LightningIRCLI

class lightning_ir.main.LightningIRCLI(model_class: type[~lightning.pytorch.core.module.LightningModule] | ~typing.Callable[[...], ~lightning.pytorch.core.module.LightningModule] | None = None, datamodule_class: type[~lightning.pytorch.core.datamodule.LightningDataModule] | ~typing.Callable[[...], ~lightning.pytorch.core.datamodule.LightningDataModule] | None = None, save_config_callback: type[~lightning.pytorch.cli.SaveConfigCallback] | None = <class 'lightning.pytorch.cli.SaveConfigCallback'>, save_config_kwargs: dict[str, ~typing.Any] | None = None, trainer_class: type[~lightning.pytorch.trainer.trainer.Trainer] | ~typing.Callable[[...], ~lightning.pytorch.trainer.trainer.Trainer] = <class 'lightning.pytorch.trainer.trainer.Trainer'>, trainer_defaults: dict[str, ~typing.Any] | None = None, seed_everything_default: bool | int = True, parser_kwargs: dict[str, ~typing.Any] | dict[str, dict[str, ~typing.Any]] | None = None, subclass_mode_model: bool = False, subclass_mode_data: bool = False, args: list[str] | dict[str, ~typing.Any] | ~jsonargparse._namespace.Namespace | None = None, run: bool = True, auto_configure_optimizers: bool = True)[source]

Bases: LightningCLI

__init__(model_class: type[~lightning.pytorch.core.module.LightningModule] | ~typing.Callable[[...], ~lightning.pytorch.core.module.LightningModule] | None = None, datamodule_class: type[~lightning.pytorch.core.datamodule.LightningDataModule] | ~typing.Callable[[...], ~lightning.pytorch.core.datamodule.LightningDataModule] | None = None, save_config_callback: type[~lightning.pytorch.cli.SaveConfigCallback] | None = <class 'lightning.pytorch.cli.SaveConfigCallback'>, save_config_kwargs: dict[str, ~typing.Any] | None = None, trainer_class: type[~lightning.pytorch.trainer.trainer.Trainer] | ~typing.Callable[[...], ~lightning.pytorch.trainer.trainer.Trainer] = <class 'lightning.pytorch.trainer.trainer.Trainer'>, trainer_defaults: dict[str, ~typing.Any] | None = None, seed_everything_default: bool | int = True, parser_kwargs: dict[str, ~typing.Any] | dict[str, dict[str, ~typing.Any]] | None = None, subclass_mode_model: bool = False, subclass_mode_data: bool = False, args: list[str] | dict[str, ~typing.Any] | ~jsonargparse._namespace.Namespace | None = None, run: bool = True, auto_configure_optimizers: bool = True) → None

Receives as input pytorch-lightning classes (or callables which return pytorch-lightning classes), which are called / instantiated using a parsed configuration file and / or command line args.

Parsing of configuration from environment variables can be enabled by setting parser_kwargs={"default_env": True}. A full configuration yaml would be parsed from PL_CONFIG if set. Individual settings are so parsed from variables named for example PL_TRAINER__MAX_EPOCHS.

For more info, read the CLI docs.

Parameters:

model_class – An optional LightningModule class to train on or a callable which returns a LightningModule instance when called. If None, you can pass a registered model with --model=MyModel.
datamodule_class – An optional LightningDataModule class or a callable which returns a LightningDataModule instance when called. If None, you can pass a registered datamodule with --data=MyDataModule.
save_config_callback – A callback class to save the config.
save_config_kwargs – Parameters that will be used to instantiate the save_config_callback.
trainer_class – An optional subclass of the Trainer class or a callable which returns a Trainer instance when called.
trainer_defaults – Set to override Trainer defaults or add persistent callbacks. The callbacks added through this argument will not be configurable from a configuration file and will always be present for this particular CLI. Alternatively, configurable callbacks can be added as explained in the CLI docs.
seed_everything_default – Number for the seed_everything() seed value. Set to True to automatically choose a seed value. Setting it to False will avoid calling seed_everything.
parser_kwargs – Additional arguments to instantiate each LightningArgumentParser.
subclass_mode_model – Whether model can be any subclass of the given class.
subclass_mode_data –
Whether datamodule can be any subclass of the given class.
args – Arguments to parse. If None the arguments are taken from sys.argv. Command line style arguments can be given in a list. Alternatively, structured config options can be given in a dict or jsonargparse.Namespace.
run – Whether subcommands should be added to run a Trainer method. If set to False, the trainer and model classes will be instantiated only.

Methods

`__init__`([model_class, datamodule_class, ...])	Receives as input pytorch-lightning classes (or callables which return pytorch-lightning classes), which are called / instantiated using a parsed configuration file and / or command line args.
`add_arguments_to_parser`(parser)
`add_core_arguments_to_parser`(parser)	Adds arguments from the core classes to the parser.
`add_default_arguments_to_parser`(parser)	Adds default arguments to the parser.
`after_instantiate_classes`()	Implement to run some code after instantiating the classes.
`before_instantiate_classes`()	Implement to run some code before instantiating the classes.
`configure_optimizers`(lightning_module, optimizer)
`init_parser`(**kwargs)	Method that instantiates the argument parser.
`instantiate_classes`()	Instantiates the classes and sets their attributes.
`instantiate_trainer`(**kwargs)	Instantiates the trainer.
`link_optimizers_and_lr_schedulers`(parser)	Creates argument links for optimizers and learning rate schedulers that specified a `link_to`.
`parse_arguments`(parser, args)	Parses command line arguments and stores it in `self.config`.
`setup_parser`(add_subcommands, main_kwargs, ...)	Initialize and setup the parser, subcommands, and arguments.
`subcommands`()

add_core_arguments_to_parser(parser: LightningArgumentParser) → None: Adds arguments from the core classes to the parser.

add_default_arguments_to_parser(parser: LightningArgumentParser) → None: Adds default arguments to the parser.

after_instantiate_classes() → None: Implement to run some code after instantiating the classes.

before_instantiate_classes() → None: Implement to run some code before instantiating the classes.

init_parser(**kwargs: Any) → LightningArgumentParser: Method that instantiates the argument parser.

instantiate_classes() → None: Instantiates the classes and sets their attributes.

instantiate_trainer(**kwargs: Any) → Trainer

Instantiates the trainer.

Parameters:: kwargs – Any custom trainer arguments.

static link_optimizers_and_lr_schedulers(parser: LightningArgumentParser) → None: Creates argument links for optimizers and learning rate schedulers that specified a link_to.

parse_arguments(parser: LightningArgumentParser, args: list[str] | dict[str, Any] | Namespace | None) → None: Parses command line arguments and stores it in self.config.

setup_parser(add_subcommands: bool, main_kwargs: dict[str, Any], subparser_kwargs: dict[str, Any]) → None: Initialize and setup the parser, subcommands, and arguments.