Task · SKT-0087.12

Add Run:ai scheduler telemetry with workload-to-GPU allocation

Description

When Run:ai is the scheduler of record on Kubernetes, the failed-job and slow-job journeys start from it. Run:ai exposes a Prometheus metrics path (MetricsExporter and WorkloadExporter) plus control-plane Metrics and Telemetry APIs for data not exposed as metrics. Telemetry covers scheduler and workload state, queue depth and pending workloads, project and department quota, GPU allocation, available and idle-allocated GPUs, wait time and failures. No source emits exemplars, so the workload-to-GPU join comes from DCGM pod labels combined with Run:ai and Kubernetes metadata mapping pod to workload, project and node pool. Project and workload names can be sensitive in regulated environments and are often pseudonymised before egress, so they must stay blueprint-only.

Acceptance Criteria

Definition of Done

Implementation Notes

loop47: admitted vendor-contract research R12; implementation waits for fixture and DCGM landing

loop47: vendor-contract research confirms public versioned API identifiers, not MetricsExporter/WorkloadExporter exposition. Core names/types/labels and failed-workload GPU query are PENDING. Resume only with pinned exporter reference/source or capture proving lifecycle and workload-to-GPU series; do not infer API identifiers as Prometheus names. Implementation attempts 0; review-repairs 0; infra retries 0; no rejection.

References

View the source file on GitHub