aws/sagemaker-xgboost-container
This is the Docker container based on open source framework XGBoost (https://xgboost.readthedocs.io/en/latest/) to allow customers use their own XGBoost scripts in SageMaker.
This project helps machine learning engineers and data scientists customize and deploy XGBoost models on Amazon SageMaker. It provides the necessary tools and Docker configurations to build your own XGBoost-enabled Docker images. You input your specific XGBoost scripts and desired configurations, and it outputs a container image ready for training and inference within the SageMaker environment.
144 stars.
Use this if you need to build custom Docker images for running XGBoost models on Amazon SageMaker, requiring specific XGBoost versions, dependencies, or runtime environments.
Not ideal if you simply want to use the standard, pre-built XGBoost images provided by SageMaker without any special customizations.
Stars
144
Forks
96
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 27, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aws/sagemaker-xgboost-container"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
aws/sagemaker-python-sdk
A library for training and deploying machine learning models on Amazon SageMaker
aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning...
aws-samples/aws-ml-enablement-workshop
組織横断的にチームを組成し、機械学習による成長サイクルを実現する計画を立てるワークショップ
aws/sagemaker-training-toolkit
Train machine learning models within a 🐳 Docker container using 🧠 Amazon SageMaker.
aws-deepracer-community/deepracer-on-the-spot
Repo for running DeepRacer on Spot or Standard instances to save money versus the AWS DeepRacer console