Registro público
Informe de salud del softwareesquema 0.27.0 · métricas 2.5.0 · 2026-07-27 00:43 UTC

ludwig-ai / ludwig

Low-code framework for building custom LLMs, neural networks, and other AI models

PythonApache-2.0★ 11.746 estrellas⑂ 1218 forksdesde dic 2018Ver en GitHub ↗
TipoBibliotecacómo se determina

ludwig-ai/ludwig tiene un índice de salud de 94 sobre 100, lo que lo sitúa en la banda Excepcional. Su puntuación más alta es Engineering Quality (96/100) y la más baja, AI Readiness (52/100). Se actualizó por última vez hoy. 3 personas concentran la mayor parte del trabajo reciente.

94
global / 100
Excepcional

Índice de salud del software

Las métricas se agrupan en categorías ponderadas sobre una escala estandarizada de 1 a 100. El resultado global parte de su media ponderada, calibrada contra la distribución del registro público para que las bandas tengan significado percentil; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe un límite «En riesgo» de 34.

94
Excepcional93-100El nivel más alto del registro (≈ el 5% superior); cumple prácticamente todos los criterios evaluados
Excelente80-92Sólido en todos los frentes; carencias menores
Bueno65-79Saludable; carencias limitadas y manejables
Moderado50-64Aceptable con carencias notables; se recomienda revisión
Débil35-49Debilidades sustanciales en varias áreas
En riesgo20-34Debilidades significativas; su adopción exige cautela
Crítico1-19Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
VitalidadComunidad yAdopciónSostenibilidady GobernanzaCalidad deIngenieríaSeguridadPreparaciónpara IA

Perfil de puntuación

Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.

El resultado global ponderado 81 se calibra a 94 en la escala publicada del índice (calibración del registro 2026-08-02).

Titularidad

LudwigOrganización
176 seguidores6 repositorios públicosdesde may 2020

Este repositorio está respaldado por una organización: una custodia compartida y responsable que puede sobrevivir a cualquier mantenedor individual.

Ecosistemas de paquetes

RegistroPaqueteVersiónDescargas / mesVersionesÚltima publicaciónEtiquetas
PyPIludwig0.17.8341377hace 0 díascomputer-visiondeep-learningludwigmachine-learningnatural-language-processing

Métricas por categoría

Vitalidad

¿Está vivo el proyecto: se escribe código y se publican versiones?

83Excelente · 21% del índice global
Cómo se puntúa
36/36Recencia de push — último push hace 0 días
8.3/36Cadencia de commits — 12/52 semanas con commits
18/18Volumen de commits — 267 commits en el último año
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
Datos de entrada utilizados
commits_last_year267
human_commit_share0,98
days_since_last_push0
active_weeks_last_year12
Cómo se puntúa
27/27Publica versiones — 73 versiones publicadas
36/36Recencia de las versiones — última versión hace 0 días
27/27Cadencia de publicación — una versión cada ~9 días
0/10OpenSSF Scorecard: Signed-Releases — sin datos
Datos de entrada utilizados
releases_count73
latest_release_tagv0.17.8
releases_from_tagsno
days_since_latest_release0
mean_days_between_releases9
Excluidos de la puntuación (sin datos o no aplicable): OpenSSF Scorecard: Signed-Releases. Los pesos restantes se han renormalizado.

Comunidad y Adopción

¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?

86Excelente · 17% del índice global
Cómo se puntúa
60/60Estrellas — 11.746 estrellas
25/25Forks — 1218 forks
12.6/15Observadores — 183 observadores
Datos de entrada utilizados
forks1218
stars11.746
watchers183
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Cómo se puntúa
22.5/22.5README
22.5/22.5Licencia — licencia reconocida (Apache-2.0)
18/18Guía CONTRIBUTING
13.5/13.5Código de conducta
0/7.2Plantilla de issues
6.3/6.3Plantilla de PR
Datos de entrada utilizados
has_readme
has_license
readme_badges
has_contributing
has_issue_templateno
has_code_of_conduct
readme_badge_services
has_pull_request_template
Cómo se puntúa
47.1/80Descargas mensuales — 3413 descargas/mes en pypi
0/20Dependientes en el registro — no lo informa este ecosistema
Datos de entrada utilizados
packagesludwig
dependents
ecosystemspypi
total_downloads
monthly_downloads3413
Excluidos de la puntuación (sin datos o no aplicable): Dependientes en el registro. Los pesos restantes se han renormalizado.

Sostenibilidad y Gobernanza

¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?

79Bueno · 23% del índice global
Cómo se puntúa
36/54Factor bus — la mitad de los commits recae en 3 contribuyente(s)
17.6/22.5Distribución de commits — el principal contribuyente firma el 22% de los commits
13.5/13.5Amplitud de contribuyentes — 98 contribuyentes
10/10OpenSSF Scorecard: Contributors — project has 17 contributing companies or organizations
Datos de entrada utilizados
bus_factor3
contributors_sampled98
top_contributor_share0,219
Cómo se puntúa
42/42Resolución de issues — 100% de issues cerradas
25.9/30Aceptación de PR — 2588/2999 PR decididos fusionados
0/13Newcomer PR acceptance — ningún PR de un contribuyente primerizo decidido en 30 d
0/15OpenSSF Scorecard: Code-Review — Found 0/28 approved changesets -- score normalized to 0
Datos de entrada utilizados
merged_prs2588
open_issues1
closed_issues1094
prs_merged_7d
prs_decided_7d
prs_merged_30d
prs_decided_30d
issue_closed_ratio0,999
closed_unmerged_prs411
first_time_authors_30d
first_time_prs_merged_30d
first_time_prs_decided_30d
Excluidos de la puntuación (sin datos o no aplicable): newcomer_pr_acceptance. Los pesos restantes se han renormalizado.
Cómo se puntúa
30/30Respaldo de la propiedad — propiedad de una organización
0/20Dominio verificado
16.2/25Alcance del propietario — 176 seguidores de ludwig-ai
18.2/25Trayectoria — 6 repos públicos, cuenta de ~6 años
Datos de entrada utilizados
followers176
owner_typeOrganization
is_verified
owner_loginludwig-ai
public_repos6
account_age_days2261
Cómo se puntúa
25/25Publicado y resoluble — 1 paquete(s) en pypi
35/35Recencia de publicación — última publicación hace 0 días
20/20Historial de versiones — 77 versiones en el registro
20/20No obsoleto — activo, ni obsoleto ni retirado
Datos de entrada utilizados
packagesludwig
ecosystemspypi
any_deprecatedno
min_days_since_publish0

Calidad de Ingeniería

¿Existen unas prácticas mínimas de ingeniería y documentación?

