Make nthreads do something useful.

Set default h2o.init() to nthreads 2.
Simplify default h2o.init() call in examples.
Fix h2o.rebalance documentation.

R/h2o-package/R/ParseImport.R

@@ -57,7 +57,7 @@ h2o.clusterInfo <- function(client) {
   cat("    H2O cluster allowed cores: ", allowedCPU, "\n")
   cat("    H2O cluster healthy:       ", clusterHealth, "\n")
 
-  cpusLimited = sapply(nodeInfo, function(x) { x[['num_cpus']] > 1 && x[['cpus_allowed']] == 1 })
+  cpusLimited = sapply(nodeInfo, function(x) { x[['num_cpus']] > 1 && x[['nthreads']] != 1 && x[['cpus_allowed']] == 1 })
   if(any(cpusLimited))
     warning("Number of CPU cores allowed is limited to 1 on some nodes.  To remove this limit, set environment variable 'OPENBLAS_MAIN_FREE=1' before starting R.")
 }

R/h2o-package/R/Wrapper.R

@@ -3,13 +3,14 @@
 # 2) If user does want to start H2O and running locally, attempt to bring up H2O launcher
 # 3) If user does want to start H2O, but running non-locally, print an error
 h2o.init <- function(ip = "127.0.0.1", port = 54321, startH2O = TRUE, forceDL = FALSE, Xmx,
-                     beta = FALSE, assertion = TRUE, license = NULL, max_mem_size = NULL, min_mem_size = NULL,
+                     beta = FALSE, assertion = TRUE, license = NULL, nthreads = 2, max_mem_size = NULL, min_mem_size = NULL,
                      ice_root = NULL, strict_version_check = TRUE) {
   if(!is.character(ip)) stop("ip must be of class character")
   if(!is.numeric(port)) stop("port must be of class numeric")
   if(!is.logical(startH2O)) stop("startH2O must be of class logical")
   if(!is.logical(forceDL)) stop("forceDL must be of class logical")
   if(!missing(Xmx) && !is.character(Xmx)) stop("Xmx must be of class character")
+  if(!is.numeric(nthreads)) stop("nthreads must be of class numeric")
   if(!is.null(max_mem_size) && !is.character(max_mem_size)) stop("max_mem_size must be of class character")
   if(!is.null(min_mem_size) && !is.character(min_mem_size)) stop("min_mem_size must be of class character")
   if(!is.null(max_mem_size) && !regexpr("^[1-9][0-9]*[gGmM]$", max_mem_size)) stop("max_mem_size option must be like 1g or 1024m")
@@ -41,7 +42,7 @@ h2o.init <- function(ip = "127.0.0.1", port = 54321, startH2O = TRUE, forceDL =
       stop(paste("Cannot connect to H2O server. Please check that H2O is running at", myURL))
     else if(ip == "localhost" || ip == "127.0.0.1") {
       cat("\nH2O is not running yet, starting it now...\n")
-      .h2o.startJar(max_memory = max_mem_size, min_memory = min_mem_size, beta = beta, assertion = assertion, forceDL = forceDL, license = license, ice_root = ice_root)
+      .h2o.startJar(nthreads, max_memory = max_mem_size, min_memory = min_mem_size, beta = beta, assertion = assertion, forceDL = forceDL, license = license, ice_root = ice_root)
       count = 0; while(!url.exists(myURL) && count < 60) { Sys.sleep(1); count = count + 1 }
       if(!url.exists(myURL)) stop("H2O failed to start, stopping execution.")
     } else stop("Can only start H2O launcher if IP address is localhost.")
@@ -219,7 +220,7 @@ h2o.clusterStatus <- function(client) {
 #     h2o.shutdown(new("H2OClient", ip=ip, port=port), prompt = FALSE)
 # }
 
