Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-09-13 21:00 UTC

kozistr / pytorch_optimizer

optimizer & lr scheduler & loss function collections in PyTorch

PythonApache-2.0★ 428 stars⑂ 44 forkssince Sep 2021View on GitHub ↗

kozistr/pytorch_optimizer holds a health index of 90 out of 100, placing it in the Excellent band. It scores highest on AI Readiness (85/100) and lowest on Sustainability & Governance (67/100). It was last updated 4 days ago. A single contributor accounts for most of its recent work.

90
overall / 100
Excellent

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.

90
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 76 is calibrated to 90 on the published index scale (record calibration 2026-08-02).

Ownership

Hyeongchan KimPersonal account
183 followers38 public repossince Oct 2015

This repository is owned by a personal account. A single-owner project carries more continuity risk than an organization-backed one.

Package ecosystems

Metrics by category

Vitality

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

80Excellent · 21% of overall
How it's scored
36/36Push recency — last push 4 days ago
14.5/36Commit cadence — 21/52 weeks with commits
15.8/18Commit volume — 57 commits in the last year
0/10OpenSSF Scorecard: Maintained — no data
Inputs used
commits_last_year57
human_commit_share1
days_since_last_push4
active_weeks_last_year21
How it's scored
27/27Ships releases — 92 releases published
27/36Release recency — latest release 113 days ago
27/27Release cadence — a release every ~43.5 days
0/10OpenSSF Scorecard: Signed-Releases — no data
Inputs used
releases_count92
latest_release_tagv3.10.1
releases_from_tagsno
days_since_latest_release113
mean_days_between_releases43.5

Community & Adoption

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

75Good · 17% of overall
How it's scored
42.7/60Stars — 428 stars
13.6/25Forks — 44 forks
3.9/15Watchers — 6 watchers
Inputs used
forks44
stars428
watchers6
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_badges8
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesshields.io
has_pull_request_templateyes

Sustainability & Governance

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

67Good · 23% of overall
How it's scored
9/54Bus factor — 1 contributor(s) cover half of all commits
0.2/22.5Commit distribution — top contributor authored 99% of commits
13.5/13.5Contributor breadth — 17 contributors
0/10OpenSSF Scorecard: Contributors — no data
Inputs used
bus_factor1
contributors_sampled17
top_contributor_share0.991
How it's scored
40.2/42Issue resolution — 96% of issues closed
28.1/30PR acceptance — 357/381 decided PRs merged
13/13Newcomer PR acceptance — 6/6 first-time contributors' PRs merged in 30d
0/15OpenSSF Scorecard: Code-Review — no data
Inputs used
merged_prs357
open_issues6
closed_issues136
prs_merged_7d3
prs_decided_7d3
prs_merged_30d6
prs_decided_30d6
issue_closed_ratio0.958
closed_unmerged_prs24
first_time_authors_30d2
first_time_prs_merged_30d6
first_time_prs_decided_30d6
How it's scored
10/30Ownership backing — personal (user) account
0/20Verified domain — not applicable to user accounts
16.3/25Owner reach — 183 followers of kozistr
23.6/25Track record — 38 public repos, account ~10 yr old
Inputs used
followers183
owner_typeUser
is_verified—
owner_loginkozistr
public_repos38
account_age_days3,974
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 113 days ago
20/20Version history — 91 published versions
20/20Not deprecated — active, not deprecated or yanked
Inputs used
packagespytorch_optimizer
ecosystemspypi
any_deprecatedno
min_days_since_publish113

Engineering Quality

Are baseline engineering and documentation practices in place?

84Excellent · 19% of overall
How it's scored
24/24CI workflows — 7 workflow(s)
24/24Tests present
16/16Linter config — pyproject.toml ([tool.ruff])
0/9.6Pre-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_configno

Documentation

90Excellent
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage site — https://pytorch-optimizers.readthedocs.io/en/latest/
10/10Repository description
10/10Topics — 19 topics
0/10Wiki
Inputs used
topicsoptimizer, pytorch, ranger, adamp, radam, madgrad, adabelief, sam, lookahead, deep-learning, diffgrad, adan, gradient-centralization, adai, learning-rate-scheduling, loss-functions, ademamix, muon, scion
has_wikino
homepagehttps://pytorch-optimizers.readthedocs.io/en/latest/
docs_sitehttps://pytorch-optimizers.readthedocs.io/en/latest/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

74Good · 16% of overall
How it's scored
30/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles — published library — lockfiles are an application concern, not expected
20/20CodeQL workflow
Inputs used
sourcefile_signals
lockfilesuv.lock
manifestspyproject.toml, requirements-docs.txt
has_codeql_workflowyes
has_security_policyyes
has_dependabot_configno
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
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_packages30
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:pytorch_optimizer@3.10.1 runtime dependency closure — what installing the published package pulls in — 30 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.

