Page Comparison

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• Introduce what Jupyter is and why it’s useful

• Differentiate between Jupyter Notebooks and Jupyter Labs and when to use each

• Identify cell types in a notebook and how they’re used

• Things that you may want to watch out for

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What is Jupyter?

Jupyter, formerly known as an ipython notebook, is a popular tool used in data science and data analysis.

• An open-source, browser-based, web application with a wide variety of functions

  • Allows users to create and share computational documents, called notebooks .

    • Notebooks facilitate the development of live code that can then be run in a number of different coding languages.

    • Code can be run step by step in “chunks” called cells.

    • Users can combine live code cells with other cells - Markdown text, images, plots, and other rich media in a single interactive canvas.

    • Can produce a wide variety of interactive output including HTML, videos, LaTeX, and custom MIME types.

    • Can be shared through e-mail, GitHub, or other cloud storage and sharing services.

    • Easily exported to other formats like, books, slides, web apps, static web pages, or PDF documents

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  We see this tool used for a number of things:

  • To organize work and display the thought process or logic associated with a project

  • Collaboration

  • As an IDE (Integrated Development Environment)

    • Easy to look at or run code line-by-line to simplify debugging

  • For teaching

  • Displaying and manipulating data frames

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  • Allows users to run code and share ideas and share in a “live” and easily available format.

• Requires a kernel to launch

• A computational lab notebook that has live code which can be run in real time.

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Jupyter is great for:

• Learning and Sharing Ideas

• Prototyping

• Documenting thought processes

• Presenting work through an interactive environment

• Exploratory Data Analysis - Investigating and Summarizing Datasets

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Jupyter Notebooks

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A web based interactive environment in which to create Jupyter notebook documents

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Launched from a kernel

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Straightforward linear flow, where you can create and run cells in a single notebook Beginners may find Notebooks easier to use, initially Lacks some of the functionality of Jupyter Lab Supports extensions, but the process of installing and managing them more difficult. Available extensions is smaller compared to JupyterLab. Does not have a built-in terminal or text editor. Users need to rely on external tools or extensions for these tasks.

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Notebook cell types

By default, there are 4 types of Notebook cells:

• Markdown

• Code

• NBConvert

• Heading

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Jupyter Labs

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Jupyter Notebook cell types

• Markdown

  • Text Cells allowing you to write and render Markdown syntax

  • This is where you describe and document your workflow

• Code

  • Code cells allow you to write and run programming code in a language of your choosing (e.g., Python)

  • Languages supported in Jupyter include Python, R, Julia, and many others

  • On ARCC HPC resources, we support jupyter code in Python and R

• NBConvert

  • Stands for Notebook Convert

  • Enables the conversion of your notebook to another format as given by the FORMAT string using Jinja templates.

    • Presenting: PDF

    • Publishing: LaTeX

    • Collaboration

    • Sharing: HTML

Running Jupyter from OnDemand

After logging into OnDemand on your favorite ARCC HPC resource, you can launch Jupyter from the main Dashboard:

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Upon request, you are taken to a web form to tailor and specify the Jupyter environment you’d like to run in your session

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Lab also allows users to execute code in a python console 

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