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:

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-jupyter-python3

Windows:

$ docker run -p 8888:8888 -v /c/data:/notebooks -it --rm asashiho/ml-jupyter-python3

This container setup:

Python 3.6

TensorFlow 1.5.0

scikit-learn

keras

sklearn

jupyter

scipy

simpy

matplotlib

numpy

pandas

plotly

sympy

mecab-python3

librosa

Pillow

h5py

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,