wookayin/gpustat

📊 A simple command-line utility for querying and monitoring GPU status

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Summary Information

Updated 52 seconds ago
Added to GitGenius on September 15th, 2026
Created on April 24th, 2016
Open Issues & Pull Requests: 26 (+0)
GitHub issues: Enabled
Number of forks: 286
Total Stargazers: 4,391 (+0)
Total Subscribers: 41 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 6.6 days
Mean response time: 18.3 days
90th percentile: 50.8 days
Tracked items: 8

Maintainer activity

1 person did triage or write work on this repository in the last 12 months.

Counts unlabeled, assigned, unassigned, milestoned, demilestoned, locked, unlocked over the last 12 months. These are issue and pull request events that require triage or write permission. Commits and code review are not counted. labeled and renamed are excluded because GitHub issue forms record the issue author as the actor. Figures from October 11, 2026. This count is not comparable across projects: each project's automation decides which of these events a person emits.

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Issue Activity (beta)

Open issues: 1
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1,114 days
Stale 30+ days: 1
Stale 90+ days: 1

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • bug (3)
  • invalid (1)
  • pynvml (1)
  • question (1)

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Detailed Description

gpustat is a command-line utility for querying and monitoring GPU status.

The tool addresses the need for a simpler, more readable alternative to nvidia-smi for checking GPU usage and health. It parses output from nvidia-smi and presents GPU information in a cleaner, more human-friendly format. Users can quickly see which GPUs are available, their memory usage, temperature, and which processes are consuming GPU resources without wading through nvidia-smi's verbose output.

The project suits developers and researchers who frequently need to check GPU availability and utilization from the command line, particularly those working with machine learning, scientific computing, or other GPU-intensive workloads. It is especially useful in shared computing environments where multiple users need to monitor GPU allocation. The tool is lightweight and straightforward, making it a good fit for quick status checks and scripting scenarios where a simpler interface than nvidia-smi is preferred.

Development on the project is active and responsive to user needs. The maintainer regularly merges pull requests that add features and fix issues. The project maintains good test coverage and addresses bug reports promptly. Documentation is kept current and reflects the tool's actual capabilities and usage patterns.