Description
Edge computing is becoming increasingly popular for applications that require low latency and high bandwidth. However, building machine learning runtimes for edge computing infra that is both scalable and optimized can be challenging. In this talk, we explore how PyTorch and WebAssembly (Wasm) can be used to build efficient edge computing runtimes. We then discuss how Wasm, a low-level bytecode format, can be used to effectively run PyTorch models and specific optimizations one could make in the models to run on the edge with Wasm, which provides a lightweight and portable runtime environment for edge applications, while we also introduce Akri, an open-source project in this context which allows us to easily discover edge devices to run the PyTorch model on. We will also cover some use cases where PyTorch and Wasm can be used together, such as building machine learning models that can run on edge devices or processing sensor data in real-time. We also share some best practices by showing how we run Neural Radiance Fields on the edge using this setup. The audience will gain a better understanding of how they can use PyTorch to run scalable and optimized machine learning on edge.