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feat(llm-inference): move astro-orbiter monitoring to GitOps (values.yaml + dashboards.yaml)

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- Prometheus scrape configs for node/gpu/llama-server exporters on
  astro-orbiter now declared in cluster/applications/monitoring/values.yaml
  (additionalScrapeConfigs), applied via ArgoCD sync instead of an
  imperative kubectl secret patch from the Ansible role.
- Grafana dashboard for astro-orbiter LLM inference added as a ConfigMap
  in cluster/applications/monitoring/dashboards.yaml (grafana_dashboard=1
  sidecar label), replacing the role's ad-hoc kubectl apply of a rendered
  Jinja template.
- ansible/roles/llm-inference/tasks/monitoring.yml: removed the kubectl
  get/patch/apply tasks and orphaned grafana-llm-dashboard.json.j2
  template; role now only stands up node_exporter + nvidia_gpu_exporter
  and verifies they're reachable — cluster-facing config lives in Git.
- host_vars/vars.yml + inventory.yml: finalize astro-orbiter as the
  llama.cpp/RTX 3090 host (jarvis user, ssh key), drop stale Ollama/AMD
  vars and ollama_server inventory group superseded by the ATX rebuild.
This commit is contained in:
Hermes Agent service account
2026-08-05 09:43:54 -05:00
parent aa8e229e64
commit aff792a061
8 changed files with 324 additions and 398 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
ansible/host_vars/astro-orbiter/vars.yml
View File

@@ -2,11 +2,14 @@
# ------------------------------------------------------------------------------
# FILE: ansible/host_vars/astro_orbiter/vars.yml
# HOST: astro-orbiter (10.1.71.130)
# ROLE: Ollama inference host with AMD RX 5700 GPU passthrough
# ROLE: llama.cpp LLM inference host — Ryzen 7 5800XT / RTX 3090 (ATX rebuild,
# 2026-08-04). Superseded the prior AMD RX 5700 / Ollama config below;
# drive was transplanted into new hardware, not reinstalled.
# ------------------------------------------------------------------------------
ansible_host: 10.1.71.130
ansible_user: wed
ansible_user: jarvis
ansible_ssh_private_key_file: ~/.ssh/id_jarvis
ansible_become: true
# LVM root expansion — xlarge template uses sda3 partition, standard VG/LV names
@@ -15,11 +18,3 @@ common_root_pv: /dev/sda3
common_root_vg: ubuntu-vg
common_root_lv: ubuntu-lv
# Ollama — all defaults apply; explicitly documented here for visibility
ollama_rocm_version: "6.2"
ollama_default_model: "qwen3:8b"
ollama_hsa_override_gfx_version: "10.1.0"
ollama_data_disk: /dev/sdb
ollama_data_vg: ollama-vg
ollama_data_lv: ollama-lv
ollama_data_dir: /var/lib/ollama

8
ansible/inventory.yml
View File

@@ -59,13 +59,6 @@ n8n_server:
hosts:
tiki-room:
ollama_server:
hosts:
astro-orbiter:
ansible_host: 10.1.71.130
ansible_user: wed
ansible_become: true
astro_orbiter:
hosts:
astro-orbiter:
@@ -76,6 +69,7 @@ hermes_server:
ansible_host: 10.1.71.131
ansible_user: wed
ansible_become: true
ansible_ssh_private_key_file: ~/.ssh/ansible
honcho_server:
hosts:

107
ansible/roles/llm-inference/tasks/monitoring.yml
View File

@@ -2,16 +2,24 @@
# ------------------------------------------------------------------------------
# FILE: roles/llm-inference/tasks/monitoring.yml
# DESCRIPTION: Phase 7 — Prometheus monitoring for the LLM inference stack.
# Deploys three metric sources on astro-orbiter:
# Deploys two metric-producing exporters on astro-orbiter:
#
# 1. node_exporter (port 9100) — system: CPU, RAM, disk, network
# 2. nvidia_gpu_exporter (port 9835) — GPU: VRAM, temp, util, power
# 3. vLLM built-in metrics (port 8000/metrics) — already exposed
# by vLLM; just needs a scrape job (no extra process)
# 3. llama-server built-in metrics (port 8000/metrics, enabled via
# --metrics) — just needs a scrape job (no extra process)
#
# Wires all three into Prometheus via additionalScrapeConfigs on
# the kube-prometheus-stack secret on carousel-of-progress.
# Deploys a Grafana dashboard ConfigMap in the monitoring namespace.
# GitOps note: the Prometheus scrape jobs for all three targets and
# the Grafana dashboard are declared in the homelab Git repo and
# applied by ArgoCD — NOT by this role:
# - cluster/applications/monitoring/values.yaml
# (prometheus.prometheusSpec.additionalScrapeConfigs)
# - cluster/applications/monitoring/dashboards.yaml
# (grafana-llm-inference-dashboard ConfigMap)
# This role's job is only to stand up the two exporters + verify
# they're reachable. Do NOT reintroduce kubectl patch/apply tasks
# here — cluster-facing changes go through Git commit + ArgoCD
# sync so state stays reproducible and self-healing.
# ------------------------------------------------------------------------------
# -----------------------------------------------------------------------
@@ -114,85 +122,10 @@
changed_when: false
# -----------------------------------------------------------------------
# 3. Patch additionalScrapeConfigs secret on carousel-of-progress
# Adds three new scrape jobs: node, gpu, vllm
# 3. Prometheus scrape configs + Grafana dashboard
#
# Intentionally NOT managed here. See file header: these are declared
# in cluster/applications/monitoring/{values.yaml,dashboards.yaml} in
# the homelab Git repo and rolled out by ArgoCD sync, keeping cluster
# state in Git rather than mutated imperatively from the control node.
# -----------------------------------------------------------------------
- name: Read current additionalScrapeConfigs from Prometheus secret
ansible.builtin.command:
cmd: >
kubectl get secret monitoring-kube-prometheus-prometheus-scrape-confg
-n monitoring
-o jsonpath='{.data.additional-scrape-configs\.yaml}'
register: current_scrape_b64
changed_when: false
delegate_to: carousel-of-progress
become: false
- name: Decode current scrape configs
ansible.builtin.set_fact:
current_scrape_yaml: "{{ current_scrape_b64.stdout | b64decode }}"
- name: Check if astro-orbiter scrape jobs already present
ansible.builtin.set_fact:
scrape_already_patched: "{{ 'astro-orbiter' in current_scrape_yaml }}"
- name: Append astro-orbiter scrape jobs to additionalScrapeConfigs
when: not scrape_already_patched
block:
- name: Build new scrape config with astro-orbiter jobs appended
ansible.builtin.set_fact:
new_scrape_yaml: |
{{ current_scrape_yaml }}
- job_name: node-astro-orbiter
scrape_interval: 30s
static_configs:
- labels:
hostname: astro-orbiter
targets:
- {{ hostvars['astro-orbiter']['ansible_host'] }}:9100
- job_name: gpu-astro-orbiter
scrape_interval: 15s
static_configs:
- labels:
hostname: astro-orbiter
gpu: rtx3090
targets:
- {{ hostvars['astro-orbiter']['ansible_host'] }}:{{ llm_gpu_exporter_port }}
- job_name: vllm-astro-orbiter
scrape_interval: 15s
metrics_path: /metrics
static_configs:
- labels:
hostname: astro-orbiter
model: "{{ llm_hf_model }}"
targets:
- {{ hostvars['astro-orbiter']['ansible_host'] }}:{{ llm_serve_port }}
- name: Patch Prometheus additionalScrapeConfigs secret
ansible.builtin.command:
cmd: >
kubectl patch secret monitoring-kube-prometheus-prometheus-scrape-confg
-n monitoring
--type=json
-p='[{"op":"replace","path":"/data/additional-scrape-configs.yaml","value":"{{ new_scrape_yaml | b64encode }}"}]'
delegate_to: carousel-of-progress
become: false
# -----------------------------------------------------------------------
# 4. Deploy Grafana dashboard
# -----------------------------------------------------------------------
- name: Deploy LLM inference Grafana dashboard ConfigMap
ansible.builtin.template:
src: grafana-llm-dashboard.json.j2
dest: /tmp/grafana-llm-dashboard-cm.yaml
delegate_to: carousel-of-progress
become: false
- name: Apply Grafana dashboard ConfigMap to cluster
ansible.builtin.command:
cmd: kubectl apply -f /tmp/grafana-llm-dashboard-cm.yaml
delegate_to: carousel-of-progress
become: false
changed_when: true

