Beignet is a work-in-progress. Contributions and feedback are encouraged!

Beignet

A standard library for biological research.

Installation

pip install beignet

Requires Python 3.10 or later and PyTorch 2.2.0 or later.

Layout

beignet: PyTorch operators
- beignet.datasets: PyTorch datasets
- beignet.func: composable PyTorch function transforms
- beignet.lightning: Lightning modules
  - beignet.lightning.datamodules: Lightning datamodules
- beignet.metrics: TorchMetrics metrics
  - beignet.metrics.functional: functional TorchMetrics metrics
- beignet.nn: PyTorch modules
  - beignet.nn.functional:
- beignet.optim: PyTorch optimizers
- beignet.samplers: PyTorch samplers
- beignet.transforms:
  - beignet.transforms.functional:

Metadata

Release files for beignet 0.0.13

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for beignet 0.0.13
File	Size	Uploaded
beignet-0.0.13.tar.gz	401.2 kB	Jun 24, 2025	Details

Built distribution (wheel)

Table of built distributions (wheels) for beignet 0.0.13
File	Interpreter	ABI	Platform	Reset
beignet-0.0.13-py3-none-any.whl 256.7 kB Jun 24, 2025	Python 3	none	any	Details

Total release size: 657.8 kB

Release files / beignet-0.0.13.tar.gz

Download URL	beignet-0.0.13.tar.gz
Size	401.2 kB
Tags	Source
SHA-256 checksum How to use checksums	`776b73bfa705ad7345ebf21c158e99f21389cc7fe96b0430ff63d2b0b24b1c80`
BLAKE2b-256 checksum How to use checksums	`3a23ed44366ef8eed08d34b676e45d4c6a4aa3fb0320e40dae0cb1382749f123`
Upload date	Jun 24, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.9`

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.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Genentech/beignet
Publishing commit: github.com/Genentech/beignet/tree/ecf7e7d46e2f542b02f780158376d6d01f21f54f

Workflow

Publishing configuration: github.com/Genentech/beignet/blob/ecf7e7d46e2f542b02f780158376d6d01f21f54f/.github/workflows/push.yml
Publishing logs: github.com/Genentech/beignet/actions/runs/15859170103/attempts/1

Artifact

Subject: beignet-0.0.13.tar.gz
SHA-256 checksum: 776b73bfa705ad7345ebf21c158e99f21389cc7fe96b0430ff63d2b0b24b1c80 Verifying attestations

Release files / beignet-0.0.13-py3-none-any.whl

Download URL	beignet-0.0.13-py3-none-any.whl
Size	256.7 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`14dffc1692f894a22eacec039b7d7f11823f0b278b02a5dda05f9544014944ab`
BLAKE2b-256 checksum How to use checksums	`cfe13fd7b93bf27ab9eccda43ef7abcc8ccc3a0b65a3936e7b07da7383983afa`
Upload date	Jun 24, 2025
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.12.9`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Genentech/beignet
Publishing commit: github.com/Genentech/beignet/tree/ecf7e7d46e2f542b02f780158376d6d01f21f54f

Workflow

Publishing configuration: github.com/Genentech/beignet/blob/ecf7e7d46e2f542b02f780158376d6d01f21f54f/.github/workflows/push.yml
Publishing logs: github.com/Genentech/beignet/actions/runs/15859170103/attempts/1

Artifact

Subject: beignet-0.0.13-py3-none-any.whl
SHA-256 checksum: 14dffc1692f894a22eacec039b7d7f11823f0b278b02a5dda05f9544014944ab Verifying attestations