API - 1/1 Blog onnx (8) runtime (3)
API - 1/1#
Numpy API for ONNX and scikit-learn (part II)#
2021-05-05
This follows blog post Numpy API for ONNX and scikit-learn (part I). It demonstrated how to insert a custom function in a pipeline and still be able to convert that pipeline into ONNX. This blog post shows how to implement a custom transformer.
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Numpy API for ONNX and scikit-learn (part I)#
2021-05-05
sklearn-onnx converts most of the pipelines including numerical preprocessing or predictors but it fails whenever custom code is involved. That covers the use of FunctionTransformer or a new model inheriting from BaseEstimator. To be successful, the conversion needs a way to convert the custom code into ONNX. The proposed solution here is bypass that complex steps (rewrite a python function with ONNX operators) by directly writing the custom code with ONNX operators. However, even though most of the operator are close to numpy functions, they are not the same. To avoid spending time looking at them, many numpy functions were implementing with ONNX operators. The custom function or predictor can then just be implemented with this API to build a unique ONNX graph executed with a runtime.
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