85Excellent · 4% of overall
How it's scored
45/45Agent instructions — AGENT.md, CLAUDE.md
0/15Machine-readable docs (llms.txt)
40/40Legible commit history — 100 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url—
legible_history_share1
agent_instruction_filesAGENT.md, CLAUDE.md
agent_instruction_max_bytes6,053
How it's scored
18/18One-command bootstrap — justfile
22/22Automated tests
11/11Lint / format config — pyproject.toml ([tool.ruff])
11/11Static type checking — pytorch_optimizer/py.typed
10/10Reproducible environment — lockfile
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 — no data
Inputs used
has_nixno
has_testsyes
lockfilesuv.lock
has_dockerfileno
typed_languageno
bootstrap_filesjustfile
has_devcontainerno
has_linter_configyes
typecheck_configspytorch_optimizer/py.typed
agent_commit_share0
toolchain_manifests—
dependency_bot_commit_share0
How it's scored
27/45Type-checkable code — Python with type-check config (pytorch_optimizer/py.typed)
55/55Manageable file sizes — 0/160 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes35,314
source_files_sampled160
oversized_source_files0
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

428GitHub stars
17contributors
57commits, last 12 months
4days since last push
92releases
1bus factor
6open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • OpenSSF Scorecard did not return a usable result (killed by SIGKILL — most likely the container's memory limit); skipping Scorecard checks

More detail

Star and fork history 0 ★ / 44 ⇿
0Stars
44Forks
80Releases

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.

010203040504322021-102024-032026-09
Major 3Minor 30Patch 47

Each point covers 5 days.

Direct dependencies 5
RegistryPackageVersion constraintManifest
PyPInumpy>1.24.4pyproject.toml
PyPInumpy<=1.24.4pyproject.toml
PyPItorch>=1.10,<2.5pyproject.toml
PyPItorch>=1.10,<2.9pyproject.toml
PyPItorch>=1.10pyproject.toml
All dependencies 61

Full resolved dependency set from the GitHub dependency graph: 13 direct and 48 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPInumpy—direct
PyPInumpy1.24.4direct
PyPInumpy2.0.2direct
PyPInumpy2.2.6direct
PyPInumpy2.4.6direct
PyPInumpy2.5.1direct
PyPItorch2.10.0direct
PyPItorch2.13.0direct
PyPItorch2.13.0+cpudirect
PyPItorch2.4.1direct
PyPItorch2.4.1+cpudirect
PyPItorch2.8.0direct
PyPItorch2.8.0+cpudirect
PyPIcolorama0.4.6indirect
PyPIcoverage7.10.7indirect
PyPIcoverage7.15.2indirect
PyPIcoverage7.6.1indirect
PyPIexceptiongroup1.3.1indirect
PyPIfilelock3.16.1indirect
PyPIfilelock3.19.1indirect
PyPIfilelock3.32.0indirect
PyPIfsspec2025.10.0indirect
PyPIfsspec2025.3.0indirect
PyPIfsspec2026.6.0indirect
PyPIiniconfig2.1.0indirect
PyPIiniconfig2.3.0indirect
PyPIjinja23.1.6indirect
PyPImarkdown-include0.8.1indirect
PyPImarkupsafe2.1.5indirect
PyPImarkupsafe3.0.3indirect
PyPImdx-truly-sane-lists1.3indirect
PyPImkdocs1.6.1indirect
PyPImkdocs-awesome-nav3.3.0indirect
PyPImkdocs-git-revision-date-localized-plugin1.5.1indirect
PyPImkdocs-material9.7.6indirect
PyPImkdocstrings-python2.0.3indirect
PyPImpmath1.3.0indirect
PyPInetworkx3.1indirect
PyPInetworkx3.2.1indirect
PyPInetworkx3.4.2indirect
PyPInetworkx3.6.1indirect
PyPInodeenv1.10.0indirect
PyPIpackaging26.2indirect
PyPIpluggy1.5.0indirect
PyPIpluggy1.6.0indirect
PyPIpygments2.20.0indirect
PyPIpymdown-extensions10.21.2indirect
PyPIpyright1.1.408indirect
PyPIpytest8.3.5indirect
PyPIpytest8.4.2indirect
PyPIpytest9.1.1indirect
PyPIpytest-cov5.0.0indirect
PyPIpytest-cov7.1.0indirect
PyPIpytorch-optimizer3.10.1indirect
PyPIruff0.16.0indirect
PyPIsetuptools83.0.0indirect
PyPIsympy1.13.3indirect
PyPIsympy1.14.0indirect
PyPItomli2.4.1indirect
PyPItyping-extensions4.13.2indirect
PyPItyping-extensions4.16.0indirect
Dependency advisories 0

Installing pypi:pytorch_optimizer@3.10.1 pulls in 30 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

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.