Coreweave observability
Set up GPU monitoring and observability for CoreWeave workloads. Use when implementing GPU metrics dashboards, configuring alerts, or tracking inference latency and throughput. Trigger with phrases like "coreweave monitoring", "coreweave observability", "coreweave gpu metrics", "coreweave grafana".From its SKILL.md
npx -y skills add ComeOnOliver/skillshub --skill coreweave-observabilityAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
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SKILL.md
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CoreWeave Observability
GPU Metrics (DCGM Exporter)
CKS clusters come with DCGM exporter pre-installed. Key metrics:
| Metric | Description |
|---|---|
DCGM_FI_DEV_GPU_UTIL | GPU core utilization % |
DCGM_FI_DEV_FB_USED | GPU memory used (MB) |
DCGM_FI_DEV_FB_FREE | GPU memory free (MB) |
DCGM_FI_DEV_POWER_USAGE | Power consumption (W) |
DCGM_FI_DEV_GPU_TEMP | GPU temperature (C) |
Prometheus Alert Rules
groups:
- name: coreweave-gpu
rules:
- alert: GPUUtilizationLow
expr: avg(DCGM_FI_DEV_GPU_UTIL) < 20
for: 30m
labels: { severity: warning }
annotations:
summary: "GPU utilization below 20% for 30min -- consider scaling down"
- alert: GPUMemoryHigh
expr: DCGM_FI_DEV_FB_USED / (DCGM_FI_DEV_FB_USED + DCGM_FI_DEV_FB_FREE) > 0.95
for: 5m
labels: { severity: critical }
annotations:
summary: "GPU memory >95% -- risk of OOM"
- alert: InferencePodDown
expr: kube_deployment_status_replicas_available{deployment=~".*inference.*"} == 0
for: 2m
labels: { severity: critical }
Resources
Next Steps
For incident response, see coreweave-incident-runbook.
What ships with it
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