semvec/pypi-packages huggingface dataset:
mlopscli 0.1.4
CLI to turn DS scripts to composable pipelines.
Requires Python: >=3.10

# mlopscli 🚀

A CLI tool to turn DS Python scripts + YAML config into an executable ML pipeline.

## Usage

1. Write your scripts focussing on different steps in ML Lifecyle (`data_prep.py`, `train_model.py`, `evaluate_model.py`)
2. Define them in a `job_order.yaml` with the dependencies.
3. Install the mlops cli : `pip install mlopscli`
4. Run the CLI command.

#### Commands Available

1. Spin up a streamlit dashboard where the metadata of all the runs will be available.

```bash
mlopscli dashboard
```

2. Dry Run the pipeline to ensure Job Config is valid. Validations like dependencies, file paths are checked during dry run

```bash
mlopscli dry-run --job prepare_train_pipeline --job_config job_order.yaml
```

3. Execute the pipeline and get results

```bash
mlopscli execute --job prepare_train_pipeline --job_config job_order.yaml --observe
```

`--observe` : If passed, the resource consumption like CPU/Memory usage will be calculated for each step.


> ⚠️ **NOTE** : 
>  - If the environments already exists, it is not recreated.
>  - Once the DAG is prepared, the steps in the same level are ran in parallel.

4. Clean up Environments

```bash
mlopscli cleanup --step-name train
```

`--step-name` : If passed, the environment associated with the input step is deleted.

```bash
mlopscli cleanup --all
```

`--all` : If passed, all the environments are cleaned up.


**Quick Demo**

[![Watch the demo](https://img.youtube.com/vi/MFBbSA-SHFU/hqdefault.jpg)](https://www.youtube.com/watch?v=MFBbSA-SHFU)
k8ops 0.0.2
Kubernetes Operations Tools
Requires Python: >=3.7
Keywords: kubernetes, k8s, operations, tools

# k8ops - Kubernetes Operations Tools

[![Publish Python Package to PyPI](https://github.com/nomagicln/k8ops/actions/workflows/publish-to-pypi.yml/badge.svg?branch=main)](https://github.com/nomagicln/k8ops/actions/workflows/publish-to-pypi.yml)

A collection of tools to help with Kubernetes cluster operations and management.

## Installation

You can install k8ops using pip:

```bash
pip install k8ops
```

Or using uv:

```bash
uv pip install k8ops
```

## Requirements

- Python 3.7+
- `kubectl` configured with access to your Kubernetes cluster

## Features

### Node Summary

The node summary command provides detailed information about cluster nodes:

```bash
k8ops cluster summary [options] [node_names...]
```

#### Options

- `--wide`: Show wide output with additional columns
- `--pods`: Show pods running on each node
- `--images`: Show container images on each node
- `--taints`: Show node taints
- `--labels`: Show node labels
- `--annotations`: Show node annotations
- `--capacity`: Show node capacity
- `--allocatable`: Show node allocatable resources
- `--conditions`: Show node conditions
- `--addresses`: Show node addresses
- `--metrics`: Show node metrics
- `--all`: Show all information

### Node Drain Analyzer

The node drain analyzer helps assess the impact and risks of draining nodes from your Kubernetes cluster:

```bash
k8ops cluster drain [options] <node_names...>
```

#### Features

- Comprehensive drain impact analysis:
  - Resource usage evaluation (CPU, memory, pods)
  - Pod scheduling constraints check
  - Architecture and OS compatibility verification
  - Taint and toleration validation
  - DaemonSet impact assessment
  - Running jobs/cronjobs detection
  - Node selector and affinity rules check
  - Resource availability calculation

#### Options

All options from the summary command are supported, plus:

- `--factor`: The factor for resource calculations when pods have no requests set (default: 1.5)

#### Analysis Details

The analyzer checks for various potential issues:

1. **Resource Constraints**
   - CPU and memory availability on remaining nodes
   - Pod capacity limits
   - Resource distribution impact

2. **Scheduling Constraints**
   - Node selectors compatibility
   - Affinity/anti-affinity rules
   - Taints and tolerations
   - Architecture/OS compatibility

