Is it useless to use the Lock() in multiprocess Pool()? [duplicate] - multiprocessing

Question

I am having troubles with the multiprocessing module. I am using a Pool of workers with its map method to concurrently analyze lots of files. Each time a file has been processed I would like to have a counter updated so that I can keep track of how many files remains to be processed. Here is sample code:
import os
import multiprocessing
counter = 0
def analyze(file):
# Analyze the file.
global counter
counter += 1
print counter
if __name__ == '__main__':
files = os.listdir('/some/directory')
pool = multiprocessing.Pool(4)
pool.map(analyze, files)
I cannot find a solution for this.

Answer 1

The problem is that the counter variable is not shared between your processes: each separate process is creating it's own local instance and incrementing that.
See this section of the documentation for some techniques you can employ to share state between your processes. In your case you might want to share a Value instance between your workers
Here's a working version of your example (with some dummy input data). Note it uses global values which I would really try to avoid in practice:
from multiprocessing import Pool, Value
from time import sleep
counter = None
def init(args):
''' store the counter for later use '''
global counter
counter = args
def analyze_data(args):
''' increment the global counter, do something with the input '''
global counter
# += operation is not atomic, so we need to get a lock:
with counter.get_lock():
counter.value += 1
print counter.value
return args * 10
if __name__ == '__main__':
#inputs = os.listdir(some_directory)
#
# initialize a cross-process counter and the input lists
#
counter = Value('i', 0)
inputs = [1, 2, 3, 4]
#
# create the pool of workers, ensuring each one receives the counter
# as it starts.
#
p = Pool(initializer = init, initargs = (counter, ))
i = p.map_async(analyze_data, inputs, chunksize = 1)
i.wait()
print i.get()

Answer 2

Counter class without the race-condition bug:
class Counter(object):
def __init__(self):
self.val = multiprocessing.Value('i', 0)
def increment(self, n=1):
with self.val.get_lock():
self.val.value += n
#property
def value(self):
return self.val.value

Answer 3

A extremly simple example, changed from jkp's answer:
from multiprocessing import Pool, Value
from time import sleep
counter = Value('i', 0)
def f(x):
global counter
with counter.get_lock():
counter.value += 1
print("counter.value:", counter.value)
sleep(1)
return x
with Pool(4) as p:
r = p.map(f, range(1000*1000))

Answer 4

Faster Counter class without using the built-in lock of Value twice
class Counter(object):
def __init__(self, initval=0):
self.val = multiprocessing.RawValue('i', initval)
self.lock = multiprocessing.Lock()
def increment(self):
with self.lock:
self.val.value += 1
#property
def value(self):
return self.val.value
https://eli.thegreenplace.net/2012/01/04/shared-counter-with-pythons-multiprocessing
https://docs.python.org/2/library/multiprocessing.html#multiprocessing.sharedctypes.Value
https://docs.python.org/2/library/multiprocessing.html#multiprocessing.sharedctypes.RawValue

