PaddlePaddle in Docker Containers

Docker container is currently the only officially-supported way to running PaddlePaddle. This is reasonable as Docker now runs on all major operating systems including Linux, Mac OS X, and Windows. Please be aware that you will need to change Dockers settings to make full use of your hardware resource on Mac OS X and Windows.

Usage of CPU-only and GPU Images

For each version of PaddlePaddle, we release 2 types of Docker images: development image and production image. Production image includes CPU-only version and a CUDA GPU version and their no-AVX versions. We put the docker images on dockerhub.com. You can find the latest versions under “tags” tab at dockerhub.com. 1. development image paddlepaddle/paddle:<version>-dev

This image has packed related develop tools and runtime environment. Users and developers can use this image instead of their own local computer to accomplish development, build, releasing, document writing etc. While different version of paddle may depends on different version of libraries and tools, if you want to setup a local environment, you must pay attention to the versions. The development image contains: - gcc/clang - nvcc - Python - sphinx - woboq - sshd Many developers use servers with GPUs, they can use ssh to login to the server and run docker exec to enter the docker container and start their work. Also they can start a development docker image with SSHD service, so they can login to the container and start work.

To run the CPU-only image as an interactive container:

docker run -it --rm paddledev/paddle:<version> /bin/bash

or, we can run it as a daemon container

docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:<version>

and SSH to this container using password root:

ssh -p 2202 root@localhost

An advantage of using SSH is that we can connect to PaddlePaddle from more than one terminals. For example, one terminal running vi and another one running Python interpreter. Another advantage is that we can run the PaddlePaddle container on a remote server and SSH to it from a laptop.

2. Production images, this image might have multiple variants:

   • GPU/AVX：paddlepaddle/paddle:<version>-gpu
   • GPU/no-AVX：paddlepaddle/paddle:<version>-gpu-noavx
   • CPU/AVX：paddlepaddle/paddle:<version>
   • CPU/no-AVX：paddlepaddle/paddle:<version>-noavx

   Please be aware that the CPU-only and the GPU images both use the AVX instruction set, but old computers produced before 2008 do not support AVX. The following command checks if your Linux computer supports AVX:

   if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
   
   
   If it doesn't, we will use the non-AVX images.
   

   Notice please don’t forget to install CUDA driver and let Docker knows about it:

   export CUDA_SO="$(\ls /usr/lib64/libcuda* | xargs -I{} echo '-v {}:{}') $(\ls /usr/lib64/libnvidia* | xargs -I{} echo '-v {}:{}')"
   export DEVICES=$(\ls /dev/nvidia* | xargs -I{} echo '--device {}:{}')
   docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:<version>-gpu
   

3. Use production image to release you AI application

   Suppose that we have a simple application program in a.py, we can test and run it using the production image:

   `bash docker run -it -v $PWD:/work paddle /work/a.py `

   But this works only if all dependencies of a.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.

PaddlePaddle Book

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text in a single browser.

PaddlePaddle Book is an interactive Jupyter Notebook for users and developers. We already exposed port 8888 for this book. If you want to dig deeper into deep learning, PaddlePaddle Book definitely is your best choice.

We provide a packaged book image, simply issue the command:

docker run -p 8888:8888 paddlepaddle/book

Then, you would back and paste the address into the local browser:

http://localhost:8888/

That’s all. Enjoy your journey!

Development Using Docker

Developers can work on PaddlePaddle using Docker. This allows developers to work on different platforms – Linux, Mac OS X, and Windows – in a consistent way.

1. Build the Development Docker Image

   git clone --recursive https://github.com/PaddlePaddle/Paddle
   cd Paddle
   docker build -t paddle:dev .
   

   Note that by default docker build wouldn’t import source tree into the image and build it. If we want to do that, we need docker the development docker image and then run the following command:

   docker run -v $PWD:/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=ON" -e "TEST=OFF" paddle:dev
   

2. Run the Development Environment

   Once we got the image paddle:dev, we can use it to develop Paddle by mounting the local source code tree into a container that runs the image:

   docker run -d -p 2202:22 -p 8888:8888 -v $PWD:/paddle paddle:dev sshd
   

   This runs a container of the development environment Docker image with the local source tree mounted to /paddle of the container.

   The above docker run commands actually starts an SSHD server listening on port 2202. This allows us to log into this container with:

   ssh root@localhost -p 2202
   

   Usually, I run above commands on my Mac. I can also run them on a GPU server xxx.yyy.zzz.www and ssh from my Mac to it:

   my-mac$ ssh root@xxx.yyy.zzz.www -p 2202
   

3. Build and Install Using the Development Environment

   Once I am in the container, I can use paddle/scripts/docker/build.sh to build, install, and test Paddle:

   /paddle/paddle/scripts/docker/build.sh
   

   This builds everything about Paddle in /paddle/build. And we can run unit tests there:

   cd /paddle/build
   ctest
   

Documentation

Paddle Docker images include an HTML version of C++ source code generated using woboq code browser. This makes it easy for users to browse and understand the C++ source code.

As long as we give the Paddle Docker container a name, we can run an additional Nginx Docker container to serve the volume from the Paddle container:

docker run -d --name paddle-cpu-doc paddle:<version>
docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx

Then we can direct our Web browser to the HTML version of source code at http://localhost:8088/paddle/