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Software-GesundheitsberichtSchema 0.12.0 · Metriken 1.13.0 · 2026-07-18 05:56 UTC

ai-hypercomputer / jetstream

JetStream is a throughput and memory optimized engine for LLM inference on XLA devices, starting with TPUs (and GPUs in future -- PRs welcome).

PythonApache-2.0★ 451 Sterne⑂ 67 Forksseit März 2024Auf GitHub ansehen ↗

ai-hypercomputer/jetstream erreicht einen Gesundheitsindex von 55 von 100 und liegt damit im Bereich Mittel. Am stärksten schneidet es bei Sustainability & Governance (76/100) ab, am schwächsten bei Vitality (28/100). Zuletzt vor 193 Tagen aktualisiert. 6 Mitwirkende tragen den Großteil der jüngsten Arbeit.

55
gesamt / 100
Mittel

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.

55
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

AI HypercomputerOrganisation
512 Follower22 öffentliche Reposseit Sept. 2024

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

Paket-Ökosysteme

RegistryPaketVersionDownloads / MonatVersionenZuletzt veröffentlicht
PyPIgoogle-jetstreamverweist auf ein anderes Repo — nicht bewertet0.2.22.1854vor 777 Tagen

Metriken nach Kategorie

Vitalität

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

28Kritisch · 22 % des Gesamtindex
Wie die Bewertung erfolgt
3.6/36Push-Aktualität — letzter Push vor 193 Tagen
0.7/36Commit-Rhythmus — 1/52 Wochen mit Commits
2.7/18Commit-Volumen — 1 Commits im letzten Jahr
0/10OpenSSF Scorecard: Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
Verwendete Eingangsdaten
commits_last_year1
human_commit_share
days_since_last_push193
active_weeks_last_year1
Wie die Bewertung erfolgt
27/27Liefert Releases aus — 4 Releases veröffentlicht
7.2/36Release-Aktualität — letztes Release vor 576 Tagen
19.8/27Release-Rhythmus — ein Release etwa alle 85,7 Tage
0/10OpenSSF Scorecard: Signed-Releases — keine Daten
Verwendete Eingangsdaten
releases_count4
latest_release_tagv0.3
releases_from_tagsnein
days_since_latest_release576
mean_days_between_releases85,7
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): OpenSSF Scorecard: Signed-Releases. Die verbleibenden Gewichte wurden renormalisiert.

Community & Verbreitung

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

68Mittel · 18 % des Gesamtindex
Wie die Bewertung erfolgt
43/60Stars — 451 Stars
15.2/25Forks — 67 Forks
7.4/15Watcher — 22 Watcher
Verwendete Eingangsdaten
forks67
stars451
watchers22
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
0/13.5Verhaltenskodex
0/7.2Issue-Vorlage
0/6.3PR-Vorlage
Verwendete Eingangsdaten
has_readmeja
has_licenseja
has_contributingja
has_issue_templatenein
has_code_of_conductnein
has_pull_request_templatenein

Nachhaltigkeit & Governance

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

76Gut · 24 % des Gesamtindex
Wie die Bewertung erfolgt
48.6/54Bus-Faktor — 6 Beitragende decken die Hälfte aller Commits ab
18.1/22.5Commit-Verteilung — wichtigste beitragende Person verfasste 20 % der Commits
13.5/13.5Breite der Beitragenden — 36 Beitragende
10/10OpenSSF Scorecard: Contributors — project has 4 contributing companies or organizations
Verwendete Eingangsdaten
bus_factor6
contributors_sampled36
top_contributor_share0,195
Wie die Bewertung erfolgt
24.9/46.8Issue-Lösungsquote — 53 % der Issues geschlossen
33.4/38.3PR-Annahme — 209/239 entschiedene PRs gemergt
15/15OpenSSF Scorecard: Code-Review — all changesets reviewed
Verwendete Eingangsdaten
merged_prs209
open_issues14
closed_issues16
issue_closed_ratio0,533
closed_unmerged_prs30
Wie die Bewertung erfolgt
30/30Organisatorische Trägerschaft — im Besitz einer Organisation
0/20Verifizierte Domain
19.5/25Reichweite des Inhabers — 512 Follower von AI-Hypercomputer
13.6/25Kontohistorie — 22 öffentliche Repos, Kontoalter ca. 1 Jahre
Verwendete Eingangsdaten
followers512
owner_typeOrganization
is_verified
owner_loginAI-Hypercomputer
public_repos22
account_age_days676

Engineering-Qualität

Sind grundlegende Engineering- und Dokumentationspraktiken vorhanden?

59Mittel · 20 % des Gesamtindex
Wie die Bewertung erfolgt
24/24CI-Workflows — 5 Workflow(s)
24/24Tests vorhanden
0/16Linter-Konfiguration
0/9.6Pre-Commit-Hooks
0/6.4.editorconfig
0/20OpenSSF Scorecard: CI-Tests — 1 out of 16 merged PRs checked by a CI test -- score normalized to 0
Verwendete Eingangsdaten
has_cija
has_testsja
has_editorconfignein
has_linter_confignein
has_precommit_confignein
Wie die Bewertung erfolgt
30/30README
25/25Dokumentationsverzeichnis
0/15Dokumentations-/Homepage-Site
10/10Repository-Beschreibung
10/10Topics — 16 Topics
0/10Wiki
Verwendete Eingangsdaten
topicsgemma, gpt, gpu, inference, jax, large-language-models, llama, llama2, llm, model-serving, pytorch, tpu, llm-inference, llmops, mlops, transformer
has_wikinein
homepage
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

44Gefährdet
Wie die Bewertung erfolgt
7.5/7.5Binary-Artifacts — no binaries found in the repo
0/7.5Branch-Protection — keine Daten
0/2.5CI-Tests — 1 out of 16 merged PRs checked by a CI test -- score normalized to 0
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 4 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
2.5/2.5Lizenz — license file detected
0/7.5Maintained — 0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
0/5Packaging — keine Daten
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
0/5Security-Policy — security policy file not detected
0/7.5Signed-Releases — keine Daten
0/7.5Token-Permissions — detected GitHub workflow tokens with excessive permissions
0/7.5Vulnerabilities — 20 existing vulnerabilities detected
Verwendete Eingangsdaten
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4,4
Von der Bewertung ausgeschlossen (keine Daten oder nicht anwendbar): branch_protection, packaging, signed_releases. 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.

39Gefährdet · 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 — Makefile
22/22Automatisierte Tests
0/11Lint-/Format-Konfiguration
0/11Statische Typprüfung
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_filesMakefile
has_devcontainernein
has_linter_confignein
typecheck_configs
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
0/45Typprüfbarer Code — Python ohne Typprüfungs-Konfiguration
54.6/55Handhabbare Dateigrößen — 1/136 Quelldateien über 60 KB
Verwendete Eingangsdaten
primary_languagePython
largest_source_bytes68.226
source_files_sampled136
oversized_source_files1
Wie die Bewertung erfolgt
40/40API-Schema (OpenAPI/GraphQL/proto) — jetstream/core/proto/jetstream.proto, jetstream/core/proto/multi_lora_decoding.proto, jetstream/engine/tokenizer.proto
0/20MCP-Server
0/40Lauffähige Beispiele
Verwendete Eingangsdaten
example_dirs
has_mcp_signalnein
api_schema_filesjetstream/core/proto/jetstream.proto, jetstream/core/proto/multi_lora_decoding.proto, jetstream/engine/tokenizer.proto

Eckdaten

451GitHub-Sterne
36Mitwirkende
1Commits, letzte 12 Monate
193Tage seit letztem Push
4Releases
6Bus-Faktor
14offene Issues
PyPIPaket-Ökosysteme

Warnungen zur Datenerhebung

  • pypi package 'google-jetstream' points at a different repository (https://github.com/google/JetStream); excluded from ecosystem scoring

Weitere Details

OpenSSF Scorecard 4.4 / 10
4.4Gesamtwert

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 · 2026-07-18 05:55 UTC

10Binary-Artifactsno binaries found in the repo
k. A.Branch-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
0CI-Tests1 out of 16 merged PRs checked by a CI test -- score normalized to 0
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
10Code-Reviewall changesets reviewed
10Contributorsproject has 4 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
10Dependency-Update-Toolupdate tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
0Maintained0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0
k. A.Packagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
k. A.Signed-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
0Vulnerabilities20 existing vulnerabilities detected
Alle Abhängigkeiten 27

Vollständig aufgelöster Abhängigkeitssatz aus dem GitHub-Abhängigkeitsgraphen: 0 direkte und 27 indirekte (transitive) Pakete. Die transitive Hülle ist vollständig, wenn das Repository eine Lockfile eincheckt.

RegistryPaketVersionBeziehung
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PyPIgrpcioindirekt
PyPIhuggingface-hubindirekt
PyPIjaxindirekt
PyPIjax0.4.33indirekt
PyPIjaxlibindirekt
PyPInltkindirekt
PyPInumpyindirekt
PyPIpandasindirekt
PyPIparameterizedindirekt
PyPIportpickerindirekt
PyPIprometheus-clientindirekt
PyPIpytestindirekt
PyPIseqioindirekt
PyPIshortuuidindirekt
PyPIsympyindirekt
PyPItensorboard-plugin-profileindirekt
PyPItiktokenindirekt
PyPItorch2.3.0+cpuindirekt
PyPItorchvision0.18.0+cpuindirekt
PyPItransformersindirekt
PyPIuvicornindirekt
JSON-Rohbericht maschinenlesbar
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          "avatar_url": "https://avatars.githubusercontent.com/u/193563974?v=4"
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          "type": "User",
          "login": "morgandu",
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          "avatar_url": "https://avatars.githubusercontent.com/u/11281451?u=819f86fc631502417bbc7db4fd58bf795b745c94&v=4"
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        {
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        {
          "type": "User",
          "login": "Lumosis",
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          "avatar_url": "https://avatars.githubusercontent.com/u/30372757?u=306296c5dbf4adee6826a8f69e90def24f71dc4e&v=4"
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        {
          "type": "User",
          "login": "mailvijayasingh",
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          "avatar_url": "https://avatars.githubusercontent.com/u/14227112?v=4"
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      "top_contributor_share": 0.195
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      "linter_configs": [],
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      "has_linter_config": false,
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            "name": "Dangerous-Workflow",
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            "reason": "no dangerous workflow patterns detected",
            "documentation_url": "https://github.com/ossf/scorecard/blob/c395761df6afe1a69e476bc60a013a94bcbc153f/docs/checks.md#dangerous-workflow"
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            "documentation_url": "https://github.com/ossf/scorecard/blob/c395761df6afe1a69e476bc60a013a94bcbc153f/docs/checks.md#sast"
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        "metrics": [
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                "key": "tests_present",
                "name": "Tests present",
                "detail": null,
                "points": 24,
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                "max_points": 24
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                "key": "linter_config",
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                "key": "editorconfig",
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                "points": 0,
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                "details": [],
                "max_points": 6.4
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              {
                "key": "openssf_scorecard_ci_tests",
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          {
            "key": "documentation",
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              "has_wiki": false,
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                "points": 25,
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                "status": "missed",
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                "key": "wiki",
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                "points": 0,
                "status": "missed",
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          }
        ],
        "description": "Are baseline engineering and documentation practices in place?"
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                "points": 7.5,
                "status": "met",
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              {
                "key": "branch_protection",
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                "points": 0,
                "status": "excluded",
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                "key": "cii_best_practices",
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                "status": "missed",
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                "max_points": 2.5
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                "key": "code_review",
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                "points": 10,
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                "key": "fuzzing",
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                "key": "sast",
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                "key": "security_policy",
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                "key": "signed_releases",
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                "points": 100,
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        "description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
      }
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    "metrics_version": "1.13.0"
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  "warnings": [
    "pypi package 'google-jetstream' points at a different repository (https://github.com/google/JetStream); excluded from ecosystem scoring"
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  "generated_at": "2026-07-18T05:56:25.095680Z",
  "schema_version": "0.12.0",
  "badge_url": "https://raw.githubusercontent.com/inspect-software/badges/main/v1/a/ai-hypercomputer/jetstream.svg",
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}

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