Introducing custom containers in SageMaker
In Chapter 6 and Chapter 7, we went over various options for training and deploying models on Amazon SageMaker. These options allow you to cover a variety of scenarios that should address most of your ML needs. As you saw, SageMaker makes heavy use of Docker containers to train and host models. By utilizing pre-built SageMaker algorithms and framework containers, you can use SageMaker to train and deploy ML models with ease. Sometimes, however, your need may not be fully addressed by the pre-built containers. This may be because you need specific software or a dependency that cannot be directly addressed by the framework and algorithm containers in SageMaker. This is when you can use the option of bringing your own container to SageMaker. To do this, you need to adapt or create a container that can work with SageMaker. Let’s now dive into the details of how to utilize this option.
