def get_args()

in tutorials/tensorflow/mlflow_gcp/trainer/task.py [0:0]

• 41 lines of code
• 1 McCabe index (conditional complexity)

def get_args():
    """Argument parser.

    Returns:
      Dictionary of arguments.
    """
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--train-files',
        help='GCS file or local paths to training data',
        default='gs://cloud-samples-data/ml-engine/census/data/adult.data.csv')
    parser.add_argument(
        '--eval-files',
        help='GCS file or local paths to evaluation data',
        default='gs://cloud-samples-data/ml-engine/census/data/adult.test.csv')
    parser.add_argument(
        '--job-dir',
        type=str,
        required=True,
        help='Local or GCS location for writing checkpoints and exporting '
             'models')
    parser.add_argument(
        '--num-epochs',
        type=int,
        default=20,
        help='Number of times to go through the data, default=20')
    parser.add_argument(
        '--batch-size',
        default=64,
        type=int,
        help='Number of records to read during each training step, default=128')
    parser.add_argument(
        '--learning-rate',
        default=.01,
        type=float,
        help='Learning rate for gradient descent, default=.01')
    parser.add_argument(
        '--eval-steps',
        help='Number of steps to run evaluation for, at each checkpoint',
        default=1,
        type=int)
    parser.add_argument(
        '--reuse-job-dir',
        action='store_true',
        default=False,
        help="""