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https://github.com/explosion/thinc
๐ฎ A refreshing functional take on deep learning, compatible with your favorite libraries
https://github.com/explosion/thinc
ai artificial-intelligence deep-learning functional-programming jax machine-learning machine-learning-library mxnet natural-language-processing nlp python pytorch spacy tensorflow type-checking
Last synced: 3 days ago
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๐ฎ A refreshing functional take on deep learning, compatible with your favorite libraries
- Host: GitHub
- URL: https://github.com/explosion/thinc
- Owner: explosion
- License: mit
- Created: 2014-10-16T16:34:59.000Z (about 10 years ago)
- Default Branch: main
- Last Pushed: 2024-10-01T10:17:18.000Z (2 months ago)
- Last Synced: 2024-12-02T12:08:02.534Z (10 days ago)
- Topics: ai, artificial-intelligence, deep-learning, functional-programming, jax, machine-learning, machine-learning-library, mxnet, natural-language-processing, nlp, python, pytorch, spacy, tensorflow, type-checking
- Language: Python
- Homepage: https://thinc.ai
- Size: 10.6 MB
- Stars: 2,824
- Watchers: 79
- Forks: 277
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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README
# Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries
### From the makers of [spaCy](https://spacy.io) and [Prodigy](https://prodi.gy)
[Thinc](https://thinc.ai) 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](https://spacy.io) and [Prodigy](https://prodi.gy). We wrote the new
version to let users **compose, configure and deploy custom models** built with
their favorite framework.
[](https://github.com/explosion/thinc/actions/workflows/tests.yml)
[](https://github.com/explosion/thinc/releases)
[](https://pypi.python.org/pypi/thinc)
[](https://anaconda.org/conda-forge/thinc)
[](https://github.com/explosion/wheelwright/releases)
[](https://github.com/ambv/black)
[![Open demo in Colab][colab]][intro_to_thinc_colab]
## ๐ฅ Features
- **Type-check** your model definitions with custom types and
[`mypy`](https://mypy.readthedocs.io/en/latest/) 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 โ](https://thinc.ai/docs)**
## ๐ Quickstart
Thinc is compatible with **Python 3.6+** and runs on **Linux**, **macOS** and
**Windows**. The latest releases with binary wheels are available from
[pip](https://pypi.python.org/pypi/thinc). 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.
```bash
pip install -U pip setuptools wheel
pip install thinc
```
See the [extended installation docs](https://thinc.ai/docs/install#extended) for
details on optional dependencies for different backends and GPU. You might also
want to
[set up static type checking](https://thinc.ai/docs/install#type-checking) to
take advantage of Thinc's type system.
> โ ๏ธ 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+.
### ๐ Selected examples and notebooks
Also see the [`/examples`](examples) directory and
[usage documentation](https://thinc.ai/docs) for more examples. Most examples
are Jupyter notebooks โ to launch them on
[Google Colab](https://colab.research.google.com) (with GPU support!) click on
the button next to the notebook name.
