多进程之Pool与多线程pool 及tqdm和for 并对比pandas处理结果

又叕又碰到这个多进程问题了，其实线程也行，下面再次进行demo测试

召回中遇到的，存储召回的items

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from multiprocessing import Process,Pool

Pool(32)与Pool(64)相比没有慢多少，一个是116s，一个是90s，因此保险考虑32为好

遇到一个问题：多进程Pool中

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

下面小demo复现

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