DCGM exporter metrics carry the exporter's own namespace
(cozy-gpu-operator), not the workload namespace. Recording rules that
filtered namespace!~"cozy-.*" silently dropped all DCGM series,
producing empty dashboard panels.
Replace namespace-level hardware aggregations with node-level
equivalents (grouped by Hostname), keep namespace-level allocation
rules that use kube_pod_container_resource_requests (which carries the
real workload namespace), and rename pod-level efficiency rules to
gpu-level since DCGM cannot attribute hardware metrics to individual
pods.
Signed-off-by: Arsolitt <arsolitt@gmail.com>
Pod-level panels on the efficiency dashboard and DCGM-level panels on
the performance dashboard ignored the $namespace template variable, so
changing it left the visualizations unchanged. Add the filter to each
query. Performance-side queries use the `$namespace|` empty-tolerant
form so host-level DCGM series without a namespace label remain
visible when a specific namespace is selected.
Signed-off-by: Arsolitt <arsolitt@gmail.com>
Strip Grafana export boilerplate (__inputs, __elements, __requires,
default annotations, embedded datasource inputs) and tighten panel
layouts across the three GPU dashboards. All three continue to use
the $ds_prometheus template variable.
Assisted-By: Claude <noreply@anthropic.com>
Signed-off-by: Arsolitt <arsolitt@gmail.com>
Revise gpu-performance and add two new dashboards, registered in
dashboards-infra.list:
- gpu-efficiency (GPU Efficiency Score) — utilization vs. capacity
and workload efficiency signals.
- gpu-quotas (GPU Quotas & Allocation) — per-namespace requested vs.
used GPUs for tenant capacity planning.
All three dashboards use the $ds_prometheus template variable, per
the project convention.
Assisted-By: Claude <noreply@anthropic.com>
Signed-off-by: Arsolitt <arsolitt@gmail.com>
Add the gpu/gpu-performance dashboard and register it in the infra
dashboard list. The dashboard provides:
- Cluster overview: total/allocated GPUs, average utilization,
aggregate power draw.
- Utilization: GPU util (NVML), tensor pipe active (realistic load
for LLM/AI workloads), graphics engine active, memory copy util.
- Memory: VRAM used/free per GPU.
- Power and temperature per GPU.
- Health: XID errors, power and thermal throttling.
The dashboard relies on DCGM_FI_* metrics plus the cluster:gpu_* and
namespace:gpu_* recording rules added to monitoring-agents.
The JSON follows the cozystack convention — Prometheus data source is
selected via the $ds_prometheus template variable.
Assisted-By: Claude <noreply@anthropic.com>
Signed-off-by: Arsolitt <arsolitt@gmail.com>
