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Currently TFX, Scikit learn and Pytorch might get different types of input data.
They then look at the type of input from the pcollection given at run time and decision what sort of path they will go on.
Also these transforms use a large ever expanding list of typehints as the input type.
The ideal way to resolve both these issues would be to allow the user (who should be aware of the input type) a way to pass that in to the model loader (which will return an inferer).
What needs to happen?
Currently TFX, Scikit learn and Pytorch might get different types of input data.
They then look at the type of input from the pcollection given at run time and decision what sort of path they will go on.
Also these transforms use a large ever expanding list of typehints as the input type.
The ideal way to resolve both these issues would be to allow the user (who should be aware of the input type) a way to pass that in to the model loader (which will return an inferer).
Something like:
model_loader = SklearnModelLoader(url='http://mymodel.com/model.pkl', input_type=pandas.DataFrame)
model_loader = PytorchModelLoader(url='http://mymodel.com/model.pkl', input_type=dict)
Issue Priority
Priority: 2
Issue Component
Component: sdk-py-core
Subtask of issue #21435
Issue Priority
Priority: 2
Issue Component
Component: sdk-py-core
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