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Saving and Loading |
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Basics
import Serialization101 from 'usage/101/_serialization.md'
Serializing the pipeline
When serializing the pipeline, keep in mind that this will only save out the binary data for the individual components to allow spaCy to restore them – not the entire objects. This is a good thing, because it makes serialization safe. But it also means that you have to take care of storing the language name and pipeline component names as well, and restoring them separately before you can load in the data.
Saving the meta and config
The
nlp.meta
attribute is a JSON-serializable dictionary and contains all model meta information like the author and license information. Thenlp.config
attribute is a dictionary containing the training configuration, pipeline component factories and other settings. It is saved out with a model as theconfig.cfg
.
### Serialize
bytes_data = nlp.to_bytes()
lang = nlp.config["nlp"]["lang"] # "en"
pipeline = nlp.config["nlp"]["pipeline"] # ["tagger", "parser", "ner"]
### Deserialize
nlp = spacy.blank(lang)
for pipe_name in pipeline:
nlp.add_pipe(pipe_name)
nlp.from_bytes(bytes_data)
This is also how spaCy does it under the hood when loading a model: it loads the
model's config.cfg
containing the language and pipeline information,
initializes the language class, creates and adds the pipeline components based
on the defined
factories and then
loads in the binary data. You can read more about this process
here.
Serializing Doc objects efficiently
If you're working with lots of data, you'll probably need to pass analyses
between machines, either to use something like Dask or
Spark, or even just to save out work to disk. Often
it's sufficient to use the Doc.to_array
functionality for
this, and just serialize the numpy arrays – but other times you want a more
general way to save and restore Doc
objects.
The DocBin
class makes it easy to serialize and deserialize a
collection of Doc
objects together, and is much more efficient than calling
Doc.to_bytes
on each individual Doc
object. You can
also control what data gets saved, and you can merge pallets together for easy
map/reduce-style processing.
### {highlight="4,8,9,13,14"}
import spacy
from spacy.tokens import DocBin
doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=True)
texts = ["Some text", "Lots of texts...", "..."]
nlp = spacy.load("en_core_web_sm")
for doc in nlp.pipe(texts):
doc_bin.add(doc)
bytes_data = doc_bin.to_bytes()
# Deserialize later, e.g. in a new process
nlp = spacy.blank("en")
doc_bin = DocBin().from_bytes(bytes_data)
docs = list(doc_bin.get_docs(nlp.vocab))
If store_user_data
is set to True
, the Doc.user_data
will be serialized as
well, which includes the values of
extension attributes
(if they're serializable with msgpack).
Including the Doc.user_data
and extension attributes will only serialize the
values of the attributes. To restore the values and access them via the
doc._.
property, you need to register the global attribute on the Doc
again.
docs = list(doc_bin.get_docs(nlp.vocab))
Doc.set_extension("my_custom_attr", default=None)
print([doc._.my_custom_attr for doc in docs])
Using Pickle
Example
doc = nlp("This is a text.") data = pickle.dumps(doc)
When pickling spaCy's objects like the Doc
or the
EntityRecognizer
, keep in mind that they all require
the shared Vocab
(which includes the string to hash mappings,
label schemes and optional vectors). This means that their pickled
representations can become very large, especially if you have word vectors
loaded, because it won't only include the object itself, but also the entire
shared vocab it depends on.
If you need to pickle multiple objects, try to pickle them together instead
of separately. For instance, instead of pickling all pipeline components, pickle
the entire pipeline once. And instead of pickling several Doc
objects
separately, pickle a list of Doc
objects. Since they all share a reference to
the same Vocab
object, it will only be included once.
### Pickling objects with shared data {highlight="8-9"}
doc1 = nlp("Hello world")
doc2 = nlp("This is a test")
doc1_data = pickle.dumps(doc1)
doc2_data = pickle.dumps(doc2)
print(len(doc1_data) + len(doc2_data)) # 6636116 😞
doc_data = pickle.dumps([doc1, doc2])
print(len(doc_data)) # 3319761 😃
Pickling Token
and Span
objects isn't supported. They're only views of the
Doc
and can't exist on their own. Pickling them would always mean pulling in
the parent document and its vocabulary, which has practically no advantage over
pickling the parent Doc
.
