The easy case#
The easy case is when the machine learned model can be converter into ONNX with a converting library without writing nay specific code. That means that a converter exists for the model or each piece of the model, the converter produces an ONNX graph where every node is part of the existing ONNX specifications, the runtime used to compute the predictions implements every node used in the ONNX graph.
- Train and deploy a scikit-learn pipeline
- Benchmark ONNX conversion
- What is the opset number?
- One model, many possible conversions with options
- Black list operators when converting
- Choose appropriate output of a classifier
- Issues when switching to float
- Funny discrepancies
- Intermediate results and investigation
- Dataframe as an input
- Transfer Learning with ONNX
- Store arrays in one onnx graph
- Modify the ONNX graph