DriConfig

A Pydantic-ish way to manage your project's YAML configurations.

Documentation: https://dribia.github.io/driconfig

Source Code: https://github.com/dribia/driconfig

The usage of YAML files to store configurations and parameters is widely accepted in the Python community, especially in Data Science environments.

DriConfig provides a clean interface between your Python code and these YAML configuration files.

It is heavily based on Pydantic's Settings Management, preserving its core functionalities and advantages.

Key features

Subclassing the DriConfig class we create an interface to any YAML configuration file.
Our project's configurations are then attributes of this class.
They are automatically filled with the values in the YAML configuration file.
We can define complex configuration structures using Pydantic models.
We preserve Pydantic's type casting and validation!

Example

Let's say we have a YAML configuration file config.yaml with the following data:

# config.yaml
model_parameters:
  eta: 0.2
  gamma: 2
  lambda: 1

date_interval:
  start: 2021-01-01
  end: 2021-12-31

Then we can configparse with driconfig as follows:

from datetime import date
from typing import Dict

from driconfig import DriConfig, DriConfigConfigDict
from pydantic import BaseModel


class DateInterval(BaseModel):
  """Model for the `date_interval` configuration."""
  start: date
  end: date


class AppConfig(DriConfig):
   """Interface for the config/config.yaml file."""

    """Configure the YAML file location."""
    model_config = DriConfigConfigDict(
        config_folder=".",
        config_file_name="config.yaml",
    )
   model_parameters: Dict[str, float]
   date_interval: DateInterval

config = AppConfig()
print(config.model_dump_json(indent=4))
"""
{
    "model_parameters": {
        "eta": 0.2,
        "gamma": 2.0,
        "lambda": 1.0
    },
    "date_interval": {
        "start": "2021-01-01",
        "end": "2021-12-31"
    }
}
"""