- data = pickle.dumps(doc[10:20])
+ data = pickle.dumps(doc)
If you really only need a span – for example, a particular sentence – you can
use Span.as_doc
to make a copy of it and convert it to a
Doc
object. However, note that this will not let you recover contextual
information from outside the span.
+ span_doc = doc[10:20].as_doc()
data = pickle.dumps(span_doc)
Implementing serialization methods
When you call nlp.to_disk
,
nlp.from_disk
or load a model package, spaCy will
iterate over the components in the pipeline, check if they expose a to_disk
or
from_disk
method and if so, call it with the path to the model directory plus
the string name of the component. For example, if you're calling
nlp.to_disk("/path")
, the data for the named entity recognizer will be saved
in /path/ner
.
If you're using custom pipeline components that depend on external data – for
example, model weights or terminology lists – you can take advantage of spaCy's
built-in component serialization by making your custom component expose its own
to_disk
and from_disk
or to_bytes
and from_bytes
methods. When an nlp
object with the component in its pipeline is saved or loaded, the component will
then be able to serialize and deserialize itself. The following example shows a
custom component that keeps arbitrary JSON-serializable data, allows the user to
add to that data and saves and loads the data to and from a JSON file.
Real-world example
To see custom serialization methods in action, check out the new
EntityRuler
component and its source. Patterns added to the component will be saved to a.jsonl
file if the pipeline is serialized to disk, and to a bytestring if the pipeline is serialized to bytes. This allows saving out a model with a rule-based entity recognizer and including all rules with the model data.
### {highlight="14-18,20-25"}
@Language.factory("my_component")
class CustomComponent:
def __init__(self):
self.data = []
def __call__(self, doc):
# Do something to the doc here
return doc
def add(self, data):
# Add something to the component's data
self.data.append(data)
def to_disk(self, path, **kwargs):
# This will receive the directory path + /my_component
data_path = path / "data.json"
with data_path.open("w", encoding="utf8") as f:
f.write(json.dumps(self.data))
def from_disk(self, path, **cfg):
# This will receive the directory path + /my_component
data_path = path / "data.json"
with data_path.open("r", encoding="utf8") as f:
self.data = json.loads(f)
return self
After adding the component to the pipeline and adding some data to it, we can
serialize the nlp
object to a directory, which will call the custom
component's to_disk
method.
### {highlight="2-4"}
nlp = spacy.load("en_core_web_sm")
my_component = nlp.add_pipe("my_component")
my_component.add({"hello": "world"})
nlp.to_disk("/path/to/model")
The contents of the directory would then look like this.
CustomComponent.to_disk
converted the data to a JSON string and saved it to a
file data.json
in its subdirectory:
### Directory structure {highlight="2-3"}
└── /path/to/model
├── my_component # data serialized by "my_component"
| └── data.json
├── ner # data for "ner" component
├── parser # data for "parser" component
├── tagger # data for "tagger" component
├── vocab # model vocabulary
├── meta.json # model meta.json
├── config.cfg # model config
└── tokenizer # tokenization rules
When you load the data back in, spaCy will call the custom component's
from_disk
method with the given file path, and the component can then load the
contents of data.json
, convert them to a Python object and restore the
component state. The same works for other types of data, of course – for
instance, you could add a
wrapper for a model
trained with a different library like TensorFlow or PyTorch and make spaCy load
its weights automatically when you load the model package.
When you load back a model with custom components, make sure that the components
are available and that the @Language.component
or @Language.factory
decorators are executed before
your model is loaded back. Otherwise, spaCy won't know how to resolve the string
name of a component factory like "my_component"
back to a function. For more
details, see the documentation on
adding factories or
use entry points to make your extension package expose your
custom components to spaCy automatically.
