.. testsetup:: * import torch from argparse import ArgumentParser, Namespace from pytorch_lightning.trainer.trainer import Trainer from pytorch_lightning.core.lightning import LightningModule import sys sys.argv = ['foo'] Hyperparameters --------------- Lightning has utilities to interact seamlessly with the command line ArgumentParser and plays well with the hyperparameter optimization framework of your choice. ---------- ArgumentParser ^^^^^^^^^^^^^^ Lightning is designed to augment a lot of the functionality of the built-in Python ArgumentParser .. testcode:: from argparse import ArgumentParser parser = ArgumentParser() parser.add_argument('--layer_1_dim', type=int, default=128) args = parser.parse_args() This allows you to call your program like so: .. code-block:: bash python trainer.py --layer_1_dim 64 ---------- Argparser Best Practices ^^^^^^^^^^^^^^^^^^^^^^^^ It is best practice to layer your arguments in three sections. 1. Trainer args (gpus, num_nodes, etc...) 2. Model specific arguments (layer_dim, num_layers, learning_rate, etc...) 3. Program arguments (data_path, cluster_email, etc...) | We can do this as follows. First, in your LightningModule, define the arguments specific to that module. Remember that data splits or data paths may also be specific to a module (ie: if your project has a model that trains on Imagenet and another on CIFAR-10). .. testcode:: class LitModel(LightningModule): @staticmethod def add_model_specific_args(parent_parser): parser = ArgumentParser(parents=[parent_parser], add_help=False) parser.add_argument('--encoder_layers', type=int, default=12) parser.add_argument('--data_path', type=str, default='/some/path') return parser Now in your main trainer file, add the Trainer args, the program args, and add the model args .. testcode:: # ---------------- # trainer_main.py # ---------------- from argparse import ArgumentParser parser = ArgumentParser() # add PROGRAM level args parser.add_argument('--conda_env', type=str, default='some_name') parser.add_argument('--notification_email', type=str, default='will@email.com') # add model specific args parser = LitModel.add_model_specific_args(parser) # add all the available trainer options to argparse # ie: now --gpus --num_nodes ... --fast_dev_run all work in the cli parser = Trainer.add_argparse_args(parser) args = parser.parse_args() Now you can call run your program like so .. code-block:: bash python trainer_main.py --gpus 2 --num_nodes 2 --conda_env 'my_env' --encoder_layers 12 Finally, make sure to start the training like so: .. code-block:: python # init the trainer like this trainer = Trainer.from_argparse_args(args, early_stopping_callback=...) # NOT like this trainer = Trainer(gpus=hparams.gpus, ...) # init the model with Namespace directly model = LitModel(args) # or init the model with all the key-value pairs dict_args = vars(args) model = LitModel(**dict_args) ---------- LightningModule hyperparameters ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Often times we train many versions of a model. You might share that model or come back to it a few months later at which point it is very useful to know how that model was trained (ie: what learning_rate, neural network, etc...). Lightning has a few ways of saving that information for you in checkpoints and yaml files. The goal here is to improve readability and reproducibility 1. The first way is to ask lightning to save the values anything in the __init__ for you to the checkpoint. This also makes those values available via `self.hparams`. .. code-block:: python class LitMNIST(LightningModule): def __init__(self, layer_1_dim=128, learning_rate=1e-2, **kwargs): super().__init__() # call this to save (layer_1_dim=128, learning_rate=1e-4) to the checkpoint self.save_hyperparameters() # equivalent self.save_hyperparameters('layer_1_dim', 'learning_rate') # this now works self.hparams.layer_1_dim 2. Sometimes your init might have objects or other parameters you might not want to save. In that case, choose only a few .. code-block:: python class LitMNIST(LightningModule): def __init__(self, loss_fx, generator_network, layer_1_dim=128 **kwargs): super().