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
DriConfigclass 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 parse with driconfig as follows:
from datetime import date
from typing import Dict
from driconfig import DriConfig
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."""
class Config:
"""Configure the YAML file location."""
config_folder = "."
config_file_name = "config.yaml"
model_parameters: Dict[str, float]
date_interval: DateInterval
config = AppConfig()
print(config.json(indent=4))
"""
{
"model_parameters": {
"eta": 0.2,
"gamma": 2.0,
"lambda": 1.0
},
"date_interval": {
"start": "2021-01-01",
"end": "2021-12-31"
}
}
"""
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