Custom images - Amazon SageMaker AI (original) (raw)

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), and the AWS Command Line Interface (AWS CLI). You can also create images and image versions using the SageMaker AI console, even if you haven't onboarded to a SageMaker AI domain. SageMaker AI provides sample Dockerfiles to use as a starting point for your custom SageMaker images in the SageMaker Studio Classic Custom Image Samples repository.

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. For notebooks that show how to bring your own image for use in training and inference, see Amazon SageMaker Studio Classic Container Build CLI.

Key terminology

The following section defines key terms for bringing your own image to use with Studio Classic.

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