priml✴️

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

ML building blocks for training experiments. 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 for local validation and the public contribution flow.

License

Apache License 2.0

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Source Distribution

priml-0.1.1.tar.gz (2.8 MB view details)

Uploaded Aug 1, 2026 Source

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Provenance

The following attestation bundles were made for priml-0.1.1.tar.gz:

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.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: priml-0.1.1.tar.gz
- Subject digest: e41f182446faf97c91b2ad70b67e536d1c70b6097bd2721123acbedddcd54a27
- Sigstore transparency entry: 2313206214
- Sigstore integration time: Aug 1, 2026
Source repository:
- Permalink: rekursiv-ai/priml@e072623210dc8c87e5659670bba8ba6ce906515a
- Branch / Tag: refs/tags/v0.1.1
- Owner: https://github.com/rekursiv-ai
- Access: public
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- Runner Environment: github-hosted
- Publication workflow: publish-pypi.yml@e072623210dc8c87e5659670bba8ba6ce906515a
- Trigger Event: release