多進程之Pool與多線程pool 及tqdm和for 並對比pandas處理結果

又叕又碰到這個多進程問題了，其實線程也行，下面再次進行demo測試

召回中遇到的，存儲召回的items

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from multiprocessing import Process,Pool

Pool(32)與Pool(64)相比沒有慢多少，一個是116s，一個是90s，因此保險考慮32爲好

遇到一個問題：多進程Pool中

AttributeError: Can't pickle local object 'update_model.<locals>.delete_copy_items'

下面小demo復現

Traceback (most recent call last):
  File "multiprocess_Process_Pool_.py", line 99, in <module>
    update_model()
  File "multiprocess_Process_Pool_.