| Notebook | Description |
| --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [`intro_to_thinc`][intro_to_thinc]
[![Open in Colab][colab]][intro_to_thinc_colab] | 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`][transformers_tagger_bert]
[![Open in Colab][colab]][transformers_tagger_bert_colab] | 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`][pos_tagger_basic_cnn]
[![Open in Colab][colab]][pos_tagger_basic_cnn_colab] | 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`][parallel_training_ray]
[![Open in Colab][colab]][parallel_training_ray_colab] | How to set up synchronous and asynchronous parameter server training with Thinc and [Ray](https://ray.readthedocs.io/en/latest/). |
**[View more โ](examples)**
[colab]:
https://gistcdn.githack.com/ines/dcf354aa71a7665ae19871d7fd14a4e0/raw/461fc1f61a7bc5860f943cd4b6bcfabb8c8906e7/colab-badge.svg
[intro_to_thinc]: examples/00_intro_to_thinc.ipynb
[intro_to_thinc_colab]:
https://colab.research.google.com/github/explosion/thinc/blob/master/examples/00_intro_to_thinc.ipynb
[transformers_tagger_bert]: examples/02_transformers_tagger_bert.ipynb
[transformers_tagger_bert_colab]:
https://colab.research.google.com/github/explosion/thinc/blob/master/examples/02_transformers_tagger_bert.ipynb
[pos_tagger_basic_cnn]: examples/03_pos_tagger_basic_cnn.ipynb
[pos_tagger_basic_cnn_colab]:
https://colab.research.google.com/github/explosion/thinc/blob/master/examples/03_pos_tagger_basic_cnn.ipynb
[parallel_training_ray]: examples/04_parallel_training_ray.ipynb
[parallel_training_ray_colab]:
https://colab.research.google.com/github/explosion/thinc/blob/master/examples/04_parallel_training_ray.ipynb
### ๐ Documentation & usage guides
| Documentation | Description |
| --------------------------------------------------------------------------------- | ----------------------------------------------------- |
| [Introduction](https://thinc.ai/docs) | Everything you need to know. |
| [Concept & Design](https://thinc.ai/docs/concept) | Thinc's conceptual model and how it works. |
| [Defining and using models](https://thinc.ai/docs/usage-models) | How to compose models and update state. |
| [Configuration system](https://thinc.ai/docs/usage-config) | Thinc's config system and function registry. |
| [Integrating PyTorch, TensorFlow & MXNet](https://thinc.ai/docs/usage-frameworks) | Interoperability with machine learning frameworks |
| [Layers API](https://thinc.ai/docs/api-layers) | Weights layers, transforms, combinators and wrappers. |
| [Type Checking](https://thinc.ai/docs/usage-type-checking) | Type-check your model definitions and more. |
## ๐บ What's where
| Module | Description |
| ----------------------------------------- | --------------------------------------------------------------------------------- |
| [`thinc.api`](thinc/api.py) | **User-facing API.** All classes and functions should be imported from here. |
| [`thinc.types`](thinc/types.py) | Custom [types and dataclasses](https://thinc.ai/docs/api-types). |
| [`thinc.model`](thinc/model.py) | The `Model` class. All Thinc models are an instance (not a subclass) of `Model`. |
| [`thinc.layers`](thinc/layers) | The layers. Each layer is implemented in its own module. |
| [`thinc.shims`](thinc/shims) | Interface for external models implemented in PyTorch, TensorFlow etc. |
| [`thinc.loss`](thinc/loss.py) | Functions to calculate losses. |
| [`thinc.optimizers`](thinc/optimizers.py) | Functions to create optimizers. Currently supports "vanilla" SGD, Adam and RAdam. |
| [`thinc.schedules`](thinc/schedules.py) | Generators for different rates, schedules, decays or series. |
| [`thinc.backends`](thinc/backends) | Backends for `numpy` and `cupy`. |
| [`thinc.config`](thinc/config.py) | Config parsing and validation and function registry system. |
| [`thinc.util`](thinc/util.py) | Utilities and helper functions. |
## ๐ Development notes
Thinc uses [`black`](https://github.com/psf/black) for auto-formatting,
[`flake8`](http://flake8.pycqa.org/en/latest/) for linting and
[`mypy`](https://mypy.readthedocs.io/en/latest/) for type checking. All code is
written compatible with **Python 3.6+**, with type hints wherever possible. See
the [type reference](https://thinc.ai/docs/api-types) for more details on
Thinc's custom types.
### ๐ทโโ๏ธ Building Thinc from source
Building Thinc from source requires the full dependencies listed in
[`requirements.txt`](requirements.txt) to be installed. You'll also need a
compiler to build the C extensions.
```bash
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:
```bash
pip install -r requirements.txt
pip install --no-build-isolation --editable .
```
Or by setting `PYTHONPATH`:
```bash
export PYTHONPATH=`pwd`
pip install -r requirements.txt
python setup.py build_ext --inplace
```
### ๐ฆ Running tests
Thinc comes with an [extensive test suite](thinc/tests). The following should
all pass and not report any warnings or errors:
```bash
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.