Standalone Usage
You can use pydantic-kedro to save and load your Pydantic models without invoking Kedro.
Pure Example
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16 | from tempfile import TemporaryDirectory
from pydantic import BaseModel
# from pydantic.v1 import BaseModel # Pydantic V2
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"
|
Arbitrary Example
Here's an example that uses a Pandas dataframe.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31 | from tempfile import TemporaryDirectory
import pandas as pd
from kedro_datasets.pandas.parquet_dataset import ParquetDataset
from pydantic_kedro import ArbConfig, ArbModel, load_model, save_model
# Arbitrary model class with a few useful defaults
class _PdModel(ArbModel):
"""Pandas model, configured to use Parquet."""
class Config(ArbConfig):
kedro_map = {pd.DataFrame: lambda x: ParquetDataset(filepath=x)}
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)
|