NVIDIA/cuml - Open Source PR Review Scorecard

NVIDIA cuML: GPU-Accelerated Machine Learning

C-Rank Grade: A (Welcoming) - 63/100

External PR Merge Rate: 65%

Response Time: 16h

First Timer Success: 50%

Frequently Asked Questions

Is NVIDIA/cuml welcoming to first-time open-source contributors?

NVIDIA/cuml has a recorded first-timer success rate of 50.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in NVIDIA/cuml respond to incoming external pull requests in approximately 15.9 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 62.6 C-Rank™ score (A Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 62.6 places NVIDIA/cuml in the A tier.

What is the external contributor pull request merge rate for NVIDIA/cuml?

The external contributor pull request merge rate for NVIDIA/cuml is 65.4%, based on public PR activity from non-core contributors.

Are there Good First Issues available in NVIDIA/cuml?

NVIDIA/cuml does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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NVIDIA/cuml

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NVIDIA cuML: GPU-Accelerated Machine Learning

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AI Maintainer Review Guidelines

Review Persona

Active Open-Source Maintainer

Warmth Score
7.8/10
Patience Score
8.2/10
Nitpick Rate
20%

Established Python ecosystem in NVIDIA/cuml. Maintains structured code review standards for external contributors.

Top PR Submission Do's

  • •Ensure code complies with Python style conventions
  • •Keep PRs scoped and well-documented
  • •Include relevant test coverage for changes

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit unlinked PRs without context
  • •Do not break existing automated test suites
  • •Do not mix unrelated refactors with feature work
Response Velocity
15 hours
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
65.4%
Moderate PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
50.0%
Accepts new contributor PRs

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
36 core
Highly collaborative maintainer core
Diagnostic Health HUD
65.4%
Merge Gauge
50.0%
1st-Timer
Community Vibe59/100

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GetMerged C-Rank badge for NVIDIA/cuml
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Active Good First Issues (1)

View on GitHub

Describe the bug As in the title. Running it multiple times with the same seed generates different results. Steps/Code to reproduce bug from cuml.datasets import make_regression import cupy as cp X1, y1 = make_regression( n_samples=100, n_features=5, n_informative=5, random_state=42 ) X2, y2 = make_regression( n_samples=100, n_features=5, n_informative=5, random_state=42 ) assert cp.array_equal(X1, X2) assert cp.array_equal(y1, y2) Additional context Add any other context about the problem here. It seems rand() is used by raft: https://github.com/rapidsai/raft/blob/bf258f39b670d65c60d8a681c8ec45d0ade5690f/cpp/include/raft/random/detail/permute.cuh#L126

📅 Opened Mar 7, 2026💬 3 comments
Quality: 30/100Contribute
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Contributor Compatibility & Review Speed Analysis for NVIDIA/cuml

When evaluating whether to contribute to NVIDIA/cuml, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of NVIDIA/cuml acknowledge new external contributions in approximately 15 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 65.4% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to NVIDIA/cuml

01

Is NVIDIA/cuml welcoming to first-time open-source contributors?

NVIDIA/cuml has a recorded first-timer success rate of 50.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~15 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 62.6 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 62.6 places NVIDIA/cuml in the Welcoming tier.

04

What is the external contributor pull request merge rate for NVIDIA/cuml?

The external pull request merge rate is 65.4%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in NVIDIA/cuml?

NVIDIA/cuml does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for NVIDIA/cuml are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.