def setup(): # Your config parsing goes here. parser = get_parser() parser = make_cli_parser(parser, parser.parse_args().config) configure_logging(f"logs/{config.trainer.experiment_name}") # Add all default command line parsers and merge with yaml config file. config = parse_config(parser, PLDataModuleFromDatasets) return config
root=".", transform=data_transform, train=True) val = MNIST(download=False, root=".", transform=data_transform, train=False) return train, val if __name__ == "__main__": # SETUP ################################################## parser = get_parser() parser = make_cli_parser(parser, PLDataModuleFromDatasets) config = parse_config(parser, parser.parse_args().config) if config.trainer.experiment_name == "experiment": config.trainer.experiment_name = "mnist-rnn-classification" configure_logging(f"logs/{config.trainer.experiment_name}") if config.seed is not None: logger.info("Seeding everything with seed={seed}") pl.utilities.seed.seed_everything(seed=config.seed) train, test = get_data() # Get data and make datamodule ##########################
config["model"] = { "intermediate_hidden": tune.choice([16, 32, 64, 100, 128, 256, 300, 512]) } config["optimizer"] = tune.choice(["SGD", "Adam", "AdamW"]) config["optim"]["lr"] = tune.loguniform(1e-4, 1e-1) config["optim"]["weight_decay"] = tune.loguniform(1e-4, 1e-1) config["data"]["batch_size"] = tune.choice([16, 32, 64, 128]) return config if __name__ == "__main__": # SETUP ################################################## parser = get_parser() parser = make_cli_parser(parser, PLDataModuleFromDatasets) # type: ignore config = parse_config(parser, parser.parse_args().config) if config.trainer.experiment_name == "experiment": config.trainer.experiment_name = "mnist-classification" configure_logging() if config.seed is not None: logger.info("Seeding everything with seed={seed}") pl.utilities.seed.seed_everything(seed=config.seed) # These arguments may be provided from the command line or a config file # config = OmegaConf.to_container(config) # config["tune"] = {
num_labels = 2 return ( raw_train, labels_train, raw_dev, labels_dev, raw_test, labels_test, num_labels, ) if __name__ == "__main__": parser = get_parser() parser = make_cli_parser(parser, PLDataModuleFromCorpus) args = parser.parse_args() config_file = args.config config = parse_config(parser, config_file) # Set these by default. config.hugging_face_model = config.data.tokenizer config.data.add_special_tokens = True config.data.lower = "uncased" in config.hugging_face_model if config.trainer.experiment_name == "experiment": config.trainer.experiment_name = "finetune-bert-smt" configure_logging(f"logs/{config.trainer.experiment_name}")