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Volume 357 - The New Era of Multi-Messenger Astrophysics (Asterics2019) - Main session
Efficient remote interactive pipelines using CASA and Jupyter
A. Keimpema,* M. Kettenis, D. Small, T.J. Dijkema, A. Szomoru
*corresponding author
Full text: Not available
In this paper we describe the Jupyter kernel which we have created for CASA. Jupyter notebooks offer great potential for remote data reduction, allowing users to interactively perform data reduction inside a web browser. The Jupyter kernel allows CASA tasks to be executed from within a Jupyter notebook and includes function wrappers which embed results from CASA tasks into the notebook.

Generally, when running a pipeline in an iterative fashion, at each iteration only a subset of pipeline steps needs to be (re-)executed. We have implemented a binding to a minimal re-computation framework which automates the process of determining which pipeline steps need to be re-executed, greatly increasing efficiency.

We have made all source code available in a public Git repository, and for ease of deployment both Singularity and Docker images have also been made available. To demonstrate the effectiveness of our Jupyter kernel for CASA we have made a public demonstration service available at \url{} in which users can perform a full CASA data reduction inside a Jupyter notebook.
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