Metadata-Version: 2.1
Name: tidygrad
Version: 0.0.1
Summary: A tidy library for gradient-based optimization
Home-page: https://github.com/xl0/tidygrad
Author: Alexey
Author-email: alexey.zaytsev@gmail.com
License: MIT License
Keywords: nbdev jupyter notebook python
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: nbdev ; extra == 'dev'
Requires-Dist: matplotlib ; extra == 'dev'
Requires-Dist: lovely-numpy ; extra == 'dev'
Requires-Dist: requests ; extra == 'dev'

TidyGrad
================

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# TidyGrad \| [Documentation](https://xl0.github.io/tidygrad/) \| [Discord](https://discord.gg/qBaqauUWXP)

A tidy library for gradient-based optimization

![Tests](https://github.com/xl0/tidygrad/actions/workflows/test.yaml/badge.svg)

## Install

``` sh
pip install -e tidygrad
```

## Dev

``` sh
pip install -e .[dev] # Local install with dev dependencies
```

- Hack
- Hack
- Hack
- (edit and run the notebooks in the `./nbs` directory).

``` sh
nbdev_prepare
```

`nbdev_prepare` will:

- Export all norebooks into the `./tidygrad` directory.
  - Note: the notebooks themselves have an export cell, so they are
    exported every time you run them.
- Run all notebooks (equivalent of testing)
- Generate REDME.md from the index.ipynb
- Generate the docs

## How to use

``` python
# Later
```
