GPU Hot
Real-time NVIDIA GPU monitoring dashboard. Lightweight, web-based, and self-hosted.
Usage
Monitor a single machine or an entire cluster with the same Docker image.
Single machine:
docker run -d --gpus all -p 1312:1312 ghcr.io/psalias2006/gpu-hot:latest
Multiple machines:
# On each GPU server
docker run -d --gpus all -p 1312:1312 -e NODE_NAME=$(hostname) ghcr.io/psalias2006/gpu-hot:latest
# On a hub machine (no GPU required)
docker run -d -p 1312:1312 -e GPU_HOT_MODE=hub -e NODE_URLS=http://server1:1312,http://server2:1312,http://server3:1312 ghcr.io/psalias2006/gpu-hot:latest
Open http://localhost:1312
Older GPUs: Add -e NVIDIA_SMI=true if metrics don't appear.
Process monitoring: Add --init --pid=host to see process names. Note: This allows the container to access host process information.
From source:
git clone https://github.com/psalias2006/gpu-hot
cd gpu-hot
docker-compose up --build
Requirements: Docker + NVIDIA Container Toolkit
Features
- Real-time metrics (sub-second)
- Automatic multi-GPU detection
- Process monitoring (PID, memory usage)
- Historical charts (utilization, temperature, power, clocks)
- System metrics (CPU, RAM)
- Scale from 1 to 100+ GPUs
Metrics: Utilization, temperature, memory, power draw, fan speed, clock speeds, PCIe info, P-State, throttle status, encoder/decoder sessions
Configuration
Environment variables:
NVIDIA_VISIBLE_DEVICES=0,1 # Specific GPUs (default: all)
NVIDIA_SMI=true # Force nvidia-smi mode for older GPUs
GPU_HOT_MODE=hub # Set to 'hub' for multi-node aggregation (default: single node)
NODE_NAME=gpu-server-1 # Node display name (default: hostname)
NODE_URLS=http://host:1312... # Comma-separated node URLs (required for hub mode)
UPDATE_INTERVAL=0.5 # Optional. NVML polling interval in seconds (default: 0.5)
NVIDIA_SMI_INTERVAL=2.0 # Optional. nvidia-smi fallback polling interval (default: 2.0)
Polling is paused automatically when no clients are connected, so idle CPU usage stays near zero.
Backend (core/config.py):
PORT = 1312 # Server port
API
HTTP
GET / # Dashboard
GET /api/gpu-data # JSON metrics snapshot
GET /api/version # Version and update info
WebSocket
const ws = new WebSocket('ws://localhost:1312/socket.io/');
ws.onmessage = (event) => {
const data = JSON.parse(event.data);
// data.gpus — per-GPU metrics
// data.processes — active GPU processes
// data.system — host CPU, RAM, swap, disk, network
};
Project Structure
gpu-hot/
├── app.py # FastAPI server + routes
├── version.py # Version info
├── core/
│ ├── config.py # Configuration
│ ├── monitor.py # NVML GPU monitoring
│ ├── handlers.py # WebSocket handlers
│ ├── hub.py # Multi-node hub aggregator
│ ├── hub_handlers.py # Hub WebSocket handlers
│ ├── nvidia_smi_fallback.py # nvidia-smi fallback for older GPUs
│ └── metrics/
│ ├── collector.py # Metrics collection
│ └── utils.py # Metric utilities
├── static/
│ ├── css/
│ │ ├── tokens.css # Design tokens (colors, spacing)
│ │ ├── layout.css # Page layout (sidebar, main)
│ │ └── components.css # UI components (cards, charts)
│ ├── js/
│ │ ├── chart-config.js # Chart.js configurations
│ │ ├── chart-manager.js # Chart data + lifecycle
│ │ ├── chart-drawer.js # Correlation drawer
│ │ ├── gpu-cards.js # GPU card rendering
│ │ ├── socket-handlers.js # WebSocket + batched rendering
│ │ ├── ui.js # Sidebar navigation
│ │ └── app.js # Init + version check
│ └── favicon.svg
├── templates/index.html
├── Dockerfile
├── docker-compose.yml
└── requirements.txt
Troubleshooting
No GPUs detected:
nvidia-smi # Verify drivers work
docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi # Test Docker GPU access
Hub can't connect to nodes:
curl http://node-ip:1312/api/gpu-data # Test connectivity
sudo ufw allow 1312/tcp # Check firewall
Performance issues: Increase UPDATE_INTERVAL (env var, seconds — e.g. -e UPDATE_INTERVAL=2.0)
Star History
Contributing
PRs welcome. Open an issue for major changes.
License
MIT - see LICENSE