The documentation for /MP says: Incompatible Options and Language Features The /MP option is incompatible with some compiler options and language features. If you use an incompatible compiler option with the /MP option, the compiler issues warning D9030 and ignores the /MP option. If you use an incompatible language feature, the compiler issues error C2813then … Read more
I tried something, and it seems to work. I put preload_app = True in my gunicorn.conf and now the lock seems to be shared. I am still looking into exactly what’s happening here but for now this is good enough, YMMV.
Just thought I would add an actual answer to make it clear for others. Quoting the answer from aix in this question: What happens is that on Unix every worker process inherits the same state of the random number generator from the parent process. This is why they generate identical pseudo-random sequences. Use the random.seed() … Read more
@Konate’s answer really helped me. Here is a simplified version using multiprocessing.pool: import multiprocessing def double(a): return a * 2 def driver_func(): PROCESSES = 4 with multiprocessing.Pool(PROCESSES) as pool: params = [(1, ), (2, ), (3, ), (4, )] results = [pool.apply_async(double, p) for p in params] for r in results: print(‘\t’, r.get()) driver_func()
Multithreading in Python is sort of a myth. There’s technically nothing forbidding multiple threads from trying to access the same resource at the same time. The result is usually not desirable, so things like locks, mutexes, and resource managers were developed. They’re all different ways to ensure that only one thread can access a given … Read more
I used cx_freeze for a project at work. I’m not sure if this is your problem… but I was using the Anaconda distribution, and cx_freeze was not properly gathering the .dll’s that I needed for my project. The solution was to: Install a plane version of Python make an environment with the packages that I … Read more
Try this: import Queue import time def dump_queue(queue): “”” Empties all pending items in a queue and returns them in a list. “”” result = [] for i in iter(queue.get, ‘STOP’): result.append(i) time.sleep(.1) return result import multiprocessing q = multiprocessing.Queue() for i in range(100): q.put([range(200) for j in range(100)]) q.put(‘STOP’) l=dump_queue(q) print len(l) Multiprocessing queues … Read more
how can I reduce the execution time using selenium when it is made to run using multiprocessing A lot of time in your solution is spent on launching the webdriver for each URL. You can reduce this time by launching the driver only once per thread: (… skipped for brevity …) threadLocal = threading.local() def … Read more