pydantic-kedro 0.8.0

Kedro

pydantic-kedro

Advanced serialization for Pydantic models via Kedro and fsspec.

This package implements custom Kedro "datasets" for both "pure" and "arbitrary" Pydantic models. You can also use it stand-alone, using Kedro just for serializing other object types.

Please see the documentation for a tutorial and more examples.

Usage with Kedro

You can use the [PydanticAutoDataset][pydantic_kedro.PydanticAutoDataset] or any other dataset from pydantic-kedro within your Kedro catalog to save your Pydantic models:

# conf/base/catalog.yml
my_pydantic_model:
 type: pydantic_kedro.PydanticAutoDataset
 filepath: folder/my_model

Direct Dataset Usage

This example works for "pure", JSON-safe Pydantic models via PydanticJsonDataset:

from pydantic import BaseModel
# from pydantic.v1 import BaseModel  # Pydantic V2
from pydantic_kedro import PydanticJsonDataset


class MyPureModel(BaseModel):
    """Your custom Pydantic model with JSON-safe fields."""

    x: int
    y: str


obj = MyPureModel(x=1, y="why?")

# Create an in-memory (temporary) file via `fsspec` and save it
ds = PydanticJsonDataset("memory://temporary-file.json")
ds.save(obj)

# We can re-load it from the same file
read_obj = ds.load()
assert read_obj.x == 1

Standalone Usage

You can also use pydantic-kedro as a generic saving and loading engine for Pydantic models:

from tempfile import TemporaryDirectory

from pydantic.v1 import BaseModel
from pydantic_kedro import load_model, save_model

class MyModel(BaseModel):
    """My custom model."""

    name: str

# We can use any fsspec URL, so we'll make a temporary folder
with TemporaryDirectory() as tmpdir:
    save_model(MyModel(name="foo"), f"{tmpdir}/my_model")
    obj = load_model(f"{tmpdir}/my_model")
    assert obj.name == "foo"