Important
As of November 30, 2023, the previous Amazon SageMaker Studio experience is now named Amazon SageMaker Studio Classic. The following section is specific to using the Studio Classic application. For information about using the updated Studio experience, see Amazon SageMaker Studio.
A SageMaker image is a file that identifies the kernels, language packages, and other dependencies required to run a Jupyter notebook in Amazon SageMaker Studio Classic. These images are used to create an environment that you then run Jupyter notebooks from. Amazon SageMaker AI provides many built-in images for you to use. For the list of built-in images, see Amazon SageMaker images available for use with Studio Classic.
If you need different functionality, you can bring your own custom images to Studio Classic. You
can create images and image versions, and attach image versions to your domain or shared space,
using the SageMaker AI control panel, the AWS SDK for Python (Boto3)
The following topics explain how to bring your own image using the SageMaker AI console or AWS CLI,
then launch the image in Studio Classic. For a similar blog article, see Bringing
your own R environment to Amazon SageMaker Studio Classic
Key terminology
The following section defines key terms for bringing your own image to use with Studio Classic.
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Dockerfile: A Dockerfile is a file that identifies the language packages and other dependencies for your Docker image.
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Docker image: The Docker image is a built Dockerfile. This image is checked into Amazon ECR and serves as the basis of the SageMaker AI image.
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SageMaker image: A SageMaker image is a holder for a set of SageMaker AI image versions based on Docker images. Each image version is immutable.
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Image version: An image version of a SageMaker image represents a Docker image and is stored in an Amazon ECR repository. Each image version is immutable. These image versions can be attached to a domain or shared space and used with Studio Classic.
Topics