35
ansible/roles/llm-inference/tasks/serve.yml
View File

@@ -22,6 +22,17 @@
state: present
update_cache: false
# NOTE: nvidia-driver-595-open provides the runtime driver only (nvidia-smi,
# libcuda.so) — it does NOT ship nvcc/CUDA headers needed to build GGML_CUDA=ON.
# Ubuntu 24.04's nvidia-cuda-toolkit (12.0.x) is sufficient to build llama.cpp
# against; it does not need to match the 595 driver's CUDA 13.2 runtime version.
- name: Install NVIDIA CUDA toolkit (nvcc) for building llama.cpp with CUDA support
ansible.builtin.apt:
name: nvidia-cuda-toolkit
state: present
update_cache: false
become: true
- name: Clone llama.cpp repository
ansible.builtin.git:
repo: https://github.com/ggml-org/llama.cpp.git
@@ -30,6 +41,20 @@
update: false
become: true
- name: Check for incomplete/stale llama.cpp CMake configuration
ansible.builtin.stat:
path: /opt/llama.cpp/build/Makefile
register: llama_cmake_generated
- name: Remove stale llama.cpp build dir if CMake configure never completed
ansible.builtin.file:
path: /opt/llama.cpp/build
state: absent
become: true
when:
- not llama_cmake_generated.stat.exists
- not (ansible_check_mode | default(false))
- name: Build llama.cpp with CUDA support
ansible.builtin.command:
cmd: cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
@@ -53,6 +78,11 @@
group: "{{ llm_venv_owner }}"
mode: "0755"
- name: Check whether GGUF already exists (avoid re-downloading 16.6GB on every run)
ansible.builtin.stat:
path: "{{ llm_gguf_path }}"
register: llm_gguf_stat
- name: Download Gemma 2 27B Q4_K_M GGUF from HuggingFace
ansible.builtin.get_url:
url: "https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/resolve/main/gemma-2-27b-it-Q4_K_M.gguf"
@@ -63,8 +93,13 @@
group: "{{ llm_venv_owner }}"
mode: "0644"
timeout: 7200
force: false
become: true
no_log: true
# Idempotency: skip entirely once the file exists and is reasonably sized
# (the finished GGUF is ~16.6GB; guard against a truncated partial download
# being mistaken for complete by only trusting files > 15GB).
when: not llm_gguf_stat.stat.exists or (llm_gguf_stat.stat.size | int) < 15000000000
- name: Disable and stop vllm-serve if present
ansible.builtin.systemd:

293
ansible/roles/llm-inference/templates/grafana-llm-dashboard.json.j2
View File

