Scale up your parallel R workloads with containers and doAzureParallel

By Guest Blogger

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by JS Tan (Program Manager, Microsoft)

The R language is by and far the most popular statistical language, and has seen massive adoption in both academia and industry. In our new data-centric economy, the models and algorithms that data scientists build in R are not just being used for research and experimentation. They are now also being deployed into production environments, and directly into products themselves.

However, taking your workload in R and deploying it at production capacity, and at scale, is no trivial matter. Because of R’s rich and robust package ecosystem, and the many versions of R, reproducing the environment of your local machine in a production setting can be challenging. Let alone ensuring your model’s reproducibility!

This is why using containers is extremely important when it comes to operationalizing your R workloads. I’m happy to announce that the doAzureParallel package, powered by Azure Batch, now supports fully containerized deployments. With this migration, doAzureParallel will not only help you scale out your workloads, but will also do it in a completely containerized fashion, letting your bypass the complexities of dealing with inconsistent environments. Now that doAzureParallel runs on containers, we can ensure a consistent immutable runtime while handling custom R versions, environments, and packages.

By default, the container used in doAzureParallel is the ‘rocker/tidyverse:latest‘ container that is developed and maintained as part of the rocker project. For most cases, and especially for beginners, this image will contain most of what is needed. However, as users become more experienced or have more complex deployment requirements, they may want to change the Docker image that is used, or even build their own. doAzureParallel supports both those options, giving you flexibility (without any compromise on reliability). Configuring the Docker image is easy. Once you know which Docker image you want to use, you can simply specify its location in the cluster configuration and doAzureParallel will just know to use it when provisioning subsequent clusters. More details on configuring your Docker container settings with doAzureParallel are included in the documentation.

With this release, we hope to unblock many users who are looking to take their R models, and scale it up in the cloud. To get started with doAzureParallel, visit our Github page. Please give it a try and let us know if you have questions, feedback, or suggestions, or via email at

Github (Azure): doAzureParallel

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Source:: R News

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