Python多处理同步

Question

我有一个功能“功能”，我想用2次5次cpus多次处理调用10次。

因此，我需要一种方法来同步流程，如下面的代码所述。

如果不使用多处理池，这可能吗？ 如果我这样做，我会得到奇怪的错误（例如“UnboundLocalError：在赋值之前引用的局部变量'fd'（我没有这样的变量））。 这些过程似乎随机终止。

如果可能的话，我想在没有游泳池的情况下这样做。 谢谢！

number_of_cpus = 5
number_of_iterations = 2

# An array for the processes.
processing_jobs = []

# Start 5 processes 2 times.
for iteration in range(0, number_of_iterations):

    # TODO SYNCHRONIZE HERE

    # Start 5 processes at a time.
    for cpu_number in range(0, number_of_cpus):

        # Calculate an offset for the current function call.
        file_offset = iteration * cpu_number * number_of_files_per_process

        p = multiprocessing.Process(target=function, args=(file_offset,))
        processing_jobs.append(p)
        p.start()

    # TODO SYNCHRONIZE HERE

这是我在池中运行代码时遇到的错误的（匿名）回溯：

Process Process-5:
Traceback (most recent call last):
  File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
    self.run()
  File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run
    self._target(*self._args, **self._kwargs)
  File "python_code_3.py", line 88, in function_x
    xyz = python_code_1.function_y(args)
  File "/python_code_1.py", line 254, in __init__
    self.WK =  file.WK(filename)
  File "/python_code_2.py", line 1754, in __init__
    self.__parse__(name, data, fast_load)
  File "/python_code_2.py", line 1810, in __parse__
    fd.close()
UnboundLocalError: local variable 'fd' referenced before assignment

大多数进程都会崩溃，但不是全部。 当我增加进程数时，它们中的更多似乎会崩溃。 我还以为这可能是由于内存限制......

Answer 1

游泳池非常容易使用。 这是一个完整的例子：

资源

import multiprocessing

def calc(num):
    return num*2

if __name__=='__main__':  # required for Windows
    pool = multiprocessing.Pool()   # one Process per CPU
    for output in pool.map(calc, [1,2,3]):
        print 'output:',output

产量

output: 2
output: 4
output: 6

Answer 2

以下是如何在不使用池的情况下执行所需的同步：

import multiprocessing

def function(arg):
    print ("got arg %s" % arg)

if __name__ == "__main__":
    number_of_cpus = 5
    number_of_iterations = 2

    # An array for the processes.
    processing_jobs = []

    # Start 5 processes 2 times.
    for iteration in range(1, number_of_iterations+1):  # Start the range from 1 so we don't multiply by zero.

        # Start 5 processes at a time.
        for cpu_number in range(1, number_of_cpus+1):

            # Calculate an offset for the current function call.
            file_offset = iteration * cpu_number * number_of_files_per_process

            p = multiprocessing.Process(target=function, args=(file_offset,))
            processing_jobs.append(p)
            p.start()

        # Wait for all processes to finish.
        for proc in processing_jobs:
            proc.join()

        # Empty active job list.
        del processing_jobs[:]

        # Write file here
        print("Writing")

这是一个Pool ：

import multiprocessing

def function(arg):
    print ("got arg %s" % arg)

if __name__ == "__main__":
    number_of_cpus = 5
    number_of_iterations = 2

    pool = multiprocessing.Pool(number_of_cpus)
    for i in range(1, number_of_iterations+1): # Start the range from 1 so we don't multiply by zero
        file_offsets = [number_of_files_per_process * i * cpu_num for cpu_num in range(1, number_of_cpus+1)] 
        pool.map(function, file_offsets)
        print("Writing")
        # Write file here

如您所见， Pool解决方案更好。

但是，这并不能解决您的追溯问题。 我很难说如何在不了解实际导致这种情况的情况下解决这个问题。 您可能需要使用multiprocessing.Lock来同步对资源的访问。