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-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>