Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-17 15:05 UTC

NVIDIA / Megatron-LM

Ongoing research training transformer models at scale

PythonCustom license★ 17,930 stars⑂ 4,524 forkssince Mar 2019View on GitHub ↗
KindLibraryCommand-line toolhow this is determined
#large-language-models#model-para#transformers

NVIDIA/Megatron-LM holds a health index of 98 out of 100, placing it in the Exceptional band. It scores highest on Vitality (100/100) and lowest on Security (62/100). It was last updated today. 6 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 88 is calibrated to 98 on the published index scale (record calibration 2026-08-02).

Ownership

NVIDIA CorporationOrganization · verified domain
30,153 followers803 public repossince May 2012

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPImegatron-core0.19.0263,72139528 days agonlpnludeepgpulanguagelearningmachinenvidiapytorchtorchtransformer

Metrics by category

Vitality

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

100Exceptional · 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 — 2,648 commits in the last year
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year2,648
human_commit_share0.99
days_since_last_push0
active_weeks_last_year52

Release discipline

100Exceptional
How it's scored
27/27Ships releases — 48 releases published
36/36Release recency — latest release 28 days ago
27/27Release cadence — a release every ~21.6 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count48
latest_release_tagcore_v0.19.0
releases_from_tagsno
days_since_latest_release28
mean_days_between_releases21.6
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?

86Excellent · 17% of overall

Popularity & adoption

97Exceptional
How it's scored
60/60Stars — 17,930 stars
25/25Forks — 4,524 forks
12.4/15Watchers — 172 watchers
Inputs used
forks4,524
stars17,930
watchers172
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

71Good
How it's scored
22.5/22.5README
16.9/22.5License — license file present, not a recognized license
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesshields.io
has_pull_request_templateyes

Ecosystem adoption (downloads)

90Excellent
How it's scored
72.3/80Monthly downloads — 263,721 downloads/month across pypi
0/20Registry dependents — not reported by this ecosystem
Inputs used
packagesmegatron-core
dependents—
ecosystemspypi
total_downloads—
monthly_downloads263,721
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?

92Excellent · 23% of overall

Maintainer resilience (bus factor)

90Excellent
How it's scored
48.6/54Bus factor — 6 contributor(s) cover half of all commits
17.6/22.5Commit distribution — top contributor authored 22% of commits
13.5/13.5Contributor breadth — 99 contributors
10/10OpenSSF Scorecard: Contributors — project has 7 contributing companies or organizations
Inputs used
bus_factor6
contributors_sampled99
top_contributor_share0.218

Issue & PR responsiveness

78Good
How it's scored
32/42Issue resolution — 76% of issues closed
19.4/30PR acceptance — 2,941/4,548 decided PRs merged
13/13Newcomer PR acceptance — 3/3 first-time contributors' PRs merged in 30d
13.5/15OpenSSF Scorecard: Code-Review — Found 29/30 approved changesets -- score normalized to 9
Inputs used
merged_prs2,941
open_issues417
closed_issues1,341
prs_merged_7d34
prs_decided_7d45
prs_merged_30d44
prs_decided_30d60
issue_closed_ratio0.763
closed_unmerged_prs1,607
first_time_authors_30d3
first_time_prs_merged_30d3
first_time_prs_decided_30d3

Ownership & stewardship

100Exceptional
How it's scored
30/30Ownership backing — organization-owned
20/20Verified domain
25/25Owner reach — 30,153 followers of NVIDIA
25/25Track record — 803 public repos, account ~14 yr old
Inputs used
followers30,153
owner_typeOrganization
is_verifiedyes
owner_loginNVIDIA
public_repos803
account_age_days5,242

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable — 1 package(s) on pypi
35/35Publish recency — latest publish 28 days ago
20/20Version history — 395 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagesmegatron-core
ecosystemspypi
any_deprecatedno
min_days_since_publish28

Engineering Quality

Are baseline engineering and documentation practices in place?

