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Software-GesundheitsberichtSchema 0.16.0 · Metriken 1.13.0 · 2026-07-21 03:12 UTC

PaddlePaddle / Paddle

PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

C++ · PythonApache-2.0★ 24.022 Sterne⑂ 6.010 Forksseit Aug. 2016Auf GitHub ansehen ↗

PaddlePaddle/Paddle erreicht einen Gesundheitsindex von 86 von 100 und liegt damit im Bereich Exzellent. Am stärksten schneidet es bei Engineering Quality (100/100) ab, am schwächsten bei Security (44/100). Zuletzt heute aktualisiert. 20 Mitwirkende tragen den Großteil der jüngsten Arbeit.

86
gesamt / 100
Exzellent

Software-Gesundheitsindex

Metriken werden auf einer Skala von 1–100 in gewichtete Kategorien gruppiert. Der Gesamtwert beginnt als ihr Mittel; sobald öffentliche Evidenz die Richtlinie für Hochrisikojurisdiktionen auslöst, wird die Bewertung angepasst und erhält die Obergrenze 49 (Gefährdet). AI Readiness liegt außerhalb.

86
Exzellent85-100Vorbildlich; erfüllt im Wesentlichen alle geprüften Kriterien
Gut70-84Gesund; geringfügige Lücken
Mittel50-69Akzeptabel mit deutlichen Lücken; Überprüfung empfohlen
Gefährdet30-49Erhebliche Schwächen; eine Übernahme erfordert Vorsicht
Kritisch1-29Schwerwiegende Probleme (aufgegeben, nur ein Maintainer, keine Hygiene)
VitalitätCommunity &VerbreitungNachhaltigkeit &GovernanceEngineering-QualitätSicherheitAI Readiness

Bewertungsprofil

Jede Achse ist eine Kategorie. Die Form zählt mehr als der Durchschnitt — ein gesundes Projekt füllt die gesamte Fläche, während ein Profil aus Spitzen und Kratern bedeutet, dass Stärke in einer Dimension Risiken in einer anderen verdeckt.

Eigentümerschaft

PaddlePaddleOrganisation
7.197 Follower109 öffentliche Reposseit Nov. 2016

Dieses Repository wird von einer Organisation getragen — geteilte, rechenschaftspflichtige Trägerschaft, die jeden einzelnen Maintainer überdauern kann.

Metriken nach Kategorie

Vitalität

Lebt das Projekt — wird Code geschrieben und werden Releases ausgeliefert?

93Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 0 Tagen
36/36Commit-Rhythmus — 52/52 Wochen mit Commits
18/18Commit-Volumen — 3.242 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — keine Daten
Verwendete Eingangsdaten
commits_last_year3.242
human_commit_share
days_since_last_push0
active_weeks_last_year52
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 69 Releases veröffentlicht
27/36Release-Aktualität — letztes Release vor 170 Tagen
19.8/27Release-Rhythmus — ein Release etwa alle 102,3 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count69
latest_release_tagv3.3.0
releases_from_tagsnein
days_since_latest_release170
mean_days_between_releases102,3

Community & Verbreitung

Hat das Projekt Nutzer, Downloads, Aufmerksamkeit und ein einladendes Umfeld für Beitragende?

96Exzellent · 18 % des Gesamtindex
Wie die Bewertung erfolgt
60/60Stars — 24.022 Stars
25/25Forks — 6.010 Forks
15/15Watcher — 707 Watcher
Verwendete Eingangsdaten
forks6.010
stars24.022
watchers707
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (Apache-2.0)
18/18CONTRIBUTING-Leitfaden
13.5/13.5Verhaltenskodex
0/7.2Issue-Vorlage
6.3/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductja
has_pull_request_templateja

Nachhaltigkeit & Governance

Überdauert das Projekt die Menschen, die es tragen — Bus-Faktor, Reaktionsfähigkeit, Trägerschaft und Paketpflege?

