Pyllelize

Easily parallelize your Python functions with pyllelize. Whether you're dealing with CPU-bound tasks or I/O-bound asynchronous functions, pyllelize offers a simple interface to distribute your workload efficiently.

Key Features

• Thread-based Parallelization: Distribute CPU-bound tasks across multiple threads with ease.

• Process-based Parallelization: Harness the full power of multi-core processors by distributing tasks across separate processes, bypassing Python's Global Interpreter Lock.

• Asynchronous Execution: Seamlessly parallelize I/O-bound asynchronous tasks.

• Error Handling: Safely capture and report errors without halting the execution of other tasks.

Installation

pip install pyllelize

Quick Start

Thread-based Execution

from pyllelize import parallel_execute_threads

def multiply(a, b):
    return a * b

arguments = [(1, 2), (3, 4), (5, 6)]
results = parallel_execute_threads(multiply, arguments)
print(results)  # Possible output: [2, 12, 30]

Process-based Execution

from pyllelize import parallel_execute_processes

def add(a, b):
    return a + b

arguments = [(1, 2), (3, 4), (5, 6)]
results = parallel_execute_processes(add, arguments)
print(results)  # Possible output: [3, 7, 11]

Asynchronous Execution

import asyncio
from pyllelize import parallel_execute_threads_async

async def fetch_data(a, b):
    await asyncio.sleep(1)  # Simulating async I/O operation
    return a + b

arguments = [(1, 2), (3, 4), (5, 6)]
results = parallel_execute_threads_async(fetch_data, arguments)
print(results)  # Possible output after 1 second: [3, 7, 11]

License

This project is licensed under the MIT License.