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{ | ||
"environment": "web", | ||
"format": "html", | ||
"prefix": "https://storage.googleapis.com", | ||
"mainga": "UA-49880327-14", | ||
"updated": "2024-05-27T08:53:14Z", | ||
"id": "container-workshop", | ||
"duration": 140, | ||
"title": "Container-driven Reproducible Research Computing Workshop", | ||
"authors": "Ronald Lencevičius", | ||
"summary": "Do you enjoy working on data science but not installing the software environment? Do you have nightmares about software library dependencies? Is your laptop slowing you down and want to use a more powerful remote server or cloud platform? In this workshop, we will show a reproducible and user-friendly approach to creating research environments using development containers. You will learn how to use Visual Studio Code to create containerized R and/or Python environments, customize them with extensions, Jupyterlab, and RStudio, and deploy them on NSF supported cloud instances like Jetstream2... All at the click of a button!", | ||
"source": "1XDhWE0jqTjqXMU26Vj03zWfl3R6-gjddJ2JvTdTnSA0", | ||
"theme": "web", | ||
"status": [ | ||
"draft" | ||
], | ||
"category": [ | ||
"web" | ||
], | ||
"tags": [], | ||
"feedback": "https://github.com/UCSB-PSTAT/computing/issues", | ||
"url": "container-workshop" | ||
} |
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