python之在 python 中使用多处理时无法使用所有处理器

我正在研究 python 并尝试学习多处理。当我尝试以下代码时，它应该并行运行，但它只使用一个处理器。我无法理解原因。可能是什么问题，为什么它没有使用我电脑的所有 4 个内核。

我试过的代码如下:

import multiprocessing 
import time 
start = time.perf_counter() 
 
def do_something(): 
    print("hello") 
    time.sleep(1) 
    print("done") 
 
p1 = multiprocessing.Process(target=do_something()) 
p2 = multiprocessing.Process(target=do_something()) 
p1.start() 
p2.start() 
p1.join() 
p2.join() 
finish = time.perf_counter() 
print(f'finished in {round(finish-start,1)} sec') 

---

结果:

hello 
done 
hello 
done 
finished in 2.1 sec 

---

它应该在 1 秒(大约)内执行

#

我用来查找核心数的代码:

#

import multiprocessing 
print("Number of cpu : ", multiprocessing.cpu_count()) 

---

结果:

Number of cpu :  4 

---

#

我尝试的另一个代码是:

#

from multiprocessing import Lock, Process, Queue, current_process 
import time 
import queue # imported for using queue.Empty exception 
 
 
def do_job(tasks_to_accomplish, tasks_that_are_done): 
    while True: 
        try: 
            ''' 
                try to get task from the queue. get_nowait() function will  
                raise queue.Empty exception if the queue is empty.  
                queue(False) function would do the same task also. 
            ''' 
            task = tasks_to_accomplish.get_nowait() 
        except queue.Empty: 
 
            break 
        else: 
            ''' 
                if no exception has been raised, add the task completion  
                message to task_that_are_done queue 
            ''' 
            print(task) 
            tasks_that_are_done.put(task + ' is done by ' + current_process().name) 
            time.sleep(.5) 
    return True 
 
 
def main(): 
    number_of_task = 10 
    number_of_processes = 4 
    tasks_to_accomplish = Queue() 
    tasks_that_are_done = Queue() 
    processes = [] 
 
    for i in range(number_of_task): 
        tasks_to_accomplish.put("Task no " + str(i)) 
 
    # creating processes 
    for w in range(number_of_processes): 
        p = Process(target=do_job(tasks_to_accomplish, tasks_that_are_done)) 
        processes.append(p) 
        p.start() 
 
    # completing process 
    for p in processes: 
        p.join() 
 
    # print the output 
    while not tasks_that_are_done.empty(): 
        print(tasks_that_are_done.get()) 
 
    return True 
 
 
if __name__ == '__main__': 
    main() 

---

结果:

Task no 0 
Task no 1 
Task no 2 
Task no 3 
Task no 4 
Task no 5 
Task no 6 
Task no 7 
Task no 8 
Task no 9 
Task no 0 is done by MainProcess 
Task no 1 is done by MainProcess 
Task no 2 is done by MainProcess 
Task no 3 is done by MainProcess 
Task no 4 is done by MainProcess 
Task no 5 is done by MainProcess 
Task no 6 is done by MainProcess 
Task no 7 is done by MainProcess 
Task no 8 is done by MainProcess 
Task no 9 is done by MainProcess 

---

#

根据建议，我做了以下几项更改:

import multiprocessing 
import time 
start = time.perf_counter() 
 
def do_something(): 
    print("hello") 
    time.sleep(1) 
    print("done") 
 
p1 = multiprocessing.Process(target=do_something) 
p2 = multiprocessing.Process(target=do_something) 
p1.start() 
p2.start() 
p1.join() 
p2.join() 
finish = time.perf_counter() 
print(f'finished in {round(finish-start,1)} sec') 

我得到的结果是:

finished in 0.2 sec 

请您参考如下方法:

目标必须是可调用的。

p1 = multiprocessing.Process(target=do_something) 

按照您的方式，您在主进程中调用方法并将结果传递给多处理。