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
.