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boilerplate-machine-learning-dockerfiles-for-miniconda Build Status License: MIT

Boilerplate of Dockerfiles for Jupyter Notebook, Tensorflow, Keras and so on with Miniconda.

Required

  • Docker installed

Getting started

  • Clone this repo
  • Select Dockerfile and Edit dockerfile value in docker-compose.yml
  • Run docker-compose up --build in terminal

Default Settings

  • Port: 8888:8888
  • Volume: ./data:/data
  • --notebook-dir: /data/notebooks

Dockerfile List

Dockerfile Name Main Packages Virtual Env. Base Image
Dockerfile.mini.base Miniconda, Jupyter Notebook Not used continuumio/miniconda3
Dockerfile.mini.tensorflow Miniconda, Jupyter Notebook, Tensorflow Not used keidrun/ml-base-mini
Dockerfile.mini.keras Miniconda, Jupyter Notebook, Tensorflow, Keras Not used keidrun/ml-base-mini
Dockerfile.mini.pytorch Miniconda, Jupyter Notebook, Pytorch Not used keidrun/ml-base-mini

Docker Image List

Docker Image Name Built Dockerfile Name
keidrun/ml-base-mini Dockerfile.mini.base
keidrun/ml-tensorflow-mini Dockerfile.mini.tensorflow
keidrun/ml-keras-mini Dockerfile.mini.keras
keidrun/ml-pytorch-mini Dockerfile.mini.pytorch

For example, if you'd like to use keidrun/ml-keras-mini image, run the following command:

docker container run -it -p 8888:8888 -v $(pwd)/data:/data keidrun/ml-keras-mini

Or edit image value in docker-compose.image.yml and run the following command:

docker-compose -f docker-compose.image.yml up