yeandy commented on code in PR #21806:
URL: https://github.com/apache/beam/pull/21806#discussion_r897138843
##########
sdks/python/apache_beam/ml/inference/base.py:
##########
@@ -125,11 +130,14 @@ def load_model(self) -> ModelT:
return self._unkeyed.load_model()
def run_inference(
- self, batch: Sequence[Tuple[KeyT, ExampleT]], model: ModelT,
- **kwargs) -> Iterable[Tuple[KeyT, PredictionT]]:
+ self,
+ batch: Sequence[Tuple[KeyT, ExampleT]],
+ model: ModelT,
+ extra_kwargs: Optional[Dict[str, Any]] = None
+ ) -> Iterable[Tuple[KeyT, PredictionT]]:
keys, unkeyed_batch = zip(*batch)
return zip(
- keys, self._unkeyed.run_inference(unkeyed_batch, model, **kwargs))
+ keys, self._unkeyed.run_inference(unkeyed_batch, model, extra_kwargs))
Review Comment:
Caught up with @ryanthompson591. We're going to stick with passing (renamed)
`inference_args` for all frameworks, but for sklearn, raise an exception if a
non-empty value is passed.
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