89Exzellent · 24 % des Gesamtindex
Wie die Bewertung erfolgt
54/54Bus-Faktor — 20 Beitragende decken die Hälfte aller Commits ab
21.1/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 6 % der Commits
13.5/13.5Breite der Beitragenden — 100 Beitragende
0/10OpenSSF Scorecard: Contributors — keine Daten
Verwendete Eingangsdaten
bus_factor20
contributors_sampled100
top_contributor_share0,064
Wie die Bewertung erfolgt
44.3/46.8Issue-Lösungsquote — 95 % der Issues geschlossen
28.6/38.3PR-Annahme — 44.356/59.309 entschiedene PRs gemergt
0/15OpenSSF Scorecard: Code-Review — keine Daten
Verwendete Eingangsdaten
merged_prs44.356
open_issues1.010
closed_issues18.326
issue_closed_ratio0,948
closed_unmerged_prs14.953
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
25/25Reichweite des Inhabers — 7.197 Follower von PaddlePaddle
25/25Kontohistorie — 109 öffentliche Repos, Kontoalter ca. 9 Jahre
Verwendete Eingangsdaten
followers7.197
owner_typeOrganization
is_verified
owner_loginPaddlePaddle
public_repos109
account_age_days3.532

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

100Exzellent · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 51 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration
9.6/9.6Pre-Commit-Hooks
6.4/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — keine Daten
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfigja
has_linter_configja
has_precommit_configja

Dokumentation

100Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — http://www.paddlepaddle.org/
10/10Repository-Beschreibung
10/10Topics — 8 Topics
10/10Wiki
Verwendete Eingangsdaten
topicspaddlepaddle, deep-learning, scalability, machine-learning, neural-network, python, efficiency, distributed-training
has_wikija
homepagehttp://www.paddlepaddle.org/
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

Sind die sichtbaren Sicherheits- und Lieferkettenpraktiken belastbar, ohne ungeklärte Exposition gegenüber Hochrisikojurisdiktionen?

44Gefährdet · 16 % des Gesamtindex

Sicherheitslage

30Gefährdet
Wie die Bewertung erfolgt
30/30Sicherheitsrichtlinie (SECURITY.md)
0/25Dependabot-Konfiguration
0/25Lockfiles für Abhängigkeiten
0/20CodeQL-Workflow
Verwendete Eingangsdaten
sourcefile_signals
lockfiles
manifestspyproject.toml, python/requirements.txt, setup.py
has_codeql_workflownein
has_security_policyja
has_dependabot_confignein
Wie die Bewertung erfolgt
35/35Direkte Abhängigkeiten ohne bekannte Advisories — keine direkte Abhängigkeit trägt ein bekanntes Advisory
0/25Indirekte Abhängigkeiten ohne bekannte Advisories — transitive Menge in diesem Bereich nicht von Entwicklungs- und Test-Abhängigkeiten trennbar
0/40Keine offenen Advisories — kein Advisory trägt ein Veröffentlichungsdatum
Verwendete Eingangsdaten
sourceosv
advisories14
affected_packages3
assessed_packages13
unassessed_packages28
affected_by_severityhigh 3
direct_affected_packages0
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Indirekte Abhängigkeiten ohne bekannte Advisories, Keine offenen Advisories. Die verbleibenden Gewichte wurden renormalisiert. 13 aufgelöste Abhängigkeiten wurden mit OSV abgeglichen. 28 konnten nicht bewertet werden — keine aufgelöste Version, ein nicht unterstütztes Ökosystem oder außerhalb der ausgewiesenen Paketliste. Dieses Repository veröffentlicht kein Paket, das der Index auflöst; bewertet wurde daher der Abhängigkeitsgraph des Repositorys. Dieser Graph vermischt Entwicklungs- und Test-Pins mit ausgelieferten Abhängigkeiten, daher werden nur die deklarierten Laufzeit-Abhängigkeiten bewertet; transitive Befunde werden als Kontext ausgewiesen und fließen nicht in die Bewertung ein. Erreichbarkeit wird nicht analysiert.

AI Readiness

Wie gut ist das Repository dafür ausgestattet, mit KI-Coding-Agenten entwickelt und gepflegt zu werden? Ein unabhängiges, experimentelles Badge — Gewicht 0,0, es wird eigenständig ausgewiesen und verändert den Gesamt-Gesundheitswert nicht.

57Mittel · 0 % des Gesamtindex
Wie die Bewertung erfolgt
0/45Agentenanweisungen — keine CLAUDE.md / AGENTS.md / Editor-Regeln
0/15Maschinenlesbare Doku (llms.txt)
0/40Lesbare Commit-Historie — keine Daten
Verwendete Eingangsdaten
has_llms_txtnein
legible_history_share
agent_instruction_files
agent_instruction_max_bytes
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Lesbare Commit-Historie. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
0/18Bootstrap mit einem Befehl
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration
11/11Statische Typprüfung — python/paddle/py.typed
10/10Reproduzierbare Umgebung — Dockerfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — keine Daten
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfileja
typed_languageja
bootstrap_files
has_devcontainernein
has_linter_configja
typecheck_configspython/paddle/py.typed
agent_commit_share
toolchain_manifests
dependency_bot_commit_share
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): Belegte Agentenpraxis, Automatisierte Wartung. Die verbleibenden Gewichte wurden renormalisiert.
Wie die Bewertung erfolgt
45/45Typprüfbarer Code — C++ (statisch typisiert)
54.2/55Handhabbare Dateigrößen — 206/13.386 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languageC++
largest_source_bytes329.789
source_files_sampled13.386
oversized_source_files206
Wie die Bewertung erfolgt
40/40API-Schema (OpenAPI/GraphQL/proto) — paddle/cinn/hlir/pe/schedule_param.proto, paddle/cinn/ir/group_schedule/config/tile_config_desc.proto, paddle/cinn/ir/schedule/schedule_desc.proto, paddle/fluid/distributed/ps.proto, paddle/fluid/distributed/the_one_ps.proto, paddle/fluid/framework/data_feed.proto, paddle/fluid/framework/distributed_strategy.proto, paddle/fluid/framework/framework.proto, paddle/fluid/framework/heter_service.proto, paddle/fluid/framework/op_def.proto, paddle/fluid/framework/pass_desc.proto, paddle/fluid/framework/trainer_desc.proto, paddle/fluid/inference/utils/shape_range_info.proto, paddle/fluid/jit/property.proto, paddle/fluid/platform/profiler/dump/nodetree.proto, paddle/phi/api/profiler/profiler.proto, paddle/phi/core/distributed/auto_parallel/auto_parallel.proto, paddle/phi/core/external_error.proto, paddle/phi/core/framework/data_feed.proto, paddle/phi/core/framework/framework.proto, paddle/phi/core/framework/heter_service.proto, paddle/phi/core/framework/pass_desc.proto, paddle/phi/core/framework/trainer_desc.proto
0/20MCP-Server
40/40Lauffähige Beispiele — example
Verwendete Eingangsdaten
example_dirsexample
has_mcp_signalnein
api_schema_filespaddle/cinn/hlir/pe/schedule_param.proto, paddle/cinn/ir/group_schedule/config/tile_config_desc.proto, paddle/cinn/ir/schedule/schedule_desc.proto, paddle/fluid/distributed/ps.proto, paddle/fluid/distributed/the_one_ps.proto, paddle/fluid/framework/data_feed.proto, paddle/fluid/framework/distributed_strategy.proto, paddle/fluid/framework/framework.proto, paddle/fluid/framework/heter_service.proto, paddle/fluid/framework/op_def.proto, paddle/fluid/framework/pass_desc.proto, paddle/fluid/framework/trainer_desc.proto, paddle/fluid/inference/utils/shape_range_info.proto, paddle/fluid/jit/property.proto, paddle/fluid/platform/profiler/dump/nodetree.proto, paddle/phi/api/profiler/profiler.proto, paddle/phi/core/distributed/auto_parallel/auto_parallel.proto, paddle/phi/core/external_error.proto, paddle/phi/core/framework/data_feed.proto, paddle/phi/core/framework/framework.proto, paddle/phi/core/framework/heter_service.proto, paddle/phi/core/framework/pass_desc.proto, paddle/phi/core/framework/trainer_desc.proto

Eckdaten

24.022GitHub-Sterne
100Mitwirkende
3.242Commits, letzte 12 Monate
0Tage seit letztem Push
69Releases
20Bus-Faktor
1.010offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • OpenSSF Scorecard did not return a usable result (2026/07/21 03:12:13 Warning: PATs stored in env variables GITHUB_AUTH_TOKEN and GITHUB_TOKEN differ. Scorecard will use the former.); skipping Scorecard checks

Weitere Details

Alle Abhängigkeiten 41

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 41 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
PyPIapache-tvm-ffi0.1.11indirekt
PyPIautograd1.4indirekt
PyPIcoverage7.10.7indirekt
PyPIdistroindirekt
PyPIgraphvizindirekt
PyPIgymnasiumindirekt
PyPIhttpxindirekt
PyPIhypothesisindirekt
PyPIjinja2indirekt
PyPIlibrosa0.11.0indirekt
PyPImockindirekt
PyPImypy2.1.0indirekt
PyPInetworkxindirekt
PyPInumpyindirekt
PyPInvidia-ml-py3indirekt
PyPIopencv-pythonindirekt
PyPIopt-einsum3.3.0indirekt
PyPIpackagingindirekt
PyPIpaddle2onnxindirekt
PyPIparameterizedindirekt
PyPIpillowindirekt
PyPIprettytableindirekt
PyPIprotobufindirekt
PyPIpybind11-stubgenindirekt
PyPIpygithubindirekt
PyPIpyyamlindirekt
PyPIsafetensorsindirekt
PyPIscipyindirekt
PyPIsetuptoolsindirekt
PyPIsetuptools57.4.0indirekt
PyPIsetuptools69.0.2indirekt
PyPIsoundfileindirekt
PyPItritonindirekt
PyPItyping-extensionsindirekt
PyPIubelt1.4.0indirekt
PyPIvisualdl2.5.3indirekt
PyPIwandbindirekt
PyPIwgetindirekt
PyPIwheel0.45.1indirekt
PyPIxdoctest1.3.0indirekt
PyPIxlsxwriter3.0.9indirekt
Abhängigkeits-Advisories 3

Dieses Repository veröffentlicht kein vom Index auflösbares Paket, daher wurde sein eigener Abhängigkeitsgraph bewertet – 13 Pakete, darunter auch Entwicklungs- und Test-Pins, die nie ausgeliefert werden: 3 tragen bekannte Advisories, davon 0 direkte. 28 konnten nicht bewertet werden – keine aufgelöste Version, ein nicht unterstütztes Ökosystem, oder außerhalb der ausgewiesenen Paketliste.

PaketVersionBeziehungSchweregradAdvisoriesBehoben in
setuptools57.4.0indirekthoch783.0.0
setuptools69.0.2indirekthoch583.0.0
wheel0.45.1indirekthoch20.46.2

Ein Advisory bedeutet, dass die im Abhängigkeitsgraphen erfasste Version in den betroffenen Bereich eines Advisories fällt. Erreichbarkeit wird nicht analysiert, und der Graph enthält Entwicklungs- und Test-Pins — ein Fund kann das Werkzeug betreffen und nicht die ausgelieferte Software.

JSON-Rohbericht maschinenlesbar
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  "generated_at": "2026-07-21T03:12:52.329713Z",
  "schema_version": "0.16.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/p/PaddlePaddle/Paddle.svg",
  "full_name": "PaddlePaddle/Paddle",
  "license_state": "standard",
  "license_spdx": "Apache-2.0"
}

Bewertungen sind Signale, keine Garantien. Sie spiegeln öffentlich sichtbare Praxis auf GitHub wider — kein Code-Audit und keine Sicherheitsgarantie.

Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.16.0 — vollständige Methodik · Metriken-Wiki.

Wie ein einzelnes Ergebnis im Gesamtregister steht: aggregierte Statistiken.