Docker keras

Keras in Docker for reproducible deep learning on CPU or GPU

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docker-keras is a minimal Docker image built from Debian 9 (amd64) for reproducible deep learning based on Keras. It features minimal images for Python 2 or 3, TensorFlow or Theano backends, processing on CPU or GPU, and uses only Debian and Python packages (no manual installations).

Open source project:

Available tags:

  • 1.2.1-py2, 1.2.1-cpu, 1.2.1, latest points to 1.2.1-py2-tf-cpu
  • 1.2.1-py3 points to 1.2.1-py3-tf-cpu
  • 1.2.1-gpu points to 1.2.1-py2-tf-gpu
  • 1.2.1-py2-tf-cpu/1.2.1-py2-tf-gpu [2017-01-20]: Python 2.7 + Keras (1.2.1) + TensorFlow (0.12.1) on CPU/GPU (Dockerfile.py2-tf-cpu/.py2-tf-gpu)
  • 1.2.1-py2-th-cpu/1.2.1-py2-th-gpu [2017-01-20]: Python 2.7 + Keras (1.2.1) + Theano (0.8.2) on CPU/GPU (Dockerfile.py2-th-cpu/.py2-th-gpu)
  • 1.2.1-py3-tf-cpu/1.2.1-py3-tf-gpu [2017-01-20]: Python 3.5 + Keras (1.2.1) + TensorFlow (0.12.1) on CPU/GPU (Dockerfile.py3-tf-cpu/.py3-tf-gpu)
  • 1.2.1-py3-th-cpu/1.2.1-py3-th-gpu [2017-01-20]: Python 3.5 + Keras (1.2.1) + Theano (0.8.2) on CPU/GPU (Dockerfile.py3-th-cpu/.py3-th-gpu)
  • 1.2.0-py2-tf-cpu/1.2.0-py2-tf-gpu [2016-12-20]: Python 2.7 + Keras (1.2.0) + TensorFlow (0.12.0) on CPU/GPU
  • 1.2.0-py2-th-cpu/1.2.0-py2-th-gpu [2016-12-20]: Python 2.7 + Keras (1.2.0) + Theano (0.8.2) on CPU/GPU
  • 1.2.0-py3-tf-cpu/1.2.0-py3-tf-gpu [2016-12-20]: Python 3.5 + Keras (1.2.0) + TensorFlow (0.12.0) on CPU/GPU
  • 1.2.0-py3-th-cpu/1.2.0-py3-th-gpu [2016-12-20]: Python 3.5 + Keras (1.2.0) + Theano (0.8.2) on CPU/GPU
  • 1.1.1-py2-tf-cpu/1.1.1-py2-tf-gpu [2016-10-31]: Python 2.7 + Keras (1.1.1) + TensorFlow (0.10.0) on CPU/GPU
  • 1.1.1-py2-th-cpu/1.1.1-py2-th-gpu [2016-10-31]: Python 2.7 + Keras (1.1.1) + Theano (0.8.2) on CPU/GPU
  • 1.1.1-py3-tf-cpu/1.1.1-py3-tf-gpu [2016-10-31]: Python 3.5 + Keras (1.1.1) + TensorFlow (0.10.0) on CPU/GPU
  • 1.1.1-py3-th-cpu/1.1.1-py3-th-gpu [2016-10-31]: Python 3.5 + Keras (1.1.1) + Theano (0.8.2) on CPU/GPU
  • 1.1.0-py2-tf-cpu/1.1.0-py2-tf-gpu [2016-09-20]: Python 2.7 + Keras (1.1.0) + TensorFlow (0.10.0) on CPU/GPU
  • 1.1.0-py2-th-cpu/1.1.0-py2-th-gpu [2016-09-20]: Python 2.7 + Keras (1.1.0) + Theano (0.8.2) on CPU/GPU
  • 1.1.0-py3-tf-cpu/1.1.0-py3-tf-gpu [2016-09-20]: Python 3.5 + Keras (1.1.0) + TensorFlow (0.10.0) on CPU/GPU
  • 1.1.0-py3-th-cpu/1.1.0-py3-th-gpu [2016-09-20]: Python 3.5 + Keras (1.1.0) + Theano (0.8.2) on CPU/GPU
  • 1.0.8-py2-tf-cpu/1.0.8-py2-tf-gpu [2016-08-28]: Python 2.7 + Keras (1.0.8) + TensorFlow (0.9.0) on CPU/GPU
  • 1.0.8-py2-th-cpu/1.0.8-py2-th-gpu [2016-08-28]: Python 2.7 + Keras (1.0.8) + Theano (0.8.2) on CPU/GPU
  • 1.0.8-py3-tf-cpu/1.0.8-py3-tf-gpu [2016-08-28]: Python 3.5 + Keras (1.0.8) + TensorFlow (0.9.0) on CPU/GPU
  • 1.0.8-py3-th-cpu/1.0.8-py3-th-gpu [2016-08-28]: Python 3.5 + Keras (1.0.8) + Theano (0.8.2) on CPU/GPU
  • 1.0.6-py2-tf-cpu/1.0.6-py2-tf-gpu [2016-07-20]: Python 2.7 + Keras (1.0.6) + TensorFlow (0.9.0) on CPU/GPU
  • 1.0.6-py2-th-cpu/1.0.6-py2-th-gpu [2016-07-20]: Python 2.7 + Keras (1.0.6) + Theano (0.8.2) on CPU/GPU
  • 1.0.6-py3-tf-cpu/1.0.6-py3-tf-gpu [2016-07-20]: Python 3.5 + Keras (1.0.6) + TensorFlow (0.9.0) on CPU/GPU
  • 1.0.6-py3-th-cpu/1.0.6-py3-th-gpu [2016-07-20]: Python 3.5 + Keras (1.0.6) + Theano (0.8.2) on CPU/GPU
  • 1.0.4-py2-tf-cpu/1.0.4-py2-tf-gpu [2016-06-16]: Python 2.7 + Keras (1.0.4) + TensorFlow (0.8.0) on CPU/GPU
  • 1.0.4-py2-th-cpu/1.0.4-py2-th-gpu [2016-06-16]: Python 2.7 + Keras (1.0.4) + Theano (0.8.2) on CPU/GPU
  • 1.0.4-py3-tf-cpu/1.0.4-py3-tf-gpu [2016-06-16]: Python 3.5 + Keras (1.0.4) + TensorFlow (0.8.0) on CPU/GPU
  • 1.0.4-py3-th-cpu/1.0.4-py3-th-gpu [2016-06-16]: Python 3.5 + Keras (1.0.4) + Theano (0.8.2) on CPU/GPU
  • 1.0.1-py2-th-cpu/1.0.1-py2-th-gpu [2016-04-16]: Python 2.7 + Keras (1.0.1) + Theano (0.8.1) on CPU/GPU
  • 0.3.3-py2-th-cpu/0.3.3-py2-th-gpu [2016-03-31]: Python 2.7 + Keras (0.3.3) + Theano (0.8.1) on CPU/GPU

