Does R run under my version of Windows? How do I update packages in my previous version of R? Should I run 32-bit or 64-bit R? Please see the R FAQ for general information about R and the R Windows FAQ for Windows-specific information. Other builds. Patches to this release are incorporated in the r-patched snapshot build. Install Microsoft R Client on Windows.; 5 minutes to read +2; In this article. Microsoft R Client is a free, data science tool for high-performance analytics that you can install on Windows client operating systems. About Microsoft R Client.; 5 minutes to read; In this article. Microsoft R Client is a free, community-supported, data science tool for high performance analytics.R Client is built on top of Microsoft R Open so you can use any open-source R package to build your analytics.
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Microsoft R Client is a free, community-supported, data science tool for high performance analytics. R Client is built on top of Microsoft R Open so you can use any open-source R package to build your analytics. Additionally, R Client includes the powerful RevoScaleR technology and its proprietary functions to benefit from parallelization and remote computing.
R Client allows you to work with production data locally using the full set of RevoScaleR functions, but there are some constraints. Data must fit in local memory, and processing is limited to two threads for RevoScaleR functions. To work with larger data sets or offload heavy processing, you can access a remote production instance of Machine Learning Server from the command line or push the compute context to the remote server. Learn more about its compatibility.
Machine Learning Server vs R ClientHow To Download R Script
Machine Learning Server and Microsoft R Client offer virtually identical R packages, but each one targets different scenarios. R Client is intended for data scientists who create solutions that run locally. Machine Learning Server is commercial software that runs on a range of platforms, at much greater scale, with infrastructure for handling major workloads, on client-server topologies that support remote access over authenticated connections.
You can work with R Client standalone. You can also use it with Machine Learning Server, where you learn and develop on R Client, and then migrate your work to Machine Learning Server or execute it remotely on a Machine Learning Server whenever you need the scale, support, and infrastructure of a server configured for operationalization.
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