3. **Workload Impact**
   - DaemonSet pods
   - Running jobs/cronjobs
   - Pods with no controllers
   - Pod density changes

#### Severity Levels

- **High**: Critical issues that will likely prevent successful node draining
- **Medium**: Significant issues that may cause problems
- **Low**: Minor issues that should be reviewed
- **None**: No issues detected

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

[MIT License](LICENSE)
ctlepy 1.7.2
Python package for ctle
Requires Python: >=3.10

# ctle
C++ Template Library Extension classes - Extension classes for Windows and Linux STL

## license
Copyright (c) 2022-2024 Ulrik Lindahl
Licensed under the MIT license https://github.com/Cooolrik/ctle/blob/main/LICENSE

MIT License

Copyright (c) 2022 Ulrik Lindahl

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
adrenaline 4.0.1
Keep your OS from sleeping (supports Windows and macOS)
Requires Python: >=3.9

`adrenaline`
============

Simple Python module to prevent your computer from going to sleep. Supports
Windows and macOS at the moment; Linux support is coming soon (hopefully).

Usage
-----

The module provides a context manager named `prevent_sleep()`. The computer
will not go to sleep while the execution is in this context:

```python
from adrenaline import prevent_sleep


with prevent_sleep():
    # do something important here
    ...
```

Optionally, you can also prevent the screen from turning off:

```python
with prevent_sleep(display=True):
    # do something important here
    ...
```

Command line interface
----------------------

You can also use this module from the command line as follows:

```sh
$ python -m adrenaline
```

The command line interface will prevent sleep mode as long as it is running.


Acknowledgments
---------------

Thanks to [Michael Lynn](https://github.com/pudquick/pypmset) for figuring out
how to do this on macOS.

Thanks to [Niko Pasanen](https://github.com/np-8/wakepy) for the Windows
version.
marketDP 0.2.0
simple python code for predicting markets

# Market Direction Predictor (MDP)
predict UP or DOWN days(candles) based on previous sources/indicators on a crypto/forex... markets using neural networks
## why i should use it when there are other libraries for this task?
well...
1. the code is simple,understandable and easy to use. 
2. use this if you don't want a headache on figuring out how to use neural network for market prediction or even reading the documentation page
## will this 100% work.
IDK. the markets no matter crypto,stock,forex... have non stationary behaviour and mostly influenced by economics,news... but if i were to make a code that trades markets i would have 2 choices:
1. is to make a very complicated program that includes many inputs. economics,news,candlestick... which is very difficult to make
2. use a simple method to trade the market like the one written in this code which only trains a neural network on candlesticks (%changes of sources) to predict next candle direction. (BTW i wouldn't suggest you to make a deep neural network it will easily overfit the data and perform poorly. just need a nn that learns the general pattern this model worked fine but don't take my word `MLPClassifier((8,8),"tanh",solver="lbfgs",max_iter=2000)`)

so to answer your question, no it won't.

## so how do i use it ?
the library is a single file called `mdp.py` and
i've put some tests on the file too. this is the simples way of using the code:
```python
from MDP.mdp import MDPC,MLPClassifier
d=[1,2,3 ,2,3,4 ,3,4,5 ,4,5,6] # this is your closing data provided by the Exchange
mdpc=MDPC(wl=2,clf=MLPClassifier()) # wl stands for window length and it controls how far the model sees the past bars to predict next 
ac,cm,npp=mdpc.train_predict(d) # ac:accuracy(number),cm:confusion table(2x2 array),npp:list of 2 numbers first one is the probability in which model thinks it is going down and UP for the second one.
print(ac)
print(cm)
print(npp)
assert npp[0]>npp[1]
``` 
there are 3 classes. `MDPC,MDPHLC,MDPMORE`. MDPHLC accepts HLC and MDPMORE accepts infinity of features (note that all of them compute %change on the data)







btw my github is https://github.com/navidpgg
check-rkn 1.2.1
Library that check blocked website on blocklist.rkn.gov.ru
Requires Python: ~=3.10