Answer 5

Here is a solution to your problem based on a different approach from that proposed in the other answers. It uses message passing with multiprocessing.Queue objects (instead of shared memory with multiprocessing.Value objects) and process-safe (atomic) built-in increment and decrement operators += and -= (instead of introducing custom increment and decrement methods) since you asked for it.
First, we define a class Subject for instantiating an object that will be local to the parent process and whose attributes are to be incremented or decremented:
import multiprocessing
class Subject:
def __init__(self):
self.x = 0
self.y = 0
Next, we define a class Proxy for instantiating an object that will be the remote proxy through which the child processes will request the parent process to retrieve or update the attributes of the Subject object. The interprocess communication will use two multiprocessing.Queue attributes, one for exchanging requests and one for exchanging responses. Requests are of the form (sender, action, *args) where sender is the sender name, action is the action name ('get', 'set', 'increment', or 'decrement' the value of an attribute), and args is the argument tuple. Responses are of the form value (to 'get' requests):
class Proxy(Subject):
def __init__(self, request_queue, response_queue):
self.__request_queue = request_queue
self.__response_queue = response_queue
def _getter(self, target):
sender = multiprocessing.current_process().name
self.__request_queue.put((sender, 'get', target))
return Decorator(self.__response_queue.get())
def _setter(self, target, value):
sender = multiprocessing.current_process().name
action = getattr(value, 'action', 'set')
self.__request_queue.put((sender, action, target, value))
#property
def x(self):
return self._getter('x')
#property
def y(self):
return self._getter('y')
#x.setter
def x(self, value):
self._setter('x', value)
#y.setter
def y(self, value):
self._setter('y', value)
Then, we define the class Decorator to decorate the int objects returned by the getters of a Proxy object in order to inform its setters whether the increment or decrement operators += and -= have been used by adding an action attribute, in which case the setters request an 'increment' or 'decrement' operation instead of a 'set' operation. The increment and decrement operators += and -= call the corresponding augmented assignment special methods __iadd__ and __isub__ if they are defined, and fall back on the assignment special methods __add__ and __sub__ which are always defined for int objects (e.g. proxy.x += value is equivalent to proxy.x = proxy.x.__iadd__(value) which is equivalent to proxy.x = type(proxy).x.__get__(proxy).__iadd__(value) which is equivalent to type(proxy).x.__set__(proxy, type(proxy).x.__get__(proxy).__iadd__(value))):
class Decorator(int):
def __iadd__(self, other):
value = Decorator(other)
value.action = 'increment'
return value
def __isub__(self, other):
value = Decorator(other)
value.action = 'decrement'
return value
Then, we define the function worker that will be run in the child processes and request the increment and decrement operations:
def worker(proxy):
proxy.x += 1
proxy.y -= 1
Finally, we define a single request queue to send requests to the parent process, and multiple response queues to send responses to the child processes:
if __name__ == '__main__':
subject = Subject()
request_queue = multiprocessing.Queue()
response_queues = {}
processes = []
for index in range(4):
sender = 'child {}'.format(index)
response_queues[sender] = multiprocessing.Queue()
proxy = Proxy(request_queue, response_queues[sender])
process = multiprocessing.Process(
target=worker, args=(proxy,), name=sender)
processes.append(process)
running = len(processes)
for process in processes:
process.start()
while subject.x != 4 or subject.y != -4:
sender, action, *args = request_queue.get()
print(sender, 'requested', action, *args)
if action == 'get':
response_queues[sender].put(getattr(subject, args[0]))
elif action == 'set':
setattr(subject, args[0], args[1])
elif action == 'increment':
setattr(subject, args[0], getattr(subject, args[0]) + args[1])
elif action == 'decrement':
setattr(subject, args[0], getattr(subject, args[0]) - args[1])
for process in processes:
process.join()
The program is guaranteed to terminate when += and -= are process-safe. If you remove process-safety by commenting the corresponding __iadd__ or __isub__ of Decorator then the program will only terminate by chance (e.g. proxy.x += value is equivalent to proxy.x = proxy.x.__iadd__(value) but falls back to proxy.x = proxy.x.__add__(value) if __iadd__ is not defined, which is equivalent to proxy.x = proxy.x + value which is equivalent to proxy.x = type(proxy).x.__get__(proxy) + value which is equivalent to type(proxy).x.__set__(proxy, type(proxy).x.__get__(proxy) + value), so the action attribute is not added and the setter requests a 'set' operation instead of an 'increment' operation).
Example process-safe session (atomic += and -=):
child 0 requested get x
child 0 requested increment x 1
child 0 requested get y
child 0 requested decrement y 1
child 3 requested get x
child 3 requested increment x 1
child 3 requested get y
child 2 requested get x
child 3 requested decrement y 1
child 1 requested get x
child 2 requested increment x 1
child 2 requested get y
child 2 requested decrement y 1
child 1 requested increment x 1
child 1 requested get y
child 1 requested decrement y 1
Example process-unsafe session (non-atomic += and -=):
child 2 requested get x
child 1 requested get x
child 0 requested get x
child 2 requested set x 1
child 2 requested get y
child 1 requested set x 1
child 1 requested get y
child 2 requested set y -1
child 1 requested set y -1
child 0 requested set x 1
child 0 requested get y
child 0 requested set y -2
child 3 requested get x
child 3 requested set x 2
child 3 requested get y
child 3 requested set y -3 # the program stalls here

Answer 6

A more sophisticated solution based on the lock-free atomic operations, as given by example on atomics library README:
from multiprocessing import Process, shared_memory
import atomics
def fn(shmem_name: str, width: int, n: int) -> None:
shmem = shared_memory.SharedMemory(name=shmem_name)
buf = shmem.buf[:width]
with atomics.atomicview(buffer=buf, atype=atomics.INT) as a:
for _ in range(n):
a.inc()
del buf
shmem.close()
if __name__ == "__main__":
# setup
width = 4
shmem = shared_memory.SharedMemory(create=True, size=width)
buf = shmem.buf[:width]
total = 10_000
# run processes to completion
p1 = Process(target=fn, args=(shmem.name, width, total // 2))
p2 = Process(target=fn, args=(shmem.name, width, total // 2))
p1.start(), p2.start()
p1.join(), p2.join()
# print results and cleanup
with atomics.atomicview(buffer=buf, atype=atomics.INT) as a:
print(f"a[{a.load()}] == total[{total}]")
del buf
shmem.close()
shmem.unlink()
(atomics could be installed via pip install atomics on most of the major platforms)

Answer 7

This is a different solution and the simplest to my taste.
The reasoning is you create an empty list and append to it each time your function executes , then print len(list) to check progress.
Here is an example based on your code :
import os
import multiprocessing
counter = []
def analyze(file):
# Analyze the file.
counter.append(' ')
print len(counter)
if __name__ == '__main__':
files = os.listdir('/some/directory')
pool = multiprocessing.Pool(4)
pool.map(analyze, files)
For future visitors, the hack to add counter to multiprocessing is as follow :
from multiprocessing.pool import ThreadPool
counter = []
def your_function():
# function/process
counter.append(' ') # you can append anything
return len(counter)
pool = ThreadPool()
result = pool.map(get_data, urls)
Hope this will help.

Answer 8

I'm working on a process bar in PyQT5, so I use thread and pool together
import threading
import multiprocessing as mp
from queue import Queue
def multi(x):
return x*x
def pooler(q):
with mp.Pool() as pool:
count = 0
for i in pool.imap_unordered(ggg, range(100)):
print(count, i)
count += 1
q.put(count)
def main():
q = Queue()
t = threading.Thread(target=thr, args=(q,))
t.start()
print('start')
process = 0
while process < 100:
process = q.get()
print('p',process)
if __name__ == '__main__':
main()
this I put in Qthread worker and it works with acceptable latency