96Excepcional · 19% del índice global
Cómo se puntúa
24/24Flujos de trabajo de CI — 6 flujo(s) de trabajo
24/24Pruebas presentes
16/16Configuración de linter — .flake8
9.6/9.6Hooks de pre-commit
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests — 2 out of 2 merged PRs checked by a CI test -- score normalized to 10
Datos de entrada utilizados
has_ci
has_tests
has_editorconfigno
has_linter_config
has_precommit_config

Documentación

100Excepcional
Cómo se puntúa
30/30README
25/25Directorio de documentación
15/15Sitio de documentación / página del proyecto — http://ludwig.ai
10/10Descripción del repositorio
10/10Topics — 20 topics
10/10Wiki
Datos de entrada utilizados
topicsdeep-learning, deeplearning, deep, learning, machine-learning, machinelearning, natural-language-processing, natural-language, computer-vision, data-centric, data-science, pytorch, neural-network, ml, llm, llm-training, fine-tuning, llama, mistral, llama2
has_wiki
homepagehttp://ludwig.ai
has_readme
has_docs_dir
has_description

Seguridad

¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?

64Moderado · 16% del índice global
Cómo se puntúa
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — sin datos
2.5/2.5CI-Tests — 2 out of 2 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
0/7.5Code-Review — Found 0/28 approved changesets -- score normalized to 0
2.5/2.5Contributors — project has 17 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
2.5/2.5Licencia — license file detected
7.5/7.5Maintained — 30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
5/5Packaging — packaging workflow detected
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
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 — sin datos
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities — 0 existing vulnerabilities detected
Datos de entrada utilizados
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate6,4
Excluidos de la puntuación (sin datos o no aplicable): branch_protection, signed_releases. Los pesos restantes se han renormalizado.

Preparación para IA

¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Tiene un peso deliberadamente pequeño (4%): las herramientas para agentes son una señal real de mantenimiento, pero un repositorio sin ninguna puede alcanzar igualmente 100/100.

52Moderado · 4% del índice global
Cómo se puntúa
0/45Instrucciones para agentes — sin CLAUDE.md / AGENTS.md / reglas de editor
0/15Documentación legible por máquinas (llms.txt)
40/40Historial de commits legible — 90 de 98 commits humanos declaran su intención (asunto estructurado o cuerpo explicativo)
Datos de entrada utilizados
has_llms_txtno
legible_history_share0,918
agent_instruction_files
agent_instruction_max_bytes
Cómo se puntúa
0/18Arranque con un solo comando
22/22Pruebas automatizadas
11/11Configuración de lint / formato — .flake8
11/11Verificación estática de tipos — ludwig/py.typed
10/10Entorno reproducible — devcontainer, Dockerfile
0/10Práctica demostrada con agentes — ningún commit con autoría de agente entre los últimos 100
0/8Mantenimiento automatizado — no se observan actualizaciones automáticas de dependencias
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Datos de entrada utilizados
has_nixno
has_tests
lockfiles
has_dockerfile
typed_languageno
bootstrap_files
has_devcontainer
has_linter_config
typecheck_configsludwig/py.typed
agent_commit_share0
toolchain_manifests
dependency_bot_commit_share0
Cómo se puntúa
27/45Código verificable por tipos — Python con configuración de verificación de tipos (ludwig/py.typed)
54.5/55Tamaños de archivo manejables — 7/805 archivos fuente de más de 60 KB
Datos de entrada utilizados
primary_languagePython
largest_source_bytes107.706
source_files_sampled805
oversized_source_files7
Cómo se puntúa
0/40Esquema de API (OpenAPI/GraphQL/proto)
0/20Servidor MCP
40/40Ejemplos ejecutables — examples, notebooks
Datos de entrada utilizados
example_dirsexamples, notebooks
has_mcp_signalno
api_schema_files

Datos clave

11.746estrellas de GitHub
98contribuidores
267commits en los últimos 12 meses
0días desde el último push
73versiones publicadas
3factor bus
1issues abiertas
PyPIecosistemas de paquetes

Advertencias de recopilación de datos

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • deps.dev does not index pypi:ludwig@0.17.8; advisories assessed against the repository dependency graph instead
  • No resolved dependencies carried a version and a supported ecosystem

Más detalle

Historial de estrellas y forks 0 ★ / 1218 ⇿
0Estrellas
1218Forks
71Versiones

Cuándo se añadió cada estrella y fork, recopilado de GitHub y agrupado por día. El crecimiento acumulado se sitúa justo encima de las adiciones diarias que lo componen, de modo que ambos se leen en conjunto: la acumulación orgánica sostenida no se parece en nada a un pico abrupto y efímero. Cuando esa diferencia es medible, se informa como autenticidad del crecimiento.

Solo se muestra el historial más reciente: este repositorio supera la ventana de recopilación, por lo que no se captura el historial más antiguo.

250500750100012501218582019-022022-112026-07
Mayor 0Menor 8Parche 49

Cada punto abarca 7 días.

OpenSSF Scorecard 6.4 / 10
6.4agregado

Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).Scorecard v5.5.0 · 2026-07-27 00:42 UTC

10Binary-Artifactsno binaries found in the repo
n/dBranch-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-Tests2 out of 2 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
0Code-ReviewFound 0/28 approved changesets -- score normalized to 0
10Contributorsproject has 17 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
10Security-Policysecurity policy file detected
n/dSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
Dependencias directas 40
RegistroPaqueteRestricción de versiónManifiesto
PyPInumpy>=1.24pyproject.toml
PyPIpandas>=2.0pyproject.toml
PyPIscipy>=1.10pyproject.toml
PyPItabulate>=0.9pyproject.toml
PyPIscikit-learn>=1.3pyproject.toml
PyPItqdm>=4.60pyproject.toml
PyPItorch>=2.11pyproject.toml
PyPItorchaudio>=2.11pyproject.toml
PyPItorchcodec>=0.1pyproject.toml
PyPItorchvision>=0.26pyproject.toml
PyPItransformers>=5.0pyproject.toml
PyPIsentencepiece>=0.2pyproject.toml
PyPIspacy>=2.3pyproject.toml
PyPIPyYAML>=6.0pyproject.toml
PyPIabsl-pypyproject.toml
PyPIkagglepyproject.toml
PyPIrequests>=2.28pyproject.toml
PyPIpy-cpuinfopyproject.toml
PyPIfsspecpyproject.toml
PyPIdataclasses-jsonpyproject.toml
PyPIjsonschema>=4.17pyproject.toml
PyPItensorboardpyproject.toml
PyPItorchmetrics>=1.0pyproject.toml
PyPItorchinfopyproject.toml
PyPIfilelockpyproject.toml
PyPIpsutilpyproject.toml
PyPIprotobuf>=4.0pyproject.toml
PyPIgpustatpyproject.toml
PyPIrich>=12.4.4pyproject.toml
PyPIpackagingpyproject.toml
PyPIretrypyproject.toml
PyPIsacremosespyproject.toml
PyPIbitsandbytes>=0.44.0pyproject.toml
PyPIxlwtpyproject.toml
PyPIxlrdpyproject.toml
PyPIopenpyxlpyproject.toml
PyPIpyarrow>=14.0pyproject.toml
PyPIlxmlpyproject.toml
PyPIdatasetspyproject.toml
PyPIsafetensors>=0.4pyproject.toml
Todas las dependencias 32

Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 22 paquetes directos y 10 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.

