winmltools 1.5.1

Converts Machine Learning models to ONNX for use in Windows ML

WinMLTools provide following tools for Windows ML:

Model Conversion

WinMLTools enables you to convert models from different machine learning toolkits into ONNX for use with Windows ML. Currently the following toolkits are supported:

• apple CoreML

• keras

• scikit-learn

• lightgbm

• xgboost

• libSVM

• tensorflow (experimental)

Here is a simple example to convert a Core ML model:

from coremltools.models.utils import load_spec
from winmltools import convert_coreml
model_coreml = load_spec('example.mlmodel')
model_onnx = convert_coreml(model_coreml, 10, name='ExampleModel')

Post Training Weight Quantization

WinMLTools provides quantization tool to reduce the memory footprint of the model.

Here is an example to convert an ONNX model to a quantized ONNX model:

import winmltools

model = winmltools.load_model('model.onnx')
quantized_model = winmltools.quantize(model, per_channel=True, nbits=8, use_dequantize_linear=True)
winmltools.save_model(quantized_model, 'quantized.onnx')

Dependencies

In order to convert from different toolkits, you may have to install the following packages for different converters:

Toolkit	Source
keras	https://pypi.org/project/Keras
tensorflow	https://pypi.org/project/tensorflow
scikit-learn	https://pypi.org/project/scikit-learn
lightgbm	https://pypi.org/project/lightgbm
xgboost	https://pypi.org/project/xgboost
libsvm	You can download libsvm wheel from various web sources. One example can be found here: https://www.lfd.uci.edu/~gohlke/pythonlibs/#libsvm
coremltools	Currenlty coreml does not distribute coreml packaging on windows. You can install from source: pip install git+https://github.com/apple/coremltools

For more information on WinMLTools, you can go to Convert ML models to ONNX with WinMLTools

License

MIT License