With Amazon SageMaker, you can package your own algorithm in a container that can then be used for model training, inference, and hyperparameter tuning. The following notebooks guide you through the process of building custom Docker containers that leverage the Scikit-Learn and TensorFlow frameworks. You will then use your custom containers for training and hyperparameter tuning with SageMaker.
By packaging an algorithm in a container, you can bring almost any code to the Amazon SageMaker environment, regardless of programming language, environment, framework, or dependencies.
Even in cases where SageMaker already provides direct SDK support (ex: an Estimator) for your desired environment or framework, you may find it more effective to build your own container.
Some of the reasons to build a container for a framework that is already support in SageMaker include: