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In our previous tutorial, we learned about Python CSV Example. In this tutorial we are going to learn Python Multiprocessing with examples.
Parallel processing is getting more attention nowadays. If you still don’t know about the parallel processing, learn from wikipedia. As CPU manufacturers start adding more and more cores to their processors, creating parallel code is a great way to improve performance. Python introduced multiprocessing module to let us write parallel code. To understand the main motivation of this module, we have to know some basics about parallel programming. After reading this article, we hope that, you would be able to gather some knowledge on this topic.
There are plenty of classes in python multiprocessing module for building a parallel program. Among them, three basic classes are
Lock. These classes will help you to build a parallel program. But before describing about those, let us initiate this topic with simple code. To make a parallel program useful, you have to know how many cores are there in you pc. Python Multiprocessing module enables you to know that. The following simple code will print the number of cores in your pc.
import multiprocessing print("Number of cpu : ", multiprocessing.cpu_count())
The following output may vary for your pc. For me, number of cores is 8.
Process class is an abstraction that sets up another Python process, provides it to run code and a way for the parent application to control execution. There are two important functions that belongs to the Process class -
join() function. At first, we need to write a function, that will be run by the process. Then, we need to instantiate a process object. If we create a process object, nothing will happen until we tell it to start processing via
start() function. Then, the process will run and return its result. After that we tell the process to complete via
join() function. Without
join() function call, process will remain idle and won’t terminate. So if you create many processes and don’t terminate them, you may face scarcity of resources. Then you may need to kill them manually. One important thing is, if you want to pass any argument through the process you need to use
args keyword argument. The following code will be helpful to understand the usage of Process class.
from multiprocessing import Process def print_func(continent='Asia'): print('The name of continent is : ', continent) if __name__ == "__main__": # confirms that the code is under main function names = ['America', 'Europe', 'Africa'] procs =  proc = Process(target=print_func) # instantiating without any argument procs.append(proc) proc.start() # instantiating process with arguments for name in names: # print(name) proc = Process(target=print_func, args=(name,)) procs.append(proc) proc.start() # complete the processes for proc in procs: proc.join()
The output of the following code will be:
You have basic knowledge about computer data-structure, you probably know about Queue. Python Multiprocessing modules provides
Queue class that is exactly a First-In-First-Out data structure. They can store any pickle Python object (though simple ones are best) and are extremely useful for sharing data between processes. Queues are specially useful when passed as a parameter to a Process’ target function to enable the Process to consume data. By using
put() function we can insert data to then queue and using
get() we can get items from queues. See the following code for a quick example.
from multiprocessing import Queue colors = ['red', 'green', 'blue', 'black'] cnt = 1 # instantiating a queue object queue = Queue() print('pushing items to queue:') for color in colors: print('item no: ', cnt, ' ', color) queue.put(color) cnt += 1 print('\npopping items from queue:') cnt = 0 while not queue.empty(): print('item no: ', cnt, ' ', queue.get()) cnt += 1
The task of Lock class is quite simple. It allows code to claim lock so that no other process can execute the similar code until the lock has be released. So the task of Lock class is mainly two. One is to claim lock and other is to release the lock. To claim lock the,
acquire() function is used and to release lock
release() function is used.
In this Python multiprocessing example, we will merge all our knowledge together. Suppose we have some tasks to accomplish. To get that task done, we will use several processes. So, we will maintain two queue. One will contain the tasks and the other will contain the log of completed task. Then we instantiate the processes to complete the task. Note that the python Queue class is already synchronized. That means, we don’t need to use the Lock class to block multiple process to access the same queue object. 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 starting them, then using
join() to complete the processes. Finally we are printing the log from the second queue.
from multiprocessing import Lock, Process, Queue, current_process import time import queue # imported for using queue.Empty exception def do_job(tasks_to_accomplish, tasks_that_are_done): while True: try: ''' try to get task from the queue. get_nowait() function will raise queue.Empty exception if the queue is empty. queue(False) function would do the same task also. ''' task = tasks_to_accomplish.get_nowait() except queue.Empty: break else: ''' if no exception has been raised, add the task completion message to task_that_are_done queue ''' print(task) tasks_that_are_done.put(task + ' is done by ' + current_process().name) time.sleep(.5) return True def main(): number_of_task = 10 number_of_processes = 4 tasks_to_accomplish = Queue() tasks_that_are_done = Queue() processes =  for i in range(number_of_task): tasks_to_accomplish.put("Task no " + str(i)) # creating processes for w in range(number_of_processes): p = Process(target=do_job, args=(tasks_to_accomplish, tasks_that_are_done)) processes.append(p) p.start() # completing process for p in processes: p.join() # print the output while not tasks_that_are_done.empty(): print(tasks_that_are_done.get()) return True if __name__ == '__main__': main()
Depending on the number of task, the code will take some time to show you the output. The output of the following code will vary from time to time.
Python multiprocessing Pool can be used for parallel execution of a function across multiple input values, distributing the input data across processes (data parallelism). Below is a simple Python multiprocessing Pool example.
from multiprocessing import Pool import time work = (["A", 5], ["B", 2], ["C", 1], ["D", 3]) def work_log(work_data): print(" Process %s waiting %s seconds" % (work_data, work_data)) time.sleep(int(work_data)) print(" Process %s Finished." % work_data) def pool_handler(): p = Pool(2) p.map(work_log, work) if __name__ == '__main__': pool_handler()
Below image shows the output of the above program. Notice that pool size is 2, so two executions of
work_log function is happening in parallel. When one of the function processing finishes, it picks the next argument and so on. So, that’s all for python multiprocessing module. Reference: Official Documentation
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