多处理：使用 tqdm 显示进度条

Question

为了使我的代码更多 "pythonic" 和更快，我使用 "multiprocessing" 和一个映射函数来发送 a) 函数和 b) 迭代范围。

植入的解决方案（即，直接在范围 tqdm.tqdm(range(0, 30)) 上调用 tqdm）不适用于多处理（如下面的代码所示）。

进度条显示的是0到100%（python看代码的时候？）但不表示地图功能的实际进度。

如何显示指示 'map' 函数执行到哪一步的进度条？

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()

欢迎任何帮助或建议...

Answer 1

已找到解决方案。当心！由于多重处理，估计时间（每次循环迭代、总时间等）可能不稳定，但进度条完美运行。

注意：Pool 的上下文管理器仅在 Python 3.3+ 中可用。

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()

Answer 2

使用 imap 而不是 map，后者 returns 是已处理值的迭代器。

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 3

根据 Xavi Martínez 的回答，我编写了函数 imap_unordered_bar。它的使用方式与 imap_unordered 相同，唯一的区别是显示了一个处理栏。

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 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

这种方法简单有效。

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()

Answer 6

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)))

Answer 7

抱歉来晚了，但如果您只需要一个并发映射，我在 tqdm>=4.42.0 中添加了此功能：

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)

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

它支持max_workers和chunksize，您还可以轻松地从process_map切换到thread_map。

Answer 8

当您需要从并行执行的函数中取回结果时，这是我的看法。这个函数做了一些事情（我的另一个 post 进一步解释了它）但关键是有一个任务挂起队列和一个任务完成队列。当工作人员完成待处理队列中的每个任务时，他们将结果添加到任务完成队列中。您可以使用 tqdm 进度条将检查包装到任务完成队列。我没有在这里实现 do_work() 函数，它不相关，因为这里的消息是监视任务完成队列并在每次有结果时更新进度条。

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 9

对于 apply_async 的进度条，我们可以使用下面建议的代码：

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()

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