Deploying neural networks in production environments presents unique challenges: models must run efficiently across diverse hardware, from powerful servers to resource-constrained embedded devices, while maintaining predictable performance without heavy runtime dependencies.

This talk introduces tract, Sonos's open-source neural network inference toolkit started in 2018 and written in Rust. We'll explore how tract bridges the gap between training frameworks and production deployment by offering a no-nonsense, self-contained inference solution used today to deploy deep learning on millions of devices at Sonos.

This toolkit has some unique strengths thanks to embedded graph optimization, automated streaming management, and symbolic abstraction for dynamic dimensions — plus support for multiple open exchange standards including ONNX, NNEF, and TensorFlow Lite.

tract also has a companion project coined torch-to-nnef that strive to export PyTorch models to an NNEF optimized for tract with maximum compatibility. It enables some unique features like quantization, better Fourier Transform support and easier extensibility: this will also be discussed shortly during this presentation.