drytorch 0.1.0b4

This package helps train, document and evaluate a Pytorch model.

DRYTorch

A lean, minimal framework for machine-learning experiments. It enforces best practices and maximizes code reusability via a modular, decoupled design.

🌟 Features at a Glance

• DRY Training Cycle: Minimal boilerplate for a wide range of ML applications. Includes a hook-based training cycle, adaptive data loading, and simplified checkpointing.
• Protocol-Driven Modularity: Components communicate via defined protocols and abstract classes, ensuring type safety and flexibility for custom classes.
• Decoupled Tracking: An event system notifies optional external libraries (Hydra, W&B, TensorBoard, etc.). Trackers do not impact the core experiment.
• Enforced Best Practices: All logic runs within an experimental scope to discourage dependencies, prevent data leakage, and ensure correct configuration.

🛠️ Installation

Requirements The library only requires recent versions of PyTorch and NumPy. Tracker dependencies are optional.

Installation Using pip:

pip install drytorch

Using uv

uv add drytorch

🏗️ Library Organization

The library uses a microkernel (plugin) architecture to separate concerns.

1. Core (core): The library kernel. Contains the Event System, Protocols for component communication, and internal safety Checks.
2. Standard Library (lib): Reusable implementations and abstract classes of the protocols.
3. Trackers (tracker): Optional tracker plugins that integrate via the event system.
4. Contributions (contrib): Dedicated space for community-driven extensions.
5. Utilities (utils): Functions and classes independent to the framework.

🚀 Usage

Dive into the full, runnable examples:

➡️ Getting Started Notebook