def cli_main(): pl.seed_everything(1234) # ------------ # args # ------------ parser = ArgumentParser() parser = pl.Trainer.add_argparse_args(parser) parser = LitClassifier.add_model_specific_args(parser) parser = MNISTDataModule.add_argparse_args(parser) args = parser.parse_args() # ------------ # data # ------------ dm = MNISTDataModule.from_argparse_args(args) # ------------ # model # ------------ model = LitClassifier(args.hidden_dim, args.learning_rate) # ------------ # training # ------------ trainer = pl.Trainer.from_argparse_args(args) trainer.fit(model, datamodule=dm) # ------------ # testing # ------------ # todo: without passing model it fails for missing best weights # MisconfigurationException, 'ckpt_path is "best", but ModelCheckpoint is not configured to save the best model.' result = trainer.test(model, datamodule=dm) pprint(result)
def cli_main(): pl.seed_everything(1234) # ------------ # args # ------------ parser = ArgumentParser() parser = pl.Trainer.add_argparse_args(parser) parser = LitClassifier.add_model_specific_args(parser) parser = MNISTDataModule.add_argparse_args(parser) args = parser.parse_args() # ------------ # data # ------------ dm = MNISTDataModule.from_argparse_args(args) # ------------ # model # ------------ model = LitClassifier(args.hidden_dim, args.learning_rate) # ------------ # training # ------------ trainer = pl.Trainer.from_argparse_args(args) trainer.fit(model, datamodule=dm) # ------------ # testing # ------------ result = trainer.test(model, datamodule=dm) pprint(result)
def main(args: Namespace) -> None: # ------------------------ # 1 INIT LIGHTNING MODEL # ------------------------ model = GAN(lr=args.lr, b1=args.b1, b2=args.b2, latent_dim=args.latent_dim) # ------------------------ # 2 INIT TRAINER # ------------------------ # If use distributed training PyTorch recommends to use DistributedDataParallel. # See: https://pytorch.org/docs/stable/nn.html#torch.nn.DataParallel dm = MNISTDataModule.from_argparse_args(args) trainer = Trainer.from_argparse_args(args) # ------------------------ # 3 START TRAINING # ------------------------ trainer.fit(model, dm)