# Check_rkn

[![PyPI](https://img.shields.io/pypi/v/check-rkn)](https://pypi.org/project/check-rkn/)

This is library, that check blocked websites on [blocklist.rkn.gov.ru](https://blocklist.rkn.gov.ru/). Library uses [Selenium](https://pypi.org/project/selenium/) and [anticaptchaofficial](https://pypi.org/project/anticaptchaofficial/). *WARNING!*  You need to register on [anti-captcha.com](https://anti-captcha.com/) to get the api key and top up your balance (For residents of the Russian federation: MIR cards are available, you can also pay with cryptocurrency)

## How to install

```shell
pip install check-rkn
```


## How to use

```python 
from check_rkn.check_rkn import check_website

result = check_website("your_url", "your_api_key")
print(result) # True if website is blocked or False if no

```

## License - [Apache 2.0](NOTICE)
Market-Direction-Predictor 0.2.0
simple python code for predicting markets

# Market Direction Predictor (MDP)
predict UP or DOWN days(candles) based on previous sources/indicators on a crypto/forex... markets using neural networks
## why i should use it when there are other libraries for this task?
well...
1. the code is simple,understandable and easy to use. 
2. use this if you don't want a headache on figuring out how to use neural network for market prediction or even reading the documentation page
## will this 100% work.
IDK. the markets no matter crypto,stock,forex... have non stationary behaviour and mostly influenced by economics,news... but if i were to make a code that trades markets i would have 2 choices:
1. is to make a very complicated program that includes many inputs. economics,news,candlestick... which is very difficult to make
2. use a simple method to trade the market like the one written in this code which only trains a neural network on candlesticks (%changes of sources) to predict next candle direction. (BTW i wouldn't suggest you to make a deep neural network it will easily overfit the data and perform poorly. just need a nn that learns the general pattern this model worked fine but don't take my word `MLPClassifier((8,8),"tanh",solver="lbfgs",max_iter=2000)`)

so to answer your question, no it won't.

## so how do i use it ?
the library is a single file called `mdp.py` and
i've put some tests on the file too. this is the simples way of using the code:
```python
d=[1,2,3 ,2,3,4 ,3,4,5 ,4,5,6] # this is your closing data provided by the Exchange
mdpc=MDPC(wl=2,clf=MLPClassifier()) # wl stands for window length and it controls how far the model sees the past bars to predict next 
ac,cm,npp=mdpc.train_predict(d) # ac:accuracy(number),cm:confusion table(2x2 array),npp:list of 2 numbers first one is the probability in which model thinks it is going down and UP for the second one.
print(ac)
print(cm)
print(npp)
assert npp[0]>npp[1]
``` 
there are 3 classes. `MDPC,MDPHLC,MDPMORE`. MDPHLC accepts HLC and MDPMORE accepts infinity of features (note that all of them compute %change on the data)







btw my github is https://github.com/navidpgg
pipcentral 1.0.2
PipCentral is your one-stop tool for Python lovers
Requires Python: >=3.6

# PipCentral 🐍🚀

Welcome to **PipCentral** – PipCentral is your one-stop tool for python lovers 🌟  

## Why I Created This: Let Me Tell You
"When I started learning about pip, I felt overwhelmed seeing all the packages from A to Z. My eyes would hurt so much 😣. I lost count of how many times I had to use commands to check for security, vulnerabilities, and other tasks 🔄. Sometimes, I felt completely exhausted from using pip commands all the time 😫. It became really difficult to look up packages 📦."
Let me know if you'd like to add anything else! 

## Why PipCentral? 🤔  
PipCentral is built to address real challenges faced by Python developers. Whether it’s managing dependencies or organizing virtual environments, this tool is crafted to save time and streamline your workflow.  

