Today Amazon SageMaker announced the support of SageMaker training instance fallbacks for Amazon SageMaker Automatic Model Tuning (AMT) that allow users to specify alternative compute resource configurations.
SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using the ranges of hyperparameters that you specify for your algorithm. Then, it chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose.
Previously, users only had the option to specify a single instance configuration. This can lead to problems when the specified instance type isn’t available due to high utilization. In the past, your training jobs would fail with an InsufficientCapacityError (ICE). AMT used smart retries to avoid these failures in many cases, but it remained powerless in the face of sustained low capacity.
This new feature means that you can specify a list

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