multiprocessing

问题 I have a Python 3.7 script on a Linux machine where I am trying to run a function in multiple threads, but when I try I receive the following error: Traceback (most recent call last): File "./test2.py", line 43, in <module> pt.ping_scanx() File "./test2.py", line 39, in ping_scanx par = Parallel(function=self.pingx, parameter_list=list, thread_limit=10) File "./test2.py", line 19, in __init__ self._x = self._pool.starmap(function, parameter_list, chunksize=1) File "/usr/local/lib/python3.7

问题 I have a Python 3.7 script on a Linux machine where I am trying to run a function in multiple threads, but when I try I receive the following error: Traceback (most recent call last): File "./test2.py", line 43, in <module> pt.ping_scanx() File "./test2.py", line 39, in ping_scanx par = Parallel(function=self.pingx, parameter_list=list, thread_limit=10) File "./test2.py", line 19, in __init__ self._x = self._pool.starmap(function, parameter_list, chunksize=1) File "/usr/local/lib/python3.7

问题 Say we have a script that works with no problem: import module1 class SomeClass(object): def __init__(self): self.m = module1() s = SomeCLass() However if I use multiprocessing module with "spawn" context: import module1 import multiprocessing as mp class SomeClass(object): def __init__(self): self.m = module1() s = SomeCLass() ctx = mp.get_context('spawn') proc = ctx.Process(target=SomeClass, kwargs={}) proc.daemon = True proc.start() I get the following error at proc.start() :

问题 Say we have a script that works with no problem: import module1 class SomeClass(object): def __init__(self): self.m = module1() s = SomeCLass() However if I use multiprocessing module with "spawn" context: import module1 import multiprocessing as mp class SomeClass(object): def __init__(self): self.m = module1() s = SomeCLass() ctx = mp.get_context('spawn') proc = ctx.Process(target=SomeClass, kwargs={}) proc.daemon = True proc.start() I get the following error at proc.start() :

问题 I'm getting stuck on what I think is a basic multiprocess and threading issue. I've got a multiprocess set up, and within this a thread. However, when I set up the thread class within the init function, I get the following error: "TypeError: can't pickle thread.lock objects". However, this does not happen if the thread is set up outside of the init function. Does anyone know why this is happening? Note I'm using Windows. Some code is below to illustrate the issue. As typed below, it runs fine

问题 The posted code starts two async Processes . The first publisher Process publishes data to the Queue , the second subscriber Process reads the data from the Queue and logs it to console. To make sure the Queue is not accessed at the same time, before getting the data from the Queue , the subscribe function first executes lock.acquire() , then gets the data with data = q.get() and finally releases the lock with the lock.release() statement. The same locking-releasing sequence is used in

问题 I'm trying to find a faster way to calculate factorial. In the following code, I first use recursion method , and then I use another method by dividing the whole operation into smaller calculations and then run them in parallel. Here's my code: #!/usr/bin/env python3 import time from multiprocessing import Pool import itertools def fact(n): if n==1: return 1 else: a=n*fact(n-1) return a def divid(start ,end): m=1 for i in range(start,end+1): m=m*i return m pool=Pool() a=time.time() res=fact

问题 I'm trying to find a faster way to calculate factorial. In the following code, I first use recursion method , and then I use another method by dividing the whole operation into smaller calculations and then run them in parallel. Here's my code: #!/usr/bin/env python3 import time from multiprocessing import Pool import itertools def fact(n): if n==1: return 1 else: a=n*fact(n-1) return a def divid(start ,end): m=1 for i in range(start,end+1): m=m*i return m pool=Pool() a=time.time() res=fact

问题 I'm trying to find a faster way to calculate factorial. In the following code, I first use recursion method , and then I use another method by dividing the whole operation into smaller calculations and then run them in parallel. Here's my code: #!/usr/bin/env python3 import time from multiprocessing import Pool import itertools def fact(n): if n==1: return 1 else: a=n*fact(n-1) return a def divid(start ,end): m=1 for i in range(start,end+1): m=m*i return m pool=Pool() a=time.time() res=fact

问题 I have primary.py: from tkinter import * from tkinter.filedialog import askopenfilename from tkinter import ttk import multiprocessing as mp import other_script class GUI: def __init__(self, master): self.master = master def file_select(): path = askopenfilename() if __name__ == '__main__': queue = mp.Queue() queue.put(path) import_ds_proc = mp.Process(target=other_script.dummy, args=(queue,)) import_ds_proc.daemon = True import_ds_proc.start() # GUI root = Tk() my_gui = GUI(root) # Display