__init__() self.layer_1_dim = layer_1_dim self.loss_fx = loss_fx # call this to save (layer_1_dim=128) to the checkpoint self.save_hyperparameters('layer_1_dim') # to load specify the other args model = LitMNIST.load_from_checkpoint(PATH, loss_fx=torch.nn.SomeOtherLoss, generator_network=MyGenerator()) 3. Assign to `self.hparams`. Anything assigned to `self.hparams` will also be saved automatically .. code-block:: python # using a argparse.Namespace class LitMNIST(LightningModule): def __init__(self, hparams, *args, **kwargs): super().__init__() self.hparams = hparams self.layer_1 = torch.nn.Linear(28 * 28, self.hparams.layer_1_dim) self.layer_2 = torch.nn.Linear(self.hparams.layer_1_dim, self.hparams.layer_2_dim) self.layer_3 = torch.nn.Linear(self.hparams.layer_2_dim, 10) def train_dataloader(self): return DataLoader(mnist_train, batch_size=self.hparams.batch_size) 4. You can also save full objects such as `dict` or `Namespace` to the checkpoint. .. code-block:: python # using a argparse.Namespace class LitMNIST(LightningModule): def __init__(self, conf, *args, **kwargs): super().__init__() self.hparams = conf # equivalent self.save_hyperparameters(conf) self.layer_1 = torch.nn.Linear(28 * 28, self.hparams.layer_1_dim) self.layer_2 = torch.nn.Linear(self.hparams.layer_1_dim, self.hparams.layer_2_dim) self.layer_3 = torch.nn.Linear(self.hparams.layer_2_dim, 10) conf = OmegaConf.create(...) model = LitMNIST(conf) # this works model.hparams.anything ---------- Trainer args ^^^^^^^^^^^^ To recap, add ALL possible trainer flags to the argparser and init the Trainer this way .. code-block:: python parser = ArgumentParser() parser = Trainer.add_argparse_args(parser) hparams = parser.parse_args() trainer = Trainer.from_argparse_args(hparams) # or if you need to pass in callbacks trainer = Trainer.from_argparse_args(hparams, checkpoint_callback=..., callbacks=[...]) ---------- Multiple Lightning Modules ^^^^^^^^^^^^^^^^^^^^^^^^^^ We often have multiple Lightning Modules where each one has different arguments. Instead of polluting the main.py file, the LightningModule lets you define arguments for each one. .. testcode:: class LitMNIST(LightningModule): def __init__(self, layer_1_dim, **kwargs): super().__init__() self.layer_1 = torch.nn.Linear(28 * 28, layer_1_dim) @staticmethod def add_model_specific_args(parent_parser): parser = ArgumentParser(parents=[parent_parser], add_help=False) parser.add_argument('--layer_1_dim', type=int, default=128) return parser .. testcode:: class GoodGAN(LightningModule): def __init__(self, encoder_layers, **kwargs): super().__init__() self.encoder = Encoder(layers=encoder_layers) @staticmethod def add_model_specific_args(parent_parser): parser = ArgumentParser(parents=[parent_parser], add_help=False) parser.add_argument('--encoder_layers', type=int, default=12) return parser Now we can allow each model to inject the arguments it needs in the ``main.py`` .. code-block:: python def main(args): dict_args = vars(args) # pick model if args.model_name == 'gan': model = GoodGAN(**dict_args) elif args.model_name == 'mnist': model = LitMNIST(**dict_args) trainer = Trainer.from_argparse_args(args) trainer.fit(model) if __name__ == '__main__': parser = ArgumentParser() parser = Trainer.add_argparse_args(parser) # figure out which model to use parser.add_argument('--model_name', type=str, default='gan', help='gan or mnist') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # let the model add what it wants if temp_args.model_name == 'gan': parser = GoodGAN.add_model_specific_args(parser) elif temp_args.model_name == 'mnist': parser = LitMNIST.add_model_specific_args(parser) args = parser.parse_args() # train main(args) and now we can train MNIST or the GAN using the command line interface! .. code-block:: bash $ python main.py --model_name gan --encoder_layers 24 $ python main.py --model_name mnist --layer_1_dim 128 ---------- Hyperparameter Optimization ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Lightning is fully compatible with the hyperparameter optimization libraries! Here are some useful ones: - `Hydra `_ - `Optuna `_ - `Ray Tune `_