nvgpu
NVIDIA GPU tools
Decision gist · record as of 2026-08-14
Yes, if you have NVIDIA GPUs and need to programmatically select available devices or monitor a cluster—but with caution. The package is dormant (last release 2023-03-30) and classifiers list Python 2 support, raising questions about compatibility with modern Python and driver versions. High install friction and no recent maintenance mean you should verify it works in your environment before relying on it in production.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires nvidia-smi and NVIDIA GPU drivers to be installed on the system; CUDA_VISIBLE_DEVICES environment variable may need to be set for GPU selection to work.
- High install friction: the package is distributed as a source tarball and has no runtime dependencies listed, suggesting it may require system-level NVIDIA tools to be present.
- Maintenance is dormant—last release was 2023-03-30, with no recent commits.
License · maintenance · safety
MIT (permissive) — MIT license is permissive and poses no restrictions on use, modification, or distribution in proprietary or open-source projects.
last release 2023-03-30 (1233 days) · last repo commit 2024-05-16 · 91 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 87,481 downloads/mo, #13,794 on PyPI
Alternatives
Verify before relying
pip install nvgpu
import nvgpu
available = nvgpu.available_gpus()
info = nvgpu.gpu_info()- Whether the package works with current NVIDIA driver versions and modern Python releases (classifiers list Python 2, which is end-of-life).
- Whether NVML Python bindings are bundled or must be installed separately as a system dependency.
- Current compatibility with Flask versions if using the web application agent feature.
What it is and what it does
nvgpu wraps nvidia-smi and NVIDIA's NVML library to provide a simpler interface for querying GPU state on multi-GPU machines. It solves the problem of selecting which GPU to use when multiple devices are available and frameworks would otherwise claim all memory, blocking other processes. The package offers both a command-line tool (nvgpu available, nvgpu list) and a Python API (nvgpu.available_gpus(), nvgpu.gpu_info()) to check memory usage, temperature, and running processes per device.
It also includes an optional distributed monitoring mode: agents on each node expose GPU status as JSON over REST, and a master node aggregates and displays the cluster status in a web dashboard. This is useful for coordinating GPU allocation across a cluster of machines. The package is marked Alpha and has been dormant since its latest release, so it may not be actively maintained or tested against recent driver and Python versions.
Use it for
- Select an available GPU for a single training job before launching a framework to avoid memory conflicts.
- Monitor GPU utilization and temperature across a cluster of machines via a web dashboard for capacity planning.
- Query which GPUs are free and their memory state programmatically to implement custom job scheduling logic.
- Display a colored table of all GPUs, their users, and running processes for quick status checks on a multi-user system.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you have NVIDIA GPUs and need to programmatically select available devices or monitor a cluster—but with caution.
The package is dormant (last release 2023-03-30) and classifiers list Python 2 support, raising questions about compatibility with modern Python and driver versions. High install friction and no recent maintenance mean you should verify it works in your environment before relying on it in production.
Install
nvgpu on PyPI
Before you install
High install friction: the package is distributed as a source tarball and has no runtime dependencies listed, suggesting it may require system-level NVIDIA tools to be present. Maintenance is dormant—last release was 2023-03-30, with no recent commits.
Requires nvidia-smi and NVIDIA GPU drivers to be installed on the system; CUDA_VISIBLE_DEVICES environment variable may need to be set for GPU selection to work.
License in practice
MIT license is permissive and poses no restrictions on use, modification, or distribution in proprietary or open-source projects.
Quickstart
pip install nvgpu
import nvgpu
available = nvgpu.available_gpus()
info = nvgpu.gpu_info()Verify before relying
- Whether the package works with current NVIDIA driver versions and modern Python releases (classifiers list Python 2, which is end-of-life).
- Whether NVML Python bindings are bundled or must be installed separately as a system dependency.
- Current compatibility with Flask versions if using the web application agent feature.
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Dormant 1,233 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 87,481 / month, #13,794 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: MIT LicenseOperating System :: POSIX :: LinuxProgramming Language :: Python :: 2Programming Language :: Python :: 3 |
Evidence: nvgpu-0.10.0.tar.gz
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See also gpustat · GPUtil · py3nvml · nvitop · nvidia-nvshmem-cu13 · nvidia-nvshmem-cu12 · pynvml · nvidia-nvvm · egl-probe · tensorflow