A cross-platform desktop application for JupyterLab, providing the easiest way to run Jupyter notebooks locally.
JupyterLab Desktop is a cross-platform desktop application that packages JupyterLab as a native installable program. It allows users to run Jupyter notebooks locally on their personal computers without needing to start a Jupyter server manually or rely on a browser. The application provides a streamlined way to work with notebooks, files, and data science projects directly from the desktop.
Data scientists, researchers, educators, and developers who use Jupyter notebooks for interactive computing, data analysis, and prototyping on their local machines.
It offers the quickest and easiest way to get started with Jupyter notebooks locally, combining the full JupyterLab web interface with the convenience of a native desktop application. Users benefit from seamless file integration, session management, and a simplified setup compared to traditional Jupyter server deployments.
JupyterLab desktop application, based on Electron.
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Offers native installers for Windows, macOS, and Linux, eliminating browser-based access and server management for a seamless desktop experience.
Starts JupyterLab sessions instantly without manual server setup, making it the quickest way to get started with notebooks locally as per the README.
Supports double-clicking .ipynb files and drag-and-drop, allowing direct file access and workflow integration from the desktop.
Restores previous sessions with configurations and working directories, aiding in project continuity and reducing setup time.
Provides the jlab command for launching with custom paths, Python environments, and server connections, offering control for advanced use cases.
The README explicitly states it is not actively maintained since August 2025 and lacks security bug fixes, making it risky for production use.
Only compatible with prebuilt extensions; source extensions requiring rebuilding are unsupported, restricting customization options.
Built on Electron, which can introduce higher resource usage and performance overhead compared to lightweight web-based JupyterLab.