Presentation: JupyterLab: The Next Generation Jupyter Web Interface

Track: ML in Action

Location: Cyril Magnin III

Duration: 12:50pm - 1:00pm

Day of week: Tuesday

Share this on:

Abstract

Project Jupyter provides building blocks for interactive and exploratory computing, which make science and data science reproducible across over 40 programming languages (Python, Julia, R, etc.). Central to the project is the Jupyter Notebook, a web-based interactive computing platform that allows users to author “computational narratives” that combine live code, equations, narrative text, visualizations, interactive dashboards, and other media. We will give an overview of JupyterLab, the next generation of the Jupyter Notebook.

JupyterLab goes beyond the classic Jupyter Notebook by providing a flexible and extensible web application with a set of reusable components. Users can arrange multiple notebooks, text editors, terminals, output areas, and custom components using tabs and collapsible sidebars. These components are carefully designed to enable the user to use them together or separately (for example, a user can send code from a file to a console with a keystroke, or can pop out an output from a notebook to work with it alone).

JupyterLab is based on a flexible application plugin system provided by PhosphorJS that makes it easy to customize existing components or extend it with new components. For example, users can install or write third-party plugins to view custom file formats, such as GeoJSON, interact with external services, such as Dask or Apache Spark, or display their data in effective and useful ways, such as interactive maps, tables, or plots.

Note: This is a short talk. Short talks are 10-minute talks designed to offer breadth across the areas of machine learning, artificial intelligence, and data engineering. The short talks are focused on the tools and practices of data science with an eye towards the software engineer.

Speaker: Jason Grout

Scientific Software Developer @Bloomberg & JupyterLab / Sage Core Contributor

I am a Jupyter developer working at Bloomberg in New York. I work primarily on JupyterLab and the interactive widget system. I have also contributed to the open source Sage mathematical software system for many years. I co-organize the PyDataNYC Meetup. Previously, I was an assistant professor of mathematics at Drake University in Des Moines, Iowa.

Find Jason Grout at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.