ezconfy 0.1.3

YAML-based configuration framework with Pydantic validation and dynamic object instantiation

Why ezconfy?

EzConfy is designed for ML/data science projects that want typed, validated configs with automatic object wiring — without the complexity of a full framework like Hydra.

Installation

pip install ezconfy

Quick Start

Define a schema and a config file, then load them:

from ezconfy import ConfigBuilder

cfg = ConfigBuilder.from_files(config_paths="config.yaml", schema_path="schema.yaml")
print(cfg.training.batch_size)  # validated, typed access

Schema

A schema file describes the expected shape and types of your configuration. It can define custom types and the root structure:

types:
  OptimizerType:
    - adam
    - sgd
    - rmsprop

schema:
  model:
    hidden_dims: list[int]
  training:
    batch_size: int = 32
    num_epochs: int = 10
    shuffle: bool = true
    dropout: float?
    optimizer: OptimizerType

Supported type syntax

Syntax	Meaning
int, float, str, bool	Primitive types
type?	Optional (defaults to None)
type = value	Type with default
list[T]	List of T
A | B	Union type
[a, b, c]	Enum
Child < Parent	Model inheritance
pathlib:Path	External type (import path)
/path/to/file.py:ClassName	External type (file path)

If no types are needed, the entire YAML is treated as the root schema (no schema: wrapper required).

Object Instantiation

ezconfy can instantiate Python objects directly from config using _target_type_:

dataset:
  _target_type_: mypackage.data:MyDataset
  _init_args_:
    num_classes: 100
    root: /data

Use _init_method_ to call an alternative constructor (e.g. from_pretrained):

encoder:
  _target_type_: mypackage.models:BertEncoder
  _init_method_: from_pretrained
  _init_args_:
    model_name: bert-base-uncased

Placeholder Injection

Reference other config values with ${key}. Supports attribute access and method calls:

num_classes: 10

dataset:
  _target_type_: mypackage.data:MyDataset
  _init_args_:
    num_classes: ${num_classes}   # scalar reference

model:
  _target_type_: mypackage.models:Classifier
  _init_args_:
    in_features: ${dataset.num_classes}   # attribute access
    params: ${encoder.parameters()}       # method call

Objects are instantiated in topological order based on their dependencies, so forward references work automatically.

Multi-file Configs & Overrides

Pass multiple files — they are deep-merged in order (later files win on conflicts):

cfg = ConfigBuilder.from_files(config_paths=["base.yaml", "experiment.yaml"])

Apply programmatic overrides on top:

cfg = ConfigBuilder.from_files(
    config_paths="config.yaml",
    overrides={"training": {"batch_size": 64}},
)

Code Generation CLI

Generate a Pydantic model file from a schema:

ezconfy generate schema.yaml output.py

This produces a standalone .py file with BaseModel classes matching the schema, useful for editor autocompletion and static analysis.

Requirements

• Python 3.11+