Extended from Jupyter Notebook Scientific Python Stack which contains :
- Jupyter Notebook 5.2.x
- Conda Python 3.x environment
- pandas, matplotlib, scipy, seaborn, scikit-learn, scikit-image, sympy, cython, patsy, statsmodel, cloudpickle, dill, numba, bokeh, vincent, beautifulsoup, xlrd pre-installed
Check Jupyter Github given above for full reference and usage. Everything is pre-installed for Python 3.x.
Image build is triggered with each update of Jupyter Notebook Scientific Python Stack and install bleeding edge versions of Keras.
For advanced usage and options, read more at Jupyter Notebook Scientific Python Stack.
I use this image to work with neural networks, so I added the following libraries :
- Keras: a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation.
- TensorFlow: open source software library for numerical computation using data flow graphs, the default backend for Keras.
- HDF5 for Python: optional dependency of Keras used to save / load weights for neural networks.
Note : in this setting, TensorFlow will be CPU enabled only.
docker run -d -v /$(pwd)/:/home/jovyan/work \ -p 8888:8888 gaarv/jupyter-keras start-notebook.sh --NotebookApp.token=''
This will allow to have the current working directory path mounted directly into the guest and with no password