winmltools 1.4.1

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

• sparkml (experimental)

• 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')

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')