semvec/pypi-packages huggingface dataset:
redner-test 0.0.1
Differentiable rendering without approximation.
License: MIT
Keywords: rendering,Monte Carlo ray tracing,computer vision,computer graphics,differentiable rendering,PyTorch,TensorFlow

redner is a differentiable renderer that can take the
derivatives of rendering output with respect to arbitrary
scene parameters, that is, you can backpropagate from the
image to your 3D scene. One of the major usages of redner
is inverse rendering (hence the name redner) through gradient
descent. What sets redner apart are: 1) it computes correct
rendering gradients stochastically without any approximation
and 2) it has a physically-based mode -- which means it can
simulate photons and produce realistic lighting phenomena,
such as shadow and global illumination, and it handles the
derivatives of these features correctly. You can also use
redner in a fast deferred rendering mode for local shading:
in this mode it still has correct gradient estimation and
more elaborate material models compared to most differentiable
renderers out there.
letra 0.5.1
Utility for managing GitHub issue labels
License: MIT

# letra
Label management utility for GitHub issues

Early, pre-Alpha, non-functional (yet)

[![Version Badge][version-badge]][pypi-package-url]
[![Downloads Badge][downloads-badge]][pypi-package-url]
[![License Badge][license-badge]][license-url] 

[![Linux CI Badge][linux-ci-badge]][linux-ci-url]
[![Mac CI Badge][mac-ci-badge]][mac-ci-url]
[![Windows CI Badge][windows-ci-badge]][windows-ci-url]  

[![Test Results Badge][tests-badge]][tests-url]
[![Coverage Badge][coverage-badge]][coverage-url]
[![Sonar Quality GateBadge][quality-gate-badge]][sonar-project-url]
[![Code Style Badge][black-badge]][black]


[pypi-package-url]: https://pypi.org/project/letra/
[version-badge]: https://img.shields.io/pypi/v/letra?style=flat-square
[downloads-badge]: https://img.shields.io/pypi/dm/letra?style=flat-square
[license-url]: ./LICENSE
[license-badge]: https://img.shields.io/github/license/swellaby/letra?style=flat-square&color=blue
[downloads-badge]: https://img.shields.io/pypi/dm/letra?style=flat-square
[linux-ci-badge]: https://img.shields.io/azure-devops/build/swellaby/opensource/113/master?label=linux%20build&style=flat-square
[linux-ci-url]: https://dev.azure.com/swellaby/OpenSource/_build/latest?definitionId=113
[mac-ci-badge]: https://img.shields.io/azure-devops/build/swellaby/opensource/90/master?label=mac%20build&style=flat-square
[mac-ci-url]: https://dev.azure.com/swellaby/OpenSource/_build/latest?definitionId=90
[windows-ci-badge]: https://img.shields.io/azure-devops/build/swellaby/opensource/91/master?label=windows%20build&style=flat-square
[windows-ci-url]: https://dev.azure.com/swellaby/OpenSource/_build/latest?definitionId=91
[coverage-badge]: https://img.shields.io/azure-devops/coverage/swellaby/opensource/91/master?style=flat-square
[coverage-url]: https://codecov.io/gh/swellaby/letra
[tests-badge]: https://img.shields.io/azure-devops/tests/swellaby/opensource/113/master?label=unit%20tests&style=flat-square
[tests-url]: https://dev.azure.com/swellaby/OpenSource/_build/latest?definitionId=113&view=ms.vss-test-web.build-test-results-tab
[quality-gate-badge]: https://img.shields.io/sonar/quality_gate/swellaby:letra?server=https%3A%2F%2Fsonarcloud.io&style=flat-square
[sonar-project-url]: https://sonarcloud.io/dashboard?id=swellaby%3Aletra
[black-badge]: https://img.shields.io/badge/code%20style-black-black?style=flat-square
[black]: https://black.readthedocs.io/en/stable/
[contributing]: .github/CONTRIBUTING.md
textgenrnn 2.0.0
Easily train your own text-generating neural network of any size and complexity
License: MIT
Requires Python: >=3.5
Keywords: deep learning,tensorflow,keras,text generation

Easily train your own text-generating neural network of
any size and complexity on any text dataset with a few lines
of code, or quickly train on a text using a pretrained model.

- A modern neural network architecture which utilizes new techniques as
attention-weighting and skip-embedding to accelerate training
and improve model quality.
- Able to train on and generate text at either the
character-level or word-level.
- Able to configure RNN size, the number of RNN layers,
and whether to use bidirectional RNNs.
- Able to train on any generic input text file, including large files.
- Able to train models on a GPU and then use them with a CPU.
- Able to utilize a powerful CuDNN implementation of RNNs
when trained on the GPU, which massively speeds up training time as
opposed to normal LSTM implementations.
- Able to train the model using contextual labels,
allowing it to learn faster and produce better results in some cases.
- Able to generate text interactively for customized stories.