macOS
Homebrewverified · 100%
brew install djl-servinglocal Homebrew formula metadata
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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
Additional install commands
overview
Package summary
This module contains an universal model serving implementation
Project links
Commands and aliases
- djl-serving
history
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 posture
Risk level: orange
formula declares a Homebrew service.
Risk classifier
orange risk · medium confidence · infrastructure
Why
- formula declares a Homebrew service
Signals
- metadata:service
Install behavior
- No Homebrew bottle metadata was recorded.
Recommended review
Before unattended agent use, check whether the tool reads plaintext credentials, writes remote state, publishes artifacts, or shells out to 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
executables
Installed executables
| Command | Kind | Exposure | Note |
|---|---|---|---|
djl-serving | executable | indexed executable | Discovered from the local executable index. |
freshness
Version and freshness
These signals separate page generation age, package-manager activity, and upstream release comparison. Version lag is warned only when an evidence URL and comparable versions are present.
page generated2026-08-03
manager version0.36.0
manager updated
local dataunknown
upstreamnot available
latest detectednot detected
- okNo freshness warnings were generated.
install metadata
Package metadata
| Package key | brew:djl-serving |
|---|---|
| Version | 0.36.0 |
| Package manager | Homebrew |
| Homepage | https://github.com/deepjavalibrary/djl-serving |
| Bottle | not recorded |
| Service | none declared |
source trail
Generated from repository data
This page is generated by av-web from the private package SQLite artifact built by scripts/generate-pkg-sqlite.py.
Combined YAML source
View the package source record on GitHub.
Used sources
- 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