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

.. py:module:: anomalib.utils.cli

.. autoapi-nested-parse::

   Anomalib CLI.



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

   cli/index.rst


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

Classes
~~~~~~~

.. autoapisummary::

   anomalib.utils.cli.AnomalibCLI




.. py:class:: AnomalibCLI(model_class: Optional[Union[Type[pytorch_lightning.LightningModule], Callable[Ellipsis, pytorch_lightning.LightningModule]]] = None, datamodule_class: Optional[Union[Type[pytorch_lightning.LightningDataModule], Callable[Ellipsis, pytorch_lightning.LightningDataModule]]] = None, save_config_callback: Optional[Type[pytorch_lightning.utilities.cli.SaveConfigCallback]] = SaveConfigCallback, save_config_filename: str = 'config.yaml', save_config_overwrite: bool = False, save_config_multifile: bool = False, trainer_class: Union[Type[pytorch_lightning.Trainer], Callable[Ellipsis, pytorch_lightning.Trainer]] = Trainer, trainer_defaults: Optional[Dict[str, Any]] = None, seed_everything_default: Optional[int] = None, description: str = 'Anomalib trainer command line tool', env_prefix: str = 'Anomalib', env_parse: bool = False, parser_kwargs: Optional[Union[Dict[str, Any], Dict[str, Dict[str, Any]]]] = None, subclass_mode_model: bool = False, subclass_mode_data: bool = False, run: bool = True, auto_registry: bool = True)

   Bases: :py:obj:`pytorch_lightning.utilities.cli.LightningCLI`

   Implementation of a fully configurable CLI tool for anomalib.

   The advantage of this tool is its flexibility to configure the pipeline
   from both the CLI and a configuration file (.yaml or .json). It is even
   possible to use both the CLI and a configuration file simultaneously.
   For more details, the reader could refer to PyTorch Lightning CLI documentation.

   .. py:method:: add_arguments_to_parser(parser: pytorch_lightning.utilities.cli.LightningArgumentParser) -> None

      Add default arguments.

      :param parser: Lightning Argument Parser.
      :type parser: LightningArgumentParser


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

      Sets the default root directory depending on the subcommand type. <train, fit, predict, tune.>.


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

      Sets the default callbacks used within the pipeline.


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

      Modify the configuration to properly instantiate classes.