One post tagged with "benchmark"

As batch training, inference, AI Agent, HPC, big-data and other diverse workloads are increasingly co-located in the same Kubernetes cluster, the scheduler must make higher-quality decisions under intensifying resource contention while preserving job-level semantics, queue fairness, topology affinity, and operational stability. v1.15.0 delivers enhancements across the scheduling core, heterogeneous resource management, multi-scheduler coordination, and performance observability.

The most notable new capability is Gang-Aware Preemption and Resource Reclamation: preemption decisions are evaluated at gang granularity on both the preemptor and victim sides — the preemptor is placed as a whole gang, and victim candidates are organized and evaluated at job/gang granularity, preferring surplus replicas to avoid per-Pod random eviction that disrupts multiple training jobs while the preemptor itself still cannot start. In addition, v1.15.0 introduces DRA queue quota in the capacity plugin, a pluggable multi-sharding policy framework, a Benchmark and performance observability tool, Kubernetes 1.35 support, NodeGroup preferred ordering, Agent Scheduler stability fixes, GPU/vGPU incremental enhancements, and Scheduling Gates for queue admission control.