new_contents: import sys
  sys.path.insert(1, '../../h2o-py/src/main/py')
  
  from h2o import H2OConnection
  from h2o import H2OFrame
  from h2o import H2OGBM
  from tabulate import tabulate
  
  ######################################################
  # Parse command-line args.
  #
  # usage:  python test_name.py --usecloud ipaddr:port
  #
  
  ip_port = sys.argv[2].split(":")
  print ip_port
  ip = ip_port[0]
  port = int(ip_port[1])
  
  ######################################################
  #
  # Sample Running GBM on prostate.csv
  
  # Connect to a pre-existing cluster
  cluster = H2OConnection(ip = ip, port = port)
  
  df = H2OFrame(remote_fname="../../../smalldata/logreg/prostate.csv")
  print df.describe()
  
  # Remove ID from training frame
  del df['ID']
  
  # For VOL & GLEASON, a zero really means "missing"
  vol = df['VOL']
  vol[vol==0] = None
  gle = df['GLEASON']
  gle[gle==0] = None
  
  # Convert CAPSULE to a logical factor
  df['CAPSULE'] = df['CAPSULE'].asfactor()
  
  # Test/train split
  r = vol.runif()
  train = df[r< 0.8]
  test  = df[r>=0.8]
  
  # See that the data is ready
  print train.describe()
  print test .describe()
  
  # Run GBM
  gbm = H2OGBM(dataset=train,x="CAPSULE",validation_dataset=test,ntrees=50,max_depth=5,learn_rate=0.1)
  #print gbm._model
  mm = gbm.metrics()
  mm0 = mm[0]
  cm = mm0['cm']
  conf = cm['confusion_matrix']
  print tabulate(conf)
  
  
  
subject: Add first example test of using h2o python API to gradle build regression suite.