# Using Enterprise Steam with R¶

This section describes how to use the Enterprise Steam for R. Note that this requires “urltools”. Refer to https://github.com/Ironholds/urltools/ for more information.

1. Go to https://s3.amazonaws.com/steam-release/enterprise-steam/latest-stable.html to retrieve the latest version of Enterprise Steam.
3. Open a Terminal window, and navigate to the location where the Enterprse Steam file was downloaded. For example:
cd ~/Downloads

1. Install Enterprise Steam for R using R CMD INSTALL <file_name>. For example:
R CMD INSTALL h2osteam_1.5.1.tar.gz


## Available Functions¶

### get_h2o_cluster¶

Use the get_h2o_cluster to retrieve information about a specific cluster using the cluster name.

> h2osteam.get_h2o_cluster(conn, 'first-cluster-from-R')
$id [1] 109$connect_params
$connect_params$ip
[1] "steam.0xdata.loc"

$connect_params$port
[1] 9999

$connect_params$cookies
[1] "first-cluster-from-R=YW5nZWxhOnVoYzdyeTNtM3g="

$connect_params$context_path
[1] "jsmith_first-cluster-from-R"

$connect_params$https
[1] TRUE

$connect_params$insecure
[1] TRUE


### get_h2o_clusters¶

Use the get_h2o_clusters to retrieve all running H2O clusters accessible to current user

> h2osteam.get_h2o_clusters(conn)


### show_profiles¶

Use the show_profiles to show available profiles.

> h2osteam.show_profiles(conn)


### start_h2o_cluster¶

Use the start_h2o_cluster function to create a new cluster. This function takes the following parameters:

• cluster_name: Specify a name for this cluster.
• profile_name: Specify the profile to use for this cluster.
• num_nodes: Specify the number of nodes for the cluster.
• node_memory: Specify the amount of memory that should be available on each node.
• v_cores: Specify the number of virtual cores.
• n_threads: Specify the number of threads (CPUs) to use in the cluster. Specify 0 to use all available threads.
• max_idle_time: Specify the maximum number of hours that the cluster can be idle before gracefully shutting down. Specify 0 to turn off this setting and allow the cluster to remain idle for an unlimited amount of time.
• max_uptime: Specify the maximum number of hours that the cluster can be running. Specify 0 to turn off this setting and allow the cluster to remain up for an unlimited amount of time.
• extramempercent: Specify the amount of extra memory for internal JVM use outside of the Java heap. This is a percentage of memory per node. The default (and recommended) value is 10%.
• h2o_engine_id: The H2O engine version that this cluster will use. Note that the Enterprise Steam Admin is responsible for adding engines to Enterprise Steam.
• yarn_queue: If your cluster contains queues for allocating cluster resources, specify the queue for this cluster. Note that the YARN Queue cannot contain spaces.
> cluster_config <- h2osteam.start_h2o_cluster(conn = conn,
cluster_name = "first-cluster-from-R",
profile_name = "default",
num_nodes = 2,
node_memory = "30g",
h2o_version = "3.26.0.11",
max_idle_time = 1,
max_uptime = 1)

# Call the cluster to retrieve its ID and configuration params.
> cluster_config
$id [1] 109$connect_params
$connect_params$ip
[1] "steam.0xdata.loc"

$connect_params$port
[1] 9999

$connect_params$cookies
[1] "first-cluster-from-R=YW5nZWxhOnVoYzdyeTNtM3g="

$connect_params$context_path
[1] "jsmith_first-cluster-from-R"

$connect_params$https
[1] TRUE

$connect_params$insecure
[1] TRUE


Note that after you create a cluster, you can immediately connect to that cluster and begin using H2O. Refer to the following for a complete R example.

> library(h2o)
> h2o.connect(config = cluster_config)

# import the cars dataset
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
> cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
> cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])

# set the predictor names and the response column name
> predictors <- c("displacement","power","weight","acceleration","year")
> response <- "economy_20mpg"

# split into train and validation sets
> cars.split <- h2o.splitFrame(data = cars,ratios = 0.8, seed = 1234)
> train <- cars.split[[1]]
> valid <- cars.split[[2]]

# your 'y' the response column, training_frame, and validation_frame
> cars_gbm <- h2o.gbm(x = predictors,
y = response,
training_frame = train,
validation_frame = valid,
seed = 1234)

# print the auc for your model
> print(h2o.auc(cars_gbm, valid = TRUE))


### stop_h2o_cluster¶

Use the stop_h2o_cluster function to stop a cluster.

> h2osteam.stop_h2o_cluster(conn, cluster_config)