This post will demonstrate setting up your You’d have access to a Kubernetes service running at your institutionĮxperimented with Google Kubernetes Engine (GKE) and Amazon’s Elastic Many of the cloud providers have Kubernetes services (and it’s also possible The setup described here) allows one to run a Python Dask cluster accessed via a A similar effort (which I heavily borrowed from in developing Standard platform that can be used on various cloud providers. And there is now something called AWSīut doing it via Kubernetes allows you to build upon an industry Some tools that are no longer actively maintained are Machines on AWS easily from the command line on your There have been (and are) approaches to starting up a cluster of Provider such as Google Cloud Platform (GCP) or Amazon Web Services (AWS), youĬan easily start up a cluster of (virtual) machines. By using the Kubernetes service of a cloud YouĬan think of the containers as lightweight Linux machines on which youĬan do your computation. Kubernetes is a platform for managing containers. Why use Kubernetes to start a cluster in the cloud? One advantage of doing this in the cloud is the ability to easily scale the number and type of (virtual) machines across which you run your parallel computation. This allows you to easily doing parallel computing in R in the cloud. In this post, I’ll demonstrate that you can easily use the future package in R on aĬluster of machines running in the cloud, specifically on a Kubernetes This is a guest post by Chris Paciorek, Department of Statistics, University of California at Berkeley.
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