Multiprocessing in Python: Pool
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- Опубликовано: 18 окт 2024
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In this video, we will be continuing our treatment of the multiprocessing module in Python. Specifically, we will be taking a look at the "Pool" class, and how we can go about using the Pool class to instantiate tasks that are run across multiple processors on our machine.
According to the official documentation (docs.python.or...
"multiprocessing is a package that supports spawning processes using an API similar to the threading module. The multiprocessing package offers both local and remote concurrency, effectively side-stepping the Global Interpreter Lock by using subprocesses instead of threads. Due to this, the multiprocessing module allows the programmer to fully leverage multiple processors on a given machine. It runs on both Unix and Windows."
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I have looked at A LOT of python parallel videos and this is the first one that worked! I also added a print line to the serial function to confirm the output; they were consistent. Furthermore, it worked well and can be adapted easily for more advanced applications. Thank you!
Great to hear, David. Thank you for watching!
Finally a clear tutorial on this! Was finally able to get it to work thanks to you.
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thanks for the video bro, I was looking for several and several and could not understand as clearly as it was in this video!
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This is a phenomenally well made video, well done sir on your presentation and clarity.
Thank you kindly! I appreciate your support!
you are best teacher . Greetings from Azerbaijan
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you are a legend! thanks for this tutorial.
Nice work!! Thanks for this series of videos. Greetings from Chile
Greetings from Canada :). I am happy to have produced content that was found to be valuable by people like yourself.
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I had problem with real time OCR and QR detection. when using single thread, it takes time save the images and process the OCR and QR which make the screen freeze about 0.3 seconds . so I used multiprocessing and make two process to capture images and process QR OCR and one shared list to put and read numpy arrays (ROI). It works better. Thank you very much.
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Awesome explanation!
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Fantastic Python class!
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Great example! I have almost faced up with the same situation where I expected to speed up my execution time using Multiprocessing however I got longer run time! Thanks.
Awesome to hear, and thanks for watching!
Excellent video, greetings from Germany
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@@LucidProgramming all set: subscribed to your channel, donated and subscribed to your mailing list
Oh, what I forgot to mention: if you ever run out of ideas for topics I would be really interested into a series about object oriented programming. I get the point of what oop can be used for, but I cannot think of any usecase why I (as someone who is into python for less than a year) would ever want to use oop
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@@schogaia Noted! I think this is a great idea for a series of videos. Once I get my recording setup back in proper order, I think I will add this one to the list. Again, thank you for the suggestion!
Awesome video as usual!
great example and explanation bro!
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Once again ! Very interesting video ! Thank you very much
Really appreciate your comments! I'm glad to hear you've enjoyed this series!
great examplwe - thanks!
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Nice work!!
Thank you! :)
Under what condition do you recommend using Pool as opposed to Process?
what about compared to threading?
which is faster and whats the difference
It is work mentioning that it isn't possible to call p.join() before calling p.call(). At least it isn't possible, based on the official Python documentation.
Thank you so much for your demonstration! Very clear and helpful. Can I ask why do we need the line "if __name__ == '__mian__' "? Thank you!
because each process basically calls the file in wich this function is in and executes that function. However, if you call a function from another file, it also does everything outside of the function that is being called (except for stuff in other functions). So with that "if", each process would also again start more processes and endless processes would be spawned
thank you!
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Excellent. I'm trying to implement this along with a timeout that will kill processes that are running longer than a set time. If anyone knows of a video that shows how to do that please pass it on.
Thanks for the great contents. I'm really learning a lot from these videos. I have a question, is there a smart way to give pool class multiple arguments? For example, in this video, function 'sum_square' takes 1 integer as an argument and to execute parallel computation we make a list of integers[numbers] and use 'p.map(sum_square, numbers)' . What I see is that the map function takes the function to parallelize and then a list of arguments. But for a function that takes multiple arguments other than only one, lets say for 2 integers, how do you design the pool multiprocessing?
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And w.r.t. your question, checkout the "functools" module. Cheers!
I'm having the same issue !
This may not be a satisfying solution, hope it helps
python.omics.wiki/multiprocessing_map/multiprocessing_partial_function_multiple_arguments
@@JWNam98 Did you look into using "functools"? That should solve your problem.
This is the way on how you can do that:
from multiprocessing import Pool
def multiply(a, b):
return a * b
if __name__ == '__main__':
args_list = [(2, 3), (4, 5), (6, 7)] # a list of argument tuples
with Pool(processes=2) as pool:
results = pool.starmap(multiply, args_list)
print(results)
well explained :)
Thanks! :)
cool video - what is the diffeerence with using 'Process' instead of ''Pool'?
Thanks for watching! W.r.t. your question, I honestly think Google has some great answers on these differences. Far better than anything I can convey in a RUclips comment. Thanks again for watching, and have a nice day!
Nice, one comment, you can use double the process of the number of cores so 16 cores is 32 process you can run.
Neat, thanks for sharing that!
I like your vim setup. How can I get the style/customization like yours? Thanks.
Thanks! I have a series on this that can be found here: bit.ly/lp_vim. Cheers!
@@LucidProgramming Just to let you know that your video is super useful. I dig further into the documentation and figure out how to send multiple arguments. Mucho gracias!! stackoverflow.com/questions/5442910/python-multiprocessing-pool-map-for-multiple-arguments
Hi! Is there anyway to use a multiprocessing within a function?
Specifically I'd like to know if for example it is possible to define a function "sum_square_with_mp(numbers)", where if a run the code, then I would be able to type sum_square_with_mp([1,3,5]) and get 1, 14 and 55.
Yeah, I would say check the docs for multiprocessing.
