How to use H2O with R on HDInsight

By David Smith

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

H2O.ai is an open-source AI platform that provides a number of machine-learning algorithms that run on the Spark distributed computing framework. Azure HDInsight is Microsoft’s fully-managed Apache Hadoop platform in the cloud, which makes it easy to spin up and manage Azure clusters of any size. It’s also easy to to run H2O on HDInsight: H2O AI Platform is available as an application on HDInsight, which pre-installs everything you need as the cluster is created.

You can also drive H2O from R, but the R packages don’t come auto-installed on HDInsight. To make this easy, the Azure HDInsight team has provided a couple of scripts that will install the necessary components on the cluster for you. These include RStudio (to provide an R IDE on the cluster) and the rsparkling package. With these components installed, from R you can:

  • Query data in Spark using the dplyr interface, and add new columns to existing data sets.
  • Convert data for training, validation, and testing to “H2O Frames” in preparation for modeling.
  • Apply any of the machine learning models provided by Sparkling Water to your data, using the distributed computing capabilities provided by the HDInsight platform.

For details on how to install the R components on HDInsight, follow the link below.

Azure Data Lake & Azure HDInsight Blog: Run H2O.ai in R on Azure HDInsight

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