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- README.md: Fleet overview and quick access commands - FLEET.md: Complete device specifications and inventory - NETWORK.md: IP addresses, Tailscale mesh, network diagram - SSH_CONFIG.md: SSH configuration reference - HAILO.md: Hailo-8 AI accelerator documentation Fleet: 8 devices, 52 TOPS AI compute, 167 Docker containers 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Hailo-8 AI Accelerator Setup
Overview
Two Pironman Pi 5 units equipped with Hailo-8 M.2 AI accelerators.
| Device | Serial Number | Firmware | TOPS |
|---|---|---|---|
| octavia | HLLWM2B233704606 | 4.23.0 | 26 |
| cecilia | HLLWM2B233704667 | 4.23.0 | 26 |
Total AI Compute: 52 TOPS
Hailo-8 Specifications
- Architecture: HAILO8
- Part Number: HM218B1C2FAE
- Product Name: HAILO-8 AI ACC M.2 M KEY MODULE EXT TEMP
- Performance: 26 TOPS (Tera Operations Per Second)
- Interface: M.2 M-Key (PCIe)
Quick Commands
Check Hailo Status
# Identify device
ssh octavia "hailortcli fw-control identify"
# Scan for devices
ssh octavia "hailortcli scan"
# Check device exists
ssh octavia "ls -la /dev/hailo0"
Run Benchmark
# On cecilia (has sample models)
ssh cecilia "hailortcli benchmark /usr/share/hailo-models/resnet_v1_50_h8l.hef -t 5"
Sample Output
Network resnet_v1_50/resnet_v1_50: 100% | FPS: 23.98
Available Models
Located at /usr/share/hailo-models/ on cecilia:
resnet_v1_50_h10.hefresnet_v1_50_h8l.hefscrfd_2.5g_h8l.hef
Pironman 5 Fan Service
Both units run the Pironman 5 fan control service:
# Check status
ssh octavia "systemctl status pironman5"
# View settings
ssh octavia "pironman5 --help"
# RGB LED control
ssh octavia "pironman5 -rc '#FF1D6C' -rs solid"
HailoRT Service
The Hailo runtime service runs automatically:
ssh cecilia "systemctl status hailort"
Use Cases
- Real-time Object Detection - YOLO models at 30+ FPS
- Image Classification - ResNet, MobileNet
- Face Detection - SCRFD models
- Edge AI Inference - Low latency local processing
Integration Notes
- Both devices have Docker installed for containerized AI workloads
- Cecilia runs Ollama for LLM inference
- NVMe storage available for model caching
- Tailscale mesh enables remote AI inference calls