TensorFlow and scikit-learn with Python3.6 via Docker

This contains Dockerfiles to make it easy to get up and running with TensorFlow and scikit-learn via Docker.

1. Installing Docker

General installation instructions are on the Docker site, but we give some quick links here:

• OSX
• Windows
• Ubuntu

2. Running the container

2.1 create a new Data directory at local

Linux/MacOS:

$ mkdir /data

Windows:

$ mkdir c:\data

[Note] if you are useing 'Docker for Windows',you need to configuring Shared Drives

2.2 run a new Docker container

Linux/MacOS:

$ docker run -p 8888:8888 -v /data:/notebooks -it--rm asashiho/ml-jupyterlab

Windows:

$ docker run -p 8888:8888 -v /c/data:/notebooks -it --rm asashiho/ml-jupyterlab

This container setup:

• Python 3.6
• tensorflow
• keras
• nomkl
• ipywidgets
• pandas
• numexpr
• matplotlib
• scipy
• seaborn
• scikit-learn
• scikit-image
• sympy
• cython
• patsy
• statsmodels
• cloudpickle
• dill
• numba
• bokeh
• sqlalchemy
• hdf5
• h5py
• vincent
• beautifulsoup4
• protobuf
• xlrd'
• plotly
• Pillow
• google-api-python-client

This container is CPU Only.If you want to use GPU, rebuilding GPU images requires nvidia-docker.

3. How To Use Jupyter Notebooks

Copy/paste this URL into your browser when you connect for the first time,

to login with a token:
    http://localhost:8888/?token=<your token>

4. How To Use JupyterLab

Copy/paste this URL into your browser when you connect for the first time,

to login with a token:
    http://localhost:8888/lab/?token=<your token>