Title

AWS re:Invent 2022 - Accelerate deep learning and innovate faster with AWS Trainium (CMP313)

Summary

• AWS introduced Trainium-based instances for high-performance ML training workloads, offering low cost per training.
• Nitin Nagarkatte, Ron Diamond, and Hamid Shozanazeri presented the session.
• AI advancements are driven by larger and more complex models, creating a demand for better compute and acceleration technologies.
• AWS has invested in AI/ML services and infrastructure, including accelerators like Inferentia (for inference) and Trainium (for training).
• Inferentia-based Inf1 instances launched in 2019, offering high inference performance at low cost, with significant adoption across various customers.
• Trainium is designed for ML training workloads, delivering the highest performance at the lowest cost in Amazon EC2.
• TRN1 instances, built with Trainium accelerators, provide substantial compute and network capabilities, including 3.4 petaflops of BFloat 16 compute and 800 Gbps of network bandwidth.
• AWS is also working on TRN1N instances with 1.6 Tbps network bandwidth.
• TRN1 ultra-clusters can scale up to 30,000 accelerators, offering over six exaflops of training compute.
• Trainium offers 1.5 times the throughput and up to 50% lower cost to train compared to GPU-based instances.
• Trainium is integrated with AWS services and supports industry-standard ML frameworks and managed services like SageMaker.
• Ron Diamond discussed the design and innovations in Trainium, including support for various data types, stochastic rounding, dynamic execution, and collective communication optimizations.
• Hamid Shozanazeri highlighted the collaboration with PyTorch and the ease of use and performance benefits of using Trainium with PyTorch XLA.
• AWS is committed to delivering best-in-class deep learning infrastructure and announced the preview of Inferentia 2-based Inf2 instances.

Insights

• The exponential growth in AI model complexity necessitates advancements in hardware and software to maintain performance and cost efficiency.
• AWS's investment in AI-specific hardware like Trainium and Inferentia demonstrates a commitment to supporting the evolving needs of AI and ML workloads.
• The introduction of Trainium-based instances reflects AWS's strategy to provide customers with scalable, high-performance, and cost-effective solutions for training increasingly large and complex ML models.
• The integration of Trainium with AWS services and popular ML frameworks like PyTorch and TensorFlow ensures a seamless experience for developers and data scientists, enabling them to focus on innovation rather than infrastructure management.
• The advancements in data types, stochastic rounding, and dynamic execution presented by Ron Diamond indicate AWS's focus on both performance optimization and flexibility for ML training workloads.
• The collaboration with the PyTorch team and the emphasis on ease of use and debuggability suggest that AWS values the developer experience and is actively working to reduce barriers to adopting new technologies like Trainium.
• The announcement of Inferentia 2-based Inf2 instances indicates ongoing innovation in AWS's machine learning infrastructure, promising continued improvements in performance and cost efficiency for inference workloads.