Title

AWS re:Invent 2023 - Cutting-edge AI with AWS and NVIDIA (CMP208)

Summary

• Speakers: Dvij Vajpayee (Senior Product Manager, Amazon EC2), Samantha Pham (Principal Product Manager, EC2), Saurabh (Senior Staff Engineer, Pinterest).
• Trends in AI/ML: Growth in large language models (LLMs), increasing dataset sizes, and scaling of training jobs to over 10,000 GPUs.
• Amazon Bedrock: Launched to provide access to pre-trained foundational models, reducing the need for training from scratch.
• Open Source Contributions: AI companies contributing frameworks and libraries, such as Ray and Mosaic ML, to facilitate AI development.
• Customer Considerations: Performance, cost, and energy efficiency/sustainability are top considerations for ML infrastructure.
• AWS Offerings: A broad set of services for different stages of AI journey, including AI services, Amazon SageMaker, Amazon Bedrock, and EC2 instances.
• EC2 Instances: Portfolio includes NVIDIA and AMD GPUs, custom accelerators from Intel and Qualcomm, and AWS custom AI accelerators Tranium and Infrentria.
• Ultra Clusters: Redesigned for P5 launch, can fit over 20,000 GPUs, and offer improved latency for distributed workloads.
• Customer Stories: Adobe's Firefly and Aurora's autonomous transportation leveraging AWS's ML infrastructure.
• Pinterest's ML Platform: Utilizes a variety of AWS services and instance types for training and serving ML models, with a focus on co-optimizing model code and infrastructure.

Insights

• Rapid Growth in AI: The AI industry is rapidly evolving, with LLMs exceeding 1 trillion parameters and training jobs using tens of thousands of GPUs, indicating a significant increase in computational demand.
• Cost-Efficiency and Accessibility: Amazon Bedrock democratizes access to powerful AI models, enabling smaller entities to leverage advanced AI without prohibitive costs.
• Open Source Ecosystem: The active open source community in AI is accelerating innovation and adoption of new AI trends, such as generative AI, by providing accessible tools and frameworks.
• Performance and Cost Balance: AWS's diverse infrastructure portfolio allows customers to balance performance and cost, which is crucial as the demand for more powerful GPUs increases.
• Ultra Clusters for Scalability: AWS's EC2 Ultra Clusters are designed to support massive, network-intensive workloads, enabling efficient scaling for large ML training jobs.
• Sustainability Considerations: AWS's commitment to net zero emissions by 2040 reflects a growing trend of sustainability being a key factor in technology infrastructure decisions.
• Customer-Centric Infrastructure: AWS's infrastructure offerings are tailored to meet the diverse needs of customers at different stages of their AI journey, from novices to experts managing their own ML models and infrastructure.
• Pinterest's ML Strategy: Pinterest's approach to ML, which includes leveraging a mix of AWS instance types and services, underscores the importance of a flexible and tailored infrastructure to support diverse and evolving ML workloads.
• Co-Optimization of Model and Infrastructure: Saurabh's detailed account of Pinterest's ML platform highlights the necessity of co-optimizing ML models with the underlying infrastructure to achieve optimal performance and cost efficiency.
• The Importance of Profiling and Right Sizing: The emphasis on profiling and right-sizing GPU usage for efficiency parallels similar practices in CPU optimization, indicating a maturation of ML operations practices.