semvec/pypi-packages huggingface dataset:
focker-pypitest 0.0.4
intended for testing release automation
Requires Python: >=3.9
Keywords: release,pypi,test

![test and build](https://github.com/felixocker/focker-pypitest/actions/workflows/publish-to-testpypi.yml/badge.svg)
![pypi release](https://github.com/felixocker/focker-pypitest/actions/workflows/publish-to-pypi.yml/badge.svg)
[![PyPI](https://img.shields.io/pypi/v/focker-pypitest)](https://pypi.org/project/focker-pypitest/)
[![License](https://img.shields.io/badge/license-GPLv3-blue)](https://www.gnu.org/licenses/gpl-3.0.html)

# pypitest

this repo serves as a reference and for testing CI/CD pipelines, especially via GitHub actions
natural-units 0.0.2
uncertainty quantification for numerical computations
Requires Python: >=3.6

# natural_units

Wrapper for various units:

| Unit      | Description | Notes | 
| ----------- | ----------- | ------------ | 
| base      | general        | super class | 
| si        | m, kg, s, A, K, mol, cd       |
| hep   | $c=\hbar=\epsilon_0=k_B=1$         | checks mass dimension compatibility | 
| plain_hep       | $c=\hbar=\epsilon_0=k_B=1$         | like natural, but without mass dimension checks |
| geometrized | $c=G=1$ | checks length dimension compatibility |
| planck | $c=\hbar=G=k_B=1$ | |
| stoney | $c=G=k_e=e=1$ | |
| hartree_atomic | $e=m_e=\hbar=k_e=1$ | |
| particle_atomic | $c=m_e=\hbar=\epsilon_0=1$ | |
| qcd | $c = m_p=\hbar=\epsilon_0 = 1$ | |
mitts 0.0.2
Don't get burned with common torch training errors
Requires Python: >=3.6

Mitts

- TorchedHops - great beer ML name
- Mitts
- Torch oopsie
- Fire safety
- Smoke alarm

Goal: Prevent “oopsies” with PyTorch

Examples
- All of an input or output are the same (or missing)
- Gradients aren’t updating
- Inputs aren’t normalized
- Vanishing/Exploding gradient?
- Training/Test Overlap



To Deploy
python3 -m build
python3 -m twine upload dist/*