Lifecycle configurations within Amazon SageMaker Studio - Amazon SageMaker AI
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Lifecycle configurations within Amazon SageMaker Studio

Administrators and users can create and attach lifecycle configurations (LCCs) to automate the customization of the following applications within your Amazon SageMaker Studio environment:

  • Amazon SageMaker AI JupyterLab

  • Code Editor, based on Code-OSS, Visual Studio Code - Open Source

  • Studio Classic

  • Notebook instance

Customizing your application includes:

  • Installing custom packages

  • Configuring extensions

  • Preloading datasets

  • Setting up source code repositories

Users create and attach built-in lifecycle configurations to their own user profiles. Administrators create and attach default or built-in lifecycle configurations at the domain, space, or user profile level.

Important

Amazon SageMaker Studio first runs the built-in lifecycle configuration and then runs the default LCC. Amazon SageMaker AI won't resolve package conflicts between the user and administrator LCCs. For example, if the built-in LCC installs python3.11 and the default LCC installs python3.12, Studio installs python3.12.