我需要在Python3.6的多进程中做一些事情。也就是说,我必须更新一个添加对象列表的字典。因为这些对象是不可拾取的,所以我需要使用dill而不是pickle,从pathos使用multiprocess而不是multiprocessing,但这不应该是问题所在。
向字典添加列表需要在向字典添加之前重新序列化列表。这会减慢一切,并且所需的时间与没有多处理时相同。你能给我推荐一种解决方法吗?
这是我用Python3.6编写的代码:init1工作但很慢,init2很快但坏了。其余部分仅用于测试目的。
import time
def init1(d: dict):
for i in range(1000):
l = []
for k in range(i):
l.append(k)
d[i] = l
def init2(d: dict):
for i in range(1000):
l = []
d[i] = l
for k in range(i):
l.append(i)
def test1():
import multiprocess as mp
with mp.Manager() as manager:
d = manager.dict()
p = mp.Process(target=init1, args=(d,))
p.start()
p.join()
print(d)
def test2():
import multiprocess as mp
with mp.Manager() as manager:
d = manager.dict()
p = mp.Process(target=init2, args=(d,))
p.start()
p.join()
print(d)
start = time.time()
test1()
end = time.time()
print('test1: ', end - start)
start = time.time()
test2()
end = time.time()
print('test2: ', end - start)发布于 2018-02-11 02:19:52
使用管道的可能解决方案。在我的pc上,这需要870ms,相比之下,1.10s的test1和200ms的test2。
def init3(child_conn):
d = {}
for i in range(1000):
l = []
for k in range(i):
l.append(i)
d[i] = l
child_conn.send(d)
def test3():
import multiprocess as mp
parent_conn, child_conn = mp.Pipe(duplex=False)
p = mp.Process(target=init3, args=(child_conn,))
p.start()
d = parent_conn.recv()
p.join()在jupyter上,通过使用神奇的%timeit,我得到:
In [01]: %timeit test3()
872 ms ± 11.9 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [02]: %timeit test2()
199 ms ± 1.72 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
In [03]: %timeit test1()
1.09 s ± 10.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)https://stackoverflow.com/questions/48720252
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