Bring your own model with Amazon SageMaker AI: Script mode in SDK v3

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In 2021, we published Bring your own model with Amazon SageMaker script mode. That post showed how to use script mode on managed framework containers from AWS to write custom training and inference code. Script mode was a leap forward: you didn’t need to build or maintain Docker images to run your own algorithm on Amazon SageMaker AI. The v3 SDK delivers a redesign from scratch that makes many workflows like the bring-your-own-model workflow even more streamlined. The new SDK replaces framework-specific estimator classes (SKLearn, PyTorch, XGBoost) with a single, unified ModelTrainer for training and ModelBuilder for deployment. In v3, the SDK syncs a local source code directory into the training job at runtime using the new SourceCode configuration object. You bring a container image from Amazon Elastic Container Registry (Amazon ECR): one you build, an AWS Deep Learning Container, or a third-party image. The SDK handles injecting your code at runtime. This means: Faster iterations: Change your training script, rerun. No container rebuild necessary. Full container control: Install system packages or CUDA libraries in your image. The SDK doesn’t assume what’s inside. One API for multiple frameworks: Whether you’re training with frameworks like scikit-learn, PyTorch, Stable Diffusion, or a custom C++ inference binary, the interface is identical. Solution overview In this post, we walk through two end-to-end examples that demonstrate how script mode works in the SageMaker Python SDK v3: Train and deploy a scikit-learn Random Forest – a classic tabular …

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