https://github.com/genentech/beignet

A standard library for biological research.
https://github.com/genentech/beignet

Last synced: 11 months ago
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A standard library for biological research.

• Host: GitHub
• URL: https://github.com/genentech/beignet
• Owner: Genentech
• License: mit
• Created: 2024-04-01T20:09:03.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2025-06-24T00:19:44.000Z (12 months ago)
• Last Synced: 2025-06-24T01:27:39.738Z (12 months ago)
• Language: Python
• Homepage: https://genentech.github.io/beignet/
• Size: 5.97 MB
• Stars: 26
• Watchers: 10
• Forks: 6
• Open Issues: 54
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

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

# Beignet

A standard library for biological research.

## Installation

```bash

pip install beignet

```

_Requires Python 3.10 or later and PyTorch 2.2.0 or later._

## Layout

*   `beignet`: PyTorch operators

    *   `beignet.datasets`: PyTorch [datasets](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset)

    *   `beignet.func`: [composable PyTorch function transforms](https://pytorch.org/docs/stable/func.html)

    *   `beignet.lightning`: Lightning [modules](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html)

        *   `beignet.lightning.datamodules`: Lightning [datamodules](https://lightning.ai/docs/pytorch/stable/data/datamodule.html)

    *   `beignet.metrics`: [TorchMetrics](https://lightning.ai/docs/torchmetrics/stable/) metrics

        *   `beignet.metrics.functional`: functional [TorchMetrics](https://lightning.ai/docs/torchmetrics/stable/) metrics

    *   `beignet.nn`: PyTorch [modules](https://pytorch.org/docs/stable/generated/torch.nn.Module.html)

        *   `beignet.nn.functional`: 

    *   `beignet.optim`: PyTorch [optimizers](https://pytorch.org/docs/stable/optim.html#torch.optim.Optimizer)

    *   `beignet.samplers`: PyTorch [samplers](https://pytorch.org/docs/stable/data.html#torch.utils.data.Sampler)

    *   `beignet.transforms`: 

        *   `beignet.transforms.functional`: