Learing Experiment As Running contaiNer

Motivation

This project is developed by whom are working at teaching assistant that is teach AI (Artificial Intelligence) in experimence class.
AI relating libraries are large and many , so installing their're not finished in class time, and futher, their install process are complex.
Therefore, we have to work suppotting install proccess at "service overtime" without pay.
Thus we have developed easier install process.
This Container is installed many AI relating libraries, by using Docker is easy introduce.
Many students aren't to need worrying about install process, what you need is all in this container.
We contained simple sample program. So, you can immediately validation.
We named "LEARN", want that many learning students in experimence class used.

Who needs this ?

Beginner AI enginner
Who want an easier installion

Usage

docker pull ghcr.io/i13302/i13302/learn/superml:latest

Terminal on Linux,Mac

docker run -it --rm --publish 8888:8888 --volume $(pwd)/work:/home/jovyan/work/ ghcr.io/i13302/i13302/learn/superml:latest

Powershell on Windows

docker run -it --rm --publish 8888:8888 --volume ${PWD}/work:/home/jovyan/work/ ghcr.io/i13302/i13302/learn/superml:latest

In work directory of current directory in host machine with In work directory of /home/jovyan/work in container (top directory on jupyter) are synceing files.
Access to http://127.0.0.1:8888 in your browser.
By the way, you are AI engineer too !

Local Build

docker build . -t superml

Branch Rule

Git-Flow.

master
  |- develop
     |- future/*

License

Apache License.
This is LICENSE file.

Member

Install Library

Calculation

NumPy - https://numpy.org/
Extension module for efficient numerical calculation
pandas - https://pandas.pydata.org/
A library that provides functions to support data analysis

Visualization

matplotlib - https://matplotlib.org/
Graph drawing library
seaborn - https://seaborn.pydata.org/
Graph drawing library
Graphviz - https://graphviz.org/
Draw a graph structure written in DOT language

Model

scikit-learn - https://scikit-learn.org/stable/
Machine learning library
TensorFlow - https://www.tensorflow.org/
Software library for use in machine learning
MeCab - https://taku910.github.io/mecab/
Morphological analysis engine

i13302 / learn