Python Multiprocessccessing Pool - 每个进程共享一个变量?

Question

我一直在尝试找到一个简单的示例，其中我共享一个常量变量，其中我共享一个常量变量，在我的进程池中启动的每个进程。大多数示例向您展示如何跨进程共享变量，这不是我想要的。

import multiprocessing
import time
data = (
{"var":1, "shared": None}, {"var":2, "shared": None}, {"var":3, "shared": None}, {"var":4, "shared": None}
)
def mp_worker(input):
print input
# print " Processs %stWaiting %s seconds" % (inputs, the_time)
# time.sleep(int(the_time))
# print " Process %stDONE" % inputs
def mp_handler():
p = multiprocessing.Pool(2)
p.map(mp_worker, data)
if __name__ == '__main__':
mp_handler()

例如，如果我运行此代码，我希望为每个进程初始化一次"共享"组件。

我想做这样的事情(这不起作用(：

from multiprocessing import Pool, Process
class Worker(Process):
def __init__(self):
print 'Worker started'
# do some initialization here
super(Worker, self).__init__()
def compute(self, data):
print 'Computing things!'
return data * data

if __name__ == '__main__':
# This works fine
worker = Worker()
#print worker.compute(3)
# workers get initialized fine
pool = Pool(processes = 4,
initializer = Worker)
data = range(10)
# How to use my worker pool?
# result = pool.map(Worker.compute, data)
result = pool.map(Worker.compute, data)

Answer 1

使用共享c_types：

from multiprocessing import Process, Lock
from multiprocessing.sharedctypes import Value
from ctypes import Structure, c_double
class Point(Structure):
_fields_ = [('x', c_double), ('y', c_double)]
def modify(parmMap):
parmMap['point'].x = parmMap['var']
parmMap['point'].y = parmMap['var'] * 2

if __name__ == '__main__':
lock = Lock()
data = ( {'var' : 1, 'shared' : Value(Point, (0,0), lock=lock) },
{'var' : 2, 'shared' : Value(Point, (0,0), lock=lock) },
{'var' : 3, 'shared' : Value(Point, (0,0), lock=lock) },
{'var' : 4, 'shared' : Value(Point, (0,0), lock=lock) }
)
p = multiprocessing.Pool(2)
print p.map(mp_worker, data)
print data

Answer 2

def init(args, num_gpu):
pid = int(str(multiprocessing.current_process()).split(" ")[0].split("-")[-1].split(",")[0]) - 1
gpu_id = pid % num_gpu
global testModule
testModule = TestModuleShared(args, gpu_id)
def worker(datum):
pid = int(str(multiprocessing.current_process()).split(" ")[0].split("-")[-1].split(",")[0]) - 1
params = datum["params"]
# print str(datum["fc"]) + " " + str(pid) 
# print testModule.openpose
# Reset State
testModule.run()
p = multiprocessing.Pool(per_gpu_threads*num_gpu, initializer=init, initargs=(params["test_module_param"],num_gpu,))

事实证明，您只需使用全局变量关键字以及初始值设定项回调来初始化它。