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We introduce Ivy, a templated Deep Learning (DL) framework which abstractsexisting DL frameworks. Ivy unifies the core functions of these frameworks toexhibit consistent call signatures, syntax and input-output behaviour. Newhigh-level framework-agnostic functions and classes, which are usable alongsideframework-specific code, can then be implemented as compositions of the unifiedlow-level Ivy functions. Ivy currently supports TensorFlow, PyTorch, MXNet, Jaxand NumPy. We also release four pure-Ivy libraries for mechanics, 3D vision,robotics, and differentiable environments. Through our evaluations, we showthat Ivy can significantly reduce lines of code with a runtime overhead of lessthan 1% in most cases. We welcome developers to join the Ivy community bywriting their own functions, layers and libraries in Ivy, maximizing theiraudience and helping to accelerate DL research through inter-frameworkcodebases. More information can be found at https://ivy-dl.org.

Ivy: Templated Deep Learning for Inter-Framework Portability