architecture-design

Architecture Design - ML Project Template

This skill defines the standard code architecture for machine learning projects based on the template structure. When modifying or extending code, follow these patterns to maintain consistency.

Overview

The project follows a modular, extensible architecture with clear separation of concerns. Each module (data, model, trainer, analysis) is independently organized using factory and registry patterns for maximum flexibility.

When to Use

Use this skill when:

• Creating a new Dataset class that needs @register_dataset
• Creating a new Model class that needs @register_model
• Creating a new module directory with __init__.py factory wiring
• Initializing a new ML project structure from scratch
• Adding new component types such as Augmentation, CollateFunction, or Metrics

When Not to Use

Do not use this skill when:

• Modifying existing functions or methods
• Fixing bugs in existing code
• Adding helper functions or utilities
• Refactoring without adding new registrable components
• Making simple code changes to a single file
• Modifying configuration files
• Reading or understanding existing code

Key indicator: if the task does not require a @register_* decorator or a Factory pattern, skip this skill.

Core Design Patterns

Factory Pattern

Each module uses a factory to create instances dynamically:

# Example from data_module/dataset/__init__.py
DATASET_FACTORY: Dict = {}

def DatasetFactory(data_name: str):
    dataset = DATASET_FACTORY.get(data_name, None)
    if dataset is None:
        print(f"{data_name} dataset is not implementation, use simple dataset")
        dataset = DATASET_FACTORY.get('simple')
    return dataset

For detailed guidance, refer to references/factory_pattern.md.

Registry Pattern

Components register themselves via decorators:

# Example from data_module/dataset/simple_dataset.py
@register_dataset("simple")
class SimpleDataset(Dataset):
    def __init__(self, data):
        self.data = data