priml✴️

ML building blocks for training experiments.

Quick Start

# Mac:
#   # Required for quick install.
#   brew install uv

# Ubuntu/Debian:
#   # Required for quick install.
#   sudo apt-get install -y curl
#   curl -LsSf https://astral.sh/uv/install.sh | sh

uv add priml

# Alternatively: python -m pip install priml

What's inside

model -- 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

See CONTRIBUTING.md for local validation and the public contribution flow.

Citing

If you find our work useful, please consider citing:

@misc{rekursivai2026priml,
      title={Priml - ML building blocks for training experiments.},
      author={Joshua V. Dillon and Dan Kondratyuk},
      year={2026},
      howpublished={Github},
      url={https://github.com/rekursiv-ai/priml},
}

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Source Distribution

priml-0.1.4.tar.gz (3.9 MB view details)

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Provenance

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Publisher: publish-pypi.yml on rekursiv-ai/priml

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

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Source repository:
- Permalink: rekursiv-ai/priml@cbb7a7696397db26a25ca44804f51ab6f4c59c02
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- Owner: https://github.com/rekursiv-ai
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