:py:mod:`neural_compressor.compression.pruner`
==============================================

.. py:module:: neural_compressor.compression.pruner

.. autoapi-nested-parse::

   Pruning init.



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

   model_slim/index.rst
   patterns/index.rst
   pruners/index.rst
   wanda/index.rst


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

   criteria/index.rst
   pruning/index.rst
   regs/index.rst
   schedulers/index.rst
   tf_criteria/index.rst
   utils/index.rst


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


Functions
~~~~~~~~~

.. autoapisummary::

   neural_compressor.compression.pruner.save
   neural_compressor.compression.pruner.prepare_pruning



.. py:function:: save(obj: object, f, pickle_module=None, pickle_protocol=None, _use_new_zipfile_serialization=None)

   A rewrite function for torch save.

   :param obj:
   :param f:
   :param pickle_module:
   :param pickle_protocol:
   :param _use_new_zipfile_serialization:
   :return:


.. py:function:: prepare_pruning(model, config, optimizer=None, dataloader=None, loss_func=None, framework='pytorch', device: str = None)

   Get registered pruning class, wrapper the model and optimizer to support all the pruning functionality.

   Get a pruning object from PRUNINGS.

   :param modules: A dict {"module_name": Tensor} that stores the pruning modules' weights.
   :param config: A config dict object that contains the pruners information.

   :returns: A pruning object.

   Raises: AssertionError: Currently only support prunings that have been registered in PRUNINGS.