-.h2o.startJar <- function(max_memory = NULL, min_memory = NULL, beta = FALSE, assertion = TRUE, forceDL = FALSE, license = NULL, ice_root) {
+.h2o.startJar <- function(nthreads = -1, max_memory = NULL, min_memory = NULL, beta = FALSE, assertion = TRUE, forceDL = FALSE, license = NULL, ice_root) {
   command <- .h2o.checkJava()
 
   if (! is.null(license)) {
@@ -278,6 +279,7 @@ http://www.oracle.com/technetwork/java/javase/downloads/jdk7-downloads-1880260.h
   args <- c(args, "-ip", "127.0.0.1")
   args <- c(args, "-port", "54321")
   args <- c(args, "-ice_root", slashes_fixed_ice_root)
+  if(nthreads > 0) args <- c(args, "-nthreads", nthreads)
   if(beta) args <- c(args, "-beta")
   if(!is.null(license)) args <- c(args, "-license", license)
 

R/h2o-package/man/AAA_DownloadAndStartBeforeExamples.Rd

@@ -8,5 +8,5 @@ Download H2O jar file and Start H2O cloud before examples run (for H2O developer
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 }

R/h2o-package/man/Extremes.Rd

@@ -26,7 +26,7 @@ Returns the maximum or minimum over all the input arguments. For a \code{\linkS4
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 ausPath = system.file("extdata", "australia.csv", package = "h2o")
 australia.hex = h2o.importFile(localH2O, path = ausPath, key = "australia.hex")
 min(australia.hex)

R/h2o-package/man/apply.Rd

@@ -30,7 +30,7 @@ Produces a new \code{\linkS4class{H2OParsedData}} of the output of the applied f
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris.csv", package="h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
 summary(apply(iris.hex, 1, sum))

R/h2o-package/man/as.Date.H2OParsedData.Rd

@@ -25,7 +25,7 @@ Note that resulting values are in milliseconds and not the seconds stored by the
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 dates = c("Fri Jan 10 00:00:00 1969 -0800",
           "Tue Jan 10 04:00:00 2068 -0800",
           "Mon Dec 30 01:00:00 2002 -0800",

R/h2o-package/man/as.data.frame.Rd

@@ -14,7 +14,7 @@ Returns a data frame in the R environment. Note that this call establishes the d
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package="h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.data.frame <- as.data.frame(prostate.hex)

R/h2o-package/man/as.factor.Rd

@@ -14,7 +14,7 @@ Returns the original object of class \code{\linkS4class{H2OParsedData}}, with th
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package="h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.hex[,4] = as.factor(prostate.hex[,4])

R/h2o-package/man/as.h2o.Rd

@@ -36,11 +36,11 @@ Converts an R object to an H2O Parsed data object.
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 
 data(iris)
 summary(iris)
 iris.r <- iris
 iris.h2o <- as.h2o(localH2O, iris.r, key="iris.h2o")
 class(iris.h2o)
-}
+}

R/h2o-package/man/as.matrix.Rd

@@ -14,7 +14,7 @@ Returns a matrix in the R environment. Note that this call establishes the data
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package="h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.matrix <- as.matrix(prostate.hex)

R/h2o-package/man/as.table.Rd

@@ -14,7 +14,7 @@ Returns a table in the R environment. Note that this call establishes the data s
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package="h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.table <- as.table(prostate.hex)

R/h2o-package/man/cbind.H2OParsedData.Rd

@@ -21,9 +21,9 @@ An \code{\linkS4class{H2OParsedData}} object containing the combined \dots argum
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package="h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.cbind = cbind(prostate.hex, prostate.hex)
 head(prostate.cbind)
-}
+}

R/h2o-package/man/colnames.Rd

@@ -15,7 +15,7 @@ colnames(x, do.NULL = TRUE, prefix = "col")
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris.csv", package="h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
 summary(iris.hex)

R/h2o-package/man/ddply.H2OParsedData.Rd

@@ -55,7 +55,7 @@ Hadley Wickham (2011). The Split-Apply-Combine Strategy for Data Analysis. Journ
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 
 # Import iris dataset to H2O
 irisPath = system.file("extdata", "iris_wheader.csv", package = "h2o")

