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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. Switch between PyTorch, TensorFlow and MXNet models without changing your application, or even create mutant hybrids using zero-copy array interchange. Develop faster and catch bugs sooner with sophisticated type checking. Trying to pass a 1-dimensional array into a model that expects two dimensions? That’s a type error. Your editor can pick it up as the code leaves your fingers.
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
Project Samples
