From 58d5be1ab663de2cc53acae4e518d0e233f3eaaa Mon Sep 17 00:00:00 2001 From: Helin Wang Date: Thu, 9 Mar 2017 09:09:30 -0800 Subject: [PATCH] update docker tag, add translate for jupyter notebook --- .../build_and_install/docker_install_cn.rst | 32 +++++++++++--- .../build_and_install/docker_install_en.rst | 42 +++++++++---------- 2 files changed, 47 insertions(+), 27 deletions(-) diff --git a/doc/getstarted/build_and_install/docker_install_cn.rst b/doc/getstarted/build_and_install/docker_install_cn.rst index 78f518cfe49..cf7dddd073c 100644 --- a/doc/getstarted/build_and_install/docker_install_cn.rst +++ b/doc/getstarted/build_and_install/docker_install_cn.rst @@ -56,6 +56,26 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do cd /paddle/build ctest +4. 在Docker容器中运行PaddlePaddle书籍 + + Jupyter Notebook是一个开源的web程序，大家可以通过它制作和分享带有代码、公式、图表、文字的交互式文档。用户可以通过网页浏览文档。 + + PaddlePaddle书籍是为用户和开发者制作的一个交互式的Jupyter Nodebook。 + 如果您想要更深入了解deep learning，PaddlePaddle书籍一定是您最好的选择。 + + 当您进入容器内之后，只用运行以下命令： + + .. code-block:: bash + + jupyter notebook + + 然后在浏览器中输入以下网址： + + .. code-block:: text + + http://localhost:8888/ + + 就这么简单，享受您的旅程！ 纯CPU和GPU的docker镜像 ---------------------- @@ -64,20 +84,20 @@ PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Do .. code-block:: bash - docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile . - docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . + docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON . + docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON . 以交互容器方式运行纯CPU的镜像： .. code-block:: bash - docker run -it --rm paddledev/paddle:cpu-latest /bin/bash + docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash 或者，可以以后台进程方式运行容器： .. code-block:: bash - docker run -d -p 2202:22 paddledev/paddle:cpu-latest + docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu 然后用密码 :code:`root` SSH进入容器： @@ -94,7 +114,7 @@ SSH方式的一个优点是我们可以从多个终端进入容器。比如， 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:gpu-latest + docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu 非AVX镜像 @@ -128,7 +148,7 @@ Paddle的Docker镜像带有一个通过 `woboq code browser .. code-block:: bash - docker run -d --name paddle-cpu-doc paddle:cpu + docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx 接着我们就能够打开浏览器在 http://localhost:8088/paddle/ 浏览代码。 diff --git a/doc/getstarted/build_and_install/docker_install_en.rst b/doc/getstarted/build_and_install/docker_install_en.rst index a92201c618c..a4f62b28356 100644 --- a/doc/getstarted/build_and_install/docker_install_en.rst +++ b/doc/getstarted/build_and_install/docker_install_en.rst @@ -84,27 +84,27 @@ Windows -- in a consistent way. 4. Run PaddlePaddle Book under Docker Container - 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. + 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. + 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. - Once you are inside the container, simply issue the command: + Once you are inside the container, simply issue the command: - .. code-block:: bash - - jupyter notebook - - Then, you would back and paste the address into the local browser: + .. code-block:: bash + + jupyter notebook - .. code-block:: text + Then, you would back and paste the address into the local browser: + + .. code-block:: text - http://localhost:8888/ + http://localhost:8888/ - That's all. Enjoy your journey! + That's all. Enjoy your journey! CPU-only and GPU Images ----------------------- @@ -116,21 +116,21 @@ automatically runs the following commands: .. code-block:: bash - docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile . - docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu . + docker build -t paddle:cpu -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON . + docker build -t paddle:gpu -f paddle/scripts/docker/Dockerfile.gpu --build-arg BUILD_AND_INSTALL=ON . To run the CPU-only image as an interactive container: .. code-block:: bash - docker run -it --rm paddledev/paddle:cpu-latest /bin/bash + docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash or, we can run it as a daemon container .. code-block:: bash - docker run -d -p 2202:22 paddledev/paddle:cpu-latest + docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu and SSH to this container using password :code:`root`: @@ -152,7 +152,7 @@ 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:gpu-latest + docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:0.10.0rc1-gpu Non-AVX Images @@ -194,7 +194,7 @@ container: .. code-block:: bash - docker run -d --name paddle-cpu-doc paddle:cpu + docker run -d --name paddle-cpu-doc paddle:0.10.0rc1-cpu docker run -d --volumes-from paddle-cpu-doc -p 8088:80 nginx -- GitLab