Frank
af05c890fd
The scraped metrics have no rules over them, so every graph was read after the
fact by a human. This adds shared/prometheus/rules/llm-fleet.yml, loaded via
rule_files and installed by the same copier that ships prometheus.yml (a rule
file nothing loads evaluates nothing, and Prometheus reports no error for it):
LlmKvPoolSaturated kv pool > 90% for 10m — the 122,636-token wall
LlmRequestsQueueing waiting > 0 for 10m — MAXSEQS=4 reached
LlmPrefixCacheCollapse reuse < 20% for 15m with traffic — the thrash signature
LlmEndpointDown scrape target down 2m — both replicas gone
LlmLatencyHigh p95 end-to-end > 60s for 10m — what an operator feels
job:llm_prefix_cache_reuse:rate5m — recording rule shared by the alert and the
dashboard, so the two cannot drift
Every threshold is tied to something measured on this fleet, not to a guess,
and the file says plainly at the top that **nothing is notified**: there is no
Alertmanager here, so a firing alert reaches Prometheus' /alerts page, the
ALERTS metric and the dashboard panel added alongside it — and nowhere else.
Choosing how Frank actually gets paged is his call, not a missing config line.
Seam covers the silent failure mode: rules exist, the config references the
directory, the deployer installs it, and the dashboard graphs ALERTS.
85 lines
2.7 KiB
YAML
85 lines
2.7 KiB
YAML
# Prometheus configuration for the homelab.
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# Deployed to /opt/prometheus/config/prometheus.yml on Dankclaw.
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global:
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scrape_interval: 15s
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scrape_timeout: 10s
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evaluation_interval: 15s
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# Alerting and recording rules for the LLM fleet. They evaluate and expose state
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# through /alerts, the ALERTS metric and the Grafana dashboard; nothing is
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# notified, because this stack has no Alertmanager (see the rules file header).
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rule_files:
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- /etc/prometheus/rules/*.yml
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scrape_configs:
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# Prometheus itself
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- job_name: "prometheus"
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static_configs:
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- targets: ["localhost:9090"]
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# Host metrics — both hosts
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- job_name: "node-dankclaw"
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static_configs:
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- targets: ["192.168.0.143:9100"]
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- job_name: "node-dankserver"
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static_configs:
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- targets: ["192.168.0.235:9100"]
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# Gitea metrics (quadlet on an isolated network; use the published LAN port)
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- job_name: "gitea"
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static_configs:
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- targets: ["192.168.0.143:3000"]
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metrics_path: "/metrics"
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# Grafana metrics
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- job_name: "grafana"
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static_configs:
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- targets: ["grafana:3000"]
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metrics_path: "/metrics"
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# Loki metrics
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- job_name: "loki"
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static_configs:
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- targets: ["loki:3100"]
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metrics_path: "/metrics"
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# ComfyUI (no native /metrics; probe the UI)
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- job_name: "comfyui"
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metrics_path: "/"
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static_configs:
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- targets: ["comfyui:8188"]
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# Blackbox-style probes for service up/down (published LAN ports;
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# gitea and comfyui are covered by their own jobs above)
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- job_name: "services"
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metrics_path: "/"
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static_configs:
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- targets:
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- "192.168.0.143:8081" # SearXNG (via the Gluetun tunnel)
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- "192.168.0.143:3005" # OpenWebUI
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# LLM fleet. Scraped on the LAN addresses, not the guarded vhosts: these are
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# machine clients and Prometheus cannot solve an Anubis challenge.
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# frank-medium is vLLM, and its series are the ones that explain behaviour
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# under load — vllm:kv_cache_usage_perc (how full the per-card pool is),
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# vllm:num_requests_waiting (queue depth), the request-latency histograms,
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# and the prefix-cache pair vllm:prefix_cache_hits_total over
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# vllm:prefix_cache_queries_total (their rates give the hit rate, which says
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# whether pinned conversations are actually being reused).
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# frank-large is llama.cpp and exposes throughput counters under llamacpp:.
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- job_name: "frank-medium"
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metrics_path: "/metrics"
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static_configs:
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- targets: ["192.168.0.235:7770"]
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labels:
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service: frank-medium
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- job_name: "frank-large"
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metrics_path: "/metrics"
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static_configs:
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- targets: ["192.168.0.143:7770"]
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labels:
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service: frank-large
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