOUBLE||Build with double precision||OFF|
|WITH_DSO||Dynamically load CUDA libraries||ON|
|WITH_AVX||Build with AVX support||ON|
|WITH_PYTHON||Build with integrated Python interpreter||ON|
|WITH_STYLE_CHECK||Check code style when building||ON|
|WITH_TESTING||Build unit tests||ON|
|WITH_SWIG_PY||Build Python SWIG interface for V2 API||Auto|
|WITH_GOLANG||Build fault-tolerant parameter server written in go||ON|
|WITH_MKL||Use MKL as BLAS library, else use OpenBLAS||ON|
If you choose not to use MKL, then OpenBlAS will be used.
PaddlePaddle will automatically find CUDA and cuDNN when compiling and running.
-DCUDA_ARCH_NAME=Auto can be used to detect SM architecture
automatically in order to speed up the build.
PaddlePaddle can build with any version later than cuDNN v5.1, and we intend to keep on with latest cuDNN versions. Be sure to run with the same version of cuDNN you built.
Pass Compile Options¶
You can pass compile options to use intended BLAS/CUDA/Cudnn libraries.
When running cmake command, it will search system paths like
/usr/lib:/usr/local/lib and then search paths that you
passed to cmake, i.e.
cmake .. -DWITH_GPU=ON -DWITH_TESTING=OFF -DCUDNN_ROOT=/opt/cudnnv5
NOTE: These options only take effect when running cmake for the first time, you need to clean the cmake cache or clean the build directory (
rm -rf ) if you want to change it.