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}")
