:py:mod:`lightning_template.utils.cli`
======================================

.. py:module:: lightning_template.utils.cli


Subpackages
-----------
.. toctree::
   :titlesonly:
   :maxdepth: 3

   argument_parsers/index.rst


Submodules
----------
.. toctree::
   :titlesonly:
   :maxdepth: 1

   cli/index.rst
   instantiate_class/index.rst
   trainer/index.rst


Package Contents
----------------

Classes
~~~~~~~

.. autoapisummary::

   lightning_template.utils.cli.LightningCLI
   lightning_template.utils.cli.Trainer



Functions
~~~~~~~~~

.. autoapisummary::

   lightning_template.utils.cli.recursive_instantate_class



.. py:class:: LightningCLI(save_config_callback: Optional[Type[lightning.pytorch.cli.SaveConfigCallback]] = SaveAndLogConfigCallback, trainer_class: Union[Type[lightning_template.utils.cli.trainer._Trainer], Callable[Ellipsis, lightning_template.utils.cli.trainer._Trainer]] = Trainer, *args, **kwargs)


   Bases: :py:obj:`lightning.pytorch.cli.LightningCLI`

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

   .. py:method:: _setup_parser_kwargs(*args, **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any]]


   .. py:method:: add_default_arguments_to_parser(parser: lightning.pytorch.cli.LightningArgumentParser) -> None

      Adds default arguments to the parser.


   .. py:method:: randomly_select_seed() -> int
      :staticmethod:


   .. py:method:: _set_seed() -> None

      Sets the seed.


   .. py:method:: before_instantiate_classes() -> None

      Implement to run some code before instantiating the classes.


   .. py:method:: _add_configure_optimizers_method_to_model(*args, **kwargs) -> None

      Overrides the model's :meth:`~lightning.pytorch.core.LightningModule.configure_optimizers` method if a
      single optimizer and optionally a scheduler argument groups are added to the parser as 'AUTOMATIC'.



.. py:function:: recursive_instantate_class(config)


.. py:class:: Trainer(num_folds: Optional[int] = None, *args, **kwargs)


   Bases: :py:obj:`lightning.pytorch.Trainer`