spaCy/website/docs/usage/_spacy-101/_serialization.jade
2017-05-24 19:24:40 +02:00

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//- 💫 DOCS > USAGE > SPACY 101 > SERIALIZATION
p
| If you've been modifying the pipeline, vocabulary vectors and entities, or made
| updates to the model, you'll eventually want
| to #[strong save your progress] for example, everything that's in your #[code nlp]
| object. This means you'll have to translate its contents and structure
| into a format that can be saved, like a file or a byte string. This
| process is called serialization. spaCy comes with
| #[strong built-in serialization methods] and supports the
| #[+a("http://www.diveintopython3.net/serializing.html#dump") Pickle protocol].
+aside("What's pickle?")
| Pickle is Python's built-in object persistance system. It lets you
| transfer arbitrary Python objects between processes. This is usually used
| to load an object to and from disk, but it's also used for distributed
| computing, e.g. with
| #[+a("https://spark.apache.org/docs/0.9.0/python-programming-guide.html") PySpark]
| or #[+a("http://dask.pydata.org/en/latest/") Dask]. When you unpickle an
| object, you're agreeing to execute whatever code it contains. It's like
| calling #[code eval()] on a string so don't unpickle objects from
| untrusted sources.
p
| All container classes and pipeline components, i.e.
for cls in ["Doc", "Language", "Tokenizer", "Tagger", "DependencyParser", "EntityRecognizer", "Vocab", "StringStore"]
| #[+api(cls.toLowerCase()) #[code=cls]],
| have the following methods available:
+table(["Method", "Returns", "Example"])
- style = [1, 0, 1]
+annotation-row(["to_bytes", "bytes", "nlp.to_bytes()"], style)
+annotation-row(["from_bytes", "object", "nlp.from_bytes(bytes)"], style)
+annotation-row(["to_disk", "-", "nlp.to_disk('/path')"], style)
+annotation-row(["from_disk", "object", "nlp.from_disk('/path')"], style)