Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 17:39 UTC

NVIDIA / cuml

NVIDIA cuML: GPU-Accelerated Machine Learning

Python · C++ · CudaApache-2.0★ 5,281 stars⑂ 677 forkssince Oct 2018View on GitHub ↗

NVIDIA/cuml holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (97/100) and lowest on Security (67/100). It was last updated today. 5 contributors account for most of its recent work.

98
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

98
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 87 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

NVIDIA CorporationOrganization · verified domain
30,155 followers804 public repossince May 2012

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIcumlpoints to another repo — not scored0.6.1.post176312298 days ago

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

97Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recency — last push 0 days ago
36/36Commit cadence — 52/52 weeks with commits
18/18Commit volume — 869 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year869
human_commit_share1
days_since_last_push0
active_weeks_last_year52
How it's scored
27/27Ships releases — 53 releases published
36/36Release recency — latest release 42 days ago
19.8/27Release cadence — a release every ~53.7 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count53
latest_release_tagv26.08.00
releases_from_tagsno
days_since_latest_release42
mean_days_between_releases53.7
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

83Excellent · 17% of overall
How it's scored
60/60Stars — 5,281 stars
23.6/25Forks — 677 forks
10.3/15Watchers — 72 watchers
Inputs used
forks677
stars5,281
watchers72
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges0
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_services—
has_pull_request_templateno

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

87Excellent · 23% of overall
How it's scored
45.9/54Bus factor — 5 contributor(s) cover half of all commits
18.9/22.5Commit distribution — top contributor authored 16% of commits
13.5/13.5Contributor breadth — 100 contributors
10/10OpenSSF Scorecard: Contributors — project has 33 contributing companies or organizations
Inputs used
bus_factor5
contributors_sampled100
top_contributor_share0.161
How it's scored
32.5/42Issue resolution — 77% of issues closed
25.3/30PR acceptance — 4,478/5,303 decided PRs merged
0/13Newcomer PR acceptance — 0/2 first-time contributors' PRs merged in 30d
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Inputs used
merged_prs4,478
open_issues748
closed_issues2,558
prs_merged_7d24
prs_decided_7d40
prs_merged_30d24
prs_decided_30d40
issue_closed_ratio0.774
closed_unmerged_prs825
first_time_authors_30d1
first_time_prs_merged_30d0
first_time_prs_decided_30d2
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 30,155 followers of NVIDIA
25/25Track record — 804 public repos, account ~14 yr old
Inputs used
followers30,155
owner_typeOrganization
is_verifiedyes
owner_loginNVIDIA
public_repos804
account_age_days5,242

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows — 8 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff]), python/cuml/pyproject.toml ([tool.black], [tool.isort])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://docs.nvidia.com/cuml/
10/10Repository description
10/10Topics — 5 topics
10/10Wiki
Inputs used
topicsmachine-learning-algorithms, machine-learning, cuda, gpu, nvidia
has_wikiyes
homepagehttps://docs.nvidia.com/cuml/
docs_sitehttps://docs.nvidia.com/cuml/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

67Good · 16% of overall
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — no data
2.5/2.5CI-Tests — 30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 33 contributing companies or organizations
0/10Dangerous-Workflow — dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — no data
7.5/7.5Token-Permissions — GitHub workflow tokens follow principle of least privilege
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate5.9
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
0/25Indirect dependencies free of known advisories — transitive set not separable from development and test dependencies in this scope
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages1
unassessed_packages47
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): Indirect dependencies free of known advisories, No advisories left outstanding. Remaining weights renormalized. Matched 1 resolved dependencies against OSV. 47 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list. This repository publishes no package the index resolves, so the repository dependency graph was assessed instead. That graph mixes development and test pins with shipped dependencies, so only the declared runtime dependencies are scored; transitive findings are reported as context and excluded from the score. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

78Good · 4% of overall
How it's scored
45/45Agent instructions — cpp/agents.md, python/agents.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.nvidia.com/llms.txt)
40/40Legible commit history — 96 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://docs.nvidia.com/llms.txt
legible_history_share0.96
agent_instruction_filescpp/agents.md, python/agents.md
agent_instruction_max_bytes16,045
How it's scored
18/18One-command bootstrap — docs/Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff]), python/cuml/pyproject.toml ([tool.black], [tool.isort])
0/11Static type checking
10/10Reproducible environment — devcontainer, Dockerfile
0/10Demonstrated agent practice — no agent-authored commits among the last 100
0/8Automated maintenance — no automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesdocs/Makefile
has_devcontaineryes
has_linter_configyes
typecheck_configs—
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
0/45Type-checkable code — Python without a type-check config
54.2/55Manageable file sizes — 8/554 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes1,260,083
source_files_sampled554
oversized_source_files8
How it's scored
0/40API schema (OpenAPI/GraphQL/proto) — not applicable to this kind of software
0/20MCP server — not applicable to this kind of software
40/40Runnable examples — examples, notebooks, recipes
Inputs used
example_dirsexamples, notebooks, recipes
has_mcp_signalno
api_schema_files—
interfaces_expected_of—
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

5,281GitHub stars
100contributors
869commits, last 12 months
0days since last push
53releases
5bus factor
748open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 16 authors (cap 12)
  • pypi package 'cuml' points at a different repository (https://github.com/rapidsai/dask-cuda); excluded from ecosystem scoring
  • Could not fetch pypi package 'libcuml' from its registry

More detail

Star and fork history 0 ★ / 677 ⇿
0Stars
677Forks
53Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

0125250375500625750673112018-102022-092026-09
Major 0Minor 36Patch 12

Each point covers 8 days.

