Build from Sources


To build PaddlePaddle, you need

  1. A computer – Linux, Windows, MacOS.
  2. Docker.

Nothing else. Not even Python and GCC, because you can install all build tools into a Docker image. We run all the tools by running this image.

How To Build

You need to use Docker to build PaddlePaddle to avoid installing dependencies by yourself. We have several pre-built Docker images here , you can also find how to build and use paddle_manylinux_devel Docker image from here Or you can build your own image from source as the optional step below:

If you don’t wish to use docker,you need to install several compile dependencies manually as Compile Dependencies shows to start compilation.

# 1. clone the source code
git clone
cd Paddle
# 2. Optional: build development docker image from source
docker build -t paddle:dev .
# 3. Run the following command to build a CPU-Only binaries
docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 ./paddle/scripts/ build
# 4. Or, use your built Docker image to build PaddlePaddle (must run step 2)
docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=OFF" paddle:dev ./paddle/scripts/ build

NOTE: The above command try to mount the current working directory (root directory of source code) into /paddle directory inside docker container.

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 the machine has installed PaddlePaddle before, there are two methods:

1. uninstall and reinstall
pip uninstall paddlepaddle
pip install build/python/dist/*.whl

2. upgrade directly
pip install build/python/dist/*.whl -U

Run Tests

If you wish to run the tests, you may follow the below steps:

When using Docker, set RUN_TEST=ON and WITH_TESTING=ON will run test immediately after the build. Set WITH_GPU=ON Can also run tests on GPU.

docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=ON" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 ./paddle/scripts/ test

If you wish to run only one unit test, like test_sum_op:

docker run -it -v $PWD:/paddle -w /paddle -e "WITH_GPU=OFF" -e "WITH_TESTING=ON" -e "RUN_TEST=OFF" paddlepaddle/paddle_manylinux_devel:cuda8.0_cudnn5 /bin/bash
./paddle/scripts/ build
cd build
ctest -R test_sum_op -V

Frequently Asked Questions

  • What is Docker?

    If you haven’t heard of it, consider it something like Python’s virtualenv.

  • Docker or virtual machine?

    Some people compare Docker with VMs, but Docker doesn’t virtualize any hardware nor running a guest OS, which means there is no compromise on the performance.

  • Why Docker?

    Using a Docker image of build tools standardizes the building environment, which makes it easier for others to reproduce your problems and to help.

    Also, some build tools don’t run on Windows or Mac or BSD, but Docker runs almost everywhere, so developers can use whatever computer they want.

  • Can I choose not to use Docker?

    Sure, you don’t have to install build tools into a Docker image; instead, you can install them on your local computer. This document exists because Docker would make the development way easier.

  • How difficult is it to learn Docker?

    It takes you ten minutes to read an introductory article and saves you more than one hour to install all required build tools, configure them, especially when new versions of PaddlePaddle require some new tools. Not even to mention the time saved when other people trying to reproduce the issue you have.

  • Can I use my favorite IDE?

    Yes, of course. The source code resides on your local computer, and you can edit it using whatever editor you like.

    Many PaddlePaddle developers are using Emacs. They add the following few lines into their ~/.emacs configure file:

    (global-set-key "\C-cc" 'compile)
    (setq compile-command "docker run --rm -it -v $(git rev-parse --show-toplevel):/paddle paddle:dev")

    so they could type Ctrl-C and c to build PaddlePaddle from source.

  • Does Docker do parallel building?

    Our building Docker image runs a Bash script , which calls make -j$(nproc) to starts as many processes as the number of your CPU cores.

  • Docker requires sudo

    An owner of a computer has the administrative privilege, a.k.a., sudo, and Docker requires this privilege to work properly. If you use a shared computer for development, please ask the administrator to install and configure Docker. We will do our best to support rkt, another container technology that doesn’t require sudo.

  • Docker on Windows/MacOS builds slowly

    On Windows and MacOS, Docker containers run in a Linux VM. You might want to give this VM some more memory and CPUs so to make the building efficient. Please refer to this issue for details.

  • Not enough disk space

    Examples in this article use option –rm with the docker run command. This option ensures that stopped containers do not exist on hard disks. We can use docker ps -a to list all containers, including stopped. Sometimes docker build generates some intermediate dangling images, which also take disk space. To clean them, please refer to this article .

Appendix: Compile Dependencies

PaddlePaddle need the following dependencies when compiling, other dependencies will be downloaded automatically.

PaddlePaddle Compile Dependencies
Dependency Version Description
CMake >=3.2  
GCC 4.8.2 Recommend devtools2 for CentOS
Python 2.7.x Need
pip >=9.0  
SWIG >=2.0  
Go >=1.8 Optional

Appendix: Build Options

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

You can add -D argument to pass such options, like:

cmake .. -DWITH_GPU=OFF
Bool Type Options
Option Description Default
WITH_GPU Build with GPU support ON
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 OFF
WITH_DOC Build documentations OFF
WITH_SWIG_PY Build Python SWIG interface for V2 API Auto
WITH_GOLANG Build fault-tolerant parameter server written in go OFF
WITH_MKL Use MKL as BLAS library, else use OpenBLAS ON


PaddlePaddle supports MKL and OpenBlAS as BLAS library。By default it uses MKL. If you are using MKL and your machine supports AVX2, MKL-DNN will also be downloaded and used, for more details .

If you choose not to use MKL, then OpenBlAS will be used.


PaddlePaddle will automatically find CUDA and cuDNN when compiling and running. parameter -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.


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.