Training-Time Quantization
==========================

Quantization refers to techniques for performing neural network computations in lower precision than
floating point. Quantization can reduce a model’s size and also improve a model’s inference latency and
memory bandwidth requirement, because many hardware platforms offer high-performance implementations of quantized
operations.

.. autoclass::  coremltools.optimize.torch.quantization.ModuleLinearQuantizerConfig
    :members: from_dict, as_dict, from_yaml

.. autoclass::  coremltools.optimize.torch.quantization.LinearQuantizerConfig
    :members: set_global, set_module_type, set_module_name, from_dict, as_dict, from_yaml

.. autoclass:: coremltools.optimize.torch.quantization.LinearQuantizer
    :members: prepare, step, report, finalize

.. autoclass::  coremltools.optimize.torch.quantization.ObserverType

.. autoclass::  coremltools.optimize.torch.quantization.QuantizationScheme