Let’s start with a slightly simpler problem. All keys are 8 numeric digits, and you have 10 machines. Simple enough – one machine checks 0???????, another checks 1??????? and so on. Note that this slicing doesn’t care that you’re doing multiprocessing – it’s just allocating ranges by fixing one of the digits. Your version is … Read more
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 google … Read more
It is generally a bad idea to have file processing start immediately a file appears in a folder. For example if 100 files arrive at once then you will end up with 100 threads all competing for the same physical resources, e.g. disk heads etc. The best approach, in my opinion, is to have a … Read more
Well, yes, to some extent this is true. But it’s a simplified statement, there are significant differences. Shortly… Multiprocessing Processes are isolated from each other by OS. Each process has its own virtual memory space. They need to use IPC methods for communication with each other. If a process encounters e.g. segmentation fault, only this … Read more
So off the top of my head you can look at 2 things. 1) Asyncio (be careful this example uses threading and is not thread safe specifically the function asyncio.gather) import asyncio for work in [1,2,3,4,5]: tasks.append(method_to_be_called(work)) results = await asyncio.gather(*tasks) 2) Asyncio + multiprocessing https://github.com/jreese/aiomultiprocess 1 solved Multi-tasking with Multiprocessing or Threading or Asyncio, … Read more