multiprocessing

问题 I want to search for pre-defined list of keywords in a given article and increment the score by 1 if keyword is found in article. I want to use multiprocessing since pre-defined list of keyword is very large - 10k keywords and number of article is 100k. I came across this question but it does not address my question. I tried this implementation but getting None as result. keywords = ["threading", "package", "parallelize"] def search_worker(keyword): score = 0 article = """ The multiprocessing

问题 I have a list of dictionaries. list_of_dict = [{'name' : 'bob', 'weight': 50}, {'name' : 'ramesh', 'weight': 60}] I want to process both the dictionaries at the same time What should I use for this multiprocessing Pool or Process ? 回答1: I have tried with Multiprocessing Pool from multiprocessing.pool import ThreadPool as Pool pool_size = 5 def worker(item1, itme2): try: print(item1.get('weight')) print(itme2) except: print('error with item') pool = Pool(pool_size) items = [{'name' : 'bob',

问题 I am trying to apply a function to 5 cross validation sets in parallel using Python's multiprocessing and repeat that for different parameter values, like so: import pandas as pd import numpy as np import multiprocessing as mp from sklearn.model_selection import StratifiedKFold #simulated datasets X = pd.DataFrame(np.random.randint(2, size=(3348,868), dtype='int8')) y = pd.Series(np.random.randint(2, size=3348, dtype='int64')) #dummy function to apply def _work(args): del(args) for C in np

问题 I am trying to apply a function to 5 cross validation sets in parallel using Python's multiprocessing and repeat that for different parameter values, like so: import pandas as pd import numpy as np import multiprocessing as mp from sklearn.model_selection import StratifiedKFold #simulated datasets X = pd.DataFrame(np.random.randint(2, size=(3348,868), dtype='int8')) y = pd.Series(np.random.randint(2, size=3348, dtype='int64')) #dummy function to apply def _work(args): del(args) for C in np

问题 Frown as much as you want, I'm going to do it anyway :) My question is: in the following code, what is the proper way to handle the termination of the std::thread in the subprocess generated by fork() ? std::thread::detach() or std::thread::join() ? #include <thread> #include <iostream> #include <unistd.h> struct A { void Fork() { std::thread t(&A::Parallel, this); pid_t pid = fork(); if(pid) { //parent t.join(); } else { //child t.join(); // OR t.detach()? } } void Parallel() { std::cout <<

问题 Basically I have trained a few models using keras to do isolated word recognition. Currently i can record the audio using sound device record function for a pre-fixed duration and save the audio file as a wav file. I have implemented silence detection to trim out unwanted samples. But this is all working after the whole recording is complete. I would like to get the trimmed audio segments immediately while recording simultaneously so that i can do speech recognition in real-time. I'm using

问题 context is an optional argument in the constructor of class multiprocessing.pool.Pool . Documentation only says: context can be used to specify the context used for starting the worker processes. Usually a pool is created using the function multiprocessing.Pool() or the Pool() method of a context object. In both cases context is set appropriately. It doesn't clarify what a "context object" is, why class Pool constructor needs it, and what it means that it "is set appropriately" in the

问题 I am trying to update a key in a nested dictionary of multiprocessing module's manager.dict() but not able to do so. It doesn't update the value and doesn't throw any error too. Code: import time import random from multiprocessing import Pool, Manager def spammer_task(d, token, repeat): success = 0 fail = 0 while success+fail<repeat: time.sleep(random.random()*2.0) if (random.random()*100)>98.0: fail+=1 else: success+=1 d[token] = { 'status': 'ongoing', 'fail': fail, 'success': success,

问题 I understand that threads in Python use the same instance of Python interpreter. My question is it the same with process created by os.fork ? Or does each process created by os.fork has its own interpreter? 回答1: Whenever you fork, the entire Python process is duplicated in memory ( including the Python interpreter, your code and any libraries, current stack etc.) to create a second process - one reason why forking a process is much more expensive than creating a thread. This creates a new

问题 I understand that threads in Python use the same instance of Python interpreter. My question is it the same with process created by os.fork ? Or does each process created by os.fork has its own interpreter? 回答1: Whenever you fork, the entire Python process is duplicated in memory ( including the Python interpreter, your code and any libraries, current stack etc.) to create a second process - one reason why forking a process is much more expensive than creating a thread. This creates a new