semvec/pypi-packages huggingface dataset:
sentry-plugin-dingtalk 0.1.2
A Sentry extension which send errors stats to dingtalk
License: MIT
Keywords: sentry dingtalk

# sentry-plugin-dingtalk

> Sentry 钉钉通知插件

## 安装

新版本在 self-hosted 目录的 `sentry/enhance-image.sh` 中加入 `pip install sentry-plugin-dingtalk`  
老版本在 onpremise 目录的 `sentry/requirements.txt` 中添加 `sentry-plugin-dingtalk`  

**如果新版本中已经在使用 requirements.txt 配置了，那保持原状，在 requirements.txt 添加即可**

然后执行:

```sh
docker-compose down
./install.sh
docker-compose up -d
```

## 使用

在项目的所有集成页面找到 `DingTalk-EX` 插件，启用，并设置模板
yeet-api 0.2.0
Core API for yeet tools
License: MIT
Requires Python: >=3.10,<4.0

# yeet-api
Welcome to `yeet-api`! This is where the API used by (the upcoming) yeet-cli
resides. This API is written in Python and is aimed to be as friendly as
possible towards the community, so you can build your own tools, too.

_Note that currently `yeet-api` is in major development, and is currently
unstable to be used._

## Contribution
To contribute, please make sure you have black installed,
and preferably, use our provided pre-commit hook to run
black automatically before commiting.
```bash
# To install the pre-commit hook:
poetry run install-hook
```
NeuroSurgeon 0.1.0
A toolkit for subnetwork analysis
License: MIT

# NeuroSurgeon
<p align="center">
    <img src=https://github.com/mlepori1/NeuroSurgeon/assets/25048682/9e96d377-7a65-4441-8492-e6b0c635886f>
</p>

NeuroSurgeon is a python toolkit built to enable deep learning researchers to easily uncover and manipulate subnetworks within trained models. NeuroSurgeon provides a simple API to inject differentiable binary masks techniques into linear, attention, and convolution layers in BERT, GPT, ResNet, and ViT-style models within Huggingface Transformers. Differentiable masking has a variety of use cases for deep learning research, such as:

- Pruning to uncover functional subnetworks
    - Relevant Papers:
        - [Csordás et al. 2021](https://arxiv.org/abs/2010.02066)
        - [Lepori et al. 2023](https://arxiv.org/abs/2301.10884)
        - [Panigrahi et al. 2023](https://arxiv.org/abs/2302.06600)
- Subnetwork Probing
    - Relevant Papers:
        - [Cao et al. 2021](https://arxiv.org/abs/2104.03514)
        - [Conmy et al. 2023](https://arxiv.org/abs/2304.14997)
- Training with L0 Regularization
    - Relevant Papers:
        - [Louizos et al. 2018](https://arxiv.org/abs/1712.01312)
        - [Savarese et al. 2020](https://arxiv.org/abs/1912.04427)
     
## Documentation     
[Read the NeuroSurgeon Documentation](https://neurosurgeon.readthedocs.io/en/latest/)

## Tutorial
To get started with NeuroSurgeon, check out the tutorial [here](https://colab.research.google.com/drive/190_zGHEI8BjPgdxWaRrogFmbgruP6y4l?usp=sharing). This covers the basic workflow for using NeuroSurgeon to uncover functional subnetworks within a trained model.

## Install

NeuroSurgeon requires python 3.9 or higher and several libraries, including Transformers and PyTorch. Installation can be done using PyPi:

`pip install NeuroSurgeon`

### Logo Prompt
NeuroSurgeon's logo was created with the help of DALL-E 2, using the prompt "A cute cartoon robot doctor smiling with a stethoscope".