https://github.com/nverchev/drytorch

This package helps you training, documenting, and evaluating a Pytorch model.
https://github.com/nverchev/drytorch

machine-learning pytorch reproducible-research self-documenting

Last synced: 6 months ago
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This package helps you training, documenting, and evaluating a Pytorch model.

• Host: GitHub
• URL: https://github.com/nverchev/drytorch
• Owner: nverchev
• License: mit
• Created: 2023-11-27T17:22:03.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2026-01-12T16:54:32.000Z (6 months ago)
• Last Synced: 2026-01-12T20:57:55.350Z (6 months ago)
• Topics: machine-learning, pytorch, reproducible-research, self-documenting
• Language: Python
• Homepage:
• Size: 6.86 MB
• Stars: 4
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

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# DRYTorch

## 💡 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**. Tracker dependencies are optional.

**Commands**

```bash

pip install drytorch

```

or:

```bash

uv add drytorch

```

## 🗂️ Library Organization

Folders are organized as follows:

- **Core (`core`):** The library kernel. Contains the **Event System**, **Protocols** for component communication, and internal safety **Checks**.

- **Standard Library (`lib`):** Reusable implementations and abstract classes of the protocols.

- **Trackers (`tracker`):** Optional tracker plugins that integrate via the event system.

- **Contributions (`contrib`):** Dedicated space for community-driven extensions.

- **Utilities (`utils`):** Functions and classes independent to the framework.

## 📚 Documentation

**[Read the full documentation on Read the Docs →](https://drytorch.readthedocs.io/)**

The documentation includes:

- **[Tutorials](https://drytorch.readthedocs.io/en/latest/tutorials.html)** - Complete walkthrough

- **[API Reference](https://drytorch.readthedocs.io/en/latest/api.html)** - Detailed API documentation

- **[Architecture Overview](https://drytorch.readthedocs.io/en/latest/architecture.html)** - Design principles and structure

## 📝 **[Changelog](CHANGELOG.md)**