Jupyter: Difference between revisions
(Created page with "== Project Jupyter == [https://jupyter.org/ 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 would be one of the great platform in education for [https://jupyter4edu.github.io...") |
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Jupyter would be one of the great platform in education for [https://jupyter4edu.github.io/jupyter-edu-book/ teaching and Learning with Jupyter], interactive data science<ref>https://jakevdp.github.io/PythonDataScienceHandbook/index.html</ref> and reproducible computing documents for sharing. | Jupyter would be one of the great platform in education for [https://jupyter4edu.github.io/jupyter-edu-book/ teaching and Learning with Jupyter], interactive data science<ref>https://jakevdp.github.io/PythonDataScienceHandbook/index.html</ref> and reproducible computing documents for sharing. | ||
Project Jupyter compose three major releases, | Project Jupyter compose three major releases, | ||
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* 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. | * 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. | * JupyterHub is encapsulated environments for multiple users for servers for an entire office or classroom. | ||
== Jupyter kernel == | |||
According to [https://docs.jupyter.org/en/latest/projects/architecture/content-architecture.html Architecture Guides — Jupyter Documentation], Jupyter provides language-agnostic interfactive computing environment such as Python, R, Julia and so on through the well designed kernel API. | |||
With [[IPython]]'s built-in magic commands<ref>https://jakevdp.github.io/PythonDataScienceHandbook/01.03-magic-commands.html</ref> can solve various common problems in standard data analysis. | |||
[[Manual:DLSSystem|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 [https://www.databricks.com/ Databrics] | |||
== References == | == References == | ||
<references /> | <references /> | ||
Revision as of 13:19, 28 November 2023
Project Jupyter
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 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 compose 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.
Jupyter kernel
According to Architecture Guides — Jupyter Documentation, Jupyter provides language-agnostic interfactive computing environment such as Python, R, Julia and so on through the well designed kernel API.
With IPython's built-in magic commands[2] can solve various common problems in standard data analysis.
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