def train()

in autogluon-tab-with-test.py [0:0]

• 12 lines of code
• 3 McCabe index (conditional complexity)

def train(args):
  # SageMaker passes num_cpus, num_gpus and other args we can use to tailor training to
  # the current container environment, but here we just use simple cpu context.

  num_gpus = int(os.environ['SM_NUM_GPUS'])
  current_host = args.current_host
  hosts = args.hosts
  model_dir = args.model_dir
  target = args.target

  # load training and validation data

  training_dir = args.train
  filename = args.filename
  logging.info(training_dir)
  train_data = task.Dataset(file_path=training_dir + '/' + filename)
  predictor = task.fit(train_data = train_data, label=target, output_directory=model_dir)
  
  return predictor