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Languages & runtimes · #jupyter
Guide to pick the right Jupyter delivery method based on interactivity, deployment effort, and version‑control needs.
JupyterHub splits one shared Jupyter Server into per-user servers behind a single login. Here is the minimal four-component design, where the trust boundaries actually fall, and the checks that catch orphaned servers and leaked routes.
Deploying JupyterHub with OAuth2 requires a secure, minimal design that delegates identity verification, enforces TLS, and isolates notebook servers. This guide details requirements, trust boundaries, operational checks, failure modes, and a practical verification checklist.
Learn how to export any notebook to a live Reveal.js presentation, control slide visibility with cell tags, and brand your talks with custom themes—all without leaving Jupyter.
Learn how to create a virtual environment, register it as a Jupyter kernel, and verify that notebooks run with the isolated dependencies you expect.
Learn how to enable, validate, and troubleshoot the JupyterLab integrated terminal to manage shell commands and environments directly from your notebook interface.
Stop manually updating variables in your Jupyter cells. Learn how to use ipywidgets to create interactive sliders and dropdowns that make data exploration faster and more accessible.
Stop re-running cells every time someone asks "what if the threshold were different?" ipywidgets turns a static notebook analysis into an interactive parameter explorer with a few lines of Python — if you respect its live-kernel limits.
In Jupyter Server 2.0, the behavior of the TZ environment variable has shifted regarding how internal timestamps are handled. When this variable is defined prior to launch, the server adopts this zone for all internal operations, potentially overriding the host's local time settings. This creates a compatibility boundary for notebooks that previously relied
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