Jupyter
Overview
Jupyter에 대한 기술 문서입니다.
Summary
- 무엇인가? - Jupyter
- 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
- 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
Purpose
이 문서가 존재하는 이유
- Goal: Jupyter에 대한 기술 정보 제공
- Scope: Jupyter의 개념, 사용법, 설정
- Non-goals: 다른 주제로의 확장
Key Concepts
| Concept | Description | Related |
|---|---|---|
| Jupyter | HPC/서버 환경에서 중요한 기술 개념 | Linux, Server |
Detailed Explanation
Project Jupyter is a non-profit, open-source project, born out of the IPython Project in 2014 as it evolved to support interactive data science and scientific computing across all programming languages. Jupyter will always be 100% open-source software, free for all to use and released under the liberal terms of the modified BSD license. Jupyter is an interactive computational environment managed by Jupyter Project. A Notebook is a shareable document that combines both inputs and outputs to a single file. A notebooks can consist of
- Code
- Mathematical equations
- Narrative text
- Visualizations
- Statistical modeling
- Other rich media
With these features, Jupyter would be one of the great platform in education for teaching and Learning with Jupyter, interactive data science[1] and reproducible computing documents for sharing. Project Jupyter has three major releases,
- Jupyter Notebook plugins may not work with JupyterLab (which is currently beta now).
- JupyterLab is a new version (for the lack of better words) of Jupyter Notebook. It is notebook, text editor and python console together with a file explorer.
- JupyterHub is encapsulated environments for multiple users for servers for an entire office or classroom.
According to Architecture Guides — Jupyter Documentation, Jupyter provides language-agnostic interfactive computing environment such as Python, R, Julia, Spark and so on through the well designed kernel API. HM DLS framework ship with pre-installed jupyterHub for multi users with optimized Deep learning framework for medical research and development with optional customize conda environment for Spark, Physics dynamic, Bioinformatics, and so on. with optional plutin of UCM, customers are able to up and run on-premise scalable computing cluster farm for various research like Databrics
Best Practices
- 최신 버전 사용 권장
- 공식 문서 참고
- 테스트 환경에서 먼저 검증
References
Related Pages
Knowledge Graph
Related