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. Working With Docker ------------------- Docker is simple as long as we understand a few basic concepts: - *container*: considering a Docker image a program, a container is a "process" that runs the image. Indeed, a container is exactly an operating system process, but with a virtualized filesystem, network port space, and other virtualized environment. We can type .. code-block:: bash docker run paddlepaddle/paddle:0.10.0rc2 to start a container to run a Docker image, paddlepaddle/paddle in this example. - *image*: A Docker image is a pack of software. It could contain one or more programs and all their dependencies. For example, the PaddlePaddle's Docker image includes pre-built PaddlePaddle and Python and many Python packages. We can run a Docker image directly, other than installing all these software. We can type .. code-block:: bash docker images to list all images in the system. We can also run .. code-block:: bash docker pull paddlepaddle/paddle:0.10.0rc2 to download a Docker image, paddlepaddle/paddle in this example, from Dockerhub.com. - By default docker container have an isolated file system namespace, we can not see the files in the host file system. By using *volume*, mounted files in host will be visible inside docker container. Following command will mount current dirctory into /data inside docker container, run docker container from debian image with command :code:`ls /data`. .. code-block:: bash docker run --rm -v $(pwd):/data debian ls /data Usage of CPU-only and GPU Images ---------------------------------- For each version of PaddlePaddle, we release two 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. Production images, this image might have multiple variants: - GPU/AVX：:code:`paddlepaddle/paddle:-gpu` - GPU/no-AVX：:code:`paddlepaddle/paddle:-gpu-noavx` - CPU/AVX：:code:`paddlepaddle/paddle:` - CPU/no-AVX：:code:`paddlepaddle/paddle:-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: .. code-block:: bash if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi To run the CPU-only image as an interactive container: .. code-block:: bash docker run -it --rm paddlepaddle/paddle:0.10.0rc2 /bin/bash Above method work with the GPU image too -- the recommended way is using `nvidia-docker `_. Please install nvidia-docker first following this `tutorial `_. Now you can run a GPU image: .. code-block:: bash nvidia-docker run -it --rm paddlepaddle/paddle:0.10.0rc2-gpu /bin/bash 2. development image :code:`paddlepaddle/paddle:-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 :code:`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. Train Model Using Python API ---------------------------- Our official docker image provides a runtime for PaddlePaddle programs. The typical workflow will be as follows: Create a directory as workspace: .. code-block:: bash mkdir ~/workspace Edit a PaddlePaddle python program using your favourite editor .. code-block:: bash emacs ~/workspace/example.py Run the program using docker: .. code-block:: bash docker run -it --rm -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2 python /workspace/example.py Or if you are using GPU for training: .. code-block:: bash nvidia-docker run -it --rm -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2-gpu python /workspace/example.py Above commands will start a docker container by running :code:`python /workspace/example.py`. It will stop once :code:`python /workspace/example.py` finishes. Another way is to tell docker to start a :code:`/bin/bash` session and run PaddlePaddle program interactively: .. code-block:: bash docker run -it -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2 /bin/bash # now we are inside docker container cd /workspace python example.py Running with GPU is identical: .. code-block:: bash nvidia-docker run -it -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2-gpu /bin/bash # now we are inside docker container cd /workspace python example.py Develop PaddlePaddle or Train Model Using C++ API --------------------------------------------------- We will be using PaddlePaddle development image since it contains all compiling tools and dependencies. Let's clone PaddlePaddle repo first: .. code-block:: bash git clone https://github.com/PaddlePaddle/Paddle.git && cd Paddle Mount both workspace folder and paddle code folder into docker container, so we can access them inside docker container. There are two ways of using PaddlePaddle development docker image: - run interactive bash directly .. code-block:: bash # use nvidia-docker instead of docker if you need to use GPU docker run -it -v ~/workspace:/workspace -v $(pwd):/paddle paddlepaddle/paddle:0.10.0rc2-dev /bin/bash # now we are inside docker container - or, we can run it as a daemon container .. code-block:: bash # use nvidia-docker instead of docker if you need to use GPU docker run -d -p 2202:22 -p 8888:8888 -v ~/workspace:/workspace -v $(pwd):/paddle paddlepaddle/paddle:0.10.0rc2-dev /usr/sbin/sshd -D and SSH to this container using password :code:`root`: .. code-block:: bash ssh -p 2202 root@localhost An advantage is that we can run the PaddlePaddle container on a remote server and SSH to it from a laptop. When developing PaddlePaddle, you can edit PaddlePaddle source code from outside of docker container using your favoriate editor. To compile PaddlePaddle, run inside container: .. code-block:: bash WITH_GPU=OFF WITH_AVX=ON WITH_TEST=ON bash /paddle/paddle/scripts/docker/build.sh This builds everything about Paddle in :code:`/paddle/build`. And we can run unit tests there: .. code-block:: bash cd /paddle/build ctest When training model using C++ API, we can edit paddle program in ~/workspace outside of docker. And build from /workspace inside of docker. 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: .. code-block:: bash docker run -p 8888:8888 paddlepaddle/book Then, you would back and paste the address into the local browser: .. code-block:: text http://localhost:8888/ That's all. Enjoy your journey! 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: .. code-block:: bash docker run -d --name paddle-cpu-doc paddle: 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/