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Scientific workflows facilitate computational, data manipulation, andsometimes visualization steps for scientific data analysis. They are vital forreproducing and validating experiments, usually involving computational stepsin scientific simulations and data analysis. These workflows are oftendeveloped by domain scientists using Jupyter notebooks, which are convenientyet face limitations: they struggle to scale with larger data sets, lackfailure tolerance, and depend heavily on the stability of underlying tools andpackages. To address these issues, Jup2Kup has been developed. This softwaresystem translates workflows from Jupyter notebooks into a distributed,high-performance Kubernetes environment, enhancing fault tolerance. It alsomanages software dependencies to maintain operational stability amidst changesin tools and packages.

Jup2Kub: algorithms and a system to translate a Jupyter Notebook pipeline to a fault tolerant distributed Kubernetes deployment