Intel Releases Open Source AI Reference Kits – Investor Relations :: Intel Corporation (INTC)
Open source designs simplify AI development for solutions across healthcare, manufacturing, retail and other industries.
SANTA CLARA, Calif.–(BUSINESS WIRE)– What’s New: Intel has released the first set of open source AI reference kits specifically designed to make AI more accessible to organizations in on-prem, cloud and edge environments. First introduced at Intel Vision, the reference kits include AI model code, end-to-end machine learning pipeline instructions, libraries and Intel oneAPI components for cross-architecture performance. These kits enable data scientists and developers to learn how to deploy AI faster and more easily across healthcare, manufacturing, retail and other industries with higher accuracy, better performance and lower total cost of implementation.
“Innovation thrives in an open, democratized environment. The Intel accelerated open AI software ecosystem including optimized popular frameworks and Intel’s AI tools are built on the foundation of an open, standards-based, unified oneAPI programming model. These reference kits, built with components of Intel’s end-to-end AI software portfolio, will enable millions of developers and data scientists to introduce AI quickly and easily into their applications or boost their existing intelligent solutions.”
–Wei Li, Ph.D., Intel vice president and general manager of AI and Analytics
About AI Reference Kits: AI workloads continue to grow and diversify with use cases in vision, speech, recommender systems and more. Intel’s AI reference kits, built in collaboration with Accenture, are designed to accelerate the adoption of AI across industries. They are open source, pre-built AI with meaningful enterprise contexts for both greenfield AI introduction and strategic changes to existing AI solutions.
Four kits are available for download today:
Download free on the Intel.com AI Reference Kits website. The kits are also available on Github.
Why It Matters: Developers are looking to infuse AI into their solutions and the reference kits contribute to that goal. These kits build on and complement Intel’s AI software portfolio of end-to-end tools and framework optimizations. Built on the foundation of the oneAPI open, standards-based, heterogeneous programming model, which delivers performance across multiple types of architectures, these tools help data scientists train models faster and at lower cost by overcoming the limitations of proprietary environments.
What’s Next: Over the next year, Intel will release a series of additional open source AI reference kits with trained machine learning and deep learning models to help organizations of all sizes in their digital transformation journey.
More Context: oneAPI Dev Summit for AI | Intel oneAPI | Intel AI Tools
About Intel
Intel (Nasdaq: INTC) is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better. To learn more about Intel’s innovations, go to newsroom.intel.com and intel.com.
Notices & Disclaimers
1Predictive Utility Analytics Reference Kit, measured on June 29, 2022. HW Configuration: Microsoft Azure Standard D4_v5, OS: Ubuntu 20.04.4 LTS (Focal Fossa), 8 X Intel® Xeon® Platinum 8370C CPU @ 2.80GHz, 2 threads/core, 4 cores/socket, 1 socket. SW Configuration: Config 1 (Python v3.9, Scikit-learn v 1.0.2, XGBoost v0.81), Config 2 (Intel® Distribution for Python 3.9.12 2022.0.0, Scikit-learn 0.24.2, Intel® Extension for Scikit-learn 2021.5.1, XGBoost 1.4.3, daap4py 2021.6.0). Additional details at https://github.com/oneapi-src/predictive-health-analytics. Results may vary.
2Visual Quality Inspection Reference Kit, measured on June 29, 2022. HW Configuration: Microsoft Azure Standard D4_v5, OS: Ubuntu 20.04.4 LTS (Focal Fossa), 4 X Intel® Xeon® Platinum 8370C CPU @ 2.80GHz, 2 threads/core, 2 cores/socket, 1 socket. SW Configuration: Config 1 (PyTorch v1.8.0), Config 2 (Intel® Extension for PyTorch v1.8.0, Intel® Neural Compressor v1.12, Intel® Distribution of OpenVINO Toolkit 2021.4.2). Additional details at https://github.com/oneapi-src/visual-quality-inspection. Results may vary.
3Customer Chatbot Reference Kit, measured on June 22, 2022. HW Configuration: Microsoft Azure Standard D4_v5, OS: Red Hat Enterprise Linux Server 7.9, 4 X Intel® Xeon® Platinum 8370C CPU @ 2.80GHz, 2 threads/core, 2 cores/socket, 1 socket. SW Configuration: Config 1 (PyTorch v1.11), Config 2 (PyTorch v1.11.0, Intel® Extension for PyTorch v1.11.200, Intel® Neural Compressor v1.12). Additional details at https://github.com/oneapi-src/customer-chatbot. Results may vary.
4Intelligent Indexing Reference Kit, measured on June 22, 2022. HW Configuration: Amazon AWS m6i.xlarge, OS: Red Hat Enterprise Linux Server 7.9, 4 X Intel® Xeon® Platinum 8370C CPU @ 2.80GHz, 2 threads/core, 2 cores/socket, 1 socket. SW Configuration: Config 1 (Pandas, Scikit-learn), Config 2 (Intel® AI Analytics Toolkit v 2021.4.1, Intel® Extension for Scikit-learn, Intel® Distribution of Modin). Additional details at https://github.com/oneapi-src/intelligent-indexing. Results may vary.
Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.
Results may vary. Performance results are based on testing as of dates shown in configurations and may not reflect all publicly available updates.
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