@@ -1,293 +0,0 @@
apiVersion: v1
kind: ConfigMap
metadata:
name: grafana-llm-inference-dashboard
namespace: monitoring
labels:
grafana_dashboard: "1"
data:
llm-inference.json: |
{
"title": "LLM Inference — astro-orbiter",
"uid": "llm-astro-orbiter",
"timezone": "browser",
"refresh": "30s",
"schemaVersion": 38,
"tags": ["llm", "gpu", "vllm", "astro-orbiter"],
"time": { "from": "now-1h", "to": "now" },
"templating": {
"list": [
{
"name": "instance",
"type": "constant",
"label": "Host",
"query": "{{ hostvars['astro-orbiter']['ansible_host'] }}",
"hide": 0
}
]
},
"panels": [
{
"id": 1,
"title": "GPU Utilization %",
"type": "timeseries",
"gridPos": { "x": 0, "y": 0, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "percent",
"min": 0, "max": 100,
"color": { "mode": "palette-classic" },
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 70 },
{ "color": "red", "value": 90 }
]
}
}
},
"targets": [
{
"expr": "nvidia_smi_utilization_gpu_ratio{hostname=\"astro-orbiter\"} * 100",
"legendFormat": "GPU Util"
}
]
},
{
"id": 2,
"title": "GPU VRAM Used",
"type": "timeseries",
"gridPos": { "x": 8, "y": 0, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "bytes",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "nvidia_smi_memory_used_bytes{hostname=\"astro-orbiter\"}",
"legendFormat": "VRAM Used"
},
{
"expr": "nvidia_smi_memory_total_bytes{hostname=\"astro-orbiter\"}",
"legendFormat": "VRAM Total"
}
]
},
{
"id": 3,
"title": "GPU Temperature",
"type": "gauge",
"gridPos": { "x": 16, "y": 0, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "celsius",
"min": 0, "max": 100,
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 70 },
{ "color": "red", "value": 85 }
]
}
}
},
"targets": [
{
"expr": "nvidia_smi_temperature_gpu{hostname=\"astro-orbiter\"}",
"legendFormat": "GPU Temp"
}
]
},
{
"id": 4,
"title": "GPU Power Draw",
"type": "timeseries",
"gridPos": { "x": 0, "y": 8, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "watt",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "nvidia_smi_power_draw_watts{hostname=\"astro-orbiter\"}",
"legendFormat": "Power Draw"
},
{
"expr": "nvidia_smi_power_limit_watts{hostname=\"astro-orbiter\"}",
"legendFormat": "Power Limit"
}
]
},
{
"id": 5,
"title": "vLLM — Token Throughput",
"type": "timeseries",
"gridPos": { "x": 8, "y": 8, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "reqps",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "rate(vllm:generation_tokens_total{hostname=\"astro-orbiter\"}[1m])",
"legendFormat": "Tokens/s (gen)"
},
{
"expr": "rate(vllm:prompt_tokens_total{hostname=\"astro-orbiter\"}[1m])",
"legendFormat": "Tokens/s (prompt)"
}
]
},
{
"id": 6,
"title": "vLLM — Request Queue Depth",
"type": "timeseries",
"gridPos": { "x": 16, "y": 8, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "short",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "vllm:num_requests_running{hostname=\"astro-orbiter\"}",
"legendFormat": "Running"
},
{
"expr": "vllm:num_requests_waiting{hostname=\"astro-orbiter\"}",
"legendFormat": "Waiting"
},
{
"expr": "vllm:num_requests_swapped{hostname=\"astro-orbiter\"}",
"legendFormat": "Swapped"
}
]
},
{
"id": 7,
"title": "vLLM — E2E Request Latency (p50/p95/p99)",
"type": "timeseries",
"gridPos": { "x": 0, "y": 16, "w": 12, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "s",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "histogram_quantile(0.50, rate(vllm:e2e_request_latency_seconds_bucket{hostname=\"astro-orbiter\"}[5m]))",
"legendFormat": "p50"
},
{
"expr": "histogram_quantile(0.95, rate(vllm:e2e_request_latency_seconds_bucket{hostname=\"astro-orbiter\"}[5m]))",
"legendFormat": "p95"
},
{
"expr": "histogram_quantile(0.99, rate(vllm:e2e_request_latency_seconds_bucket{hostname=\"astro-orbiter\"}[5m]))",
"legendFormat": "p99"
}
]
},
{
"id": 8,
"title": "vLLM — KV Cache Utilization %",
"type": "timeseries",
"gridPos": { "x": 12, "y": 16, "w": 12, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "percent",
"min": 0, "max": 100,
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 75 },
{ "color": "red", "value": 90 }
]
}
}
},
"targets": [
{
"expr": "vllm:gpu_cache_usage_perc{hostname=\"astro-orbiter\"} * 100",
"legendFormat": "KV Cache %"
}
]
},
{
"id": 9,
"title": "System — CPU Usage %",
"type": "timeseries",
"gridPos": { "x": 0, "y": 24, "w": 8, "h": 7 },
"fieldConfig": {
"defaults": {
"unit": "percent",
"min": 0, "max": 100
}
},
"targets": [
{
"expr": "100 - (avg by(instance) (rate(node_cpu_seconds_total{mode=\"idle\",instance=~\"{{ hostvars['astro-orbiter']['ansible_host'] }}:.*\"}[1m])) * 100)",
"legendFormat": "CPU Used"
}
]
},
{
"id": 10,
"title": "System — Memory Usage",
"type": "timeseries",
"gridPos": { "x": 8, "y": 24, "w": 8, "h": 7 },
"fieldConfig": {
"defaults": {
"unit": "bytes"
}
},
"targets": [
{
"expr": "node_memory_MemTotal_bytes{instance=~\"{{ hostvars['astro-orbiter']['ansible_host'] }}:.*\"} - node_memory_MemAvailable_bytes{instance=~\"{{ hostvars['astro-orbiter']['ansible_host'] }}:.*\"}",
"legendFormat": "Used"
},
{
"expr": "node_memory_MemTotal_bytes{instance=~\"{{ hostvars['astro-orbiter']['ansible_host'] }}:.*\"}",
"legendFormat": "Total"
}
]
},
{
"id": 11,
"title": "System — Disk Usage (root)",
"type": "gauge",
"gridPos": { "x": 16, "y": 24, "w": 8, "h": 7 },
"fieldConfig": {
"defaults": {
"unit": "percentunit",
"min": 0, "max": 1,
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 0.75 },
{ "color": "red", "value": 0.90 }
]
}
}
},
"targets": [
{
"expr": "1 - (node_filesystem_avail_bytes{instance=~\"{{ hostvars['astro-orbiter']['ansible_host'] }}:.*\",mountpoint=\"/\"} / node_filesystem_size_bytes{instance=~\"{{ hostvars['astro-orbiter']['ansible_host'] }}:.*\",mountpoint=\"/\"})",
"legendFormat": "Root disk"
}
]
}
]
}

