parallelizer

Parallel execution of your tasks simplified! This package can be used as an alternative to Python's concurrent.futures executors, or as an analog of Java's ExecutorService.

Two simple wrappers (thread-based and process-based), that allow for easy split and parallel processing of your jobs.

Description

This package provides two classes:

• ThreadParallelizer to execute your jobs in new threads
• ProcessParallelizer to execute your jobs in new processes

This is a pure python implementation, with usage of threading and multiprocessing packages

Note that the wrappers appear to be in sync (from the caller's perspective), they will wait until all inner tasks are completed.

Installation

Normal installation

pip install parallelizer

Development installation

git clone https://github.com/jpleorx/parallelizer.git
cd parallelizer
pip install --editable .

Example

import time
from parallelizer import ThreadParallelizer, ProcessParallelizer, repeat

def power_function(base: int, power: int) -> int:
    time.sleep(1)
    return base ** power

inputs = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
number_of_threads = 5

thread_parallelizer = ThreadParallelizer(number_of_threads)
results = thread_parallelizer.execute(power_function, [inputs, repeat(2, len(inputs))])

process_parallelizer = ProcessParallelizer(number_of_threads)
results = process_parallelizer.execute(power_function, [inputs, repeat(2, len(inputs))])