![]() Each individual R session or job remains on a single Docker container (Kubernetes) or compute node (Slurm). RStudio Workbench’s Launcher feature spawns R sessions and jobs on external resource managers such as Kubernetes and Slurm. Q: Will RStudio Workbench’s Launcher run my R jobs in parallel across a cluster? The load balancer ensures that a new R session will go to machine with the most availability, and that features like the admin dashboard and project sharing will scale as you add RStudio nodes. (See scaling for HPC).Ī load-balanced RStudio Workbench cluster is designed to support larger teams of data scientists. Any parallelization across the cores on the server or across the cluster will require the R analyst to write or submit parallel code. Each individual R session remains on a single server. RStudio Workbench’s load balancer balances R sessions across the cluster. Q: Will RStudio Workbench’s load balancer run my R job across a cluster? Refer to the overview on RStudio Workbench with Launcher and FAQ for RStudio Workbench with Launcher and Kubernetes for more information on scaling your R workloads with external resource managers. When expanding a cluster that is configured with Launcher, you can provision additional worker nodes (Kubernetes) or additional compute nodes (Slurm) separate from the base installation of RStudio Workbench. Scaling out horizontally - When expanding a load-balanced cluster, you will need to provision additional servers with RStudio Workbench and the required R and system dependencies. Whereas R sessions and jobs that are spawned via Launcher will be submitted alongside other jobs on your external resource manager such as Kubernetes or Slurm. Shared workloads and resource management - R sessions and jobs that are running on a load-balanced cluster will not be aware of workloads running on your external resource manager such as Kubernetes and Slurm and could overload and oversubscribe the system resources. Launcher allows you to configure RStudio Workbench with an external resource manager such as Kubernetes or Slurm. The Load Balancer allows you to configure two or more servers with RStudio Workbench and balance R sessions and jobs between servers. Launcher?īoth RStudio Workbench's Load Balancer and Launcher are designed to support larger teams of data scientists. Q: What is the difference between RStudio Workbench's Load Balancer vs. The tool includes features for project sharing, collaborative editing, session management, and IT administration tools like authentication, audit logs, and server performance metrics. RStudio Workbench (previously RStudio Server Pro) is designed to help your organization scale for a team of R users. Hadoop, Spark, Tensorflow, Oracle BDA, Microsoft R Server, Aster, H2O.ai R syntax is used to construct pipelines, and R is used to analyze results. Heavy lifting is done by a different compute engine on the cluster. Local: parallel, Rmpi, snow, Rcpp parallel Ĭluster: RStudio Workbench + Launcher, Kubernetes, Slurm, LSF, Torque, Docker īig data, black box routines that require fitting a model against an entire domain space. R must be installed on all compute nodes. Submit code in batch jobs on compute R processes. RStudio Server, RStudio Workbench + Load Balancer, RStudio Workbench + LauncherĮmbarrassingly parallel tasks like: bootstrapping, cross validation, scoring, model fitting on independent groupsĭevelop code in an interactive R session in RStudio. Includes loading data subsets from files or warehousesĬreate a platform to support large-scale individual interactive R session(s) and jobs The following table presents an overview of the three most common cases. The first step is to determine the type of scale you are hoping to achieve. Can RStudio Workbench or RStudio Server help? Q: I want to develop a platform to scale R for my organization. The following document presents some FAQs for scaling R and the RStudio IDE.
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