max_runtime_secs

• Available in: GBM, DRF, Deep Learning, GLM, PCA, GLRM, Naïve-Bayes, K-Means, AutoML, XGBoost, Word2vec, Isolation Forest
• Hyperparameter: yes

Description

Model Building

When building a model, this option specifes the maximum runtime in seconds that you want to allot in order to complete the model. If this maximum runtime is exceeded before the model build is completed, then the model will fail.

Using with Grid Search

When performing a grid search, this option specifies the maximum runtime in seconds for the entire grid. This option can also be combined with max_runtime_secs in the model parameters. If max_runtime_secs is not set in the model parameters, then each model build is launched with a limit equal to the remainder of the grid time. On the other hand, if max_runtime_secs is set in the model parameters, then each build is launched with a limit equal to the minimum of the model time limit and the remaining time for the grid.

Specifying max_runtime_secs=0 disables this option, thus allowing for an unlimited amount of runtime.

Related Parameters

• max_models
• max_runtime_secs_per_model

Example

library(h2o)
h2o.init()

# 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]]

# try using the `max_runtime_secs` parameter:
# train your model
# set max_runtime_secs to 10 seconds to limit how long the model can take to build
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
                    validation_frame = valid, max_runtime_secs = 10, ntrees = 10000, max_depth = 10, seed = 1234)

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

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()

# 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.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()

# set the predictor names and the response column name
predictors = ["displacement","power","weight","acceleration","year"]
response = "economy_20mpg"

# split into train and validation sets
train, valid = cars.split_frame(ratios = [.8], seed = 1234)

# try using the `max_runtime_secs` parameter:
# first initialize your estimator
# set max_runtime_secs to 10 seconds to limit how long the model can take to build
cars_gbm = H2OGradientBoostingEstimator(max_runtime_secs = 10, ntrees = 10000, max_depth = 10, seed = 1234)

# then train your model
cars_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

# print the auc for the validation data
cars_gbm.auc(valid = True)