Introducing H2O Lagrange (2.6.0.11) to R

September 1, 2014
By

(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 (2.6.0.11),
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 (2.6.0.11) 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
install.packages("h2o",
                 repos = c("http://h2o-release.s3.amazonaws.com/h2o/rel-lagrange/11/R", getOption("repos")))
library(h2o)
localH2O <- h2o.init()

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

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 support@0xdata.com. Until then, happy analyzing.

Related Blogs

R-bloggers

To leave a comment for the author, please follow the link and comment on his blog: 0xdata Blog.

R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...



If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.