Researchers and developers working with Machine Learning (ML) models and algorithms using PyTorch can now use AMD ROCm 5.7 on Ubuntu® Linux® to tap into the parallel computing power of the Radeon™ RX 7900 XTX and the Radeon™ PRO W7900 graphics cards which are based on the AMD RDNA™ 3 GPU architecture.
A client solution built on these two high-end GPUs enables a local, private, and cost-effective workflow for ML training and inference for those who previously relied on cloud-based solutions alone.
“We are excited to offer the AI community new support for machine learning development using PyTorch built on the AMD Radeon RX 7900 XTX and Radeon PRO W7900 GPUs and the ROCm open software platform. This is our first RDNA 3 architecture-based implementation, and we are looking forward to partnering with the community.”, says Dan Wood, Corp. Vice President, Radeon Product Management at AMD.
ACCELERATE MACHINE LEARNING ON YOUR DESKTOP
With today’s models easily exceeding the capabilities of standard hardware and software not designed for AI, ML engineers are looking for cost-effective solutions to develop and train their ML-powered applications. A local PC or workstation system with a Radeon 7900 series GPU presents a capable, yet affordable solution to address these growing workflow challenges thanks to large GPU memory sizes of 24GB and even 48GB.
Radeon™ 7900 series GPUs built on the RDNA™ 3 GPU architecture now come with up to 192 AI accelerators and feature more than 2x higher AI performance per Compute Unit (CU)1 compared to the previous generation.
UNIFIED SOFTWARE STACK FOR THE DESKTOP AND THE DATACENTER
AMD ROCm (short for Radeon Open Compute) platform is the open AMD software stack for graphics processing unit (GPU) programming. ROCm spans several domains: general-purpose computing on GPUs (GPGPU), high performance computing (HPC) and heterogeneous computing.
The latest AMD ROCm 5.7 software stack for GPU programming unlocks the massively parallel compute power of these RDNA™ 3 architecture-based GPUs for use with PyTorch, one of the leading ML frameworks. The same unified software stack also supports the CDNA™ GPU architecture of the AMD Instinct™ MI series accelerators.
FREEDOM TO CUSTOMIZE
The AMD ROCm platform is primarily Open-Source Software (OSS). It allows developers the freedom to customize and tailor their GPU software for their own needs while collaborating with a community of other developers, and helping each other find solutions in an agile, flexible, and rapid manner. The AMD ROCm platform’s goal is to allow users to maximize their GPU hardware investment. The AMD ROCm platform is designed to help develop, test, and deploy GPU accelerated HPC, AI, scientific computing, CAD, and other applications in a free, open source, integrated and secure software ecosystem.
As the industry moves towards an ecosystem that supports a broad set of systems, frameworks and accelerators, AMD is determined to continue to make AI more accessible to developers and researchers that benefit from a local client-based setup for ML development using RDNA™ 3 architecture-based desktop GPUs.
AMD is also looking to broaden support for additional ML frameworks and operating systems.
Radeon™ AI technology is compatible with all AMD Radeon 7000 Series graphics cards and newer. Please check with your system manufacturer for feature availability prior to purchase. GD-232.
Based on AMD internal measurements, November 2022, comparing the Radeon RX 7900 XTX at 2.5GHz boost clock with 96 CUs issuing 2X the Bfloat16 math operations per clocks vs. the RX 6900 XT GPU at 2.25 GHz boost clock and 80 CUs issue 1X the Bfloat16 math operations per clock. RX-821