RegistroPaqueteVersiónRelación
PyPIbitsandbytesdirecta
PyPIjsonschemadirecta
PyPInumpydirecta
PyPIpandasdirecta
PyPIprotobufdirecta
PyPIpyarrowdirecta
PyPIpyyamldirecta
PyPIrequestsdirecta
PyPIrichdirecta
PyPIsafetensorsdirecta
PyPIscikit-learndirecta
PyPIscipydirecta
PyPIsentencepiecedirecta
PyPIspacydirecta
PyPItabulatedirecta
PyPItorchdirecta
PyPItorchaudiodirecta
PyPItorchcodecdirecta
PyPItorchmetricsdirecta
PyPItorchvisiondirecta
PyPItqdmdirecta
PyPItransformersdirecta
PyPIconfigspaceindirecta
PyPIdaskindirecta
PyPIfutureindirecta
PyPImatplotlibindirecta
PyPIpeftindirecta
PyPIpredibaseindirecta
PyPIrayindirecta
PyPIruffindirecta
PyPIs3fsindirecta
PyPItorchaoindirecta
Avisos de dependencias sin evaluar

El cotejo de avisos no pudo ejecutarse para este informe: No resolved dependencies carried a version and a supported ecosystem

Informe JSON sin procesar legible por máquina
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          "tag": "v0.14.1",
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          "tag": "v0.14.0",
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          "tag": "v0.13.0",
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          "published_at": "2026-04-12T06:12:27Z"
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          "body": "torchaudio 2.11 uses torchcodec for all audio I/O, which requires FFmpeg.\nCI was not installing ffmpeg and the torch pin (2.7.1) was below the new\ntorch>=2.11 lower bound, causing pip to upgrade to 2.12.0+cu130 (CUDA build\nfrom default PyPI) instead of the intended CPU build.\n\n- Add ffmpeg to apt-ge\n[…]\norch==2.12.0, torchvision==0.27.0, torchaudio==2.11.0\n- Use --extra-index-url https://download.pytorch.org/whl/cpu on all pip\n  install steps so torchcodec resolves to the +cpu build, not the CUDA one",
          "is_bot": false,
          "headline": "fix: update CI torch pins to 2.12.0 and add ffmpeg for torchcodec",
          "author_name": "w4nderlust",
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          "committed_at": "2026-05-23T01:50:15Z",
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          "body": "- Require torch>=2.11, torchaudio>=2.11, torchvision>=0.26, transformers>=5.0\n  in core deps to match torchao>=0.17.0 (which requires torch>=2.11); fixes\n  LLM fine-tuning crash caused by torchao/torch version mismatch in Docker images\n- Update all four Docker images to pin torch==2.12.0, torchvisio\n[…]\na; torchao>=0.17.0 replaces old >=0.9.0\n- Change AutoML default tabular combiner from tabnet to ft_transformer;\n  add ft_transformer and tabtransformer to combiner_defaults with tuned hyperopt configs",
          "is_bot": false,
          "headline": "chore: bump version to 0.17.2; upgrade torch stack and automl defaults",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-23T00:43:39Z",
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        {
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          "body": "- Extract _NDJSONChannel helper to eliminate _open/_emit copy-paste\n- Use NumpyEncoder instead of default=str for Ludwig JSON convention\n- _NDJSONChannel.__del__ closes file handle if on_hyperopt_end never fires\n- Remove import-inside-method for time in trainer.py and trainer_utils.py\n- Replace logger.info() in SIGUSR1/2 signal handlers with print() to\n  avoid potential deadlock when logging lock is held by main thread\n- Exclude ephemeral ProgressTracker fields from JSON serialization",
          "is_bot": false,
          "headline": "Refactor StudioCallback and trainer pause/resume for correctness",
          "author_name": "w4nderlust",
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          "body": "… bug\n\nTwo root causes for CI failure in test_experiment[1919-0]:\n\n1. eval_loss() double-update bug: for non-MeanMetric eval_loss_metrics\n   (e.g. MSEMetric), calling self.eval_loss_metric(preds, targets) invokes\n   forward() which updates the metric's running state — but update_metrics()\n   already\n[…]\nx: module-scoped single_threaded_blas fixture sets\n   torch.set_num_threads(1) for the duration of test_reproducibility.py,\n   eliminating BLAS non-determinism without affecting the rest of the suite.",
          "is_bot": false,
          "headline": "fix: deterministic reproducibility tests; fix eval_loss double-update…",
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          "body": "Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>",
          "is_bot": true,
          "headline": "[pre-commit.ci] pre-commit suggestions (#4191)",
          "author_name": "pre-commit-ci[bot]",
          "author_login": "pre-commit-ci[bot]",
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          "oid": "45a33bf9c6e8512aa943ad30db197832bf1750ed",
          "body": ".claude/worktrees/ paths were committed as gitlink submodule entries,\nbreaking CI checkout with submodules:recursive. Removed from index\nand added to .gitignore.",
          "is_bot": false,
          "headline": "Remove accidental Claude worktree gitlinks from index",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-18T21:35:03Z",
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          "body": "0.17.0 was tagged before the version bump commit landed, so the\nPyPI build produced ludwig-0.16.2 and was rejected as a duplicate.\nThis patch release also includes:\n- Preprocessing pipeline hardening for output features without\n  preprocessing config (e.g. anomaly type)\n- StudioCallback for Ludwig Studio metrics/hyperopt integration\n- Per-call callbacks on train() and hyperopt ray-free hardening",
          "is_bot": false,
          "headline": "chore: bump version to 0.17.1",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
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          "body": "…ssing config\n\nOutput features like `anomaly` do not include a `preprocessing` key in their\nconfig dict (unlike all standard input/output features). This caused KeyErrors\nin four places in the preprocessing pipeline:\n\n- build_preprocessing_parameters: skip features with no PREPROCESSING key\n- build_\n[…]\nin the metric registry) that prevents training; these fixes are\ndefensive guards that prevent the preprocessing stage from crashing on any\nfuture output feature type that omits a preprocessing config.",
          "is_bot": false,
          "headline": "Fix preprocessing pipeline crash for output features without preproce…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
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          "body": "…onments\n\n- Add callbacks parameter to LudwigModel.train() so per-call callbacks can\n  be merged with model-level callbacks without rebuilding the model\n- Make ray imports in execution.py optional so OptunaExecutor works without ray\n- Add on_hyperopt_trial_start/end dispatch in OptunaExecutor so callbacks\n  receive trial lifecycle events during optuna-based hyperopt runs",
          "is_bot": false,
          "headline": "Add per-call callbacks to train(), harden hyperopt for ray-free envir…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-18T06:30:25Z",
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          "oid": "44512d165418a86c0378cf3dd1b1ef7fbf7c2fb7",
          "body": "- Rename ludwig/callbacks.py → ludwig/callbacks/__init__.py so that\n  ludwig.callbacks.studio is importable as a submodule\n- Add on_hyperopt_trial_start / on_hyperopt_trial_end / on_hyperopt_end\n  hooks to StudioCallback: write trial_start, trial_end, hyperopt_end\n  events to <group_output_dir>/trials.jsonl for Ludwig Studio to stream\n- Add optional group_id / group_output_dir constructor params\n- Track best_eval_metric_value per trial via on_epoch_end for reporting",
          "is_bot": false,
          "headline": "Convert callbacks.py to package, add hyperopt hooks to StudioCallback",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-18T01:24:05Z",
          "body_truncated": false,
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        },
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          "oid": "5663a5d226c58e823ce269d098efb02bfad83888",
          "body": "StudioCallback (ludwig/callbacks/studio.py):\n- Built-in Ludwig callback for Ludwig Studio integration\n- Streams lifecycle events (phase transitions, per-epoch metrics) to\n  <output_dir>/metrics.jsonl (line-buffered NDJSON)\n- Emits progress_pct and eta_seconds on every metric event using the\n  new Pr\n[…]\n\n- Register SIGUSR2 handler: resume from SIGUSR1 pause\n- Initialize _training_paused=False in __init__\n- Restore SIG_DFL at end of training\n- Populate progress_tracker fields after batcher initializes",
