Description
High developer velocity is crucial to shipping new ML-enabled experiences from a server-trained model to a customers’ device. ExecuTorch is an on-device runtime that seamlessly integrates with the PyTorch stack with a focus on developer productivity. We present the ExecuTorch Dev Tools and highlight key features that tighten the iteration loop when optimizing models for deployment and execution on edge devices. We demonstrate how ExecuTorch’s built-in profiler and bundled tools tackle key pain-points, such as: 1. Examining the memory footprint of an ExecuTorch program ahead-of-time; 2. Collecting runtime performance metrics and intermediate outputs for accuracy analysis; 3. Correlating runtime data with the underlying graph of an exported model.