# Install djl-serving with Homebrew

This module contains an universal model serving implementation. Version 0.36.0 via Homebrew; verified from local package data.

## Install

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

Additional install commands:

### macOS

- Homebrew (100%):

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

  Evidence: local Homebrew formula metadata

## Package facts

- **Package key:** brew:djl-serving
- **Package manager:** Homebrew
- **Package manager page:** <https://formulae.brew.sh/formula/djl-serving>
- **Version:** 0.36.0
- **Source summary:** This module contains an universal model serving implementation
- **Homepage:** <https://github.com/deepjavalibrary/djl-serving>
- **Repository:** <https://github.com/deepjavalibrary/djl-serving>
- **Upstream docs:** <https://docs.djl.ai/master/docs/serving/serving/docs/configurations.html>
- **License:** Apache-2.0
- **Source archive:** <https://publish.djl.ai/djl-serving/serving-0.36.0.tar>
- **Generated:** 2026-07-08T07:18:31+00:00

## Executables

- djl-serving (cli)
- djl-serving (alias)

## Dependencies

- openjdk

## Install behavior

- Post-install hook: not defined
- Service: declared
- Bottle: available on all

## Freshness

- Page generated: 2026-07-08
- Package-manager version: 0.36.0
- Local data: ok
- Upstream repository: https://github.com/deepjavalibrary/djl-serving
- info: No package-manager update timestamp was available.
- info: No cached GitHub release or tag data was available.
## Project history and usage

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.

### Project history

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.

### Adoption history

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.

### How it is used

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.

### Why package nerds care

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.

### Timeline

- 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>


## Security Notes

formula declares a Homebrew service.

- **Geiger risk:** orange / medium
- 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
## Source Database Details

- **Source Database:** Homebrew formula API
- **Tap:** homebrew/core
- **Full Name:** djl-serving
- **Version Scheme:** 0
- **Revision:** 0
- **Bottle Stable Root URL:** <https://ghcr.io/v2/homebrew/core>
- **Deprecated:** no
- **Disabled:** no
- **Keg Only:** no
- **URL Keys:** stable


## Related links

- [Source-control packages](https://www.automicvault.com/pkg/source-control-tools/) - Belongs to a source-control command family.
- [Secret-risk packages](https://www.automicvault.com/pkg/secret-risk-packages/) - Has protected-tool coverage, approval-gate, or non-low Geiger security signals.
- [Terminal utility packages](https://www.automicvault.com/pkg/terminal-utilities/) - Matched terminal and command-line workflow metadata.
- [Text processing packages](https://www.automicvault.com/pkg/text-processing-tools/) - Matched text, document, or structured-data processing metadata.
- [openjdk](https://www.automicvault.com/pkg/brew/openjdk/) - Runtime dependency declared by Homebrew.
- [mallet](https://www.automicvault.com/pkg/brew/mallet/) - Shares av.db curated category or tags: cli, data, java, machine-learning.
- [ncnn](https://www.automicvault.com/pkg/brew/ncnn/) - Shares av.db curated category or tags: cli, data, inference, machine-learning.
- [classifier](https://www.automicvault.com/pkg/brew/classifier/) - Shares av.db curated category or tags: cli, data, machine-learning.
- [lightgbm](https://www.automicvault.com/pkg/brew/lightgbm/) - Shares av.db curated category or tags: cli, data, machine-learning.
- [rgf](https://www.automicvault.com/pkg/brew/rgf/) - Shares av.db curated category or tags: cli, data, machine-learning.
- [vowpal-wabbit](https://www.automicvault.com/pkg/brew/vowpal-wabbit/) - Shares av.db curated category or tags: cli, data, machine-learning.
- [alluxio](https://www.automicvault.com/pkg/brew/alluxio/) - Shares av.db curated category or tags: cli, data, java.
- [carrot2](https://www.automicvault.com/pkg/brew/carrot2/) - Shares av.db curated category or tags: cli, data, java.

## Combined YAML source

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


## Sources

- Nucleus package database
- Geiger risk classifier
- package-page enrichment
- curated configuration and credential file locations
- curated package history
- package version freshness
- av.db category and tag curation
- package relationship graph
- cross-ecosystem install command graph