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