Last month, Apple announced Mac powered by the M1 chip, featuring a powerful machine learning accelerator and high-performance GPU. ML Compute, a new framework available in macOS Big Sur, enables developers to accelerate the training of neural networks using the CPU and GPU.

In this talk, we discuss how we use ML Compute to speed up the training of ML models on M1-powered Mac with popular deep learning frameworks such as TensorFlow. We show how to replace the TensorFlow ops in graph and eager mode with an ML Compute graph. We also present the performance and watt improvements when training neural networks on Mac with M1. Finally, we examine how unified memory and other memory optimizations on M1-powered Mac allow us to minimize the memory footprint when training neural networks.