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Software-GesundheitsberichtSchema 0.15.0 · Metriken 1.13.0 · 2026-07-19 11:19 UTC

triton-inference-server / server

The Triton Inference Server provides an optimized cloud and edge inferencing solution.

Python · Shell · C++BSD-3-Clause★ 10.853 Sterne⑂ 1.817 Forksseit Okt. 2018Auf GitHub ansehen ↗

triton-inference-server/server erreicht einen Gesundheitsindex von 84 von 100 und liegt damit im Bereich Gut. Am stärksten schneidet es bei Vitality (94/100) ab, am schwächsten bei AI Readiness (54/100). Zuletzt vor 1 Tag aktualisiert. 4 Mitwirkende tragen den Großteil der jüngsten Arbeit.

84
gesamt / 100
Gut

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.

84
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

2.024 Follower36 öffentliche Reposseit Juli 2020

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?

94Exzellent · 22 % des Gesamtindex
Wie die Bewertung erfolgt
36/36Push-Aktualität — letzter Push vor 1 Tagen
32.5/36Commit-Rhythmus — 47/52 Wochen mit Commits
18/18Commit-Volumen — 201 Commits im letzten Jahr
10/10OpenSSF Scorecard: Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
Verwendete Eingangsdaten
commits_last_year201
human_commit_share
days_since_last_push1
active_weeks_last_year47
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 92 Releases veröffentlicht
36/36Release-Aktualität — letztes Release vor 22 Tagen
27/27Release-Rhythmus — ein Release etwa alle 29,1 Tage
0/10OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases.
Verwendete Eingangsdaten
releases_count92
latest_release_tagv2.70.0
releases_from_tagsnein
days_since_latest_release22
mean_days_between_releases29,1

Community & Verbreitung

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

88Exzellent · 18 % des Gesamtindex
Wie die Bewertung erfolgt
60/60Stars — 10.853 Stars
25/25Forks — 1.817 Forks
12/15Watcher — 145 Watcher
Verwendete Eingangsdaten
forks1.817
stars10.853
watchers145
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history
Wie die Bewertung erfolgt
22.5/22.5README
22.5/22.5Lizenz — anerkannte Lizenz (BSD-3-Clause)
18/18CONTRIBUTING-Leitfaden
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
6.3/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductnein
has_pull_request_templateja

Nachhaltigkeit & Governance

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

83Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
43.2/54Bus-Faktor — 4 Beitragende decken die Hälfte aller Commits ab
18.2/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 19 % der Commits
13.5/13.5Breite der Beitragenden — 100 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 10 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor4
contributors_sampled100
top_contributor_share0,192
Wie die Bewertung erfolgt
37.8/46.8Issue-Lösungsquote — 81 % der Issues geschlossen
34.8/38.3PR-Annahme — 3.981/4.372 entschiedene PRs gemergt
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Verwendete Eingangsdaten
merged_prs3.981
open_issues792
closed_issues3.360
issue_closed_ratio0,809
closed_unmerged_prs391
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
23.8/25Reichweite des Inhabers — 2.024 Follower von triton-inference-server
23.4/25Kontohistorie — 36 öffentliche Repos, Kontoalter ca. 6 Jahre
Verwendete Eingangsdaten
followers2.024
owner_typeOrganization
is_verified
owner_logintriton-inference-server
public_repos36
account_age_days2.200

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

91Exzellent · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 2 Workflow(s)
24/24Tests vorhanden
16/16Linter-Konfiguration
9.6/9.6Pre-Commit-Hooks
0/6.4.editorconfig
18/20OpenSSF Scorecard: CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_configja
has_precommit_configja

Dokumentation

90Exzellent
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
15/15Dokumentations-/Homepage-Site — https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
10/10Repository-Beschreibung
10/10Topics — 7 Topics
0/10Wiki
Verwendete Eingangsdaten
topicsinference, gpu, machine-learning, deep-learning, cloud, datacenter, edge
has_wikinein
homepagehttps://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html
has_readmeja
has_docs_dirja
has_descriptionja

