XGBoost Serving

This is a fork of TensorFlow Serving, extended with the support for XGBoost, alphaFM and alphaFM_softmax frameworks. For more information about TensorFlow Serving, switch to the master branch or visit the TensorFlow Serving website.

---

XGBoost Serving is a flexible, high-performance serving system for XGBoost && FM models, designed for production environments. It deals with the inference aspect of XGBoost && FM models, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. XGBoost Serving derives from TensorFlow Serving and is used widely inside iQIYI.

To note a few features:

• Can serve multiple models, or multiple versions of the same model simultaneously
• Exposes gRPC inference endpoints
• Allows deployment of new model versions without changing any client code
• Supports canarying new versions and A/B testing experimental models
• Adds minimal latency to inference time due to efficient, low-overhead implementation
• Supports XGBoost servables, XGBoost && FM servables and XGBoost && alphaFM_Softmax servables
• Supports computation latency distribution statistics

Documentation

Set up

The easiest and most straight-forward way of building and using XGBoost Serving is with Docker images. We highly recommend this route unless you have specific needs that are not addressed by running in a container.

• Build XGBoost Serving from Source
• Build XGBoost Serving from Source with Docker(Recommended)

Use

Export your XGBoost && FM model

In order to serve a XGBoost && FM model, simply export your XGBoot model, leaf mapping and FM model.

Please refer to Export XGBoost && FM model for details about the models's specification and how to export XGBoost && FM model.

Configure and Use XGBoost Serving

• Follow a tutorial on Serving XGBoost && FM models
• Configure XGBoost Serving to make it fit your serving use case

Extend

XGBoost Serving derives from TensorFlow Serving and thanks to Tensorflow Serving's highly modular architecture. You can use some parts individually and/or extend it to serve new use cases.

• Ensure you are familiar with building Tensorflow Serving
• Learn about Tensorflow Serving's architecture
• Explore the Tensorflow Serving C++ API reference
• Create a new type of Servable
• Create a custom Source of Servable versions

Contribute

If you'd like to contribute to XGBoost Serving, be sure to review the contribution guidelines.

Feedback and Getting involved

• Report bugs, ask questions or give suggestions by Github Issues