parallize 0.1.1

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Parallize

Parallize is a Python package that provides utilities to parallelize both synchronous and asynchronous functions using the concurrent.futures.ProcessPoolExecutor. This allows you to execute functions in separate processes, leveraging multiple CPU cores for improved performance.

Features

• Parallelize Synchronous Functions: Execute synchronous functions in parallel using multiple processes.
• Parallelize Asynchronous Functions: Execute asynchronous functions in parallel using multiple processes.
• Customizable Worker Count: Specify the maximum number of worker processes to use, or let the package use the number of available CPU cores by default.

Parallize

Parallize is a Python package that provides utilities to parallelize both synchronous and asynchronous functions using the concurrent.futures.ProcessPoolExecutor. This allows you to execute functions in separate processes, leveraging multiple CPU cores for improved performance.

Features

• Parallelize Synchronous Functions: Execute synchronous functions in parallel using multiple processes.
• Parallelize Asynchronous Functions: Execute asynchronous functions in parallel using multiple processes.
• Customizable Worker Count: Specify the maximum number of worker processes to use, or let the package use the number of available CPU cores by default.

Mini benchmark

• parallel are the same function but with @parallize decorator or parallize function Here are the benchmark results comparing serial and parallel execution times:

Test Case	Serial Execution Time	Parallel Execution Time
test_aparallize	0:00:04.073584	0:00:00.017007
test_aparallize_decorator	0:00:04.027461	0:00:00.014918
test_parallize	0:00:04.057500	0:00:00.013410
test_parallize_decorator	0:00:04.042075	0:00:00.015465

Installation

To install Parallize, you can use pip:

pip install parallize

Usage

Parallelizing Synchronous Functions Using Decorator

To parallelize a synchronous function, use the parallize decorator:

from parallize import parallize

@parallize
def my_function(x, y):
    return x + y

# Call the function as usual
result = my_function(1, 2)
print(result)  # This will be executed in a separate process

Parallelizing Asynchronous Functions Using Decorator

To parallelize an asynchronous function, use the aparallize decorator:

import asyncio
from parallize import aparallize

@aparallize
async def my_async_function(x, y):
    await asyncio.sleep(1)
    return x + y

# Call the function as usual
result = my_async_function(1, 2)
print(result)  # This will be executed in a separate process

Another usage without decorator

from parallize import parallize

def my_function(x, y):
    return x + y

# Call the function as usual
result = parallize(my_function)(1, 2)
print(result)  # This will be executed in a separate process

Customizing the Number of Workers

You can specify the maximum number of worker processes to use by passing the max_workers argument to the decorator:

@parallize(max_workers=4)
def my_function(x, y):
    return x + y

@aparallize(max_workers=4)
async def my_async_function(x, y):
    await asyncio.sleep(1)
    return x + y

or

def my_function(x, y):
    return x + y

async def my_async_function(x, y):
    await asyncio.sleep(1)
    return x + y

parallize(my_function,max_workers=4)(1, 2)
aparallize(my_async_function,max_workers=4)(1, 2)

License

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

Contributing

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

Issues

If you encounter any issues or have suggestions for improvements, please open an issue on the GitHub repository.

Acknowledgments

• Thanks to the Python community for providing powerful tools and libraries for parallel processing.

@vikyw89-20240804