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Introducing JobSet
- JobSet is an open source API introduced by Daniel Vega-Myhre, Abdullah Gharaibeh, and Kevin Hannon for representing distributed jobs, focusing on distributed ML training and HPC workloads on Kubernetes.
- JobSet aims to solve gaps in Kubernetes primitives for distributed ML training, offering a unified API for large-scale distributed HPC and ML use cases.
- Key features of JobSet include Replicated Jobs, automatic headless service management, configurable success and failure policies, and exclusive placement per topology domain.
- It models distributed batch workloads as Kubernetes Jobs and uses ReplicatedJobs to manage child Jobs, allowing users to define different pod templates for various groups of pods.
- JobSet integrates with Kueue for workload queuing, oversubscription, multi-tenancy, and more, enhancing cluster resource utilization.
- An example use case of JobSet involves distributed ML training on multiple TPU slices using Jax, showcasing its capabilities for TPU-based workloads.
- Future JobSet features include configurable success and failure policies, seamless integration with Kubernetes, and providing a rich API for distributed computing tasks.
- Developers and contributors are encouraged to engage with the JobSet project, offer feedback, report bugs, suggest features, and participate in its development.
- For more information on JobSet, its roadmap, and how to get involved, interested parties can visit the project repository, mailing list, or reach out on Slack.
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