Python Runtime for ONNX models, other helpers to convert machine learned models in C++.
Project description
mlprodict
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 = 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 skl2onnx import to_onnx
model_onnx = to_onnx(lr, X.astype(numpy.float32))
# Predictions with onnxruntime
oinf = OnnxInference(model_onnx, runtime='onnxruntime1')
ypred = oinf.run({'X': X[:5]})
print(ypred)
# Measuring the maximum difference.
print(measure_relative_difference(expected, ypred))
History
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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