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41 lines
1.7 KiB
Plaintext
41 lines
1.7 KiB
Plaintext
//- 💫 DOCS > USAGE > PROCESSING PIPELINES > MULTI-THREADING
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p
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| If you have a sequence of documents to process, you should use the
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| #[+api("language#pipe") #[code Language.pipe()]] method. The method takes
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| an iterator of texts, and accumulates an internal buffer,
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| which it works on in parallel. It then yields the documents in order,
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| one-by-one. After a long and bitter struggle, the global interpreter
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| lock was freed around spaCy's main parsing loop in v0.100.3. This means
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| that #[code .pipe()] will be significantly faster in most
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| practical situations, because it allows shared memory parallelism.
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+code.
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for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
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pass
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p
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| To make full use of the #[code .pipe()] function, you might want to
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| brush up on #[strong Python generators]. Here are a few quick hints:
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+list
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+item
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| Generator comprehensions can be written as
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| #[code (item for item in sequence)].
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+item
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| The
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| #[+a("https://docs.python.org/2/library/itertools.html") #[code itertools] built-in library]
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| and the
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| #[+a("https://github.com/pytoolz/cytoolz") #[code cytoolz] package]
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| provide a lot of handy #[strong generator tools].
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+item
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| Often you'll have an input stream that pairs text with some
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| important meta data, e.g. a JSON document. To
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| #[strong pair up the meta data] with the processed #[code Doc]
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| object, you should use the #[code itertools.tee] function to split
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| the generator in two, and then #[code izip] the extra stream to the
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| document stream. Here's
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| #[+a(gh("spacy") + "/issues/172#issuecomment-183963403") an example].
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