R/h2o-package/man/diff.H2OParsedData.Rd

@@ -21,8 +21,8 @@ An \code{\linkS4class{H2OParsedData}} object with a single numeric column contai
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package="h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 diff(prostate.hex$AGE)
-}
+}

R/h2o-package/man/doNotCallThisMethod...Unsupported.Rd

@@ -22,7 +22,7 @@ doNotCallThisMethod...Unsupported(h2o, key)
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 hex <- as.h2o(localH2O, iris)
 m <- h2o.randomForest(x = 1:4, y = 5, data = hex)
 doNotCallThisMethod...Unsupported(localH2O, m@key)

R/h2o-package/man/h2o.SpeeDRF.Rd

@@ -91,10 +91,9 @@ An object of class \code{\linkS4class{H2OSpeeDRFModel}} with slots key, data, va
 }
 
 \examples{
-# Currently still in beta, so don't automatically run example
 \dontrun{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, beta = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris.csv", package = "h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
 h2o.SpeeDRF(x = c(2,3,4), y = 5, data = iris.hex, ntree = 50, depth = 100)

R/h2o-package/man/h2o.addFunction.Rd

@@ -32,6 +32,6 @@ This method is intended to be used in conjunction with \code{\link{ddply.H2OPars
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 h2o.addFunction(localH2O, function(x) { 2*x + 5 }, "simpleFun")
 }

R/h2o-package/man/h2o.anomaly.Rd

@@ -25,7 +25,7 @@ Returns an \code{\linkS4class{H2OParsedData}} object with a single column contai
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package = "h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.dl = h2o.deeplearning(x = 3:9, y = 2, data = prostate.hex, autoencoder = TRUE,

R/h2o-package/man/h2o.anyFactor.Rd

@@ -23,7 +23,7 @@ Returns a logical value indicating whether any of the columns in \code{x} are fa
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris_wheader.csv", package="h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath)
 h2o.anyFactor(iris.hex)

R/h2o-package/man/h2o.assign.Rd

@@ -12,7 +12,7 @@
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package = "h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 psa.qs = quantile(prostate.hex$PSA)

R/h2o-package/man/h2o.clearLogs.Rd

@@ -15,7 +15,7 @@ h2o.clearLogs()
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 h2o.startLogging()
 ausPath = system.file("extdata", "australia.csv", package="h2o")
 australia.hex = h2o.importFile(localH2O, path = ausPath)

R/h2o-package/man/h2o.clusterInfo.Rd

@@ -20,6 +20,6 @@ h2o.clusterInfo(client)
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 h2o.clusterInfo(localH2O)
 }

R/h2o-package/man/h2o.clusterStatus.Rd

@@ -47,6 +47,6 @@ Furthermore, for each node, this function displays:
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 h2o.clusterStatus(localH2O)
 }

R/h2o-package/man/h2o.confusionMatrix.Rd

@@ -23,7 +23,7 @@ Returns a confusion matrix with the actual value counts along the rows and the p
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package="h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.gbm = h2o.gbm(x = 3:9, y = 2, data = prostate.hex)

R/h2o-package/man/h2o.coxph.Rd

@@ -75,7 +75,7 @@ An object of class \code{\linkS4class{H2OCoxPHModel}}.
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 
 library(survival)
 bladder.hex <- as.h2o(localH2O, bladder, key = "bladder.hex")

R/h2o-package/man/h2o.createFrame.Rd

@@ -35,7 +35,7 @@ Returns an H2O data frame.
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, beta = TRUE)
+localH2O = h2o.init(beta = TRUE)
 myframe = h2o.createFrame(localH2O, 'myframekey', rows = 1000, cols = 10, 
                           seed = -12301283, randomize = TRUE, value = 0, real_range = 2.0,
                           categorical_fraction = 0.2, factors = 100, 

R/h2o-package/man/h2o.cut.Rd

@@ -20,7 +20,7 @@ A \code{\linkS4class{H2OParsedData}} object containing the factored data with in
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris_wheader.csv", package="h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
 summary(iris.hex)

