\nimport multiprocessing as mp\n\nprint(mp.cpu_count())\n<\/pre><\/div>\n\n\nOutput: 12<\/strong><\/p>\n\n\n\nThe count here is the total number of cores between multiple processors, summed up. <\/p>\n\n\n\n
The four most important classes of this module are- <\/strong><\/p>\n\n\n\n- Process Class<\/li>
- Lock Class<\/li>
- Queue Class<\/li>
- Pool Class <\/li><\/ul>\n\n\n\n
Let’s look at each of these classes individually… <\/p>\n\n\n\n
1. Process Class<\/h3>\n\n\n\n
Process is the forked copy of the current process. It creates a new process identifier and tasks run as independent child process. <\/p>\n\n\n\n
start()<\/em><\/strong> and join()<\/strong><\/em> functions belong to this class. To pass an argument through a process, we use args<\/em><\/strong> keyword.<\/p>\n\n\n\nExample of start() function-<\/p>\n\n\n\n
Here, we have created a function calc_square<\/em> and calc_cube<\/em> for finding square and cube of the number respectively. In the main function we have created the objects p1<\/em> and p2<\/em>. p1.start()<\/em> and p2.start()<\/em> will start the function and calling p1.join() and p2.join<\/em> will terminate the process.<\/p>\n\n\n\nimport time\nimport multiprocessing\n\ndef calc_square(numbers):\n\tfor n in numbers:\n\t\tprint('square ' + str(n*n))\n\ndef calc_cube(numbers):\n\tfor n in numbers:\n\t\tprint('cube '+ str(n*n*n))\n\nif __name__ == "__main__":\n\tarr=[2,3,8,9]\n\tp1=multiprocessing.Process(target=calc_square,args=(arr,))\n\tp2=multiprocessing.Process(target=calc_cube,args=(arr,))\n\n\tp1.start()\n\tp2.start()\n\n\tp1.join()\n\tp2.join()\n\n\tprint("Done")\n<\/pre><\/div>\n\n\nOutput:<\/p>\n\n\n
\nsquare 4\nsquare 9\nsquare 64\nsquare 81\ncube 8\ncube 27\ncube 512\ncube 729\nDone\n<\/pre><\/div>\n\n\n2. Lock Class<\/h3>\n\n\n\n
The lock class allows the code to be locked in order to make sure that no other process can execute the similar code until it is released. <\/p>\n\n\n\n
To claim the lock, acquire() <\/strong><\/em>function is used and to release the lock, release()<\/em><\/strong> function is used.<\/p>\n\n\n\nfrom multiprocessing import Process, Lock\n\nlock=Lock()\ndef printer(data):\n lock.acquire()\n try:\n print(data)\n finally:\n lock.release()\n\nif __name__=="__main__":\n items=['mobile','computer','tablet']\n for item in items:\n p=Process(target=printer,args=(item,))\n p.start()\n<\/pre><\/div>\n\n\nOutput<\/p>\n\n\n
\nmobile\ncomputer\ntablet\n<\/pre><\/div>\n\n\n3. Queue Class<\/h3>\n\n\n\n
Queue is a data structure which uses First In First Out (FIFO) technique.It helps us perform inter process communication using native Python objects. <\/p>\n\n\n\n
Queue enables the Process to consume shared data when passed as a parameter.<\/p>\n\n\n\n
put()<\/strong><\/em> function is used to insert data to the queue and get()<\/em><\/strong> function is used to consume data from the queue. <\/p>\n\n\n\nimport multiprocessing as mp\n\ndef sqr(x,q):\n\tq.put(x*x)\n\nif __name__ == "__main__":\n\tq=mp.Queue() # Instance of queue class created\n\tprocesses=[mp.Process(target=sqr,args=(i,q))for i in range (2,10)] # List of processes within range 2 to 10\n\tfor p in processes:\n\t\tp.start()\n\n\tfor p in processes:\n\t\tp.join()\n\n\tresult = [q.get() for p in processes]\n\tprint(result)\n<\/pre><\/div>\n\n\nOutput:<\/p>\n\n\n
\n[4, 9, 16, 25, 36, 64, 49, 81]\n<\/pre><\/div>\n\n\n4. Pool Class<\/h3>\n\n\n\n
The pool class helps us execute a function against multiple input values in parallel. This concept is called Data Parallelism.<\/p>\n\n\n\n
Here, array [5,9,8] is mapped as input in the function call. pool.map() function is used to pass a list of multiple arguments.<\/p>\n\n\n
\nimport multiprocessing as mp\n\ndef my_func(x):\n print(x**x)\n\ndef main():\n pool = mp.Pool(mp.cpu_count())\n result = pool.map(my_func, [5,9,8])\n\nif __name__ == "__main__":\n main()\n<\/pre><\/div>\n\n\nOutput:<\/p>\n\n\n
\n3125\n387420489\n16777216\n<\/pre><\/div>\n\n\nConclusion<\/h2>\n\n\n\n
In this article, we learned the four most important classes in multiprocessing in Python – Process, Lock, Queue, and Pool which enables better utilization of CPU cores and improves performance.<\/p>\n\n\n\n
References<\/h2>\n\n\n\n
Official Module documentation<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"Hey guys! In this article, we will learn about multiprocessing in Python. So, let’s get started. What is multiprocessing? Multiprocessing is a package in python that supports the ability to spawn processes that make use of a Python API. It similar to the threading module in Python. Understanding Multiprocessing in Python A multiprocessor is a […]<\/p>\n","protected":false},"author":18,"featured_media":11496,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[],"class_list":["post-11383","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-python-modules"],"blocksy_meta":[],"_links":{"self":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/11383","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/users\/18"}],"replies":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/comments?post=11383"}],"version-history":[{"count":0,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/posts\/11383\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media\/11496"}],"wp:attachment":[{"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/media?parent=11383"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/categories?post=11383"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.askpython.com\/wp-json\/wp\/v2\/tags?post=11383"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}