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== IPython vs Ipykernel ==
== Overview ==
IPython provides a rich architecture for interactive computing. IPython itself is focused on interactive Python, part of which is providing a Python kernel for Jupyter. [https://ipython.readthedocs.io/en/stable/index.html This URL] describes more about features by version of IPython.


HM DLS framework<ref>Manual:DLSSystem</ref> utilize IPython to connect Jupyter and Conda/Mamba based on the [https://docs.jupyter.org/en/latest/projects/architecture/content-architecture.html Architecture Guides — Jupyter Documentation]  which shows  IPython and Jupyter are connected and related.
IPython에 대한 기술 문서입니다.


=== Summary ===


* 무엇인가? - IPython
* 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
* 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시
== Purpose ==
이 문서가 존재하는 이유
* Goal: IPython에 대한 기술 정보 제공
* Scope: IPython의 개념, 사용법, 설정
* Non-goals: 다른 주제로의 확장
== Key Concepts ==
{| class="wikitable"
! Concept
! Description
! Related
|-
| IPython
| HPC/서버 환경에서 중요한 기술 개념
| [[Linux]], [[Server]]
|}
== Detailed Explanation ==
IPython provides a rich architecture for interactive computing. IPython itself is focused on interactive Python, part of which is providing a Python [[kernel]] for [[Jupyter]]. [https://ipython.readthedocs.io/en/stable/index.html This URL] describes more about features by version of IPython.
IPykernel(IPython kernel) is a Jupyter kernel for '''Python code execution'''. Jupyter, and other compatible notebooks, use the IPython kernel for executing Python notebook code.  pip show command shows the relationship ipython and ipykernel.<syntaxhighlight lang="bash">
IPykernel(IPython kernel) is a Jupyter kernel for '''Python code execution'''. Jupyter, and other compatible notebooks, use the IPython kernel for executing Python notebook code.  pip show command shows the relationship ipython and ipykernel.<syntaxhighlight lang="bash">
DLS$ pip show ipython
DLS$ pip show ipython
Line 13: Line 40:
Requires: backcall, decorator, jedi, matplotlib-inline, pexpect, pickleshare, prompt-toolkit, pygments, stack-data, traitlets
Requires: backcall, decorator, jedi, matplotlib-inline, pexpect, pickleshare, prompt-toolkit, pygments, stack-data, traitlets
Required-by: ipykernel
Required-by: ipykernel
</syntaxhighlight>
</syntaxhighlight>[[Manual:DLSSystem|HM DLS framework]] utilize IPython to connect Jupyter and Conda/Mamba based on the [https://docs.jupyter.org/en/latest/projects/architecture/content-architecture.html Architecture Guides — Jupyter Documentation]  which shows  IPython and Jupyter are connected and related.
 
== Python Data Science Handbook ==
[https://jakevdp.github.io/PythonDataScienceHandbook/index.html This website] contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks.
[https://jakevdp.github.io/PythonDataScienceHandbook/index.html This website] contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks.
Beginning with version 6.0, IPython '''<u>stopped supporting compatibility with Python versions lower than 3.3 including all versions of Python 2.7</u>.'''
If you are looking for an IPython version compatible with Python 2.7, need to use the IPython 5.x LTS release and refer to its documentation (LTS is the long term [[support]] release)<ref>https://ipython.readthedocs.io/en/stable/interactive/magics.html</ref>
<references />


== IPython and Python version compatiblity ==
== Best Practices ==
Beginning with version 6.0, IPython '''<u>stopped supporting compatibility with Python versions lower than 3.3 including all versions of Python 2.7</u>.'''


If you are looking for an IPython version compatible with Python 2.7, need to use the IPython 5.x LTS release and refer to its documentation (LTS is the long term [[support]] release)<ref>https://ipython.readthedocs.io/en/stable/interactive/magics.html</ref>
* 최신 버전 사용 권장
* 공식 문서 참고
* 테스트 환경에서 먼저 검증


== References ==
== References ==
<references />
 
* [https://wiki.hpcmate.com IPython]
 
== Related Pages ==
 
* [[Linux]]
* [[Server]]
* [[Hardware]]
* [[Network]]
 
[[Category:Server]]
== Knowledge Graph ==
 
Related
 
→ [[Linux]]
→ [[Server]]
→ [[Hardware]]
→ [[Network]]
 
[[Category:Reference]]

Latest revision as of 13:24, 2 October 2026

Overview

IPython에 대한 기술 문서입니다.

Summary

  • 무엇인가? - IPython
  • 왜 필요한가? - HPC 및 서버 환경에서 필수 개념
  • 언제 사용하는가? - 서버 구성, 성능 튜닝, 문제 해결 시

Purpose

이 문서가 존재하는 이유

  • Goal: IPython에 대한 기술 정보 제공
  • Scope: IPython의 개념, 사용법, 설정
  • Non-goals: 다른 주제로의 확장

Key Concepts

Concept Description Related
IPython HPC/서버 환경에서 중요한 기술 개념 Linux, Server

Detailed Explanation

IPython provides a rich architecture for interactive computing. IPython itself is focused on interactive Python, part of which is providing a Python kernel for Jupyter. This URL describes more about features by version of IPython.

IPykernel(IPython kernel) is a Jupyter kernel for Python code execution. Jupyter, and other compatible notebooks, use the IPython kernel for executing Python notebook code. pip show command shows the relationship ipython and ipykernel.

DLS$ pip show ipython
Name: ipython
Version: 8.15.0
Summary: IPython: Productive Interactive Computing
...
Requires: backcall, decorator, jedi, matplotlib-inline, pexpect, pickleshare, prompt-toolkit, pygments, stack-data, traitlets
Required-by: ipykernel

HM DLS framework utilize IPython to connect Jupyter and Conda/Mamba based on the Architecture Guides — Jupyter Documentation which shows IPython and Jupyter are connected and related.

This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks. Beginning with version 6.0, IPython stopped supporting compatibility with Python versions lower than 3.3 including all versions of Python 2.7. If you are looking for an IPython version compatible with Python 2.7, need to use the IPython 5.x LTS release and refer to its documentation (LTS is the long term support release)[1]

  1. ↑ https://ipython.readthedocs.io/en/stable/interactive/magics.html

Best Practices

  • 최신 버전 사용 권장
  • 공식 문서 참고
  • 테스트 환경에서 먼저 검증

References

Related Pages

Knowledge Graph

Related

→ Linux → Server → Hardware → Network

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