提交 1c8a2ecb 编写于 作者: H Helin Wang 提交者: Yu Yang

update docker install English version

上级 8ce0008a
......@@ -53,12 +53,20 @@ Docker is simple as long as we understand a few basic concepts:
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
We package PaddlePaddle's compile environment into a Docker image,
called the develop image, it contains all compiling tools that
PaddlePaddle needs. We package compiled PaddlePaddle program into a
Docker image as well, called the production image, it contains all
runtime environment that running PaddlePaddle needs. For each version
of PaddlePaddle, we release both of them. Production image includes
CPU-only version and a CUDA GPU version and their no-AVX versions.
We put the docker images on `dockerhub.com
<https://hub.docker.com/r/paddledev/paddle/>`_. You can find the
latest versions under "tags" tab at dockerhub.com
latest versions under "tags" tab at dockerhub.com. If you are in
China, you can use our Docker image registry mirror to speed up the
download process. To use it, please replace all paddlepaddle/paddle in
the commands to docker.paddlepaddle.org/paddle.
1. Production images, this image might have multiple variants:
......@@ -179,59 +187,40 @@ 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:
1. Build PaddlePaddle develop image
.. 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
Use following command to build PaddlePaddle develop image:
.. 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
.. code-block:: bash
- or, we can run it as a daemon container
git clone https://github.com/PaddlePaddle/Paddle.git && cd Paddle
docker build -t paddle:dev .
.. code-block:: bash
2. Build PaddlePaddle production image
# 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
There are two steps for building production image, the first step is to run:
and SSH to this container using password :code:`root`:
.. code-block:: bash
.. code-block:: bash
ssh -p 2202 root@localhost
docker run -v $(pwd):/paddle -e "WITH_GPU=OFF" -e "WITH_AVX=OFF" -e "WITH_TEST=ON" paddle:dev
An advantage is that we can run the PaddlePaddle container on a
remote server and SSH to it from a laptop.
The above command will compile PaddlePaddle and create a Dockerfile for building production image. All the generated files are in the build directory. "WITH_GPU" controls if the generated production image supports GPU. "WITH_AVX" controls if the generated production image supports AVX. "WITH_TEST" controls if the unit test will be generated.
When developing PaddlePaddle, you can edit PaddlePaddle source code
from outside of docker container using your favoriate editor. To
compile PaddlePaddle, run inside container:
The second step is to run:
.. code-block:: bash
.. code-block:: bash
WITH_GPU=OFF WITH_AVX=ON WITH_TEST=ON bash /paddle/paddle/scripts/docker/build.sh
docker build -t paddle:prod -f build/Dockerfile .
This builds everything about Paddle in :code:`/paddle/build`. And we
can run unit tests there:
The above command will generate the production image by copying the compiled PaddlePaddle program into the image.
.. code-block:: bash
3. Run unit test
cd /paddle/build
ctest
Following command will run unit test:
When training model using C++ API, we can edit paddle program in
~/workspace outside of docker. And build from /workspace inside of
docker.
.. code-block:: bash
docker run -it -v $(pwd):/paddle paddle:dev bash -c "cd /paddle/build && ctest"
PaddlePaddle Book
------------------
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