multiprocessing

问题 I'm a total node.js newbie who's just started tinkering with it. I have a piece of code that executes a function that processes strings on all cpu cores, and I wish to determine which worker completed the function first by it's id, and after that kill every worker (or just exit node). Here's the simplified code of my program: var cluster = require('cluster'), cpus = require("os").cpus().length, // 4 cores myArray = ["foo","bar","baz","qux"]; // 1 string per core if (cluster.isMaster) { for

问题 I'm a total node.js newbie who's just started tinkering with it. I have a piece of code that executes a function that processes strings on all cpu cores, and I wish to determine which worker completed the function first by it's id, and after that kill every worker (or just exit node). Here's the simplified code of my program: var cluster = require('cluster'), cpus = require("os").cpus().length, // 4 cores myArray = ["foo","bar","baz","qux"]; // 1 string per core if (cluster.isMaster) { for

问题 I am trying to receive/send data at the same time, and my idea to doing this was import multiprocessing import time from reprint import output import time import random def receiveThread(queue): while True: queue.put(random.randint(0, 50)) time.sleep(0.5) def sendThread(queue): while True: queue.put(input()) if __name__ == "__main__": send_queue = multiprocessing.Queue() receive_queue = multiprocessing.Queue() send_thread = multiprocessing.Process(target=sendThread, args=[send_queue],)

问题 I have use all types of multiprocessing Pool but still the program only using single core (available=8).Any help will be appreciated and thanks in advance. import multiprocessing from multiprocessing import Pool class pdf_gen(): def __init__(self): pdf = self.pdf = FPDF() pdf.set_auto_page_break(True,0.1) def get_data_from_mysql(self) : pdf = self.pdf # connection is established and result is stored in 'res'. dup = [] dup.insert(0,res) z = tuple(dup) pool = multiprocessing.Pool

问题 The simplest way I've found so far to use a parallel lapply in R was through the following example code: library(parallel) library(pbapply) cl <- makeCluster(10) clusterExport(cl = cl, {...}) clusterEvalQ(cl = cl, {...}) results <- pblapply(1:100, FUN = function(x){rnorm(x)}, cl = cl) This has a very useful feature of providing a progress bar for the results, and is very easy to reuse the same code when no parallel computations are needed, by setting cl = NULL . However, one issue that I've

问题 I want to decrease training time of my models by using a high end EC2 instance. So I tried c5.18xlarge instance with 2 CPUs and run a few models with parameter n_jobs=-1 but I noticed that only one CPU was utilized: Can I somehow make Scikit-learn to use all CPUs? 回答1: Try adding: import multiprocessing multiprocessing.set_start_method('forkserver') at the top of your code, before running or importing anything. That's a well-known issue with multiprocessing in python. 来源： https:/

问题 I want to get the result of the function run by Pool.apply_async in Python. How to assign the result to a variable in the parent process? I tried to use callback but it seems complicated. 回答1: The solution is very simple: import multiprocessing def func(): return 2**3**4 p = multiprocessing.Pool() result = p.apply_async(func).get() print(result) Since Pool.apply_async() returns an AsyncResult, you can simply get the result from the AsyncResult.get() method. Hope this helps! 回答2: Well an easy

问题 >>> import concurrent.futures >>> from collections import namedtuple >>> #1. Initialise namedtuple here >>> # tm = namedtuple("tm", ["pk"]) >>> class T: ... #2. Initialise named tuple here ... #tm = namedtuple("tm", ["pk"]) ... def __init__(self): ... #3: Initialise named tuple here ... tm = namedtuple("tm", ["pk"]) ... self.x = {'key': [tm('value')]} ... def test1(self): ... with concurrent.futures.ProcessPoolExecutor(max_workers=1) as executor: ... results = executor.map(self.test, ["key"])

问题 I am trying to run a generator process in parallel by child processes. But when I tried to do this, I see the function with generator was processed by the parent process!!! from multiprocessing import Process import os import time class p(Process): def __init__(self): Process.__init__(self) def run(self): print('PID:', os.getpid()) def genfunc(self): time.sleep(1) yield os.getpid() p1 = p() p2 = p() p1.start() p2.start() print('Iterators:') print('Ran by:',next(p1.genfunc())) print('Ran by:'

问题 I've done some research on this and found somewhat similar questions but none answer what I'm really looking for. I understand how to create and use processes with the multiprocessing module. But when I create a new process, I would like to spawn a new console window just for the use of that process, for printing and so on, so that the child processes don't share the parent process's console window. Is there a way of doing that with the multiprocessing module? 回答1: If you're going to spawn a