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:
- home: http://gw.tnode.com/docker/keras-full/
- github: http://github.com/gw0/docker-keras-full/
- technology: debian, keras, theano, tensorflow, openblas, cuda toolkit, python, numpy, h5py, matplotlib, jupyter
- docker hub: http://hub.docker.com/r/gw000/keras-full/
Available tags:
1.1.0,latest[2016-09-20]: Python 2.7/3.5 + Keras (1.1.0) + TensorFlow (0.10.0) + Theano (0.8.2) on CPU/GPU1.0.8[2016-08-28]: Python 2.7/3.5 + Keras (1.0.8) + TensorFlow (0.9.0) + Theano (0.8.2) on CPU/GPU1.0.6[2016-07-20]: Python 2.7/3.5 + Keras (1.0.6) + TensorFlow (0.9.0) + Theano (0.8.2) on CPU/GPU1.0.4[2016-06-16]: Python 2.7/3.5 + Keras (1.0.4) + TensorFlow (0.8.0) + Theano (0.8.2) on CPU/GPU
Quick experiment from console IPython:
$ docker run -it --rm gw000/keras-full ipythonTo start the Jupyter IPython web interface on http://<ip>:8888/ and notebooks stored in /srv/notebooks:
$ docker run -d -p=6006:6006 -p=8888:8888 -v=/srv/notebooks:/srv gw000/keras-fullTo 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-fullIf 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.
Copyright © 2016 gw0 [http://gw.tnode.com/] <gw.2016@tnode.com>
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.