[英]How to retrieve values from a function run in parallel processes?
多处理模块对于python初学者来说非常困惑,特别是那些刚刚从MATLAB迁移并且使用并行计算工具箱变得懒惰的人。 我有以下功能需要大约80秒运行,我想通过使用Python的多处理模块来缩短这个时间。
from time import time
xmax = 100000000
start = time()
for x in range(xmax):
y = ((x+5)**2+x-40)
if y <= 0xf+1:
print('Condition met at: ', y, x)
end = time()
tt = end-start #total time
print('Each iteration took: ', tt/xmax)
print('Total time: ', tt)
这按预期输出:
Condition met at: -15 0
Condition met at: -3 1
Condition met at: 11 2
Each iteration took: 8.667453265190124e-07
Total time: 86.67453265190125
由于循环的任何迭代都不依赖于其他循环,我尝试从官方文档中采用此服务器进程来在单独的进程中扫描范围的块。 最后我想出了vartec对这个问题的回答,可以准备以下代码。 我还根据Darkonaut对当前问题的回答更新了代码。
from time import time
import multiprocessing as mp
def chunker (rng, t): # this functions makes t chunks out of rng
L = rng[1] - rng[0]
Lr = L % t
Lm = L // t
h = rng[0]-1
chunks = []
for i in range(0, t):
c = [h+1, h + Lm]
h += Lm
chunks.append(c)
chunks[t-1][1] += Lr + 1
return chunks
def worker(lock, xrange, return_dict):
'''worker function'''
for x in range(xrange[0], xrange[1]):
y = ((x+5)**2+x-40)
if y <= 0xf+1:
print('Condition met at: ', y, x)
return_dict['x'].append(x)
return_dict['y'].append(y)
with lock:
list_x = return_dict['x']
list_y = return_dict['y']
list_x.append(x)
list_y.append(y)
return_dict['x'] = list_x
return_dict['y'] = list_y
if __name__ == '__main__':
start = time()
manager = mp.Manager()
return_dict = manager.dict()
lock = manager.Lock()
return_dict['x']=manager.list()
return_dict['y']=manager.list()
xmax = 100000000
nw = mp.cpu_count()
workers = list(range(0, nw))
chunks = chunker([0, xmax], nw)
jobs = []
for i in workers:
p = mp.Process(target=worker, args=(lock, chunks[i],return_dict))
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
end = time()
tt = end-start #total time
print('Each iteration took: ', tt/xmax)
print('Total time: ', tt)
print(return_dict['x'])
print(return_dict['y'])
这大大减少了运行时间到~17秒。 但是,我的共享变量无法检索任何值。 请帮我找出代码的哪个部分出错了。
我得到的输出是:
Each iteration took: 1.7742713451385497e-07
Total time: 17.742713451385498
[]
[]
从中我期望:
Each iteration took: 1.7742713451385497e-07
Total time: 17.742713451385498
[0, 1, 2]
[-15, -3, 11]
您的示例中的问题是不会传播对Manager.dict
标准可变结构的修改。 我首先向您展示如何与经理进行修复,以便向您展示更好的选择。
multiprocessing.Manager
有点沉重,因为它仅为Manager
使用单独的Process,并且使用锁来处理共享对象需要数据一致性。 如果你在一台机器上运行它,那么multiprocessing.Pool
有更好的选择,如果你不必运行自定义的Process
类,如果必须, multiprocessing.Process
.Process与multiprocessing.Queue
一起是常见的做法它。
引用部分来自多处理文档。
经理
如果标准(非代理)列表或dict对象包含在引用对象中,则对这些可变值的修改将不会通过管理器传播,因为代理无法知道何时修改其中包含的值。 但是,将值存储在容器代理中(在代理对象上触发setitem )确实会传播通过管理器,因此为了有效地修改这样的项,可以将修改后的值重新分配给容器代理...
在你的情况下,这将是:
def worker(xrange, return_dict, lock):
"""worker function"""
for x in range(xrange[0], xrange[1]):
y = ((x+5)**2+x-40)
if y <= 0xf+1:
print('Condition met at: ', y, x)
with lock:
list_x = return_dict['x']
list_y = return_dict['y']
list_x.append(x)
list_y.append(y)
return_dict['x'] = list_x
return_dict['y'] = list_y
这里的lock
是一个manager.Lock
实例你必须作为参数传递,因为整个(现在)锁定操作本身不是原子的。 ( 这里是用一个简单的例子Manager
使用锁)
对于大多数用例而言,这种方法可能不如使用嵌套代理对象方便,但也展示了对同步的控制级别。
由于Python 3.6代理对象是可嵌套的:
在版本3.6中更改:共享对象能够嵌套。 例如,共享容器对象(如共享列表)可以包含其他共享对象,这些对象将由SyncManager进行管理和同步。
从Python 3.6开始,您可以在使用manager.list
作为值开始多处理之前填充manager.dict
,然后直接追加到worker中而无需重新分配。
return_dict['x'] = manager.list()
return_dict['y'] = manager.list()
编辑:
以下是Manager
的完整示例:
import time
import multiprocessing as mp
from multiprocessing import Manager, Process
from contextlib import contextmanager
# mp_util.py from first link in code-snippet for "Pool"
# section below
from mp_utils import calc_batch_sizes, build_batch_ranges
# def context_timer ... see code snippet in "Pool" section below
def worker(batch_range, return_dict, lock):
"""worker function"""
for x in batch_range:
y = ((x+5)**2+x-40)
if y <= 0xf+1:
print('Condition met at: ', y, x)
with lock:
return_dict['x'].append(x)
return_dict['y'].append(y)
if __name__ == '__main__':
N_WORKERS = mp.cpu_count()
X_MAX = 100000000
batch_sizes = calc_batch_sizes(X_MAX, n_workers=N_WORKERS)
batch_ranges = build_batch_ranges(batch_sizes)
print(batch_ranges)
with Manager() as manager:
lock = manager.Lock()
return_dict = manager.dict()
return_dict['x'] = manager.list()
return_dict['y'] = manager.list()
tasks = [(batch_range, return_dict, lock)
for batch_range in batch_ranges]
with context_timer():
pool = [Process(target=worker, args=args)
for args in tasks]
for p in pool:
p.start()
for p in pool:
p.join()
