{"id":18721,"date":"2022-11-01T18:43:17","date_gmt":"2022-11-01T13:13:17","guid":{"rendered":"https:\/\/jassweb.com\/solved\/solved-how-to-launch-100-workers-in-multiprocessing\/"},"modified":"2022-11-01T18:43:17","modified_gmt":"2022-11-01T13:13:17","slug":"solved-how-to-launch-100-workers-in-multiprocessing","status":"publish","type":"post","link":"https:\/\/jassweb.com\/solved\/solved-how-to-launch-100-workers-in-multiprocessing\/","title":{"rendered":"[Solved] How to launch 100 workers in multiprocessing?"},"content":{"rendered":"

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The literally first example on the page you link to works. So I’m just going to copy and paste it here and change two values.<\/p>\n

from multiprocessing import Pool\n\ndef f(x):\n    return x*x\n\nif __name__ == '__main__':\n    with Pool(100) as p:\n        print(p.map(f, range(100)))\n<\/code><\/pre>\n
EDIT:<\/strong> you just said that you’re using Google colab. I think google colab offers you two<\/em> cpu cores, not more. (you can check by running !cat \/proc\/cpuinfo<\/code>). With 2 cpu cores, you can only execute two pieces of computation at once.<\/p>\n
So, if your function is not primarily something that waits for external IO (e.g. from network), then this makes no sense: you’ve got 50 executions competing for one core. The magic of modern multiprocessing is that this means that suddenly, one function will be interrupted, its state saved to RAM, the other function then may run for a while, gets interrupted, and so on.<\/p>\n
This whole exchanging of state of course is overhead. You’d be faster just running as many instances your function in parallel as you have cores. Read the documentation on Pool<\/code> as used above for more information.<\/p>\n<\/p><\/div>\n
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solved How to launch 100 workers in multiprocessing? <\/p>\n","protected":false},"excerpt":{"rendered":"
[ad_1] The literally first example on the page you link to works. So I’m just going to copy and paste it here and change two values. from multiprocessing import Pool def f(x): return x*x if __name__ == ‘__main__’: with Pool(100) as p: print(p.map(f, range(100))) EDIT: you just said that you’re using Google colab. I think … Read more<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[320],"tags":[4507,2844,4506,349,4505],"class_list":["post-18721","post","type-post","status-publish","format-standard","hentry","category-solved","tag-concurrent-processing","tag-multiprocessing","tag-pool","tag-python","tag-python-multiprocessing"],"yoast_head":"\n[Solved] How to launch 100 workers in multiprocessing? - JassWeb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jassweb.com\/solved\/solved-how-to-launch-100-workers-in-multiprocessing\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"[Solved] How to launch 100 workers in multiprocessing? - JassWeb\" \/>\n<meta property=\"og:description\" content=\"[ad_1] The literally first example on the page you link to works. So I’m just going to copy and paste it here and change two values. from multiprocessing import Pool def f(x): return x*x if __name__ == '__main__': with Pool(100) as p: print(p.map(f, range(100))) EDIT: you just said that you’re using Google colab. I think ... 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So I’m just going to copy and paste it here and change two values. from multiprocessing import Pool def f(x): return x*x if __name__ == '__main__': with Pool(100) as p: print(p.map(f, range(100))) EDIT: you just said that you’re using Google colab. I think ... 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