Emptying mp queue fast
I have an multiprocess queue which is shared between multiple running process. At some point (while all the processes are not using it for reading or writing), I need to empty that queue.
What is the closest thing to Python multiprocessing’s Pool.imap when the iterator only works on the main thread?
Suppose I have an iterator that only works on the main thread (throws an exception otherwise), but I still want to distribute work (one task per item from the iterator) over several processes. (Because the cost of the work per item is much higher than the cost of the iteration.)
Multiprocessing with gramformer
I am trying to implement multiprocessing with gramformer which is an open source model to correct grammatical errors. I have tried several approaches but i keep getting errors cant be pickled. i am also using spacy which seems to have no problem with multiprocessing.
Python multiprocess pool – slower than without multiprocessing
I have written a (very dirty) Python raytracer with a simple scene, for educational purpose.
Not optimized at all, but it generates the image in a few seconds on my 8 core PC.
How to prevent multiprocessing spawn from using an edited python script?
I am running multiprocessing in Python with start method ‘spawn’. I have the following code:
Is there a way to shut down the Python multiprocessing resource tracker process?
I submitted a question a week ago about persistent processes after terminating the ProcessPoolExecutor, but there have been no replies. I think this might be because not enough people are familiar with how ProcessPoolExecutor is coded, so I thought it would be helpful to ask a more general question to those who use the multiprocessing module.
Multiprocessing as slow as sequential
def blend(s1_num, ind_f, ind_t, connection): s2_num = 1.0 – s1_num blended_array = [] blended_vec = [] lt = len(template) for i in range(ind_f, ind_t): p = np.array(nlp.vocab[s1[i]].vector) o = np.array(nlp.vocab[s2[i]].vector) p *= s1_num o *= s2_num blended_vec = p + o ms = nlp.vocab.vectors.most_similar(np.asarray([blended_vec]), n=16) words = [nlp.vocab.strings[w] for w in ms[0][0]] blended_array = blended_array […]
Why are `_parent_pid` and `_parent_name` attributes of class BaseProcess(object) in multiprocessing/process.py assigned with current info?
According to the multiprocessing/process.py file for Python 3.10.12:
How do python main process and forked process share gc information?
From the post /a/53504673/9191338 :
Python multiprocessing apply_async with callback does not update the dictionary data structure
I’m trying to count all the tokens in a catalog. Due to the large amount of document, I’d like to do this counting using multiprocessing (or any other parallel calculation tool that you’re free to mention). My problem is that naive construction does not work.