pypbars 0.1.7

Provides a convenient way to display progress bars for concurrent asyncio or multiprocessing Pool processes.

pypbars

The pypbars module provides a convenient way to display progress bars for concurrent asyncio or multiprocessing Pool processes. The pypbars class is a subclass of list2term that displays a list to the terminal, and uses progress1bar to render the progress bar.

Installation

pip install pypbars

example1 - ProgressBars with asyncio

Create ProgressBars using a lookup list containing unique values, these identifiers will be used to get the index of the appropriate ProgressBar to be updated. The convention is for the function to include logger.write calls containing the identifier and a message for when and how the respective progress bar should be updated. In this example the default regex dict is used but the caller can specify their own, so long as it contains regular expressions for how to detect when total, count and optional alias are set.

Code

import asyncio
import random
from faker import Faker
from pypbars import ProgressBars

async def do_work(worker, logger=None):
    logger.write(f'{worker}->worker is {worker}')
    total = random.randint(10, 65)
    logger.write(f'{worker}->processing total of {total} items')
    for count in range(total):
        # mimic an IO-bound process
        await asyncio.sleep(.1)
        logger.write(f'{worker}->processed {count}')
    return total

async def run(workers):
    with ProgressBars(lookup=workers, show_prefix=False, show_fraction=False) as logger:
        doers = (do_work(worker, logger=logger) for worker in workers)
        return await asyncio.gather(*doers)

def main():
    workers = [Faker().user_name() for _ in range(10)]
    print(f'Total of {len(workers)} workers working concurrently')
    results = asyncio.run(run(workers))
    print(f'The {len(workers)} workers processed a total of {sum(results)} items')

if __name__ == '__main__':
    main()

example2 - ProgressBars with multiprocessing Pool

This example demonstrates how pypbars can be used to display progress bars from processes executing in a multiprocessing Pool. The pypbars.multiprocessing module contains a progress_bars method that fully abstracts the required multiprocessing constructs, you simply pass it the function to execute along with an iterable of arguments to pass each process invocation. The method will execute the functions asynchronously and return a multiprocessing.pool.AsyncResult object. Additional key word arguments can be passed to the progress_bars method to control ProgressBars instance. Each line in the terminal represents a background worker process.

If you do not wish to use the abstraction, the list2term.multiprocessing module contains helper classes that define a LinesQueue as well as a QueueManager to facilitate communication between worker processes and the main process. Refer to example3 for how the helper methods can be used.

Note the function being executed must accept a logger object that is used to write status messages, this is the mechanism for how status messages are sent from the worker processes to the main process, it is the main process that is displaying the progress bars to the terminal. The messages must be written using the format {identifier}->{message}, where (by default) {identifier} is a colon delimited string consisting of the function arguments, it uniquely identifies a process to the ProgressBars instance. You may choose to define your own identifer so long as you provide it via the lookup argument to the ProgressBars class or progress_bars method.

Code

import time
from pypbars.multiprocessing import progress_bars
from list2term.multiprocessing import CONCURRENCY

def is_prime(num):
    if num == 1:
        return False
    for i in range(2, num):
        if (num % i) == 0:
            return False
    else:
        return True

def count_primes(start, stop, logger):
    workerid = f'{start}:{stop}'
    logger.write(f'{workerid}->worker is {workerid}')
    logger.write(f'{workerid}->processing total of {stop - start} items')
    primes = 0
    for number in range(start, stop):
        if is_prime(number):
            primes += 1
        logger.write(f'{workerid}->processed {number}')
    return primes

def main(number):
    step = int(number / CONCURRENCY)
    iterable = [(index, index + step) for index in range(0, number, step)]
    results = progress_bars(count_primes, iterable, use_color=True, show_prefix=False, show_fraction=False)
    return sum(results.get())

if __name__ == '__main__':
    start = time.perf_counter()
    number = 50_000
    result = main(number)
    stop = time.perf_counter()
    print(f"Finished in {round(stop - start, 2)} seconds\nTotal number of primes between 0-{number}: {result}")

Development

Clone the repository and ensure the latest version of Docker is installed on your development server.

Build the Docker image:

docker image build \
-t \
pypbars:latest .

Run the Docker container:

docker container run \
--rm \
-it \
-v $PWD:/code \
pypbars:latest \
bash

Execute the build:

pyb -X