Optimizers
===============================================

To deploy models on devices such as the iPhone, you often need to optimize the models to
use less storage space, reduce power consumption, and reduce latency during inference.
For an overview, see Optimizing Models Post-Training
(`Compressing ML Program Weights <https://coremltools.readme.io/docs/compressing-ml-program-weights>`_
and `Compressing Neural Network Weights <https://coremltools.readme.io/docs/quantization>`_).

Post-Training Compression
-------------------------

Post-training compression for Core ML models:

.. toctree::
   :maxdepth: 1
   
   coremltools.optimize.coreml.post_training_quantization.rst
   coremltools.optimize.coreml.config.rst


Training-Time Compression
-------------------------

Training-time compression for PyTorch models:

.. toctree::
   :maxdepth: 1
   
   coremltools.optimize.torch.pruning.rst
   coremltools.optimize.torch.palettization.rst
   coremltools.optimize.torch.quantization.rst
   coremltools.optimize.torch.examples.rst