92Excellent · 19% of overall

Engineering practices

94Exceptional
How it's scored
24/24CI workflows — 39 workflow(s)
24/24Tests present
16/16Linter config — .pylintrc, pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 29 out of 29 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://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html
10/10Repository description
10/10Topics — 3 topics
0/10Wiki
Inputs used
topicslarge-language-models, model-para, transformers
has_wikino
homepagehttps://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html
docs_sitehttps://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

62Moderate · 16% of overall

Security posture

53Moderate
How it's scored
7.5/7.5Binary-Artifacts — no binaries found in the repo
3/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.5/2.5CI-Tests — 29 out of 29 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 29/30 approved changesets -- score normalized to 9
2.5/2.5Contributors — project has 7 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.2/2.5License — license file detected
7.5/7.5Maintained — 30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — no data
2/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
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
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 26 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.3
Excluded from scoring (no data or not applicable): 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
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_packages31
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:megatron-core@0.19.0 runtime dependency closure — what installing the published package pulls in — 31 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.

83Excellent · 4% of overall

Agent context & guidance

100Exceptional
How it's scored
45/45Agent instructions — .cursorrules, AGENTS.md, CLAUDE.md, megatron/core/models/hybrid/AGENTS.md, megatron/core/models/hybrid/CLAUDE.md
15/15Machine-readable docs (llms.txt) — llms.txt served by the project website (https://docs.nvidia.com/llms.txt)
40/40Legible commit history — 92 of 99 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_share0.929
agent_instruction_files.cursorrules, AGENTS.md, CLAUDE.md, megatron/core/models/hybrid/AGENTS.md, megatron/core/models/hybrid/CLAUDE.md
agent_instruction_max_bytes3,333

Verify loop (build / test / typecheck)

75Good
How it's scored
18/18One-command bootstrap — megatron/core/datasets/Makefile
22/22Automated tests
11/11Lint / format config — .pylintrc, pyproject.toml ([tool.ruff], [tool.black], [tool.isort])
0/11Static type checking
10/10Reproducible environment — Dockerfile, lockfile
10/10Demonstrated agent practice — 13 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenance — no automated dependency updates observed
4/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 4
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileyes
typed_languageno
bootstrap_filesmegatron/core/datasets/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs—
agent_commit_share0.13
toolchain_manifests—
dependency_bot_commit_share0

Code legibility for models

53Moderate
How it's scored
0/45Type-checkable code — Python without a type-check config
52.6/55Manageable file sizes — 63/1,474 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes302,723
source_files_sampled1,474
oversized_source_files63

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, recipes
Inputs used
example_dirsexamples, 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

17,930GitHub stars
99contributors
2,648commits, last 12 months
0days since last push
48releases
6bus factor
417open 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 33 authors (cap 12)

More detail

Star and fork history 0 ★ / 4,524 ⇿
0Stars
4,524Forks
10Releases

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.

3,4003,6003,8004,0004,2004,4004,6004,524152026-012026-052026-09
Major 0Minor 0Patch 0
OpenSSF Scorecard 5.3 / 10
5.3aggregate

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 · 2026-09-17 15:04 UTC

10Binary-Artifactsno binaries found in the repo
4Branch-Protectionbranch protection is not maximal on development and all release branches
10CI-Tests29 out of 29 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 29/30 approved changesets -- score normalized to 9
10Contributorsproject has 7 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
9Licenselicense file detected
10Maintained30 commit(s) and 1 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
4Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 4
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities26 existing vulnerabilities detected
Direct dependencies 3
RegistryPackageVersion constraintManifest
PyPItorch>=2.6.0pyproject.toml
PyPInumpy—pyproject.toml
PyPIpackaging>=24.2pyproject.toml
All dependencies 329