## Get Started 🚀
```bash
pip install pipcentral==1.0.2
```
## After Installation
run this command

```bash
pipcentral
```

## Features 💡  
- 🛠️ **Installed Package Management**: Quickly view, update, and manage your installed Python packages.  
- 🌍 **Virtual Environment Support**: Create and manage virtual environments for seamless project isolation.  
- 💾 **Local Repository Setup**: Easily set up and use local repositories for added convenience.  
- 🕒 **Pip Scheduler**: Automate pip operations with a built-in scheduler to save time.  
- 🔍 **Dependency Graph Visualization**: Dive deep into your project's structure with clear dependency graphs.
- ✨ **And More**: Discover additional features

## snapshots
![image](https://github.com/user-attachments/assets/ee271d77-3e0e-4f28-bb3f-a9ea8bb11046)
joetorch 0.0.42
Useful code for testing new Deep Learning algorithms.
Requires Python: >=3.7

# JoeTorch

A Python library containing useful utilities for testing new Deep Learning algorithms.

## Installation

Install using pip:
```bash
pip install joetorch
```

## Features

- **Dataset Utilities**
  - MNIST dataset loader with validation split and augmentation options
  - PreloadedDataset class for efficient data handling
  - Support for custom datasets

- **Neural Network Components**
  - MLP (Multi-Layer Perceptron) module
  - Convolutional blocks (Encoder/Decoder)
  - Auto-encoder architectures

- **Training Utilities**
  - Learning rate schedulers (Cosine, Step, Flat)
  - Mixed precision training support
  - TensorBoard logging integration
  - Optimized weight decay handling

- **Loss Functions**
  - MSE reconstruction loss
  - BCE reconstruction loss
  - KL divergence loss
  - Smooth L1 loss
  - Negative cosine similarity

- **Feature Analysis**
  - Feature correlation analysis
  - Feature standard deviation metrics
  - Representation analysis tools

## Example Usage

```python
from joetorch.datasets import MNIST
from joetorch.nn import MNIST_AE
from joetorch.optim import get_optimiser, train

# Load MNIST dataset
train_dataset = MNIST(root='datasets/', split='train', val_ratio=0.1, 
                     augment=True, device='cuda')
val_dataset = MNIST(root='datasets/', split='val', val_ratio=0.1, 
                   device='cuda')

# Create model and optimizer
model = MNIST_AE(out_dim=20, mode='cnn').to('cuda')
optimizer = get_optimiser(model, optim='AdamW')

# Train the model
train(model, train_dataset, val_dataset, optimizer, 
      num_epochs=50, batch_size=256)
```

## Requirements

- Python >= 3.7
- PyTorch >= 1.19.2
- NumPy >= 1.19.2

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Author

Joe Griffith (joeagriffith@gmail.com)
numcurios 0.1.0
Библиотека для работы с интересными числами и комбинаторикой
Requires Python: >=3.7

# numcurios

Р‘РёР±Р»РёРѕС‚РµРєР° РґР»СЏ СЂР°Р±РѕС‚С‹ СЃ РёРЅС‚РµСЂРµСЃРЅС‹РјРё С‡РёСЃР»Р°РјРё Рё РёС… СЃРІРѕР№СЃС‚РІР°РјРё.  
Р РµР°Р»РёР·РѕРІР°РЅР° С‡Р°СЃС‚СЊ С„СѓРЅРєС†РёР№ РЅР° C++ РґР»СЏ РїРѕРІС‹С€РµРЅРёСЏ СЃРєРѕСЂРѕСЃС‚Рё.
aio-net-events 8.0.1
Asynchronous network configuration event detector for Python 3.9 and above
Requires Python: >=3.9

# aio-net-events

`aio-net-events` is a Python library that provides asynchronous generators
yielding events when the network configuration of the machine changes.
Currently only network interface additions / removals and IP address additions /
removals are supported; more events may be added later.

Supports Windows, Linux and macOS at the moment.

Requires Python >= 3.8.

Works with [`asyncio`](https://docs.python.org/3/library/asyncio.html) and
[`trio`](https://trio.readthedocs.io/en/stable/).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install
`aio-net-events`.

```bash
pip install aio-net-events
```

## Usage

## Contributing

Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://choosealicense.com/licenses/mit/)
myrich_mohammed 0.1.0
Simple library for rich-based input and color preview
Requires Python: >=3.7

# My_first_lib

**Hi!!**  
This is my first lib, so I'm testing things.  
This lib does two things for now (2025/4/18):

1. **rich_input**  
   It lets you color the input message.

