text: import os
  import time
  
  from dotenv import load_dotenv
  
  from mev.azure.run import get_auth_ws, run_train, run_featurize
  
  load_dotenv()
  
  ENVIRONMENT_VARIABLES = dict(
      TENANT_ID=os.getenv("TENANT_ID"),
  )
  
  
  if __name__ == "__main__":
  
      # Params
      compute_target_name_1 = "mev-compute"
      compute_target_name_2 = "mev-compute2"
  
      source_dir_featurize = "./mev/azure/src"
      script_name_featurize = "featurize.py"
  
      source_dir_train = "./mev/azure/src_train"
      script_name_train = "train.py"
  
      output_name_featurize = 'featurize_all'
      output_name_train = 'train_all'
  
      dataset_name_featurize = "prepare_all"
      dataset_name_train = output_name_featurize
  
      kind = 'both'
      time_limit = 10 * 3600
  
      # Auth to Azure ML
      ws = get_auth_ws(ENVIRONMENT_VARIABLES["TENANT_ID"])
  
      print("Running 'featurize' step...")
      run_featurize(
          dataset_name=dataset_name_featurize,
          compute_target_name=compute_target_name_1,
          source_dir=source_dir_featurize,
          script_name=script_name_featurize,
          ws=ws,
          environment_variables=ENVIRONMENT_VARIABLES,
          with_labels=True,
          output_name=output_name_featurize
      )
      print("Featurize step finished.")
  
      print("Sleeping 4 mins to wait for compute being ready for next pipeline.")
      time.sleep(4 * 60)
  
      print("Running 'train' step...")
      run_train(
          dataset_name=dataset_name_train,
          compute_target_names=[compute_target_name_1, compute_target_name_2],
          source_dir=source_dir_train,
          script_name=script_name_train,
          ws=ws,
          environment_variables=ENVIRONMENT_VARIABLES,
          time_limit=time_limit,
          output_name=output_name_train,
          kind=kind
      )
      print("Train step finished.")
      print("Done.")