Take Advantage of Scalable Cloud Compute Directly from Your R Session, with doAzureParallel
Re-posted from the Azure blog.
For users of the R language, scaling up their work to take advantage of cloud compute has generally been a complex undertaking. We are therefore excited to announce doAzureParallel, a lightweight R package built on Azure Batch that allows you to easily use Azure's flexible compute resources right from your R session. doAzureParallel complements Microsoft R Server and provides the infrastructure for you to run massively parallel simulations.
The doAzureParallel package is a parallel backend for the popular foreach package that lets you execute multiple processes across a cluster of Azure virtual machines. With just a few lines of code, the package helps you create and manage a cluster in Azure, and register it as a parallel backend to be used with foreach.
With doAzureParallel, there is no need to manually create, configure and manage a cluster of individual VMs. Running your scale jobs is as easy running algorithms on your local machine. With Azure Batch's autoscaling capabilities, you can also increase or decrease your cluster size to fit your workloads, saving you time and money. doAzureParallel also uses the Azure Data Science Virtual Machine (DSVM), allowing Azure Batch to easily and quickly configure the appropriate environment in as little time as possible.
doAzureParallel is ideal for running embarrassingly parallel work such as parametric sweeps or Monte Carlo simulations, making it a great fit for many financial modelling algorithms (back-testing, portfolio scenario modelling, etc).
There is no additional cost for these capabilities – you only pay for the Azure VMs you use.
For more detailed information, including installation steps and demo code, check out the original blog post here.
CIML Blog Team