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 = 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'}, target_opset=__max_supported_opset__) 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 = 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.