parallel-processing 0.1.3

No project description provided

Parallel Processing

A minimal, easy-to-use Python utility to parallelize function execution using threads or processes.
This library helps you speed up processing over iterable items by distributing work across multiple workers.

---

Features

• Lightweight and dependency-free
• Simple interface for threading and multiprocessing
• Easily process a list of items with any custom function
• Ideal for I/O-bound or CPU-bound tasks

---

Installation

pip install parallel-processing

---

Usage

You can use either threading or multiprocessing to parallelize your task over a list of items.

Example: Using Threads

from parallel_processing import ParallelProcessing

def fetch(x):
    print(f"Processing item: {x}")

workers = 10
its = 100

ParallelProcessing.thread(
    workers=workers,
    processor=fetch,
    items=range(its)
)

---

Example: Using Processes

from parallel_processing import ParallelProcessing

def compute(x):
    return x * x

results = ParallelProcessing.process(
    workers=4,
    processor=compute,
    items=range(10)
)

print(list(results))

---

API

ParallelProcessing.thread(workers, processor, items)

Runs processor(item) for each item in items using workers threads.

• workers (int): Number of threads to use
• processor (callable): Function to apply to each item
• items (iterable): The list or iterable of items to process

---

ParallelProcessing.process(workers, processor, items)

Runs processor(item) for each item in items using workers processes and returns results.

• workers (int): Number of processes to use
• processor (callable): Function to apply to each item
• items (iterable): The list or iterable of items to process

---

License

MIT License

---

Author

Louis Nguyen
[e-mail]