``export_checkpoints_dir``
--------------------------

- Available in: GBM, DRF, Deep Learning, GLM, GAM, PCA, GLRM, Naïve-Bayes, K-Means, Word2Vec, Stacked Ensembles, XGBoost, Aggregator, CoxPH, Isolation Forest, AutoML
- Hyperparameter: no

Description
~~~~~~~~~~~

This option is used to automatically export generated models to a specified directory.

Related Parameters
~~~~~~~~~~~~~~~~~~

- None

Example
~~~~~~~

.. tabs::
   .. code-tab:: r R

		library(h2o)
		h2o.init()

		# import the airlines dataset
		airlines = h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")

		# set the predictors and response
		predictors <- c("DayofMonth", "DayOfWeek")
		response <- "IsDepDelayed"

		# set hyperparameters to build one model with 5 trees and one with 10 trees
		hyper_parameters <- list(ntrees = c(5, 10))

		# specify the export checkpoints directory
		checkpoints_dir <- tempfile()

		# perform grid search using GBM
		gbm_grid <- h2o.grid("gbm", 
		                     x = predictors, 
		                     y = response, 
		                     training_frame = airlines, 
		                     distribution = "bernoulli", 
		                     stopping_rounds = 3, 
		                     stopping_metric = "AUTO", 
		                     stopping_tolerance = 1e-2, 
		                     learn_rate = 0.1, 
		                     max_depth = 3, 
		                     hyper_params = hyper_parameters, 
		                     export_checkpoints_dir = checkpoints_dir, 
		                     seed = 1234)

		# retrieve the number of files in the exported checkpoints directory
		num_files <- length(checkpoints_dir)
		num_files
		[1] 1

   .. code-tab:: python

		import h2o
		h2o.init()

		# import necessary modules
		from h2o.estimators.glm import H2OGeneralizedLinearEstimator
		import tempfile
		from os import listdir
		from h2o.estimators.gbm import H2OGradientBoostingEstimator
		from h2o.grid.grid_search import H2OGridSearch

		# import the airlines dataset
		airlines = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip", destination_frame="air.hex")

		# set the predictors and response
		predictors = ["DayofMonth", "DayOfWeek"]
		response = "IsDepDelayed"

		# set hyperparameters to build one model with 5 trees and one with 10 trees
		hyper_parameters = {'ntrees': [5,10]}

		# specify modeling options
		search_crit = {'strategy': "RandomDiscrete",
		               'seed': 1234,
		               'stopping_rounds' : 3,
		               'stopping_metric' : "AUTO",
		               'stopping_tolerance': 1e-2}

		# create an export checkpoints directory
		checkpoints_dir = tempfile.mkdtemp()

		# perform grid search using GBM
		air_grid = H2OGridSearch(H2OGradientBoostingEstimator, 
		                         hyper_params=hyper_parameters, 
		                         search_criteria=search_crit)
		air_grid.train(x=predictors, 
		               y=response, 
		               training_frame=airlines, 
		               distribution="bernoulli",
		               learn_rate=0.1,
		               max_depth=3,
		               export_checkpoints_dir=checkpoints_dir)

		# retrieve the number of files in the exported checkpoints directory
		num_files = len(listdir(checkpoints_dir))
		num_files
		2