Run in Docker Containers ================================= Run PaddlePaddle in Docker container so that you don't need to care about runtime dependencies, also you can run under Windows system. You can get tutorials at `here `_ . If you are using Windows, please refer to `this `_ tutorial to start running docker under windows. After you've read above tutorials you may proceed the following steps. .. _docker_pull: Pull PaddlePaddle Docker Image ------------------------------ Run the following command to download the latest Docker images: .. code-block:: bash docker pull paddlepaddle/paddle For users in China, we provide a faster mirror: .. code-block:: bash docker pull docker.paddlepaddle.org/paddle Download GPU version images: .. code-block:: bash docker pull paddlepaddle/paddle:latest-gpu docker pull docker.paddlepaddle.org/paddle:latest-gpu Choose between different BLAS version: .. code-block:: bash # image using MKL by default docker pull paddlepaddle/paddle # image using OpenBLAS docker pull paddlepaddle/paddle:latest-openblas If you want to use legacy versions, choose a tag from `DockerHub `_ and run: .. code-block:: bash docker pull paddlepaddle/paddle:[tag] # i.e. docker pull docker.paddlepaddle.org/paddle:0.10.0-gpu .. _docker_run: Launch your training program in Docker ------------------------------ Assume that you have already written a PaddlePaddle program named :code:`train.py` under directory :code:`/home/work` (refer to `PaddlePaddleBook `_ for more samples), then run the following command: .. code-block:: bash cd /home/work docker run -it -v $PWD:/work paddlepaddle/paddle /work/train.py In the above command, :code:`-it` means run the container interactively; :code:`-v $PWD:/work` means mount the current directory ($PWD will expand to current absolute path in Linux) under :code:`/work` in the container. :code:`paddlepaddle/paddle` to specify image to use; finnally :code:`/work/train.py` is the command to run inside docker. Also, you can go into the container shell, run or debug your code interactively: .. code-block:: bash docker run -it -v $PWD:/work paddlepaddle/paddle /bin/bash cd /work python train.py **NOTE: We did not install vim in the default docker image to reduce the image size, you can run** :code:`apt-get install -y vim` **to install it if you need to edit python files.** .. _docker_run_book: PaddlePaddle Book ------------------ You can create a container serving PaddlePaddle Book using Jupyter Notebook in one minute using Docker. PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.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! .. _docker_run_gpu: Train with Docker with GPU ------------------------------ We recommend using `nvidia-docker `_ to run GPU training jobs. Please ensure you have latest GPU driver installed before move on. .. code-block:: bash nvidia-docker run -it -v $PWD:/work paddlepaddle/paddle:latest-gpu /bin/bash **NOTE: If you don't have nvidia-docker installed, try the following method to mount CUDA libs and devices into the container.** .. code-block:: bash export CUDA_SO="$(\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 paddlepaddle/paddle:latest-gpu **About AVX:** AVX is a kind of CPU instruction can accelerate PaddlePaddle's calculations. The latest PaddlePaddle Docker image turns AVX on by default, so, if your computer doesn't support AVX, you'll probably need to `build <./build_from_source_en.html>`_ with :code:`WITH_AVX=OFF`. The following command will tell you whether your computer supports AVX. .. code-block:: bash if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi