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To target underlying GPU devices, you can write applications to use the MPS API directly. MPS is the main building block for Core ML to run ML workloads on graphics processing units (GPUs). Core ML is built on top of low-level frameworks such as MPS (Intel Processor Graphics) and accelerates basic neural network subroutines (BNNS on Intel processors) that are highly tuned and optimized for Intel hardware to maximize the hardware capability.

This removes the dependency on network connectivity, security, and privacy concerns. Core ML allows you to take advantage of Intel processors and Intel Processor Graphics technology to build and run ML workloads on a device so that your data does not need to leave it. Core ML*Ĭore ML, available on Apple devices, is the main framework for accelerating domain-specific ML inference capability such as image analysis, object detection, natural language processing, and more. Read this section for a recap on inference and training architecture with macOS Catalina on Intel Processor Graphics. Software developers, platform architects, data scientists, and academics seeking to maximize machine learning performance on Intel Processor Graphics on macOS platforms will find this content useful. The paper also describes the Create ML feature, through which a model (mlmodel) can be created on an Intel® powered device running macOS without the need for data leaving the device.Ī follow up to the Apple Machine Learning on Intel Processor Graphics paper, the information here builds on the content of this previous paper Target Audience It summarizes some of the improvements Intel and Apple have made to Core ML*, the Metal Performance Shaders (MPS) software stack, to take full advantage of Intel Processor Graphics architecture. This paper describes advancements and improvements made to the macOS* machine learning stack on Intel® Processor Graphics through Apple*’s latest Mac* operating system, macOS Catalina* (macOS 10.15).

Macos catalina coregraphics xnugatlanbleepingcomputer