gw000/keras-full repository overview

⁠docker-keras-full

docker-keras-full is a Docker⁠ image built from Debian 9 (amd64) with a deep learning research environment based on Keras⁠ and Jupyter⁠. It supports CPU and GPU processing with TensorFlow⁠, Theano⁠ and CNTK⁠ 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, cntk, openblas, cuda toolkit, python, python3, numpy, h5py, jupyter, tensorboard, matplotlib, pillow, pandas, scikit-learn, statsmodels
• docker hub: http://hub.docker.com/r/gw000/keras-full/⁠

Available tags:

• 2.1.4, latest [2018-02-15]: Python 2.7/3.5 + Keras (2.1.4) + TensorFlow (1.5.0) + Theano (1.0.1) + CNTK (2.4) on CPU/GPU
• 2.1.1 [2017-12-01]: Python 2.7/3.5 + Keras (2.1.1) + TensorFlow (1.4.0) + Theano (1.0.0) + CNTK (2.3) on CPU/GPU
• 2.0.2 [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 web interface on http://<ip>:8888/ (password: keras) and notebooks stored in current directory (will be mapped to /srv):

$ docker run -d -p 8888:8888 -v $(pwd):/srv gw000/keras-full

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

$ docker run -d $(ls /dev/nvidia* | xargs -I{} echo '--device={}') $(ls /usr/lib/*-linux-gnu/{libcuda,libnvidia}* | xargs -I{} echo '-v {}:{}:ro') -p 8888:8888 -v $(pwd):/srv gw000/keras-full

To change the default password and token authentication, prepare a new hashed password⁠ and a token string and pass it as environment variables:

$ docker run -d -p 8888:8888 -e PASSWD="sha1:..." -e TOKEN="..." -v $(pwd):/srv gw000/keras-full

If the TensorFlow backend is used, it is possible to start the TensorBoard visualization tool directly from the Jupyter web interface (see usage⁠).

⁠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 © 2016-2018 gw0 [http://gw.tnode.com/⁠] <[email protected]⁠>

All code 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 publicly available to any user.