# thinc **Repository Path**: deeplearningrepos/thinc ## Basic Information - **Project Name**: thinc - **Description**: 🔮 A refreshing functional take on deep learning, compatible with your favorite libraries - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-03-30 - **Last Updated**: 2021-08-31 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Thinc: A refreshing functional take on deep learning, compatible with your favorite libraries ### From the makers of [spaCy](https://spacy.io), [Prodigy](https://prodi.gy) and [FastAPI](https://fastapi.tiangolo.com) [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. [![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/7/master.svg?logo=azure-pipelines&style=flat-square)](https://dev.azure.com/explosion-ai/public/_build?definitionId=7) [![codecov](https://img.shields.io/codecov/c/gh/explosion/thinc?logo=codecov&logoColor=white&style=flat-square)](https://codecov.io/gh/explosion/thinc) [![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 --pre ``` > ⚠️ Note that Thinc 8.0 is currently **in alpha preview** and not necessarily ready > for production yet. 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 | | | | --------------------------------------------------------------------------------- | ----------------------------------------------------- | | [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.py) | 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.