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

.. py:module:: neural_compressor.experimental.graph_optimization

.. autoapi-nested-parse::

   Graph Optimization Entry.



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

Classes
~~~~~~~

.. autoapisummary::

   neural_compressor.experimental.graph_optimization.Graph_Optimization




.. py:class:: Graph_Optimization(conf_fname_or_obj=None)


   Graph_Optimization class.

   automatically searches for optimal quantization recipes for low
   precision model inference, achieving best tuning objectives like inference performance
   within accuracy loss constraints.
   Tuner abstracts out the differences of quantization APIs across various DL frameworks
   and brings a unified API for automatic quantization that works on frameworks including
   tensorflow, pytorch and mxnet.
   Since DL use cases vary in the accuracy metrics (Top-1, MAP, ROC etc.), loss criteria
   (<1% or <0.1% etc.) and tuning objectives (performance, memory footprint etc.).
   Tuner class provides a flexible configuration interface via YAML for users to specify
   these parameters.

   :param conf_fname_or_obj: The path to the YAML configuration file or
                             Graph_Optimization_Conf class containing accuracy goal, tuning objective and
                             preferred calibration & quantization tuning space etc.
   :type conf_fname_or_obj: string or obj