January 7, 2020

336 words 2 mins read

Jupyter's configuration system

Jupyter's configuration system

Jupyter's straightforward, out-of-the-box experience has been important for its success in widespread adoption. But good defaults only go so far. Join Afshin Darian, M Pacer, Min Ragan-Kelley, and Matthias Bussonnier to go beyond the defaults and make Jupyter your own.

Talk Title Jupyter's configuration system
Speakers Afshin Darian (Two Sigma
Conference JupyterCon in New York 2018
Conf Tag The Official Jupyter Conference
Location New York, New York
Date August 22-24, 2018
URL Talk Page
Slides Talk Slides
Video

Jupyter’s straightforward, out-of-the-box experience has been important for its success in widespread adoption. But good defaults only go so far. Jupyter’s modular design and powerful, flexible configuration systems inspire users’ creativity and enthusiasm. By enabling people to customize their tools, Jupyter’s configuration systems allow people to determine their own computational destiny. However, with great power comes great fragility; the same config files that enable user freedom and empowerment are a potential source of user frustration and confusion. Because of how Jupyter’s configuration systems work, tracing down the source of a configuration issue can be an arduous task. Join Afshin Darian, M Pacer, Min Ragan-Kelley, and Matthias Bussonnier to go beyond the defaults and make Jupyter your own, focusing on Jupyter’s config systems—how to use them productively and how to debug them when things (inevitably) break. This is all the more important to do today, as the number and diversity of configuration systems is continuing to grow. For instance, Jupyter’s Python applications use the classic traitlets system, a versatile typed system that allows defining a common interface across CLIs, APIs, and configuration files, while JupyterLab introduces new configuration systems, as well as user-friendly interfaces for viewing and setting those configuration values. And managed deployments present their own challenges. Through concrete examples and demos, you’ll explore the ideas needed to use, understand, and debug Jupyter’s configuration system. Some topics will be demonstrations of JupyterLab UIs, while others will show the role of configuration across a number of Jupyter libraries, including nbconvert, the notebook server, JupyterLab LaTeX, and traitlets.config. Topics include:

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