Amazon Web Services, Inc. (AWS), an Amazon.com, Inc. company (NASDAQ: AMZN), announced general availability of Amazon Elastic Compute Cloud (Amazon EC2) DL1 instances, a new instance type designed for training machine learning models. DL1 instances are powered by Gaudi accelerators from Habana Labs (an Intel company) to provide up to 40% better price performance for training machine learning models than the latest GPU-powered Amazon EC2 instances. With DL1 instances, customers can train their machine learning models faster and more cost effectively for use cases like natural language processing, object detection and classification, fraud detection, recommendation and personalization engines, intelligent document processing, business forecasting, and more. DL1 instances are available on demand via a low-cost pay-as-you-go usage model with no upfront commitments. To get started with DL1 instances, visit aws.amazon.com/ec2/instance-types/dl1.
Machine learning has become mainstream as customers have realized tangible business impact from deploying machine learning models at scale in the cloud. To use machine learning in their business applications, customers start by building and training a model to recognize patterns by learning from sample data, and then apply the model on new data to make predictions. For example, a machine learning model trained on large numbers of contact center transcripts can make predictions to provide real-time personalized assistance to customers through a conversational chatbot. To improve a model’s prediction accuracy, data scientists and machine learning engineers are building increasingly larger and more complex models. To maintain prediction accuracy and high quality of the models, these engineers need to tune and retrain their models frequently. This requires a considerable amount of high-performance compute resources, resulting in increased infrastructure costs. These costs can be prohibitive for customers to retrain their models at the frequency they need to maintain high-accuracy predictions, while also posing an obstacle to customers that want to begin experimenting with machine learning.
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New DL1 instances use Gaudi accelerators built specifically to accelerate machine learning model training by delivering higher compute efficiency at a lower cost compared to general purpose GPUs. DL1 instances feature up to eight Gaudi accelerators, 256 GB of high-bandwidth memory, 768 GB of system memory, 2nd generation Amazon custom Intel Xeon Scalable (Cascade Lake) processors, 400 Gbps of networking throughput, and up to 4 TB of local NVMe storage. Together, these innovations translate to up to 40% better price performance than the latest GPU-powered Amazon EC2 instances for training common machine learning models. Customers can quickly and easily get started with DL1 instances using the included Habana SynapseAI SDK, which is integrated with leading machine learning frameworks (e.g. TensorFlow and PyTorch), helping customers to seamlessly migrate their existing machine learning models currently running on GPU-based or CPU-based instances onto DL1 instances, with minimal code changes. Developers and data scientists can also start with reference models optimized for Gaudi accelerators available in Habana’s GitHub repository, which includes popular models for diverse applications, including image classification, object detection, natural language processing, and recommendation systems.
“The use of machine learning has skyrocketed. One of the challenges with training machine learning models, however, is that it is computationally intensive and can get expensive as customers refine and retrain their models,” said David Brown, Vice President, of Amazon EC2, at AWS. “AWS already has the broadest choice of powerful compute for any machine learning project or application. The addition of DL1 instances featuring Gaudi accelerators provides the most cost-effective alternative to GPU-based instances in the cloud to date. Their optimal combination of price and performance makes it possible for customers to reduce the cost to train, train more models, and innovate faster.”
Customers can launch DL1 instances using AWS Deep Learning AMIs or using Amazon Elastic Kubernetes Service (Amazon EKS) or Amazon Elastic Container Service (Amazon ECS) for containerized applications. For a more managed experience, customers can access DL1 instances through Amazon SageMaker, making it even easier and faster for developers and data scientists to build, train, and deploy machine learning models in the cloud and at the edge. DL1 instances benefit from the AWS Nitro System, a collection of building blocks that offload many of the traditional virtualization functions to dedicated hardware and software to deliver high performance, high availability, and high security while also reducing virtualization overhead. DL1 instances are available for purchase as On-Demand Instances, with Savings Plans, as Reserved Instances, or as Spot Instances. DL1 instances are currently available in the US East (N. Virginia) and US West (Oregon) AWS Regions.
