代码

Question

以下代码可以正常工作，但由于传递了大型数据集，因此速度非常慢。在实际实现中，创建流程和发送数据所需的速度与计算时间几乎相同，因此在创建第二个流程时，第一个流程几乎完成了计算，并行化了？没有意义的。

代码与此问题Multiprocessing has cutoff at 992 integers being joined as result中的代码相同，建议的更改在下面工作并实施。然而，我遇到了常见问题，因为我认为其他人需要长时间腌制大量数据。

我看到使用multiprocessing.array传递共享内存数组的答案。我有一个~4000个索引的数组，但每个索引都有一个包含200个键/值对的字典。每个过程只读取数据，完成一些计算，然后返回矩阵（4000x3）（没有dicts）。

像Is shared readonly data copied to different processes for Python multiprocessing?这样的答案使用地图。是否可以维护以下系统并实现共享内存？有没有一种有效的方法将数据发送到每个进程的数组，例如将dict包装在某个管理器中，然后将其放在multiprocessing.array中？

import multiprocessing

def main():
    data = {}
    total = []
    for j in range(0,3000):
        total.append(data)
        for i in range(0,200):
            data[str(i)] = i

    CalcManager(total,start=0,end=3000)

def CalcManager(myData,start,end):
    print 'in calc manager'
    #Multi processing
    #Set the number of processes to use.  
    nprocs = 3
    #Initialize the multiprocessing queue so we can get the values returned to us
    tasks = multiprocessing.JoinableQueue()
    result_q = multiprocessing.Queue()
    #Setup an empty array to store our processes
    procs = []
    #Divide up the data for the set number of processes 
    interval = (end-start)/nprocs 
    new_start = start
    #Create all the processes while dividing the work appropriately
    for i in range(nprocs):
        print 'starting processes'
        new_end = new_start + interval
        #Make sure we dont go past the size of the data 
        if new_end > end:
            new_end = end 
        #Generate a new process and pass it the arguments 
        data = myData[new_start:new_end]
        #Create the processes and pass the data and the result queue
        p = multiprocessing.Process(target=multiProcess,args=(data,new_start,new_end,result_q,i))
        procs.append(p)
        p.start()
        #Increment our next start to the current end 
        new_start = new_end+1
    print 'finished starting'    

    #Print out the results
    for i in range(nprocs):
        result = result_q.get()
        print result

    #Joint the process to wait for all data/process to be finished
    for p in procs:
        p.join()

#MultiProcess Handling
def multiProcess(data,start,end,result_q,proc_num):
    print 'started process'
    results = []
    temp = []
    for i in range(0,22):
        results.append(temp)
        for j in range(0,3):
            temp.append(j)
    result_q.put(results)
    return

if __name__== '__main__':   
    main()

解决

只需将字典列表放入经理，问题就解决了。

manager=Manager()
d=manager.list(myData)

看起来持有该列表的经理也管理该列表所包含的字典。启动时间有点慢，所以看起来数据仍然被复制，但它在开始时完成一次，然后在数据被切片的过程中完成。

import multiprocessing
import multiprocessing.sharedctypes as mt
from multiprocessing import Process, Lock, Manager
from ctypes import Structure, c_double

def main():
    data = {}
    total = []
    for j in range(0,3000):
        total.append(data)
        for i in range(0,100):
            data[str(i)] = i

    CalcManager(total,start=0,end=500)

def CalcManager(myData,start,end):
    print 'in calc manager'
    print type(myData[0])

    manager = Manager()
    d = manager.list(myData)

    #Multi processing
    #Set the number of processes to use.  
    nprocs = 3
    #Initialize the multiprocessing queue so we can get the values returned to us
    tasks = multiprocessing.JoinableQueue()
    result_q = multiprocessing.Queue()
    #Setup an empty array to store our processes
    procs = []
    #Divide up the data for the set number of processes 
    interval = (end-start)/nprocs 
    new_start = start
    #Create all the processes while dividing the work appropriately
    for i in range(nprocs):
        new_end = new_start + interval
        #Make sure we dont go past the size of the data 
        if new_end > end:
            new_end = end 
        #Generate a new process and pass it the arguments 
        data = myData[new_start:new_end]
        #Create the processes and pass the data and the result queue
        p = multiprocessing.Process(target=multiProcess,args=(d,new_start,new_end,result_q,i))
        procs.append(p)
        p.start()
        #Increment our next start to the current end 
        new_start = new_end+1
    print 'finished starting'    

    #Print out the results
    for i in range(nprocs):
        result = result_q.get()
        print len(result)

    #Joint the process to wait for all data/process to be finished
    for p in procs:
        p.join()

#MultiProcess Handling
def multiProcess(data,start,end,result_q,proc_num):
    #print 'started process'
    results = []
    temp = []
    data = data[start:end]
    for i in range(0,22):
        results.append(temp)
        for j in range(0,3):
            temp.append(j)
    print len(data)        
    result_q.put(results)
    return

if __name__ == '__main__':
    main()

Answer 1

看看你的问题，我假设如下：

对于myData中的每个项目，您想要返回一个输出（某种矩阵）
您创建了一个JoinableQueue（tasks）可能用于保存输入，但不确定如何使用它

代码

import logging
import multiprocessing


def create_logger(logger_name):
    ''' Create a logger that log to the console '''
    logger = logging.getLogger(logger_name)
    logger.setLevel(logging.DEBUG)

    # create console handler and set appropriate level
    ch = logging.StreamHandler()
    formatter = logging.Formatter("%(processName)s %(funcName)s() %(levelname)s: %(message)s")
    ch.setFormatter(formatter)
    logger.addHandler(ch)
    return logger

def main():
    global logger
    logger = create_logger(__name__)
    logger.info('Main started')
    data = []
    for i in range(0,100):
        data.append({str(i):i})

    CalcManager(data,start=0,end=50)
    logger.info('Main ended')

def CalcManager(myData,start,end):
    logger.info('CalcManager started')
    #Initialize the multiprocessing queue so we can get the values returned to us
    tasks = multiprocessing.JoinableQueue()
    results = multiprocessing.Queue()

    # Add tasks
    for i in range(start, end):
        tasks.put(myData[i])

    # Create processes to do work
    nprocs = 3
    for i in range(nprocs):
        logger.info('starting processes')
        p = multiprocessing.Process(target=worker,args=(tasks,results))
        p.daemon = True
        p.start()

    # Wait for tasks completion, i.e. tasks queue is empty
    try:
        tasks.join()
    except KeyboardInterrupt:
        logger.info('Cancel tasks')

    # Print out the results
    print 'RESULTS'
    while not results.empty():
        result = results.get()
        print result

    logger.info('CalManager ended')

def worker(tasks, results):
    while True:
        try:
            task = tasks.get()  # one row of input
            task['done'] = True # simular work being done
            results.put(task)   # Save the result to the output queue
        finally:
            # JoinableQueue: for every get(), we need a task_done()
            tasks.task_done()


if __name__== '__main__':   
    main()

讨论

对于多个流程情况，我建议使用logging模块，因为它提供了一些优势：
- 它是线程和过程安全的;意味着你不会有一个进程的输出混合在一起的情况
- 您可以配置日志记录以显示进程名称，函数名称 - 非常便于调试
CalcManager本质上是一个任务管理器，它执行以下操作
1. 创建三个流程
2. 填充输入队列tasks
3. 等待任务完成
4. 打印出结果
请注意，在创建进程时，我将它们标记为守护程序，这意味着它们将在主程序退出时被终止。你不必担心杀死他们
worker是完成工作的地方
- 每个都永远运行（while True循环）
- 每次循环，他们将得到一个输入单位，做一些处理，然后把结果放在输出中
- 任务完成后，它会调用task_done()，以便主进程知道所有作业何时完成。我将task_done放在finally子句中，以确保即使处理过程中发生错误也会运行

Answer 2

通过使用multiprocessing.Manager将列表存储在管理器服务器中，并让每个子进程通过从一个共享列表中提取项目来处理来自dict的项目，而不是将切片复制到每个子进程：

def CalcManager(myData,start,end):
    print 'in calc manager'
    print type(myData[0])

    manager = Manager()
    d = manager.list(myData)

    nprocs = 3 
    result_q = multiprocessing.Queue()
    procs = []

    interval = (end-start)/nprocs 
    new_start = start

    for i in range(nprocs):
        new_end = new_start + interval
        if new_end > end:
            new_end = end 
        p = multiprocessing.Process(target=multiProcess,
                                    args=(d, new_start, new_end, result_q, i))
        procs.append(p)
        p.start()
        #Increment our next start to the current end 
        new_start = new_end+1
    print 'finished starting'        

    for i in range(nprocs):
        result = result_q.get()
        print len(result)

    #Joint the process to wait for all data/process to be finished
    for p in procs:
        p.join()

在创建任何工作人员之前，这会将您的整个data列表复制到Manager进程。 Manager返回Proxy对象，该对象允许对list进行共享访问。然后，您只需将Proxy传递给工作人员，这意味着他们的启动时间将大大减少，因为不再需要复制data列表的切片。这里的缺点是访问列表的孩子会更慢，因为访问需要通过IPC进入管理器进程。这是否真的有助于提高性能在很大程度上取决于您在工作流程中list上正在做什么工作，但值得一试，因为它只需要很少的代码更改。

多处理通过共享内存传递一系列dicts

2 个答案:

代码

讨论