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)
