Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose, configure and deploy custom models built with their favorite framework.
๐ฅ Featuresmypy plugin.Thinc is compatible with Python 3.6+ and runs on Linux, macOS and Windows. The latest releases with binary wheels are available from pip. Before you install Thinc and its dependencies, make sure that your pip, setuptools and wheel are up to date. For the most recent releases, pip 19.3 or newer is recommended.
pip install -U pip setuptools wheel pip install thinc
See the extended installation docs for details on optional dependencies for different backends and GPU. You might also want to set up static type checking to take advantage of Thinc's type system.
๐ Selected examples and notebooksโ ๏ธ If you have installed PyTorch and you are using Python 3.7+, uninstall the package
dataclasseswithpip uninstall dataclasses, since it may have been installed by PyTorch and is incompatible with Python 3.7+.
Also see the /examples directory and usage documentation for more examples. Most examples are Jupyter notebooks โ to launch them on Google Colab (with GPU support!) click on the button next to the notebook name.
thinc.api User-facing API. All classes and functions should be imported from here. thinc.types Custom types and dataclasses. thinc.model The Model class. All Thinc models are an instance (not a subclass) of Model. thinc.layers The layers. Each layer is implemented in its own module. thinc.shims Interface for external models implemented in PyTorch, TensorFlow etc. thinc.loss Functions to calculate losses. thinc.optimizers Functions to create optimizers. Currently supports "vanilla" SGD, Adam and RAdam. thinc.schedules Generators for different rates, schedules, decays or series. thinc.backends Backends for numpy and cupy. thinc.config Config parsing and validation and function registry system. thinc.util Utilities and helper functions. ๐ Development notes
Thinc uses black for auto-formatting, flake8 for linting and mypy for type checking. All code is written compatible with Python 3.6+, with type hints wherever possible. See the type reference for more details on Thinc's custom types.
Building Thinc from source requires the full dependencies listed in requirements.txt to be installed. You'll also need a compiler to build the C extensions.
git clone https://github.com/explosion/thinc cd thinc python -m venv .env source .env/bin/activate pip install -U pip setuptools wheel pip install -r requirements.txt pip install --no-build-isolation .
Alternatively, install in editable mode:
pip install -r requirements.txt pip install --no-build-isolation --editable .
Or by setting PYTHONPATH:
export PYTHONPATH=`pwd` pip install -r requirements.txt python setup.py build_ext --inplace๐ฆ Running tests
Thinc comes with an extensive test suite. The following should all pass and not report any warnings or errors:
python -m pytest thinc # test suite python -m mypy thinc # type checks python -m flake8 thinc # linting
To view test coverage, you can run python -m pytest thinc --cov=thinc. We aim for a 100% test coverage. This doesn't mean that we meticulously write tests for every single line โ we ignore blocks that are not relevant or difficult to test and make sure that the tests execute all code paths.
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