new_contents: import pandas as pd
  from sklearn.cross_validation import train_test_split
  from sklearn import linear_model
  from sklearn.metrics import accuracy_score
  
  from modeldb.sklearn_native.ModelDbSyncer import *
  from modeldb.sklearn_native import SyncableMetrics
  
  DATA_PATH = '../../../../data/'
  
  '''
  Source: http://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
  '''
  
  # modeldb start
  
  name = "simple sample"
  author = "srinidhi"
  description = "simple LR for credit default prediction"
  syncer_obj = Syncer(
     NewOrExistingProject(name, author, description),
     DefaultExperiment(),
     NewExperimentRun("credit test"))
  
  # modeldb end
  
  # modeldb start
  df = pd.read_csv_sync(DATA_PATH + 'credit-default.csv', skiprows=[0])
  # modeldb end
  
  target = df['default payment next month']
  df = df[["LIMIT_BAL", "SEX", "EDUCATION", "MARRIAGE", "AGE"]]
  
  x_train, x_test, y_train, y_test = cross_validation.train_test_split_sync(df, target, test_size=0.3)
  
  lr = linear_model.LogisticRegression(C=2)
  
  # modeldb start
  lr.fit_sync(x_train, y_train)
  # modeldb end
  
  # modeldb start
  y_pred = lr.predict_sync(x_test)
  # modeldb end
  
  # modeldb start
  score = SyncableMetrics.compute_metrics(lr, accuracy_score, y_test, y_pred, x_train, "features", 'default payment next month')
  # modeldb end
  
  # modeldb start
  syncer_obj.sync()
  # modeldb end
  
subject: add a simple python sample using modelDB