djl-serving

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  "desc": "This module contains an universal model serving implementation",
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  "enrichment_fetched_at": "2026-06-20T23:36:59+00:00",
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  "full_name": "djl-serving",
  "github_default_branch": "master",
  "github_last_commit_at": "2026-06-17T07:21:10Z",
  "github_readme_excerpt": "# DJL Serving\n\n## Overview\n\nDJL Serving is a high performance universal stand-alone model serving solution powered by [DJL](https://djl.ai).\nIt takes a deep learning model, several models, or workflows and makes them available through an\nHTTP endpoint. It can serve the following model types out of the box:\n\n- PyTorch TorchScript model\n- TensorFlow SavedModel bundle\n- ONNX model (CPU)\n- Python script model\n\nYou can install extra extensions to enable the following models:\n\n- XGBoost model\n- LightGBM model\n- Sentencepiece model\n- fastText/BlazingText model\n\n## Key features\n\n- **Performance** - DJL serving running multithreading inference in a single JVM. Our benchmark shows\nDJL serving has higher throughput than most C++ model servers on the market\n- **Ease of use** - DJL serving can serve most models out of the box\n- **Easy to extend** - DJL serving plugins make it easy to add custom extensions\n- **Auto-scale** - DJL serving automatically scales up/down worker threads based on the load\n- **Dynamic batching** - DJL serving supports dynamic batching to increase throughput\n- **Model versioning** - DJL allows users to load different versions of a model on a single endpoint\n- **Multi-engine support** - DJL allows users to serve models from different engines at the same time\n\n## Installation\n\nFor macOS\n\n```\nbrew install djl-serving\n\n# Start djl-serving as service:\nbrew services start djl-serving\n\n# Stop djl-serving service\nbrew services stop djl-serving\n```\n\nFor Ubuntu\n\n```\ncurl -O https://publish.djl.ai/djl-serving/djl-serving_0.30.0-1_all.deb\nsudo dpkg -i djl-serving_0.30.0-1_all.deb\n```\n\nFor Windows\n\nWe are considering to create a `chocolatey` package for Windows. For the time being, you can\ndownload djl-serving zip file from [here](https://publish.djl.ai/djl-serving/serving-0.30.0.zip).\n\n```\ncurl -O https://publish.djl.ai/djl-serving/serving-0.30.0.zip\nunzip serving-0.30.0.zip\n# start djl-serving\nserving-0.30.0\\bin\\serving.bat\n```\n\n### Docker\n\nYou can also use docker to",
  "github_repo": "deepjavalibrary/djl-serving",
  "github_stars": 253,
  "github_topics": [
    "deep-learning",
    "deployment",
    "djl",
    "inference",
    "pytorch",
    "serving"
  ],
  "homepage": "https://github.com/deepjavalibrary/djl-serving",
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  "last_seen": "2026-06-20T23:34:18+00:00",
  "license": "Apache-2.0",
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  "name": "djl-serving",
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  "raw_hash": "f54f74d5e309230e",
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  "ruby_source_path": "Formula/d/djl-serving.rb",
  "tap": "homebrew/core",
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  "version_stable": "0.36.0",
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