          "is_bot": false,
          "headline": "Add StudioCallback, SIGUSR1/2 pause-resume, enhanced ProgressTracker",
          "author_name": "w4nderlust",
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          "committed_at": "2026-05-18T01:09:54Z",
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          "oid": "5003d760d08ec0fb052b44da2e81b4d076b5524c",
          "body": "… (CWE-502) (#4190)",
          "is_bot": false,
          "headline": "fix(security): remove pickle from auto-dispatch and harden torch.load…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T19:05:10Z",
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          "body": "…#4189)",
          "is_bot": false,
          "headline": "fix(api): remove stale type: ignore and dead tuple check in train() (…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T17:41:55Z",
          "body_truncated": false,
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          "oid": "7c526ab6beda3a965c36af1d618f98a366583575",
          "body": "…guide (#4187)",
          "is_bot": false,
          "headline": "docs: fix stale BaseFeatureMixin references in adding_a_feature_type …",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T17:11:14Z",
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          "oid": "0512de059c33a3dce0185ed5f0c81e3752fb81b6",
          "body": null,
          "is_bot": false,
          "headline": "refactor: full codebase improvement plan (Phase 0–7) (#4186)",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-17T07:53:31Z",
          "body_truncated": false,
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        },
        {
          "oid": "0cd82690695d1930deafd59563470c4b90ccbb2b",
          "body": "…; fix visualize types\n\n- Add name() and description() classmethods to VeRA, LoHa, LoKr, FourierFT, BOFT adapter configs\n- Annotate **kwargs: Any in api.py public methods and kfold_cross_validate\n- Annotate hyperopt_hiplot_cli/hyperopt_hiplot with full type signatures\n- Add dict annotation to metadata parameter in confidence_thresholding_2thresholds_{2,3}d",
          "is_bot": false,
          "headline": "fix(docs): add name/description to adapter schemas; annotate **kwargs…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T20:48:52Z",
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          "oid": "ec46fa5fa37c971ce575d227dc7de92ff980c14b",
          "body": null,
          "is_bot": false,
          "headline": "chore: bump version to 0.17.0",
          "author_name": "w4nderlust",
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          "committed_at": "2026-05-16T20:30:03Z",
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        },
        {
          "oid": "b6b6bc917d8af755263ba6f497a421d22e794806",
          "body": "…hed memmap (#4173)",
          "is_bot": false,
          "headline": "feat(data): preprocessing mode enum + prefetch_size config + lazy_cac…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T19:14:03Z",
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        },
        {
          "oid": "84d180d796860fc71780a3e930a0f7875d8928ef",
          "body": "Two root-cause fixes that together bring lazy-decode throughput to\nnear-eager performance:\n\n1. Audio FBANK over-subscription fix\n   LazyColumn for audio now uses max(1, cpu_count // torch_threads)\n   workers instead of the previous default of min(16, cpu_count+4).\n   FBANK is CPU-bound and already u\n[…]\n:           already 99.9% util (async reader was correct)\n\nAlso add:\n- 30 unit tests in tests/ludwig/data/test_prefetch_batcher.py\n- scripts/benchmark_training_pipeline.py for per-step timing analysis",
          "is_bot": false,
          "headline": "feat(data): prefetch background decoder for lazy audio/image features",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T05:21:55Z",
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        },
        {
          "oid": "991efde40be4c8275164a59c8b1f121176a11f39",
          "body": "…ession tests and benchmark\n\n- _with_lazy_decode now emits a WARNING (not silent skip) when lazy=True but\n  lazy_audio_params / lazy_image_params is absent in training_set_metadata,\n  so stale preprocessing caches fail loudly rather than silently passing path\n  strings to workers.\n- Add parametrized\n[…]\nnchmark_lazy_decode.py to measure throughput across\n  eager_local / lazy_local / eager_ray / lazy_ray paths; confirms lazy=False\n  (eager_ray) is unaffected by the decode pipeline change (~3 700 sps).",
          "is_bot": false,
          "headline": "fix(ray): warn on missing lazy_audio/image_params; add lazy-mode regr…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T04:34:22Z",
          "body_truncated": true,
          "is_coding_agent": false
        },
        {
          "oid": "39a774cd2df5828163a85b7a6ac9d8adddea699c",
          "body": "… training actors\n\nLazy audio/image features store file paths in the dataset. PandasDataset wraps\nthese with LazyColumn objects so local training decodes per-batch. RayDataset had\nno equivalent, so Ray workers received raw path strings instead of tensors. The\nbatcher then tried to np.stack strings, \n[…]\naset — which is the whole point of lazy=True.\n\nAlso remove the workaround lazy=False from audio_feature() test helper; with the\nfix, lazy=True (schema default) works correctly in distributed training.",
          "is_bot": false,
          "headline": "fix(ray): decode lazy media features in Ray data pipeline, not inside…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T04:00:47Z",
          "body_truncated": true,
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        },
        {
          "oid": "c6b3b1fd5c10fa1cc0a34ebb9cc670094b5bb264",
          "body": "…ributed tests 6x (#4172)",
          "is_bot": false,
          "headline": "test(ci): rename integration groups to sequential letters, split dist…",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T02:56:15Z",
          "body_truncated": false,
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        },
        {
          "oid": "4e94923dde41f7ab97b91d73dcc40a8b7cca2f90",
          "body": null,
          "is_bot": false,
          "headline": "feat(data): lazy preprocessing for audio and image features (#4171)",
          "author_name": "Piero Molino",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-16T00:22:30Z",
          "body_truncated": false,
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        },
        {
          "oid": "0743a27c15a0b0df9f606529c39fe983cdde5893",
          "body": "…r in load_pretrained_from_config\n\ntorchao>=0.9.0 calls torch.utils._pytree.register_constant() at class-definition\ntime (via @register_as_pytree_constant), which was added in PyTorch 2.7.0. Users\non PyTorch 2.6.x get an AttributeError the moment transformers imports the torchao\nquantizer module — e\n[…]\nient HuggingFace Hub download failures; catching\n  all Exception was causing 8 retries over ~2.5 minutes before surfacing the real\n  error (AttributeError from the broken torchao import).\n\nFixes #4170",
          "is_bot": false,
          "headline": "fix(llm): require torch>=2.7 with llm extra; stop retrying non-OSErro…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T04:11:22Z",
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        },
        {
          "oid": "910a45ed6aaa16e332b9dc3da38beab9ffd0c88d",
          "body": "…ed data\n\nPrevents OOM on image datasets (e.g. rendered_sst2) where streaming\n40k images into a large shuffle buffer exhausted all available RAM.\nSequential sampling from skip position is sufficient for diversity.\n\nAlso marks intentionally-deleted dataset configs as skipped in results.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): use buffer=1 for diversity-retry skip-sampl…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:37:45Z",
          "body_truncated": false,
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        },
        {
          "oid": "426f348ff761cb586c040ccdda419b2ef6a563e4",
          "body": "…t outputs\n\nNatural Questions rows are ~1MB each; a 10k buffer wastes 10GB RAM.\nText/number output datasets have no minimum-diversity requirement, so\na 2k shuffle buffer is sufficient while keeping memory usage bounded.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): reduce default shuffle buffer to 2k for tex…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:16:31Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "a17f0b756b7adc3e9e1cbb55c194c686baa31103",