7
ansible/roles/llm-inference/templates/llama-server.service.j2
View File

@@ -15,7 +15,12 @@ ExecStart=/opt/llama.cpp/build/bin/llama-server \
--ctx-size {{ llm_max_model_len }} \
--n-gpu-layers {{ llm_gpu_layers }} \
--parallel {{ llm_parallel_slots }} \
--chat-template gemma
--metrics
# NOTE: no --chat-template flag — llama-server auto-detects and uses the
# GGUF's own embedded Jinja chat template (verified correct Gemma-2
# start_of_turn/end_of_turn format for bartowski's gemma-2-27b-it-Q4_K_M).
# The built-in "--chat-template gemma" name does NOT match this model's
# expected format on this llama.cpp build and produced garbled completions.
Restart=on-failure
RestartSec=10
TimeoutStartSec=120

227
cluster/applications/monitoring/dashboards.yaml
View File

@@ -9199,3 +9199,230 @@ data:
"version": 1,
"description": "Ingress-nginx supports a rich collection of prometheus metrics. If you have prometheus and grafana installed on your cluster then prometheus will already be scraping this data due to the scrape annotation on the deployment."
}
---
apiVersion: v1
kind: ConfigMap
metadata:
name: grafana-llm-inference-dashboard
namespace: monitoring
labels:
grafana_dashboard: "1"
data:
llm-inference-astro-orbiter.json: |
{
"title": "LLM Inference — astro-orbiter",
"uid": "llm-astro-orbiter",
"timezone": "browser",
"refresh": "30s",
"schemaVersion": 38,
"tags": ["llm", "gpu", "llama-server", "astro-orbiter"],
"time": { "from": "now-1h", "to": "now" },
"panels": [
{
"id": 1,
"title": "GPU Utilization %",
"type": "timeseries",
"gridPos": { "x": 0, "y": 0, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "percent",
"min": 0, "max": 100,
"color": { "mode": "palette-classic" },
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 70 },
{ "color": "red", "value": 90 }
]
}
}
},
"targets": [
{
"expr": "nvidia_smi_utilization_gpu_ratio{hostname=\"astro-orbiter\"} * 100",
"legendFormat": "GPU Util"
}
]
},
{
"id": 2,
"title": "GPU VRAM Used",
"type": "timeseries",
"gridPos": { "x": 8, "y": 0, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "bytes",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "nvidia_smi_memory_used_bytes{hostname=\"astro-orbiter\"}",
"legendFormat": "VRAM Used"
},
{
"expr": "nvidia_smi_memory_total_bytes{hostname=\"astro-orbiter\"}",
"legendFormat": "VRAM Total"
}
]
},
{
"id": 3,
"title": "GPU Temperature",
"type": "gauge",
"gridPos": { "x": 16, "y": 0, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "celsius",
"min": 0, "max": 100,
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 70 },
{ "color": "red", "value": 85 }
]
}
}
},
"targets": [
{
"expr": "nvidia_smi_temperature_gpu{hostname=\"astro-orbiter\"}",
"legendFormat": "GPU Temp"
}
]
},
{
"id": 4,
"title": "GPU Power Draw",
"type": "timeseries",
"gridPos": { "x": 0, "y": 8, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "watt",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "nvidia_smi_power_draw_watts{hostname=\"astro-orbiter\"}",
"legendFormat": "Power Draw"
},
{
"expr": "nvidia_smi_power_limit_watts{hostname=\"astro-orbiter\"}",
"legendFormat": "Power Limit"
}
]
},
{
"id": 5,
"title": "llama-server — Requests per Second",
"type": "timeseries",
"gridPos": { "x": 8, "y": 8, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "reqps",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "rate(llamacpp:tokens_predicted_total{hostname=\"astro-orbiter\"}[1m])",
"legendFormat": "Tokens/s (predicted)"
},
{
"expr": "rate(llamacpp:prompt_tokens_total{hostname=\"astro-orbiter\"}[1m])",
"legendFormat": "Tokens/s (prompt)"
}
]
},
{
"id": 6,
"title": "llama-server — Requests Processing/Deferred",
"type": "timeseries",
"gridPos": { "x": 16, "y": 8, "w": 8, "h": 8 },
"fieldConfig": {
"defaults": {
"unit": "short",
"color": { "mode": "palette-classic" }
}
},
"targets": [
{
"expr": "llamacpp:requests_processing{hostname=\"astro-orbiter\"}",
"legendFormat": "Processing"
},
{
"expr": "llamacpp:requests_deferred{hostname=\"astro-orbiter\"}",
"legendFormat": "Deferred"
}
]
},
{
"id": 9,
"title": "System — CPU Usage %",
"type": "timeseries",
"gridPos": { "x": 0, "y": 16, "w": 8, "h": 7 },
"fieldConfig": {
"defaults": {
"unit": "percent",
"min": 0, "max": 100
}
},
"targets": [
{
"expr": "100 - (avg by(instance) (rate(node_cpu_seconds_total{mode=\"idle\",instance=~\"10.1.71.130:.*\"}[1m])) * 100)",
"legendFormat": "CPU Used"
}
]
},
{
"id": 10,
"title": "System — Memory Usage",
"type": "timeseries",
"gridPos": { "x": 8, "y": 16, "w": 8, "h": 7 },
"fieldConfig": {
"defaults": {
"unit": "bytes"
}
},
"targets": [
{
"expr": "node_memory_MemTotal_bytes{instance=~\"10.1.71.130:.*\"} - node_memory_MemAvailable_bytes{instance=~\"10.1.71.130:.*\"}",
"legendFormat": "Used"
},
{
"expr": "node_memory_MemTotal_bytes{instance=~\"10.1.71.130:.*\"}",
"legendFormat": "Total"
}
]
},
{
"id": 11,
"title": "System — Disk Usage (root)",
"type": "gauge",
"gridPos": { "x": 16, "y": 16, "w": 8, "h": 7 },
"fieldConfig": {
"defaults": {
"unit": "percentunit",
"min": 0, "max": 1,
"thresholds": {
"mode": "absolute",
"steps": [
{ "color": "green", "value": null },
{ "color": "yellow", "value": 0.75 },
{ "color": "red", "value": 0.90 }
]
}
}
},
"targets": [
{
"expr": "1 - (node_filesystem_avail_bytes{instance=~\"10.1.71.130:.*\",mountpoint=\"/\"} / node_filesystem_size_bytes{instance=~\"10.1.71.130:.*\",mountpoint=\"/\"})",
"legendFormat": "Root disk"
}
]
}
]
}

30
cluster/applications/monitoring/values.yaml
View File

@@ -212,6 +212,36 @@ prometheus:
# array: utilidor
# array_type: physical
# astro-orbiter — LLM inference host (Ryzen 7 5800XT / RTX 3090)
# Managed by roles/llm-inference (Phase monitoring). Three targets:
# node (system), gpu (nvidia_gpu_exporter), llama-server (inference metrics)
- job_name: node-astro-orbiter
scrape_interval: 30s
static_configs:
- targets:
- 10.1.71.130:9100
labels:
hostname: astro-orbiter
- job_name: gpu-astro-orbiter
scrape_interval: 15s
static_configs:
- targets:
- 10.1.71.130:9835
labels:
hostname: astro-orbiter
gpu: rtx3090
- job_name: llama-server-astro-orbiter
scrape_interval: 15s
metrics_path: /metrics
static_configs:
- targets:
- 10.1.71.130:8000
labels:
hostname: astro-orbiter
model: bartowski/gemma-2-27b-it-GGUF
# ─── Grafana ──────────────────────────────────────────────────────────────────
grafana:
enabled: true