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Building PaddlePaddle

Goals

We want to make the building procedures:

  1. Static, can reproduce easily.
  2. Generate python whl packages that can be widely use cross many distributions.
  3. Build different binaries per release to satisfy different environments:
    • Binaries for different CUDA and CUDNN versions, like CUDA 7.5, 8.0, 9.0
    • Binaries containing only capi
    • Binaries for python with wide unicode support or not.
  4. Build docker images with PaddlePaddle pre-installed, so that we can run PaddlePaddle applications directly in docker or on Kubernetes clusters.

To achieve this, we maintain a dockerhub repo:https://hub.docker.com/r/paddlepaddle/paddle which provides pre-built environment images to build PaddlePaddle and generate corresponding whl binaries.(We strongly recommend building paddlepaddle in our pre-specified Docker environment.)

Development Workflow

Here we describe how the workflow goes on. We start from considering our daily development environment.

Developers work on a computer, which is usually a laptop or desktop:

or, they might rely on a more sophisticated box (like with GPUs):

A principle here is that source code lies on the development computer (host) so that editors like Eclipse can parse the source code to support auto-completion.

Build With Docker

Build Environments

The latest pre-built build environment images are:

Image Tag
paddlepaddle/paddle latest-dev

Start Build

git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
./paddle/scripts/paddle_docker_build.sh build

After the build finishes, you can get output whl package under build/python/dist.

This command will download the most recent dev image from docker hub, start a container in the backend and then run the build script /paddle/paddle/scripts/paddle_build.sh build in the container. The container mounts the source directory on the host into /paddle. When it writes to /paddle/build in the container, it writes to $PWD/build on the host indeed.

Build Options

Users can specify the following Docker build arguments with either "ON" or "OFF" value:

Option Default Description
WITH_GPU OFF Generates NVIDIA CUDA GPU code and relies on CUDA libraries.
WITH_AVX OFF Set to "ON" to enable AVX support.
WITH_TESTING OFF Build unit tests binaries.
WITH_MKL ON Build with Intel® MKL and Intel® MKL-DNN support.
WITH_PYTHON ON Build with python support. Turn this off if build is only for capi.
WITH_STYLE_CHECK ON Check the code style when building.
PYTHON_ABI "" Build for different python ABI support, can be cp27-cp27m or cp27-cp27mu
RUN_TEST OFF Run unit test immediately after the build.

Docker Images

You can get the latest PaddlePaddle docker images by docker pull paddlepaddle/paddle:<version> or build one by yourself.

Official Docker Releases

Official docker images at here, you can choose either latest or images with a release tag like 0.10.0, Currently available tags are:

Tag Description
latest latest CPU only image
latest-gpu latest binary with GPU support
0.10.0 release 0.10.0 CPU only binary image
0.10.0-gpu release 0.10.0 with GPU support

Build Your Own Image

Build PaddlePaddle docker images are quite simple since PaddlePaddle can be installed by just running pip install. A sample Dockerfile is:

FROM nvidia/cuda:7.5-cudnn5-runtime-centos6
RUN yum install -y centos-release-SCL
RUN yum install -y python27
# This whl package is generated by previous build steps.
ADD python/dist/paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl /
RUN pip install /paddlepaddle-0.10.0-cp27-cp27mu-linux_x86_64.whl && rm -f /*.whl

Then build the image by running docker build -t [REPO]/paddle:[TAG] . under the directory containing your own Dockerfile.

We also release a script and Dockerfile for building PaddlePaddle docker images across different cuda versions. To build these docker images, run:

bash ./build_docker_images.sh
docker build -t [REPO]/paddle:tag -f [generated_docker_file] .
  • NOTE: note that you can choose different base images for your environment, you can find all the versions here.

Use Docker Images

Suppose that you have written an application program train.py using PaddlePaddle, we can test and run it using docker:

docker run --rm -it -v $PWD:/work paddlepaddle/paddle /work/a.py

But this works only if all dependencies of train.py are in the production image. If this is not the case, we need to build a new Docker image from the production image and with more dependencies installs.

Run PaddlePaddle Book In Docker

Our book repo also provide a docker image to start a jupiter notebook inside docker so that you can run this book using docker:

docker run -d -p 8888:8888 paddlepaddle/book

Please refer to https://github.com/paddlepaddle/book if you want to build this docker image by your self.

Run Distributed Applications

In our API design doc, we proposed an API that starts a distributed training job on a cluster. This API need to build a PaddlePaddle application into a Docker image as above and calls kubectl to run it on the cluster. This API might need to generate a Dockerfile look like above and call docker build.

Of course, we can manually build an application image and launch the job using the kubectl tool:

docker build -f some/Dockerfile -t myapp .
docker tag myapp me/myapp
docker push
kubectl ...

More Options

Build Without Docker

Follow the Dockerfile in the paddlepaddle repo to set up your local dev environment and run:

./paddle/scripts/paddle_build.sh build

Additional Tasks

You can get the help menu for the build scripts by running with no options:

./paddle/scripts/paddle_build.sh
or ./paddle/scripts/paddle_docker_build.sh