          "body": "…ification outputs\n\n100k buffer rows for text/number outputs (NQ, ASR, etc.) wastes RAM and time.\nOnly use the 100k buffer when output features are category/binary (need label\ndiversity in sorted datasets). Media datasets always use 5k.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): smart shuffle buffer — large only for class…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:14:55Z",
          "body_truncated": false,
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        },
        {
          "oid": "427279981b479dfa138b5fcf3c275117fa7bcac6",
          "body": null,
          "is_bot": false,
          "headline": "fix(datasets): change peoples_speech duration_ms from category to number",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:09:47Z",
          "body_truncated": false,
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        },
        {
          "oid": "92e1b20cac811b9a5a12173f39e6750417f90748",
          "body": "The GLUE ax diagnostic split only has a test split with all labels=-1\n(benchmark labels are hidden). Unusable for training smoke tests.",
          "is_bot": false,
          "headline": "fix(datasets): remove glue_diagnostic config (hidden test labels)",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:06:00Z",
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        },
        {
          "oid": "2c83326c57c879a7d7aeb8ec416e34264193f18c",
          "body": "…on datasets\n\nWhen an output category/binary column has only 1 distinct value after sampling\n(dataset is sorted by label), retry by skipping 40k rows into the stream and\ntaking a second half-sample from a different label region. Handles cases like\nrendered_sst2 (SST-2 as images, sorted: all negatives first then positives).\n\nAlso adds skip parameter to stream_sample() for targeted offset sampling.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): add diversity retry for sorted classificati…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:05:25Z",
          "body_truncated": false,
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        },
        {
          "oid": "e596e904ef2121cb901799c825a6e95df3074d51",
          "body": "Audio and image datasets stream large files — use a 5k buffer to avoid\nstreaming 100k large files. Text datasets use 100k buffer to ensure\nlabel diversity in sorted classification datasets (dbpedia_14, imdb, etc).",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): use media-aware shuffle buffer size",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T03:00:51Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "de8bb8d16bada7382cf125e3308a086bd8328afd",
          "body": "…iversity\n\nSorted datasets like imdb (25k rows/class), rotten_tomatoes (5k/class),\nand dbpedia_14 (40k/class) require a larger shuffle buffer to ensure\nat least 2 distinct label values appear in the 1000-row sample.",
          "is_bot": false,
          "headline": "fix(datasets/smoke-test): increase shuffle buffer to 100k for label d…",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T02:58:56Z",
          "body_truncated": false,
          "is_coding_agent": false
        },
        {
          "oid": "6ae10bbea70483a438570848f5ae7a5300c899f9",
          "body": "- Fix CauldronVQALoader._transform(): add default split_name=\"train\" so\n  apply_custom_loader() can call it without positional arg\n- Fix NewYorkerCaptionContestLoader._transform(): same default arg fix\n- Fix NaturalQuestionsLoader: safe indexing when short_answers is []\n- Fix OpenBookQALoader: remov\n[…]\nor streaming smoke tests:\n  fsd50k (labels always null), legalbench (all subsets <32 rows),\n  mmmu (5 rows per split), gift_eval (streaming schema incompatible),\n  samsum (dataset removed from HF Hub)",
          "is_bot": false,
          "headline": "fix(datasets): fix loaders and configs for smoke test failures",
          "author_name": "w4nderlust",
          "author_login": "w4nderlust",
          "committed_at": "2026-05-14T02:58:17Z",
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        },
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          "headline": "refactor: split 4144-line visualize.py into domain-scoped visualize/ …",
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            "key": "maintainer_resilience",
            "band": "good",
            "name": "Maintainer resilience (bus factor)",
            "note": null,
            "notes": [],
            "value": 77,
            "inputs": {
              "bus_factor": 3,
              "contributors_sampled": 98,
              "top_contributor_share": 0.219
            },
            "components": [
              {
                "key": "bus_factor",
                "name": "Bus factor",
                "detail": "3 contributor(s) cover half of all commits",
                "points": 36,
                "status": "partial",
                "details": [
                  {
                    "code": "bus_factor",
                    "params": {
                      "count": 3
                    }
                  }
                ],
                "max_points": 54
              },
              {
                "key": "commit_distribution",
                "name": "Commit distribution",
                "detail": "top contributor authored 22% of commits",
                "points": 17.6,
                "status": "partial",
                "details": [
                  {
                    "code": "top_contributor_share",
                    "params": {
                      "share": 22
                    }
                  }
                ],
                "max_points": 22.5
              },
              {
                "key": "contributor_breadth",
                "name": "Contributor breadth",
                "detail": "98 contributors",
                "points": 13.5,
                "status": "met",
                "details": [
                  {
                    "code": "contributors_sampled",
                    "params": {
                      "count": 98
                    }
                  }
                ],
                "max_points": 13.5
              },
              {
                "key": "openssf_scorecard_contributors",
                "name": "OpenSSF Scorecard: Contributors",
                "detail": "project has 17 contributing companies or organizations",
                "points": 10,
                "status": "met",
                "details": [],
                "max_points": 10
              }
            ]
          },
          {
            "key": "responsiveness",
            "band": "good",
            "name": "Issue & PR responsiveness",
            "note": "Excluded from scoring (no data or not applicable): Newcomer PR acceptance. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "newcomer_pr_acceptance"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 78,
            "inputs": {
              "merged_prs": 2588,
              "open_issues": 1,
              "closed_issues": 1094,
              "prs_merged_7d": null,
              "prs_decided_7d": null,
              "prs_merged_30d": null,
              "prs_decided_30d": null,
              "issue_closed_ratio": 0.999,
              "closed_unmerged_prs": 411,
              "first_time_authors_30d": null,
              "first_time_prs_merged_30d": null,
              "first_time_prs_decided_30d": null
            },
            "components": [
              {
                "key": "issue_resolution",
                "name": "Issue resolution",
                "detail": "100% of issues closed",
                "points": 42,
                "status": "partial",
                "details": [
                  {
                    "code": "issues_closed_share",
                    "params": {
                      "share": 100
                    }
                  }
                ],
                "max_points": 42
              },
              {
                "key": "pr_acceptance",
                "name": "PR acceptance",
                "detail": "2588/2999 decided PRs merged",
                "points": 25.9,
                "status": "partial",
                "details": [
                  {
                    "code": "decided_prs_merged",
                    "params": {
