Distributed systems are made up of many components such as authentication, a persistence layer, stateless services, load balancers, and stateful coordination services. These coordination services are central to the operation of the system, performing tasks such as maintaining system configuration state, ensuring service availability, name resolution, and storing other system metadata. Given their central role in the system it is essential that these systems remain available, fault tolerant and consistent. By providing a highly available file system-like abstraction as well as powerful recipes such as leader election, Apache Zookeeper is often used to implement these services. This talk will go over a generic example of stateful coordination service moving from Zookeeper to Raft.
Big Data Bellevue & Cloudy With a Chance of Data Meetup
October 20, 2022
Distributed systems are made up of many components such as authentication, a persistence layer, stateless services, load balancers, and stateful coordination services. These coordination services are central to the operation of the system, performing tasks such as maintaining system configuration state, ensuring service availability, name resolution, and storing other system metadata. Given their central role in the system it is essential that these systems remain available, fault tolerant and consistent. By providing a highly available file system-like abstraction as well as powerful recipes such as leader election, Apache Zookeeper is often used to implement these services. This talk will go over a generic example of stateful coordination service moving from Zookeeper to Raft.
Meetup Groups
Big Data Bellevue: https://www.meetup.com/big-data-bellevue-bdb/
Cloudy With a Chance of Data: https://www.meetup.com/meetup-datascience/
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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.