https://github.com/yantonov/ml-docker

Playground for common python ml libraries
https://github.com/yantonov/ml-docker

anaconda catboost docker docker-image jupiter numpy pyplot python scipy sklearn

Last synced: 15 days ago
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Playground for common python ml libraries

• Host: GitHub
• URL: https://github.com/yantonov/ml-docker
• Owner: yantonov
• Created: 2017-11-06T08:59:50.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2024-08-20T12:13:12.000Z (3 months ago)
• Last Synced: 2024-08-20T14:12:43.189Z (3 months ago)
• Topics: anaconda, catboost, docker, docker-image, jupiter, numpy, pyplot, python, scipy, sklearn
• Language: Makefile
• Homepage:
• Size: 91.8 KB
• Stars: 1
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
These scripts help to use python machine learning libraries through docker images.

This is just a playground.   

For the specific task, you may choose a specific set of packages and create a smaller image.

Docker file provides:

1. python 3

2. [anaconda](https://anaconda.org/anaconda/python)

3. [jupiter](http://jupyter.org/)

4. [jupiterlab](https://jupyterlab.readthedocs.io/en/stable/)

Some additional libraries:  

[Pip packages](https://github.com/yantonov/ml-docker/blob/master/docker/files/requirements.txt)  

Usage:  

Add repository to PATH variable (or create alias/shortcut for run.sh script).

Run jupiter notebook:

```bash

    run.sh notebook

```

Run jupiter lab:

```bash

    run.sh lab

    # or

    run.sh

```

After that you can connect to notebook at localhost:8888, current working directory will be mounted also.

Links:

1. [Docker hub](https://hub.docker.com/r/yantonov/ml/)

2. [Repository](https://github.com/yantonov/anaconda-docker) of the base image.