使用python的多处理池和地图功能进行测量

Question

以下代码我用于并行csv处理:

#!/usr/bin/env python

import csv
from time import sleep
from multiprocessing import Pool
from multiprocessing import cpu_count
from multiprocessing import current_process
from pprint import pprint as pp

def init_worker(x):
  sleep(.5)
  print "(%s,%s)" % (x[0],x[1])
  x.append(int(x[0])**2)
  return x

def parallel_csv_processing(inputFile, outputFile, header=["Default", "header", "please", "change"], separator=",", skipRows = 0, cpuCount = 1):
  # OPEN FH FOR READING INPUT FILE
  inputFH   = open(inputFile,  "rt")
  csvReader = csv.reader(inputFH, delimiter=separator)

  # SKIP HEADERS
  for skip in xrange(skipRows):
    csvReader.next()

  # PARALLELIZE COMPUTING INTENSIVE OPERATIONS - CALL FUNCTION HERE
  try:
    p = Pool(processes = cpuCount)
    results = p.map(init_worker, csvReader, chunksize = 10)
    p.close()
    p.join()
  except KeyboardInterrupt:
    p.close()
    p.join()
    p.terminate()

  # CLOSE FH FOR READING INPUT
  inputFH.close()

  # OPEN FH FOR WRITING OUTPUT FILE
  outputFH  = open(outputFile, "wt")
  csvWriter = csv.writer(outputFH, lineterminator='\n')

  # WRITE HEADER TO OUTPUT FILE
  csvWriter.writerow(header)

  # WRITE RESULTS TO OUTPUT FILE
  [csvWriter.writerow(row) for row in results]

  # CLOSE FH FOR WRITING OUTPUT
  outputFH.close()

  print pp(results)
  # print len(results)

def main():
  inputFile  = "input.csv"
  outputFile = "output.csv"
  parallel_csv_processing(inputFile, outputFile, cpuCount = cpu_count())

if __name__ == '__main__':
  main()

我想以某种方式衡量脚本的进度(只是纯文本而不是任何奇特的ASCII艺术).我想到的一个选项是将成功处理的init_worker行与input.csv中的所有行进行比较,并打印实际状态,例如每秒,你能指出我正确的解决方案吗？我发现了几篇有类似问题的文章,但我无法根据自己的需要调整它,因为它们都没有使用过Pool类和map方法.我还想问一下p.close(), p.join(), p.terminate()方法,我看过他们主要是Process没有Pool上课,他们是否需要Pool上课,我是否正确使用它们？使用的p.terminate()目的是用ctrl + c来杀死进程,但这个不同的故事尚未结束.谢谢.

PS:我的input.csv看起来像这样,如果重要的话:

0,0
1,3
2,6
3,9
...
...
48,144
49,147

PPS:正如我所说,我是新手,multiprocessing而且我把它放在一起的代码才有效.我可以看到的一个缺点是整个csv存储在内存中,所以如果你们有更好的想法,请不要犹豫,分享它.

编辑

回复@JFSebastian

以下是基于您的建议的实际代码:

#!/usr/bin/env python

import csv
from time import sleep
from multiprocessing import Pool
from multiprocessing import cpu_count
from multiprocessing import current_process
from pprint import pprint as pp
from tqdm import tqdm

def do_job(x):
  sleep(.5)
  # print "(%s,%s)" % (x[0],x[1])
  x.append(int(x[0])**2)
  return x

def parallel_csv_processing(inputFile, outputFile, header=["Default", "header", "please", "change"], separator=",", skipRows = 0, cpuCount = 1):

  # OPEN FH FOR READING INPUT FILE
  inputFH   = open(inputFile,  "rb")
  csvReader = csv.reader(inputFH, delimiter=separator)

  # SKIP HEADERS
  for skip in xrange(skipRows):
    csvReader.next()

  # OPEN FH FOR WRITING OUTPUT FILE
  outputFH  = open(outputFile, "wt")
  csvWriter = csv.writer(outputFH, lineterminator='\n')

  # WRITE HEADER TO OUTPUT FILE
  csvWriter.writerow(header)

  # PARALLELIZE COMPUTING INTENSIVE OPERATIONS - CALL FUNCTION HERE
  try:
    p = Pool(processes = cpuCount)
    # results = p.map(do_job, csvReader, chunksize = 10)
    for result in tqdm(p.imap_unordered(do_job, csvReader, chunksize=10)):
      csvWriter.writerow(result)
    p.close()
    p.join()
  except KeyboardInterrupt:
    p.close()
    p.join()

  # CLOSE FH FOR READING INPUT
  inputFH.close()

  # CLOSE FH FOR WRITING OUTPUT
  outputFH.close()

  print pp(result)
  # print len(result)

def main():
  inputFile  = "input.csv"
  outputFile = "output.csv"
  parallel_csv_processing(inputFile, outputFile, cpuCount = cpu_count())

if __name__ == '__main__':
  main()

这是输出tqdm:

1 [elapsed: 00:05,  0.20 iters/sec]

这个输出是什么意思？在您引用的页面上tqdm以循环方式使用:

>>> import time
>>> from tqdm import tqdm
>>> for i in tqdm(range(100)):
...     time.sleep(1)
... 
|###-------| 35/100  35% [elapsed: 00:35 left: 01:05,  1.00 iters/sec]

这个输出很有意义,但我的输出是什么意思？此外它似乎没有修复ctrl + c问题:点击ctrl + c脚本后抛出一些Traceback,如果我再次点击ctrl + c然后我得到新的Traceback等等.杀死它的唯一方法是将其发送到后台(ctr + z)然后杀死它(杀死%1)

Answer 1

要显示进度,请替换pool.map为pool.imap_unordered:

from tqdm import tqdm # $ pip install tqdm

for result in tqdm(pool.imap_unordered(init_worker, csvReader, chunksize=10)):
    csvWriter.writerow(result)

tqdmpart是可选的,请参阅控制台中的Text Progress Bar

无意中,它修复了"整个csv存储在内存中"和"KeyboardInterrupt未引发"问题.

这是一个完整的代码示例:

#!/usr/bin/env python
import itertools
import logging
import multiprocessing
import time

def compute(i):
    time.sleep(.5)
    return i**2

if __name__ == "__main__":
    logging.basicConfig(format="%(asctime)-15s %(levelname)s %(message)s",
                        datefmt="%F %T", level=logging.DEBUG)
    pool = multiprocessing.Pool()
    try:
        for square in pool.imap_unordered(compute, itertools.count(), chunksize=10):
            logging.debug(square) # report progress by printing the result
    except KeyboardInterrupt:
        logging.warning("got Ctrl+C")
    finally:
        pool.terminate()
        pool.join()

您应该每秒批量查看输出.5 * chunksize.如果按Ctrl+C; 你应该看到KeyboardInterrupt在子进程和主进程中提出.在Python 3中,主进程立即退出.在Python 2中,它会KeyboardInterrupt被延迟,直到下一批应该被打印出来(Python中的错误).