Using entry points
Entry points let you expose parts of a Python package you write to other Python
packages. This lets one application easily customize the behavior of another, by
exposing an entry point in its setup.py
. For a quick and fun intro to entry
points in Python, check out
this excellent blog post.
spaCy can load custom function from several different entry points to add
pipeline component factories, language classes and other settings. To make spaCy
use your entry points, your package needs to expose them and it needs to be
installed in the same environment – that's it.
Entry point | Description |
---|---|
spacy_factories |
Group of entry points for pipeline component factories, keyed by component name. Can be used to expose custom components defined by another package. |
spacy_languages |
Group of entry points for custom Language subclasses, keyed by language shortcut. |
spacy_lookups 2.2 |
Group of entry points for custom Lookups , including lemmatizer data. Used by spaCy's spacy-lookups-data package. |
spacy_displacy_colors 2.2 |
Group of entry points of custom label colors for the displaCy visualizer. The key name doesn't matter, but it should point to a dict of labels and color values. Useful for custom models that predict different entity types. |
Custom components via entry points
When you load a model, spaCy will generally use the model's config.cfg
to set
up the language class and construct the pipeline. The pipeline is specified as a
list of strings, e.g. pipeline = ["tagger", "paser", "ner"]
. For each of those
strings, spaCy will call nlp.add_pipe
and look up the name in all factories
defined by the decorators @Language.component
and
@Language.factory
. This means that you have to import
your custom components before loading the model.
Using entry points, model packages and extension packages can define their own
"spacy_factories"
, which will be loaded automatically in the background when
the Language
class is initialized. So if a user has your package installed,
they'll be able to use your components – even if they don't import them!
To stick with the theme of this entry points blog post, consider the following custom spaCy pipeline component that prints a snake when it's called:
Package directory structure
├── snek.py # the extension code └── setup.py # setup file for pip installation
### snek.py
from spacy.language import Language
snek = """
--..,_ _,.--.
`'.'. .'`__ o `;__. {text}
'.'. .'.'` '---'` `
'.`'--....--'`.'
`'--....--'`
"""
@Language.component("snek")
def snek_component(doc):
print(snek.format(text=doc.text))
return doc
Since it's a very complex and sophisticated module, you want to split it off
into its own package so you can version it and upload it to PyPi. You also want
your custom model to be able to define pipeline = ["snek"]
in its
config.cfg
. For that, you need to be able to tell spaCy where to find the
component "snek"
. If you don't do this, spaCy will raise an error when you try
to load the model because there's no built-in "snek"
component. To add an
entry to the factories, you can now expose it in your setup.py
via the
entry_points
dictionary:
Entry point syntax
Python entry points for a group are formatted as a list of strings, with each string following the syntax of
name = module:object
. In this example, the created entry point is namedsnek
and points to the functionsnek_component
in the modulesnek
, i.e.snek.py
.
### setup.py {highlight="5-7"}
from setuptools import setup
setup(
name="snek",
entry_points={
"spacy_factories": ["snek = snek:snek_component"]
}
)
The same package can expose multiple entry points, by the way. To make them available to spaCy, all you need to do is install the package in your environment:
$ python setup.py develop
spaCy is now able to create the pipeline component "snek"
– even though you
never imported snek_component
. When you save the
nlp.config
to disk, it includes an entry for your
"snek"
component and any model you train with this config will include the
component and know how to load it – if your snek
package is installed.
config.cfg (excerpt)
[nlp] lang = "en" + pipeline = ["snek"] [components] + [components.snek] + factory = "snek"
>>> from spacy.lang.en import English
>>> nlp = English()
>>> nlp.add_pipe("snek") # this now works! 🐍🎉
>>> doc = nlp("I am snek")
--..,_ _,.--.
`'.'. .'`__ o `;__. I am snek
'.'. .'.'` '---'` `
'.`'--....--'`.'
