Speaker
Description
Title: Scalable Data Management Techniques for AI workloads
Abstract: The advent of complex AI workflows that involve large learning models (training using data/pipeline/tensor parallelism, retrieval augmented generation, chaining) has prompted the need for scalable system-level building blocks that enable running them efficiently at large scale on high end machines. Of particular interest in this context are data management techniques and their implementation that bridge the gap between high-level required capabilities (fine-grain tensor access, support for transfer learning and versioning, streaming and transformation of training samples, transparent augmentation, vector databases, etc.) and the existing storage hierarchy (parallel file systems, node-local memories, etc.). This talk discusses the challenges and opportunities in the design and development of such techniques and presents several results based on VELOC and DataStates, two efforts at ANL aimed at leveraging checkpointing to capture the evolution of datasets (including AI models and their training data).
