def setUpClass(cls): cls._spark = SparkSession.builder.config( conf=unit_test_utils.get_default_spark_conf()).getOrCreate() unit_test_utils.set_up_class(cls) cls._hc = H2OContext.getOrCreate( cls._spark, H2OConf(cls._spark).set_num_of_external_h2o_nodes(2))
def setUpClass(cls): cls._conf = unit_test_utils.get_default_spark_conf( cls._spark_options_from_params) cls._spark = SparkSession.builder.config(conf=cls._conf).getOrCreate() cls._hc = H2OContext.getOrCreate( cls._spark, H2OConf(cls._spark).set_num_of_external_h2o_nodes(1))
def setUpClass(cls): cls._conf = unit_test_utils.get_default_spark_conf( cls._spark_options_from_params) cls._spark = SparkSession.builder.config(conf=cls._conf).getOrCreate() cls._hc = H2OContext.getOrCreate( cls._spark, H2OConf(cls._spark).set_cluster_size(1))
def setUpClass(cls): cls._conf = unit_test_utils.get_default_spark_conf(cls._spark_options_from_params) cls._spark = SparkSession.builder.config(conf=cls._conf).getOrCreate() dataset = cls._spark.read\ .options(header='true', inferSchema='true')\ .csv("file://" + unit_test_utils.locate("smalldata/prostate/prostate.csv")) [cls._trainingDataset, cls._testingDataset] = dataset.randomSplit([0.8, 0.2], 1)
def setUpClass(cls): cls._cloud_name = generic_test_utils.unique_cloud_name("h2o_conf_test") cls._spark = SparkSession.builder.config( conf=unit_test_utils.get_default_spark_conf().set( "spark.ext.h2o.cloud.name", cls._cloud_name)).getOrCreate() unit_test_utils.set_up_class(cls) h2o_conf = H2OConf(cls._spark).set_num_of_external_h2o_nodes(2) cls._hc = H2OContext.getOrCreate(cls._spark, h2o_conf)
def setUpClass(cls): cls._spark = SparkSession.builder.config( conf=unit_test_utils.get_default_spark_conf().setMaster( "yarn-client")).getOrCreate() unit_test_utils.set_up_class(cls) cls._hc = H2OContext.getOrCreate( cls._spark, H2OConf(cls._spark).set_cluster_size(1))
def setUpClass(cls): cls._cloud_name = generic_test_utils.unique_cloud_name("h2o_conf_test") cls._conf = unit_test_utils.get_default_spark_conf(cls._spark_options_from_params). \ set("spark.ext.h2o.cloud.name", cls._cloud_name) cls._spark = SparkSession.builder.config(conf=cls._conf).getOrCreate() cls._hc = H2OContext.getOrCreate( cls._spark, H2OConf(cls._spark).set_cluster_size(1))
def setUpClass(cls): cls._conf = unit_test_utils.get_default_spark_conf( cls._spark_options_from_params) cls._spark = SparkSession.builder.config(conf=cls._conf).getOrCreate() cls._hc = H2OContext.getOrCreate( cls._spark, H2OConf(cls._spark).set_cluster_size(1)) cls.dataset = cls._spark.read.csv( "file://" + unit_test_utils.locate("smalldata/iris/iris_wheader.csv"), header=True, inferSchema=True)
def setUpClass(cls): cls._cloud_name = generic_test_utils.unique_cloud_name("h2o_conf_test") cls._conf = unit_test_utils.get_default_spark_conf(cls._spark_options_from_params). \ set("spark.ext.h2o.cloud.name", cls._cloud_name) cls._spark = SparkSession.builder.config(conf=cls._conf).getOrCreate() cls._hc = H2OContext.getOrCreate(cls._spark, H2OConf(cls._spark).set_num_of_external_h2o_nodes(1))
def setUpClass(cls): cls._cloud_name = generic_test_utils.unique_cloud_name("h2o_mojo_predictions_test") cls._spark = SparkSession.builder.config(conf = unit_test_utils.get_default_spark_conf()).getOrCreate()
def setUpClass(cls): cls._spark = SparkSession.builder.config( conf=unit_test_utils.get_default_spark_conf().setMaster("yarn-client")).getOrCreate() unit_test_utils.set_up_class(cls) cls._hc = H2OContext.getOrCreate(cls._spark, H2OConf(cls._spark).set_num_of_external_h2o_nodes(1))
def setUpClass(cls): cls._conf = unit_test_utils.get_default_spark_conf( cls._spark_options_from_params) cls._spark = SparkSession.builder.config(conf=cls._conf).getOrCreate()