Skip to main content

Run function in multiple processes

This project has been archived.

The maintainers of this project have marked this project as archived. No new releases are expected.

Project description

gy-multiprocessing

Installation

via Github

pip install git+https://github.com/guangyu-he/gy-multiprocessing

via PyPI

pip install gy-multiprocessing

Usage

  • initialize a mp pool with a dictionary of lists of processes (and their output) and their runtimes
  • generate a mp object inside the loop you want to run parallel
  • run the function with the given arguments and callback the mp pool

Examples

Multi Processing

import gy_multiprocessing.multiprocessing.multi_process as gymp
import time


def your_func(a_string: int, queue):
    # NOTE! you MUST add a argument for queue and use put() method to fetch the returning value

    print(a_string)
    if a_string % 5 == 0:
        time.sleep(2)

    # NOTE! This is a MUST-have line, or the multi_processing will not end!!!
    queue.put(a_string)


if __name__ == '__main__':
    # the multiprocessing must work in a function or entrance
    # do not use it barely

    """
    # initializing the multi threading instance
    # the default max_process are your cpu max cores
    # max_process could be infinite, but performance will get suffered when the hardware is overloaded
    """
    mp = gymp.MultiProcess(max_process=8)

    # example for multithreading in the loop
    outer_loop_times = 5
    for current_loop_index in range(outer_loop_times):
        # your running arguments, must be tuple
        args = (current_loop_index,)

        """
        # adding tasks in multiprocessing pool
        """
        mp.add(your_func, args)

    # it is also possible to add task outside the loop
    mp.add(your_func, (10,))

    """
    # running tasks in multi threading pool (returned values are optional)
    """
    result = mp.run()
    print(result)

Multi Threads

Note: you can not use multi "children" threads inside the multi threads' method! If you want to use such structure,please consider using Multi Threads inside the Multi Processing.

import gy_multiprocessing.multithreading.multi_thread as gymt
import time


def your_func(a_string):
    # your single task function

    print(a_string)
    return a_string + "!"


if __name__ == '__main__':
    # the multithreading must work in a function or entrance
    # do not use it barely

    # timing (optional)
    start = time.time()

    """
    # initializing the multi threading instance
    # the default max_threads are your cpu max cores number - 1
    # max_threads can not larger than your cpu max core number
    """
    mt = gymt.MultiThread(max_threads=4)

    # example for multithreading in the loop
    outer_loop_times = 5
    for current_loop_index in range(outer_loop_times):
        args = (str(current_loop_index),)

        """
        # adding tasks in multi threading pool
        """
        mt.add(your_func, args)

    # it is also possible to work without loop
    args = (str(1),)
    mt.add(your_func, args)
    args = (str(2),)
    mt.add(your_func, args)

    """
    # running tasks in multi threading pool (returned values are optional)
    """
    results = mt.run()
    print(results)

    # timing (optional)
    end = time.time() - start
    print("done in {}s".format("%.2f" % end))

2022©Guangyu He, for further support please contact author.
Email: me@heguangyu.net

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

gy_multiprocessing-0.2.0.tar.gz (4.9 kB view details)

Uploaded Source

File details

Details for the file gy_multiprocessing-0.2.0.tar.gz.

File metadata

  • Download URL: gy_multiprocessing-0.2.0.tar.gz
  • Upload date:
  • Size: 4.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.8

File hashes

Hashes for gy_multiprocessing-0.2.0.tar.gz
Algorithm Hash digest
SHA256 4db22a104d700ea6d9370be4375e5379c0b91edcb2f96a667d924ea4ad0c36a6
MD5 3d88053bff2cdd332d8fa1aa4d6630f3
BLAKE2b-256 76ecc6e9ae8f19c05f3e590885d62937656cc52f9251ce4a1c6c84bc5a8fd813

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page