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

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

• OSX
• Windows
• Ubuntu

Linux/MacOS:

$ mkdir /data

Windows:

$ mkdir c:\data

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

Linux/MacOS:

$ docker run -p 8888:8888 -p 6006:6006 -v /data:/notebooks -it --rm asashiho/ml-jupyter-python3

Windows:

$ docker run -p 8888:8888 -p 6006:6006 -v /c/data:/notebooks -it --rm asashiho/ml-jupyter-python3

This container setup:

• Python 3.7
• TensorFlow 1.13.1
• 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.

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>

1. cd mnt/data/
2. git clone https://github.com/tensorflow/tensorflow
3. cd mnt/data/tensorflow
4. git clone https://github.com/tensorflow/models
5. cd mnt/data/
6. git clone https://github.com/cocodataset/cocoapi.git
7. exec container
8. cd /notebooks/cocoapi/PythonAPI
9. make
10. cp -r pycocotools /notebooks/tensorflow/models
11. cd /notebooks/tensorflow/models/research
12. protoc object_detection/protos/*.proto --python_out=.