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Containers increase reproducablity and scalablity, and Carme helps data scientists to use containers.

Working with containers allows your infrastructure to become code [this is often referred to as or infrastructure as code (IAC)]. This ensures that others will be able to reproduce your work, and the Carme package system makes sharing your work a snap. Containers are also useful when you want to scale an analysis, perhaps moving from your local machine to a GPU in the cloud or from a single GPU server to a Kubernetes cluster for a classroom Jupyterhub instance.