Usage

Quick experiment with latest Keras (with TensorFlow backend on CPU) and your Python 2 code in /srv/ai:

$ docker run -it --rm -v /srv/ai:/srv/ai gw000/keras /srv/ai/run.py

Or using TensorFlow backend on GPUs (see docker-debian-cuda) in Python 2:

$ docker run -it --rm $(ls /dev/nvidia* | xargs -I{} echo '--device={}') -v /srv/ai:/srv/ai gw000/keras:1.2.0-py2-tf-gpu /srv/ai/run.py

Or using Theano backend on GPUs (see docker-debian-cuda) in Python 3:

$ docker run -it --rm $(ls /dev/nvidia* | xargs -I{} echo '--device={}') -v /srv/ai:/srv/ai gw000/keras:1.2.0-py3-th-gpu /srv/ai/run.py

In practice you are supposed to extend this image by writing your own Dockerfile that installs all your application dependencies (either using apt-get or pip). Eg. if you need Matplotlib, PIL/pillow, Pandas, Scikit-learn, and Statsmodels:

FROM gw000/keras:1.2.0-py2-th-cpu

# install dependencies from debian packages
RUN apt-get update -qq \
 && apt-get install --no-install-recommends -y \
    python-matplotlib \
    python-pillow

# install dependencies from python packages
RUN pip --no-cache-dir install \
    pandas \
    scikit-learn \
    statsmodels

# install your app
ADD ai/ /srv/ai/
RUN chmod +x /srv/ai/run.py

CMD ["/srv/ai/run.py"]

If you are looking for a full deep learning research environment based on Keras and Jupyter, check out docker-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/] <>

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.