Machine Learning
day39 (multi-process)-pipeline, process pool, return value of process pool, process callback function, data sharing between processes
1. Pipeline.py (Understand, basically use queues more)from multiprocessing import Process, Lock, Pipe # Pipeline import time import random def producer_action(producer, pro, consumer_acc): # production function for item in range(20): time.sleep(random.random()) data = pro +'production data' + str(item) producer.send(data) print(data) for item in range(consumer_acc): # If there are several consumer ends, send several None producer.send(None) producer.close() def consumer_action(consumer, pro, loc