semvec/pypi-packages huggingface dataset:
liquidnet 0.0.5
Liquid Net - Pytorch
License: MIT
Requires Python: >=3.9,<4.0
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# LiquidNet
This is a simple implementation of the Liquid net official repo translated into pytorch for simplicity. [Find the original repo here:](https://github.com/raminmh/liquid_time_constant_networks)

## Install
`pip install liquidnet`

## Usage
```python
import torch
from liquidnet.main import LiquidNet

# Create an LiquidNet with a specified number of units
num_units = 64
ltc_cell = LiquidNet(num_units)

# Generate random input data with batch size 4 and input size 32
batch_size = 4
input_size = 32
inputs = torch.randn(batch_size, input_size)

# Initialize the cell state (hidden state)
initial_state = torch.zeros(batch_size, num_units)

# Forward pass through the LiquidNet
outputs, final_state = ltc_cell(inputs, initial_state)

# Print the shape of outputs and final_state
print("Outputs shape:", outputs.shape)
print("Final state shape:", final_state.shape)

```

## `VisionLiquidNet`
- Simple model with 2 convolutions with 2 max pools, alot of room for improvement

```python
import torch 
from liquidnet.vision_liquidnet import VisionLiquidNet

# Random Input Image
x = torch.randn(4, 3, 32, 32)

# Create a VisionLiquidNet with a specified number of units
model = VisionLiquidNet(64, 10)

# Forward pass through the VisionLiquidNet
print(model(x).shape)


```


# Citation
```bibtex
@article{DBLP:journals/corr/abs-2006-04439,
  author       = {Ramin M. Hasani and
                  Mathias Lechner and
                  Alexander Amini and
                  Daniela Rus and
                  Radu Grosu},
  title        = {Liquid Time-constant Networks},
  journal      = {CoRR},
  volume       = {abs/2006.04439},
  year         = {2020},
  url          = {https://arxiv.org/abs/2006.04439},
  eprinttype    = {arXiv},
  eprint       = {2006.04439},
  timestamp    = {Fri, 12 Jun 2020 14:02:57 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2006-04439.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

```


# License
MIT


# Todo:
- [ ] Implement LiquidNet for vision and train on CIFAR
xmlclone-pdesign 0.0.7.dev4
My package description
License: MIT
Requires Python: >=3.7

[![PyPI](https://img.shields.io/pypi/v/xmlclone-pdesign.svg)](https://pypi.org/project/xmlclone-pdesign/)
[![Read the Docs (version)](https://img.shields.io/readthedocs/pdesign/latest)](https://pdesign.readthedocs.io/en/latest/)
[![PyPI](https://img.shields.io/pypi/pyversions/xmlclone-pdesign.svg)](https://pypi.org/project/xmlclone-pdesign/)
[![license](https://img.shields.io/github/license/xmlclone/pdesign.svg)](https://github.com/xmlclone/pdesign/blob/main/LICENSE)
<!-- [![PyPI - Implementation](https://img.shields.io/pypi/implementation/xmlclone-pdesign)](https://pypi.org/project/xmlclone-pdesign/) -->
<!-- [![GitHub contributors](https://img.shields.io/github/contributors/xmlclone/pdesign.svg)](https://github.com/xmlclone/pdesign/graphs/contributors) -->

# 说明

设计模式&常用算法的Python实现，本文环境如下:

```shell
$ cat /proc/version
Linux version 5.15.90.1-microsoft-standard-WSL2 (oe-user@oe-host) (x86_64-msft-linux-gcc (GCC) 9.3.0, GNU ld (GNU Binutils) 2.34.0.20200220) #1 SMP Fri Jan 27 02:56:13 UTC 2023

$ conda --version
conda 23.7.4

$ conda create -n vpdesign python=3.12 -y
$ conda activate vpdesign

$ python --version
Python 3.12.0

$ make --version
GNU Make 4.3
Built for x86_64-pc-linux-gnu
Copyright (C) 1988-2020 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law.
```

# Demo使用

```shell
# 安装
pip install xmlclone-pdesign

# 测试
make test
```

# 项目环境

```shell
# 创建并激活虚拟环境
conda create -n vpdesign python=3.12 -y
conda activate vpdesign

# 安装依赖
pip install -r requirements.txt

# 测试
make test

# 打包发布
make release
```

# 其它配置

## readthedocs

1. 登录[readthedocs](https://about.readthedocs.com/)
2. 导入本项目进行编译即可

## pypi

1. 当需要发布版本时，通过`git tag -a [version]`的方式创建版本并`git push --tags`推送到`git`
2. 通过`make release`即可发布到[pypi](https://pypi.org/)

# 参考链接

1. [shields.io](https://shields.io/badges)
kmh-gpu-monitor 0.2.3
Multi-server GPU monotoring tools
License: MIT

# kairos-smi
Multi-server gpu monitoring program  

[![PyPI version](https://badge.fury.io/py/ksmi.svg)](https://badge.fury.io/py/ksmi) [![CircleCI](https://circleci.com/gh/kairos03/kairos-smi.svg?style=svg)](https://circleci.com/gh/kairos03/kairos-smi)

See change Log: [change log](change_logs/v0.1.3.1.md)

![sample.png](img/sample.png)

```
usage: ksmi [-h] [-l] [-c CONFIG]

optional arguments:
  -h, --help            show this help message and exit
  -l, --loop            loop forever
  -c CONFIG, --config CONFIG
                        set config file location
```

# quick start
## 1. install 
Install with pip
```shell
$ pip install ksmi
or
$ pip3 install ksmi
```

## 2. Setup config file
Edit `config.json`. Add your gpu server address in `config.json`.
```json
{
	"hosts": [
		"<username>@<host>[:port]",
		"<username>@<host>[:port]"
	]
}
```

## 3. Add rsa_id to server

create new rea_id and add to your server

```shell
$ python3 -m ksmi.copy_id -c config.json -n
```

## 4. Run It!
```shell
$ python3 -m ksmi -c config.json -l
```

# kairos-smi
```
usage: kairos_smi.py [-h] [-l] [-c CONFIG]

optional arguments:
  -h, --help            show this help message and exit
  -l, --loop            loop forever
  -c CONFIG, --config CONFIG
                        set config file location
```

### Simple usage
Run once
```
$ python3 -m ksmi -c config.json
```

Run forever
```
$ python3 -m ksmi -c config.json -l
```

# utils
## copy_id
```
usage: copy_id.py [-h] [-n] [-c CONFIG] [-s SERVER]

optional arguments:
  -h, --help            show this help message and exit
  -n, --new_id          generate new id_rsa key
  -c CONFIG, --config CONFIG
                        set config file to use host list
  -s SERVER, --server SERVER
                        set a server to copy id
```

## copy id(rsa) to server

### Use with `config.json`
```
$ python3 -m ksmi.copy_id -c config.json
or
$ python3 -m ksmi.copy_id --config config.json
```

### Use with individual Server address
```
$ python3 -m ksmi.copy_id -s [username@]<IP>[:port]
or
$ python3 -m ksmi.copy_id --server [username@]<IP>[:port]
```

### Make a new id_rsa 
```
$ python3 -m ksmi.copy_id -n
or
$ python3 -m ksmi.copy_id --new_id
```