run_fn.py

def run_fn(fn_args: TrainerFnArgs):
    tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)
    train_dataset = _input_fn(
        fn_args.train_files, tf_transform_output, 32)
    eval_dataset = _input_fn(
        fn_args.eval_files, tf_transform_output, 32)
 
    mirrored_strategy = tf.distribute.MirroredStrategy()
    with mirrored_strategy.scope():
        model = get_model(tf_transform_output=tf_transform_output)
 
    model.fit(
        train_dataset,
        steps_per_epoch=fn_args.train_steps,
        validation_data=eval_dataset,
        validation_steps=fn_args.eval_steps)
 
    signatures = {
        'serving_default':
            _get_serve_tf_examples_fn(model, tf_transform_output
            ).get_concrete_function(
                                 tf.TensorSpec(
                                 shape=[None],
                                 dtype=tf.string,
                                 name='examples')),
    }
    model.save(
        fn_args.serving_model_dir, 
        save_format='tf', 
        signatures=signatures)