zenml Fixed optional typing on non-serializable types

Describe changes

This code:

from typing import Optional

from zenml import step, pipeline
from sklearn.neighbors import NearestNeighbors
import numpy as np


@step
def one():
    X = np.array([[1, 1], [2, 2]])
    return NearestNeighbors().fit(X)


@step
def two(a: Optional[int] = None, nn: Optional[NearestNeighbors] = None):
    pass


@pipeline()
def simple_pipeline():
    nn = one()
    two(nn=None, a=None)


simple_pipeline()

Was failing with the following error message:

│    404 │   def _unknown_type_schema(self, obj: Any) -> CoreSchema:                               │
│ ❱  405 │   │   raise PydanticSchemaGenerationError(                                              │
│    406 │   │   │   f'Unable to generate pydantic-core schema for {obj!r}. '                      │
│    407 │   │   │   'Set `arbitrary_types_allowed=True` in the model_config to ignore this error  │
│    408 │   │   │   ' or implement `__get_pydantic_core_schema__` on your type to fully support   │
╰──────────────────────────────────────────────────────────────────────────────────────────────────╯
PydanticSchemaGenerationError: Unable to generate pydantic-core schema for <class 'sklearn.neighbors._unsupervised.NearestNeighbors'>. Set `arbitrary_types_allowed=True` in the model_config to ignore this error or 
implement `__get_pydantic_core_schema__` on your type to fully support it.

If you got this error by calling handler(<some type>) within `__get_pydantic_core_schema__` then you likely need to call `handler.generate_schema(<some type>)` since we do not call `__get_pydantic_core_schema__` on 
`<some type>` otherwise to avoid infinite recursion.

For further information visit https://errors.pydantic.dev/2.7/u/schema-for-unknown-type

def two(a: Optional[int] = None, nn: Optional[NearestNeighbors] = None):

While primitive types allowed for optional typing, non-serializable types like NearestNeighbors lead to pydantic errors, as the utility functions were only checking serializable types (as the assumption probably was that at this point we are handling only parameters, not artifacts).

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[ ] I have added tests to cover my changes.
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Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)
[ ] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)
[ ] Other (add details above)

Aug 21 '24 10:08 AlexejPenner

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Aug 21 '24 10:08 coderabbitai[bot]

Also, this really screams for a test case

already had some, are any obvious ones missing?

Aug 27 '24 12:08 AlexejPenner

I'm still not entirely sure how to handle this. On the one hand, it would be nice to have your code work as is. On the other hand, if someone now we're to switch the default value to something Non-Null, we would fail and say a parameter has to be JSON-serializable, which also seems weird.

In case we want this though, I think we could achieve this by allowing arbitrary types in case the value is None:

def _validate_parameter_input_value(
  self, parameter: inspect.Parameter, value: Any
) -> None:
  arbitrary_types_allowed = value is None
  config_dict = ConfigDict(arbitrary_types_allowed=arbitrary_types_allowed)
  ...

Aug 27 '24 14:08 schustmi

Closing in favor of #3215

Nov 25 '24 11:11 schustmi