                      "merged": 2588,
                      "decided": 2999
                    }
                  }
                ],
                "max_points": 30
              },
              {
                "key": "newcomer_pr_acceptance",
                "name": "Newcomer PR acceptance",
                "detail": "no first-time contributor's PR decided in 30d",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_newcomer_prs",
                    "params": {
                      "days": 30
                    }
                  }
                ],
                "max_points": 13
              },
              {
                "key": "openssf_scorecard_code_review",
                "name": "OpenSSF Scorecard: Code-Review",
                "detail": "Found 0/28 approved changesets -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              }
            ]
          },
          {
            "key": "stewardship",
            "band": "moderate",
            "name": "Ownership & stewardship",
            "note": null,
            "notes": [],
            "value": 64,
            "inputs": {
              "followers": 176,
              "owner_type": "Organization",
              "is_verified": null,
              "owner_login": "ludwig-ai",
              "public_repos": 6,
              "account_age_days": 2261
            },
            "components": [
              {
                "key": "ownership_backing",
                "name": "Ownership backing",
                "detail": "organization-owned",
                "points": 30,
                "status": "met",
                "details": [
                  {
                    "code": "owner_organization",
                    "params": {}
                  }
                ],
                "max_points": 30
              },
              {
                "key": "verified_domain",
                "name": "Verified domain",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 20
              },
              {
                "key": "owner_reach",
                "name": "Owner reach",
                "detail": "176 followers of ludwig-ai",
                "points": 16.2,
                "status": "partial",
                "details": [
                  {
                    "code": "owner_followers",
                    "params": {
                      "count": 176,
                      "login": "ludwig-ai"
                    }
                  }
                ],
                "max_points": 25
              },
              {
                "key": "track_record",
                "name": "Track record",
                "detail": "6 public repos, account ~6 yr old",
                "points": 18.2,
                "status": "partial",
                "details": [
                  {
                    "code": "public_repos",
                    "params": {
                      "count": 6
                    }
                  },
                  {
                    "code": "account_age_years",
                    "params": {
                      "years": 6
                    }
                  }
                ],
                "max_points": 25
              }
            ]
          },
          {
            "key": "package_maintenance",
            "band": "exceptional",
            "name": "Package maintenance",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "packages": [
                "ludwig"
              ],
              "ecosystems": "pypi",
              "any_deprecated": false,
              "min_days_since_publish": 0
            },
            "components": [
              {
                "key": "published_resolvable",
                "name": "Published & resolvable",
                "detail": "1 package(s) on pypi",
                "points": 25,
                "status": "met",
                "details": [
                  {
                    "code": "packages_published",
                    "params": {
                      "count": 1,
                      "ecosystems": "pypi"
                    }
                  }
                ],
                "max_points": 25
              },
              {
                "key": "publish_recency",
                "name": "Publish recency",
                "detail": "latest publish 0 days ago",
                "points": 35,
                "status": "met",
                "details": [
                  {
                    "code": "publish_recency",
                    "params": {
                      "days": 0
                    }
                  }
                ],
                "max_points": 35
              },
              {
                "key": "version_history",
                "name": "Version history",
                "detail": "77 published versions",
                "points": 20,
                "status": "met",
                "details": [
                  {
                    "code": "published_versions",
                    "params": {
                      "count": 77
                    }
                  }
                ],
                "max_points": 20
              },
              {
                "key": "not_deprecated",
                "name": "Not deprecated",
                "detail": "active, not deprecated or yanked",
                "points": 20,
                "status": "met",
                "details": [
                  {
                    "code": "package_not_deprecated",
                    "params": {}
                  }
                ],
                "max_points": 20
              }
            ]
          }
        ],
        "description": "Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?"
      },
      {
        "key": "engineering",
        "band": "exceptional",
        "name": "Engineering Quality",
        "value": 96,
        "weight": 0.19,
        "metrics": [
          {
            "key": "engineering_practices",
            "band": "exceptional",
            "name": "Engineering practices",
            "note": null,
            "notes": [],
            "value": 94,
            "inputs": {
              "has_ci": true,
              "has_tests": true,
              "has_editorconfig": false,
              "has_linter_config": true,
              "has_precommit_config": true
            },
            "components": [
              {
                "key": "ci_workflows",
                "name": "CI workflows",
                "detail": "6 workflow(s)",
                "points": 24,
                "status": "met",
                "details": [
                  {
                    "code": "ci_workflows",
                    "params": {
                      "count": 6
                    }
                  }
                ],
                "max_points": 24
              },
              {
                "key": "tests_present",
                "name": "Tests present",
                "detail": null,
                "points": 24,
                "status": "met",
                "details": [],
                "max_points": 24
              },
              {
                "key": "linter_config",
                "name": "Linter config",
                "detail": ".flake8",
                "points": 16,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": ".flake8"
                    }
                  }
                ],
                "max_points": 16
              },
              {
                "key": "pre_commit_hooks",
                "name": "Pre-commit hooks",
                "detail": null,
                "points": 9.6,
                "status": "met",
                "details": [],
                "max_points": 9.6
              },
              {
                "key": "editorconfig",
                "name": ".editorconfig",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 6.4
              },
              {
                "key": "openssf_scorecard_ci_tests",
                "name": "OpenSSF Scorecard: CI-Tests",
                "detail": "2 out of 2 merged PRs checked by a CI test -- score normalized to 10",
                "points": 20,
                "status": "met",
                "details": [],
                "max_points": 20
              }
            ]
          },
          {
            "key": "documentation",
            "band": "exceptional",
            "name": "Documentation",
            "note": null,
            "notes": [],
            "value": 100,
            "inputs": {
              "topics": [