OpenSSF Scorecard 5.9 / 10
5.9aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-09-17 17:38 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10CI-Tests30 out of 30 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 33 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
10Token-PermissionsGitHub workflow tokens follow principle of least privilege
10Vulnerabilities0 existing vulnerabilities detected
Direct dependencies 25
RegistryPackageVersion constraintManifest
PyPIcuda-bindings>=13.0.2,<14.0python/cuml/pyproject.toml
PyPIcuda-toolkit==13.*python/cuml/pyproject.toml
PyPIcudf==26.12.*,>=0.0.0a0python/cuml/pyproject.toml
PyPIcupy-cuda13x>=14.0.1,!=14.1.0python/cuml/pyproject.toml
PyPIjoblib>=0.11python/cuml/pyproject.toml
PyPIlibcuml==26.12.*,>=0.0.0a0python/cuml/pyproject.toml
PyPInumba-cuda>=0.22.2python/cuml/pyproject.toml
PyPInumba>=0.60.0,<0.65.0python/cuml/pyproject.toml
PyPInumpy>=2.0,<3.0python/cuml/pyproject.toml
PyPInvforest==26.12.*,>=0.0.0a0python/cuml/pyproject.toml
PyPInvidia-nvjitlink>=13.3,<14python/cuml/pyproject.toml
PyPIpackaging—python/cuml/pyproject.toml
PyPIpylibraft==26.12.*,>=0.0.0a0python/cuml/pyproject.toml
PyPIrich—python/cuml/pyproject.toml
PyPIrmm==26.12.*,>=0.0.0a0python/cuml/pyproject.toml
PyPIscikit-learn>=1.6python/cuml/pyproject.toml
PyPIscipy>=1.14.0python/cuml/pyproject.toml
PyPItreelite>=4.7.2,<5.0.0python/cuml/pyproject.toml
PyPIcuda-toolkit==13.*python/libcuml/pyproject.toml
PyPIlibcuvs==26.12.*,>=0.0.0a0python/libcuml/pyproject.toml
PyPIlibnvforest==26.12.*,>=0.0.0a0python/libcuml/pyproject.toml
PyPIlibraft==26.12.*,>=0.0.0a0python/libcuml/pyproject.toml
PyPIlibrmm==26.12.*,>=0.0.0a0python/libcuml/pyproject.toml
PyPInvidia-nvjitlink>=13.3,<14python/libcuml/pyproject.toml
PyPIrapids-logger==0.3.*python/libcuml/pyproject.toml
All dependencies 48

Full resolved dependency set from the GitHub dependency graph: 26 direct and 22 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIcuda-bindings—direct
PyPIcuda-toolkit—direct
PyPIcudf—direct
PyPIcudf24.6.*direct
PyPIcupy-cuda13x—direct
PyPIjoblib—direct
PyPIlibcuml—direct
PyPIlibcuvs—direct
PyPIlibnvforest—direct
PyPIlibraft—direct
PyPIlibrmm—direct
PyPInumba—direct
PyPInumba-cuda—direct
PyPInumpy—direct
PyPInvforest—direct
PyPInvidia-nvjitlink—direct
PyPIpackaging—direct
PyPIpylibraft—direct
PyPIpylibraft24.6.*direct
PyPIrapids-logger—direct
PyPIrmm—direct
PyPIrmm24.6.*direct
PyPIscikit-learn—direct
PyPIscipy—direct
PyPItreelite—direct
PyPItreelite4.1.2direct
PyPIcupy-cuda11x—indirect
PyPIcython—indirect
PyPIdask-cuda—indirect
PyPIdask-cuda24.6.*indirect
PyPIdask-cudf—indirect
PyPIdask-cudf24.6.*indirect
PyPIdask-ml—indirect
PyPIhdbscan—indirect
PyPIhypothesis—indirect
PyPIipython—indirect
PyPInumpydoc—indirect
PyPInvidia-ml-py—indirect
PyPIraft-dask—indirect
PyPIraft-dask24.6.*indirect
PyPIrapids-build-backend—indirect
PyPIrapids-dask-dependency—indirect
PyPIrapids-dask-dependency24.6.*indirect
PyPIscikit-build-core—indirect
PyPIsphinx—indirect
PyPIsphinx-rtd-theme—indirect
PyPIumap-learn—indirect
PyPIxgboost—indirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 1 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 47 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statistics — PyPI.