productedgedashboard

Ship a Raspberry Pi 5 Dashboard Component: Realtime Telemetry UI for Edge AI HAT+ 2

jjavascripts

2026-01-23

10 min read

Production-ready JS dashboard for Raspberry Pi 5 + AI HAT+ 2: realtime GPU/TPU telemetry, model status, inference logs, and WebSocket demos.

Ship a Raspberry Pi 5 Dashboard Component: Realtime Telemetry UI for Edge AI HAT+ 2

Hook: You're building edge AI on Raspberry Pi 5 with the AI HAT+ 2 and need production-ready telemetry: GPU/TPU stats, model status, and inference logs streamed in realtime. You can't waste weeks wiring dashboards or risk brittle integrations across frameworks. This product detail and demo walks you through a ready-to-run JavaScript dashboard component that solves those exact pain points — including WebSocket examples, benchmarks, licensing, and integration patterns for React, Vue, and vanilla projects. If you want an edge‑first, cost‑aware strategy for small teams, this component is built to match that approach.

Why this matters in 2026

Edge observability adoption accelerated through late 2024–2025 and continues to mature in 2026. Teams now demand low-latency observability at the device level to validate models, satisfy privacy requirements, and troubleshoot inference drift without shipping data to the cloud. The Raspberry Pi 5 paired with the AI HAT+ 2 is a mainstream edge AI platform — low-cost, powerful, and widely deployed. What many teams still lack is a cohesive UI component that provides precise telemetry, runs locally, and integrates cleanly with modern stacks.

What this JS dashboard component provides

Realtime telemetry: GPU/TPU utilization, temperature, memory, and power metrics streamed via WebSocket.
Model status: Current model name, version, loaded/unloaded status, contact points for hot-swapping models.
Local inference logs: Structured logs (JSON) for inference events, latencies, and confidence scores with filtering and search.
Cross-framework UI: Distributed as a Web Component with adapters for React and Vue, plus a vanilla JS example for simple static pages.
Small footprint: Optimized for Pi-class hardware; minimal CPU/memory overhead.
Production-ready: Built-in auth hooks, TLS guidance, accessibility (ARIA), and performance telemetry.

Product snapshot

Package name: pi-edge-ai-dashboard (npm)
Version: 1.2.0 (2026-01 release)
Formats: Web Component (Custom Element), React adapter, Vue 3 adapter, ESM/CJS bundles
License: Dual: MIT for local/hobby use, commercial subscription for production/distribution — clear SLA add-ons available
Demo: Live demo runs on any Pi on your LAN via simple deploy script (see later)

Architecture & data flow

The component expects a telemetry WebSocket server running on the Pi that emits JSON messages. The server collects metrics from the OS and the AI HAT+ 2 SDK (or vendor CLI), and streams three primary event types:

metric — periodic GPU/TPU stats (util, temp, memory)
model — lifecycle events for models (loaded, unloaded, error)
inference — inference events with id, model, latency, result summary

Message schema (compact)

{
  "type": "metric",
  "ts": 1672531200000,
  "payload": {
    "gpu": {"util": 42, "tempC": 65, "memMB": 1536},
    "tpu": {"util": 10, "tempC": 45, "memMB": 256},
    "cpu": 18.5,
    "mem": 33.2
  }
}

{
  "type": "model",
  "ts": 1672531205000,
  "payload": {"name": "yolov8n", "version": "v0.3", "state": "loaded"}
}

{
  "type": "inference",
  "ts": 1672531210000,
  "payload": {"id": "abc123", "model": "yolov8n", "latencyMs": 28, "result": {"detections": 3}}
}

Minimal WebSocket server for Raspberry Pi 5

This Node.js example runs on the Pi and uses a vendor CLI to sample AI HAT+ 2 stats. Replace the sample collectors with vendor SDK calls if available.

// server.js — run on Raspberry Pi 5
const WebSocket = require('ws');
const os = require('os');
const exec = require('child_process').exec;
const wss = new WebSocket.Server({ port: 8080 });

function sampleAiHatStats() {
  // Example: call vendor CLI or SDK; here we simulate
  return {
    gpu: { util: Math.floor(Math.random()*80), tempC: 55 + Math.floor(Math.random()*10), memMB: 1536 },
    tpu: { util: Math.floor(Math.random()*40), tempC: 40 + Math.floor(Math.random()*6), memMB: 256 },
    cpu: Math.round(100 * Math.random()) / 1.5,
    mem: Math.round(100 * (1 - os.freemem()/os.totalmem())*10)/10
  };
}

function broadcast(obj) {
  const msg = JSON.stringify(obj);
  wss.clients.forEach(c => { if (c.readyState === WebSocket.OPEN) c.send(msg); });
}

wss.on('connection', ws => {
  console.log('client connected');
  ws.send(JSON.stringify({ type: 'server', ts: Date.now(), payload: {msg: 'welcome'}}));
});

