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！

推荐答案

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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