星图与tqdm结合?
问题描述
我在做一些并行处理,如下:
I am doing some parallel processing, as follows:
with mp.Pool(8) as tmpPool:
results = tmpPool.starmap(my_function, inputs)
输入如下所示:[(1,0.2312),(5,0.52) ...]即,int 和 float 的元组.
where inputs look like: [(1,0.2312),(5,0.52) ...] i.e., tuples of an int and a float.
代码运行良好,但我似乎无法将其包裹在加载栏 (tqdm) 周围,例如可以使用 imap 方法完成,如下所示:
The code runs nicely, yet I cannot seem to wrap it around a loading bar (tqdm), such as can be done with e.g., imap method as follows:
tqdm.tqdm(mp.imap(some_function,some_inputs))
星图也可以这样做吗?
谢谢!
解决方案
starmap()
是不行的,但是通过添加 Pool.istarmap()
的补丁是可以的>.它基于 imap()
的代码.您所要做的就是创建 istarmap.py
-文件并导入模块以应用补丁,然后再进行常规的多处理导入.
It's not possible with starmap()
, but it's possible with a patch adding Pool.istarmap()
. It's based on the code for imap()
. All you have to do, is create the istarmap.py
-file and import the module to apply the patch before you make your regular multiprocessing-imports.
Python <3.8
# istarmap.py for Python <3.8
import multiprocessing.pool as mpp
def istarmap(self, func, iterable, chunksize=1):
"""starmap-version of imap
"""
if self._state != mpp.RUN:
raise ValueError("Pool not running")
if chunksize < 1:
raise ValueError(
"Chunksize must be 1+, not {0:n}".format(
chunksize))
task_batches = mpp.Pool._get_tasks(func, iterable, chunksize)
result = mpp.IMapIterator(self._cache)
self._taskqueue.put(
(
self._guarded_task_generation(result._job,
mpp.starmapstar,
task_batches),
result._set_length
))
return (item for chunk in result for item in chunk)
mpp.Pool.istarmap = istarmap
Python 3.8+
# istarmap.py for Python 3.8+
import multiprocessing.pool as mpp
def istarmap(self, func, iterable, chunksize=1):
"""starmap-version of imap
"""
self._check_running()
if chunksize < 1:
raise ValueError(
"Chunksize must be 1+, not {0:n}".format(
chunksize))
task_batches = mpp.Pool._get_tasks(func, iterable, chunksize)
result = mpp.IMapIterator(self)
self._taskqueue.put(
(
self._guarded_task_generation(result._job,
mpp.starmapstar,
task_batches),
result._set_length
))
return (item for chunk in result for item in chunk)
mpp.Pool.istarmap = istarmap
然后在你的脚本中:
import istarmap # import to apply patch
from multiprocessing import Pool
import tqdm
def foo(a, b):
for _ in range(int(50e6)):
pass
return a, b
if __name__ == '__main__':
with Pool(4) as pool:
iterable = [(i, 'x') for i in range(10)]
for _ in tqdm.tqdm(pool.istarmap(foo, iterable),
total=len(iterable)):
pass
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