Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 23:19 UTC

openvinotoolkit / nncf

Neural Network Compression Framework for enhanced OpenVINO™ inference

PythonApache-2.0★ 1,199 stars⑂ 305 forkssince May 2020View on GitHub ↗

openvinotoolkit/nncf holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (96/100) and lowest on Security (78/100). It was last updated today. 4 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

1,868 followers48 public repossince Sep 2019

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

Package ecosystems

Metrics by category

Vitality

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

96Exceptional · 21% of overall
How it's scored
36/36Push recency — last push 0 days ago
34.6/36Commit cadence — 50/52 weeks with commits
18/18Commit volume — 373 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year373
human_commit_share0.83
days_since_last_push0
active_weeks_last_year50
How it's scored
27/27Ships releases — 37 releases published
36/36Release recency — latest release 0 days ago
19.8/27Release cadence — a release every ~65.3 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count37
latest_release_tagv3.4.0
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases65.3

Community & Adoption

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

89Excellent · 17% of overall
How it's scored
49.9/60Stars — 1,199 stars
20.7/25Forks — 305 forks
8.3/15Watchers — 32 watchers
Inputs used
forks305
stars1,199
watchers32
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5License — recognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateyes
How it's scored
80/80Monthly downloads — 1,333,362 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesnncf
dependents—
ecosystemspypi
total_downloads—
monthly_downloads1,333,362
unverified_packages_excluded—
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

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

83Excellent · 23% of overall
How it's scored
43.2/54Bus factor — 4 contributor(s) cover half of all commits
17.4/22.5Commit distribution — top contributor authored 23% of commits
13.5/13.5Contributor breadth — 77 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor4
contributors_sampled77
top_contributor_share0.228
How it's scored
39.4/42Issue resolution — 94% of issues closed
26.1/30PR acceptance — 3,267/3,756 decided PRs merged
0/13Newcomer PR acceptance — 0/2 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs3,267
open_issues24
closed_issues360
prs_merged_7d5
prs_decided_7d6
prs_merged_30d24
prs_decided_30d32
issue_closed_ratio0.938
closed_unmerged_prs489
first_time_authors_30d2
first_time_prs_merged_30d0
first_time_prs_decided_30d2
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
23.5/25Owner reach — 1,868 followers of openvinotoolkit
24.3/25Track record — 48 public repos, account ~7 yr old
Inputs used
followers1,868
owner_typeOrganization
is_verifiedno
owner_loginopenvinotoolkit
public_repos48
account_age_days2,557

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 0 days ago
20/20Version history — 35 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesnncf
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

89Excellent · 19% of overall
How it's scored
24/24CI workflows — 25 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — no data
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

85Excellent
How it's scored
30/30README
25/25Documentation directory
0/15Documentation / homepage site
10/10Repository description
10/10Topics — 19 topics
10/10Wiki
Inputs used
topicsquantization, pruning, sparsity, quantization-aware-training, mixed-precision-training, compression, semantic-segmentation, object-detection, classification, nlp, bert, transformers, pytorch, tensorflow, onnx, openvino, deep-learning, genai, llm
has_wikiyes
homepage—
docs_site—
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

78Good · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifestspyproject.toml
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes
Excluded from scoring (no data or not applicable): Dependency lockfiles. 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
advisories14
affected_packages7
assessed_packages73
unassessed_packages36
affected_by_severityhigh 3, moderate 2, unknown 2
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 73 resolved dependencies against OSV. 36 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.

80Excellent · 4% of overall
How it's scored
45/45Agent instructions — .github/copilot-instructions.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://pytorch.org/llms.txt)
40/40Legible commit history — 83 of 83 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtyes
llms_txt_urlhttps://pytorch.org/llms.txt
legible_history_share1
agent_instruction_files.github/copilot-instructions.md
agent_instruction_max_bytes2,356
How it's scored
18/18One-command bootstrap — Makefile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
0/11Static type checking
0/10Reproducible environment
2/10Demonstrated agent practice — 1 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 17 of the last 100 commits are automated dependency updates
0/10OpenSSF Scorecard: Pinned-Dependencies — no data
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileno
typed_languageno
bootstrap_filesMakefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.01
toolchain_manifests—
dependency_bot_commit_share0.17
How it's scored
0/45Type-checkable code — Python without a type-check config
54.3/55Manageable file sizes — 10/752 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes109,688
source_files_sampled752
oversized_source_files10
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
Inputs used
example_dirsexamples, notebooks
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

1,199GitHub stars
77contributors
373commits, last 12 months
0days since last push
37releases
4bus factor
24open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi download statistics for 'nncf' unavailable this scan (stats endpoint failed after retries); adoption may be under-evidenced
  • pypi download figures for nncf carried forward from the previous scan (stats endpoint unavailable this scan)
  • deps.dev does not index pypi:nncf@3.4.0; advisories assessed against the repository dependency graph instead
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit; 2026/09/17 23:18:32 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 305 ⇿
0Stars
305Forks
36Releases

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.

05010015020025030029572020-052023-072026-09
Major 2Minor 25Patch 8

Each point covers 6 days.

