Our mission is to help AI developers easily build, optimize, and deploy deep learning models. As part of this mission, we developed Infery, a Python runtime engine that transforms running inference on optimized models into a light and easy process. It involves just three lines of code and supports the major frameworks and hardware types. Imagine having the power of all frameworks at your fingertips with one friendly yet powerful API. That’s exactly what Infery is all about.
We took the best practices of all APIs and bundled them in a single, always-up-to-date Python library. It simplifies running inference for deep learning models — whether for quick experiments or for production purposes. In this post, you’ll learn about Infery: what makes it so exciting, what you can use it for, and how to get started using it right away.
Infery is a stand-alone library, but when coupled with the Deci Lab, you can optimize and deploy your models in a matter of minutes to boost inference performance on your preferred hardware, while maintaining the same accuracy. Once you optimize your model in the Deci Lab, only three simple copy-pastes separate you from running local inference for your optimized model.