python-multiprocessing

问题 I am trying to use Python multiprocessing. I wrapped my statements inside a function then I used the multiprocessing map to loop over the function. I found that only the first iteration was really processed yet the rest did not ( I checked from that by printing the result). Here are my problems : Why only the first iteration was computed. How to return each array separately B, C, and D. My real calculations do have too many staff to calculate and to return, so is there is a more efficient

问题 >>> import multiprocessing >>> print multiprocessing.Manager().Lock() <thread.lock object at 0x7f64f7736290> >>> type(multiprocessing.Lock()) <class 'multiprocessing.synchronize.Lock'> Why the produced object is a thread.lock and not a multiprocessing.synchronize.Lock as it would be expected from a multiprocessing object? 回答1: Managed objects are always proxies; the goal of the manager is to make non-multiprocessing-aware objects into multiprocessing aware. There is no point in doing this for

问题 I'm using map_async to create a pool of 4 workers. And giving it a list of image files to process [Set 1]. At times, I need to cancel the processing in between, so that I can instead get a different set of files processed [Set 2]. So an example situation is, I gave map_async 1000 files to process. And then want to cancel the processing of remaining jobs after about 200 files have been processed. Additionally, I want to do this cancellation without destroying/terminating the pool. Is this

问题 I'm using SQLAlchemy and multiprocessing. I also use scoped_session sinse it avoids share the same session but I've found an error and their solution but I don't understand why does it happend. You can see my code below: db.py engine = create_engine(connection_string) Session = sessionmaker(bind=engine) DBSession = scoped_session(Session) script.py from multiprocessing import Pool, current_process from db import DBSession def process_feed(test): session = DBSession() print(current_process()

问题 So I have a bunch of functions, that don't depend on each other to do their stuff, and each of them takes quite some time. So i thought i would safe runtime if I could use Multiple Threads. For example: axial_velocity = calc_velocity(data_axial, factors_axial) radial_velocity = calc_velocity(data_radial, factors_radial) circumferential_velocity = calc_velocity(data_circ, factors_circ) All my variables so far are lists (pretty long lists too) I have to do this for every input file, and this

问题 I am getting familiar with Python's multiprocessing module. The following code works as expected: #outputs 0 1 2 3 from multiprocessing import Pool def run_one(x): print x return pool = Pool(processes=12) for i in range(4): pool.apply_async(run_one, (i,)) pool.close() pool.join() Now, however, if I wrap a function around the above code, the print statements are not executed (or the output is redirected at least): #outputs nothing def run(): def run_one(x): print x return pool = Pool(processes

问题 The end goal is to execute a method in background, but not in parallel : when multiple objects are calling this method, each should wait for their turn to proceed. To achieve running in background, I have to run the method in a subprocess (not a thread), and I need to start it using spawn (not fork). To prevent parallel executions, the obvious solution is to have a global lock shared between processes. When processes are forked, which is the default on Unix, it is easy to achieve, as

问题 I have two versions of Python (these are actually two conda environments) /path/to/bin-1/python /path/to/bin-2/python From one version of python I want to launch a function that runs in the other version using something like the multiprocessing.Process object. It turns out that this is doable using the set_executable method: ctx = multiprocess.get_context('spawn') ctx.set_executable('/path/to/bin-2/python') And indeed we can see that this does in fact launch using that executable: def f(q):

问题 I need to say that multiprocessing is something new to me. I read some about it but it makes me more confused. I want to understand it on a simple example. Let's assume that we have 2 functions in first one I just increment 'a' variable and then assign it to 'number' variable, in second I start first function and each every one second I want to print 'number' variable. It should looks like: global number def what_number(): a=1 while True: a+=1 number=a def read_number(): while True: --> #here

问题 I am trying to pass a list as a parameter to the pool.map(co_refresh, input_list) . However, pool.map didn't trigger the function co_refresh . And also no error returned. It looks like the process hung in there. Original Code: from multiprocessing import Pool import pandas as pd import os account='xxx' password='xxx' threads=5 co_links='file.csv' input_list=[] pool = Pool(processes=threads) def co_refresh(url, account, password, outputfile): print(url + ' : ' + account + ' : ' + password + '