Note The examples from Figures 5 and 6 can be found here. Chromium open source project running with WebML API powered by MPS. Chromium* open source project running with WebGL API.įigure 6 shows running the same model using WebML implementation and performance gain that can be achieved with WebML powered by Metal Performance Shaders and Intel Processor Graphics compared to WebGL implementation running on the same hardware.įigure 6. #MACOS CATALINA SYSTEM REQUIREMENTS PRO#Figure 3 and figure 4 from below demonstrates the result of running Intel® Open Image Denoiser using CoreML through Intel Processor Graphics Gen9 architecture.įigure 5 shows Web Machine Learning (WebML) improvements compared to a legacy WebGL* API, with macOS Catalina on a 13-inch MacBook Pro 2016 powered by Intel Processor Graphics Gen9 architecture.įigure 5. Figure 3 shows the output running Intel Open Image Denoise through Core ML on a 13-inch MacBook Pro 2016 powered by Intel Processor Graphics Gen9 architecture. The denoiser model is using an Intel® Open Image Denoise trained model to denoise a high-resolution noisy image. The numbers in Figure 2 were generated from macOS Catalina and Mojave using a 13-inch MacBook Pro* running Intel® Processor Graphics Architecture.įigure 2. Such improvements are a continuous process, and Figure 2 gives you an idea on where the performance gains are (see the disclaimer). As previously mentioned, with macOS Catalina we deployed an improved machine learning algorithm for key primitives, optimized how we deploy work to the underlying hardware, and more. Significant improvements were made with macOS Catalina on top of the earlier release, with macOS Mojave* using Intel Processor Graphics technology. macOS Catalina machine learning on Intel Processor Graphics. You can integrate those improvements into part of your Core ML applications.įigure 1. Major improvements and additions were made to Create ML so you can now create and train custom machine learning models on macOS platforms that use Intel Processor Graphics. It uses transfer learning technology with Intel Processor Graphics to train models faster. Create MLĬreate ML, upgraded to be a standalone app, lets you build various types of custom machine learning models, including image classifier, object detector, activity classifier, and others. With MPS, you can encode the machine learning dispatches and commands tasks using the same command buffers that are used with metal-based 3D, and compute workloads for traditional graphics applications. 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. #MACOS CATALINA SYSTEM REQUIREMENTS SOFTWARE#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 #MACOS CATALINA SYSTEM REQUIREMENTS FULL#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).
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