GPU-Hot

GPU-Hot

Docker app from grtgbln's Repository

Overview

Real-time NVIDIA GPU Monitoring Dashboard

GPU Hot

Real-time NVIDIA GPU monitoring dashboard. Lightweight, web-based, and self-hosted.

Python Docker License: MIT NVIDIA

GPU Hot Dashboard

Live Demo


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

Star History Chart

Contributing

PRs welcome. Open an issue for major changes.

License

MIT - see LICENSE

Media gallery

Install GPU-Hot on Unraid in a few clicks.

Find GPU-Hot in Community Apps on your Unraid server, review the template, and click Install. Unraid handles the Docker app or plugin setup from the published template.

Open the Apps tab on your Unraid server Search Community Apps for GPU-Hot Review the template variables and paths Click Install

Related apps

Details

Repository
ghcr.io/psalias2006/gpu-hot:latest
Last Updated2026-07-15
First Seen2025-10-14

Runtime arguments

Web UI
http://[IP]:[PORT:1312]/
Network
bridge
Privileged
false
Extra Params
--gpus all

Template configuration

Web UI PortPorttcp

Container Port: 1312

Target
1312
Default
1312
Value
1312
Nvidia SMI ModeVariable

Force nvidia-smi mode (for older GPUs)

Target
NVIDIA_SMI
Default
false|true