nvidia/nvidia-container-toolkit

Build and run containers leveraging NVIDIA GPUs

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Summary Information

Updated 46 minutes ago
Added to GitGenius on September 15th, 2026
Created on November 14th, 2019
Open Issues & Pull Requests: 54 (+0)
GitHub issues: Enabled
Number of forks: 592
Total Stargazers: 4,576 (+0)
Total Subscribers: 49 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 43.7 hours
Mean response time: 127.1 days
90th percentile: 506.3 days
Tracked items: 575

Most active contributors

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How this project is maintained

About 5% of issues opened in the past year have never received a reply. 64% of open issues come from outside the core team, a mix of external reports and the maintainers' own roadmap. Work labelled "bug" is answered fastest, typically in about 11 hours, while "feature" waits about 5 days. 87% of issues opened in the past year have been closed, leaving a working backlog. Three people close 82% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 25
New in 7 days: 2
Closed in 7 days: 0
Avg open age: 396 days
Stale 30+ days: 20
Stale 90+ days: 14

Recent activity

Opened in 7 days: 2
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 5

Top labels

  • lifecycle/stale (346)
  • bug (46)
  • needs-triage (44)
  • feature (29)
  • lifecycle/frozen (18)
  • question (17)
  • package-management (9)
  • release (6)

Detailed Description

NVIDIA Container Toolkit is a container runtime that enables GPU-accelerated workloads in containerized environments.

The toolkit solves the problem of making NVIDIA GPUs accessible to containerized applications. It provides a container runtime library and utilities that automatically configure containers to leverage available GPUs without requiring the CUDA Toolkit to be installed on the host system. The approach works by intercepting container initialization and injecting the necessary GPU device access and driver libraries at runtime.

Teams running GPU-accelerated workloads in Docker or other container environments should adopt this toolkit. It suits projects that need to distribute machine learning models, scientific computing applications, or other GPU-intensive tasks across containerized infrastructure. The only requirement is that the NVIDIA driver be installed on the host system; the toolkit handles the rest of the configuration automatically.

The project maintains an active contribution process with a documented code of conduct and clear guidelines for submitting issues and pull requests. Development activity centers on addressing user-reported issues and integrating community contributions through the public repository.