using tqdm with multiprocessing

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  • Techknowledgy :

Use imap instead of map, which returns an iterator of the 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))

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)

Note: Context manager for Pool is only available in Python 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 _ in p.imap_unordered(_foo, range(0, max_)):
   pbar.update()
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)))

based on the answer of Xavi Martínez I wrote the function imap_unordered_bar. It can be used in the same way as imap_unordered with the only difference that a processing bar is shown.

from multiprocessing
import Pool
import time
from tqdm
import *

def imap_unordered_bar(func, args, n_processes = 2):
   p = Pool(n_processes)
res_list = []
with tqdm(total = len(args)) as pbar:
   for i, res in tqdm(enumerate(p.imap_unordered(func, args))):
   pbar.update()
res_list.append(res)
pbar.close()
p.close()
p.join()
return res_list

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

if __name__ == '__main__':
   result = imap_unordered_bar(_foo, range(5))
import multiprocessing as mp
import tqdm

iterable = ...
   num_cpu = mp.cpu_count() - 2 # dont use all cpus.

def func():
   # your logic
   ...

   if __name__ == '__main__':
   with mp.Pool(num_cpu) as p:
   list(tqdm.tqdm(p.imap(func, iterable), total = len(iterable)))

Suggestion : 2

Using queues, tqdm-multiprocess supports multiple worker processes, each with multiple tqdm progress bars, displaying them cleanly through the main process. The worker processes also have access to a single global tqdm for aggregate progress monitoring.,Due to the performance limits of the default Python multiprocess queue you need to update your global and worker process tqdms infrequently to avoid flooding the main process. I will attempt to implement a lock free ringbuffer at some point to see if things can be improved.,TqdmMultiProcessPool creates a standard python multiprocessing pool with the desired number of processes. Under the hood it uses async_apply with an event loop to monitor a tqdm and logging queue, allowing the worker processes to redirect both their tqdm objects and logging messages to your main process. There is also a queue for the workers to update the single global tqdm.,Easy multiprocessing with tqdm and logging redirected to main process.

Installation

pip install tqdm - multiprocess

Suggestion : 3

But when using from tqdm.auto import tqdm or from tqdm.notebook import tqdm it does not work. The strange thing is that I remember that it was working before using notebook. ,I am struggling to work out how to get a single progress bar to update on every completion from a multiprocessed, mapped function. Consider the following example:,The idea is to have a single progress bar that updates every time a call to myfunc completes anywhere in the pool. However, what I see is that in each process the bar seems to have an independent counter, so I only get to 50% (or thereabouts, because of random()) when the job finishes:,Is there a clean way to implement a single tqdm progress bar that I can then update from inside one of many child processes?

import time
import random
from multiprocessing
import Pool
import tqdm

pbar = tqdm.tqdm(total = 100)

def myfunc(a):
   time.sleep(random.random())
pbar.update(1)
return a ** 2

pool = Pool(2)
pool.map(myfunc, range(100))
pool.close()
pool.join()
pbar.close()
 48 % | ████████████████████▏ | 48 / 100[00: 24 < 00: 21, 2.43 it / s]
import time
import random
from multiprocessing
import Pool
import tqdm

def myfunc(a):
   time.sleep(random.random())
pbar.update(1)
return a ** 2

pool = Pool(2)
for _ in tqdm.tqdm(pool.imap_unordered(myfunc, range(100), total = 100):
      pass pool.close() pool.join() pbar.close()
import time
import random
from multiprocessing
import Pool
import tqdm

def myfunc(a):
   time.sleep(random.random())
return a ** 2

pool = Pool(2)
for _ in tqdm.tqdm(pool.imap_unordered(myfunc, range(100)), total = 100):
   pass
pool.close()
pool.join()
100 % | ████████████████████████████ | 100 / 100[00: 23 < 00: 00, 4.20 it / s]
for k, task in enumerate(tasks):
   pool.apply_async(task_runner, args = (), callback = task_callback)
pool.close()
pool.join()
import time
import random
from multiprocessing
import Pool
from tqdm
import tqdm

def myfunc(a):
   time.sleep(random.random())
return a ** 2

pool = Pool(2)
''
'
for _ in tqdm(pool.imap_unordered(myfunc, range(100)), total = 100):
   pass ''
'
pbar = tqdm(total = 100)
def update( * a):
   pbar.update()
# tqdm.write(str(a))
for i in range(pbar.total):
   pool.apply_async(myfunc, args = (i, ), callback = update)
# tqdm.write('scheduled')
pool.close()
pool.join()

Suggestion : 4

The implanted solution (i.e., call tqdm directly on the range tqdm.tqdm(range(0, 30)) does not work with multiprocessing (as formulated in the code below). ,I"m only going to do the performance analysis of the usages known to behave correctly. (Self-contained so you can copy and paste yourself.),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 progress bar is displayed from 0 to 100% (when python reads the code?) but it does not indicate the actual progress of the map function.

How to display a progress bar that indicates at which step the "map" function is ?

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__":
   p = Pool(2)
r = p.map(_foo, tqdm.tqdm(range(0, 30)))
p.close()
p.join()

This has always confused me. It seems like this would be nicer:

my_list = ["Hello", "world"]
print(my_list.join("-"))
# Produce: "Hello-world"

Than this:

my_list = ["Hello", "world"]
print("-".join(my_list))
# Produce: "Hello-world"
"_".join(["welcome", "to", "stack", "overflow"])
"_".join(("welcome", "to", "stack", "overflow"))
"welcome_to_stack_overflow"

Using something other than strings will raise the following error:

TypeError: sequence item 0: expected str instance, int found

This has always confused me. It seems like this would be nicer:

my_list = ["Hello", "world"]
print(my_list.join("-"))
# Produce: "Hello-world"

Than this:

my_list = ["Hello", "world"]
print("-".join(my_list))
# Produce: "Hello-world"

For example:

"_".join(["welcome", "to", "stack", "overflow"])
"_".join(("welcome", "to", "stack", "overflow"))
"_".join(["welcome", "to", "stack", "overflow"])
"_".join(("welcome", "to", "stack", "overflow"))
"welcome_to_stack_overflow"

Using something other than strings will raise the following error:

TypeError: sequence item 0: expected str instance, int found

I have two Python dictionaries, and I want to write a single expression that returns these two dictionaries, merged (i.e. taking the union). The update() method would be what I need, if it returned its result instead of modifying a dictionary in-place.

>>> x = {
      "a": 1,
      "b": 2
   } >>>
   y = {
      "b": 10,
      "c": 11
   } >>>
   z = x.update(y) >>>
   print(z)
None
   >>>
   x {
      "a": 1,
      "b": 10,
      "c": 11
   }

In Python 3.9.0 or greater (released 17 October 2020): PEP-584, discussed here, was implemented and provides the simplest method:

z = x | y # NOTE: 3.9 + ONLY

In Python 3.5 or greater:

z = {
   ** x,
   ** y
}