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Python Runtime for ONNX models, other helpers to convert machine learned models in C++.

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mlprodict was initially started to help implementing converters to ONNX. The main features is a python runtime for ONNX (class OnnxInference), visualization tools (see Visualization), and a numpy API for ONNX). The package also provides tools to compare predictions, to benchmark models converted with sklearn-onnx.

import numpy
from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_iris
from mlprodict.onnxrt import OnnxInference
from mlprodict.onnxrt.validate.validate_difference import measure_relative_difference
from mlprodict import __max_supported_opset__, get_ir_version

iris = load_iris()
X =[:, :2]
y =
lr = LinearRegression(), y)

# Predictions with scikit-learn.
expected = lr.predict(X[:5])

# Conversion into ONNX.
from mlprodict.onnx_conv import to_onnx
model_onnx = to_onnx(lr, X.astype(numpy.float32),
print("ONNX:", str(model_onnx)[:200] + "\n...")

# Predictions with onnxruntime
model_onnx.ir_version = get_ir_version(__max_supported_opset__)
oinf = OnnxInference(model_onnx, runtime='onnxruntime1')
ypred ={'X': X[:5].astype(numpy.float32)})
print("ONNX output:", ypred)

# Measuring the maximum difference.
print("max abs diff:", measure_relative_difference(expected, ypred['variable']))

# And the python runtime
oinf = OnnxInference(model_onnx, runtime='python')
ypred ={'X': X[:5].astype(numpy.float32)},
                 verbose=1, fLOG=print)
print("ONNX output:", ypred)


Installation from pip should work unless you need the latest development features.

pip install mlprodict

The package includes a runtime for ONNX. That’s why there is a limited number of dependencies. However, some features relies on sklearn-onnx, onnxruntime, scikit-learn. They can be installed with the following instructions:

pip install mlprodict[all]

The code is available at GitHub/mlprodict and has online documentation.

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