LightningArgumentParser¶
- class lightning.pytorch.cli.LightningArgumentParser(*args, description='pytorch-lightning trainer command line tool', env_prefix='PL', default_env=False, **kwargs)[source]¶
- Bases: - object- Extension of jsonargparse’s ArgumentParser for pytorch-lightning. - Initialize argument parser that supports configuration file input. - For full details of accepted arguments see ArgumentParser.__init__. - Parameters
 - add_lightning_class_args(lightning_class, nested_key, subclass_mode=False, required=True)[source]¶
- Adds arguments from a lightning class to a nested key of the parser. - Parameters
- lightning_class¶ ( - Union[- Callable[- ...,- Union[- Trainer,- LightningModule,- LightningDataModule,- Callback]],- Type[- Trainer],- Type[- LightningModule],- Type[- LightningDataModule],- Type[- Callback]]) – A callable or any subclass of {Trainer, LightningModule, LightningDataModule, Callback}.
- nested_key¶ ( - str) – Name of the nested namespace to store arguments.
- subclass_mode¶ ( - bool) – Whether allow any subclass of the given class.
 
- Return type
- Returns
- A list with the names of the class arguments added. 
 
 - add_lr_scheduler_args(lr_scheduler_class=(<class 'torch.optim.lr_scheduler.LRScheduler'>, <class 'lightning.pytorch.cli.ReduceLROnPlateau'>), nested_key='lr_scheduler', link_to='AUTOMATIC')[source]¶
- Adds arguments from a learning rate scheduler class to a nested key of the parser. - Parameters
- lr_scheduler_class¶ ( - Union[- Type[- LRScheduler],- Type[- ReduceLROnPlateau],- Tuple[- Union[- Type[- LRScheduler],- Type[- ReduceLROnPlateau]],- ...]]) – Any subclass of- torch.optim.lr_scheduler.{_LRScheduler, ReduceLROnPlateau}. Use tuple to allow subclasses.
- nested_key¶ ( - str) – Name of the nested namespace to store arguments.
- link_to¶ ( - str) – Dot notation of a parser key to set arguments or AUTOMATIC.
 
- Return type