With Jupyter, python 3, TensorFlow, Keras and others popular libraries.
- Python 3.6.4
- Jupyter Notebook 5.0.0
- TensorFlow 1.4.1
- Keras 2.1.2
- Numpy 1.13.3
- Scikit-learn 0.19.1
- Scipy 1.0.0
- HDF5 1.10.1
- Pandas 0.21.0
- Bokeh 0.12.13
- Graphviz 2.40.1
- Matplotlib 2.1.1
- Seaborn 0.8.1
Default folder used by Jupyter to store notebooks is /home/jupyter, inside the container. Any data (notebook, datasets, etc.) created inside the container will be available only from the container, while the container is running and will be ephemeral.
I strongly advise you to map this volume to a folder on your computer (named <LOCAL_DIR>).
docker run -v <LOCAL_DIR>:/home/jupyter -p 8888:8888 mverriez/datascience-python-toolbox
This command starts a container with the Jupyter server listening for HTTP connections on port 8888, protected by a generated token.
[I 10:39:51.685 NotebookApp] Writing notebook server cookie secret to /home/jupyter/.local/share/jupyter/runtime/notebook_cookie_secret
[I 10:39:51.881 NotebookApp] Serving notebooks from local directory: /home/jupyter
[I 10:39:51.881 NotebookApp] 0 active kernels
[I 10:39:51.881 NotebookApp] The Jupyter Notebook is running at: http://0.0.0.0:8888/?token=6a354d94975c9d5b37ea0959e22a6f3b5d5b0d646f6a9cdc
[I 10:39:51.881 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 10:39:51.881 NotebookApp]
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://0.0.0.0:8888/?token=6a354d94975c9d5b37ea0959e22a6f3b5d5b0d646f6a9cdc