IPython: Difference between revisions

From HPCWIKI
Jump to navigation Jump to search
(Add categories: Linux, Reference)
(Template migration to LLM-Optimized Wiki Template)
Line 1: Line 1:
== IPython vs Ipykernel ==
{{Status
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.
|status=Draft
|owner=Knowledge Agent
|last_update=2026-07-16
|review=Pending
}}
 
{{TOC}}
 
== 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 ==
 
{| 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 10: Line 56:
Required-by: ipykernel
Required-by: ipykernel
</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.
</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.
[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 />


== 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.
 
== Best Practices ==


== IPython and Python version compatiblity ==
* 최신 버전 사용 권장
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>.'''
* 공식 문서 참고
* 테스트 환경에서 먼저 검증


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 ==
== References ==
<references />
[[Category:Linux]]


* [https://wiki.hpcmate.com IPython]
---
== Related Pages ==
* [[Linux]]
* [[Server]]
* [[Hardware]]
* [[Network]]
---
[[Category:Server]]
[[Category:Reference]]
[[Category:Reference]]

Revision as of 15:23, 16 July 2026

Template:Status

Template:TOC

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]

---

Best Practices

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

---

References

---

Related Pages

---