Prebuilt Amazon SageMaker Docker Images for Scikit-learn and Spark ML - Amazon SageMaker

Prebuilt Amazon SageMaker Docker Images for Scikit-learn and Spark ML

SageMaker provides prebuilt Docker images that install the scikit-learn and Spark ML libraries. These libraries also include the dependencies needed to build Docker images that are compatible with SageMaker using the Amazon SageMaker Python SDK. With the SDK, you can use scikit-learn for machine learning tasks and use Spark ML to create and tune machine learning pipelines. For instructions on installing and using the SDK, see SageMaker Python SDK.

Using the SageMaker Python SDK

The following table contains links to the GitHub repositories with the source code for the scikit-learn and Spark ML containers. The table also contains links to instructions that show how use these containers with Python SDK estimators to run your own training algorithms and hosting your own models.

For more information and links to github repositories, see Use Scikit-learn with Amazon SageMaker and Use SparkML Serving with Amazon SageMaker.

Specifying the Prebuilt Images Manually

If you are not using the SageMaker Python SDK and one of its estimators to manage the container, you have to retrieve the relevant prebuilt container manually. The SageMaker prebuilt Docker images are stored in Amazon Elastic Container Registry (Amazon ECR). You can push or pull them using their fullname registry addresses. SageMaker uses the following Docker Image URL patterns for scikit-learn and Spark ML:


    For example,


    For example,

For account IDs and AWS Region names, see Docker Registry Paths and Example Code.

Finding Available Images

Use the following commands to find out which versions of the images are available. For example, use the following to find the available sagemaker-sparkml-serving image in the ca-central-1 Region:

aws \ ecr describe-images \ --region ca-central-1 \ --registry-id 341280168497 \ --repository-name sagemaker-sparkml-serving