Public record
Software health reportschema 0.31.0 · metrics 2.5.0 · 2026-08-05 01:47 UTC

Lightning-AI / pytorch-lightning

Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.

PythonApache-2.0★ 31,269 stars⑂ 3,771 forkssince Mar 2019View on GitHub ↗
#python#deep-learning#artificial-intelligence#ai#pytorch#data-science#machine-learning

Lightning-AI/pytorch-lightning holds a health index of 96 out of 100, placing it in the Exceptional band. It scores highest on Vitality (95/100) and lowest on AI Readiness (64/100). It was last updated today. 3 contributors account for most of its recent work.

96
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.

96
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 84 is calibrated to 96 on the published index scale (record calibration 2026-08-02).

Ownership

⚡️ Lightning AI Organization
6,847 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 publish
PyPIassistantpoints to another repo — not scored2.0.2b2-4399 days ago

Metrics by category

Vitality

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

95Exceptional · 21% of overall

Development activity

92Excellent
How it's scored
36/36Push recency — last push 0 days ago
28.4/36Commit cadence — 41/52 weeks with commits
18/18Commit volume — 413 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year413
human_commit_share0.68
days_since_last_push0
active_weeks_last_year41

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 100 releases published
36/36Release recency — latest release 69 days ago
27/27Release cadence — a release every ~44.1 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count100
latest_release_tag2.6.5
releases_from_tagsno
days_since_latest_release69
mean_days_between_releases44.1

Community & Adoption

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

95Exceptional · 17% of overall

Popularity & adoption

98Exceptional
How it's scored
60/60Stars — 31,269 stars
25/25Forks — 3,771 forks
13.4/15Watchers — 256 watchers
Inputs used
forks3,771
stars31,269
watchers256
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_badges10
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, codecov.io, dev.azure.com, github.com, shields.io
has_pull_request_templateyes

Sustainability & Governance

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

76Good · 23% of overall

Maintainer resilience (bus factor)

74Good
How it's scored
36/54Bus factor — 3 contributor(s) cover half of all commits
16.7/22.5Commit distribution — top contributor authored 26% of commits
13.5/13.5Contributor breadth — 97 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor3
contributors_sampled97
top_contributor_share0.259

Issue & PR responsiveness

76Good
How it's scored
37.3/42Issue resolution — 89% of issues closed
25.7/30PR acceptance — 9,444/11,036 decided PRs merged
1.6/13Newcomer PR acceptance — 1/8 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs9,444
open_issues844
closed_issues6,712
prs_merged_7d2
prs_decided_7d9
prs_merged_30d18
prs_decided_30d26
issue_closed_ratio0.888
closed_unmerged_prs1,592
first_time_authors_30d8
first_time_prs_merged_30d1
first_time_prs_decided_30d8

Ownership & stewardship

78Good
How it's scored
30/30Ownership backing — organization-owned
0/20Verified domain
25/25Owner reach — 6,847 followers of Lightning-AI
22.8/25Track record — 29 public repos, account ~6 yr old
Inputs used
followers6,847
owner_typeOrganization
is_verified—
owner_loginLightning-AI
public_repos29
account_age_days2,438

Engineering Quality

Are baseline engineering and documentation practices in place?

91Excellent · 19% of overall

Engineering practices

92Excellent
How it's scored
24/24CI workflows — 18 workflow(s)
24/24Tests present
16/16Linter config
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

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme
10/10Repository description
10/10Topics — 7 topics
0/10Wiki
Inputs used
topicspython, deep-learning, artificial-intelligence, ai, pytorch, data-science, machine-learning
has_wikino
homepagehttps://lightning.ai/pytorch-lightning/?utm_source=ptl_readme&utm_medium=referral&utm_campaign=ptl_readme
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

64Moderate · 16% of overall

Security posture

55Moderate
How it's scored
30/30Security policy (SECURITY.md)
25/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles—
manifests.actions/requirements.txt, pyproject.toml, requirements.txt, setup.py
has_codeql_workflowno
has_security_policyyes
has_dependabot_configyes

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisories — no direct dependency carries a known advisory
25/25Indirect dependencies free of known advisories — no indirect dependency carries a known advisory
0/40No advisories left outstanding — no advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages28
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:assistant@2.0.2b2 runtime dependency closure — what installing the published package pulls in — 28 packages. 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.

64Moderate · 4% of overall

Agent context & guidance

40Weak
How it's scored
0/45Agent instructions — no CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 65 of 68 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
legible_history_share0.956
agent_instruction_files—
agent_instruction_max_bytes—

Verify loop (build / test / typecheck)

84Excellent
How it's scored
18/18One-command bootstrap — Makefile, docs/source-fabric/Makefile, docs/source-pytorch/Makefile
22/22Automated tests
11/11Lint / format config
11/11Static type checking — src/lightning/py.typed, src/lightning_fabric/py.typed, src/pytorch_lightning/py.typed
0/10Reproducible environment
6/10Demonstrated agent practice — 3 of the last 100 commits agent-authored or agent-credited
8/8Automated maintenance — 31 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, docs/source-fabric/Makefile, docs/source-pytorch/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configssrc/lightning/py.typed, src/lightning_fabric/py.typed, src/pytorch_lightning/py.typed
agent_commit_share0.03
toolchain_manifests—
dependency_bot_commit_share0.31

