Files
serverAppConfigs/shared/prometheus/prometheus.yml
Frank b02647b992 feat(observability): per-GPU metrics for the mixed AMD/NVIDIA fleet
Add a host-process GPU exporter, scrape it on both servers, and graph it
on the host-system dashboard. The fleet has four GPUs across three vendors
of interest (2x R9700 on Dankserver, the 8060S iGPU + RTX 3090 on Dankclaw)
and nothing exposed them until now.

scripts/gpu-exporter.py reads each AMD card's amdgpu sysfs
(gpu_busy_percent / mem_info_vram_* / hwmon temp1_input / power1_average)
plus one nvidia-smi --query-gpu CSV, and emits unified gpu_* Prometheus
families (utilization_percent, memory_used/total_bytes, temperature_celsius,
power_watts) labelled gpu/vendor/model. Absent hardware yields ABSENT series,
never a fabricated 0: the R9700s expose no sysfs power, so they simply carry
no gpu_power_watts line. Both sources are injectable, so the seam runs on a
GPU-less dev box; live-verified on Dankserver's real sysfs.

It runs as a plain user unit (not a quadlet): it must read the host's own DRM
sysfs and shell to nvidia-smi, which a container cannot do without GPU
passthrough. Same posture as signal-relay. The base unit binds loopback; the
deploy drop-in appends the host LAN address, never a wildcard (matching
node-exporter/cadvisor). deploy_gpu_exporter installs/enables/health-gates it
on both hosts and is wired into the planner (both targets), the test-changed
router, and two Prometheus scrape jobs using the node/cadvisor reach rules
(gateway on Dankclaw, LAN on Dankserver).
2026-10-20 23:42:26 -07:00

