# 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

```sh
sudo av install brew:djl-serving
```

Commandes d'installation supplémentaires:

### macOS

- Homebrew (100%):

```sh
brew install djl-serving
```

  Preuve: local Homebrew formula metadata

## Faits du paquet

- **Clé du paquet:** brew:djl-serving
- **Gestionnaire de paquets:** Homebrew
- **Version:** 0.36.0
- **Résumé source:** This module contains an universal model serving implementation
- **Page d'accueil:** <https://github.com/deepjavalibrary/djl-serving>
- **Généré:** 2026-08-03T19:37:03+00:00

## exécutables

- djl-serving (alias)

## Comportement d'installation

- Bouteille: non disponible

## Version et fraîcheur

- page générée: 2026-08-03
- version du gestionnaire: 0.36.0
## 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>


## Notes de sécurité

formula declares a Homebrew service.

- **Risque Geiger:** orange / moyen
- formula declares a Homebrew service


## 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

- Unix: /opt/ml/model/serving.properties, serving.properties

## Combined YAML source

View the package source record on GitHub. [combined/djl-serving.yml](https://github.com/mxcl/pkgdb/blob/main/combined/djl-serving.yml)


## Sources

- pkg.so package database
- Geiger risk classifier
- curated configuration and credential file locations
- curated package history
- pkgdb category and tag curation
- cross-ecosystem install command graph