.skl

modules

to_onnx

experimental_experiment.skl.to_onnx(model: BaseEstimator, args: Sequence[torch.Tensor] | None = None, target_opset: Dict[str, int] | int | None = None, as_function: bool = False, options: OptimizationOptions | None = None, optimize: bool = True, filename: str | None = None, inline: bool = False, input_names: Sequence[str] | None = None, output_names: List[str] | None = None, large_model: bool = False, verbose: int = 0, return_builder: bool = False, raise_list: Set[str] | None = None, external_threshold: int = 1024, return_optimize_report: bool = False, function_options: FunctionOptions | None = None) → ModelProto | ModelContainer | Tuple[ModelProto | ModelContainer, GraphBuilder][source]

Exports a scikit-learn model into ONNX.

Parameters:

  • model – estimator

  • args – input arguments

  • kwargs – keyword attributes

  • input_names – input names

  • target_opset – targeted opset or targeted opsets as a dictionary

  • as_function – export as a ModelProto or a FunctionProto

  • options – optimization options

  • verbose – verbosity level

  • return_builder – returns the builder as well

  • raise_list – the builder stops any time a name falls into that list, this is a debbuging tool

  • optimize – optimize the model before exporting into onnx

  • large_model – if True returns a onnx.model_container.ModelContainer, it lets the user to decide later if the weights should be part of the model or saved as external weights

  • external_threshold – if large_model is True, every tensor above this limit is stored as external

  • return_optimize_report – returns statistics on the optimization as well

  • filename – if specified, stores the model into that file

  • inline – inline the model before converting to onnx, this is done before any optimization takes place

  • export_options – to apply differents options before to get the exported program

  • function_options – to specify what to do with the initializers in local functions, add them as constants or inputs

  • output_names – to rename the output names

Returns:

onnx model

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