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

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

Amazon SageMaker provides prebuilt Docker images that install the scikit-learn and Spark ML libraries and the dependencies they need to build Docker images that are compatible with Amazon 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. The following table contains links to the GitHub repositories with the source code and the Dockerfiles for scikit-learn and Spark ML frameworks and to instructions that show how use the Python SDK estimators to run your own training algorithms on Amazon SageMaker Learner and your own models on Amazon SageMaker Hosting.

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


    For example,

  • <ACCOUNT_ID>.dkr.ecr.<REGION_NAME>

    For example,

The following table lists the supported values for account IDs and corresponding AWS Region names.

746614075791 us-west-1
246618743249 us-west-2
683313688378 us-east-1
257758044811 us-east-2
354813040037 ap-northeast-1
366743142698 ap-northeast-2
121021644041 ap-southeast-1
783357654285 ap-southeast-2
720646828776 ap-south-1
141502667606 eu-west-1
764974769150 eu-west-2
492215442770 eu-central-1
341280168497 ca-central-1
414596584902 us-gov-west-1

The supported values listed in the table are also available on the page of the Amazon SageMaker Python SDK GitHub repository.

Finding the Images

To find out which versions of the image are available use the following commands. For example, use the following for the ca-central-1 Region and the sagemaker-sparkml-serving image:

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

Amazon SageMaker also provides prebuilt Docker images for popular deep learning frameworks. For information about Docker images that enable using deep learning frameworks in Amazon SageMaker, see Prebuilt Amazon SageMaker Docker Images for TensorFlow, MXNet, Chainer, and PyTorch.

For information on Docker images for developing reinforcement learning (RL) solutions in Amazon SageMaker, see Amazon SageMaker RL Containers.