def _log_model_missing_warning()

in src/sagemaker_tensorflow_container/training.py [0:0]

• 32 lines of code
• 9 McCabe index (conditional complexity)

def _log_model_missing_warning(model_dir):
    pb_file_exists = False
    file_exists = False
    for dirpath, dirnames, filenames in os.walk(model_dir):
        if filenames:
            file_exists = True
        for f in filenames:
            if "saved_model.pb" in f or "saved_model.pbtxt" in f:
                pb_file_exists = True
                path, direct_parent_dir = os.path.split(dirpath)
                if not str.isdigit(direct_parent_dir):
                    logger.warn(
                        "Your model will NOT be servable with SageMaker TensorFlow Serving containers. "
                        'The SavedModel bundle is under directory "{}", not a numeric name.'.format(
                            direct_parent_dir
                        )
                    )

    if not file_exists:
        logger.warn(
            "No model artifact is saved under path {}."
            " Your training job will not save any model files to S3.\n"
            "For details of how to construct your training script see:\n"
            "https://sagemaker.readthedocs.io/en/stable/using_tf.html#adapting-your-local-tensorflow-script".format(
                model_dir
            )
        )
    elif not pb_file_exists:
        logger.warn(
            "Your model will NOT be servable with SageMaker TensorFlow Serving container. "
            "The model artifact was not saved in the TensorFlow SavedModel directory structure:\n"
            "https://www.tensorflow.org/guide/saved_model#structure_of_a_savedmodel_directory"
        )