Abstract

Cloud computing can accelerate scientific workflows, discovery, and collaborations by reducing research and data friction. We describe the deployment of Unidata and JupyterHub technologies on the NSF-funded XSEDE Jetstream cloud. With the aid of virtual machines and Docker technology, we deploy a... Show moreCloud computing can accelerate scientific workflows, discovery, and collaborations by reducing research and data friction. We describe the deployment of Unidata and JupyterHub technologies on the NSF-funded XSEDE Jetstream cloud. With the aid of virtual machines and Docker technology, we deploy a Unidata JupyterHub server co-located with a Local Data Manager (LDM), THREDDS data server (TDS), and RAMADDA geoscience content management system. We provide Jupyter Notebooks and the pre-built Python environments needed to run them. The notebooks can be used for instruction and as templates for scientific experimentation and discovery. We also supply a large quantity of NCEP forecast model results to allow data-proximate analysis and visualization. In addition, users can transfer data using Globus command line tools, and perform their own data-proximate analysis and visualization with Notebook technology. These data can be shared with others via a dedicated TDS server for scientific distribution and collaboration. There are many benefits of this approach. Not only is the cloud computing environment fast, reliable and scalable, but scientists can analyze, visualize, and share data using only their web browser. No local specialized desktop software or a fast internet connection is required. This environment will enable scientists to spend less time managing their software and more time doing science. Show less