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: ```yaml 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: ```python 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: ```python 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" ```
Latest version: 0.8.0 Released: 2024-04-01