Complete documentation on data import / processing and model creation is available here:
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/index.html
Installation
2 options are available, you can download H2O from internet every time you launch a job, or you can install it from HDFS to speed up the process, both options are described below
Option 1 (recommended): Install from HDFS
Automatic upload (recommended)
Run the script found here Upload H2O library to HDFS.
Manual upload
Download H2O : http://h2o-release.s3.amazonaws.com/h2o/rel-yau/3/index.html
Unzip it, go to the R/ folder, and upload the file "h2o_3.20.0.2.tar.gz" to HDFS in the folder of your choice (recommended in /user/h2o/install_R/).
Install to R
Use the following code in you script to install H2O:
# Install the package directly from hdfs. Replace nn1 by the correct value if needed
install.packages('http://nn1:50070/webhdfs/v1/user/h2o/install_R/h2o_3.20.0.2.tar.gz?op=OPEN', repos = NULL, type = 'source')
library(h2o)
If the previous code does not work you can try the alternatives below:
# This line works in the R capsule and notebooks. Replace nn1 by the correct value if needed
download.file('http://nn1:50070/webhdfs/v1/user/hdfs/h2o_3.20.0.2.tar.gz?op=OPEN', destfile = 'h2o_3.20.0.2.tar.gz')
# This line is simpler but only works in the capsule
# system('hdfs dfs -get /user/hdfs/h2o_3.20.0.2.tar.gz', intern = T)
install.packages('h2o_3.20.0.2.tar.gz', repos = NULL, type = 'source')
library(h2o)
Option 2: Install from internet
pkgs <- c("RCurl","jsonlite")
for (pkg in pkgs) {
if (! (pkg %in% rownames(installed.packages()))) { install.packages(pkg) }
}
install.packages("h2o", type="source", repos="http://h2o-release.s3.amazonaws.com/h2o/rel-wright/1/R")
library(h2o)
Connection from R to H2O
# Replace the ip by the correct value
h2o.connect(ip = 'h2o_custom_url.internal.pX', port = 80)
Import data
From HDFS
# Change the url as needed
iris_h2o <- h2o.importFile('hdfs://nn1:8020/user/h2o/data/iris/iris.csv')
From a local R object
# Change the url as needed
iris_h2o <- as.h2o(iris_local)
Creating a new model in H2O
# Create a split for train and test dataset
iris.split <- h2o.splitFrame(iris_h2o)
train <- iris.split[[1]]
test <- iris.split[[2]]
# Create a Random forest model with our dataset as input
rf <- h2o.randomForest(y = 'Species', training_frame = train, validation_frame = test)
# Print the result in console
rf
# Results are also available in the H2O web interface, with more details than this simple print
Saving model to HDFS
# Change the url as needed
h2o.saveModel(rf, 'hdfs://nn1:8020/user/h2o/models/', force = T)
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