# Create standard container with data from manager before exiting
# the manager.
result = {k: list(v) for k, v in return_dict.items()}
print(result)
池
通常是multiprocessing.Pool
会这样做。 由于您希望在一个范围内分配迭代,因此您的示例中还有一个额外的挑战。 您的chunker
函数无法划分范围,即使每个进程都要完成相同的工作:
chunker((0, 21), 4)
# Out: [[0, 4], [5, 9], [10, 14], [15, 21]] # 4, 4, 4, 6!
对于下面的代码,请抢代码片段mp_utils.py
从我的答案在这里 ,它提供了两个功能块,因为即使范围成为可能。
使用multiprocessing.Pool
您的worker
函数只需返回结果, Pool
将负责将结果通过内部队列传回给父进程。 result
将是一个列表,因此您必须以您希望的方式再次重新排列结果。 您的示例可能如下所示:
import time
import multiprocessing as mp
from multiprocessing import Pool
from contextlib import contextmanager
from itertools import chain
from mp_utils import calc_batch_sizes, build_batch_ranges
@contextmanager
def context_timer():
start_time = time.perf_counter()
yield
end_time = time.perf_counter()
total_time = end_time-start_time
print(f'\nEach iteration took: {total_time / X_MAX:.4f} s')
print(f'Total time: {total_time:.4f} s\n')
def worker(batch_range):
"""worker function"""
result = []
for x in batch_range:
y = ((x+5)**2+x-40)
if y <= 0xf+1:
print('Condition met at: ', y, x)
result.append((x, y))
return result
if __name__ == '__main__':
N_WORKERS = mp.cpu_count()
X_MAX = 100000000
batch_sizes = calc_batch_sizes(X_MAX, n_workers=N_WORKERS)
batch_ranges = build_batch_ranges(batch_sizes)
print(batch_ranges)
with context_timer():
with Pool(N_WORKERS) as pool:
results = pool.map(worker, iterable=batch_ranges)
print(f'results: {results}')
x, y = zip(*chain.from_iterable(results)) # filter and sort results
print(f'results sorted: x: {x}, y: {y}')
示例输出:
[range(0, 12500000), range(12500000, 25000000), range(25000000, 37500000),
range(37500000, 50000000), range(50000000, 62500000), range(62500000, 75000000), range(75000000, 87500000), range(87500000, 100000000)]
Condition met at: -15 0
Condition met at: -3 1
Condition met at: 11 2
Each iteration took: 0.0000 s
Total time: 8.2408 s
results: [[(0, -15), (1, -3), (2, 11)], [], [], [], [], [], [], []]
results sorted: x: (0, 1, 2), y: (-15, -3, 11)
Process finished with exit code 0
如果您的worker
有多个参数,您将构建一个带有参数元组的“任务”列表,并使用pool.starmap(...iterable=tasks)
交换pool.map(...)
pool.starmap(...iterable=tasks)
。 有关详细信息,请参阅文档。
流程和队列
如果由于某种原因不能使用multiprocessing.Pool
,则必须自己处理进程间通信(IPC),方法是将multiprocessing.Queue
作为参数传递给子进程中的worker-functions并让它们入队。他们的结果将被发送回父母。
您还必须构建类似Pool的结构,以便可以迭代它以启动并加入进程,并且必须从队列中get()
结果。 有关Queue.get
用法的更多信息,我已经在这里写了。
这种方法的解决方案可能如下所示:
def worker(result_queue, batch_range):
"""worker function"""
result = []
for x in batch_range:
y = ((x+5)**2+x-40)
if y <= 0xf+1:
print('Condition met at: ', y, x)
result.append((x, y))
result_queue.put(result) # <--
if __name__ == '__main__':
N_WORKERS = mp.cpu_count()
X_MAX = 100000000
result_queue = mp.Queue() # <--
batch_sizes = calc_batch_sizes(X_MAX, n_workers=N_WORKERS)
batch_ranges = build_batch_ranges(batch_sizes)
print(batch_ranges)
with context_timer():
pool = [Process(target=worker, args=(result_queue, batch_range))
for batch_range in batch_ranges]
for p in pool:
p.start()
results = [result_queue.get() for _ in batch_ranges]
for p in pool:
p.join()
print(f'results: {results}')
x, y = zip(*chain.from_iterable(results)) # filter and sort results
print(f'results sorted: x: {x}, y: {y}')
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