PyTorch Edge: Enabling On-Device Inference Across Mobile and Edge Devices with ExecuTorch

End-to-end workflow from Training to Deployment for iOS and Android mobile devices

PyTorch Edge is a new framework for deploying PyTorch models to edge devices. It is designed to be efficient and lightweight, making it ideal for devices such as smartphones, tablets, and wearables.

PyTorch Edge provides a number of features that make it easy to deploy PyTorch models to edge devices, including:

• Quantization: PyTorch Edge can quantize your model to reduce its size and improve its performance on edge devices.

• Code generation: PyTorch Edge can generate optimized C++ code for your model, which can be deployed to edge devices without any additional dependencies.

• Model serialization: PyTorch Edge can serialize your model to a compact format that can be loaded and executed on edge devices.

PyTorch Edge is still under development, but it has already been used to deploy PyTorch models to a variety of edge devices, including smartphones, tablets, wearables, and embedded systems.

I am excited to see how PyTorch Edge develops in the future. It has the potential to make it much easier to deploy PyTorch models to edge devices, which could open up new possibilities for machine learning applications.
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