func-parallelizer 0.4.1

Func_Parallelizer is a simple Python module for parallel execution of functions using multiprocessing. Ideal for parallel execution of heavy cpu operations

func_parallelizer

Func_Parallelizer is a simple Python module for parallel execution of functions using multiprocessing.

Ideal for parallel execution of heavy cpu operations like processing huge no of files in a directory or reading files for pandas.

Installation

Use the package manager pip to install func_parallelizer.

pip install func-parallelizer

Usage

Import the required packages.

from func_parallelizer import parallel_runner

Create a class that has your function and arguments.

If your function doesn't require any arguments you can skip this and directly add them in tasks.

class task:
    def __init__(self, arg1, arg2):
        self.arg1 = arg1
        self.arg2 = arg2

    def this(self):
        consequences = do_this_with(self.arg1, self.arg2)
        return consequences

    def that(self):
        consequences = do_that_with(self.arg1, self.arg2)
        return consequences

Create objects using the above class.

def do_nothing():
    print("Eat 5 Star")
    print("⭐⭐⭐⭐⭐")

do = task(arg1, arg2)

Make a list of tasks you have to do.

Note: Don't use parentheses for functions otherwise function will execute in this stage also.

tasks = [
    do.this,
    do.that,
    do_nothing
]

Pass the tasks to the parallel_runner function

By default all cores are used but if you have a life and want to do some other things, I would recommend to use 50% to 80% of your CPU cores.

total_mayhem = parallel_runner(tasks, cpu_cores=2)

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

Feel free to ask any help(If it's about this package, dealing a lot IRL can't help yours too.)

License

MIT

Code of Conduct

Everyone interacting in the func-parallelizer project's codebases, issue trackers, chat rooms, and mailing lists is expected to follow the PSF Code of Conduct.

Copyright © 2014, [The Python Packaging Authority].