具有 textacy 或 spacy 的多处理

Question

我正在尝试通过文本并行化来加快处理大型文本列表的速度。当我从多处理中使用 Pool 时，生成的文本语料库是空的。我不确定问题出在我使用 textacy 还是多处理范例的方式上？这是说明我的问题的示例：

import spacy
import textacy
from multiprocessing import Pool

texts_dict={
"key1":"First text 1."
,"key2":"Second text 2."
,"key3":"Third text 3."
,"key4":"Fourth text 4."
}

model=spacy.load('en_core_web_lg')

# this works

corpus = textacy.corpus.Corpus(lang=model)

corpus.add(tuple([value, {'key':key}],) for key,value in texts_dict.items())

print(corpus) # prints Corpus(4 docs, 8 tokens)
print([doc for doc in corpus])

# now the same thing with a worker pool returns empty corpus

corpus2 = textacy.corpus.Corpus(lang=model)

pool = Pool(processes=2) 
pool.map( corpus2.add, (tuple([value, {'key':key}],) for key,value in texts_dict.items()) )

print(corpus2) # prints Corpus(0 docs, 0 tokens)
print([doc for doc in corpus2])

# to make sure we get the right data into corpus.add
pool.map( print, (tuple([value, {'key':key}],) for key,value in texts_dict.items()) )

Textacy 基于 spacy。 Spacy 不支持多线程，但应该可以在多个进程中使用运行。 https://github.com/explosion/spaCy/issues/2075

根据@constt 的重要建议，将结果收集到语料库适用于 n_docs= 10273 n_sentences= 302510 n_tokens= 2053129。

对于更大的数据集（16000 个 docs 3MM 标记），我收到以下错误：

result_corpus=corpus.get() 
  File "<string>", line 2, in get
  File "/usr/lib/python3.6/multiprocessing/managers.py", line 772, in _callmethod
    raise convert_to_error(kind, result)
multiprocessing.managers.RemoteError: 
---------------------------------------------------------------------------
Unserializable message: Traceback (most recent call last):
  File "/usr/lib/python3.6/multiprocessing/managers.py", line 283, in serve_client
    send(msg)
  File "/usr/lib/python3.6/multiprocessing/connection.py", line 206, in send
    self._send_bytes(_ForkingPickler.dumps(obj))
  File "/usr/lib/python3.6/multiprocessing/connection.py", line 393, in _send_bytes
    header = struct.pack("!i", n)
struct.error: 'i' format requires -2147483648 <= number <= 2147483647

´ 我会调查，但如果您有直接的解决方案 - 非常感谢！

Answer 1

由于 python 在单独的内存空间中处理运行，您必须在池中的进程之间共享您的 corpus 对象。为此，您必须将 corpus 对象包装到一个可共享的 class 中，您将使用 BaseManager class 注册它。以下是重构代码以使其工作的方法：

#!/usr/bin/python3
from multiprocessing import Pool
from multiprocessing.managers import BaseManager

import spacy
import textacy


texts = {
    'key1': 'First text 1.',
    'key2': 'Second text 2.',
    'key3': 'Third text 3.',
    'key4': 'Fourth text 4.',
}


class PoolCorpus(object):

    def __init__(self):
        model = spacy.load('en_core_web_sm')
        self.corpus = textacy.corpus.Corpus(lang=model)

    def add(self, data):
        self.corpus.add(data)

    def get(self):
        return self.corpus


BaseManager.register('PoolCorpus', PoolCorpus)


if __name__ == '__main__':

    with BaseManager() as manager:
        corpus = manager.PoolCorpus()

        with Pool(processes=2) as pool:
            pool.map(corpus.add, ((v, {'key': k}) for k, v in texts.items()))

        print(corpus.get())

输出：

Corpus(4 docs, 16 tokens)

具有 textacy 或 spacy 的多处理

multiprocessing with textacy or spacy

python

multiprocessing

spacy

pool

textacy