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Enabling HPC and ML Workloads with Latest Kubernetes Job Features

Offered By: CNCF [Cloud Native Computing Foundation] via YouTube

Tags

Kubernetes Courses Machine Learning Courses High Performance Computing Courses Distributed Computing Courses Batch Processing Courses

Course Description

Overview

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Explore the latest Kubernetes Job API features for running distributed Batch, AI, and HPC workloads at scale in this conference talk. Learn how Indexed Jobs simplify parallel workloads requiring pod-to-pod communication, with examples from DeepMind's distributed machine learning applications. Discover the Flux Operator's ability to orchestrate HPC workloads by creating a "Mini Cluster" within Kubernetes. Understand how Pod Failure Policy can maintain job execution despite pod disruptions while optimizing costs. Gain insights from real-world experiences at DeepMind and Lawrence Livermore National Laboratory to enhance your ability to manage complex computational workloads in Kubernetes environments.

Syllabus

Enabling HPC & ML Workloads with the Latest Kubernetes Job Features- Michał Woźniak & Vanessa Sochat


Taught by

CNCF [Cloud Native Computing Foundation]

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