Public record
Software health reportschema 0.12.0 · metrics 2.10.0 · 2026-07-18 12:47 UTC

Lightning-AI / torchmetrics

Machine learning metrics for distributed, scalable PyTorch applications.

PythonApache-2.0★ 2,449 stars⑂ 501 forkssince Dec 2020View on GitHub ↗
KindLibraryhow this is determined
#python#data-science#machine-learning#pytorch#deep-learning#metrics#analyses

Lightning-AI/torchmetrics holds a health index of 95 out of 100, placing it in the Exceptional band. It scores highest on Engineering Quality (92/100) and lowest on Security (63/100). It was last updated 2 days ago. 2 contributors account for most of its recent work.

95
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.

95
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 82 is calibrated to 95 on the published index scale (record calibration 2026-08-02).

Ownership

⚡️ Lightning AI Organization
6,836 followers29 public repossince Dec 2019

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPItorchmetrics1.9.0-73130 days agodeep-learningmachine-learningpytorchmetricsai

Metrics by category

Vitality

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

81Excellent · 21% of overall

Development activity

81Excellent
How it's scored
36/36Push recency — last push 2 days ago
17.3/36Commit cadence — 25/52 weeks with commits
18/18Commit volume — 110 commits in the last year
10/10OpenSSF Scorecard: Maintained — 12 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year110
human_commit_share—
days_since_last_push2
active_weeks_last_year25

Release discipline

81Excellent
How it's scored
27/27Ships releases — 65 releases published
27/36Release recency — latest release 130 days ago
27/27Release cadence — a release every ~40 days
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Inputs used
releases_count65
latest_release_tagv1.9.0
releases_from_tagsno
days_since_latest_release130
mean_days_between_releases40

Community & Adoption

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

88Excellent · 17% of overall

Popularity & adoption

85Excellent
How it's scored
55/60Stars — 2,449 stars
22.5/25Forks — 501 forks
7.8/15Watchers — 26 watchers
Inputs used
forks501
stars2,449
watchers26
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_badges—
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_services—
has_pull_request_templateyes

Sustainability & Governance

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

86Excellent · 23% of overall

Maintainer resilience (bus factor)

64Moderate
How it's scored
25.2/54Bus factor — 2 contributor(s) cover half of all commits
14.9/22.5Commit distribution — top contributor authored 34% of commits
13.5/13.5Contributor breadth — 100 contributors
10/10OpenSSF Scorecard: Contributors — project has 46 contributing companies or organizations
Inputs used
bus_factor2
contributors_sampled100
top_contributor_share0.336

Issue & PR responsiveness

91Excellent
How it's scored
38.3/42Issue resolution — 91% of issues closed
27.2/30PR acceptance — 1,911/2,109 decided PRs merged
0/13Newcomer PR acceptance — no first-time contributor's PR decided in 30d
13.5/15OpenSSF Scorecard: Code-Review — Found 14/15 approved changesets -- score normalized to 9
Inputs used
merged_prs1,911
open_issues90
closed_issues924
prs_merged_7d—
prs_decided_7d—
prs_merged_30d—
prs_decided_30d—
issue_closed_ratio0.911
closed_unmerged_prs198
first_time_authors_30d—
first_time_prs_merged_30d—
first_time_prs_decided_30d—
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.

Ownership & stewardship

97Exceptional
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain — verified-domain status not read for this organization
25/25Owner reach — 6,836 followers of Lightning-AI
22.8/25Track record — 29 public repos, account ~6 yr old
Inputs used
followers6,836
owner_typeOrganization
is_verified—
owner_loginLightning-AI
public_repos29
account_age_days2,421
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 130 days ago
20/20Version history — 73 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagestorchmetrics
ecosystemspypi
any_deprecatedno
min_days_since_publish130

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 12 workflow(s)
24/24Tests present
16/16Linter config
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

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://lightning.ai/docs/torchmetrics/
10/10Repository description
10/10Topics — 7 topics
0/10Wiki
Inputs used
topicspython, data-science, machine-learning, pytorch, deep-learning, metrics, analyses
has_wikino
homepagehttps://lightning.ai/docs/torchmetrics/
docs_sitehttps://lightning.ai/docs/torchmetrics/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

63Moderate · 16% of overall

Security posture

63Moderate
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
6.8/7.5Code-Review — Found 14/15 approved changesets -- score normalized to 9
2.5/2.5Contributors — project has 46 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
7.5/7.5Dependency-Update-Tool — update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5License — license file detected
7.5/7.5Maintained — 12 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
4/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8
0/5SAST — SAST tool is not run on all commits -- score normalized to 0
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
6/7.5Vulnerabilities — 2 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate6.3
Excluded from scoring (no data or not applicable): Branch-Protection. Remaining weights renormalized.

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.

65Good · 4% of overall

Agent context & guidance

1Critical
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
0/40Legible commit history — no data
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share—
agent_instruction_files—
agent_instruction_max_bytes—
Excluded from scoring (no data or not applicable): Legible commit history. Remaining weights renormalized.

