Multiprocessing : use tqdm to display a progress bar

Question

To make my code more \"pythonic\" and faster, I use \"multiprocessing\" and a map function to send it a) the function and b) the range of iterations.

The implanted s

Answer 1

You can use p_tqdm instead.

https://github.com/swansonk14/p_tqdm

from p_tqdm import p_map
import time

def _foo(my_number):
   square = my_number * my_number
   time.sleep(1)
   return square 

if __name__ == '__main__':
   r = p_map(_foo, list(range(0, 30)))

Answer 2

This approach simple and it works.

from multiprocessing.pool import ThreadPool
import time
from tqdm import tqdm

def job():
    time.sleep(1)
    pbar.update()

pool = ThreadPool(5)
with tqdm(total=100) as pbar:
    for i in range(100):
        pool.apply_async(job)
    pool.close()
    pool.join()

Answer 3

Solution Found : Be careful! Due to multiprocessing, estimation time (iteration per loop, total time, etc.) could be unstable, but the progress bar works perfectly.

Note: Context manager for Pool is only available from Python version 3.3

from multiprocessing import Pool
import time
from tqdm import *

def _foo(my_number):
   square = my_number * my_number
   time.sleep(1)
   return square 

if __name__ == '__main__':
    with Pool(processes=2) as p:
        max_ = 30
        with tqdm(total=max_) as pbar:
            for i, _ in enumerate(p.imap_unordered(_foo, range(0, max_))):
                pbar.update()

Answer 4

Here is my take for when you need to get results back from your parallel executing functions. This function does a few things (there is another post of mine that explains it further) but the key point is that there is a tasks pending queue and a tasks completed queue. As workers are done with each task in the pending queue they add the results in the tasks completed queue. You can wrap the check to the tasks completed queue with the tqdm progress bar. I am not putting the implementation of the do_work() function here, it is not relevant, as the message here is to monitor the tasks completed queue and update the progress bar every time a result is in.

def par_proc(job_list, num_cpus=None, verbose=False):

# Get the number of cores
if not num_cpus:
    num_cpus = psutil.cpu_count(logical=False)

print('* Parallel processing')
print('* Running on {} cores'.format(num_cpus))

# Set-up the queues for sending and receiving data to/from the workers
tasks_pending = mp.Queue()
tasks_completed = mp.Queue()

# Gather processes and results here
processes = []
results = []

# Count tasks
num_tasks = 0

# Add the tasks to the queue
for job in job_list:
    for task in job['tasks']:
        expanded_job = {}
        num_tasks = num_tasks + 1
        expanded_job.update({'func': pickle.dumps(job['func'])})
        expanded_job.update({'task': task})
        tasks_pending.put(expanded_job)

# Set the number of workers here
num_workers = min(num_cpus, num_tasks)

# We need as many sentinels as there are worker processes so that ALL processes exit when there is no more
# work left to be done.
for c in range(num_workers):
    tasks_pending.put(SENTINEL)

print('* Number of tasks: {}'.format(num_tasks))

# Set-up and start the workers
for c in range(num_workers):
    p = mp.Process(target=do_work, args=(tasks_pending, tasks_completed, verbose))
    p.name = 'worker' + str(c)
    processes.append(p)
    p.start()

# Gather the results
completed_tasks_counter = 0

with tqdm(total=num_tasks) as bar:
    while completed_tasks_counter < num_tasks:
        results.append(tasks_completed.get())
        completed_tasks_counter = completed_tasks_counter + 1
        bar.update(completed_tasks_counter)

for p in processes:
    p.join()

return results

Answer 5

Sorry for being late but if all you need is a concurrent map, I added this functionality in tqdm>=4.42.0:

from tqdm.contrib.concurrent import process_map  # or thread_map
import time

def _foo(my_number):
   square = my_number * my_number
   time.sleep(1)
   return square 

if __name__ == '__main__':
   r = process_map(_foo, range(0, 30), max_workers=2)

References: https://tqdm.github.io/docs/contrib.concurrent/ and https://github.com/tqdm/tqdm/blob/master/examples/parallel_bars.py

It supports max_workers and chunksize and you can also easily switch from process_map to thread_map.

Answer 6

Use imap instead of map, which returns an iterator of processed values.

from multiprocessing import Pool
import tqdm
import time

def _foo(my_number):
   square = my_number * my_number
   time.sleep(1)
   return square 

if __name__ == '__main__':
   with Pool(2) as p:
      r = list(tqdm.tqdm(p.imap(_foo, range(30)), total=30))