Clarify that the "Average utilization" panel on gpu-fleet reflects the
legacy NVML view (DCGM_FI_DEV_GPU_UTIL) rather than engine-active
profiling. For AI/LLM workloads the NVML number is optimistic; the
gpu-efficiency dashboard carries the profiling-based view.
Signed-off-by: Arsolitt <arsolitt@gmail.com>
- gpu-efficiency: scope Tensor Saturation, Util-per-Watt and Power
Throttle stats to the $namespace selector. Cluster-wide means were
misleading when a user had narrowed the dashboard to specific
tenants — the headline numbers lied relative to the panels below.
- gpu-fleet: show per-node power draw as % of combined TDP cap
(DCGM_FI_DEV_POWER_MGMT_LIMIT) instead of raw watts. Thresholds
(60 / 80 %) generalize across GPU SKUs without per-model tuning.
- gpu-quotas: read cluster:gpu_count:allocated from the recording
rules instead of recomputing sum(kube_pod_container_resource_requests)
inline. Keeps the dashboard aligned with the canonical definition
in gpu-recording.rules.yaml so the two can't drift.
Signed-off-by: Arsolitt <arsolitt@gmail.com>
- gpu-fleet: cluster-wide admin view — inventory, capacity (total /
allocated / free), per-node utilization and power, throttling,
temperatures, XID errors.
- gpu-tenants: per-namespace view — live allocation, utilization,
tensor saturation, power, and 24h GPU-hours / kWh integrations for
billing inputs.
Register both under gpu/* in dashboards-infra.list so they ship as
GrafanaDashboard CRs and fall under the bats cross-check introduced
earlier on this branch.
Update examples/README to spell out which DCGM counters each of the
five gpu/* dashboards actually needs on top of the upstream default
CSV — gpu-performance needs profiling and throttling counters,
gpu-efficiency needs profiling, gpu-tenants needs only
DCGM_FI_PROF_PIPE_TENSOR_ACTIVE for its tensor panel, and gpu-fleet
and gpu-quotas work on the default counter set alone.
Signed-off-by: Arsolitt <arsolitt@gmail.com>
Arsolitt