提交 58d5be1a 编写于 作者: H Helin Wang

update docker tag, add translate for jupyter notebook

上级 438c3145
......@@ -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。
......@@ -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
......
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