Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Prebuilt SageMaker AI Docker images for deep learning

Amazon SageMaker AI provides prebuilt Docker images that include deep learning frameworks and other dependencies needed for training and inference. For a complete list of the prebuilt Docker images managed by SageMaker AI, see Docker Registry Paths and Example Code.

Using the SageMaker AI Python SDK

With the SageMaker Python SDK, you can train and deploy models using these popular deep learning frameworks. For instructions on installing and using the SDK, see Amazon SageMaker Python SDK. The following table lists the available frameworks and instructions on how to use them with the SageMaker Python SDK:

Extending Prebuilt SageMaker AI Docker Images

You can customize these prebuilt containers or extend them as needed. With this customization, you can handle any additional functional requirements for your algorithm or model that the prebuilt SageMaker AI Docker image doesn't support. For an example of this, see Fine-tuning and deploying a BERTopic model on SageMaker AI with your own scripts and dataset, by extending existing PyTorch containers.

You can also use prebuilt containers to deploy your custom models or models that have been trained in a framework other than SageMaker AI. For an overview of the process, see Bring Your Own Pretrained MXNet or TensorFlow Models into Amazon SageMaker. This tutorial covers bringing the trained model artifacts into SageMaker AI and hosting them at an endpoint.