architecture-design
Use only when creating new registrable ML components that require Factory or Registry patterns.
Оригинален текст · няма потвърден превод на българскиПърво прочетете инструкцията.
Файлът се зарежда от посочения източник само по ваше действие. Можете да го прочетете тук, да го копирате или да го запазите като .md.
GitHub хранилище- Звезди на репото
- 5611
- Лиценз
- MIT
- Последна проверка на записа
- 21.09.2026 г.
Добавете в своя инструмент.
Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.
npx skills add Galaxy-Dawn/claude-scholarСтраницата копира текст. Не инсталира и не изпълнява нищо на устройството ви.
Откъс от SKILL.md
Началото на файла на автора, без превод.
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__.pyfactory 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
For detailed guidance, refer to references/registry_pattern.md.
Auto-Import Pattern
Modules automatically discover and import submodules:
# Example from data_module/dataset/__init__.py
models_dir = os.path.dirname(__file__)
import_modules(models_dir, "src.data_module.dataset")
For detailed guidance, refer to references/auto_import.md.