Quick Summary: 📝
GPU Hot is a real-time, web-based dashboard for monitoring NVIDIA GPUs. It provides detailed metrics for utilization, memory, temperature, and processes, accessible without SSH, and can scale from single machines to large clusters.
Key Takeaways: 💡
✅ GPU Hot provides a real-time, web-based dashboard for NVIDIA GPU monitoring, eliminating the need for constant SSH access and manual
nvidia-smichecks.✅ The project is highly scalable, supporting easy deployment for single machines or aggregating data from large clusters using a simple Docker 'hub' configuration.
✅ It delivers comprehensive metrics, including utilization, temperature, power draw, clock speeds, and detailed process monitoring (PID and memory usage) in sub-second intervals.
✅ Utilizing WebSockets, the dashboard ensures instant data updates and provides historical charts for analyzing performance trends and diagnosing bottlenecks.
Project Statistics: 📊
- ⭐ Stars: 1398
- 🍴 Forks: 60
- ❗ Open Issues: 4
Tech Stack: 💻
- ✅ JavaScript
Tired of constantly SSHing into your GPU servers and running nvidia-smi just to see if your training jobs are actually utilizing the hardware? That tedious routine is officially obsolete thanks to GPU Hot. This project delivers a sleek, real-time web dashboard specifically designed for monitoring NVIDIA GPUs, eliminating the friction associated with remote terminal checks. It solves the pain point of scattered monitoring by giving you a centralized, immediate visual representation of your compute resources, making hardware management dramatically easier and more efficient.
GPU Hot is built around simplicity and speed. It leverages the power of Docker, meaning setup is incredibly fast—often just one command is enough to get started. Once running, it uses the NVIDIA Management Library (NVML) to pull metrics directly from your hardware in sub-second intervals. This data isn't static; it's pushed instantly to your browser via WebSockets, ensuring you see exactly what your GPUs are doing right now. The dashboard automatically detects every GPU on the machine and presents critical information like utilization, temperature, power draw, clock speeds, and even detailed process monitoring, showing exactly which process ID is currently consuming VRAM.
For developers and MLOps engineers, this translates directly into saved time and better resource allocation. You can quickly diagnose bottlenecks—is the GPU underutilized, or is it hitting thermal limits? Are those rogue processes eating up memory? By providing system metrics alongside GPU data (like CPU and RAM usage), GPU Hot helps you understand the entire context of your workload. It’s a crucial tool for maximizing hardware investment and ensuring your demanding computational tasks run smoothly and efficiently without manual, repetitive checks.
The true power of GPU Hot lies in its scalability, making it ideal whether you run one workstation or manage a massive deep learning cluster. For a single machine, it’s a drop-in Docker solution. But if you have multiple servers, you can easily configure one instance as a "hub." This hub aggregates data from all your individual GPU nodes, giving you a single pane of glass to oversee your entire fleet. No more jumping between terminals; just one browser tab shows you the health and performance of dozens of GPUs, complete with historical charts to track performance trends over time and ensure continuous optimal operation.
Learn More: 🔗
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