mlprodict 0.7.1672

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

mlprodict

mlprodict was initially started to help implementing converters to ONNX. The main feature is a python runtime for ONNX. It gives feedback when the execution fails. The package 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.tools import get_ir_version_from_onnx

iris = load_iris()
X = iris.data[:, :2]
y = iris.target
lr = LinearRegression()
lr.fit(X, y)

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

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

# Predictions with onnxruntime
model_onnx.ir_version = get_ir_version_from_onnx()
oinf = OnnxInference(model_onnx, runtime='onnxruntime1')
ypred = oinf.run({'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 = oinf.run({'X': X[:5].astype(numpy.float32)},
                 verbose=1, fLOG=print)
print("ONNX output:", ypred)

Installation

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.