Nvprobe – Open-source, zero-setup CLI for CUDA benchmarks

Nvprobe, an open-source, zero-setup CLI tool for CUDA benchmarks, has been released on GitHub. The tool automates CUDA workloads including HPL, HPCG, MLPerf inference, and custom kernels, and generates interactive HTML reports. It bundles a CUDA runtime, supports Slurm integration, and stores results in SQLite with CSV/JSON export.

NVIDIA GPU & CUDA Benchmark Suite Automate CUDA workloads • HPL & HPCG • MLPerf inference • Custom kernels • Interactive reports nvprobe.scszero.com https://nvprobe.scszero.com/ pip install nvprobe && nvprobe setup && nvprobe run --local Bandwidth | MatMul / Attention | Conv2D | |---|---|---| | H2D / D2H / D2D across buffer sizes | fp32, fp16, int8 custom CUDA kernels | 2D convolution benchmarks | HPL FP64 Linpack | HPCG | MLPerf Inference | | Datacenter GPUs: A100, H100, B200, L40S… | Conjugate Gradients | ONNX Runtime via cmx4mlperf | Bundled CUDA runtime — CuPy ctk via pip, no system toolkit required Auto-downloaded HPC tools — NVIDIA HPC Benchmarks cached in ~/.nvprobe/tools/ Interactive HTML reports — Chart.js charts with GPU / transfer / precision dropdowns and oscilloscope-style glow A/B comparison — compare two result sets side-by-side Slurm integration — generate, submit, monitor, collect from HPC clusters SQLite storage — all results persisted; CSV / JSON export | Step | Command | What it does | |---|---|---| | 1 | pip install nvprobe | Install the package | | 2 | nvprobe setup | Install CuPy, download HPL/HPCG, generate configs | | 3 | nvprobe env | Verify GPU detection, driver, CUDA version | | 4 | nvprobe run --local | Run all benchmarks locally | | 5 | nvprobe report --open | Generate & open interactive HTML report | Or from source: git clone https://github.com/SergioZ3R0/nvprobe.git && cd nvprobe pip install -e . && nvprobe setup && nvprobe run --local | Command | Description | |---|---| nvprobe compare --a results/run1 --b results/run2 | Compare two runs | nvprobe run --config configs/cluster.yaml | Run with custom YAML config | nvprobe slurm submit --config configs/cluster.yaml | Submit Slurm job | nvprobe slurm status | Check Slurm job status | nvprobe setup --cuda 13 | Setup with specific CUDA version | Chart.js canvas-based charts with interactive controls: Bandwidth — filter by GPU and transfer type H2D / D2H / D2D MatMul / Attention — filter by GPU and precision fp32 / fp16 Range slider — zoom into any x-axis region Moving average — smoother trend lines for dense data name: my-run gpu: models: "L40S", "B200" slurm: enabled: true partition: gpu gpus per node: 8 precisions: fp32, fp16 benchmarks: - name: bandwidth params: sizes mb: 1, 4, 16, 64, 256, 1024 - name: custom params: kernels: matmul, attention nvprobe/ ├── nvprobe/ │ ├── cli.py CLI entry point │ ├── config.py YAML config loader │ ├── runner.py Benchmark orchestration │ ├── slurm.py Slurm job management │ ├── reporter.py Plotly HTML report generator │ ├── db.py SQLite storage + CSV/JSON export │ └── benchmarks/ │ ├── base.py Base class, GPU detection, diagnostics │ ├── bandwidth.py Memory bandwidth tests │ ├── custom.py Custom CUDA kernels │ ├── hpl.py HPL wrapper │ ├── hpcg.py HPCG wrapper │ ├── mlperf.py MLPerf via cmx4mlperf │ └── cuda/ Raw CUDA kernels ├── configs/ │ ├── default.yaml │ └── local.yaml ├── nvprobe.svg ├── index.html ├── README.md └── pyproject.toml HPL / HPCG — NVIDIA HPC Benchmarks binaries are validated for datacenter GPUs A100, H100, B200, L40S… . They may crash SIGSEGV on RTX series. Bandwidth and custom kernels work on any CUDA GPU. MLPerf cuDNN — mlcr discovers cuDNN via system CUDA paths. If installed via pip install nvidia-cudnn-cuXX , pre-register with: mlcr get,cudnn,nvidia --input=$ python3 -c 'import nvidia.cudnn; print nvidia.cudnn. path 0 ' Python 3.10+ • NVIDIA GPU with CUDA drivers • nvidia-smi in PATH • Slurm optional