Jeremy Tuloup

Jupyter Notebooks have been a widely popular tool for data science in recent years due to their ability to combine code, text, and visualizations in a single document. Despite its popularity, the core functionality and user experience of the Classic Jupyter Notebook interface has remained largely unchanged over the past years. Lately the Jupyter Notebook project decided to base its next major version 7 on JupyterLab components and extensions, which means many JupyterLab features are also available to Jupyter Notebook users. In this presentation, we will demo the new features coming in Jupyter Notebook version 7 and how they are relevant to existing users of the Classic Notebook.

Jupyter Notebook 7 is based on the JupyterLab codebase, but provides an equivalent user experience to the current (version 6) application. Notebook 7 keeps the document-centric user experience at its core, and brings many new features that were not previously available: - Debugger - Real-time collaboration - Theming and dark mode - Internationalization - Improved Web Content Accessibility Guidelines (WCAG) compliance - Support for many JupyterLab extensions, including Jupyter LSP (Language Server Protocol) for enhanced code completions - Performance improvements This talk will be about demoing the new features coming to Notebook 7, and how uses of the Classic Notebook interface should approach. We will also cover other aspects mentioned in the related Jupyter Enhancement Proposal, such as support for popular extensions and future developments: https://jupyter.org/enhancement-proposals/79-notebook-v7/notebook-v7.html

Jupyter notebooks are a popular tool for data science and scientific computing, allowing users to mix code, text, and multimedia in a single document. However, sharing Jupyter notebooks can be challenging, as they require installing a specific software environment to be viewed and executed. JupyterLite is a Jupyter distribution that runs entirely in the web browser without any server components. A significant benefit of this approach is the ease of deployment. With JupyterLite, the only requirement to provide a live computing environment is a collection of static assets. In this talk, we will show how you can create such static website and deploy it to your users.

We will cover the basics of JupyterLite, including how to use its command-line interface to generate and customize the appearance and behavior of your Jupyter website. This will be a guided walkthrough with step-by-steps instructions for adding content, extensions and configuration. By the end of this tutorial, you will be able to create your own interactive Jupyter website using JupyterLite. Outline: - Introduction to Jupyter and JupyterLite - Examples of JupyterLite used for interactive documentation and educational content (NumPy, Try Jupyter, SymPy) - Step-by-step demo for creating a Jupyter website - Quickstart with the demo repository - Adding content: notebooks, files and static assets - Adding extensions to the user interface - Adding packages to the Python runtime - Customization and custom settings - Deploy JupyterLite as a static website on GitHub Pages, Vercel or your own server - Conclusion and next steps for learning more about the Jupyter ecosystem The tutorial will be based on resources already publicly available: - try JupyterLite in your browser: https://jupyterlite.github.io/demo/ - the JupyterLite documentation: https://jupyterlite.readthedocs.io/en/latest/quickstart/deploy.html - the JupyterLite repositories: https://github.com/jupyterlite At the end of the tutorial the attendees will have something very concrete to present and a functioning Jupyter website.

JupyterLite is a Jupyter distribution that runs entirely in the web browser, backed by in-browser language kernels including WebAssembly powered Jupyter Xeus kernels and Pyodide. JupyterLite enables data science and interactive computing with the PyData scientific stack, directly in the browser, without installing anything or running a server. JupyterLite leverages the Emscripten and Conda Forge infrastructure, making it possible to easily install custom packages with binary extensions in the browser, such as numpy, scipy and scikit-learn.

This will be a functional talk to present JupyterLite with concrete examples and live demos in the web browser. The structure of the presentation is as follows: - Introduction - Easy interactive computing in the browser - A lightweight Jupyter Frontend running in the browser - Boots in seconds - Deployment and scalability made easy - Demos of the Features: - the WebAssembly powered Python kernel - Support for existing JupyterLab Extensions, IPython, Jupyter Widgets, interactive visualizations - Support for Real Time Collaboration - Working with files and notebooks - A quick overview of the underlying architecture - Plugin-based as a Lumino application - Extensible by design: most JupyterLab extensions are compatible by default and new kernels can easily be added - Emscripten Forge - Presentation of the distribution that enables the emscripten platform for the mamba (conda) package manager - Allows freezing in time a given WebAssembly environment. This will enable a new level of computational reproducibility. - Based on the WebAssembly standard supported by all web browsers - Demo of using a custom set of Python packages in a JupyterLite deployment - A wide range of use cases - Embed a live Python console on your website 🚀 - Powering the numpy and sympy documentation to let anyone try the libraries in the browser without installing anything - Education: easy access to computing environment without the trouble of installing anything - Reducing the load on public services like mybinder.org - What's coming up next

Other people: Martin Renou Thorsten Beier