Shawn Sun, Alluxio’s software engineer, shares how to get started with Alluxio on Kubernetes in April’s Product School Webinar.
To simplify the DevOps of the stack of Alluxio with a query engine, Alluxio has provided two ways to deploy on Kubernetes, helm and operator. They significantly simplify the deployment, configuration, and life cycle management of resources on Kubernetes.
Through this webinar, you will learn step-by-step how to deploy and run Alluxio on Kubernetes to accelerate analytics workloads.
Shawn Sun, Alluxio’s software engineer, shares how to get started with Alluxio on Kubernetes in April’s Product School Webinar.
To simplify the DevOps of the stack of Alluxio with a query engine, Alluxio has provided two ways to deploy on Kubernetes, helm and operator. They significantly simplify the deployment, configuration, and life cycle management of resources on Kubernetes.
Through this webinar, you will learn step-by-step how to deploy and run Alluxio on Kubernetes to accelerate analytics workloads.
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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.