提交 9201a4c2 编写于 作者: L liaogang

Change the order of docker doc session

上级 dbc87e3e
......@@ -4,138 +4,137 @@ PaddlePaddle的Docker容器使用方式
PaddlePaddle目前唯一官方支持的运行的方式是Docker容器。因为Docker能在所有主要操作系统(包括Linux,Mac OS X和Windows)上运行。 请注意,您需要更改 `Dockers设置 <https://github.com/PaddlePaddle/Paddle/issues/627>`_ 才能充分利用Mac OS X和Windows上的硬件资源。
通过Docker容器开发PaddlePaddle
------------------------------
纯CPU和GPU的docker镜像
----------------------
开发人员可以在Docker中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux,Mac OS X和Windows。
对于每一个PaddlePaddle版本,我们都会发布两个Docker镜像:纯CPU的和GPU的。
我们通过设置 `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_ 自动生成最新的docker镜像:
`paddledev/paddle:0.10.0rc1-cpu` 和 `paddledev/paddle:0.10.0rc1-gpu`。
1. 将开发环境构建为Docker镜像
.. code-block:: bash
以交互容器方式运行纯CPU的镜像:
git clone --recursive https://github.com/PaddlePaddle/Paddle
cd Paddle
docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
.. code-block:: bash
docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
请注意,默认情况下,:code:`docker build` 不会将源码导入到镜像中并编译它。如果我们想这样做,需要设置一个参数
或者,可以以后台进程方式运行容器
.. code-block:: bash
.. code-block:: bash
docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
docker run -d -p 2202:22 -p 8888:8888 paddledev/paddle:0.10.0rc1-cpu
然后用密码 :code:`root` SSH进入容器:
2. 运行开发环境
.. code-block:: bash
当我们编译好了 :code:`paddle:dev`, 我们可以在docker容器里做开发,源代码可以通过挂载本地文件来被载入Docker的开发环境里面:
.. code-block:: bash
ssh -p 2202 root@localhost
docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
SSH方式的一个优点是我们可以从多个终端进入容器。比如,一个终端运行vi,另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上,并在笔记本上通过SSH与其连接。
以上代码会启动一个带有PaddlePaddle开发环境的docker容器,源代码会被挂载到 :code:`/paddle` 。
请注意, :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样,我们就能SSH进入我们的开发容器了:
.. code-block:: bash
以上方法在GPU镜像里也能用-只是请不要忘记按装CUDA驱动,以及告诉Docker:
ssh root@localhost -p 2202
.. code-block:: bash
3. 在Docker开发环境中编译与安装PaddlPaddle代码
export CUDA_SO="$(\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:0.10.0rc1-gpu
当在容器里面的时候,可以用脚本 :code:`paddle/scripts/docker/build.sh` 来编译、安装与测试PaddlePaddle:
.. code-block:: bash
/paddle/paddle/scripts/docker/build.sh
以上指令会在 :code:`/paddle/build` 中编译PaddlePaddle。通过以下指令可以运行单元测试:
.. code-block:: bash
运行PaddlePaddle书籍
---------------------
cd /paddle/build
ctest
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。
4. 在Docker容器中运行PaddlePaddle书籍
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。
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。
当您进入容器内之后,只用运行以下命令:
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
.. code-block:: bash
jupyter notebook
然后在浏览器中输入以下网址:
.. code-block:: text
然后在浏览器中输入以下网址:
.. code-block:: text
http://localhost:8888/
http://localhost:8888/
就这么简单,享受您的旅程!
就这么简单,享受您的旅程!
纯CPU和GPU的docker镜像
----------------------
非AVX镜像
---------
对于每一个PaddlePaddle版本,我们都会发布两个Docker镜像:纯CPU的和GPU的。我们通过设置 `dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_ 自动运行以下两个命令
纯CPU镜像以及GPU镜像都会用到AVX指令集,但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX
.. code-block:: bash
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 .
if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
以交互容器方式运行纯CPU的镜像:
如果输出是No,我们就需要手动编译一个非AVX版本的镜像:
.. code-block:: bash
docker run -it --rm paddledev/paddle:0.10.0rc1-cpu /bin/bash
cd ~
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
或者,可以以后台进程方式运行容器:
.. code-block:: bash
通过Docker容器开发PaddlePaddle
------------------------------
docker run -d -p 2202:22 paddledev/paddle:0.10.0rc1-cpu
开发人员可以在Docker中开发PaddlePaddle。这样开发人员可以以一致的方式在不同的平台上工作 - Linux,Mac OS X和Windows。
然后用密码 :code:`root` SSH进入容器:
1. 将开发环境构建为Docker镜像
.. code-block:: bash
.. code-block:: bash
git clone --recursive https://github.com/PaddlePaddle/Paddle
cd Paddle
docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile .
ssh -p 2202 root@localhost
SSH方式的一个优点是我们可以从多个终端进入容器。比如,一个终端运行vi,另一个终端运行Python。另一个好处是我们可以把PaddlePaddle容器运行在远程服务器上,并在笔记本上通过SSH与其连接。
请注意,默认情况下,:code:`docker build` 不会将源码导入到镜像中并编译它。如果我们想这样做,需要设置一个参数:
.. code-block:: bash
以上方法在GPU镜像里也能用-只是请不要忘记按装CUDA驱动,以及告诉Docker:
docker build -t paddle:dev -f paddle/scripts/docker/Dockerfile --build-arg BUILD_AND_INSTALL=ON .
