Standalone Usage
You can use pydantic-kedro to save and load your Pydantic models without invoking Kedro.
Pure Example
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15 | from tempfile import TemporaryDirectory
from pydantic 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"
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Arbitrary Example
Here's an example that uses a Pandas dataframe.
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31 | from tempfile import TemporaryDirectory
import pandas as pd
from kedro.extras.datasets.pandas import ParquetDataSet
from pydantic import validator
from pydantic_kedro import ArbModel, load_model, save_model
# Arbitrary model class with a few useful defaults
class _PdModel(ArbModel):
"""Pandas model, configured to use Parquet."""
class Config(ArbModel.Config):
kedro_map = {pd.DataFrame: ParquetDataSet}
class MyModel(_PdModel):
"""My custom model."""
name: str
data: pd.DataFrame
df = pd.DataFrame({"x": [1, 2, 3], "y": ["a", "b", "c"]})
# We can use any fsspec URL, so we'll make a temporary folder
with TemporaryDirectory() as tmpdir:
save_model(MyModel(name="foo", data=df), f"{tmpdir}/my_model")
obj = load_model(f"{tmpdir}/my_model")
assert obj.data.equals(df)
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