Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
This library addresses the need for efficient online inference in deep learning applications, though it is incremental as it builds on existing PyTorch frameworks.
The authors introduced Continual Inference, a PyTorch library for implementing Continual Inference Networks to enable efficient inference in online and batch processing, providing best-practices and code examples for practical use.
We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inference in both online and batch processing scenarios. We offer a comprehensive introduction and guide to CINs and their implementation in practice, and provide best-practices and code examples for composing complex modules for modern Deep Learning. Continual Inference is readily downloadable via the Python Package Index and at \url{www.github.com/lukashedegaard/continual-inference}.