Code legibility for models

82Excellent
How it's scored
27/45Type-checkable code — Python with type-check config (src/lightning/py.typed, src/lightning_fabric/py.typed, src/pytorch_lightning/py.typed)
54.6/55Manageable file sizes — 5/648 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes82,856
source_files_sampled648
oversized_source_files5

Machine-readable interfaces

40Weak
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)
0/20MCP server
40/40Runnable examples — demos, examples, notebooks
Inputs used
example_dirsdemos, examples, notebooks
has_mcp_signalno
api_schema_files—

Key facts

31,269GitHub stars
97contributors
413commits, last 12 months
0days since last push
100releases
3bus factor
844open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • pypi package 'assistant' points at a different repository (https://gitlab.com/waser-technologies/technologies/assistant); excluded from ecosystem scoring
  • OpenSSF Scorecard did not return a usable result (2026/08/05 01:46:35 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 ★ / 3,771 ⇿
0Stars
3,771Forks
37Releases

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.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

2,6002,8003,0003,2003,4003,6003,8003,771412023-082025-022026-08
Major 0Minor 6Patch 24

Each point covers 3 days.

All dependencies 103

Full resolved dependency set from the GitHub dependency graph: 0 direct and 103 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIawscli—indirect
PyPIbitsandbytes—indirect
PyPIclick8.1.8indirect
PyPIclick8.3.1indirect
PyPIcloudpickle—indirect
PyPIcoverage7.10.7indirect
PyPIcoverage7.13.5indirect
PyPIdeepspeed—indirect
PyPIdocutils—indirect
PyPIfastapi—indirect
PyPIfire—indirect
PyPIfsspec—indirect
PyPIgymnasium—indirect
PyPIhuggingface-hub—indirect
PyPIhydra-core—indirect
PyPIimportlib-metadata—indirect
PyPIipython—indirect
PyPIjinja2—indirect
PyPIjsonargparse—indirect
PyPIjupytext—indirect
PyPIlai-sphinx-theme—indirect
PyPIlightning—indirect
PyPIlightning-utilities—indirect
PyPIlitdata—indirect
PyPImatplotlib—indirect
PyPImoviepy—indirect
PyPImypy1.20.0indirect
PyPImyst-parser—indirect
PyPInbconvert—indirect
PyPInbformat—indirect
PyPInbsphinx—indirect
PyPInumpy—indirect
PyPIomegaconf—indirect
PyPIonnx—indirect
PyPIonnx-ir—indirect
PyPIonnxruntime—indirect
PyPIonnxscript—indirect
PyPIpackaging—indirect
PyPIpandas—indirect
PyPIpandoc—indirect
PyPIpapermill—indirect
PyPIpip—indirect
PyPIpkginfo1.12.1.2indirect
PyPIpsutil—indirect
PyPIpytest9.0.2indirect
PyPIpytest-cov7.0.0indirect
PyPIpytest-doctestplus1.7.1indirect
PyPIpytest-random-order1.2.0indirect
PyPIpytest-rerunfailures16.0.1indirect
PyPIpytest-rerunfailures16.1indirect
PyPIpytest-timeout2.4.0indirect
PyPIpyyaml—indirect
PyPIrequests—indirect
PyPIrich—indirect
PyPIscikit-learn—indirect
PyPIsetuptools—indirect
PyPIsphinx—indirect
PyPIsphinx-autobuild—indirect
PyPIsphinx-autodoc-typehints—indirect
PyPIsphinx-copybutton—indirect
PyPIsphinx-multiproject—indirect
PyPIsphinx-paramlinks—indirect
PyPIsphinx-rtd-dark-mode—indirect
PyPIsphinx-togglebutton—indirect
PyPIsphinx-toolbox4.1.2indirect
PyPIsphinxcontrib-fulltoc—indirect
PyPIsphinxcontrib-mockautodoc—indirect
PyPIsphinxcontrib-video0.4.2indirect
PyPItensorboard—indirect
PyPItensorboardx—indirect
PyPItomlkit—indirect
PyPItorch—indirect
PyPItorch2.9.1indirect
PyPItorch-tensorrt—indirect
PyPItorchao—indirect
PyPItorchmetrics—indirect
PyPItorchvision—indirect
PyPItqdm—indirect
PyPItwine6.2.0indirect
PyPItypes-bleach—indirect
PyPItypes-cachetools—indirect
PyPItypes-croniter—indirect
PyPItypes-decorator—indirect
PyPItypes-markdown—indirect
PyPItypes-paramiko—indirect
PyPItypes-protobuf—indirect
PyPItypes-python-dateutil—indirect
PyPItypes-pyyaml—indirect
PyPItypes-redis—indirect
PyPItypes-requests—indirect
PyPItypes-setuptools—indirect
PyPItypes-six—indirect
PyPItypes-tabulate—indirect
PyPItypes-toml—indirect
PyPItypes-tzlocal—indirect
PyPItypes-ujson—indirect
PyPItyping-extensions—indirect
PyPIurllib3—indirect
PyPIuvicorn—indirect
PyPIvirtualenv—indirect
PyPIwcmatch—indirect
PyPIwget—indirect
PyPIwheel—indirect
Dependency advisories 0

Installing pypi:assistant@2.0.2b2 pulls in 28 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

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

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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 v2.5.0, schema v0.31.0 — full methodology · metrics wiki.

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