Interactive exploration and analysis of large amounts of data from scientific simulations, in-situ visualization and application control are convincing scenarios for explorative sciences. Based on the open source software Jupyter or JupyterLab, a way has been available for some time now that combines interactive with reproducible computing while at the same time meeting the challenges of support for the wide range of different software workflows.

Even on supercomputers, the method enables the creation of documents that combine live code with narrative text, mathematical equations, visualizations, interactive controls, and other extensive output. However, a number of challenges must be mastered in order to make existing workflows ready for interactive high-performance computing. With so many possibilities, it's easy to lose sight of the big picture. This course provides a detailed introduction to interactive high-performance computing.

The following topics are covered:

  • Introduction to JupyterLab
  • Customizing JupyterLab
  • JupyterLab on HPC resources
  • Using JupyterLab as a proxy
  • Remote visualization within JupyterLab
  • Jupyter Interactive Widget Ecosystem
  • Utilizing supercomputers with JupyterLab
  • Extending JupyterLab
  • Jupyter-JSC under the hood

Prerequisites:

Experience in Python

Date:

20-22 April 2021, 09:00-13:00

Application
Registrations are only considered until 1 April 2021, the maximal number of participants is limited. Applicants will be notified, whether they are accepted for participitation.

Instructors:

Jens Henrik Göbbert, Alice Grosch, Jülich Supercomputing Centre

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