nvsonar 1.1.0

Active GPU diagnostic tool that identifies performance bottlenecks

NVSonar

Active GPU diagnostic tool with real-time bottleneck detection and performance analysis.

Why NVSonar?

Traditional GPU monitoring tools show utilization percentages, but this can be misleading. A GPU reporting 100% utilization may actually be computing useful work, or wastefully stalled waiting on memory transfers, thermal throttling, or power limits.

NVSonar analyzes real-time patterns from NVML metrics to identify what's actually limiting your GPU performance.

Features

• Real-time Bottleneck Detection
• Subsystem Utilization Analysis
• Peak Value Tracking
• Visual Progress Bars
• Multi-GPU Support**

Installation

pip install nvsonar

Quick Start

# Launch interactive TUI with all GPUs and live metrics
nvsonar

Interface

┌─ NVSonar ──────────────────────────────────────────────────────┐
│  [Overview] [History] [Settings]                               │
├────────────────────────────────────────────────────────────────┤
│                        Available GPUs                          │ 
┡━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━╇━━━━━━┩
│ Index │ Name                       │ Memory │ Driver    │ CUDA │
│   0   │ NVIDIA GeForce GTX 1650 Ti │ 4.0 GB │ 580.95.05 │ 13.0 │
└───────┴────────────────────────────┴────────┴───────────┴──────┘

╭────────── NVIDIA GeForce GTX 1650 Ti Metrics ─────────────────╮
│  Compute           ████████████░░░░░░░░ 65%                   │
│  Memory            ████████░░░░░░░░░░░░ 35%                   │
│  Thermal           ████████████░░░░░░░░ 68%                   │
│                                                               │
│  Status            GPU cores are the limiting factor          │
│                                                               │
│  Power             ████████████████░░░░ 32.5W / 50.0W         │
│  Temperature       ████████████░░░░░░░░ 68°C / 83°C           │
│  GPU Utilization   ████████████░░░░░░░░ 65%                   │
│  Memory Utilization████████░░░░░░░░░░░░ 35%                   │
│  Memory Used       ██████░░░░░░░░░░░░░░ 2.2 / 4.0 GB          │
│  GPU Clock         1740 MHz                                   │
│  Memory Clock      5000 MHz                                   │
╰───────────────────────────────────────────────────────────────╯

Requirements

• Python 3.10+
• NVIDIA GPU with driver installed
• CUDA toolkit (for active probes)
• Linux (tested on Ubuntu)

License

Apache License 2.0 - see LICENSE for details.

Author

Maintained by Bekmukhamed Tursunbayev
GitHub: https://github.com/btursunbayev · PyPI: https://pypi.org/user/btursunbayev/