semvec/pypi-packages huggingface dataset:
PloudOS 1.0.1
Unofficial API for PloudOS
Requires Python: >=3

# PloudOS-API
An unofficial API for PloudOS.

This library contains a lot of functions and is fully asynchronous.

# Example
```py
import asyncio
from ploudos import PloudOS

myserverid = 0
ploudos = PloudOS("myusername", "mypassword", myserverid)

async def main():
    await ploudos.login()
    info = await ploudos.get_server_info()
    if info.get("isRunning") is True and info.get("isStarted") is True:
        # It's already started and running
        return
    print(info)
    cr = await ploudos.can_restart()
    print(cr)
    if cr:
        await ploudos.restart()
    else:
        q = await ploudos.queue()
        if q:
            print("Server accepting is necessary. Executing the command.")
            await ploudos.accept_server()
    print(await ploudos.get_server_info())
    await asyncio.sleep(10)
    print(await ploudos.stop())

asyncio.run(main())

```
cancel-token 0.1.6
Simple CancellationToken

CancellationToken for Python
---

This library provide simple cancellation token. 

# Usage

First create token
```python
from cancel_token import CancellationToken

token = CancellationToken()
```

At any point in time you can check if token has been cancelled:
```python
if token.cancelled:
    print('Operation already cancelled')
    return None  
```

To cancel token call its `cancel` method:
```python
token.cancel()
assert token.cancelled
```

It is also possible to add callbacks that will be called on when token is cancelled:
```python
def handler():
    print('Operation has been cancelled')

token.on_cancel(handler)
```

**Note**:
* If token is already cancelled, callback will be invoked immediately.
* All registered callbacks will be called sequentially during `cancel` call.
* Callback throwing exception will prevent remaining callbacks from calling. However token will be cancelled
* During callback invocation token is already cancelled
* It is possible to add callback from within callback
* Removing callback from within callback **will not** prevent its execution
EasyUtils-SPeak 0.0.4
a easy utils set
Requires Python: >=3.6

# EasyUtils
a easy utils set .....

## 1.Basic Feature/Tools

- ETaskManager: a simple task manager
- ELog: a log tools

## 2.Install

> use pip or clone source code directly

```
pip install EasyUtils-SPeak
```

or

```
git clone git@github.com:Sunrisepeak/EasyUtils.git
```

## 3. Example

### ELog

```python

from eutils import getLogger

getLogger("EUtils", logLevel=4).info("Hello ELog")


```

### ETaskManager

```python
#...

```

>
> More Detils: [test code](https://github.com/Sunrisepeak/EasyUtils/tree/main/tests/) , [demo](https://github.com/Sunrisepeak/EasyUtils/tree/main/demo)
>


## Other

[HomePage](https://github.com/Sunrisepeak/EasyUtils)

[PyPi](https://pypi.org/project/EasyUtils-SPeak/)
torch-components 1.0.3
Torch Components is an open-source high-level API above the PyTorch framework. It provides functionality, which can be easily inserted into any PyTorch training and validation scripts.

<img src="https://i.ibb.co/Qrvyg8S/Gallery-1649448825801.png" alt="Gallery-1649448825801" border="0">

Torch Components is an open-source high-level API above the PyTorch framework. It provides functionality, which can be easily inserted into any PyTorch training and validation scripts.

# Installation

```
git clone -q https://github.com/vad13irt/torch-components.git
```

```py
import sys
sys.path.append('./torch-components/')
import torch_components
```


# API Reference

### Averager
Averager calculates some statistics (sum, average, and count) for given values.

### Timer
Timer calculates how much time was elapsed after initializing an instance of the class, and also calculates how much time remains for a certain fraction of the process.

### Configuration
Configuration is an analog of TrainingArguments or Arguments from fast.ai and HuggingFace Transformers respectively, it provides a good interface for saving configuration variables.


## Callbacks
### Early Stopping
Early Stopping is a simple method of regularization used to avoid overfitting during training Machine Learning models by stopping the training when the monitored value has not been improved.

### Model Checkpoint
Model Checkpoint saves the model's weights (optimizer's state, and scheduler's state) when the monitored value has been improved.

### Timing
Timing stops training when the duration of the training stage reaches a certain limit of time. It is useful when you are using time-limit sources, e.g. Google Colab or Kaggle Kernels/Notebooks.


# Contribution
Torch Components is open for contributions and issues! We are waiting for your first contribution!
vs-kernels 1.0.2
Kernel objects for scaling and format conversion within VapourSynth
Requires Python: >=3.9

# vs-kernels

<p align="center">
    <a href="https://vskernels.encode.moe"><img alt="Read the Docs" src="https://img.shields.io/readthedocs/vs-kernels"></a>
    <img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/vs-kernels">
    <a href="https://pypi.org/project/vs-kernels/"><img alt="PyPI" src="https://img.shields.io/pypi/v/vs-kernels"></a>
    <a href="https://github.com/Irrational-Encoding-Wizardry/vs-kernels/commits/master"><img alt="GitHub commits since tagged version" src="https://img.shields.io/github/commits-since/Irrational-Encoding-Wizardry/vs-kernels/latest"></a>
    <a href="https://github.com/Irrational-Encoding-Wizardry/vs-kernels/blob/master/LICENSE"><img alt="PyPI - License" src="https://img.shields.io/pypi/l/vs-kernels"></a>
    <a href="https://discord.gg/qxTxVJGtst"><img alt="Discord" src="https://img.shields.io/discord/856381934052704266?label=discord"></a>
</p>

Kernels are a collection of wrappers pertaining to (de)scaling, format conversion,
and other related operations, all while providing a consistent and clean interface.
This allows for easy expansion and ease of use for any other maintainers
who wishes to use them in their own functions.

You can create presets for common scaling algorithms or settings,
while ensuring the interface will always remain the same,
even across different plugins with their own settings and expected behavior.

Full information on how every function/wrapper works,
as well as a list of dependencies and links,
can be found in the [documentation](https://vskernels.encode.moe/en/latest/).
For further support,
drop by `#kernels` in the [IEW Discord server](https://discord.gg/qxTxVJGtst).

## How to install

Install `vs-kernels` with the following command:

```sh
$ pip3 install vskernels --no-cache-dir -U
```

Or if you want the latest git version, install it with this command:

```sh
$ pip3 install git+https://github.com/Irrational-Encoding-Wizardry/vs-kernels.git --no-cache-dir -U
```