lazyregistry 0.2.0

A lightweight Python library for lazy-loading registries with namespace support and type safety.

lazyregistry

A lightweight Python library for lazy-loading registries with namespace support and type safety

Installation

# Install with pip
$ pip install lazyregistry

# Add to your project with uv
$ uv add "lazyregistry"

Quick Start

from lazyregistry import Registry

registry = Registry(name="plugins")

# Register by import string (lazy - imported on access)
registry.register("json", "json:dumps")

# Register by instance (immediate - already imported)
import pickle
registry.register("pickle", pickle.dumps, is_instance=True)

# Import happens here
serializer = registry["json"]

Features

• Lazy imports - Defer expensive imports until first access
• Instance registration - Register both import strings and direct objects
• Namespaces - Organize multiple registries
• Type-safe - Full generic type support
• Eager loading - Optional immediate import for critical components
• Pretrained models - Built-in support for save_pretrained/from_pretrained pattern

Examples

Run examples: uv run python examples/<example>.py

1. Plugin System

examples/plugin_system.py - Extensible plugin architecture with decorator-based registration:

from lazyregistry import Registry

PLUGINS = Registry(name="plugins")

def plugin(name: str):
    def decorator(cls):
        PLUGINS.register(name, cls, is_instance=True)
        return cls
    return decorator

@plugin("uppercase")
class UppercasePlugin:
    def process(self, text: str) -> str:
        return text.upper()

# Execute plugins
PluginManager.execute("uppercase", "hello")  # "HELLO"
PluginManager.pipeline("hello", "uppercase", "reverse")  # "OLLEH"

2. Pretrained Models

examples/pretrained.py - HuggingFace-style save/load with two patterns:

Basic (config only):

from pydantic import BaseModel
from lazyregistry import NAMESPACE
from lazyregistry.pretrained import AutoRegistry, PretrainedMixin

class ModelConfig(BaseModel):
    model_type: str
    hidden_size: int = 768

class AutoModel(AutoRegistry):
    registry = NAMESPACE["models"]
    config_class = ModelConfig
    type_key = "model_type"

@AutoModel.register("bert")
class BertModel(PretrainedMixin[ModelConfig]):
    config_class = ModelConfig

# Save and auto-load
model = BertModel(ModelConfig(model_type="bert"))
model.save_pretrained("./model")
loaded = AutoModel.from_pretrained("./model")  # Auto-detects type

Advanced (config + custom state):

class Tokenizer(PretrainedMixin[TokenizerConfig]):
    def __init__(self, config, vocab: dict[str, int] | None = None):
        super().__init__(config)
        self.vocab = vocab or {}

    def save_pretrained(self, path):
        super().save_pretrained(path)
        # Save additional state (vocabulary)
        Path(path).joinpath("vocab.txt").write_text(...)

    @classmethod
    def from_pretrained(cls, path):
        config = cls.config_class.model_validate_json(...)
        vocab = ...  # Load vocabulary
        return cls(config, vocab=vocab)

API Reference

Core Classes

ImportString - String that represents an import path with lazy loading capability

from lazyregistry import ImportString

# Create an import string
import_str = ImportString("json:dumps")

# Load the object when needed
func = import_str.load()
func({"key": "value"})  # '{"key": "value"}'

Registry[K, V] - Named registry with lazy import support

registry = Registry(name="plugins")
registry.register("key", "module:object")           # Lazy
registry.register("key", obj, is_instance=True)     # Immediate
registry.register("key", "module:object", eager_load=True)  # Load now
value = registry["key"]

Namespace - Container for multiple registries

from lazyregistry import NAMESPACE

NAMESPACE["models"].register("bert", "transformers:BertModel")
model = NAMESPACE["models"]["bert"]

LazyImportDict[K, V] - Base class for custom implementations (same API as Registry without name)

Pretrained Pattern

PretrainedMixin[ConfigT] - Save/load with Pydantic config

class MyModel(PretrainedMixin[MyConfig]):
    config_class = MyConfig

model.save_pretrained("./path")
loaded = MyModel.from_pretrained("./path")

AutoRegistry - Auto-detect model type from config

class AutoModel(AutoRegistry):
    registry = NAMESPACE["models"]
    config_class = ModelConfig
    type_key = "model_type"

@AutoModel.register("bert")
class BertModel(PretrainedMixin[ModelConfig]):
    config_class = ModelConfig

loaded = AutoModel.from_pretrained("./path")  # Auto-detects type

Why?

Before:

# All imports happen upfront
from heavy_module_1 import ClassA
from heavy_module_2 import ClassB
from heavy_module_3 import ClassC

REGISTRY = {"a": ClassA, "b": ClassB, "c": ClassC}

After:

# Import only what you use
from lazyregistry import Registry

registry = Registry(name="components")
registry.register("a", "heavy_module_1:ClassA")
registry.register("b", "heavy_module_2:ClassB")
registry.register("c", "heavy_module_3:ClassC")

# Only ClassA is imported
component = registry["a"]

Testing

Run tests with coverage:

uv run pytest tests/ --cov=lazyregistry --cov-report=term-missing

The test suite includes:

• Core registry tests - LazyImportDict, Registry, Namespace functionality
• Pretrained tests - save/load patterns, AutoRegistry, custom state
• Example tests - Verify all examples run correctly

License

MIT