macOS
Homebrew已验证 · 100%
brew install djl-servinglocal Homebrew formula metadata
安装
其他安装命令
概览
软件包摘要
This module contains an universal model serving implementation
历史
项目历史与用法
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.
项目历史
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.
采用历史
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.
使用方式
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.
为什么软件包爱好者会关心
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.
时间线
- 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.
来源
- 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
安全态势
风险级别：orange
formula declares a Homebrew service.
风险分类器
orange 风险 · 中 置信度 · infrastructure
原因
- formula declares a Homebrew service
信号
- metadata:service
安装行为
- formula 元数据中未记录 Homebrew post-install 钩子。
- formula 元数据声明了服务或守护进程块。
- Homebrew bottle 元数据适用于 1 个平台目标。
- 安装时包含 1 个运行时依赖。
建议审查
在无人值守的代理使用前，请检查该工具是否读取明文凭据、写入远程状态、发布制品或调用插件。
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
可执行文件
已安装的可执行文件
| 命令 | 类型 | 暴露范围 | 备注 |
|---|---|---|---|
djl-serving | cli | 全局可执行文件 |
新鲜度
版本和新鲜度
这些信号区分页生成时间、软件包管理器活动和上游发布比较。只有存在证据 URL 和可比较版本时，才会提示版本落后。
页面生成时间2026-07-25
管理器版本0.36.0
管理器更新时间
本地数据OK
上游not checked
检测到的最新版本未检测到
https://github.com/deepjavalibrary/djl-serving
- 信息No package-manager update timestamp was available.低 置信度
- 信息No cached GitHub release or tag data was available.https://github.com/deepjavalibrary/djl-servingnone 置信度
安装元数据
软件包元数据
| 软件包键 | brew:djl-serving |
|---|---|
| 版本 | 0.36.0 |
| 软件包管理器 | Homebrew |
| 软件包管理器页面 | https://formulae.brew.sh/formula/djl-serving |
| 主页 | https://github.com/deepjavalibrary/djl-serving |
| 仓库 | https://github.com/deepjavalibrary/djl-serving |
| 上游文档 | https://docs.djl.ai/master/docs/serving/serving/docs/configurations.html |
| 许可证 | Apache-2.0 |
| 源码归档 | https://publish.djl.ai/djl-serving/serving-0.36.0.tar |
| 依赖 | openjdk |
| Bottle | 可用 (于 all) |
| Homebrew post-install | 未定义 |
| 服务 | declared |
注册表事实
源数据库详情
| 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 |
|
来源线索
由仓库数据生成
此页面由 av-web 从 scripts/generate-pkg-sqlite.py 生成的私有软件包 SQLite 工件提供。
Combined YAML source
View the package source record on GitHub.
使用的来源
- 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