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

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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

Proposed Tracks

  • Real-World Data Engineering

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

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.