Public record
Software health reportschema 0.30.0 · metrics 2.10.0 · 2026-08-03 15:42 UTC

rwth-i6 / returnn

The RWTH extensible training framework for universal recurrent neural networks

PythonCustom license★ 375 stars⑂ 133 forkssince Jun 2016View on GitHub ↗
KindLibraryCommand-line toolhow this is determined

rwth-i6/returnn holds a health index of 73 out of 100, placing it in the Good band. It scores highest on Engineering Quality (81/100) and lowest on Security (21/100). It was last updated today. A single contributor accounts for most of its recent work.

73
overall / 100
Good

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.

73
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 64 is calibrated to 73 on the published index scale (record calibration 2026-08-02).

Ownership

43 followers27 public repossince Jun 2016

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

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publish
PyPIreturnn1.20260803.172340-2,0540 days ago

Metrics by category

Vitality

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

77Good · 21% of overall
How it's scored
36/36Push recencylast push 0 days ago
29.8/36Commit cadence43/52 weeks with commits
18/18Commit volume523 commits in the last year
0/10OpenSSF Scorecard: Maintainedno data
Inputs used
commits_last_year523
human_commit_share1
days_since_last_push0
active_weeks_last_year43
How it's scored
27/27Ships releases3 releases published
0/36Release recencylatest release 2,813 days ago
19.8/27Release cadencea release every ~72 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count3
latest_release_tagv2.1.0-beta
releases_from_tagsno
days_since_latest_release2,813
mean_days_between_releases72

Community & Adoption

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

66Good · 17% of overall
How it's scored
41.7/60Stars375 stars
17.7/25Forks133 forks
7.2/15Watchers21 watchers
Inputs used
forks133
stars375
watchers21
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
How it's scored
22.5/22.5README
16.9/22.5Licenselicense file present, not a recognized license
18/18CONTRIBUTING guide
0/13.5Code of conduct
0/7.2Issue template
0/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges1
has_contributingyes
has_issue_templateno
has_code_of_conductno
readme_badge_servicesgithub.com
has_pull_request_templateno

Sustainability & Governance

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

70Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
6/22.5Commit distributiontop contributor authored 74% of commits
13.5/13.5Contributor breadth57 contributors
0/10OpenSSF Scorecard: Contributorsno data
Inputs used
bus_factor1
contributors_sampled57
top_contributor_share0.735
How it's scored
30.9/42Issue resolution74% of issues closed
27.4/30PR acceptance949/1,040 decided PRs merged
0/13Newcomer PR acceptanceno first-time contributor's PR decided in 30d
0/15OpenSSF Scorecard: Code-Reviewno data
Inputs used
merged_prs949
open_issues198
closed_issues553
prs_merged_7d0
prs_decided_7d0
prs_merged_30d0
prs_decided_30d0
issue_closed_ratio0.736
closed_unmerged_prs91
first_time_authors_30d0
first_time_prs_merged_30d0
first_time_prs_decided_30d0
Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
11.8/25Owner reach43 followers of rwth-i6
22.5/25Track record27 public repos, account ~10 yr old
Inputs used
followers43
owner_typeOrganization
is_verified
owner_loginrwth-i6
public_repos27
account_age_days3,702
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 0 days ago
20/20Version history2,054 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesreturnn
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows4 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Testsno data
Inputs used
has_ciyes
has_testsyes
has_editorconfigyes
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttp://returnn.readthedocs.io/
10/10Repository description
10/10Topics5 topics
10/10Wiki
Inputs used
topicsrecurrent-neural-networks, gpu, tensorflow, theano, deep-learning
has_wikiyes
homepagehttp://returnn.readthedocs.io/
docs_sitehttp://returnn.readthedocs.io/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

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

21At Risk · 16% of overall
How it's scored
0/30Security policy (SECURITY.md)
0/25Dependabot config
0/25Dependency lockfiles
0/20CodeQL workflow
Inputs used
sourcefile_signals
lockfiles
manifestsdocs/requirements.txt, pyproject.toml, requirements.txt, setup.py
has_codeql_workflowno
has_security_policyno
has_dependabot_configno

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
0/25Indirect dependencies free of known advisoriestransitive set not separable from development and test dependencies in this scope
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages1
unassessed_packages11
affected_by_severitynone
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 1 resolved dependencies against OSV. 11 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.

40Weak · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
1.1/40Legible commit history2 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.02
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapdocs/Makefile, tools/lattice_rescorer/Makefile
22/22Automated tests
0/11Lint / format config
0/11Static type checking
0/10Reproducible environment
0/10Demonstrated agent practiceno agent-authored commits among the last 100
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesno data
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesdocs/Makefile, tools/lattice_rescorer/Makefile
has_devcontainerno
has_linter_configno
typecheck_configs
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
50.1/55Manageable file sizes34/380 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes617,682
source_files_sampled380
oversized_source_files34
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesdemos, example
Inputs used
example_dirsdemos, example
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

375GitHub stars
57contributors
523commits, last 12 months
0days since last push
3releases
1bus factor
198open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:returnn@1.20260803.172340; advisories assessed against the repository dependency graph instead
  • OpenSSF Scorecard did not return a usable result (2026/08/03 15:42:18 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 ★ / 133 ⇿
0Stars
133Forks
3Releases

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.

025507510012515012952016-062021-042026-01
Major 0Minor 0Patch 0

Each point covers 9 days.

All dependencies 12

Full resolved dependency set from the GitHub dependency graph: 0 direct and 12 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIbetter-exchookindirect
PyPIdm-treeindirect
PyPIfuroindirect
PyPIh5pyindirect
PyPInumpyindirect
PyPInumpydocindirect
PyPIpytestindirect
PyPIscipyindirect
PyPIsphinxindirect
PyPItensorflow2.12.1indirect
PyPItorchindirect
PyPItorchaudioindirect
Dependency advisories 0

This repository publishes no package the index resolves, so its own dependency graph was assessed — 1 packages, which also include development and test pins that never ship: 0 carry known advisories, of which 0 are direct. 11 could not be assessed — no resolved version, an unsupported ecosystem, or beyond the reported package list.

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 v0.30.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.