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For full details of accepted arguments see `ArgumentParser.__init__ `_. Args: description: Description of the tool shown when running ``--help``. env_prefix: Prefix for environment variables. Set ``default_env=True`` to enable env parsing. default_env: Whether to parse environment variables. rDN)"_JSONARGPARSE_SIGNATURES_AVAILABLEModuleNotFoundErrorr0r1 callback_keys _optimizers_lr_schedulers)r2rErFrGr+r,r3s r4r1 LightningArgumentParser.__init__`sG&21%)K(LN N $Jrkqr(*QSTVr6lightning_class. nested_key subclass_moderequiredc [U5(a [U[5(d [U5n[U[5(a[ U[ [ [[45(an[ U[5(aURRU5 U(aURXSUS9$URUUS[ U[ 5(+SS9$[SUS35e)aAdds arguments from a lightning class to a nested key of the parser. Args: lightning_class: A callable or any subclass of {Trainer, LightningModule, LightningDataModule, Callback}. nested_key: Name of the nested namespace to store arguments. subclass_mode: Whether allow any subclass of the given class. required: Whether the argument group is required. 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You can delete the previous config file, set `LightningCLI(save_config_callback=None)` to disable config saving, or set `LightningCLI(save_config_kwargs={"overwrite": True})` to overwrite the config file.T)exist_ok) skip_nonerr)rrlog_dirospathjoinrrris_global_zeroisfilestrategy broadcast RuntimeErrorr3r7makedirsrsaverrr)r2rrrr config_pathfs file_existss r4setupSaveConfigCallback.setupsL        ooG& &&'',,w0D0DEK(B>>8?8N8Nbii 4TY %..88E &>>223:k]Kww%% Gd 3   KKcgcqcq!  ! !   W 7!%D %--778J8JKr6cg)aImplement to save the config in some other place additional to the standard log_dir. Example: def save_config(self, trainer, pl_module, stage): if isinstance(trainer.logger, Logger): config = self.parser.dump(self.config, skip_none=False) # Required for proper reproducibility trainer.logger.log_hyperparams({"config": config}) Note: This method is only called on rank zero. This allows to implement a custom save config without having to worry about ranks or race conditions. 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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 :ref:`the CLI docs `. Args: model_class: An optional :class:`~pytorch_lightning.core.LightningModule` class to train on or a callable which returns a :class:`~pytorch_lightning.core.LightningModule` instance when called. If ``None``, you can pass a registered model with ``--model=MyModel``. datamodule_class: An optional :class:`~pytorch_lightning.core.datamodule.LightningDataModule` class or a callable which returns a :class:`~pytorch_lightning.core.datamodule.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 :class:`~pytorch_lightning.trainer.trainer.Trainer` class or a callable which returns a :class:`~pytorch_lightning.trainer.trainer.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 :ref:`the CLI docs `. seed_everything_default: Number for the :func:`~lightning_fabric.utilities.seed.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 :class:`~pytorch_lightning.trainer.trainer.Trainer` method. 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