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This is a modern environment for building deep learning applications. It has the latest stable versions of the most common tools and frameworks with CPU and GPU support.
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README.md

Deep Learning libraries Docker Image

This is a complete environment for building deep learning applications. It has the latest stable versions of the most common tools and frameworks that you're likely to need.

Included Libraries

  • Ubuntu 16.04 LTS
  • Python 3.5.2
  • Tensorflow > 1
  • OpenCV 3.2
  • ipython[notebook]
  • Numpy, Scipy, Scikit Learn, Scikit Image, Pandas, Matplotlib, Pillow

TODO:

  • Torch
  • GPU/CUDA

Running the Docker Image

If you haven't yet, start by installing Docker. It should take a few minutes. Then run this command at your terminal:

docker run -it -p 8888:8888 -p 6006:6006 -v $(pwd):/code akkefa/docker-deep-learning-libraries

Note the -v option. It maps your user directory ($(pwd)) to /code in the container. Change it if needed. The two -p options expose the ports used by Jupyter Notebook and Tensorboard respectively.

Running the ipython[notebook]

 jupyter notebook --ip=0.0.0.0 --port=8888 --allow-root
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