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