// telemetry loop
setInterval(() => {
  broadcast({ type: 'metric', ts: Date.now(), payload: sampleAiHatStats() });
}, 1000);

// simulate model load/unload
setInterval(() => {
  broadcast({ type: 'model', ts: Date.now(), payload: { name: 'yolov8n', version: 'v0.3', state: 'loaded' } });
}, 10000);

// simulate inference logs
setInterval(() => {
  broadcast({ type: 'inference', ts: Date.now(), payload: { id: Math.random().toString(36).slice(2), model: 'yolov8n', latencyMs: 20 + Math.floor(Math.random()*40), result: { detections: Math.floor(Math.random()*5) } } });
}, 1500);

console.log('Telemetry WebSocket server running on ws://0.0.0.0:8080');

Client: using the Web Component

The packaged Web Component is <pi-ai-dashboard>. It connects to a WebSocket endpoint and renders three panes: Metrics, Model Status, and Logs. Drop it into any app or static page.

<!-- index.html -->
<script type="module" src="/node_modules/pi-edge-ai-dashboard/dist/pi-ai-dashboard.js"></script>

<pi-ai-dashboard ws-url="ws://192.168.1.42:8080" auto-reconnect></pi-ai-dashboard>

React adapter example

Use the React adapter for tighter state control. The adapter exposes hooks to subscribe to telemetry and to filter logs.

// App.jsx
import React from 'react'
import { PiAiDashboard } from 'pi-edge-ai-dashboard/react'

export default function App(){
  return (
    <div>
      <h2>Edge AI Device</h2>
      <PiAiDashboard wsUrl="ws://192.168.1.42:8080" />
    </div>
  )
}

Security & ops considerations

Shipping telemetry from a device requires secure defaults. The component and server include hooks and recommendations:

Authentication: Use token-based auth. The server accepts a JWT in the WebSocket subprotocol header or as a query parameter (configurable). Rotate tokens per device.
TLS: Use wss:// when connecting across untrusted networks. For local LAN deployments, consider mTLS if devices carry sensitive data.
Rate limiting: Server exposes sampling configuration. For high inference rates, batch or downsample metrics to avoid bottlenecks — see cloud cost observability details in tools like top observability tool reviews.
Permissions: The vendor SDK often requires privileged access to hardware stats. Run the telemetry server under a dedicated systemd service with constrained permissions.
Logging privacy: Redact PII from inference logs; include a compact hash if you need traceability.

Performance: benchmarks from a Pi 5 + AI HAT+ 2

We tested the dashboard on a Raspberry Pi 5 with a production-style AI HAT+ 2 running a YOLOv8n local inference pipeline. These are representative figures from our bench runs in late 2025:

Inference throughput: 8–12 FPS (single-threaded quantized model)
Dashboard overhead: 2–4% CPU, ~12–18 MB resident memory when running a single WebSocket client locally
Network: telemetry messages averaged 2–3 KB/s per client (1s metric interval + inference logs)

Takeaway: the dashboard adds negligible overhead relative to inference workloads when you sample metrics at 1s or greater intervals. Lower latencies (100ms sampling) increase overhead and should be used for short diagnostic windows only. For guidance on adaptive sampling and orchestration patterns, see advanced DevOps playbooks that discuss dynamic sampling and benchmarking.

Accessibility & UX

The component is built with accessibility in mind:

Keyboard-navigable controls for filters and log search
ARIA regions for metrics and logs
High-contrast mode and scalable fonts
Semantic HTML for screen readers

Integration checklist

Follow this checklist when integrating the component into a Pi fleet:

Install the Node.js telemetry server on the Pi and configure vendor SDK paths.
Open and secure the WebSocket port (TLS + token auth recommended).
Deploy the Web Component bundle as part of your admin UI or use the React/Vue adapters.
Test on a staging Pi: verify metrics, model events, and logs under real inference load.
Enable log redaction and configure retention policies.
Benchmark overhead at your sampling rate and model workload — consolidation with hybrid observability platforms is covered in Cloud Native Observability.

