Introducing H2O Lagrange ( to R

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From my perspective the most important event that happened at useR! 2014 was that I got to meet the 0xdata team and now, long story short, here I am introducing the latest version of H2O, labeled Lagrange (, to the R and greater data science communities. Before joining 0xdata, I was working at a competitor on a rival project and was repeatedly asked why my generalized linear model analytic didn’t run as fast as H2O’s GLM. The answer then as it is now is the same — because H2O has a cutting edge distributed in-memory parallel computing architecture — but I no longer receive an electric shock every time I say so.

For those hearing about H2O for the first time, it is an open-source distributed in-memory data analysis tool designed for extremely large data sets and the H2O Lagrange ( release provides scalable solutions for the following analysis techniques:

In my first blog post at 0xdata, I wanted to keep it simple and make sure R users know how to get the h2o package, which is cross-referenced on the High-Performance and Parallel Computing and Machine and Statistical Learning CRAN Task Views, up and running on their computers. To so do, open an R console of your choice and type

# Download, install, and initialize the H2O package
                 repos = c("", getOption("repos")))
localH2O <- h2o.init()

# List and run some demos to see H2O at work
demo(package = "h2o")

After you are done experimenting with the demos in R, you can open up a web browser to http://localhost:54321/ to give the H2O web interface a once over and then hop over to 0xdata’s YouTube channel for some in-depth talks.

Over the coming weeks we at 0xdata will continue to blog about how to use H2O through R and other interfaces. If there is a particular use case you would like to see addressed, join our h2ostream Google Groups conversation or e-mail us at [email protected]. Until then, happy analyzing.

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