code: def gbm_on_hadoop():
      local_frame = h2o.import_file(
          path=pyunit_utils.locate("smalldata/logreg/prostate.csv")
      )
      hdfs_path = "hdfs:///user/jenkins/tests/prostate_export"
      h2o.export_file(local_frame, hdfs_path, force=True)
      df = h2o.import_file(hdfs_path)
      train = df.drop("ID")
      vol = train["VOL"]
      vol[vol == 0] = None
      gle = train["GLEASON"]
      gle[gle == 0] = None
      train["CAPSULE"] = train["CAPSULE"].asfactor()
      my_gbm = H2OGradientBoostingEstimator(
          ntrees=50, learn_rate=0.1, distribution="bernoulli"
      )
      my_gbm.train(
          x=list(range(1, train.ncol)),
          y="CAPSULE",
          training_frame=train,
          validation_frame=train,
      )
      my_gbm.predict(train)
  
imports: import sys
  import os
  from tests import pyunit_utils
  import h2o
  from h2o.estimators.gbm import H2OGradientBoostingEstimator
label_desc: Use image node_id 1 for calling a global function with example usage: gbm_on_hadoop() without return types