model = Pipeline.from_argparse_args(args, ) save_args(args, log_dir) trainer = Trainer.from_argparse_args( args, logger=tt_logger, checkpoint_callback=chkpt_callback, # early_stop_callback=False, weights_summary='full', gpus=1, profiler=True, ) trainer.fit(model, data_loader) # trainer.test(model) if __name__ == "__main__": parser = ArgumentParser() parser = CustomDataLoader.add_argparse_args(parser) parser = Pipeline.add_argparse_args(parser) parser = Pipeline.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) args = parser.parse_args() if 'params' in locals(): args.__dict__.update(params.__dict__) main(args)