@@ -8,199 +8,255 @@ Please be aware that you will need to change `Dockers settings
...
@@ -8,199 +8,255 @@ Please be aware that you will need to change `Dockers settings
<https://github.com/PaddlePaddle/Paddle/issues/627>`_ to make full use
<https://github.com/PaddlePaddle/Paddle/issues/627>`_ to make full use
of your hardware resource on Mac OS X and Windows.
of your hardware resource on Mac OS X and Windows.
Working With Docker
-------------------
Docker is simple as long as we understand a few basic concepts:
- *image*: A Docker image is a pack of software. It could contain one or more programs and all their dependencies. For example, the PaddlePaddle's Docker image includes pre-built PaddlePaddle and Python and many Python packages. We can run a Docker image directly, other than installing all these software. We can type
.. code-block:: bash
docker images
to list all images in the system. We can also run
.. code-block:: bash
docker pull paddlepaddle/paddle:0.10.0rc2
to download a Docker image, paddlepaddle/paddle in this example,
from Dockerhub.com.
- *container*: considering a Docker image a program, a container is a
"process" that runs the image. Indeed, a container is exactly an
operating system process, but with a virtualized filesystem, network
port space, and other virtualized environment. We can type
.. code-block:: bash
docker run paddlepaddle/paddle:0.10.0rc2
to start a container to run a Docker image, paddlepaddle/paddle in this example.
- By default docker container have an isolated file system namespace,
we can not see the files in the host file system. By using *volume*,
mounted files in host will be visible inside docker container.
Following command will mount current dirctory into /data inside
docker container, run docker container from debian image with
command :code:`ls /data`.
.. code-block:: bash
docker run --rm -v $(pwd):/data debian ls /data
Usage of CPU-only and GPU Images
Usage of CPU-only and GPU Images
----------------------------------
----------------------------------
For each version of PaddlePaddle, we release 2 types of Docker images: development
For each version of PaddlePaddle, we release two types of Docker images:
image and production image. Production image includes CPU-only version and a CUDA
development image and production image. Production image includes
GPU version and their no-AVX versions. We put the docker images on
CPU-only version and a CUDA GPU version and their no-AVX versions. We
`dockerhub.com <https://hub.docker.com/r/paddledev/paddle/>`_. You can find the
put the docker images on `dockerhub.com
latest versions under "tags" tab at dockerhub.com.
<https://hub.docker.com/r/paddledev/paddle/>`_. You can find the
1. development image :code:`paddlepaddle/paddle:<version>-dev`
latest versions under "tags" tab at dockerhub.com
This image has packed related develop tools and runtime environment. Users and
1. Production images, this image might have multiple variants:
developers can use this image instead of their own local computer to accomplish
development, build, releasing, document writing etc. While different version of
paddle may depends on different version of libraries and tools, if you want to
setup a local environment, you must pay attention to the versions.
The development image contains:
- gcc/clang
- nvcc
- Python
- sphinx
- woboq
- sshd
Many developers use servers with GPUs, they can use ssh to login to the server
and run :code:`docker exec` to enter the docker container and start their work.
Also they can start a development docker image with SSHD service, so they can login to
the container and start work.
To run the CPU-only image as an interactive container:
docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:<version>-gpu
Run the program using docker:
3. Use production image to release you AI application
.. code-block:: bash
Suppose that we have a simple application program in :code:`a.py`, we can test and run it using the production image:
```bash
docker run --rm -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2 python /workspace/example.py
docker run -it -v $PWD:/work paddle /work/a.py
```
But this works only if all dependencies of :code:`a.py` are in the production image. If this is not the case, we need to build a new Docker image from the production image and with more dependencies installs.
Or if you are using GPU for training:
.. code-block:: bash
PaddlePaddle Book
nvidia-docker run --rm -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2-gpu python /workspace/example.py
------------------
The Jupyter Notebook is an open-source web application that allows
Above commands will start a docker container by running :code:`python
you to create and share documents that contain live code, equations,
/workspace/example.py`. It will stop once :code:`python
visualizations and explanatory text in a single browser.
/workspace/example.py` finishes.
PaddlePaddle Book is an interactive Jupyter Notebook for users and developers.
Another way is to tell docker to start a :code:`/bin/bash` session and
We already exposed port 8888 for this book. If you want to
run PaddlePaddle program interactively:
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 -it -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2 /bin/bash
# now we are inside docker container
cd /workspace
python example.py
Running with GPU is identical:
.. code-block:: bash
.. code-block:: bash
docker run -p 8888:8888 paddlepaddle/book
nvidia-docker run -it -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2-gpu /bin/bash
# now we are inside docker container
cd /workspace
python example.py
Then, you would back and paste the address into the local browser:
