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.

• Diagnostics: bottleneck classification (compute, memory, power, thermal, data-starved), temporal pattern detection (clock oscillation, temperature trends, utilization dips, memory leaks)
• Multi-GPU: outlier detection via Z-scores, flags the GPU slowing down distributed training
• Health scoring: 0-100 per GPU with A-F grades, actionable recommendations with specific commands
• Benchmarks: memory bandwidth, compute throughput, PCIe speed vs theoretical specs
• History: tracks GPU health over time, detects degradation trends
• Python API: session monitoring during training (nvsonar.start(), nvsonar.stop())
• Output: terminal report, JSON, CSV

• Python 3.10+
• NVIDIA GPU with driver installed
• Linux
• CUDA toolkit (only for nvsonar benchmark, not required for other commands)

pip install nvsonar

nvsonar                  # interactive TUI
nvsonar report           # one-shot diagnostic
nvsonar report --json    # structured output for scripts/LLMs
nvsonar report --csv     # CSV output for spreadsheets
nvsonar report --gpu 0   # specific GPU
nvsonar benchmark        # GPU performance benchmarks
nvsonar history          # health trends over time

• Architecture
• Contributing
• Changelog

Apache License 2.0

Bekmukhamed Tursunbayev