2026-06-06 –, Grand Hall 1
JupyterLite is a JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels. Using it you can run Python, R and C++ in your browser via WebAssembly, use git and vim in a terminal, and access AI agents in a safe, sandboxed environment.
This talk will present a comprehensive summary of all things JupyterLite, and provide live demonstrations of many of its key features and how easy it is to deploy.
The talk assumes basic familiarity with JupyterLab but not necessarily JupyterLite. It will be of benefit to anyone who wishes to learn about this emerging technology and its potential for scalable, accessible interactive computing.
JupyterLite is a JupyterLab distribution that runs entirely in the web browser, backed by in-browser language kernels. Standard JupyterLab uses kernels run in separate processes and communicate with the client by message passing, whereas JupyterLite uses kernels that run entirely in the browser, based on JavaScript and WebAssembly, such as pyodide and xeus-python.
This means that JupyterLite deployments can be scaled to millions of users without the need for individual containers for each user session, only static files need to be served which can be done with a simple web server like GitHub pages.
This talk will present a comprehensive summary of all things JupyterLite, and demonstrate key features. Highlights include the wide variety of language kernels supported, a terminal for those who wish to run git or vim at a command line in the browser, and access to AI agents in a safe sandboxed browser environment. It will explain the technology behind JupyerLite and how your favourite packages are built to run in the browser.
JupyterLite sites are easy to deploy and there will be a live demonstration of a deployment to illustrate this.
Talk outline:
- Overview
- Comparison of JupyterLab and JupyterLite
- Live demonstration of basic functionality
- How it works
- Kernels, including why are there two different python kernels (pyodide and xeus-python) and how to choose between them
- Emscripten-forge package building
- Key features such as shared in-browser filesystem
- More detailed demos such as installing packages on the fly
- What it is good and bad at
- Terminal for
vim,git, etc - Jupyterlite AI
- Use in project documentation using jupyterlite-sphinx
- Deployment, including live demo
- Making it easier to deploy and share using notebook.link
- Where JupyterLite is going
Ian is a Scientific Software Developer at QuantStack. He has been an Open Source contributor for over 15 years, is a core maintainer of the libraries Matplotlib and ContourPy and a significant contributor to Bokeh and Datashader. Recently Ian has been involved throughout the Jupyter stack, from kernels and widgets through to JupyterLite.