How to explicitily limit the number of process , like I want to run only 3 process at a time .
how can I use pool to count words in a huge file (genome = dna) to speed up my results.
for ex if I look for palindrome words in a text and need to make a dict (word:[list positions]) it takes a 2 minuts and I have 198000 text to look for. 8)
I was passing the arguments like alphabet = 'acgt' and k = integer representing the length of the words to look for.
i was using partial to pass the text to the function and then pool.apply_async(func, args). But still not good performance at all. do you have any suggestions?
A lot of that would depend on some of the specifics of the problem. It's hard for me to offer anything that doesn't come off as super general. I have done work with clients who have had large datasets for biostatistics, etc. If you think you could benefit from some outside help, I do offer such services if you want to PM me. Hope that helps, cheers!
The code at 5:00 gives me this output:
[0, 0, 1, 5, 14]
Traceback (most recent call last):
File "main.py", line 17, in
p.join()
File "/usr/lib/python3.8/multiprocessing/pool.py", line 659, in join
raise ValueError("Pool is still running")
ValueError: Pool is still running
You probably need to stop the process?
@@LucidProgramming how do I do this?
@@socialrupt8040 Go fish.
@@LucidProgramming what's go fish?
@@socialrupt8040 Go search it on your own.
How to solve broken process pool on multiprocessing?
what is the terminal that you are using?
You mean what editor am I using? It's vim. I have a whole series on it you can check out here: bit.ly/lp_vim
What should I do if my function needs two arguments? What would be the correct syntax? Should I create a list of two arguments on each element? result = p.map(function, (arg1,arg2)) ??
Did you try this?
@@LucidProgramming Yes. Let's suppose that I want to multiply two numbers. When I use this for two sets (four numbers), it always asks for the second argument. I don't know what I can do to solve it. I tried many ways to write the arguments.
@@gametester905 Just put a comma after the last argument.
Is it possible to map the function on gpu instead of cpu? Thank you very much for the tutorial.
Hi Nicky. The multiprocessing module has to do with the cores on your machine, not on your GPU, so this would not work as a one-to-one mapping.
@@LucidProgramming Get it. Many thanks.
@@saber291996 Great. Thanks again for the comment! :)
I would like to understand if my VM is having only 10 core, and if I allocate 200, what will happen and how does p.map( fun, [1....300]) , fucntion work if I'm calling function more than number core and args are more number of cores? thank you
You'd crash your machine. Don't do that.
os.cpu_count() returns the number of virtual cores. On a 4-core, hyper-threading machine, it returns 8. However, python cannot efficiently run two interpreters on a single hyper-threaded core as the processes have separate memory spaces but the hyperthreaded core halves share a single memory space. To really operate at maximum efficiency, you should separate your code into a pool of 4 processes, each of which consisted of 2 threads running on a single interpreter. Thus the thread pairs could run concurrently taking advantage of overlapping IO and computation for example, while the 4 cores could each be doing some computationally intensive operation. Too much detail for this level of tutorial, but you might have significant savings for large memory processes by avoiding the default argument to Pool() and instead do Pool(os.cpu_count()/2) or Pool(psutil.cpu_count(logical = False))
Quick example on my machine: numbers=range(10000) Pool() generated 8 processes and the times were 1.016 and 3.289 seconds so multiprocessing was a bit less than 4x faster. And if I run again using Pool(4) it generates 4 processes and the times were 1.013 and 3.220 seconds, so actually a bit faster with less context switching even though I was using half as many processes.
Thanks a lot for this rich content, I really learned a lot from it. I have a question: What is the difference between Pooling and using the classical Process modules as in the previous videos?
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Regarding your question, I think this post does a nice coverage of the two comparatively. Thanks again for watching!
www.ellicium.com/python-multiprocessing-pool-process/
@@LucidProgramming Thank you very much for your reply. I already subscribed to your channel wishing you all the best.
@@marwanfouad5781 Thank you kindly, Marwan! I appreciate your support :). Wishing you the best as well! Cheers!
Hi, I have code which interacts with network devices and takes some commands output from the devices and processes that output to take useful data and writes to a csv file. Now in the code i want to introduce multiprocessing but the problem is that the main() function calls the other functions and these functions also call other local functions as per requirement. Can you please help me out here as I am really confused on how to write the same code with multiprocessing because this operation is done on more than 100 devices and consumes about an hour to complete.
It's hard for me to help as the details you've provided are a bit sparse. I think there would be a lot of unique things specific to your situation that I would not be aware of.
How the Pool object differs from ProcessPoolExecutor?
You can try Googling for this. I the answer you would find would be better than something I can provide here in a comment.
This work for py3 or no ???
Have you tried it? Why wouldn't it?
While using Multiprocessing Pool, I am getting a permission denied error. Can you help me with that
sudo?
@@LucidProgramming I am working on a Windows Machine, and I am already using a Virtual Environment
@@aanchalagarwal6886 Google?
@@aanchalagarwal6886 Not sure if this is related, but in Windows, you must have your processes creation code inside: "if __name__ == "__main__":
"
multiprocessing.cpu_count() finds the number of cores
Is this not what I said?
I was using this code to understand what the code does and if it's converted into a exe on Windows, It'll eat all of your RAM in 1-5 seconds and create over 1000 processes with a system crash. (I saw the code create 5000+ at one time.)
Yep, it'll consume all your resources. Thought I mentioned a caution against that, but I didn't, I apologize!
import multiprocessing as mp
mp.cpu_count()
What is this comment in reference to? Am I missing something?
Thanks for your video. I do have a question about multiprocessing. Here I asked it on StackOverflow.
stackoverflow.com/questions/57529308/solving-a-problem-utilizing-multiprocessing-and-value-in-python
I appreciate any help.
Solving the same problem using the new approach provided in your video, still, I get no benefits in term of speed.