multiprocessing

问题 Im running image processing on a huge dataset with multiprocessing and Im wondering if running ThreadPoolExecutor inside a Pool provides any benefit vs just simply running Pool on all items. The dataset contains multiple folders with each folder containing images, so my initial though was to split up each folder in to a process and each image in that folder to a thread. Other way would be to just get every image and run that as a process. for instance, each folder as a process and each image

问题 I have created a simple remote function with ray that utilizes very little memory. However, after running for a short period of time the memory increases steadily and I get a RayOutOfMemoryError Exception. The following code is a VERY simple example of this problem. The "result_transformed" numpy array is being sent to the workers where each worker can do work on this. My simplified calc_similarity function does nothing, but it still runs out of memory. I have added much longer sleep times to

问题 I have to use spring batch to read the file and process the contents in batches. I thought of using remote chunking, but it wont meet my actual requirement. I just mentioned my requirement below; ____Item Processor1----> | | Item Reader --->Item --Item Processor2----> ---ItemWriter | | | -- Item Processor n---> Say for example, if my commit count is 20, I wanted the ItemReader to read 20 items and should distribute to the concurrent processors and the same should commit by the ItemWriter, and

问题 Based on the Python documentation, daemon threads are threads that die once the main thread dies. This seems to be the complete opposite behavior of daemon processes which involve creating a child process and terminating the parent process in order to have init take over the child process (aka killing the parent process does NOT kill the child process). So why do daemon threads die when the parent dies, is this a misnomer? I would think that "daemon" threads would keep running after the main

问题 Based on the Python documentation, daemon threads are threads that die once the main thread dies. This seems to be the complete opposite behavior of daemon processes which involve creating a child process and terminating the parent process in order to have init take over the child process (aka killing the parent process does NOT kill the child process). So why do daemon threads die when the parent dies, is this a misnomer? I would think that "daemon" threads would keep running after the main

问题 I have a very large datasets distributed in 10 big clusters and the task is to do some computations for each cluster and write (append) the results line by line into 10 files where each file contains the results obtained corresponding to each one of the 10 clusters, each cluster can be computed independently, and I want to parallelize the code into ten CPUs (or threads) such that I can do the computations on all the clusters at once, a simplified pseudo code for my task is the following: for

问题 I asked a question here about multiprocessing a few days ago, and one user sent me the answer that you can see below. Only problem is that this answer worked on his machine and does not work on my machine. I have tried on Windows (Python 3.6) and on Mac(Python 3.8). I have ran the code on basic Python IDLE that came with installation, in PyCharm on Windows and on Jupyter Notebook and nothing happens. I have 32 bit Python. This is the code: from bs4 import BeautifulSoup import requests from

问题 I have a function with two parameters reqs =[1223,1456,1243,20455] url = "pass a url" def crawl(i,url): print("%s is %s" % (i, url)) I want to trigger above function by multi processing concept. from multiprocessing import Pool if __name__ == '__main__': p = Pool(5) print(p.map([crawl(i,url) for i in reqs])) above code is not working for me. can anyone please help me on this! ----- ADDING NEW CODE --------- from multiprocessing import Pool reqs = [1223,1456,1243,20455] url = "pass a url" def

问题 I'm not sure if this title is appropriate for my situation: the reason why I want to share numpy array is that it might be one of the potential solutions to my case, but if you have other solutions that would also be nice. My task: I need to implement an iterative algorithm with multiprocessing , while each of these processes need to have a copy of data(this data is large, and read-only , and won't change during the iterative algorithm). I've written some pseudo code to demonstrate my idea:

问题 The following code is runnable, you can just copy/paste: from tkinter import * import multiprocessing startingWin = Tk() def createClientsWin(): def startProcess(): clientsWin = Tk() label = Label(clientsWin, text="Nothing to show") label.grid() clientsWin.mainloop() if __name__ == "__main__": p = multiprocessing.Process(target=startProcess) p.start() button = Button(startingWin, text="create clients", command=lambda: createClientsWin()) button.grid() startingWin.mainloop() So I simply want