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Converts Machine Learning models to ONNX for use in Windows ML

Project description

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, 7, name='ExampleModel')

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

Project details


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