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 0.1.0rc11
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| drytorch-0.1.0rc11.tar.gz | 948.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| drytorch-0.1.0rc11-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.0 MB
Release files / drytorch-0.1.0rc11.tar.gz
| Download URL | drytorch-0.1.0rc11.tar.gz |
|---|---|
| Size | 948.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
03256af0aa68d1ab98efc233d36bc9e1976177eba955076d958990a310e25c44
|
|
BLAKE2b-256 checksum How to use checksums |
290e4368daf7dfc3c931db1139a287025622350e685fff8830e314cd3967cbbd
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 31, 2026.
Transparency logRelease files / drytorch-0.1.0rc11-py3-none-any.whl
| Download URL | drytorch-0.1.0rc11-py3-none-any.whl |
|---|---|
| Size | 97.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
a97f28799fb6fb3d0e972a07842498a1e9224ebdd7b2615f2376ab135ed6ff2e
|
|
BLAKE2b-256 checksum How to use checksums |
d02c4882f926d1b6f5be670f019f7b44786f0ce1d522c79f61ca42a44ffbc9c9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jul 31, 2026.
Transparency log
