DRYTorch’s Documentation

Reproducible machine learning experiments with PyTorch.

Design

Applies Don’t Repeat Yourself principles: replicable, documented, reusable.

• Reproducibility: experimental isolation to prevent unintended dependencies, data leakage, and misconfiguration.

• Modularity: flexible protocols preserving type inference in custom implementations.

• Decoupled Tracking: execution independent of tracking events (logging, plotting, and storing metadata).

• Optional Dependencies: support for external libraries (Hydra, W&B, TensorBoard, etc.) but minimal requirements.

• Self-Documentation: automatic metadata extraction and standardization.

• Ready-to-use: high-level implementations for advanced applications and workflows.

Installation

Requirements:

• The library only requires recent versions of PyTorch and NumPy.

• PyYAML and tqdm are recommended.

pip:

pip install drytorch

UV:

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