``max_runtime_secs_per_model`` ------------------------------ - Available in: AutoML - Hyperparameter: no Description ~~~~~~~~~~~ Use this option to specify the maximum amount of seconds dedicated to the training of each individual model in the AutoML run. This option defaults to 0 (disabled). Note that models constrained by a time budget are not guaranteed reproducible. Related Parameters ~~~~~~~~~~~~~~~~~~ - `max_runtime_secs `__ - `max_models `__ Example ~~~~~~~ .. tabs:: .. code-tab:: r R library(h2o) h2o.init() # Import the prostate dataset prostate <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip") # Set the predictor names and the response column name y <- "CAPSULE" x <- setdiff(names(prostate), c(p_y, "ID")) # Train AutoML aml <- h2o.automl(x = x, y = y, training_frame = prostate, seed = 1234, max_models = 5, max_runtime_secs = 200, max_runtime_secs_per_model = 40) # View the AutoML Leaderboard lb <- aml@leaderboard lb model_id mean_residual_deviance 1 StackedEnsemble_BestOfFamily_AutoML_20190321_110032 0.0009730593 2 StackedEnsemble_AllModels_AutoML_20190321_110032 0.0009730593 3 DRF_1_AutoML_20190321_110032 0.0012766064 4 XRT_1_AutoML_20190321_110032 0.0038347775 5 XGBoost_2_AutoML_20190321_110032 0.0064206276 6 XGBoost_1_AutoML_20190321_110032 0.0544174809 rmse mse mae rmsle 1 0.03119390 0.0009730593 0.02086672 0.02368844 2 0.03119390 0.0009730593 0.02086672 0.02368844 3 0.03572963 0.0012766064 0.01406001 0.02661268 4 0.06192558 0.0038347775 0.03330358 0.04958889 5 0.08012882 0.0064206276 0.06873394 0.06112533 6 0.23327555 0.0544174809 0.18390358 0.16640402 [7 rows x 6 columns] .. code-tab:: python import h2o from h2o.automl import H2OAutoML h2o.init() # Import a sample binary outcome training set into H2O prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate_complete.csv.zip") # Set the predictor names and the response column name response = "CAPSULE" predictor = prostate.names[2:9] # Train AutoML aml = H2OAutoML(max_models = 5, max_runtime_secs = 200, max_runtime_secs_per_model = 40, seed = 1234) aml.train(x = predictor, y = response, training_frame = prostate) # View the AutoML Leaderboard lb = aml.leaderboard lb model_id mean_residual_deviance rmse mse mae rmsle --------------------------------------------------- ------------------------ --------- ----------- --------- --------- StackedEnsemble_AllModels_AutoML_20190321_111608 0.000282073 0.016795 0.000282073 0.0103226 0.0129982 StackedEnsemble_BestOfFamily_AutoML_20190321_111608 0.000282073 0.016795 0.000282073 0.0103226 0.0129982 DRF_1_AutoML_20190321_111608 0.000334287 0.0182835 0.000334287 0.0076525 0.0140754 XRT_1_AutoML_20190321_111608 0.0015397 0.039239 0.0015397 0.0217268 0.0293752 XGBoost_2_AutoML_20190321_111608 0.0118094 0.108671 0.0118094 0.0888375 0.0804565 XGBoost_1_AutoML_20190321_111608 0.0675672 0.259937 0.0675672 0.213536 0.184793 GLM_grid_1_AutoML_20190321_111608_model_1 0.193551 0.439944 0.193551 0.397327 0.306996 [7 rows x 6 columns]