Packaging Jupyter notebooks as installable desktop apps using LabConstrictor

2026-03-11

AI Summary

This work proposes LabConstrictor, a novel framework that addresses the challenges of sharing and reproducing open-source computational tools in the life sciences, where Jupyter notebooks are often hindered by complex environmental dependencies and difficult deployment. LabConstrictor introduces, for the first time, an automated packaging mechanism requiring no DevOps expertise, leveraging GitHub Actions to build a CI/CD pipeline that automatically converts notebooks into one-click-installable desktop applications. These applications integrate documentation, version checking, and interactive controls, thereby transforming executable notebooks into application-like experiences. The approach significantly streamlines distribution, enhances accessibility of computational methods, and improves cross-laboratory reusability.

Technology Category

• Constraint Satisfaction and Optimization: Solvers and Tools
• Computer Vision: Applications
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Security and Privacy: Data transparency and provenance

Abstract

Life sciences research depends heavily on open-source academic software, yet many tools remain underused due to practical barriers. These include installation requirements that hinder adoption and limited developer resources for software distribution and long-term maintenance. Jupyter notebooks are popular because they combine code, documentation, and results into a single executable document, enabling quick method development. However, notebooks are often fragile due to reproducibility issues in coding environments, and sharing them, especially for local execution, does not ensure others can run them successfully. LabConstrictor closes this deployment gap by bringing CI/CD-style automation to academic developers without needing DevOps expertise. Its GitHub-based pipeline checks environments and packages notebooks into one-click installable desktop applications. After installation, users access a unified start page with documentation, links to the packaged notebooks, and version checks. Code cells can be hidden by default, and run-cell controls combined with widgets provide an app-like experience. By simplifying the distribution, installation, and sharing of open-source software, LabConstrictor allows faster access to new computational methods and promotes routine reuse across labs.

Problem

• Jupyter notebooks
• software distribution
• reproducibility
• academic software
• deployment

Innovation

• LabConstrictor
• Jupyter notebooks
• desktop application
• CI/CD automation
• reproducibility