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Introducing H2O Lagrange ( to R


By Team | minute read | August 26, 2014

Category: Uncategorized
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From my perspective the most important event that happened atuseR! 2014  was that I got to meetthe 0xdata team  and now, long story short,here I am introducing the latest version of H2 O, labeledLagrange ( ,to the R and greater data science  communities. Beforejoining 0xdata, I was working at a competitor on a rival project and wasrepeatedly asked why my generalized linear model analytic didn’t run as fast asH2 O’s GLM. The answer then as it is now is the same – becauseH2 O has a cutting edge distributed in-memory parallel computingarchitecture – but I no longer receive an electric shock every time I say so.

For those hearing about H2 O for the first time, it is an open-sourcedistributed in-memory data analysis tool designed for extremely large data setsand the H2 O Lagrange ( release provides scalable solutionsfor the followinganalysis techniques :

In my first blog post at 0xdata, I wanted to keep it simple and make sure Rusers know how to get the h2o package, which is cross-referenced on theHigh-Performance and Parallel Computing andMachine and Statistical Learning CRAN Task Views , up and running on theircomputers. 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 webbrowser to http://localhost:54321/  to give the H2 O web interface aonce over and then hop over to0xdata’s YouTube channel  for somein-depth talks.
Over the coming weeks we at 0xdata will continue toblog  about how to use H2 Othrough R and other interfaces. If there is a particular use case you would liketo see addressed, join ourh2ostream  Google Groupsconversation or e-mail us at . Until then, happy analyzing.
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