new_contents: import os
  import numpy as np
  import pandas as pd
  from sklearn import datasets
  from mljar import Mljar
  from mljar.client.dataset import DatasetClient
  
  os.environ["MLJAR_TOKEN"] = "*** your MLJAR_TOKEN ***"
  
  diabetes = datasets.load_diabetes()
  X = diabetes.data
  y = diabetes.target
  
  # Let's define MLJAR project
  mljar = Mljar(project='UCI-Diabets',  # project's title that we will use to find it among our projects in MLJAR
                experiment='First try', # experiment's title
                validation='5fold', # we will use 5-fold CV
                metric='rmse', # we will use Area Under ROC Curve (AUC) as a metric,
                tuning_mode='Normal', # select tuning mode
                algorithms=['rfr'], # we want to tune Random Forest and Nueral Network models
                create_ensemble=True, # create ensemble of all models,
                single_algorithm_time_limit=1 # time limit for single algorithm training
               )
  
  mljar.fit(X, y)
  
  print 'Selected algorithm'
  mljar.selected_algorithm.show()
  
  #pred = mljar.predict(df[input_cols])
  #print pred.shape
  
subject: add Diabets example