Python 导入模块（nltk）会导致多处理挂起

我跟踪了一个python多处理难题，一直到模块（nltk）的导入。可复制（希望）代码粘贴在下面。这对我来说毫无意义，有人有什么想法吗

from multiprocessing import Pool
import time, requests
#from nltk.corpus import stopwords   # uncomment this and it hangs

def gethtml(key, url):
    r = requests.get(url)
    return r.text

def getnothing(key, url):
    return "nothing"

if __name__ == '__main__':
    pool = Pool(processes=4)
    result = list()
    nruns = 4
    url = 'http://davidchao.typepad.com/webconferencingexpert/2013/08/gartners-magic-quadrant-for-cloud-infrastructure-as-a-service.html'
    for i in range(0,nruns):
#        print gethtml(i,url)
        result.append(pool.apply_async(gethtml, [i,url]))
#        result.append(pool.apply_async(getnothing, [i,url]))
    pool.close()

    # monitor jobs until they complete
    running = nruns
    while running > 0:
        time.sleep(1)
        running = 0
        for run in result:
            if not run.ready(): running += 1
        print "processes still running:",running

    # print results
    for i,run in enumerate(result):
        print i,run.get()[0:40]

请注意，“getnothing”函数可以工作。它是nltk模块导入和请求调用的组合。叹息

> python --version
Python 2.7.6

> python -c 'import sys;print("%x" % sys.maxsize, sys.maxsize > 2**32)'
('7fffffffffffffff', True)

> pip freeze | grep requests
requests==2.2.1

> pip freeze | grep nltk
nltk==2.0.4

---

我会将其他有类似问题的人重定向到不使用多处理模块的解决方案：

1） Apache Spark可扩展性/灵活性。然而，这似乎不是python多处理的解决方案。看起来pyspark也受到全局解释器锁的限制

2） 用于一般python异步处理的“gevent”或“twisted”

3） 异步请求的grequests