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I'm having trouble understanding how to implement queue into a multiprocessing example below. Basically, I want the code to:
1) spawn 2 processes (done)
2) split up my id_list into two portions (done)
3) have each process iterate over the list printing out each item, and only close when its done with the list. I know I have to implement some type of Queueing system, and pass that to each worker, but I'm not sure how to do that. Any help would be much appreciated.
from multiprocessing import Pool,Queue id_list = [1,2,3,4,5,6,7,8,9,10] def mp_worker(record): try: print record sleep(1) except: pass print "worker closed" def mp_handler(): p = Pool(processes = 2) #number of processes p.map(mp_worker, id_list) #devides id_list between 2 processes, defined above p.close() p.join() mp_handler()
Note - the code prints out "worker closed" 10 times. Id like for this statement to be printed only twice (once for each worker, after each worker prints out the 5 numbers from id_list)
This works for me (on Python 3). Instead of using a Pool, I spawn my own two processes:
from multiprocessing import Process, Queue from time import sleep id_list = [1,2,3,4,5,6,7,8,9,10] queue = Queue() def mp_worker(queue): while queue.qsize() >0 : record = queue.get() print(record) sleep(1) print("worker closed") def mp_handler(): # Spawn two processes, assigning the method to be executed # and the input arguments (the queue) processes = [Process(target=mp_worker, args=(queue,)) for _ in range(2)] for process in processes: process.start() print('Process started') for process in processes: process.join() if __name__ == '__main__': for id in id_list: queue.put(id) mp_handler()
Although the length of the elements to be processed is hardcoded. But it could be a second input argument to for the mp_worker method.
16.6. multiprocessing — Process-based “threading” interface , from multiprocessing import Pool def f(x): return x*x if __name__ Returns a process shared queue implemented using a pipe and a few locks/semaphores. Python Multiprocessing Process, Queue and Locks. There are plenty of classes in python multiprocessing module for building a parallel program. Among them, three basic classes are Process, Queue and Lock. These classes will help you to build a parallel program. But before describing about those, let us initiate this topic with simple code.
The print statement you have there is misleading you -- the worker process does not terminate at the end of the function. In fact, the worker processes stay alive until the pool is closed. Additionally,
multiprocessing already takes care of breaking up the list into chunks and queueing up each task for you.
As for your other question, normally you would pass a callback to
map_async if you wanted to trigger an asynchronous event upon the entire list being completed. Calling once per chunk takes some mucking about with the internals, but if you really want to you could:
def mapstar_custom(args): result = list(map(*args)) print "Task completed" return result ... pool._map_async(f, x, mapstar_custom, None, None, None).get()
Edit: we seem to be conflating terminology. When I say worker I mean the processes the pool spawns, whereas you seem to mean the processes Selenium spawns from those processes (which wasn't in your question). Opening the webdriver only once is easy enough: if you have
pool.map(module.task, ...), then in
module.py just do:
# ... selenium init here ... def task(...): # ... use webdriver ...
The module will only be imported once by the pool workers, no matter how many times you dispatch that task. So the top level init will happen only once.
17.2. multiprocessing — Process-based parallelism — Python 3.4 , The Queue class in this module implements all the required locking semantics. a “simple” FIFO queue type, SimpleQueue , whose specific implementation A queue class for use in a multi-processing (rather than multi-threading) context. Having installed a fresh copy of Debian Hurd into a VM, I am able to reproduce the described issue using this 2-line snippet of code: import multiprocessing q = multiprocessing.Queue() It was possible to reproduce the issue both using the builds of 2.7.9 and 3.4.2 that came with Debian Hurd and with clean builds of each from the Python source
One solution for this question by using Pool and Queue is
from time import sleep from multiprocessing import Pool,Queue id_list = [1,2,3,4,5,6,7,8,9,10] def mp_worker(q): try: print(q.get()) sleep(.1) except: pass print ("worker closed") if __name__ == "__main__": q = Queue() p = Pool(processes = 2) #number of processes for x in id_list: q.put(x) p.map(mp_worker, id_list) #devides id_list between 2 processes, defined above
you must add vaules to Quene by put in main section of your code and in the function read the value from Queue by get
queue — A synchronized queue class — Python 3.8.5 documentation, How does multiprocessing queue works on python? I really don't know how to implement multiprocessing.queue correctly, you cannot really� Implementation using queue.Queue. Queue is built-in module of Python which is used to implement a queue. queue.Queue(maxsize) initializes a variable to a maximum size of maxsize. A maxsize of zero ‘0’ means a infinite queue. This Queue follows FIFO rule.
Multiprocessing queue in Python?, That's why, we don't need to use Lock class in this case. Below is the implementation where we are adding tasks to the queue, then creating processes and� Class multiprocessing.Queue A queue class for use in a multi-processing (rather than multi-threading) context. collections.deque is an alternative implementation of unbounded queues with fast atomic append() and popleft() operations that do not require locking and also support indexing.
Python Multiprocessing Example, Returns a process shared queue implemented using a pipe and a few locks/ semaphores. When a process first puts an item on the queue a feeder thread is� Features of Queue in Python. 1) A queue is an ordered list of elements. 2) This data structure follows the FIFO order. 3) The deletion of the new element will be done only after all the previous elements of the new element are deleted. Implementation of Queue in Python . Source code to implement a queue using Python
Queue - multiprocessing - Python documentation, This post looks at how to implement several asynchronous task queues using the Python multiprocessing library and Redis. The queue implementation in multiprocessing that allows data to be transferred between processes relies on standard OS pipes. OS pipes are not infinitely long, so the process which queues data could be blocked in the OS during the put() operation until some other process uses get() to retrieve data from the queue.
- Just out of curiosity, is there any particular reason you need your own task queue/chunking implementation rather than the one
pool.mapalready provides for you?
- The code is a distilled down version of a scraper (worker) that uses selenium webdriver-when i used pool.map, i couldn't figure out how to assign items from the queue to one, already opened webdriver (per worker). A queue item would get assigned to the webdriver, webdriver would open, process the queue item, close... then the worker would get relaunched again - this killed my performance, since i had to launch the webdriver for every single queue item. With the solution below, I can launch webdriver once per worker, then pass on queue items to it. Let me know if there is a better way though.
- See my answer -- the worker processes last as long as the pool does. If the workers are getting relaunched, it's because you're closing and remaking the pool.
- If the trouble is keeping the web driver process open, you can do that by moving the init out of the function itself and into the top level of the module. Then it will run once for each pool worker.
- the trouble was passing on queue items one after another to each worker's webdriver, while it was open. The solution below accomplishes exactly what i was looking for. tested and working as expected
- Thank you, this does what i was looking for. Instead of the first for _ in range(5): , i replaced it with while queue.qsize() >0 : , to get rid of some hardcoding.
- That would definitely work if you make sure that you fill the queue before calling the working processes! Otherwise the processes might find the queue to be empty and exit the loop, while you wanted to feed them some extra input for computation.
- If I remove try-except block the execution fails with AttributeError: 'int' object has no attribute 'get'. So q.get() is inaccessible. q.get takes processes from the queue, so is it the correct behavior?
- I just correct the code of the question, now it works by Queue. The answer of your question is , The mp_worker() function can take any value and if a non-Queue variable come as input, it does not consider it as a Queue variable.