Metadata-Version: 2.4
Name: priml
Version: 0.1.0
Summary: Malleable ML building blocks for training experiments.
Project-URL: Homepage, https://www.rekursiv.ai
Project-URL: Repository, https://github.com/rekursiv-ai/priml
Project-URL: Documentation, https://github.com/rekursiv-ai/priml/tree/main/docs
Project-URL: Source, https://github.com/rekursiv-ai/priml
Project-URL: Issues, https://github.com/rekursiv-ai/priml/issues
Project-URL: Changelog, https://github.com/rekursiv-ai/priml/releases
Author-email: "rekursiv.ai" <hello@rekursiv.ai>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: experiments,ml,pytorch,training
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.12
Requires-Dist: configgle>=1.3.3
Requires-Dist: imagesize>=1.4
Requires-Dist: jaxtyping
Requires-Dist: lpips
Requires-Dist: numpy>=2.1
Requires-Dist: pyturbojpeg<2,>=1.8.2
Requires-Dist: scipy
Requires-Dist: torch==2.11.0
Requires-Dist: torchao==0.17.0
Requires-Dist: torchvision==0.26.0
Requires-Dist: tqdm
Requires-Dist: typeguard
Requires-Dist: typing-extensions
Requires-Dist: wandb>=0.22.2
Requires-Dist: webp>=0.4.0
Requires-Dist: wrapt
Provides-Extra: hub
Requires-Dist: accelerate>=1.13.0; extra == 'hub'
Requires-Dist: safetensors; extra == 'hub'
Requires-Dist: transformers>=4.57.0; extra == 'hub'
Description-Content-Type: text/markdown

# priml

Malleable ML building blocks for training experiments.

## Install

```bash
pip install priml
```

Requires Python 3.12 or newer.

## What's inside

- **models** — composable model definitions and building blocks.
- **optimizers** — optimizer implementations for training.
- **loss** — loss functions.
- **metrics** — evaluation metrics.
- **math** — numerical and math utilities.
- **train** — the training loop and experiment scaffolding.
- **data** — the data pipeline and dataset utilities.
- **inference** — inference helpers.

## Development

This package is developed in a monorepo and synced to this public repository.
See [CONTRIBUTING.md](CONTRIBUTING.md) for local validation and the public
contribution flow.

## License

Apache License 2.0
