nvProbe

nvProbe

NVIDIA GPU & CUDA Benchmark Suite
Automate CUDA workloads • HPL & HPCG • MLPerf inference • Custom kernels • Interactive reports
nvprobe.scszero.com

PyPI Python License CUDA 

pip install nvprobe && nvprobe setup && nvprobe run --local

Features

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

Quick Start

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

More commands

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

Charts

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

YAML Config

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]

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                # 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

Notes

  • 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]'))

Requirements

Python 3.10+ • NVIDIA GPU with CUDA drivers • nvidia-smi in PATH • Slurm (optional)

License

Apache License 2.0

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.9.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for SergioZ3R0 from gravatar.com SergioZ3R0

Credits

Author: SergioZ3R0

License expression

Apache-2.0
View SPDX License List

Requires

Python >=3.10

Provides Extra

dev cupy-cuda11x cupy-cuda12x cupy-cuda13x

Tags

gpu nvidia cuda benchmark hpc slurm mlperf

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nvprobe-0.7.1.tar.gz (57.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

nvprobe-0.7.1-py3-none-any.whl (66.7 kB view details)

Uploaded Python 3

File details

Details for the file nvprobe-0.7.1.tar.gz.

File metadata

  • Download URL: nvprobe-0.7.1.tar.gz
  • Upload date:
  • Size: 57.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.4

File hashes

Hashes for nvprobe-0.7.1.tar.gz
Algorithm Hash digest
SHA256 6610f35ed49487e91ef7d813ca74d57c596781d677eee048561df39192ccc93a
MD5 9c3c9cb270094215220c75645ffae601
BLAKE2b-256 38a6181413d709e8a1bb411af4a67a500536f80265a0c040e191df30ea6302fa

See more details on using hashes here.

File details

Details for the file nvprobe-0.7.1-py3-none-any.whl.

File metadata

  • Download URL: nvprobe-0.7.1-py3-none-any.whl
  • Upload date:
  • Size: 66.7 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.14.4

File hashes

Hashes for nvprobe-0.7.1-py3-none-any.whl
Algorithm Hash digest
SHA256 6eca6b464938734b2e272926ed6612a8477ac956193fa4d8e15e82247a64a355
MD5 bc21db501d072bed1a2f30c3a5b8ec1f
BLAKE2b-256 7faaf18f279c5ff2c16ca3155b1a2221dcc5f872f0e4fe0d82bf30a4a8f0edbc

See more details on using hashes here.

Release history Release notifications | RSS feed

0.9.0

2 files

0.8.7

2 files

0.8.6

2 files

0.8.5

2 files

0.8.4

2 files

0.8.3

2 files

0.8.2

2 files

0.8.1

2 files

0.8.0

2 files

0.7.2

2 files

This release

0.7.1 This release

2 files

0.7.0

2 files

0.6.2

2 files

0.6.1

2 files

0.6.0

2 files

0.5.57

2 files

0.5.56

2 files

0.5.55

2 files

0.5.54

2 files

0.5.53

2 files

0.5.52

2 files

0.5.51

2 files

0.5.50

2 files

0.5.49

2 files

0.5.48

2 files

0.5.47

2 files

0.5.46

2 files

0.5.45

2 files

0.5.44

2 files

0.5.43

2 files

0.5.42

2 files

0.5.41

2 files

0.5.40

2 files

0.5.39

2 files

0.5.38

2 files

0.5.37

2 files

0.5.36

2 files

0.5.35

2 files

0.5.34

2 files

0.5.33

2 files

0.5.32

2 files

0.5.31

2 files

0.5.30

2 files

0.5.29

2 files

0.5.28

2 files

0.5.27

2 files

0.5.26

2 files

0.5.25

2 files

0.5.24

2 files

0.5.23

2 files

0.5.22

2 files

0.5.21

2 files

0.5.20

2 files

0.5.19

2 files

0.5.18

2 files

0.5.17

2 files

0.5.16

2 files

0.5.15

2 files

0.5.14

2 files

0.5.13

2 files

0.5.12

2 files

0.5.11

2 files

0.5.10

2 files

0.5.9

2 files

0.5.8

2 files

0.5.7

2 files

0.5.6

2 files

0.5.5

2 files

0.5.4

2 files

0.5.3

2 files

0.5.2

2 files

0.5.1

2 files

0.1.1

2 files

0.1.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page