Verify loop (build / test / typecheck)

94Exceptional
How it's scored
18/18One-command bootstrap — Makefile, docs/Makefile
22/22Automated tests
11/11Lint / format config
11/11Static type checking — src/torchmetrics/py.typed
10/10Reproducible environment — devcontainer, Dockerfile
0/10Demonstrated agent practice — no data
5/8Automated maintenance — dependency automation configured, none observed in the sampled commits
8/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 8
Inputs used
has_nixno
has_testsyes
lockfiles—
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontaineryes
has_linter_configyes
typecheck_configssrc/torchmetrics/py.typed
agent_commit_share—
toolchain_manifests—
dependency_bot_commit_share0
Excluded from scoring (no data or not applicable): Demonstrated agent practice. Remaining weights renormalized.

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/torchmetrics/py.typed)
55/55Manageable file sizes — 0/563 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes57,049
source_files_sampled563
oversized_source_files0

Machine-readable interfaces

100Exceptional
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
Inputs used
example_dirsexamples
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

2,449GitHub stars
100contributors
110commits, last 12 months
2days since last push
65releases
2bus factor
90open issues
PyPIpackage ecosystems

More detail

OpenSSF Scorecard 6.3 / 10
6.3aggregate

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 · 2026-07-18 12:45 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
9Code-ReviewFound 14/15 approved changesets -- score normalized to 9
10Contributorsproject has 46 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained12 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
8Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 8
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
8Vulnerabilities2 existing vulnerabilities detected
All dependencies 106

Full resolved dependency set from the GitHub dependency graph: 0 direct and 106 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaeon—indirect
PyPIbert-score0.3.13indirect
PyPIcachier4.2.0indirect
PyPIcloudpickle—indirect
PyPIcodecov2.1.13indirect
PyPIcoverage7.10.7indirect
PyPIdists-pytorch0.1indirect
PyPIdocutils0.19indirect
PyPIdython0.7.9indirect
PyPIeinops—indirect
PyPIfairlearn—indirect
PyPIfast-bss-eval—indirect
PyPIfaster-coco-eval—indirect
PyPIfire—indirect
PyPIgammatone—indirect
PyPIhuggingface-hub—indirect
PyPIipadic—indirect
PyPIipython—indirect
PyPIjiwer—indirect
PyPIkornia—indirect
PyPIlai-sphinx-theme—indirect
PyPIlibrosa—indirect
PyPIlightning—indirect
PyPIlightning-utilities—indirect
PyPIlightning-utilities0.15.3indirect
PyPIlpips—indirect
PyPImatplotlib—indirect
PyPImecab-ko—indirect
PyPImecab-ko-dic—indirect
PyPImecab-python3—indirect
PyPImir-eval—indirect
PyPImonai1.4.0indirect
PyPImypy1.18.2indirect
PyPImyst-parser1.0.0indirect
PyPInetcal—indirect
PyPInltk—indirect
PyPInumpy—indirect
PyPIonnxruntime—indirect
PyPIpackaging—indirect
PyPIpandas—indirect
PyPIpandoc2.4indirect
PyPIpermetrics2.0.0indirect
PyPIpesq—indirect
PyPIphmdoctest1.4.0indirect
PyPIpiq—indirect
PyPIproperscoring0.1indirect
PyPIpsutil—indirect
PyPIpy-tree—indirect
PyPIpycocotools—indirect
PyPIpydantic—indirect
PyPIpygithub—indirect
PyPIpystoi—indirect
PyPIpytdc—indirect
PyPIpytest—indirect
PyPIpytest8.4.2indirect
PyPIpytest-cov7.0.0indirect
PyPIpytest-doctestplus—indirect
PyPIpytest-doctestplus1.4.0indirect
PyPIpytest-rerunfailures—indirect
PyPIpytest-rerunfailures16.0.1indirect
PyPIpytest-timeout2.4.0indirect
PyPIpytest-xdist3.8.0indirect
PyPIpytorch-lightning—indirect
PyPIpytorch-msssim1.0.0indirect
PyPIregex—indirect
PyPIrequests—indirect
PyPIrouge-score—indirect
PyPIsacrebleu—indirect
PyPIsafetensors—indirect
PyPIscienceplots—indirect
PyPIscikit-image—indirect
PyPIscikit-learn—indirect
PyPIscipy—indirect
PyPIsentencepiece—indirect
PyPIsetuptools—indirect
PyPIsewar—indirect
PyPIsphinx5.3.0indirect
PyPIsphinx-autobuild2024.10.3indirect
PyPIsphinx-autodoc-typehints1.23.0indirect
PyPIsphinx-copybutton0.5.2indirect
PyPIsphinx-gallery0.19.0indirect
PyPIsphinx-paramlinks0.6.0indirect
PyPIsphinx-togglebutton0.3.2indirect
PyPIsphinxcontrib-fulltoc—indirect
PyPIsphinxcontrib-mockautodoc—indirect
PyPIsrmrpy—indirect
PyPIstatsmodels—indirect
PyPItimm—indirect
PyPItorch—indirect
PyPItorch2.10.0indirect
PyPItorch-complex—indirect
PyPItorch-fidelity—indirect
PyPItorch-linear-assignment—indirect
PyPItorchaudio—indirect
PyPItorchvision—indirect
PyPItqdm—indirect
PyPItransformers—indirect
PyPItypes-emoji—indirect
PyPItypes-protobuf—indirect
PyPItypes-pyyaml—indirect
PyPItypes-requests—indirect
PyPItypes-setuptools—indirect
PyPItypes-six—indirect
PyPItypes-tabulate—indirect
PyPIvmaf-torch—indirect
PyPIwget—indirect
Raw JSON report machine-readable

Feedback

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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 v0.12.0 — full methodology · metrics wiki.

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