The Jupyter architecture (message specification, kernels, notebook documents) allows for multiple end-user applications or Jupyter frontends. The traditional application for Jupyter is the classic Jupyter Notebook, which began as the IPython Notebook in 2011. Since then, the Jupyter Notebook frontend has become a critical tool for millions of users doing interactive computing in scientific research, education, and commercial data science, machine learning, and AI. In recent years, a number of more modern end-user applications built on top of the Jupyter architecture have emerged, including Rodeo, CoCalc, Stencila, nteract, and JupyterLab. Project Jupyter is embracing the flowering of end-user applications and taking steps to document and formalize the abstractions across all Jupyter frontends.
Kyle Kelley and Brian Granger offer a broad look at Jupyter frontends, describing their common aspects and explaining how their differences help Jupyter reach a broader set of users. They also share ongoing challenges in building these frontends (real-time collaboration, security, rich output, different Markdown formats, etc.) as well as their ongoing work to address these questions.
Kyle Kelley is a senior software engineer at Netflix, a maintainer on nteract.io, and a core developer of the IPython/Jupyter project. He wants to help build great environments for collaborative analysis, development, and production workloads for everyone, from small teams to massive scale.
Brian Granger is an associate professor of physics and data science at Cal Poly State University in San Luis Obispo. Brian is a leader of the IPython project, cofounder of Project Jupyter, and an active contributor to a number of other open source projects focused on data science in Python. Recently, he cocreated the Altair package for statistical visualization in Python. He is a advisory board member of NumFOCUS and a faculty fellow of the Cal Poly Center for Innovation and Entrepreneurship.
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