spaCy/website/usage/_models/_production.jade

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//- 💫 DOCS > USAGE > MODELS > PRODUCTION USE
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| If your application depends on one or more models,
| you'll usually want to integrate them into your continuous integration
| workflow and build process. While spaCy provides a range of useful helpers
| for downloading, linking and loading models, the underlying functionality
| is entirely based on native Python packages. This allows your application
| to handle a model like any other package dependency.
+infobox("Training models for production")
| For an example of an automated model training and build process, see
| #[+a("/usage/training#example-training-spacy") this example] of how
| we're training and packaging our models for spaCy.
+h(3, "models-download") Downloading and requiring model dependencies
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| spaCy's built-in #[+api("cli#download") #[code download]] command
| is mostly intended as a convenient, interactive wrapper. It performs
| compatibility checks and prints detailed error messages and warnings.
| However, if you're downloading models as part of an automated build
| process, this only adds an unnecessary layer of complexity. If you know
| which models your application needs, you should be specifying them directly.
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| Because all models are valid Python packages, you can add them to your
| application's #[code requirements.txt]. If you're running your own
| internal PyPi installation, you can simply upload the models there. pip's
| #[+a("https://pip.pypa.io/en/latest/reference/pip_install/#requirements-file-format") requirements file format]
| supports both package names to download via a PyPi server, as well as direct
| URLs.
+code("requirements.txt", "text").
spacy>=2.0.0,<3.0.0
#{gh("spacy-models")}/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#en_core_web_sm
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| Specifying #[code #egg=] with the package name tells pip
| which package to expect from the download URL. This way, the
| package won't be re-downloaded and overwritten if it's already
| installed - just like when you're downloading a package from PyPi.
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| All models are versioned and specify their spaCy dependency. This ensures
| cross-compatibility and lets you specify exact version requirements for
| each model. If you've trained your own model, you can use the
| #[+api("cli#package") #[code package]] command to generate the required
| meta data and turn it into a loadable package.
+h(3, "models-loading") Loading and testing models
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| Downloading models directly via pip won't call spaCy's link
| #[+api("cli#link") #[code link]] command, which creates
| symlinks for model shortcuts. This means that you'll have to run this
| command separately, or use the native #[code import] syntax to load the
| models:
+code.
import en_core_web_sm
nlp = en_core_web_sm.load()
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| In general, this approach is recommended for larger code bases, as it's
| more "native", and doesn't depend on symlinks or rely on spaCy's loader
| to resolve string names to model packages. If a model can't be
| imported, Python will raise an #[code ImportError] immediately. And if a
| model is imported but not used, any linter will catch that.
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| Similarly, it'll give you more flexibility when writing tests that
| require loading models. For example, instead of writing your own
| #[code try] and #[code except] logic around spaCy's loader, you can use
| #[+a("http://pytest.readthedocs.io/en/latest/") pytest]'s
| #[+a("https://docs.pytest.org/en/latest/builtin.html#_pytest.outcomes.importorskip") #[code importorskip()]]
| method to only run a test if a specific model or model version is
| installed. Each model package exposes a #[code __version__] attribute
| which you can also use to perform your own version compatibility checks
| before loading a model.