Full resolved dependency set from the GitHub dependency graph: 6 direct and 323 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
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PyPIpyyaml-ft8.0.0indirect
PyPIquack-kernels0.4.1indirect
PyPIquart0.21.0indirect
PyPIrapidyaml0.15.2indirect
PyPIray2.56.1indirect
PyPIreferencing0.37.0indirect
PyPIregex2026.7.19indirect
PyPIrequests—indirect
PyPIrequests2.34.2indirect
PyPIrequests-toolbelt1.0.0indirect
PyPIrich15.0.0indirect
PyPIroman-numerals4.1.0indirect
PyPIrpds-py2026.6.3indirect
PyPIruff0.9.10indirect
PyPIs3fs2026.6.0indirect
PyPIsafetensors0.8.0indirect
PyPIscipy1.18.0indirect
PyPIsentencepiece—indirect
PyPIsentencepiece0.2.2indirect
PyPIsentry-sdk2.66.1indirect
PyPIsetuptools—indirect
PyPIsetuptools84.0.0indirect
PyPIshellingham1.5.4indirect
PyPIsix1.17.0indirect
PyPIslack-sdk3.43.0indirect
PyPIsmart-open8.0.1indirect
PyPIsniffio1.3.1indirect
PyPIsnowballstemmer3.1.1indirect
PyPIsoupsieve2.9.2indirect
PyPIsphinx9.1.0indirect
PyPIsphinx-autobuild2025.8.25indirect
PyPIsphinx-autodoc20.5.0indirect
PyPIsphinx-copybutton0.5.2indirect
PyPIsphinxcontrib-applehelp2.0.0indirect
PyPIsphinxcontrib-devhelp2.0.0indirect
PyPIsphinxcontrib-htmlhelp2.1.0indirect
PyPIsphinxcontrib-jsmath1.0.1indirect
PyPIsphinxcontrib-qthelp2.0.0indirect
PyPIsphinxcontrib-serializinghtml2.0.0indirect
PyPIstarlette0.52.1indirect
PyPIsympy1.14.0indirect
PyPItabulate0.10.0indirect
PyPItensorboard2.21.0indirect
PyPItensorboard-data-server0.7.2indirect
PyPItensorflow—indirect
PyPItensorstore—indirect
PyPItensorstore0.1.84indirect
PyPItensorstore0.1.85indirect
PyPItiktoken0.13.0indirect
PyPItilelang0.1.8indirect
PyPItokenizers0.22.2indirect
PyPItoml0.10.2indirect
PyPItomlkit0.15.1indirect
PyPItorch-c-dlpack-ext0.1.5indirect
PyPItorch-memory-saver0.0.10b1indirect
PyPItorchx0.7.0indirect
PyPItqdm—indirect
PyPItqdm4.67.3indirect
PyPItqdm4.70.0indirect
PyPItransformer-engine2.18.0+27486e03indirect
PyPItransformers—indirect
PyPItransformers5.14.1indirect
PyPItriton3.7.1indirect
PyPItrove-classifiers2026.6.1.19indirect
PyPItyper0.27.1indirect
PyPItyping-extensions4.16.0indirect
PyPItyping-inspect0.9.0indirect
PyPItyping-inspection0.4.3indirect
PyPItzdata2025.3indirect
PyPIurllib32.7.0indirect
PyPIuvicorn0.52.1indirect
PyPIvirtualenv21.7.3indirect
PyPIwandb0.28.1indirect
PyPIwatchfiles1.2.0indirect
PyPIwcmatch10.2.1indirect
PyPIwcwidth0.8.2indirect
PyPIwebdataset1.0.2indirect
PyPIwebsockets17.0.1indirect
PyPIwerkzeug3.1.8indirect
PyPIwget3.2indirect
PyPIwin32-setctime1.2.0indirect
PyPIwrapt2.3.0indirect
PyPIwsproto1.3.2indirect
PyPIxattr1.3.0indirect
PyPIxxhash3.8.1indirect
PyPIyarl1.24.5indirect
PyPIz3-solver4.15.4.0indirect
PyPIzipp4.1.0indirect
PyPIzstandard0.25.0indirect
Dependency advisories 0

Installing pypi:megatron-core@0.19.0 pulls in 31 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.

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98huggingface/datasets🤗 The largest hub of ready-to-use datasets for AI models with fast, easy-to-use and efficient data manipulation toolsExceptional98huggingface/peft🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.Exceptional96NVIDIA/cudnn-frontendcuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.Exceptional100huggingface/transformers🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. Exceptional99huggingface/diffusers🤗 Diffusers: State-of-the-art diffusion models for image, video, and audio generation in PyTorch.Exceptional98openvinotoolkit/nncfNeural Network Compression Framework for enhanced OpenVINO™ inferenceExceptional

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.