`'--....--'`
Instead of making your snek component a simple
stateless component, you
could also make it a
factory that takes
settings. Your users can then pass in an optional config
when they add your
component to the pipeline and customize its appearance – for example, the
snek_style
.
config.cfg (excerpt)
[components.snek] factory = "snek" + snek_style = "basic"
SNEKS = {"basic": snek, "cute": cute_snek} # collection of sneks
@Language.factory("snek", default_config={"snek_style": "basic"})
class SnekFactory:
def __init__(self, nlp: Language, name: str, snek_style: str):
self.nlp = nlp
self.snek_style = snek_style
self.snek = SNEKS[self.snek_style]
def __call__(self, doc):
print(self.snek)
return doc
### setup.py
entry_points={
- "spacy_factories": ["snek = snek:snek_component"]
+ "spacy_factories": ["snek = snek:SnekFactory"]
}
The factory can also implement other pipeline component like to_disk
and
from_disk
for serialization, or even update
to make the component trainable.
If a component exposes a from_disk
method and is included in a model's
pipeline, spaCy will call it on load. This lets you ship custom data with your
model. When you save out a model using nlp.to_disk
and the component exposes a
to_disk
method, it will be called with the disk path.
def to_disk(self, path, exclude=tuple()):
snek_path = path / "snek.txt"
with snek_path.open("w", encoding="utf8") as snek_file:
snek_file.write(self.snek)
def from_disk(self, path, exclude=tuple()):
snek_path = path / "snek.txt"
with snek_path.open("r", encoding="utf8") as snek_file:
self.snek = snek_file.read()
return self
The above example will serialize the current snake in a snek.txt
in the model
data directory. When a model using the snek
component is loaded, it will open
the snek.txt
and make it available to the component.
Custom language classes via entry points
To stay with the theme of the previous example and
this blog post on entry points,
let's imagine you wanted to implement your own SnekLanguage
class for your
custom model – but you don't necessarily want to modify spaCy's code to add a
language. In your package, you could then implement the following
custom language subclass:
### snek.py
from spacy.language import Language
class SnekDefaults(Language.Defaults):
stop_words = set(["sss", "hiss"])
class SnekLanguage(Language):
lang = "snk"
Defaults = SnekDefaults
Alongside the spacy_factories
, there's also an entry point option for
spacy_languages
, which maps language codes to language-specific Language
subclasses:
### setup.py
from setuptools import setup
setup(
name="snek",
entry_points={
"spacy_factories": ["snek = snek:SnekFactory"],
+ "spacy_languages": ["snk = snek:SnekLanguage"]
}
)
In spaCy, you can then load the custom snk
language and it will be resolved to
SnekLanguage
via the custom entry point. This is especially relevant for model
packages you train, which could then specify lang = snk
in their config.cfg
without spaCy raising an error because the language is not available in the core
library.
Custom displaCy colors via entry points
If you're training a named entity recognition model for a custom domain, you may
end up training different labels that don't have pre-defined colors in the
displacy
visualizer. The spacy_displacy_colors
entry point lets you define a dictionary of entity labels mapped to their color
values. It's added to the pre-defined colors and can also overwrite existing
values.
Domain-specific NER labels
Good examples of models with domain-specific label schemes are scispaCy and Blackstone.
### snek.py
displacy_colors = {"SNEK": "#3dff74", "HUMAN": "#cfc5ff"}
Given the above colors, the entry point can be defined as follows. Entry points
need to have a name, so we use the key colors
. However, the name doesn't
matter and whatever is defined in the entry point group will be used.
### setup.py
from setuptools import setup
setup(
name="snek",
entry_points={
+ "spacy_displacy_colors": ["colors = snek:displacy_colors"]
}
)
After installing the package, the the custom colors will be used when
visualizing text with displacy
. Whenever the label SNEK
is assigned, it will
be displayed in #3dff74
.
import DisplaCyEntSnekHtml from 'images/displacy-ent-snek.html'