Instance Types for Built-in Algorithms

For training and hosting Amazon SageMaker algorithms, we recommend using the following EC2 instance types: + ml.m4.xlarge, ml.m4.4xlarge, and ml.m4.10xlarge + ml.c4.xlarge, ml.c4.2xlarge, and ml.c4.8xlarge + ml.p2.xlarge, ml.p2.8xlarge, and ml.p2.16xlarge

Most Amazon SageMaker algorithms have been engineered to take advantage of GPU computing for training. Despite higher per-instance costs, GPUs train more quickly, making them more cost effective. Exceptions, such as XGBoost, are noted in this guide. (XGBoost implements an open-source algorithm that has been optimized for CPU computation.)

The size and type of data can have a great effect on which hardware configuration is most effective. When the same model is trained on a recurring basis, initial testing across a spectrum of instance types can discover configurations that are more cost effective in the long run. Additionally, algorithms that train most efficiently on GPUs might not require GPUs for efficient inference. Experiment to determine the most cost effectiveness solution.

For more information on Amazon SageMaker hardware specifications, see Amazon SageMaker ML Instance Types.