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binhvq avatar binhvq commented on September 17, 2024 1

Cấu hình hệ thống:

  • Server: 24 core
  • RAM 80GB
  • Process: 10

Sử dụng 2 cách:

  • Queue để get text line
  • Pool.map

Vấn đề: Process được khoảng 100MB text thì full RAM

Bên dưới là code sử dụng Pool.map còn code sử dụng Queue thì đã xóa rồi.

import multiprocessing
from gensim.models import Phrases
from gensim.models.phrases import Phraser
import pickle
from underthesea import word_sent
import time
from multiprocessing import Queue, Pool

sentences = []
with open('corpus.txt', 'r') as fs:
    for count, sentence in enumerate(fs):
        if count % 10000 == 0:
            print(count)
        sentence = sentence.strip()
        sentences.append(sentence)


def run(sent):
    p_name = multiprocessing.current_process().name
    files_fs[p_name].write("%s\n" % "".join(word_sent(sent, format="text")))


if __name__ == '__main__':
    num_process = 20
    files_fs = {}
    for i in range(1, num_process + 1):
        file_name = './corpus/ForkPoolWorker-%s' % i
        files_fs['ForkPoolWorker-%s' % i] = open(file_name, 'w')

    p = Pool(num_process)
    print(p.map(run, sentences))`

from underthesea.

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