spaCy/website/usage/_processing-pipelines/_multithreading.jade
2017-10-27 12:30:59 +02:00

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//- 💫 DOCS > USAGE > PROCESSING PIPELINES > MULTI-THREADING
p
| If you have a sequence of documents to process, you should use the
| #[+api("language#pipe") #[code Language.pipe()]] method. The method takes
| an iterator of texts, and accumulates an internal buffer,
| which it works on in parallel. It then yields the documents in order,
| one-by-one. After a long and bitter struggle, the global interpreter
| lock was freed around spaCy's main parsing loop in v0.100.3. This means
| that #[code .pipe()] will be significantly faster in most
| practical situations, because it allows shared memory parallelism.
+code.
for doc in nlp.pipe(texts, batch_size=10000, n_threads=3):
pass
p
| To make full use of the #[code .pipe()] function, you might want to
| brush up on #[strong Python generators]. Here are a few quick hints:
+list
+item
| Generator comprehensions can be written as
| #[code (item for item in sequence)].
+item
| The
| #[+a("https://docs.python.org/2/library/itertools.html") #[code itertools] built-in library]
| and the
| #[+a("https://github.com/pytoolz/cytoolz") #[code cytoolz] package]
| provide a lot of handy #[strong generator tools].
+item
| Often you'll have an input stream that pairs text with some
| important meta data, e.g. a JSON document. To
| #[strong pair up the meta data] with the processed #[code Doc]
| object, you should use the #[code itertools.tee] function to split
| the generator in two, and then #[code izip] the extra stream to the
| document stream. Here's
| #[+a(gh("spacy") + "/issues/172#issuecomment-183963403") an example].
+h(3, "multi-processing-example") Example: Multi-processing with Joblib
p
| This example shows how to use multiple cores to process text using
| spaCy and #[+a("https://pythonhosted.org/joblib/") Joblib]. We're
| exporting part-of-speech-tagged, true-cased, (very roughly)
| sentence-separated text, with each "sentence" on a newline, and
| spaces between tokens. Data is loaded from the IMDB movie reviews
| dataset and will be loaded automatically via Thinc's built-in dataset
| loader.
+github("spacy", "examples/pipeline/multi_processing.py", 500)