多處理：使用 tqdm 顯示進度條

Question

為了使我的代碼更“pythonic”和更快，我使用“multiprocessing”和 map function 發送它 a）function 和 b）迭代范圍

植入的解決方案（即直接在范圍 tqdm.tqdm(range(0, 30)) 上調用 tqdm）不適用於多處理（如下面的代碼所示）。

進度條顯示從0到100%（python讀碼時？）但並不表示map function的實際進度。

如何顯示一個進度條，指示“地圖”function 在哪一步？

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()

歡迎任何幫助或建議...

Answer 1

使用 imap 而不是 map，它返回一個處理值的迭代器。

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))

Answer 2

抱歉遲到了，但如果您只需要並發映射，我在tqdm>=4.42.0添加了此功能：

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)

參考資料： https : //tqdm.github.io/docs/contrib.concurrent/和https://github.com/tqdm/tqdm/blob/master/examples/parallel_bars.py

它支持max_workers和chunksize ，您還可以輕松地從process_map切換到thread_map 。

Answer 3

找到的解決方案：小心！ 由於多處理，估計時間（每個循環的迭代次數、總時間等）可能不穩定，但進度條工作正常。

注意：池的上下文管理器僅適用於 Python 3.3 版

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

您可以改用p_tqdm 。

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 5

根據哈維·馬丁內斯的回答，我編寫了函數imap_unordered_bar 。 它可以以與imap_unordered相同的方式使用，唯一的區別是顯示處理條。

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))

Answer 6

import multiprocessing as mp
import tqdm


some_iterable = ...

def some_func():
    # your logic
    ...


if __name__ == '__main__':
    with mp.Pool(mp.cpu_count()-2) as p:
        list(tqdm.tqdm(p.imap(some_func, iterable), total=len(iterable)))

Answer 7

當您需要從並行執行函數中獲取結果時，這是我的看法。 這個函數做了一些事情（我的另一篇文章進一步解釋了它），但關鍵是有一個待處理的任務隊列和一個完成的任務隊列。 當工作人員完成掛起隊列中的每個任務時，他們將結果添加到任務完成隊列中。 您可以使用 tqdm 進度條將檢查包裝到任務完成隊列中。 我沒有把 do_work() 函數的實現放在這里，它不相關，因為這里的消息是監視任務完成隊列並在每次有結果時更新進度條。

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 8

對於帶有 apply_async 的進度條，我們可以按照以下建議使用以下代碼：

https://github.com/tqdm/tqdm/issues/484

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)
pbar = tqdm(total=100)

def update(*a):
    pbar.update()

for i in range(pbar.total):
    pool.apply_async(myfunc, args=(i,), callback=update)
pool.close()
pool.join()

Answer 9

基於“user17242583”的回答，我創建了以下 function。 它應該和 Pool.map 一樣快，並且結果總是有序的。 另外，您可以根據需要向 function 傳遞任意數量的參數，而不僅僅是一個可迭代的參數。

from multiprocessing import Pool
from functools import partial
from tqdm import tqdm


def imap_tqdm(function, iterable, processes, chunksize=1, desc=None, disable=False, **kwargs):
    """
    Run a function in parallel with a tqdm progress bar and an arbitrary number of arguments.
    Results are always ordered and the performance should be the same as of Pool.map.
    :param function: The function that should be parallelized.
    :param iterable: The iterable passed to the function.
    :param processes: The number of processes used for the parallelization.
    :param chunksize: The iterable is based on the chunk size chopped into chunks and submitted to the process pool as separate tasks.
    :param desc: The description displayed by tqdm in the progress bar.
    :param disable: Disables the tqdm progress bar.
    :param kwargs: Any additional arguments that should be passed to the function.
    """
    if kwargs:
        function_wrapper = partial(_wrapper, function=function, **kwargs)
    else:
        function_wrapper = partial(_wrapper, function=function)

    results = [None] * len(iterable)
    with Pool(processes=processes) as p:
        with tqdm(desc=desc, total=len(iterable), disable=disable) as pbar:
            for i, result in p.imap_unordered(function_wrapper, enumerate(iterable), chunksize=chunksize):
                results[i] = result
                pbar.update()
    return results


def _wrapper(enum_iterable, function, **kwargs):
    i = enum_iterable[0]
    result = function(enum_iterable[1], **kwargs)
    return i, result

Answer 10

這種方法簡單而且有效。

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()