Automic VaultAutomic Vault

brew

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

macOS

Homebrewverified · 100%
brew install djl-serving

local Homebrew formula metadata

overview

Package summary

This module contains an universal model serving implementation

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.

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 post-install hook is recorded in formula metadata.
  • Formula metadata declares a service or daemon block.
  • Homebrew bottle metadata is available for 1 platform targets.
  • Installs with 1 runtime dependencies.

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

CommandKindExposureNote
djl-servingcliglobal executable

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 generated2026-07-08
manager version0.36.0
manager updated
local dataok
upstreamnot checked
latest detectednot detected

https://github.com/deepjavalibrary/djl-serving

install metadata

Package metadata

Package keybrew:djl-serving
Version0.36.0
Package managerHomebrew
Package manager pagehttps://formulae.brew.sh/formula/djl-serving
Homepagehttps://github.com/deepjavalibrary/djl-serving
Repositoryhttps://github.com/deepjavalibrary/djl-serving
Upstream docshttps://docs.djl.ai/master/docs/serving/serving/docs/configurations.html
LicenseApache-2.0
Source archivehttps://publish.djl.ai/djl-serving/serving-0.36.0.tar
Dependenciesopenjdk
Bottleavailable (on all)
Homebrew post-installnot defined
Servicedeclared

registry facts

Source database details

Source DatabaseHomebrew formula API
Taphomebrew/core
Full Namedjl-serving
Version Scheme0
Revision0
Bottle Stable Root URLhttps://ghcr.io/v2/homebrew/core
Deprecatedno
Disabledno
Keg Onlyno
URL Keys
  • stable

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.

Used sources

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