neural_compressor.experimental.graph_optimization

Graph Optimization Entry.

Module Contents

Classes

Graph_Optimization

Graph_Optimization class.

class neural_compressor.experimental.graph_optimization.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.

Parameters:

conf_fname_or_obj (string 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.