Direct dependencies 12
RegistryPackageVersion constraintManifest
PyPInetworkx>=2.6, <=3.6.1pyproject.toml
PyPIninja>=1.10.0.post2, <1.14pyproject.toml
PyPInumpy>=1.24.0, <2.5.0pyproject.toml
PyPIopenvino-telemetry>=2023.2.0pyproject.toml
PyPIpackaging>=20.0pyproject.toml
PyPIpsutil—pyproject.toml
PyPIpydot>=1.4.1, <=4.0.1pyproject.toml
PyPIrich>=13.5.2pyproject.toml
PyPIsafetensors>=0.4.1pyproject.toml
PyPIscikit-learn>=0.24.0pyproject.toml
PyPIscipy>=1.3.2pyproject.toml
PyPItabulate>=0.9.0pyproject.toml
All dependencies 109

Full resolved dependency set from the GitHub dependency graph: 18 direct and 91 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInetworkx—direct
PyPIninja—direct
PyPInumpy—direct
PyPInumpy1.26.4direct
PyPInumpy2.2.6direct
PyPInumpy2.4.6direct
PyPIopenvino-telemetry—direct
PyPIpackaging—direct
PyPIpsutil—direct
PyPIpydot—direct
PyPIrich—direct
PyPIsafetensors—direct
PyPIsafetensors0.6.2direct
PyPIscikit-learn—direct
PyPIscipy—direct
PyPIscipy1.18.0direct
PyPItabulate—direct
PyPItabulate0.10.0direct
PyPIaccelerate1.9.0indirect
PyPIaddict—indirect
PyPIanomalib0.6.0indirect
PyPIanomalib2.2.0indirect
PyPIastroid4.1.2indirect
PyPIatheris2.3.0indirect
PyPIdatasets4.6.1indirect
PyPIdatasets5.0.0indirect
PyPIdatasets5.0.1indirect
PyPIefficientnet-pytorch0.7.1indirect
PyPIexecutorch1.3.1indirect
PyPIfastcore1.11.5indirect
PyPIfastdownload0.0.7indirect
PyPIfastprogress1.0.5indirect
PyPIfuro2025.12.19indirect
PyPIgptqmodel—indirect
PyPIgptqmodel5.6.12indirect
PyPIhuggingface-hub0.36.2indirect
PyPIipykernel7.1.0indirect
PyPIipython8.37.0indirect
PyPIkaleido—indirect
PyPIkernels0.12.3indirect
PyPIlibrosa0.10.0indirect
PyPIlm-eval0.4.12indirect
PyPImatplotlib—indirect
PyPImatplotlib3.10.7indirect
PyPImatplotlib3.10.9indirect
PyPImdutils—indirect
PyPImemory-profiler0.61.0indirect
PyPInbclient0.10.4indirect
PyPInbformat5.10.4indirect
PyPIonnx1.21.0indirect
PyPIonnx1.22.0indirect
PyPIonnx-ir0.1.15indirect
PyPIonnx-ir1.0.0indirect
PyPIonnxruntime1.24.3indirect
PyPIonnxscript0.6.2indirect
PyPIonnxscript0.7.1indirect
PyPIopenvino—indirect
PyPIopenvino2026.1.0indirect
PyPIopenvino2026.3.0indirect
PyPIopenvino2026.3.1indirect
PyPIopenvino-tokenizers—indirect
PyPIoptimum2.3.0indirect
PyPIoptimum-intel—indirect
PyPIoptimum-intel2.1.0indirect
PyPIoptimum-onnx—indirect
PyPIpandas—indirect
PyPIpandas2.3.3indirect
PyPIpillow—indirect
PyPIplotly-express—indirect
PyPIpycocotools—indirect
PyPIpycocotools2.0.7indirect
PyPIpytest9.0.3indirect
PyPIpytest-cov7.1.0indirect
PyPIpytest-forked1.6.0indirect
PyPIpytest-mock3.15.1indirect
PyPIpytest-ordering0.6indirect
PyPIpytest-split—indirect
PyPIpytest-split0.11.0indirect
PyPIpytest-xdist3.8.0indirect
PyPIpyyaml—indirect
PyPIrequests2.33.0indirect
PyPIsentence-transformers5.6.0indirect
PyPIsetuptools—indirect
PyPIsetuptools81.0.0indirect
PyPIsoundfile0.12.1indirect
PyPIsphinx9.1.0indirect
PyPIsphinx-autoapi3.8.0indirect
PyPItensorboard2.20.0indirect
PyPItensorflow-io0.37.1indirect
PyPItimm0.9.2indirect
PyPItimm1.0.22indirect
PyPItorch—indirect
PyPItorch2.10.0indirect
PyPItorch2.13.0indirect
PyPItorchao—indirect
PyPItorchao0.17.0indirect
PyPItorchao0.18.0indirect
PyPItorchmetrics1.0.1indirect
PyPItorchvision—indirect
PyPItorchvision0.25.0indirect
PyPItorchvision0.28.0indirect
PyPItqdm—indirect
PyPItransformers—indirect
PyPItransformers5.5.0indirect
PyPIultralytics8.3.221indirect
PyPIultralytics8.4.21indirect
PyPIvirtualenv—indirect
PyPIwhowhatbench—indirect
PyPIyattag—indirect
Dependency advisories 7

This repository publishes no package the index resolves, so its own dependency graph was assessed — 73 packages, which also include development and test pins that never ship: 7 carry known advisories, of which 0 are direct. 36 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

PackageVersionRelationSeverityAdvisoriesFixed in
accelerate1.9.0indirecthigh2—
torch2.10.0indirecthigh22.13.0
transformers5.5.0indirecthigh25.10.0
onnx1.21.0indirectmoderate41.22.0
setuptools81.0.0indirectmoderate283.0.0
datasets4.6.1indirectunknown15.0.1
datasets5.0.0indirectunknown15.0.1

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.