R/h2o-package/man/h2o.deepfeatures.Rd

@@ -27,7 +27,7 @@ response with which the model was trained, then the response column will be prep
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package = "h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.dl = h2o.deeplearning(x = 3:9, y = 2, data = prostate.hex, hidden = c(100, 200),

R/h2o-package/man/h2o.deeplearning.Rd

@@ -98,7 +98,7 @@ An object of class \code{\linkS4class{H2ODeepLearningModel}} with slots key, dat
 
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris.csv", package = "h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath)
 h2o.deeplearning(x = 1:4, y = 5, data = iris.hex, activation = "Tanh", 

R/h2o-package/man/h2o.downloadAllLogs.Rd

@@ -22,7 +22,7 @@ h2o.downloadAllLogs(client, dirname = ".", filename = NULL)
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 h2o.downloadAllLogs(localH2O, dirname = getwd(), filename = "h2o_logs.log")
 file.info(paste(getwd(), "h2o_logs.log", sep = .Platform$file.sep))
 file.remove(paste(getwd(), "h2o_logs.log", sep = .Platform$file.sep))

R/h2o-package/man/h2o.downloadCSV.Rd

@@ -24,7 +24,7 @@ WARNING: Files located on the H2O server may be very large! Make sure you have e
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris_wheader.csv", package = "h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath)
 

R/h2o-package/man/h2o.exec.Rd

@@ -21,7 +21,7 @@ A \code{\linkS4class{H2OParsedData}} object containing the result of the express
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 hex <- as.h2o(localH2O, iris)
 res1 = h2o.exec(hex[,1] + hex[,2])
 head(res1)

R/h2o-package/man/h2o.exportFile.Rd

@@ -32,11 +32,11 @@ None.  (The function will stop if it fails.)
 \examples{
 \dontrun{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris.csv", package = "h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath)
 h2o.exportFile(iris.hex, path = "/path/on/h2o/server/filesystem/iris.csv")
 h2o.exportFile(iris.hex, path = "hdfs://path/in/hdfs/iris.csv")
 h2o.exportFile(iris.hex, path = "s3n://path/in/s3/iris.csv")
 }
-}
+}

R/h2o-package/man/h2o.gapStatistic.Rd

@@ -45,7 +45,7 @@ Tibshirani, R., Walther, G. and Hastie, T. (2000). Estimating the number of clus
 # Currently still in beta, so don't automatically run example
 \dontrun{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE, beta = TRUE)
+localH2O = h2o.init(beta = TRUE)
 irisPath = system.file("extdata", "iris.csv", package = "h2o")
 iris.hex = h2o.importFile(localH2O, path = irisPath)
 h2o.gapStatistic(iris.hex, K.max = 10, B = 100)

R/h2o-package/man/h2o.gbm.Rd

@@ -85,7 +85,7 @@ For more information see: http://docs.0xdata.com
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 
 # Run regression GBM on australia.hex data 
 ausPath = system.file("extdata", "australia.csv", package="h2o")

R/h2o-package/man/h2o.getFrame.Rd

@@ -20,7 +20,7 @@ Returns an object of class \code{\linkS4class{H2OParsedData}}.
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 irisPath = system.file("extdata", "iris.csv", package = "h2o")
 h2o.importFile(localH2O, path = irisPath, key = "iris.hex")
 h2o.ls(localH2O)

R/h2o-package/man/h2o.getGLMLambdaModel.Rd

@@ -19,7 +19,7 @@ Returns an object of class \code{\linkS4class{H2OGLMModel}}.
 }
 \examples{
 library(h2o)
-localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE)
+localH2O = h2o.init()
 prosPath = system.file("extdata", "prostate.csv", package = "h2o")
 prostate.hex = h2o.importFile(localH2O, path = prosPath)
 prostate.srch = h2o.glm(x = 3:9, y = 2, data = prostate.hex, family = "binomial",