//- 💫 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, i.e. #[+api("language") #[code Language]],
    |  #[+api("doc") #[code Doc]], #[+api("vocab") #[code Vocab]] and
    |  #[+api("stringstore") #[code StringStore]] 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)

p
    |  For example, if you've processed a very large document, you can use
    |  #[+api("doc#to_disk") #[code Doc.to_disk]] to save it to a file on your
    |  local machine. This will save the document and its tokens, as well as
    |  the vocabulary associated with the #[code Doc].

+aside("Why saving the vocab?")
    |  Saving the vocabulary with the #[code Doc] is important, because the
    |  #[code Vocab] holds the context-independent information about the words,
    |  tags and labels, and their #[strong integer IDs]. If the #[code Vocab]
    |  wasn't saved with the #[code Doc], spaCy wouldn't know how to resolve
    |  those IDs – for example, the word text or the dependency labels. You
    |  might be saving #[code 446] for "whale", but in a different vocabulary,
    |  this ID could map to "VERB". Similarly, if your document was processed by
    |  a German model, its vocab will include the specific
    |  #[+a("/docs/api/annotation#dependency-parsing-german") German dependency labels].

+code.
    moby_dick = open('moby_dick.txt', 'r') # open a large document
    doc = nlp(moby_dick) # process it
    doc.to_disk('/moby_dick.bin') # save the processed Doc

p
    |  If you need it again later, you can load it back into an empty #[code Doc]
    |  with an empty #[code Vocab] by calling
    |  #[+api("doc#from_disk") #[code from_disk()]]:

+code.
    from spacy.tokens import Doc # to create empty Doc
    from spacy.vocab import Vocab # to create empty Vocab

    doc = Doc(Vocab()).from_disk('/moby_dick.bin') # load processed Doc