Docker image: Full deep learning environment based on Keras and Jupyter using CPU or GPU
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README.md

README.md

docker-keras-full

docker-keras-full is a Docker image built from Debian 9 (amd64) with a full reproducible deep learning research environment based on Keras and Jupyter. It supports CPU and GPU processing with Theano and TensorFlow backends. It features Jupyter Notebook with Python 2 and 3 support and uses only Debian and Python packages (no manual installations).

Open source project:

Available tags:

  • 2.0.2, latest [2017-03-27]: Python 2.7/3.5 + Keras (2.0.2) + TensorFlow (1.0.1) + Theano (0.9.0) on CPU/GPU
  • 1.2.0 [2016-12-21]: Python 2.7/3.5 + Keras (1.2.0) + TensorFlow (0.12.0) + Theano (0.8.2) on CPU/GPU
  • 1.1.0 [2016-09-20]: Python 2.7/3.5 + Keras (1.1.0) + TensorFlow (0.10.0) + Theano (0.8.2) on CPU/GPU
  • 1.0.8 [2016-08-28]: Python 2.7/3.5 + Keras (1.0.8) + TensorFlow (0.9.0) + Theano (0.8.2) on CPU/GPU
  • 1.0.6 [2016-07-20]: Python 2.7/3.5 + Keras (1.0.6) + TensorFlow (0.9.0) + Theano (0.8.2) on CPU/GPU
  • 1.0.4 [2016-06-16]: Python 2.7/3.5 + Keras (1.0.4) + TensorFlow (0.8.0) + Theano (0.8.2) on CPU/GPU

Usage

Quick experiment from console with IPython 2.7 or 3.5:

$ docker run -it --rm gw000/keras-full ipython2
$ docker run -it --rm gw000/keras-full ipython3

To start the Jupyter IPython web interface on http://<ip>:8888/ (password: keras) and notebooks stored in /srv/notebooks:

$ docker run -d -p=6006:6006 -p=8888:8888 -v=/srv/notebooks:/srv gw000/keras-full

To utilize your GPUs this Docker image needs access to your /dev/nvidia* devices (see docker-debian-cuda), like:

$ docker run -d $(ls /dev/nvidia* | xargs -I{} echo '--device={}') -p=6006:6006 -p=8888:8888 -v=/srv/notebooks:/srv gw000/keras-full

To change the default password, prepare a new hashed password and pass it as an environment variable:

$ docker run -d -p=6006:6006 -p=8888:8888 -e PASSWD="sha1:..." -v=/srv/notebooks:/srv gw000/keras-full

Feedback

If you encounter any bugs or have feature requests, please file them in the issue tracker or even develop it yourself and submit a pull request over GitHub.

License

Copyright © 2016-2017 gw0 [http://gw.tnode.com/] <gw.2017@ena.one>

This library is licensed under the GNU Affero General Public License 3.0+ (AGPL-3.0+). Note that it is mandatory to make all modifications and complete source code of this library publicly available to any user.