lightning_template.utils

Submodules

• lightning_template.utils.callbacks
• lightning_template.utils.cli
• lightning_template.utils.loggers
• lightning_template.utils.loop
• lightning_template.utils.mixin
• lightning_template.utils.optim
• lightning_template.utils.progress
• lightning_template.utils.timer

Classes

LightningCLI	Implementation of a configurable command line tool for pytorch-lightning.
Trainer
Timer

Package Contents

class lightning_template.utils.LightningCLI(save_config_callback: Type[lightning.pytorch.cli.SaveConfigCallback] | None = SaveAndLogConfigCallback, trainer_class: Type[lightning_template.utils.cli.trainer._Trainer] | Callable[Ellipsis, lightning_template.utils.cli.trainer._Trainer] = Trainer, *args, **kwargs)

Bases: lightning.pytorch.cli.LightningCLI

Implementation of a configurable command line tool for pytorch-lightning.

_setup_parser_kwargs(*args, **kwargs) → Tuple[Dict[str, Any], Dict[str, Any]]

add_default_arguments_to_parser(parser: lightning.pytorch.cli.LightningArgumentParser) → None

Adds default arguments to the parser.

static randomly_select_seed() → int

_set_seed() → None

Sets the seed.

before_instantiate_classes() → None

Implement to run some code before instantiating the classes.

_add_configure_optimizers_method_to_model(*args, **kwargs) → None

Overrides the model’s configure_optimizers() method if a single optimizer and optionally a scheduler argument groups are added to the parser as ‘AUTOMATIC’.

class lightning_template.utils.Trainer(num_folds: int | None = None, *args, **kwargs)

Bases: lightning.pytorch.Trainer

num_folds = None

class lightning_template.utils.Timer

duration_ms = None

__enter__()

__exit__(*args)

property duration

iters_per_second(batch_size=1)