:py:mod:`neural_compressor.experimental.scheduler`
==================================================

.. py:module:: neural_compressor.experimental.scheduler

.. autoapi-nested-parse::

   Scheduler class.



Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.experimental.scheduler.Scheduler




.. py:class:: Scheduler




   Scheduler for neural_compressor component pipeline execution.

   Neural Compressor supports several separate components: Quantization, Pruning, Benchmarking.
   This scheduler will sequentially execute specified components by the order of
   appending. This interface provides an unique entry to pipeline execute all supported
   components.

   There are two typical usages:
   1) if all information are set in user configuration yaml files by using neural_compressor built-in
      dataloaders/datasets/metrics, the code usage is like below:

      prune = Pruning('/path/to/pruning.yaml')
      quantizer = Quantization('/path/to/quantization.yaml')
      scheduler = Scheduler()
      scheduler.model('/path/to/model')
      scheduler.append(prune)
      scheduler.append(quantizer)
      opt_model = scheduler()
      opt_model.save()


   2) if neural_compressor built-in dataloaders/datasets/metrics could not fully meet user requirements,
      customized dataloaders/datasets/metrics are needed, the code usage is like below:

      prune = Pruning('/path/to/pruning.yaml')
      prune.train_func = ...                   # optional if it is configured in user yaml.
      prune.eval_dataloader = ...                # optional if it is configured in user yaml.
      prune.eval_func = ...                      # optional if it is configured in user yaml.

      quantizer = Quantization('/path/to/quantization.yaml')
      quantizer.metric = ...                     # optional if it is configured in user yaml.
      quantizer.calib_dataloader = ...           # optional if it is configured in user yaml.
      quantizer.eval_dataloader = ...            # optional if it is configured in user yaml.

      scheduler = Scheduler()
      scheduler.model('/path/to/model')
      scheduler.append(prune)
      scheduler.append(quantizer)
      opt_model = scheduler()
      opt_model.save()