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

Project description

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The packages explores ways to productionize machine learning predictions. One approach uses ONNX and tries to implement a runtime in python / numpy or wraps onnxruntime into a single class. The package provides tools to compare predictions, to benchmark models converted with sklearn-onnx.

The second approach consists in converting a pipeline directly into C and is not much developed.

from sklearn.linear_model import LinearRegression
from sklearn.datasets import load_iris
from mlprodict.onnxrt import OnnxInference, measure_relative_difference
import numpy

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

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

# Conversion into ONNX.
from skl2onnx import to_onnx
model_onnx = to_onnx(lr, X.astype(numpy.float32))

# Predictions with onnxruntime
oinf = OnnxInference(model_onnx, runtime='onnxruntime1')
ypred ={'X': X[:5]})

# Measuring the maximum difference.
print(measure_relative_difference(expected, ypred))


current - 2019-07-05 - 0.00Mb

  • 13: add automated benchmark of every scikit-learn operator in the documentation (2019-07-05)
  • 12: implements a way to measure time for each node of the ONNX graph (2019-07-05)
  • 11: implements a better ZipMap node based on dedicated container (2019-07-05)
  • 7: implement python runtime for scaler, pca, knn, kmeans (2019-07-05)
  • 8: implements runtime for decision tree (2019-07-05)
  • 10: implements full runtime with onnxruntime not node by node (2019-06-16)
  • 9: implements a onnxruntime runtime (2019-06-16)
  • 6: first draft of a python runtime for onnx (2019-06-15)
  • 5: change style highlight-ipython3 (2018-01-05)

0.1.11 - 2017-12-04 - 0.03Mb

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