216 lines
8.7 KiB
YAML

# Prometheus configuration for the homelab.
# Deployed to /opt/prometheus/config/prometheus.yml on Dankclaw.
global:
scrape_interval: 15s
scrape_timeout: 10s
evaluation_interval: 15s
# Alerting and recording rules for the LLM fleet. They evaluate, expose
# state through /alerts and the ALERTS metric, and — since 2026-09-17 —
# deliver to Signal through Alertmanager (see the alerting section below).
rule_files:
- /etc/prometheus/rules/*.yml
# The delivery half. Alertmanager runs on the same dankclaw_default network,
# so it is addressed by container name, like grafana and loki below. Its
# single receiver webhooks the host-side signal-relay (192.168.0.143:18093,
# reached from the container as host.containers.internal).
alerting:
alertmanagers:
- static_configs:
- targets: ["alertmanager:9093"]
scrape_configs:
# Prometheus itself
- job_name: "prometheus"
static_configs:
- targets: ["localhost:9090"]
# Host metrics — both hosts. node-dankclaw is scraped over
# host.containers.internal, not its own LAN address: this Prometheus is a
# bridge container, and on this netavark host a container can only reach the
# host's LAN IP at a PUBLISHED port — node-exporter's net=host bind on
# 192.168.0.143:9100 is refused by construction (the same rule that sends
# frank-dragoon and frank-zealot to the gateway below). node-dankserver is a
# different machine, so its LAN address works.
- job_name: "node-dankclaw"
static_configs:
- targets: ["host.containers.internal:9100"]
- job_name: "node-dankserver"
static_configs:
- targets: ["192.168.0.235:9100"]
# Gitea metrics (quadlet on an isolated network; use the published LAN port)
- job_name: "gitea"
static_configs:
- targets: ["192.168.0.143:3000"]
metrics_path: "/metrics"
# Grafana metrics
- job_name: "grafana"
static_configs:
- targets: ["grafana:3000"]
metrics_path: "/metrics"
# Loki metrics
- job_name: "loki"
static_configs:
- targets: ["loki:3100"]
metrics_path: "/metrics"
# ComfyUI (no native /metrics; probe the UI)
- job_name: "comfyui"
metrics_path: "/"
static_configs:
- targets: ["comfyui:8188"]
# Blackbox-style probes for service up/down (published LAN ports;
# gitea and comfyui are covered by their own jobs above)
- job_name: "services"
metrics_path: "/"
static_configs:
- targets:
- "192.168.0.143:8081" # SearXNG (via the Gluetun tunnel)
- "192.168.0.143:3005" # OpenWebUI
# LLM fleet. Scraped on the LAN addresses where the endpoint is reachable from
# this container, not the guarded vhosts: these are machine clients and
# Prometheus cannot solve an Anubis challenge.
#
# One metric namespace: llamacpp:* — frank-dragoon, frank-zealot and
# frank-archon, all llama.cpp with /metrics (the family verified from
# /metrics on 2026-09-16 and 2026-09-20) and all exporting
# requests_deferred (the queue depth, the only form llama.cpp shows the
# capacity wall in), spec_decode_num_drafts_total, tokens_predicted_total,
# and the prompt_tokens_cached_total / prompt_tokens_total reuse pair.
#
# frank-dragoon and frank-zealot are scraped over host.containers.internal,
# not their own LAN addresses: this Prometheus is a bridge-networked
# container (Network=dankclaw_default), and on this netavark host a
# container can only reach the host's LAN IP at a PUBLISHED port — a
# host-process bind like llama-server's 0.0.0.0:7770 is refused at
# 192.168.0.143:7770 while it answers at 169.254.1.2:7770. That is
# why frank-zealot was red here for days (up{job="frank-zealot"} == 0,
# "dial tcp 192.168.0.143:7771") even though the endpoint was healthy and
# exporting: the target address was unreachable by construction, not the
# service down. Same host, same rule, so both local endpoints use the
# gateway.
#
# (2026-10-05) frank-archon LEFT the llamacpp family with FreeToken
# (the PR587 ROCm engine then serving it had NO /metrics — the job
# scraped 404s forever, a structural never-green) and RE-JOINED the same
# day on the radiance engine: its exporter speaks vLLM-compatible names
# plus radiance:* families, a different namespace — hence the separate
# job below, not a re-add to the llamacpp pair. The netavark quirk above
# is same-host only; plain LAN routing REACHES 192.168.0.235:7770.
- job_name: "frank-zealot"
metrics_path: "/metrics"
static_configs:
- targets: ["host.containers.internal:7771"]
labels:
service: frank-zealot
- job_name: "frank-dragoon"
metrics_path: "/metrics"
static_configs:
- targets: ["host.containers.internal:7770"]
labels:
service: frank-dragoon
# frank-archon (dankserver) — the radiance engine since 2026-10-05.
# A DIFFERENT host, so plain LAN routing is correct here (the
# host.containers.internal trick is same-host only): health answers at
# 192.168.0.235:7770 from this bridge container. Radiance exports
# vLLM-compatible names plus radiance:* families — NOT the llamacpp:*
# namespace, so llm-fleet rules must not be pointed at this target.
- job_name: "frank-archon"
metrics_path: "/metrics"
static_configs:
- targets: ["192.168.0.235:7770"]
labels:
service: frank-archon
# Per-container metrics from cadvisor (a rootless quadlet on each host).
# cadvisor keys every series by the cgroup path; for a quadlet container that
# path is …/app.slice/<name>.service[/libpod-payload-<hex>], so the unit NAME
# lives in it. The relabel below pulls that name out into a `container` label
# and keeps only the per-unit aggregate — cadvisor's raw host root,
# system.slice daemons and machine.slice scopes carry no such name and are
# dropped, as is the per-CPU-core split (only cpu="total" survives), so the
# scrape stays small. cadvisor cannot bind per-interface, so it is reached at
# the host's PUBLISHED LAN port: unlike node-exporter's host-net bind (which
# is why node-dankclaw scrapes via host.containers.internal), a published port
# IS reachable from this bridge container at the host LAN address.
- job_name: "cadvisor-dankclaw"
static_configs:
- targets: ["192.168.0.143:8080"]
labels:
host: dankclaw
metric_relabel_configs:
- source_labels: [id]
regex: '.*/app\.slice/([^.]+)\.service(?:/.*)?$'
target_label: container
replacement: '$1'
# Keep only the unit-level cgroup; drop its payload/control children so a
# container is not counted twice.
- source_labels: [id]
regex: '.*/\.service/.*'
action: drop
# Drop everything that is not a named unit (no `container` was extracted).
- source_labels: [container]
regex: '.+'
action: keep
# cpu="total" for cpu series; metrics with no cpu label (memory, network,
# filesystem) are kept.
- source_labels: [cpu]
regex: '^$|total'
action: keep
- regex: 'id|name|image|container_label.*|machine_id|boot_id'
action: labeldrop
- job_name: "cadvisor-dankserver"
static_configs:
- targets: ["192.168.0.235:8080"]
labels:
host: dankserver
metric_relabel_configs:
- source_labels: [id]
regex: '.*/app\.slice/([^.]+)\.service(?:/.*)?$'
target_label: container
replacement: '$1'
- source_labels: [id]
regex: '.*/\.service/.*'
action: drop
- source_labels: [container]
regex: '.+'
action: keep
- source_labels: [cpu]
regex: '^$|total'
action: keep
- regex: 'id|name|image|container_label.*|machine_id|boot_id'
action: labeldrop
# Per-GPU metrics (gpu_utilization_percent / gpu_memory_used_bytes /
# gpu_memory_total_bytes / gpu_temperature_celsius / gpu_power_watts) from the
# host-process gpu-exporter (scripts/gpu-exporter.py). One job per host, same
# reach rules as node/cadvisor: Dankclaw is scraped at the podman gateway
# because the netavark hairpin quirk makes Prometheus fail to dial its own
# host LAN IP; Dankserver is plain LAN. The gpu label (PCI BDF on AMD, index on
# NVIDIA) + vendor/model come from the exporter, so the GPU row on the
# host-system dashboard filters by host. Power is emitted only where readable
# (NVIDIA + the Dankclaw iGPU); the R9700s have no sysfs power, so they simply
# have no gpu_power_watts series — no fabricated zero.
- job_name: "gpu-dankclaw"
static_configs:
- targets: ["host.containers.internal:9401"]
labels:
host: dankclaw
- job_name: "gpu-dankserver"
static_configs:
- targets: ["192.168.0.235:9401"]
labels:
host: dankserver