semvec/pypi-packages huggingface dataset:
ssb-libtest4 0.0.1
SSB Libtest4
Requires Python: <4.0,>=3.10

# SSB Libtest4

[![PyPI](https://img.shields.io/pypi/v/ssb-libtest4.svg)][pypi status]
[![Status](https://img.shields.io/pypi/status/ssb-libtest4.svg)][pypi status]
[![Python Version](https://img.shields.io/pypi/pyversions/ssb-libtest4)][pypi status]
[![License](https://img.shields.io/pypi/l/ssb-libtest4)][license]

[![Documentation](https://github.com/statisticsnorway/ssb-libtest4/actions/workflows/docs.yml/badge.svg)][documentation]
[![Tests](https://github.com/statisticsnorway/ssb-libtest4/actions/workflows/tests.yml/badge.svg)][tests]
[![Coverage](https://sonarcloud.io/api/project_badges/measure?project=statisticsnorway_ssb-libtest4&metric=coverage)][sonarcov]
[![Quality Gate Status](https://sonarcloud.io/api/project_badges/measure?project=statisticsnorway_ssb-libtest4&metric=alert_status)][sonarquality]

[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)][pre-commit]
[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)][black]
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
[![Poetry](https://img.shields.io/endpoint?url=https://python-poetry.org/badge/v0.json)][poetry]

[pypi status]: https://pypi.org/project/ssb-libtest4/
[documentation]: https://statisticsnorway.github.io/ssb-libtest4
[tests]: https://github.com/statisticsnorway/ssb-libtest4/actions?workflow=Tests
[sonarcov]: https://sonarcloud.io/summary/overall?id=statisticsnorway_ssb-libtest4
[sonarquality]: https://sonarcloud.io/summary/overall?id=statisticsnorway_ssb-libtest4
[pre-commit]: https://github.com/pre-commit/pre-commit
[black]: https://github.com/psf/black
[poetry]: https://python-poetry.org/

## Features

- TODO

## Requirements

- TODO

## Installation

You can install _SSB Libtest4_ via [pip] from [PyPI]:

```console
pip install ssb-libtest4
```

## Usage

Please see the [Reference Guide] for details.

## Contributing

Contributions are very welcome.
To learn more, see the [Contributor Guide].

## License

Distributed under the terms of the [MIT license][license],
_SSB Libtest4_ is free and open source software.

## Issues

If you encounter any problems,
please [file an issue] along with a detailed description.

## Credits

This project was generated from [Statistics Norway]'s [SSB PyPI Template].

[statistics norway]: https://www.ssb.no/en
[pypi]: https://pypi.org/
[ssb pypi template]: https://github.com/statisticsnorway/ssb-pypitemplate
[file an issue]: https://github.com/statisticsnorway/ssb-libtest4/issues
[pip]: https://pip.pypa.io/

<!-- github-only -->

[license]: https://github.com/statisticsnorway/ssb-libtest4/blob/main/LICENSE
[contributor guide]: https://github.com/statisticsnorway/ssb-libtest4/blob/main/CONTRIBUTING.md
[reference guide]: https://statisticsnorway.github.io/ssb-libtest4/reference.html
pytorch-lightning-enterprise 2.6.0
Enterprise-Grade extensions for PyTorch Lightning
Requires Python: >=3.10
Keywords: deep learning, machine learning, pytorch, AI

<div align="center">

<img alt="Lightning" src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/app-2/ptl_banner.png" width="800px" style="max-width: 100%;">

<br/>
<br/>

**The deep learning framework to pretrain and finetune AI models -- enterprise-grade extensions**

*PyTorch Lightning Enterprise* is an extension package to PyTorch Lightning containing many enterprise-ready, matured and battle-tested add-ons and plugins to PyTorch Lightning ranging from loggers over low precision support to additional accelerators as well as advanced training regimes.

Contact sales@lightning.ai for for information

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<a id="why-pytorch-lightning"></a>
# Why PyTorch Lightning?   

Training models in plain PyTorch is tedious and error-prone - you have to manually handle things like backprop, mixed precision, multi-GPU, and distributed training, often rewriting code for every new project. PyTorch Lightning organizes PyTorch code to automate those complexities so you can focus on your model and data, while keeping full control and scaling from CPU to multi-node without changing your core code. But if you want control of those things, you can still opt into [expert-level control](#lightning-fabric-expert-control).   

Fun analogy: If PyTorch is Javascript, PyTorch Lightning is ReactJS or NextJS.