In this talk, we will provide a complete picture of the Hudi platform components, along with their unique design choices. We will then deep dive into two important areas of active development going forward – table metadata management and caching. Specifically, we will discuss gaps in the data lake ecosystem around these aspects and provide strawman design approaches for Hudi aims to solve them going forward.
ALLUXIO DAY VI 2021
October 12, 2021
In this talk, we will provide a complete picture of the Hudi platform components, along with their unique design choices. We will then deep dive into two important areas of active development going forward – table metadata management and caching. Specifically, we will discuss gaps in the data lake ecosystem around these aspects and provide strawman design approaches for Hudi aims to solve them going forward.
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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.