OnnxBackendConfig#

class openstef_foundation_models.presets.forecasting_workflow.OnnxBackendConfig(**data: Any) → None[source]

Bases: BaseConfig

Compute configuration for an ONNX Runtime inference backend.

Holds only how to run the model (execution providers, session options), not which weights: the checkpoint is supplied to build() by the caller, so the same compute settings can run different checkpoints.

Parameters:: data (Any)

kind: Literal['onnx']

providers: list[Annotated[CpuProvider | CudaProvider | TensorRTProvider | CoreMLProvider, FieldInfo(annotation=NoneType, required=True, discriminator='kind')]] | None

policy: DefaultProviderPolicy

session_options: SessionOptionsConfig | None

build(checkpoint: Annotated[LocalCheckpoint | HubCheckpoint, FieldInfo(annotation=NoneType, required=True, discriminator='kind')]) → InferenceBackend[source]

Resolve checkpoint and build the ONNX Runtime backend.

Importing the backend raises MissingExtraError if ONNX Runtime is not installed.

Parameters:

checkpoint (LocalCheckpoint | HubCheckpoint) – The checkpoint (weights + metadata) to load and run.
checkpoint

Returns:

A ready-to-run backend wrapping the resolved checkpoint.

Return type:

InferenceBackend

model_config: ClassVar[ConfigDict] = {'arbitrary_types_allowed': False, 'extra': 'ignore', 'protected_namespaces': ()}: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].