deploy/DEPLOYMENT.md

Sensorflow Deployment Guide - DGX Spark

Overview

This deployment sets up two persistent FastAPI services on your DGX Spark machine via Tailscale:

• Sensorflow Studio: YOLOv8 training and inference service (port 8001)
• Accident Analysis Platform: Data analysis and WebSocket API (port 8002)
• Frontend: React dashboard via Vite (served at /)

All services are reverse-proxied through nginx at sensorflow.wael.bot

Prerequisites

• DGX Spark machine accessible via Tailscale
• SSH access configured (credentials in local SSH config)
• Ubuntu/Debian-based Linux on DGX
• sudo privileges on DGX
• Node.js 18+ (for frontend build)
• Python 3.9+

Quick Start

1. Prepare Code on Local Machine

cd /path/to/DrivingRepo
# Cleanup already done, verify:
ls -la | grep -v "test_\|example_\|\.venv\|yolov8.*\.pt"

2. Transfer Code to DGX via Tailscale SSH

# Using your SSH config
rsync -avz --exclude='.venv' --exclude='node_modules' \
  /path/to/DrivingRepo/ \
  <your-dgx-host>:/tmp/sensorflow-deploy/

Replace <your-dgx-host> with your DGX Tailscale hostname or IP.

3. Run Deployment on DGX

# SSH into DGX
ssh <your-dgx-host>

# Run deployment script
cd /tmp/sensorflow-deploy
chmod +x deploy/setup-dgx.sh
sudo bash deploy/setup-dgx.sh /tmp/sensorflow-deploy

4. Configure DNS (GoDaddy)

Add an A record pointing to your DGX Spark's external IP or Tailscale IP:

• Domain: sensorflow.wael.bot
• Type: A
• Value: Your DGX Spark's IP
• TTL: 3600

If using Tailscale:

• Get your Tailscale IP: tailscale ip -4
• Use this IP for the A record

Service Management

Check service status:

sudo systemctl status sensorflow-studio accident-analysis

View logs:

# Studio backend
sudo journalctl -u sensorflow-studio -f

# Analysis backend
sudo journalctl -u accident-analysis -f

# Nginx
sudo tail -f /var/log/nginx/error.log

Restart services:

sudo systemctl restart sensorflow-studio accident-analysis

Stop services:

sudo systemctl stop sensorflow-studio accident-analysis

API Endpoints

Studio (YOLOv8)

• POST /api/studio/train - Start training
• GET /api/studio/train/logs - Stream training logs
• POST /api/studio/infer - Run inference
• POST /api/studio/export - Export model
• Interactive docs: /api/studio/docs

Analysis (Accident Data)

• POST /api/analysis/upload - Upload accident data
• GET /api/analysis/analyze - Analyze data
• WS /ws/telemetry - Real-time telemetry WebSocket
• Interactive docs: /api/analysis/docs

Frontend

• GET / - React dashboard

Directory Structure on DGX

/opt/sensorflow/
├── studio/
│   ├── app_backend.py
│   ├── main.py
│   ├── venv/              # Python venv
│   ├── dist/              # Built React app
│   ├── src/               # React source
│   ├── requirements-prod.txt
│   └── deploy/
├── analysis/
│   ├── main.py
│   ├── venv/
│   └── accident_*.py
└── models/
    ├── yolov8n.pt
    └── yolov8m.pt

Monitoring

Check if services are responding:

curl http://sensorflow.wael.bot/health/studio
curl http://sensorflow.wael.bot/health/analysis
curl http://sensorflow.wael.bot/

Monitor system resources:

# On DGX
nvidia-smi  # GPU usage
top -u sensorflow  # CPU/Memory

Troubleshooting

Services fail to start

sudo journalctl -u sensorflow-studio -n 50
sudo systemctl status sensorflow-studio

Port conflicts

sudo lsof -i :8001  # Check port 8001
sudo lsof -i :8002  # Check port 8002

Nginx issues

sudo nginx -t  # Test config
sudo systemctl restart nginx
tail -f /var/log/nginx/error.log

Model download failed

# Manual download
cd /opt/sensorflow/studio
bash deploy/download-models.sh .

Updates & Maintenance

To update code:

# On DGX
cd /opt/sensorflow/studio
git pull  # if using git
# or rsync again from local machine

# Restart services
sudo systemctl restart sensorflow-studio accident-analysis

License

This project is released under CC BY-NC-SA 4.0. See LICENSE file for details.

Support

For issues or questions, check:

1. Service logs: sudo journalctl -u sensorflow-studio
2. Nginx config: /etc/nginx/sites-available/sensorflow.conf
3. Python venv activation: /opt/sensorflow/studio/venv/bin/python