Customize Amazon SageMaker Studio Classic - Amazon SageMaker
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Customize Amazon SageMaker Studio Classic

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.

There are four options for customizing your Amazon SageMaker Studio Classic environment. You bring your own SageMaker image, use a lifecycle configuration script, attach suggested Git repos to Studio Classic, or create kernels using persistent Conda environments in Amazon EFS. Use each option individually, or together.

  • Bring your own SageMaker image: 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. Amazon SageMaker provides many built-in images for you to use. If you need different functionality, you can bring your own custom images to Studio Classic.

  • Use lifecycle configurations with Amazon SageMaker Studio Classic: Lifecycle configurations are shell scripts triggered by Amazon SageMaker Studio Classic lifecycle events, such as starting a new Studio Classic notebook. You can use lifecycle configurations to automate customization for your Studio Classic environment. For example, you can install custom packages, configure notebook extensions, preload datasets, and set up source code repositories.

  • Attach suggested Git repos to Studio Classic: You can attach suggested Git repository URLs at the Amazon SageMaker domain or user profile level. Then, you can select the repo URL from the list of suggestions and clone that into your environment using the Git extension in Studio Classic.

  • Persist Conda environments to the Studio Classic Amazon EFS volume: Studio Classic uses an Amazon EFS volume as a persistent storage layer. You can save your Conda environment on this Amazon EFS volume, then use the saved environment to create kernels. Studio Classic automatically picks up all valid environments saved in Amazon EFS as KernelGateway kernels. These kernels persist through restart of the kernel, app, and Studio Classic. For more information, see the Persist Conda environments to the Studio Classic EFS volume section in Four approaches to manage Python packages in Amazon SageMaker Studio Classic notebooks.

The following topics show how to use these three options to customize your Amazon SageMaker Studio Classic environment.

Topics