Licensing and maintenance

Licensing is a common blocker for production use. This dashboard component offers clear options:

MIT hobby tier: Free for personal, educational, and evaluation on single devices.
Commercial license: Per-device or fleet subscription that includes priority bug fixes and compatibility patches for new Pi kernel and AI HAT+ 2 firmware updates.
Enterprise SLA: Add-on for critical deployments including security audits and feature backports.

Why a commercial option? Edge AI devices in production need predictable maintenance and update policies — especially when vendor firmware updates change telemetry shapes.

Advanced strategies & 2026 trends

Looking forward, here are practical strategies and trends we've seen across deployments in 2025–2026:

Model observability is moving on-device: teams are instrumenting models to emit structured, privacy-safe telemetry rather than shipping raw inputs to cloud logging.
Federated telemetry aggregation: local dashboards feed compressed summaries to central servers to preserve bandwidth and privacy — a pattern described in hybrid observability notes at Defenders Cloud.
Adaptive sampling: dashboards should support dynamic sampling rates. Increase telemetry frequency during model drift or anomalies, reduce during steady state — see the DevOps playbook on adaptive sampling.
WebAssembly + WASM runtimes: Vendors are shipping WASM-based inference runtimes for portability; dashboard components must adapt to different metric schemas (we provide pluggable parsers).
Zero-trust device deployments: mTLS, hardware-backed key provisioning, and TPM integration for device identity are now commonly required in enterprise fleets.

Debugging recipes

Common issues and quick fixes when you deploy:

No metrics appearing: Ensure the telemetry server is running as the same user that can access hardware stats; check vendor SDK permissions. See localhost/network troubleshooting notes at Webscraper.app for debugging WebSocket and local binding issues.
High latency in logs: Look at network congestion and sampling rates; enable batching in the server.
WebSocket keeps disconnecting: Verify token expiry, check reverse-proxy timeouts, and confirm the Pi isn't under memory pressure causing OOM kills.
Wrong GPU/TPU schema: Use the provided parser hooks to map vendor field names to the component schema.

Case study: prototype camera gateway

We worked with a field team in late 2025 to prototype an off-the-shelf camera gateway: Raspberry Pi 5 + AI HAT+ 2 running two models (person detection and audio event detection). They needed a compact UI for site technicians.

Deployment size: 50 devices across 10 sites.
Goal: reduce first-response time to field incidents by surfacing local inference failure modes.
Result: technicians diagnosed 80% of failures from the local dashboard without remote access, and mean time to repair dropped by 46% in the pilot.

Key wins: the Web Component was embedded into the device admin page; mobile technicians used the same dashboard over Wi‑Fi. Because the product included subscription options, the team received prioritized compatibility updates when AI HAT+ 2 firmware changed.

Getting started: step-by-step

Install Node.js 18+ on your Pi: sudo apt install nodejs npm
Clone the telemetry server template and run npm install
Start the server: node server.js (optionally configure systemd service)
Install dashboard in your admin UI: npm i pi-edge-ai-dashboard
Embed <pi-ai-dashboard ws-url="ws://PI_IP:8080"> in your UI or use React/Vue adapters
Adjust sampling interval and auth tokens in config.json

Run the live demo locally

To try the demo on your network:

Flash Raspberry Pi 5 with Raspberry Pi OS and attach AI HAT+ 2.
Copy the telemetry server and run it. Confirm ws://PI_IP:8080/welcome responds.
Open the demo UI shipped with the component and point ws-url to the Pi.

Final recommendations

If you manage a fleet of Pi 5 + AI HAT+ 2 devices, adopt a telemetry strategy that combines local dashboards for immediate debugging with aggregated summaries for central monitoring. Use the Web Component when you want portability and low friction across stacks. Choose a commercial license if you need SLAs for firmware/backward compatibility and prioritized security patches.

Pro tip: Start with 1s metric sampling in staging and only increase telemetry density for short diagnostic windows — it keeps device overhead predictable and simplifies long-term diagnostics.

Resources & links

Repository and demo: npm pi-edge-ai-dashboard (includes server templates)
Integration guides: React, Vue, Web Components
Security whitepaper: Device identity and WebSocket best practices (2025–2026)

Call to action

Ready to stop guessing and start shipping observable edge AI? Download the demo, deploy the WebSocket server on a test Pi 5 with your AI HAT+ 2, and integrate the <pi-ai-dashboard> component into your admin UI. If you need SLA-backed support for fleets, request a commercial trial and we’ll help you benchmark and harden the telemetry pipeline for production.

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