thinc

Thinc: An Innovative Approach to Deep Learning

Introduction

Thinc is a lightweight deep learning library that offers a unique and efficient functional-programming interface for building and composing models. Created by the team behind spaCy and Prodigy, Thinc is designed to integrate seamlessly with popular frameworks like PyTorch, TensorFlow, and MXNet. Whether used as a standalone toolkit or an interface layer, Thinc empowers users to create, configure, and deploy custom models effortlessly.

Key Features

• Type Checking: Thinc supports type-checked model definitions using custom types and a mypy plugin, ensuring robust and error-free code.
• Framework Integration: Users can wrap models from PyTorch, TensorFlow, and MXNet, integrating them smoothly into Thinc's network.
• Functional Programming: Emphasizing a composition approach rather than inheritance, Thinc's API allows for concise and clear model definitions.
• Operator Overloading: Custom infix notation is available, making the code more expressive and easier to write.
• Config System: Thinc includes an integrated configuration system for describing object trees and hyperparameters, enhancing flexibility and control.
• Extensible Backends: Users can choose from various backends according to their needs.

Getting Started

Thinc is compatible with Python 3.6+ and works on Linux, macOS, and Windows. To install the latest version:

pip install -U pip setuptools wheel
pip install thinc

Make sure your pip, setuptools, and wheel are up to date for a smooth installation. Optional dependencies for different backends and GPU support are detailed in the extended installation documentation.

Selected Examples

Thinc provides several example notebooks to help users get started:

• Introduction to Thinc: This notebook covers the basics of model composition and training on the MNIST dataset, using config files and integrating custom functions and models from PyTorch, TensorFlow, and MXNet.
• Transformers Tagger with BERT: Learn to use Thinc with transformers and PyTorch for training a part-of-speech tagger, covering model definition, configuration, and the training loop.
• POS Tagger with Basic CNN: Implement and train a basic CNN for POS tagging without external dependencies, utilizing different levels of Thinc’s config system.
• Parallel Training with Ray: Set up both synchronous and asynchronous parameter server training using Thinc and Ray.

These examples are available as Jupyter notebooks and can be executed on Google Colab with GPU support.

Documentation & Guides

Thinc offers comprehensive documentation and usage guides:

• Concept & Design: Understanding Thinc's conceptual model.
• Model Definition & Usage: Guidance on composing models and updating their state.
• Configuration System: Details on the config system and function registry.
• Framework Integration: Instructions on working with PyTorch, TensorFlow, and MXNet.
• Layers API: Information on weight layers, transformations, and combinators.
• Type Checking: Ensuring the integrity of model definitions with type checks.

Thinc's rich feature set and user-friendly approach make it a valuable tool for anyone looking to explore deep learning with flexibility and efficiency. Whether you're integrating it with existing frameworks or developing new models from scratch, Thinc provides the tools and support to achieve your goals.