Seagate Technology has been a global leader offering data storage and management solutions for over 40 years. Seagate’s data science and machine learning engineers have built an advanced deep learning (DL) defect detection system and deployed it globally across the company’s manufacturing facilities. In a recent proof of concept project, Habana Gaudi exceeded the performance targets for training one of the DL semantic segmentation models currently used in Seagate’s production. “We expect the significant price performance advantage of Amazon EC2 DL1 instances, powered by Habana Gaudi accelerators, could make a compelling future addition to AWS compute clusters,” said Darrell Louder, Senior Engineering Director of Operations, Technology and Advanced Analytics, at Seagate. “As Habana Labs continues to evolve and enables broader coverage of operators, there is potential for expanding to additional enterprise use cases, and thereby harnessing additional cost savings.”
Intel has created 3D Athlete Tracking technology that analyzes athlete-in-action video in real time to inform performance training processes and enhance audience experiences during competitions. “Training our models on Amazon EC2 DL1 instances, powered by Gaudi accelerators from Habana Labs, will enable us to accurately and reliably process thousands of videos and generate associated performance data, while lowering training cost,” said Rick Echevarria, Vice President, Sales and Marketing Group, Intel. “With DL1 instances, we can now train at the speed and cost required to productively serve athletes, teams, and broadcasters of all levels across a variety of sports.”
Riskfuel provides real-time valuations and risk sensitivities to companies managing financial portfolios, helping them increase trading accuracy and performance. “Two factors drew us to Amazon EC2 DL1 instances based on Habana Gaudi AI accelerators,” said Ryan Ferguson, CEO of Riskfuel. “First, we want to make sure our banking and insurance clients can run Riskfuel models that take advantage of the newest hardware. We found migrating our models to DL1 instances to be simple and straightforward—really, it was just a matter of changing a few lines of code. Second, training costs are a big component of our spending, and the promise of up to 40% improvement in price performance offers potentially substantial benefit to our bottom line.”
Leidos is recognized as a top 10 health IT provider delivering a broad range of customizable, scalable solutions to hospitals and health systems, biomedical organizations, and every U.S. federal agency focused on health. “One of the numerous technologies we are enabling to advance healthcare today is the use of machine learning and deep learning for disease diagnosis based on medical imaging data. Our massive data sets require timely and efficient training to aid researchers seeking to solve some of the most urgent medical mysteries,” said Chetan Paul, CTO Health and Human Services at Leidos. “Given Leidos’ and its customers’ need for quick, easy, and cost-effective training for deep learning models, we are excited to have begun this journey with Intel and AWS to use Amazon EC2 DL1 instances based on Habana Gaudi AI processors. Using DL1 instances, we expect an increase in model training speed and efficiency, with a subsequent reduction in risk and cost of research and development.”
Fractal is a global leader in artificial intelligence and analytics, powering decisions in Fortune 500 companies. “AI and deep learning are at the core of our healthcare imaging business, enabling customers to make better medical decisions. In order to improve accuracy, medical datasets are becoming larger and more complex, requiring more training and retraining of models, and driving the need for improved computing price performance,” said Srikanth Velamakanni, Group CEO of Fractal. “The new Amazon EC2 DL1 instances promise significantly lower cost training than GPU-based EC2 instances, which can help us contain costs and make AI decision-making more accessible to a broader array of customers.”
About Amazon Web Services
For over 15 years, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud offering. AWS has been continually expanding its services to support virtually any cloud workload, and it now has more than 200 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, virtual and augmented reality (VR and AR), media, and application development, deployment, and management from 81 Availability Zones (AZs) within 25 geographic regions, with announced plans for 24 more Availability Zones and eight more AWS Regions in Australia, India, Indonesia, Israel, New Zealand, Spain, Switzerland, and the United Arab Emirates. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com.
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Source: Amazon Web Services, Inc.
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