semvec/pypi-packages huggingface dataset:
queueio 0.6.0
Python background queues with an async twist
Requires Python: >=3.14
Keywords: async, background, queue, rabbitmq, tasks

![queueio](https://raw.githubusercontent.com/ryanhiebert/queueio/main/logo.svg)


Python background queues with an async twist
============================================

Use async functions to manage complex background task workflows,
and keep using synchronous functions for everything else.

Getting Started
---------------

Install `queueio`:

```sh
pip install queueio
```

Create your routines:

```python
# basic.py
from time import sleep

from queueio import gather
from queueio import pause
from queueio import routine


@routine(name="blocking", queue="basic")
def blocking():
    sleep(0.1)  # Regular blocking call


@routine(name="yielding", queue="basic")
async def yielding(iterations: int):
    # Do them two at a time
    for _ in range(iterations // 2):
        await gather(blocking(), blocking())
        await pause(0.2)  # Release processing capacity
    if iterations % 2 == 1:
        await blocking()
```

Add the configuration to your `pyproject.toml`:

```toml
[tool.queueio]
# Configure RabbitMQ
pika = "amqp://guest:guest@localhost:5672/"
# Register the modules that the worker should load to find your routines
register = ["basic"]
```

The pika configuration can be overridden with an environment variable
to allow a project to be deployed in multiple environments.

```sh
QUEUEIO_PIKA='amqp://guest:guest@localhost:5672/'
```

Sync the queues to the broker:

```python
queueio sync
```

Submit the routine to run on a worker:

```python
from queueio import activate
from basic import yielding

with activate():
    yielding(7).submit()
```

Then run the worker to process submitted routines:

```sh
queueio run basic=4
```

Monitor the status of active routine invocations:

```sh
queueio monitor
```

Stability
---------

The design of the public API is under active development
and is likely to change with any release.
Release notes will provide upgrade instructions,
but backward compatibility and deprecation warnings
will not generally be implemented.
napistu-torch 0.3.11
PyTorch-based toolkit for working with Napistu network graphs
Requires Python: >=3.11

# Napistu PyTorch Library

Python package supporting integration of Napistu network graphs and PyTorch NNs

[![PyPI version](https://badge.fury.io/py/napistu_torch.svg)](https://badge.fury.io/py/napistu_torch)
[![Documentation](https://readthedocs.org/projects/napistu-torch/badge/?version=latest)](https://napistu-torch.readthedocs.io/)
[![CI](https://github.com/napistu/napistu-torch/actions/workflows/ci.yml/badge.svg)](https://github.com/napistu/napistu-torch/actions/workflows/ci.yml)
[![Release](https://github.com/napistu/napistu-torch/actions/workflows/release.yml/badge.svg)](https://github.com/napistu/napistu-torch/actions/workflows/release.yml)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

This Python package builds on the [Napistu Python package](https://github.com/napistu/napistu-py) with PyTorch-specific data, model, and results management. As part of the broader [Napistu project](https://github.com/napistu/napistu) this work is intended to create and interrogate genome-scale networks of cellular physiology.

## Setup

Napistu is available on [PyPI](https://pypi.org/project/napistu-torch) so the recommended way to use it is just to pip install with:

```bash
pip install napistu-torch
```

Alternatively, you can clone this repository and perform a local install. e.g., from this directory:

```bash
pip install .
```

### Mac setup

```bash
uv pip install torch==2.8.0
uv pip install torch-scatter torch-sparse -f https://data.pyg.org/whl/torch-2.8.0+cpu.html
uv install 'napistu-torch[dev]'
```

## Documentation
- 🚸 **Project Documentation**: [napistu/wiki](https://github.com/napistu/napistu/wiki)

## Advanced setup

### Wandb

If this is your first time using wandb, you'll need to authenticate:

1. Go to https://wandb.ai/ and create an account
2. Get your API key from https://wandb.ai/authorize
3. Run: wandb login
4. Or set environment variable: export WANDB_API_KEY=your_api_key_here
Cambo 0.0.1
A Web Development Framework For Python.
Requires Python: >=3.6

![Image Alt](https://github.com/joelclaude0817-beep/Cambo-Web-Development-Framework-Python/blob/8b5a27dbbadae1e9563717f43edb6f312d7c46df/Untitled97_20260214112256.png)

An Web Development Framework Called "Cambo".
## Installation
```bash
pip install Cambo
```
## Usage
```python
import Cambo

cambo = Cambo.Cambo()

@cambo.page('/')
def main():
    return 'Welcome to Cambo!'

@cambo.page('/about')
def about():
    return 'This is a simple framework called Cambo.'

if __name__ == "__main__":
    cambo.run()
```
## Output
```
Running on http://127.0.0.1:8000
```