                "deep-learning",
                "deeplearning",
                "deep",
                "learning",
                "machine-learning",
                "machinelearning",
                "natural-language-processing",
                "natural-language",
                "computer-vision",
                "data-centric",
                "data-science",
                "pytorch",
                "neural-network",
                "ml",
                "llm",
                "llm-training",
                "fine-tuning",
                "llama",
                "mistral",
                "llama2"
              ],
              "has_wiki": true,
              "homepage": "http://ludwig.ai",
              "has_readme": true,
              "has_docs_dir": true,
              "has_description": true
            },
            "components": [
              {
                "key": "readme",
                "name": "README",
                "detail": null,
                "points": 30,
                "status": "met",
                "details": [],
                "max_points": 30
              },
              {
                "key": "documentation_directory",
                "name": "Documentation directory",
                "detail": null,
                "points": 25,
                "status": "met",
                "details": [],
                "max_points": 25
              },
              {
                "key": "documentation_homepage_site",
                "name": "Documentation / homepage site",
                "detail": "http://ludwig.ai",
                "points": 15,
                "status": "met",
                "details": [],
                "max_points": 15
              },
              {
                "key": "repository_description",
                "name": "Repository description",
                "detail": null,
                "points": 10,
                "status": "met",
                "details": [],
                "max_points": 10
              },
              {
                "key": "topics",
                "name": "Topics",
                "detail": "20 topics",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "topics_count",
                    "params": {
                      "count": 20
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "wiki",
                "name": "Wiki",
                "detail": null,
                "points": 10,
                "status": "met",
                "details": [],
                "max_points": 10
              }
            ]
          }
        ],
        "description": "Are baseline engineering and documentation practices in place?"
      },
      {
        "key": "security",
        "band": "moderate",
        "name": "Security",
        "value": 64,
        "weight": 0.16,
        "metrics": [
          {
            "key": "security_posture",
            "band": "moderate",
            "name": "Security posture",
            "note": "Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.",
            "notes": [
              {
                "code": "excluded_no_data",
                "params": {
                  "components": [
                    "branch_protection",
                    "signed_releases"
                  ]
                }
              },
              {
                "code": "weights_renormalized",
                "params": {}
              }
            ],
            "value": 64,
            "inputs": {
              "source": "openssf_scorecard",
              "checks_evaluated": 16,
              "scorecard_version": "v5.5.0",
              "checks_inconclusive": 2,
              "scorecard_aggregate": 6.4
            },
            "components": [
              {
                "key": "binary_artifacts",
                "name": "Binary-Artifacts",
                "detail": "no binaries found in the repo",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "branch_protection",
                "name": "Branch-Protection",
                "detail": "internal 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",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 7.5
              },
              {
                "key": "ci_tests",
                "name": "CI-Tests",
                "detail": "2 out of 2 merged PRs checked by a CI test -- score normalized to 10",
                "points": 2.5,
                "status": "met",
                "details": [],
                "max_points": 2.5
              },
              {
                "key": "cii_best_practices",
                "name": "CII-Best-Practices",
                "detail": "no effort to earn an OpenSSF best practices badge detected",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 2.5
              },
              {
                "key": "code_review",
                "name": "Code-Review",
                "detail": "Found 0/28 approved changesets -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "contributors",
                "name": "Contributors",
                "detail": "project has 17 contributing companies or organizations",
                "points": 2.5,
                "status": "met",
                "details": [],
                "max_points": 2.5
              },
              {
                "key": "dangerous_workflow",
                "name": "Dangerous-Workflow",
                "detail": "no dangerous workflow patterns detected",
                "points": 10,
                "status": "met",
                "details": [],
                "max_points": 10
              },
              {
                "key": "dependency_update_tool",
                "name": "Dependency-Update-Tool",
                "detail": "update tool detected",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "fuzzing",
                "name": "Fuzzing",
                "detail": "project is not fuzzed",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "license",
                "name": "License",
                "detail": "license file detected",
                "points": 2.5,
                "status": "met",
                "details": [],
                "max_points": 2.5
              },
              {
                "key": "maintained",
                "name": "Maintained",
                "detail": "30 commit(s) and 9 issue activity found in the last 90 days -- score normalized to 10",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "packaging",
                "name": "Packaging",
                "detail": "packaging workflow detected",
                "points": 5,
                "status": "met",
                "details": [],
                "max_points": 5
              },
              {
                "key": "pinned_dependencies",
                "name": "Pinned-Dependencies",
                "detail": "dependency not pinned by hash detected -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "sast",
                "name": "SAST",
                "detail": "SAST tool is not run on all commits -- score normalized to 0",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 5
              },
              {
                "key": "security_policy",
                "name": "Security-Policy",
                "detail": "security policy file detected",
                "points": 5,
                "status": "met",
                "details": [],
                "max_points": 5
              },
              {
                "key": "signed_releases",
                "name": "Signed-Releases",
                "detail": "no releases found",
                "points": 0,
                "status": "excluded",
                "details": [
                  {
                    "code": "no_data",
                    "params": {}
                  }
                ],
                "max_points": 7.5
              },
              {
                "key": "token_permissions",
                "name": "Token-Permissions",
                "detail": "detected GitHub workflow tokens with excessive permissions",
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 7.5
              },
              {
                "key": "vulnerabilities",
                "name": "Vulnerabilities",
                "detail": "0 existing vulnerabilities detected",
                "points": 7.5,
                "status": "met",
                "details": [],
                "max_points": 7.5
              }
            ]
          },
          {
            "key": "high_risk_jurisdiction_exposure",
            "band": "exceptional",
            "name": "High-Risk Jurisdiction Exposure",
            "note": "Only high-confidence self-published location evidence affects this multiplier. Ambiguous matches are review-only; country evidence is not proof of nationality, citizenship, legal registration, malicious intent, or sanctions status.",
            "notes": [
              {
                "code": "jurisdiction_evidence_limits",
                "params": {}
              }
            ],
            "value": 100,
            "inputs": {
              "meaning": "self-published location evidence; not nationality or citizenship",
              "red_flag": false,
              "exposures": [],
              "policy_countries": [
                "Russia",
                "Iran",
                "North Korea"
              ],
              "commit_weight_rule": {
                "min_commits": 50,
                "min_commit_share": 0.1
              },
              "review_only_matches": 0,
              "below_threshold_exposures": [],
              "assessed_self_published_locations": 8
            },
            "components": [
              {
                "key": "policy_exposure_multiplier",
                "name": "Policy exposure multiplier",
                "detail": "no confirmed policy-scope location match",
                "points": 100,
                "status": "met",
                "details": [
                  {
                    "code": "jurisdiction_no_match",
                    "params": {}
                  }
                ],
                "max_points": 100
              }
            ]
          }
        ],
        "description": "Are visible security and supply-chain practices strong, with no malicious dependency and no unresolved high-risk jurisdiction exposure?"
      },
      {
        "key": "ai_readiness",
        "band": "moderate",
        "name": "AI Readiness",
        "value": 52,
        "weight": 0.04,
        "metrics": [
          {
            "key": "ai_agent_context",
            "band": "weak",
            "name": "Agent context & guidance",
            "note": null,
            "notes": [],
            "value": 40,
            "inputs": {
              "has_llms_txt": false,
              "legible_history_share": 0.918,
              "agent_instruction_files": [],
              "agent_instruction_max_bytes": null
            },
            "components": [
              {
                "key": "agent_instructions",
                "name": "Agent instructions",
                "detail": "no CLAUDE.md / AGENTS.md / editor rules",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_instructions",
                    "params": {}
                  }
                ],
                "max_points": 45
              },
              {
                "key": "machine_readable_docs_llms_txt",
                "name": "Machine-readable docs (llms.txt)",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 15
              },
              {
                "key": "legible_commit_history",
                "name": "Legible commit history",
                "detail": "90 of 98 human commits state their intent (structured subject or explanatory body)",
                "points": 40,
                "status": "met",
                "details": [
                  {
                    "code": "legible_history",
                    "params": {
                      "legible": 90,
                      "sampled": 98
                    }
                  }
                ],
                "max_points": 40
              }
            ]
          },
          {
            "key": "ai_verify_loop",
            "band": "moderate",
            "name": "Verify loop (build / test / typecheck)",
            "note": null,
            "notes": [],
            "value": 54,
            "inputs": {
              "has_nix": false,
              "has_tests": true,
              "lockfiles": [],
              "has_dockerfile": true,
              "typed_language": false,
              "bootstrap_files": [],
              "has_devcontainer": true,
              "has_linter_config": true,
              "typecheck_configs": [
                "ludwig/py.typed"
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              "agent_commit_share": 0,
              "toolchain_manifests": [],
              "dependency_bot_commit_share": 0
            },
            "components": [
              {
                "key": "one_command_bootstrap",
                "name": "One-command bootstrap",
                "detail": null,
                "points": 0,
                "status": "missed",
                "details": [],
                "max_points": 18
              },
              {
                "key": "automated_tests",
                "name": "Automated tests",
                "detail": null,
                "points": 22,
                "status": "met",
                "details": [],
                "max_points": 22
              },
              {
                "key": "lint_format_config",
                "name": "Lint / format config",
                "detail": ".flake8",
                "points": 11,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": ".flake8"
                    }
                  }
                ],
                "max_points": 11
              },
              {
                "key": "static_type_checking",
                "name": "Static type checking",
                "detail": "ludwig/py.typed",
                "points": 11,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "ludwig/py.typed"
                    }
                  }
                ],
                "max_points": 11
              },
              {
                "key": "reproducible_environment",
                "name": "Reproducible environment",
                "detail": "devcontainer, Dockerfile",
                "points": 10,
                "status": "met",
                "details": [
                  {
                    "code": "file_list",
                    "params": {
                      "files": "devcontainer, Dockerfile"
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "demonstrated_agent_practice",
                "name": "Demonstrated agent practice",
                "detail": "no agent-authored commits among the last 100",
                "points": 0,
                "status": "missed",
                "details": [
                  {
                    "code": "no_agent_authored_commits",
                    "params": {
                      "sampled": 100
                    }
                  }
                ],
                "max_points": 10
              },
              {
                "key": "automated_maintenance",
                "name": "Automated maintenance",
                "detail": "no automated dependency updates observed",
                "points": 0,
                "status": "missed",
                "details": [
                  {
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            "notes": [],
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            },
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        ],
        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight: agent tooling is a real maintenance signal, but its absence must never gate the top of the scale (calibration saturates at raw 91, so 100/100 remains reachable with AI Readiness at zero)."
      }
    ],
    "classification": {
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        "library"
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    "metrics_version": "2.5.0"
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  "warnings": [
    "Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token",
    "deps.dev does not index pypi:ludwig@0.17.8; advisories assessed against the repository dependency graph instead",
    "No resolved dependencies carried a version and a supported ecosystem"
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  "report_type": "repository",
  "generated_at": "2026-07-27T00:43:05.617223Z",
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  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/l/ludwig-ai/ludwig.svg",
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}

Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.

Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v2.5.0, esquema v0.27.0 — metodología completa · wiki de métricas.

Cómo se sitúa un resultado dentro del registro general: estadísticas agregadasPyPI.