Sicherheit

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

57Mittel · 16 % des Gesamtindex
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
4.5/7.5Branch-Protection — branch protection is not maximal on development and all release branches
2.2/2.5CI-Tests — 29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0/2.5CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected
7.5/7.5Code-Review — all changesets reviewed
2.5/2.5Contributors — project has 10 contributing companies or organizations
10/10Dangerous-Workflow — no dangerous workflow patterns detected
0/7.5Dependency-Update-Tool — no update tool detected
0/5Fuzzing — project is not fuzzed
2.5/2.5Lizenz — license file detected
7.5/7.5Maintained — 30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
0/5Packaging — keine Daten
0/5Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
4.5/5SAST — SAST tool detected but not run on all commits
5/5Security-Policy — security policy file detected
0/7.5Signed-Releases — Project has not signed or included provenance with any releases.
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
3/7.5Vulnerabilities — 6 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated17
scorecard_versionv5.5.0
checks_inconclusive1
scorecard_aggregate5,7
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): packaging. Die verbleibenden Gewichte wurden renormalisiert.

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.

54Mittel · 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
18/18Bootstrap mit einem Befehl — docs/Makefile, docs/examples/jetson/concurrency_and_dynamic_batching/Makefile
22/22Automatisierte Tests
11/11Lint-/Format-Konfiguration
11/11Statische Typprüfung — src/python/tritonfrontend/py.typed
10/10Reproduzierbare Umgebung — Dockerfile
0/10Belegte Agentenpraxis — keine Daten
0/8Automatisierte Wartung — keine Daten
0/10OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0
Verwendete Eingangsdaten
has_nixnein
has_testsja
lockfiles
has_dockerfileja
typed_languagenein
bootstrap_filesdocs/Makefile, docs/examples/jetson/concurrency_and_dynamic_batching/Makefile
has_devcontainernein
has_linter_configja
typecheck_configssrc/python/tritonfrontend/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
27/45Typprüfbarer Code — Python mit Typprüfungs-Konfiguration (src/python/tritonfrontend/py.typed)
53.9/55Handhabbare Dateigrößen — 9/451 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes178.956
source_files_sampled451
oversized_source_files9
Wie die Bewertung erfolgt
0/40API-Schema (OpenAPI/GraphQL/proto)
0/20MCP-Server
40/40Lauffähige Beispiele — examples
Verwendete Eingangsdaten
example_dirsexamples
has_mcp_signalnein
api_schema_files

Eckdaten

10.853GitHub-Sterne
100Mitwirkende
201Commits, letzte 12 Monate
1Tage seit letztem Push
92Releases
4Bus-Faktor
792offene Issues
PyPIPaket-Ökosysteme

Weitere Details

OpenSSF Scorecard 5.7 / 10
5.7Gesamtwert

Unabhängige, werkzeugneutrale Sicherheitsbewertung durch das quelloffene OpenSSF Scorecard. Jede Prüfung honoriert eine Sicherheits-Praxis, nicht das Werkzeug eines bestimmten Anbieters. Prüfungen, die Scorecard nicht ermitteln konnte, sind mit k. A. markiert und vom Sicherheitswert ausgeschlossen (nie als null gezählt).Scorecard v5.5.0 · 2026-07-19 11:18 UTC

10Binary-Artifactsno binaries found in the repo
6Branch-Protectionbranch protection is not maximal on development and all release branches
9CI-Tests29 out of 30 merged PRs checked by a CI test -- score normalized to 9
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 10 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 10
k. A.Packagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
9SASTSAST tool detected but not run on all commits
10Security-Policysecurity policy file detected
0Signed-ReleasesProject has not signed or included provenance with any releases.
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
4Vulnerabilities6 existing vulnerabilities detected
Alle Abhängigkeiten 25

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 25 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
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PyPImlflowindirekt
PyPInetworkx2.3indirekt
PyPInumpyindirekt
PyPIonnxindirekt
PyPIopenai1.107.3indirekt
PyPIpartial-json-parserindirekt
PyPIprotobufindirekt
PyPIpydantic2.10.6indirekt
PyPIpytestindirekt
PyPIpytest8.1.1indirekt
PyPIpytest-asyncio0.23.8indirekt
PyPIpython-rapidjsonindirekt
PyPIrequestsindirekt
PyPIscipy1.16.3indirekt
PyPIstarletteindirekt
PyPItest-generator0.1.1indirekt
PyPItritonclientindirekt
PyPItritonserverindirekt
JSON-Rohbericht maschinenlesbar
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Fehlende Daten werden ausgeschlossen und die Gewichte neu normiert, nie als null bewertet. Die Methodik ist versioniert und offen: Metriken v1.13.0, Schema v0.15.0 — vollständige Methodik · Metriken-Wiki.

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