Introducing H2O Lagrange ( to R

September 1, 2014

(This article was first published on 0xdata Blog, and kindly contributed to R-bloggers)

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
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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