XGBoost

XGBoost (Extreme Gradient Boosting) is a powerful machine learning framework for gradient boosting. Pick xgboost on AIchor when you want distributed XGBoost training across multiple worker pods.

How to use

Select XGBoost by setting spec.operator: xgboost in your manifest. The full field-by-field specification lives in the Manifest Reference, and more complete examples are in the Manifest Examples.

spec:
  operator: "xgboost"
  image: "xgboost-demo"
  command: "python src/train.py"

  types:
    worker:
      count: 2
      resources:
        cpus: 8
        ramRatio: 2
    ...

Injected environment variables

The following environment variables are injected into every worker container to set up the distribution between the different containers:

Variable	Description	Example
MASTER_PORT	Port the master listens on for the distributed rendezvous.	9999
MASTER_ADDR	Address of the master container that the others connect to.	xgboost-dist-demo-master-0
WORLD_SIZE	Total number of containers in the run.	3
RANK	Rank of the current container, from 0 to WORLD_SIZE - 1.	1

External documentation

• XGBoost on Kubernetes

Demo project

The AIchor team has shared this demo project that can be cloned and used for AIchor experiments using XGBoost:

• xgboost-demo