DRYTorch’s Documentation

💡 Design Philosophy

By adhering to the Don’t Repeat Yourself (DRY) principle, this library makes your machine-learning projects easier to replicate, document, and reuse.

✨ Features at a Glance

• Experimental Scope: All logic runs within a controlled scope, preventing unintended dependencies, data leakage, and misconfiguration.

• Modularity: Components communicate via defined protocols, providing type safety and flexibility for custom implementations.

• Decoupled Tracking: Logging, plotting, and metadata are handled by an event system that separates execution from tracking.

• Lean Dependencies: Minimal core requirements while supporting optional external libraries (Hydra, W&B, TensorBoard, etc.).

• Self-Documentation: Metadata is automatically extracted in a standardized and robust manner.

• Ready-to-Use Implementations: Advanced functionalities with minimal boilerplate, suitable for a wide range of ML applications.

📦 Installation

Requirements The library only requires recent versions of PyTorch and NumPy. PyYAML and tqdm are recommended for better tracking.

uv add drytorch

Contents:

• Tutorials
  • Getting Started with DRYTorch
  • Data Distributed Training
  • Experiments and Runs
  • Metrics and Losses
  • Trackers
• API Reference
  • drytorch.contrib
  • drytorch.core
  • drytorch.lib
  • drytorch.trackers
  • drytorch.utils
• Types and Protocols
  • Core Types
  • Diagram

Indices and tables

• Index