Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries

From the makers of spaCy and Prodigy

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.

🔥 Features

• Type-check your model definitions with custom types and
  mypy plugin.
• Wrap PyTorch, TensorFlow and MXNet models for use in your network.
• Concise functional-programming approach to model definition, using
  composition rather than inheritance.
• Optional custom infix notation via operator overloading.
• Integrated config system to describe trees of objects and hyperparameters.
• Choice of extensible backends.
• Read more →

🚀 Quickstart

Thinc 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.

⚠️ If you have installed PyTorch and you are using Python 3.7+, uninstall the
package dataclasses with pip uninstall dataclasses, since it may have been
installed by PyTorch and is incompatible with Python 3.7+.

📓 Selected examples and notebooks

Also see the /examples directory and
usage documentation for more examples. Most examples
are Jupyter notebooks – to launch them on
Google Colab (with GPU support!) click on
the button next to the notebook name.

Notebook	Description
intro_to_thinc	Everything you need to know to get started. Composing and training a model on the MNIST data, using config files, registering custom functions and wrapping PyTorch, TensorFlow and MXNet models.
transformers_tagger_bert	How to use Thinc, transformers and PyTorch to train a part-of-speech tagger. From model definition and config to the training loop.
pos_tagger_basic_cnn	Implementing and training a basic CNN for part-of-speech tagging model without external dependencies and using different levels of Thinc's config system.
parallel_training_ray	How to set up synchronous and asynchronous parameter server training with Thinc and Ray.

View more →

📖 Documentation & usage guides

🗺 What's where

🐍 Development notes

Thinc uses black for auto-formatting,
flake8 for linting and
mypy for type checking. All code
includes type hints wherever possible. See
the type reference for more details on
Thinc's custom types.

👷‍♀️ Building Thinc from source

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 --inplace

🚦 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 – we ignore blocks that are not relevant or difficult to test
and make sure that the tests execute all code paths.