Automate Kubernetes workload admission and scheduling with queue-based quota management.
npx skills add https://github.com/tylertitsworth/skills --skill kueue
Please help me install this Agent Skill. Skill: kueue Source: https://github.com/tylertitsworth/skills/tree/main/kueue Command: npx skills add https://github.com/tylertitsworth/skills --skill kueue
Manage and schedule Kubernetes workloads (batches, serving, and ML jobs) across clusters and teams, ensuring fair resource sharing and controlled admission.
Create ClusterQueues, LocalQueues, and ResourceFlavors; then submit a workload labeled for a queue.
High-intent search queries and answers about installing and using this skill.
Kubernetes batch workload admission is controlled via queue-based quota management. You create ClusterQueues, LocalQueues, and ResourceFlavors, then label submitted jobs to match a queue for controlled admission and scheduling.
Kubernetes scheduling handles batch jobs and ML training through queue-based admission. It supports submitting and admitting various workload types including standard Jobs, RayJobs, PyTorchJobs, and Deployments via configured LocalQueues.
Custom resource definitions are required to run Kubernetes job queuing. You need proper CRDs installed, a running Kueue controller, and configured quotas, ResourceFlavors, and admission checks to operate the scheduling system.
Sharing GPU resources across teams in Kubernetes is managed through cohorts and flavor-based resource assignment. This enables fair resource sharing, borrowing, and multi-tenant configurations across shared clusters using ClusterQueues.
Preemption in Kubernetes workload scheduling operates through queue-based quota management. When resources are constrained, ClusterQueues can preempt lower-priority workloads to admit new jobs, ensuring controlled admission and fair resource sharing across tenants.
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