docker run ${CUDA_SO} ${DEVICES} -it paddledev/paddle:<version>-gpu
emacs ~/workspace/example.py
Run the program using docker:
3. Use production image to release you AI application
Suppose that we have a simple application program in :code:`a.py`, we can test and run it using the production image:
.. code-block:: bash
```bash
docker run -it -v $PWD:/work paddle /work/a.py
```
docker run -it --rm -v ~/workspace:/workspace paddlepaddle/paddle:0.10.0rc2 python /workspace/example.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 -it --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
you to create and share documents that contain live code, equations,
visualizations and explanatory text in a single browser.
Above commands will start a docker container by running :code:`python
/workspace/example.py`. It will stop once :code:`python
/workspace/example.py` finishes.
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.
Another way is to tell docker to start a :code:`/bin/bash` session and
run PaddlePaddle program interactively:
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
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:
