macOS
Homebrewvérifié · 100%
brew install djl-servinglocal Homebrew formula metadata
brew / rang 6750
Installer djl-serving avec Homebrew
Consultez les chemins d'installation, exécutables, métadonnées et notes de sécurité de djl-serving pour les workflows d'agents IA.
installation
Commandes d'installation supplémentaires
aperçu
Résumé du paquet
This module contains an universal model serving implementation
Liens du projet
Commandes et alias
- djl-serving
historique
Historique du projet et usages
DJL Serving is the model-serving component of the Deep Java Library ecosystem. It packages deep-learning inference behind HTTP endpoints, with support for multiple engines, model stores, dynamic batching, worker scaling, plugins, and REST management APIs.
Historique du projet
The official GitHub repository was created in August 2021 and describes DJL Serving as a universal, scalable machine-learning model deployment solution. The README says it serves PyTorch TorchScript, TensorFlow SavedModel, ONNX CPU models, Python script models, and extension-backed model types such as XGBoost, LightGBM, SentencePiece, and fastText or BlazingText.
The project is tied to the larger DJL documentation set rather than only a standalone README. Official docs describe global, engine, workflow, model, and application configuration layers, while LMI documentation explains `serving.properties` and environment-variable configuration for large-model inference containers.
Historique d'adoption
DJL Serving adoption follows Java and AWS-centered inference workflows more than general desktop CLI culture. The official README includes Homebrew installation and service commands for macOS, Debian package installation for Ubuntu, Windows zip startup, and Docker images, making it approachable both as a local package and as a containerized service.
The release history shows regular model-serving maintenance across the 2020s, including v0.23-era releases in 2023, v0.29.0 in 2024, and v0.36.0 in 2026. That cadence tracks the changing model-serving world: new inference backends, LMI configuration, and operations APIs matter as much as the command itself.
Modes d'utilisation
Users start `djl-serving` from the command line or as a Homebrew service, point it at models or workflows, and interact with inference and management endpoints. Configuration commonly lives in a `serving.properties` file, while LMI container deployments use `/opt/ml/model` as the default model-artifact location.
Pourquoi les passionnés de paquets s'y intéressent
For package-history purposes, DJL Serving is interesting because it is both a Unix-installable daemon and a cloud/container serving stack. It puts JVM-based ML serving into Homebrew next to small CLI tools, but its real operational shape includes Docker, REST APIs, model stores, and SageMaker-style large-model inference configuration.
Chronologie
- 2021-08-16: Official GitHub repository created.
- 2023-06-14: v0.23.0-alpha release published.
- 2024-08-16: v0.29.0 release published.
- 2026-03-12: v0.36.0 release published.
Related projects
- Related serving systems include TorchServe, TensorFlow Serving, NVIDIA Triton Inference Server, KServe, and the broader Deep Java Library project that supplies engines and model APIs underneath DJL Serving.
Sources
- https://github.com/deepjavalibrary/djl-serving
- https://raw.githubusercontent.com/deepjavalibrary/djl-serving/master/README.md
- https://raw.githubusercontent.com/deepjavalibrary/djl-serving/master/serving/docs/configuration.md
- https://raw.githubusercontent.com/deepjavalibrary/djl-serving/master/serving/docs/lmi/deployment_guide/configurations.md
- https://api.github.com/repos/deepjavalibrary/djl-serving
- https://api.github.com/repos/deepjavalibrary/djl-serving/releases?per_page=10
posture de sécurité
Niveau de risque : orange
formula declares a Homebrew service.
Classificateur de risque
risque orange · confiance moyen · infrastructure
Pourquoi
- formula declares a Homebrew service
Signaux
- metadata:service
Comportement d'installation
- Aucune métadonnée de bottle Homebrew n’a été enregistrée.
Revue recommandée
Avant une utilisation sans surveillance par un agent, vérifiez si l'outil lit des identifiants en clair, écrit un état distant, publie des artefacts ou lance des plugins.
local files
Configuration and credential file locations
These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval.
Configuration files
Config paths the tool may read or write during local use.
- Unix
/opt/ml/model/serving.propertiesserving.properties
exécutables
Exécutables installés
| Commande | Type | Exposition | Note |
|---|---|---|---|
djl-serving | exécutable | exécutable indexé | Découvert depuis l'index local des exécutables. |
fraîcheur
Version et fraîcheur
Ces signaux séparent l'âge de génération de la page, l'activité du gestionnaire de paquets et la comparaison avec les versions amont. Un retard de version n'est signalé que lorsqu'une URL de preuve et des versions comparables sont présentes.
page générée2026-08-03
version du gestionnaire0.36.0
gestionnaire mis à jour
données localesinconnu
amontnon disponible
dernière version détectéenon détecté
- OKAucun avertissement de fraîcheur n'a été généré.
métadonnées d'installation
Métadonnées du paquet
| Clé du paquet | brew:djl-serving |
|---|---|
| Version | 0.36.0 |
| Gestionnaire de paquets | Homebrew |
| Page d'accueil | https://github.com/deepjavalibrary/djl-serving |
| Bouteille | non enregistré |
| Service | aucun déclaré |
piste source
Généré depuis les données du dépôt
Cette page est servie par av-web depuis l'artéfact SQLite privé des paquets généré par scripts/generate-pkg-sqlite.py.
Combined YAML source
View the package source record on GitHub.
Sources utilisées
- Geiger risk classifier
- cross-ecosystem install command graph
- curated configuration and credential file locations
- curated package history
- pkg.so package database
- pkgdb category and tag curation