178 lines
5.8 KiB
Markdown
178 lines
5.8 KiB
Markdown
# Pytorch-lightning
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The Keras for ML-researchers in PyTorch.
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## Usage
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To use lightning do 2 things:
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1. [Define a trainer](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/trainer_main.py) (which will run ALL your models).
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2. [Define a model](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/models/sample_model_template/model_template.py).
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### Example:
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#### Define the trainer
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```python
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# trainer.py
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from pytorch_lightning.models.trainer import Trainer
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from pytorch_lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
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from my_project import My_Model
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from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
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# --------------
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# TEST TUBE INIT
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exp = Experiment(
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name='my_exp',
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debug=True,
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save_dir='/some/path',
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autosave=False,
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description='my desc'
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)
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# --------------------
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# CALLBACKS
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early_stop = EarlyStopping(
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monitor='val_loss',
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patience=3,
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verbose=True,
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mode='min'
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)
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model_save_path = 'PATH/TO/SAVE'
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checkpoint = ModelCheckpoint(
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filepath=model_save_path,
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save_function=None,
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save_best_only=True,
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verbose=True,
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monitor='val_acc',
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mode='min'
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)
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# configure trainer
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trainer = Trainer(
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experiment=experiment,
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cluster=cluster,
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checkpoint_callback=checkpoint,
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early_stop_callback=early_stop
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)
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# init model and train
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model = My_Model()
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trainer.fit(model)
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```
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#### Define the model
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```python
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from torch import nn
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class My_Model(RootModule):
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def __init__(self):
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# define model
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self.l1 = nn.Linear(200, 10)
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# ---------------
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# TRAINING
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def training_step(self, data_batch):
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x, y = data_batch
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y_hat = self.l1(x)
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loss = some_loss(y_hat)
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return loss_val, {'train_loss': loss}
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def validation_step(self, data_batch):
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x, y = data_batch
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y_hat = self.l1(x)
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loss = some_loss(y_hat)
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return loss_val, {'val_loss': loss}
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def validation_end(self, outputs):
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total_accs = []
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for output in outputs:
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total_accs.append(output['val_acc'].item())
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# return a dict
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return {'total_acc': np.mean(total_accs)}
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# ---------------
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# SAVING
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def get_save_dict(self):
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# lightning saves for you. Here's your chance to say what you want to save
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checkpoint = {'state_dict': self.state_dict()}
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return checkpoint
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def load_model_specific(self, checkpoint):
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# lightning loads for you. Here's your chance to say what you want to load
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self.load_state_dict(checkpoint['state_dict'])
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# ---------------
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# TRAINING CONFIG
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def configure_optimizers(self):
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# give lightning the list of optimizers you want to use.
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# lightning will call automatically
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optimizer = self.choose_optimizer('adam', self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
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return [optimizer]
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@property
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def tng_dataloader(self):
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return pytorch_dataloader('train')
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@property
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def val_dataloader(self):
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return pytorch_dataloader('val')
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@property
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def test_dataloader(self):
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return pytorch_dataloader('test')
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# ---------------
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# MODIFY YOUR COMMAND LINE ARGS
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@staticmethod
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def add_model_specific_args(parent_parser):
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parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
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parser.add_argument('--out_features', default=20)
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return parser
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```
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### Details
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#### Model definition
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| Name | Description | Input | Return |
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| training_step | Called with a batch of data during training | data from your dataloaders | tuple: scalar, dict |
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| validation_step | Called with a batch of data during validation | data from your dataloaders | tuple: scalar, dict |
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| validation_end | Collate metrics from all validation steps | outputs: array where each item is the output of a validation step | dict: for logging |
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| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
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#### Model training
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| Name | Description | Input | Return |
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| configure_optimizers | called during training setup | None | list: optimizers you want to use |
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| tng_dataloader | called during training | None | pytorch dataloader |
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| val_dataloader | called during validation | None | pytorch dataloader |
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| test_dataloader | called during testing | None | pytorch dataloader |
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| add_model_specific_args | called with args you defined in your main. This lets you tailor args for each model and keep main the same | argparse | argparse |
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#### Model Saving/Loading
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| Name | Description | Input | Return |
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| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
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| load_model_specific | called when loading a model | checkpoint: dict you created in get_save_dict | dict: modified in whatever way you want |
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## Optional model hooks.
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Add these to the model whenever you want to configure training behavior.
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### Model lifecycle hooks
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Use these hooks to customize functionality
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| Method | Purpose | Input | Output | Required |
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| on_batch_start() | called right before the batch starts | - | - | N |
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| on_batch_end() | called right after the batch ends | - | - | N |
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| on_epoch_start() | called right before the epoch starts | - | - | N |
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| on_epoch_end() | called right afger the epoch ends | - | - | N |
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| on_pre_performance_check() | called right before the performance check starts | - | - | N |
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| on_post_performance_check() | called right after the batch starts | - | - | N |
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