# 使用 Homebrew 安装 djl-serving 查看 djl-serving 的安装路径、可执行文件、元数据以及面向 AI 代理工作流的安全说明。 ## 安装 ```sh sudo av install brew:djl-serving ``` 其他安装命令: ### macOS - Homebrew (100%): ```sh brew install djl-serving ``` 证据: local Homebrew formula metadata ## 软件包事实 - **软件包键:** brew:djl-serving - **软件包管理器:** Homebrew - **版本:** 0.36.0 - **来源摘要:** This module contains an universal model serving implementation - **主页:** - **已生成:** 2026-08-03T19:37:03+00:00 ## 可执行文件 - djl-serving (别名) ## 安装行为 - Bottle: 不可用 ## 版本和新鲜度 - 页面生成时间: 2026-08-03 - 管理器版本: 0.36.0 ## 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 - - - - - - ## 安全说明 formula declares a Homebrew service. - **Geiger 风险:** orange / 中 - 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) ## 来源 - 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