semvec/pypi-packages huggingface dataset:
pgnsplit 0.1.2
Simple package that splits a chess pgn file into a folder of pgns - each one containing one variation of the original pgn.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: Chess,CLI,PGN

# pgnsplit

`pgnsplit` is a simple command-line application that takes a single [pgn](https://en.wikipedia.org/wiki/Portable_Game_Notation) file (e.g. from a [Lichess Study](https://lichess.org/study/KjivNw7F)) and extracts all the variations in the pgn, putting each of them in their own individual pgn files in a folder `./pgn/`, which can then be stored in some sort of database (e.g. in SCID or ChessX).

This solves the problem in various chess software, where importing pgns only stores the mainline variation. 

This is presented as a lightweight and easy-to-use alternative to [David J. Barnes' pgn-extract](https://www.cs.kent.ac.uk/people/staff/djb/pgn-extract/help.html), which is still more appropriate for the intricate manipulation of pgn files. 

## Requirements
* Python (>3.8)
* pip (which normally comes with Python)

## Installation
```bash
$ pip install pgnsplit
```

## Usage
```bash
$ pgnsplit <filepath>
```
`<filepath>` must be the path to a properly formatted pgn file. The individual pgn files will be stored in a local folder. 

## Improvements
Further improvements/functionality could be made as requested/needed.
resmlp-flax 0.0.1
Minimal Flax implementation of ResMLP: Feedforward networks for image classification with data-efficient training
License: MIT

# Flax Implementation of ResMLP

![](https://github.com/SauravMaheshkar/ResMLP-Flax/blob/main/assets/ResMLP%20Banner.png?raw=true)

In the past few months there have been various papers proposing MLP based architectures without Attention or Convolutions In this [paper](https://arxiv.org/abs/2105.03404), titled 'ResMLP: Feedforward networks for image classification with data-efficient training', the authors introduce a new architecture for efficient image classification.

## Installation

You can install this package from PyPI:

```sh
pip install resmlp-flax
```

Or directly from GitHub:

```sh
pip install --upgrade git+https://github.com/SauravMaheshkar/ResMLP-Flax.git
```

## Usage

```python
from resmlp_flax.model import ResMLP

model = ResMLP(dim=512, depth=10, patch_size=16, num_classes=10)
```

## Development

### 1. Conda Approach

```sh
conda env create --name <env-name> sauravmaheshkar/resmlp
conda activate <env-name>
```

### 2. Docker Approach

```sh
docker pull ghcr.io/sauravmaheshkar/resmlp-dev:latest
docker run -it -d --name <container_name> ghcr.io/sauravmaheshkar/resmlp-dev
```

Use the [Remote Containers](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) Extension in VSCode and [attach to the running container](https://code.visualstudio.com/docs/remote/attach-container). The code resides in the `code/` dir.

Alternatively you can also download the image from [Docker Hub](https://hub.docker.com/r/sauravmaheshkar/resmlp-dev).

```sh
docker pull sauravmaheshkar/resmlp-dev
```

## Citations

```bibtex
@misc{touvron2021resmlp,
      title={ResMLP: Feedforward networks for image classification with data-efficient training},
      author={Hugo Touvron and Piotr Bojanowski and Mathilde Caron and Matthieu Cord and Alaaeldin El-Nouby and Edouard Grave and Gautier Izacard and Armand Joulin and Gabriel Synnaeve and Jakob Verbeek and Hervé Jégou},
      year={2021},
      eprint={2105.03404},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```