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This project has been archived.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

  • initializing multiprocessing/multithreading instance
  • adding your tasks into the pool, either using loop or sentences
  • running the instance

Note:

  • the multiprocessing must work in a function or entrance, do not use it barely in the script
  • make sure the code does require multiprocessing/multithreading, wrongly using the multiprocessing may even lose performance
  • please attention to the queue implementation when using multiprocessing, details check the example below

Examples

Multi Processing

import time
import gy_multiprocessing as gymp


def your_func(a_string: int, queue):
    # NEW from v0.3! if multiprocessing is not used, queue is not a necessary parameter
    # NOTE! you MUST add an argument for queue and use put() method to fetch the returning value if multiprocessing is used

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

    # NEW from v0.3! if multiprocessing is not used, all queue relevant codes can still be kept
    # NEW from v0.3! and the if queue is not None: is not necessary anymore
    # NOTE! if you are missing this method, there will be None result returned for current process if multiprocessing is used
    queue.put(a_string)
    return a_string


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

    """
    # initializing the multiprocessing 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
    """

    # NEW from v0.3! set max_process to 0 will disable multiprocessing
    mp = gymp.MultiProcess(max_process=8)

    # example for multiprocessing in the loop
    outer_loop_times = 5

    # NEW from v0.3! it is possible to test beforehand if the function and args is suitable for multiprocessing
    test_your_func: bool = mp.test(your_func, (1,))

    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 multiprocessing pool (returned values are optional)
    """
    result = mp.run()
    print(result)

Multi Threads

import gy_multiprocessing as gymp
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 multithreading instance
    # the default max_threads are your cpu max cores number - 1
    # max_threads can not larger than your cpu max core number
    """
    mt = gymp.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 multithreading pool (returned values are optional)
    """
    results = mt.run()
    print(results)

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

Combined Structure

Note: you can not use multiprocessing or sub-multithreading in the multithreading method

If you want to use such structure, based on your needs, considering using sub-multiprocessing or multithreading in multiprocessing structure.

import gy_multiprocessing as gymp


def your_sub_func(b_string: int, queue=None):
    # your function that needs to multithreading/multiprocessing

    b_string += 1
    if queue is not None:
        queue.put(b_string)
    return b_string


def your_mt_func(a_string: int, queue):
    # multithreading in multiprocessing structure

    mt = gymp.MultiThread()
    for current_loop_index in range(a_string):
        # your running arguments, must be tuple
        args = (current_loop_index,)
        mt.add(your_sub_func, args)
    result = mt.run()

    # Do not forget queue!
    queue.put(result)


def your_mp_func(a_string: int, queue):
    # sub-multiprocessing in multiprocessing structure

    smp = gymp.MultiProcess()
    for current_loop_index in range(a_string):
        # your running arguments, must be tuple
        args = (current_loop_index,)
        smp.add(your_sub_func, args)
    result = smp.run()

    # Do not forget queue!
    queue.put(result)


if __name__ == '__main__':

    mp = gymp.MultiProcess()

    outer_loop_times = 10
    for current_loop_index in range(outer_loop_times):
        args = (current_loop_index,)
        mp.add(your_mt_func, args)
    print(mp.run())

    print("\n-----”\n")

    mp = gymp.MultiProcess()

    for current_loop_index in range(outer_loop_times):
        args = (current_loop_index,)
        mp.add(your_mp_func, args)
    print(mp.run())

Updates Log

v0.2.3

bug fix

  • fixed an issue casing not adding new process to pool until all processed are done in current pool

v0.2.4

feature

  • a silent mode is added to multiprocessing which is possible not showing messages in console
  • solved multiprocessing not going to the end when there are internal error exceptions in input function
  • solved multiprocessing not going to the end when there are missing queue.put() method in input function

v0.2.4.1 & v0.2.4.2

bug fix

  • fixed an issue from readme long desc causing installation error

improvement

  • simplified duplicated codes

v0.2.4.3

updates

  • package import improvement. PLEASE UPDATE IMPORT AS EXAMPLES ABOVE
  • check a boolean type input argument

v0.3

feature

  • added a test method to check if the function and args is suitable for multiprocessing
  • added a max_process=0 possibility to disable multiprocessing
  • queue parameter in user function has a more flexibility to be used among multiprocessing mode on and off
  • a new multiprocessing pool logic to check if the subprocess is done

Support

feel free to check source code in GitHub, you are welcome to leave any comments.

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-0.3.tar.gz (8.9 kB view details)

Uploaded Source

File details

Details for the file gy_multiprocessing-0.3.tar.gz.

File metadata

  • Download URL: gy_multiprocessing-0.3.tar.gz
  • Upload date:
  • Size: 8.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for gy_multiprocessing-0.3.tar.gz
Algorithm Hash digest
SHA256 fd3d53b226cd8a343c1fccd0aeb536a4d7175508a43d2281bb3e36f60225c185
MD5 536092bc5af628fdf805cd596e777fba
BLAKE2b-256 fd3687767b7e3465a4eb1277d82b2f668130a4d85af915aa3c314ce2f9b8cf73

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