Type Hints for ML Code

What problem does it solve?

ML projects frequently suffer from unclear typing, leading to runtime bugs and maintenance challenges. This skill provides a structured approach for adding precise type hints to ML code, helping developers catch issues earlier and improve readability.

Core Features & Use Cases

• Public API typing: Annotate functions and classes that form the public interfaces of ML modules.
• Configuration typing: Use dataclasses or TypeAlias for training and evaluation configs.
• Torch typing patterns: Provide patterns for tensors, models, optimizers, and HF transformers with safe Any usage.
• Real-world scenario: Annotate a training step to clarify inputs/outputs and reduce mypy churn.

Quick Start

Start by annotating a simple model forward method with explicit tensor and output types, then extend to a small training loop with typed configurations.