The built-in Amazon SageMaker XGBoost algorithm provides a managed container to run the popular XGBoost machine learning (ML) framework, with added convenience of supporting advanced training or inference features like distributed training, dataset sharding for large-scale datasets, A/B model testing, or multi-model inference endpoints. You can also extend this powerful algorithm to accommodate different requirements.
Packaging the code and dependencies in a single container is a convenient and robust approach for long-term code maintenance, reproducibility, and auditing purposes. Modifying the container directly follows the base container faithfully and avoids duplicating existing functions already supported by the base container. In this post, we review the inner workings of the SageMaker XGBoost algorithm container and provide pragmatic scripts to directly customize the container.
SageMaker XGBoost container structure
The SageMaker built-in XGBoost algorithm is packaged as a stand-alone container, available on GitHub, and can be extended under the developer-friendly Apache 2.0 open-source license.

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