Metadata-Version: 2.1 Name: thinc Version: 8.2.3 Summary: A refreshing functional take on deep learning, compatible with your favorite libraries Home-page: https://github.com/explosion/thinc Author: Explosion Author-email: contact@explosion.ai License: MIT Classifier: Development Status :: 5 - Production/Stable Classifier: Environment :: Console Classifier: Intended Audience :: Developers Classifier: Intended Audience :: Science/Research Classifier: License :: OSI Approved :: MIT License Classifier: Operating System :: POSIX :: Linux Classifier: Operating System :: MacOS :: MacOS X Classifier: Operating System :: Microsoft :: Windows Classifier: Programming Language :: Cython Classifier: Programming Language :: Python :: 3 Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 Classifier: Programming Language :: Python :: 3.9 Classifier: Programming Language :: Python :: 3.10 Classifier: Programming Language :: Python :: 3.11 Classifier: Programming Language :: Python :: 3.12 Classifier: Topic :: Scientific/Engineering Requires-Python: >=3.6 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: blis <0.8.0,>=0.7.8 Requires-Dist: murmurhash <1.1.0,>=1.0.2 Requires-Dist: cymem <2.1.0,>=2.0.2 Requires-Dist: preshed <3.1.0,>=3.0.2 Requires-Dist: wasabi <1.2.0,>=0.8.1 Requires-Dist: srsly <3.0.0,>=2.4.0 Requires-Dist: catalogue <2.1.0,>=2.0.4 Requires-Dist: confection <1.0.0,>=0.0.1 Requires-Dist: setuptools Requires-Dist: pydantic !=1.8,!=1.8.1,<3.0.0,>=1.7.4 Requires-Dist: packaging >=20.0 Requires-Dist: dataclasses <1.0,>=0.6 ; python_version < "3.7" Requires-Dist: contextvars <3,>=2.4 ; python_version < "3.7" Requires-Dist: typing-extensions <4.5.0,>=3.7.4.1 ; python_version < "3.8" Requires-Dist: numpy >=1.15.0 ; python_version < "3.9" Requires-Dist: numpy >=1.19.0 ; python_version >= "3.9" Provides-Extra: cuda Requires-Dist: cupy >=5.0.0b4 ; extra == 'cuda' Provides-Extra: cuda-autodetect Requires-Dist: cupy-wheel >=11.0.0 ; extra == 'cuda-autodetect' Provides-Extra: cuda100 Requires-Dist: cupy-cuda100 >=5.0.0b4 ; extra == 'cuda100' Provides-Extra: cuda101 Requires-Dist: cupy-cuda101 >=5.0.0b4 ; extra == 'cuda101' Provides-Extra: cuda102 Requires-Dist: cupy-cuda102 >=5.0.0b4 ; extra == 'cuda102' Provides-Extra: cuda110 Requires-Dist: cupy-cuda110 >=5.0.0b4 ; extra == 'cuda110' Provides-Extra: cuda111 Requires-Dist: cupy-cuda111 >=5.0.0b4 ; extra == 'cuda111' Provides-Extra: cuda112 Requires-Dist: cupy-cuda112 >=5.0.0b4 ; extra == 'cuda112' Provides-Extra: cuda113 Requires-Dist: cupy-cuda113 >=5.0.0b4 ; extra == 'cuda113' Provides-Extra: cuda114 Requires-Dist: cupy-cuda114 >=5.0.0b4 ; extra == 'cuda114' Provides-Extra: cuda115 Requires-Dist: cupy-cuda115 >=5.0.0b4 ; extra == 'cuda115' Provides-Extra: cuda116 Requires-Dist: cupy-cuda116 >=5.0.0b4 ; extra == 'cuda116' Provides-Extra: cuda117 Requires-Dist: cupy-cuda117 >=5.0.0b4 ; extra == 'cuda117' Provides-Extra: cuda11x Requires-Dist: cupy-cuda11x >=11.0.0 ; extra == 'cuda11x' Provides-Extra: cuda12x Requires-Dist: cupy-cuda12x >=11.5.0 ; extra == 'cuda12x' Provides-Extra: cuda80 Requires-Dist: cupy-cuda80 >=5.0.0b4 ; extra == 'cuda80' Provides-Extra: cuda90 Requires-Dist: cupy-cuda90 >=5.0.0b4 ; extra == 'cuda90' Provides-Extra: cuda91 Requires-Dist: cupy-cuda91 >=5.0.0b4 ; extra == 'cuda91' Provides-Extra: cuda92 Requires-Dist: cupy-cuda92 >=5.0.0b4 ; extra == 'cuda92' Provides-Extra: datasets Requires-Dist: ml-datasets <0.3.0,>=0.2.0 ; extra == 'datasets' Provides-Extra: mxnet Requires-Dist: mxnet <1.6.0,>=1.5.1 ; extra == 'mxnet' Provides-Extra: tensorflow Requires-Dist: tensorflow <2.6.0,>=2.0.0 ; extra == 'tensorflow' Provides-Extra: torch Requires-Dist: torch >=1.6.0 ; extra == 'torch' # 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. [![tests](https://github.com/explosion/thinc/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/thinc/actions/workflows/tests.yml) [![Current Release Version](https://img.shields.io/github/v/release/explosion/thinc.svg?include_prereleases&sort=semver&style=flat-square&logo=github)](https://github.com/explosion/thinc/releases) [![PyPi Version](https://img.shields.io/pypi/v/thinc.svg?include_prereleases&sort=semver&style=flat-square&logo=pypi&logoColor=white)](https://pypi.python.org/pypi/thinc) [![conda Version](https://img.shields.io/conda/vn/conda-forge/thinc.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/thinc) [![Python wheels](https://img.shields.io/badge/wheels-%E2%9C%93-4c1.svg?longCache=true&style=flat-square&logo=python&logoColor=white)](https://github.com/explosion/wheelwright/releases) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black) [![Open demo in Colab][colab]][intro_to_thinc_colab] ## 🔥 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 →](https://thinc.ai/docs)** ## 🚀 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. > ⚠️ 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`](examples) directory and [usage documentation](https://thinc.ai/docs) for more examples. Most examples are Jupyter notebooks – 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 →](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 ### 📖 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. | ## 🗺 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. | ## 🐍 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. ### 👷‍♀️ 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 ``` ### 🚦 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 – we ignore blocks that are not relevant or difficult to test and make sure that the tests execute all code paths.