This release is a pre-release and may not be stable for production use.
DRYTorch
Reproducible machine learning experiments with PyTorch.
Design
Don't Repeat Yourself (DRY) 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
Package Structure
Modules are organized into the following subpackages:
core: internal routines and the interfaces for library and custom components.lib: reusable implementations and abstract classes of the protocols.tracker: optional tracker plugins that integrate via the event system.contrib: community-driven extensions and support for external libraries.utils: general utilities independent to the framework.
Documentation
Read the full documentation on Read the Docs →
The documentation includes:
- Tutorials: walkthrough through the core features.
- API Reference: detailed API documentation.
- Architecture Overview: design structure.
See also
1 maintainer
Metadata
1 maintainer
Release files for drytorch 0.1.0rc9
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| drytorch-0.1.0rc9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.0 MB
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