Data platforms span multiple clusters, regions and clouds to meet the business needs for agility, cost effectiveness, and efficiency. Organizations building data platforms for structured and unstructured data have standardized on separation of storage and compute to remain flexible while avoiding vendor lock-in. Data orchestration has emerged as the foundation of such a data platform for multiple use cases all the way from data ingestion to transformations to analytics and AI.
In this keynote from Haoyuan Li, founder and CEO of Alluxio, we will showcase how organizations have built data platforms based on data orchestration. The need to simplify data management and acceleration across different business personas has given rise to data orchestration as a requisite piece of the modern data platform. In addition, we will outline typical journeys for realizing a hybrid and multi-cloud strategy.
Data platforms span multiple clusters, regions and clouds to meet the business needs for agility, cost effectiveness, and efficiency. Organizations building data platforms for structured and unstructured data have standardized on separation of storage and compute to remain flexible while avoiding vendor lock-in. Data orchestration has emerged as the foundation of such a data platform for multiple use cases all the way from data ingestion to transformations to analytics and AI.
In this keynote from Haoyuan Li, founder and CEO of Alluxio, we will showcase how organizations have built data platforms based on data orchestration. The need to simplify data management and acceleration across different business personas has given rise to data orchestration as a requisite piece of the modern data platform. In addition, we will outline typical journeys for realizing a hybrid and multi-cloud strategy.
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Videos
TorchTitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is a repo that showcases PyTorch's latest distributed training features in a clean, minimal codebase.
In this talk, Tianyu will share TorchTitan’s design and optimizations for the Llama 3.1 family of LLMs, spanning 8 billion to 405 billion parameters, and showcase its performance, composability, and scalability.
As large-scale machine learning becomes increasingly GPU-centric, modern high-performance hardware like NVMe storage and RDMA networks (InfiniBand or specialized NICs) are becoming more widespread. To fully leverage these resources, it’s crucial to build a balanced architecture that avoids GPU underutilization. In this talk, we will explore various strategies to address this challenge by effectively utilizing these advanced hardware components. Specifically, we will present experimental results from building a Kubernetes-native distributed caching layer, utilizing NVMe storage and high-speed RDMA networks to optimize data access for PyTorch training.