Checking progress of Python multiprocessing pools

Use tqdm or roll your own code snippets to quickly check the progress of your Python multiprocessing pools!


Process pools, such as those afforded by Python’s multiprocessing.Pool class, are often used to parallelize loops or map a function over an iterable. It can be helpful sometimes to monitor the progress over the loop or iterable, and we demonstrate below several ways to do so.

In the sample code below, we consider a situation where you want to apply a function my_func to each value in a dictionary data.

Option 1: Manually check status of AsyncResult objects

This option assumes you are working with one of the _async pool methods (apply_async, map_async, or starmap_async). These are non-blocking and return AsyncResult objects, which allow you to check on the status of results.

Specifically, we take advantage of AsyncResult.successful(), which does one of the following:

  • Returns True: the call completed successfully
  • Returns False: the call completed but raised an exception
  • Raises ValueError: the call is still running

By keeping track of calls that completed but raised an exception, this option makes it easier to debug what went wrong. For example, if we see that an error occurred when calling my_func on the value corresponding to 'key3', we can run results['key3'].get() to recover and analyze the exception.

Option summary

  • Non-blocking
  • Easy to debug exceptions
  • Supports any _async method
  • Does not support using the process pool as a context manager

Sample code

import multiprocessing
import time

# Start the pool.
pool = multiprocessing.Pool(nproc)
results = {}
start_time = time.time()
for key, value in data.items():
    results[key] = pool.apply_async(my_func, (value,))

# This code block can be (re)run whenever you want to check on the progress of the pool.
running, successful, error = [], [], []
current_time = time.time()
for key, result in results.items():
        if result.successful():
    except ValueError:
rate = (len(successful) + len(error)) / (current_time - start_time)
print('Running:', sorted(running))
print('Successful:', sorted(successful))
print('Error:', sorted(error))
print('Rate:', round(rate, 3))
print('Estimated time to completion:', time.strftime('%H:%M:%S', time.gmtime(len(running) / rate)))

# Don't forget to join the pool after it finishes running!

Example output

Running: ['key4', 'key5']
Successful: ['key1', 'key2']
Error: ['key3']
Rate: 1.234
Estimated time to completion: 00:01:23

Option 2: Using tqdm

We take advantage of the very helpful progress bar provided by the tqdm package. How to use tqdm, however, depends on the pool method we choose.

  • Each _async method includes callback and error_callback arguments that take functions to be called when the result completes, with or without error, respectively. We can use these callback functions to update our tqdm progress bar.
  • imap_unordered and imap each return an iterator, so we can simply wrap it with tqdm.

Option summary

  • Blocking
  • Succinct progress bar
  • Supports any non-blocking pool method (i.e., any method except apply, map, and starmap)
  • Supports using the process pool as a context manager

Sample code

import multiprocessing
from tqdm import tqdm

# imap_unordered example
results = []
with multiprocessing.Pool(nproc) as pool:
    for result in tqdm(pool.imap_unordered(my_func, data.values()), total=len(data)):

# imap example
with mulitprocessing.Pool(nproc) as pool:
    results = list(tqdm(pool.imap(my_func, data.values()), total=len(data)))

# apply_async example
pbar = tqdm(total=len(data))
def update(*a):

results = []
with multiprocessing.Pool(nproc) as pool:
    for key, value in data.items():
        pool.apply_async(my_func, (value,), callback=update)


Posted: Jul 22, 2019.