Using `configargparse.ArgumentParser`?
❓ Questions and Help
What is your question?
Is it possible to use somehow configargparse.ArgumentParser in PyTorchLightning? I tried the following code, but neither patience nor any of the Trainer's parameters are changed to values from YAML file. Do I really need to create custom Trainer and override add_argparse_args method?
Note that the following code works for the argparse.ArgumentParser.
Code
def main(hparams):
    model = LitModel(hparams)

    early_stop_callback = EarlyStopping(
        monitor='val_loss',
        mode='min',
        patience=hparams.patience,
    )

    trainer = Trainer.from_argparse_args(
        hparams,
        early_stop_callback=early_stop_callback,
    )

    trainer.fit(model)


if __name__ == '__main__':
    parser = configargparse.ArgumentParser(config_file_parser_class=configargparse.YAMLConfigFileParser)
    parser.add_argument('-c', '--config_file', is_config_file=True)
    parser.add_argument('--patience', type=int)

    parser = LitModel.add_model_specific_args(parser)
    parser = Trainer.add_argparse_args(parser)
    hparams = parser.parse_args()

    main(hparams)

What's your environment?

OS: Ubuntu 18.04
Packaging: pip
Version 0.7.5