.. 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 paddledev/paddle:0.10.0rc1-gpu
2. 运行开发环境
当我们编译好了 :code:`paddle:dev`, 我们可以在docker容器里做开发,源代码可以通过挂载本地文件来被载入Docker的开发环境里面:
.. code-block:: bash
非AVX镜像
---------
docker run -d -p 2202:22 -v $PWD:/paddle paddle:dev
纯CPU镜像以及GPU镜像都会用到AVX指令集,但是2008年之前生产的旧电脑不支持AVX。以下指令能检查Linux电脑是否支持AVX:
以上代码会启动一个带有PaddlePaddle开发环境的docker容器,源代码会被挂载到 :code:`/paddle` 。
请注意, :code:`paddle:dev` 的默认入口是 :code:`sshd` 。以上的 :code:`docker run` 命令其实会启动一个在2202端口监听的SSHD服务器。这样,我们就能SSH进入我们的开发容器了:
.. code-block:: bash
.. code-block:: bash
ssh root@localhost -p 2202
if cat /proc/cpuinfo | grep -i avx; then echo Yes; else echo No; fi
3. 在Docker开发环境中编译与安装PaddlPaddle代码
如果输出是No,我们就需要手动编译一个非AVX版本的镜像:
当在容器里面的时候,可以用脚本 :code:`paddle/scripts/docker/build.sh` 来编译、安装与测试PaddlePaddle:
.. code-block:: bash
/paddle/paddle/scripts/docker/build.sh
.. code-block:: bash
以上指令会在 :code:`/paddle/build` 中编译PaddlePaddle。通过以下指令可以运行单元测试:
.. code-block:: bash
cd ~
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
cd /paddle/build
ctest
文档
......
......@@ -9,6 +9,100 @@ Please be aware that you will need to change `Dockers settings
of your hardware resource on Mac OS X and Windows.
CPU-only and GPU Images
------------------------
For each version of PaddlePaddle, we release 2 Docker images, a
CPU-only one and a CUDA GPU one. We do so by configuring
`dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_
automatically generate the latest docker images `paddledev/paddle:0.10.0rc1-cpu`
and `paddledev/paddle:0.10.0rc1-gpu`.
To run the CPU-only image as an interactive container:
.. code-block:: 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 -p 8888:8888 paddledev/paddle:0.10.0rc1-cpu
and SSH to this container using password :code:`root`:
.. code-block:: bash
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.
Above methods work with the GPU image too -- just please don't forget
to install CUDA driver and let Docker knows about it:
.. 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 paddledev/paddle:0.10.0rc1-gpu
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.
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:: text
http://localhost:8888/
That's all. Enjoy your journey!
Non-AVX Images
--------------
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
If it doesn't, we will need to build non-AVX images manually from
source code:
.. code-block:: bash
cd ~
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
Development Using Docker
------------------------
......@@ -82,103 +176,6 @@ Windows -- in a consistent way.
cd /paddle/build
ctest
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.
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:
.. code-block:: bash
jupyter notebook
Then, you would back and paste the address into the local browser:
.. code-block:: text
http://localhost:8888/
That's all. Enjoy your journey!
CPU-only and GPU Images
-----------------------
For each version of PaddlePaddle, we release 2 Docker images, a
CPU-only one and a CUDA GPU one. We do so by configuring
`dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_
automatically runs the following commands:
.. code-block:: bash
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: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:0.10.0rc1-cpu
and SSH to this container using password :code:`root`:
.. code-block:: bash
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.
Above methods work with the GPU image too -- just please don't forget
to install CUDA driver and let Docker knows about it:
.. 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 paddledev/paddle:0.10.0rc1-gpu
Non-AVX Images
--------------
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
If it doesn't, we will need to build non-AVX images manually from
source code:
.. code-block:: bash
cd ~
git clone https://github.com/PaddlePaddle/Paddle.git
cd Paddle
docker build --build-arg WITH_AVX=OFF -t paddle:cpu-noavx -f paddle/scripts/docker/Dockerfile .
docker build --build-arg WITH_AVX=OFF -t paddle:gpu-noavx -f paddle/scripts/docker/Dockerfile.gpu .
Documentation
-------------
......
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