Interactive High-Performance Computing with JupyterLab
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.
Practical information:
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- Prerequisites: Experience in Python; A personal institutional email address is required to register for JSC training courses
- Price: Price upon request
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Course content
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.
The following topics are covered:
- Introduction to Jupyter
- Parallel computing using Jupyter
- Interactive & in-situ visualization
- From ipywidgets to dashboards
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