193 lines
6.0 KiB
ReStructuredText
193 lines
6.0 KiB
ReStructuredText
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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LightiningModule hparams
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^^^^^^^^^^^^^^^^^^^^^^^^
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Normally, we don't hard-code the values to a model. We usually use the command line to
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modify the network. The `Trainer` can add all the available options to an ArgumentParser.
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.. code-block:: python
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from argparse import ArgumentParser
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parser = ArgumentParser()
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# parametrize the network
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parser.add_argument('--layer_1_dim', type=int, default=128)
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parser.add_argument('--layer_2_dim', type=int, default=256)
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parser.add_argument('--batch_size', type=int, default=64)
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args = parser.parse_args()
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Now we can parametrize the LightningModule.
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.. code-block:: python
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:emphasize-lines: 5,6,7,12,14
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class CoolMNIST(pl.LightningModule):
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def __init__(self, hparams):
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super(CoolMNIST, self).__init__()
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self.hparams = hparams
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self.layer_1 = torch.nn.Linear(28 * 28, hparams.layer_1_dim)
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self.layer_2 = torch.nn.Linear(hparams.layer_1_dim, hparams.layer_2_dim)
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self.layer_3 = torch.nn.Linear(hparams.layer_2_dim, 10)
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def forward(self, x):
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...
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def train_dataloader(self):
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...
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return DataLoader(mnist_train, batch_size=self.hparams.batch_size)
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def configure_optimizers(self):
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return Adam(self.parameters(), lr=self.hparams.learning_rate)
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hparams = parse_args()
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model = CoolMNIST(hparams)
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.. note:: Bonus! if (hparams) is in your module, Lightning will save it into the checkpoint and restore your
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model using those hparams exactly.
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Trainer args
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^^^^^^^^^^^^
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It also gets annoying to map each argument into the Argparser. Luckily we have
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a default parser
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.. code-block:: python
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parser = ArgumentParser()
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# add all options available in the trainer such as (max_epochs, etc...)
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parser = Trainer.add_argparse_args(parser)
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We set up the main training entry point file like this:
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.. code-block:: python
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def main(args):
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model = CoolMNIST(hparams=args)
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trainer = Trainer(max_epochs=args.max_epochs)
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trainer.fit(model)
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if __name__ == '__main__':
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parser = ArgumentParser()
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# adds all the trainer options as default arguments (like max_epochs)
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parser = Trainer.add_argparse_args(parser)
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# parametrize the network
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parser.add_argument('--layer_1_dim', type=int, default=128)
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parser.add_argument('--layer_1_dim', type=int, default=256)
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parser.add_argument('--batch_size', type=int, default=64)
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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 like this:
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.. code-block:: bash
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$ python main.py --layer_1_dim 128 --layer_2_dim 256 --batch_size 64 --max_epochs 64
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But it would also be nice to pass in any arbitrary argument to the trainer.
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We can do it by changing how we init the trainer.
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.. code-block:: python
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def main(args):
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model = CoolMNIST(hparams=args)
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# makes all trainer options available from the command line
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trainer = Trainer.from_argparse_args(args)
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and now we can do this:
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.. code-block:: bash
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$ python main.py --gpus 1 --min_epochs 12 --max_epochs 64 --arbitrary_trainer_arg some_value
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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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.. code-block:: python
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class CoolMNIST(pl.LightningModule):
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def __init__(self, hparams):
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super(CoolMNIST, self).__init__()
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self.layer_1 = torch.nn.Linear(28 * 28, hparams.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])
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parser.add_argument('--layer_1_dim', type=int, default=128)
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return parser
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class GoodGAN(pl.LightningModule):
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def __init__(self, hparams):
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super(GoodGAN, self).__init__()
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self.encoder = Encoder(layers=hparams.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])
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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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# pick model
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if args.model_name == 'gan':
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model = GoodGAN(hparams=args)
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elif args.model_name == 'mnist':
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model = CoolMNIST(hparams=args)
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model = CoolMNIST(hparams=args)
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trainer = Trainer(max_epochs=args.max_epochs)
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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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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 = CoolMNIST.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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Hyperparameter Optimization
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Lightning is fully compatible with the hyperparameter optimization libraries!
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Here are some useful ones:
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- `Hydra <https://medium.com/pytorch/hydra-a-fresh-look-at-configuration-for-machine-learning-projects-50583186b710>`_
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- `Optuna <https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py>`_
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