https://github.com/iqiyi/xgboost-serving

A flexible, high-performance serving system for machine learning models
https://github.com/iqiyi/xgboost-serving

Last synced: 4 days ago
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A flexible, high-performance serving system for machine learning models

• Host: GitHub
• URL: https://github.com/iqiyi/xgboost-serving
• Owner: iqiyi
• License: apache-2.0
• Created: 2021-06-23T09:51:16.000Z (over 3 years ago)
• Default Branch: xgboost
• Last Pushed: 2021-11-24T03:07:18.000Z (almost 3 years ago)
• Last Synced: 2023-11-07T18:12:34.463Z (about 1 year ago)
• Language: C++
• Homepage:
• Size: 6 MB
• Stars: 138
• Watchers: 10
• Forks: 18
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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# XGBoost Serving

This is a fork of TensorFlow Serving, extended with the support for [XGBoost](https://github.com/dmlc/xgboost), [alphaFM](https://github.com/CastellanZhang/alphaFM) and [alphaFM_softmax](https://github.com/CastellanZhang/alphaFM_softmax) frameworks. For more information about TensorFlow Serving, switch to the `master` branch or visit the [TensorFlow Serving website](https://github.com/tensorflow/serving).

----

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](tensorflow_serving/docs/en/building.md)

*   [Build XGBoost Serving from Source with Docker](tensorflow_serving/docs/en/building_with_docker.md)*(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](tensorflow_serving/docs/en/export_model.md)

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](tensorflow_serving/docs/en/serving_basic.md)

* [Configure XGBoost Serving to make it fit your serving use case](tensorflow_serving/docs/en/serving_config.md)

### 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](tensorflow_serving/g3doc/building_with_docker.md)

* [Learn about Tensorflow Serving's architecture](tensorflow_serving/g3doc/architecture.md)

* [Explore the Tensorflow Serving C++ API reference](https://www.tensorflow.org/tfx/serving/api_docs/cc/)

* [Create a new type of Servable](tensorflow_serving/g3doc/custom_servable.md)

* [Create a custom Source of Servable versions](tensorflow_serving/g3doc/custom_source.md)

## Contribute

**If you'd like to contribute to XGBoost Serving, be sure to review the

[contribution guidelines](CONTRIBUTING.md).**

## Feedback and Getting involved

* Report bugs, ask questions or give suggestions by [Github

  Issues](https://github.com/iqiyi/xgboost-serving/issues)