2. **show_all_colors**  
   It shows all colors you can use (total 256).

It depends on the [rich](https://github.com/Textualize/rich) lib, and I made this lib just to increase speed.
url-instax 0.1.3
Screenshot for web page.
Requires Python: <4.0,>=3.10
Keywords: python

# url-instax

[![Release](https://img.shields.io/github/v/release/wh1isper/url-instax)](https://img.shields.io/github/v/release/wh1isper/url-instax)
[![Build status](https://img.shields.io/github/actions/workflow/status/wh1isper/url-instax/main.yml?branch=main)](https://github.com/wh1isper/url-instax/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/wh1isper/url-instax/branch/main/graph/badge.svg)](https://codecov.io/gh/wh1isper/url-instax)
[![Commit activity](https://img.shields.io/github/commit-activity/m/wh1isper/url-instax)](https://img.shields.io/github/commit-activity/m/wh1isper/url-instax)
[![License](https://img.shields.io/github/license/wh1isper/url-instax)](https://img.shields.io/github/license/wh1isper/url-instax)

Screenshot for web page.

- **Github repository**: <https://github.com/wh1isper/url-instax/>
- **Documentation** <https://wh1isper.github.io/url-instax/>

## Quickstart

```bash
uvx url-instax http
```

or use docker image

```bash
docker run -p 8890:8890 ghcr.io/wh1isper/url-instax:latest
```

Access `http://localhost:8890/docs` for openapi docs.

## Usage

Open https://url-instax.wh1isper.top:8890/api/v1/screenshot?url=https://example.com in your browser, and you will see a screenshot of the page.

I created a demo server, check https://url-instax.wh1isper.top:8890/docs for `GET`/`POST` API specification. This is only for testing and you should not use it for production.

## MCP Server

This package includes a simple MCP server for LLM to view web page via screenshot. You can use this config to set up the server:

```json
{
  "mcpServers": {
    "yourware-mcp": {
      "command": "uvx",
      "args": ["url-instax@latest", "mcp"],
      "env": {
        "API_BASE_URL": "http://localhost:8890"
      }
    }
  }
}
```

if `API_BASE_URL` is not provided, it will directly use [playwright](https://playwright.dev/) to take screenshot, which you need to install Playwright first via `playwright install` or `uv tool run playwright install`.

You can use `https://url-instax.wh1isper.top:8890` as the `API_BASE_URL` for testing.
cappa-sqlplag 1.1.0
SQL-детектор плагиата, предназначенный для использования в комплексе автоматической проверки программ CAPPA
Keywords: cappa, sql, plagiarism

# CAPPA SQLPlag
SQL-детектор, предназначенный для использования в комплексе автоматической проверки программ [CAPPA](http://cappa.csu.ru/).

### Установка
 
```shell
pip install cappa_sqlplag
```

[Пример использования](docs/usage.md)

### Контакты
Официальный сайт: [cappa.csu.ru](http://cappa.csu.ru/)   

Ответственный разработчик: Перепеленко Анастасия

По вопросам сотрудничества: mig19@mail.ru

При поддержке:
 * [Математический факультет ЧелГУ](http://math.csu.ru)

### Лицензия
Распространяется под лицензией [MIT](LICENSE).
pyserpent 1.0.1
Pure Python implementation of the Serpent block cipher with CBC mode and PKCS#7 padding
Requires Python: >=3.6

# pyserpent

Pure Python implementation of the Serpent block cipher with CBC mode and PKCS#7 padding.

## Install

```bash
pip install pyserpent

## Example

from pyserpent import Serpent, serpent_cbc_encrypt, serpent_cbc_decrypt

key = Serpent.generate_key()
iv = Serpent.generateIV()
data = "Hello, Serpent!"

encrypted = serpent_cbc_encrypt(key, data, iv)
decrypted = serpent_cbc_decrypt(key, encrypted, iv)

print(decrypted.decode())