nvprobe 0.5.56

NVIDIA GPU benchmark suite for CUDA workload automation, reporting, and comparison

nvProbe: NVIDIA GPU & CUDA Benchmark Suite

NVIDIA GPU benchmark suite for CUDA workload automation, reporting, and comparison.

nvProbe runs standardized benchmarks across your GPU fleet, captures environment details, stores results in SQLite, and generates self-contained HTML reports — helping HPC engineers and ML teams make data-driven hardware purchasing decisions.

Features

• Benchmark modules: Bandwidth, custom CUDA kernels (matmul, conv2d, attention), HPL, HPCG, MLPerf

Known Limitations

• HPL / HPCG on workstation GPUs: the NVIDIA HPC Benchmarks binaries (xhpl/xhpcg) used by nvprobe are validated by NVIDIA against datacenter GPUs (A100, H100, etc.). On professional workstation GPUs (e.g. the RTX Axxx series), these binaries may crash with a segmentation fault during GPU initialization. This is a limitation of NVIDIA's precompiled binaries, not of nvprobe, and cannot be fixed from this project. Bandwidth and custom kernel benchmarks are unaffected and work correctly on any CUDA-capable GPU.

• MLPerf cuDNN detection: the MLPerf pipeline (cmx4mlperf / mlcr) discovers cuDNN by searching system CUDA toolkit paths. If cuDNN is installed via pip install nvidia-cudnn-cuXX, you may need to pre-register it with mlcr first:

  mlcr get,cudnn,nvidia --input=$(python3 -c 'import nvidia.cudnn; print(nvidia.cudnn.__path__[0])')
  

• Slurm integration: Generate and submit sbatch scripts, run across multiple nodes/GPUs

• Environment fingerprinting: Driver version, CUDA version, GPU model, memory, PCI bus ID — captured automatically

• SQLite storage: All results persisted with full query capability

• CSV/JSON export: Raw data for programmatic access

• HTML reports: Self-contained reports with matplotlib charts, sidebar navigation, and comparison views

• YAML configs: Define test matrices (GPU models, precisions, batch sizes) declaratively

• Reproducible: Same config + same hardware = same results

Quick Start

pip install nvprobe
nvprobe setup                      # install cupy + HPL/HPCG + generate configs
nvprobe run --config nvprobe/configs/local.yaml --local

Or install from source

git clone https://github.com/SergioZ3R0/nvprobe.git
cd nvprobe
pip install -e .
nvprobe setup                      # installs nvprobe + self-contained CuPy
nvprobe run --config nvprobe/configs/local.yaml --local

Detect GPU environment

nvprobe env

Run benchmarks (dry run)

nvprobe run --config configs/default.yaml --dry-run

Run benchmarks

nvprobe run --config configs/default.yaml

Generate report

nvprobe report

Compare two runs

nvprobe compare --a results/run1 --b results/run2

Configuration

Edit configs/default.yaml to define your test matrix:

name: my-benchmark-run
description: "Comparing L40S vs B200"

gpu:
  models: ["L40S", "B200"]

slurm:
  enabled: true
  partition: gpu
  gpus_per_node: 8

precisions:
  - fp32
  - fp16
  - int8

benchmarks:
  - name: bandwidth
    enabled: true
    params:
      sizes_mb: [1, 4, 16, 64, 256, 1024]
  - name: custom
    enabled: true
    params:
      kernels: [matmul, conv2d, attention]

Project Structure

nvprobe/
├── nvprobe/
│   ├── cli.py              # CLI entry point
│   ├── config.py           # YAML config loader
│   ├── runner.py           # Benchmark orchestration
│   ├── slurm.py            # Slurm job management
│   ├── reporter.py         # HTML report generator with charts
│   ├── db.py               # SQLite storage + CSV/JSON export
│   └── benchmarks/
│       ├── base.py         # Base benchmark class
│       ├── bandwidth.py    # Memory bandwidth tests
│       ├── custom.py       # Custom CUDA kernels
│       ├── hpl.py          # HPL wrapper
│       ├── hpcg.py         # HPCG wrapper
│       ├── mlperf.py       # MLPerf wrapper
│       └── _cuda/
│           ├── bandwidth_test.py   # CUDA bandwidth implementation
│           ├── custom_kernels.py   # matmul/conv2d/attention
│           └── utils.py            # Shared GPU utilities
├── configs/
│   └── default.yaml        # Default test configuration
├── reports/                 # Generated HTML reports
├── results/                 # Benchmark results (SQLite + JSON)
├── README.md
└── pyproject.toml

Roadmap

v0.1.0 — Project base ✓

• CLI with Typer (run, report, compare, env, version)
• YAML config system for test matrices
• Benchmark module framework (base class + stubs)
• Runner with nvidia-smi environment detection
• SQLite storage for results
• HTML report generator (basic)
• Default config for L40S/B200 GPUs

v0.2.0 — CUDA benchmarks ✓

• Bandwidth test (host↔device, device↔device) via cupy
• Custom CUDA kernels: matmul, conv2d, attention
• HPL/HPCG binary wrappers with Slurm script generation
• MLPerf inference/training wrapper
• Optional cupy dependency (pip install nvprobe[cuda])

v0.3.0 — Slurm integration ✓

• sbatch script generation
• Job submission and monitoring
• Multi-GPU parallel execution
• Result collection from Slurm output

v0.4.0 — Reporting ✓

• Matplotlib charts (bandwidth, matmul, attention, GPU comparison)
• Corporate branding (sidebar, color palette, env cards)
• Comparison reports (A vs B)
• CSV/JSON auto-export alongside HTML

v0.5.0 — Reproducibility ✓

• Singularity container support (CUDA 12.4 runtime)
• Makefile for common dev operations
• Git-tracked configs and results
• Singularity container support
• Environment fingerprinting
• Git-tracked configs and results

Requirements

• Python 3.10+
• NVIDIA GPU with CUDA drivers installed
• Slurm (for multi-node execution)
• nvidia-smi available in PATH
• Singularity (optional, for containerized execution)

Container

make container-build    # builds nvprobe.sif
make container-run      # runs with --nv GPU passthrough

License

Apache License 2.0