LightningCLI¶
- class lightning.pytorch.cli.LightningCLI(model_class=None, datamodule_class=None, save_config_callback=<class 'lightning.pytorch.cli.SaveConfigCallback'>, save_config_kwargs=None, trainer_class=<class 'lightning.pytorch.trainer.trainer.Trainer'>, trainer_defaults=None, seed_everything_default=True, parser_kwargs=None, subclass_mode_model=False, subclass_mode_data=False, args=None, run=True, auto_configure_optimizers=True)[소스]¶
기반 클래스:
object
Implementation of a configurable command line tool for pytorch-lightning.
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 fromPL_CONFIG
if set. Individual settings are so parsed from variables named for examplePL_TRAINER__MAX_EPOCHS
.For more info, read the CLI docs.
- 매개변수
model_class¶ (
Union
[Type
[LightningModule
],Callable
[...
,LightningModule
],None
]) – An optionalLightningModule
class to train on or a callable which returns aLightningModule
instance when called. IfNone
, you can pass a registered model with--model=MyModel
.datamodule_class¶ (
Union
[Type
[LightningDataModule
],Callable
[...
,LightningDataModule
],None
]) – An optionalLightningDataModule
class or a callable which returns aLightningDataModule
instance when called. IfNone
, you can pass a registered datamodule with--data=MyDataModule
.save_config_callback¶ (
Optional
[Type
[SaveConfigCallback
]]) – A callback class to save the config.save_config_kwargs¶ (
Optional
[Dict
[str
,Any
]]) – Parameters that will be used to instantiate the save_config_callback.trainer_class¶ (
Union
[Type
[Trainer
],Callable
[...
,Trainer
]]) – An optional subclass of theTrainer
class or a callable which returns aTrainer
instance when called.trainer_defaults¶ (
Optional
[Dict
[str
,Any
]]) – 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¶ (
Union
[bool
,int
]) – Number for theseed_everything()
seed value. Set to True to automatically choose a seed value. Setting it to False will avoid callingseed_everything
.parser_kwargs¶ (
Union
[Dict
[str
,Any
],Dict
[str
,Dict
[str
,Any
]],None
]) – Additional arguments to instantiate eachLightningArgumentParser
.subclass_mode_model¶ (
bool
) – Whether model can be any subclass of the given class.Whether datamodule can be any subclass of the given class.
args¶ (
Union
[List
[str
],Dict
[str
,Any
],object
,None
]) – Arguments to parse. IfNone
the arguments are taken fromsys.argv
. Command line style arguments can be given in alist
. Alternatively, structured config options can be given in adict
orjsonargparse.Namespace
.run¶ (
bool
) – Whether subcommands should be added to run aTrainer
method. If set toFalse
, the trainer and model classes will be instantiated only.
- add_arguments_to_parser(parser)[소스]¶
Implement to add extra arguments to the parser or link arguments.
- 매개변수
parser¶ (
LightningArgumentParser
) – The parser object to which arguments can be added- 반환 형식
- before_instantiate_classes()[소스]¶
Implement to run some code before instantiating the classes.
- 반환 형식
- static configure_optimizers(lightning_module, optimizer, lr_scheduler=None)[소스]¶
Override to customize the
configure_optimizers()
method.
- static link_optimizers_and_lr_schedulers(parser)[소스]¶
Creates argument links for optimizers and learning rate schedulers that specified a
link_to
.- 반환 형식
- parse_arguments(parser, args)[소스]¶
Parses command line arguments and stores it in
self.config
.- 반환 형식