The instructions to install Docker CLI and Docker Compose plugin based on SageMaker Studio Environment are documented below. Follow specific instructions based on applicable Studio Application Type / Images. These instructions adhere to Studio platforms requirements for enabling Local Mode/Docker Access.

• SageMaker Distribution Docker CLI Install Directions: This script provides instructions for Docker CLI Install in Studio JupyterLab/Studio Code Editor and Studio Classic SageMaker Distribution Images which are Ubuntu-Jammy based. Do cat /etc/os-release to verify the OS in App Image terminal.
  • Applicable Studio AppType/Images:
    • JupyterLab
    • Code Editor
    • Amazon SageMaker Studio Classic [Kernel Gateway Applications]
      • Applicable Images: SageMaker Distribution v0 CPU, SageMaker Distribution v0 GPU, SageMaker Distribution v1 CPU, SageMaker Distribution v1 GPU.
• SageMaker Classic - Debian-Bullseye Docker CLI Install Directions: This script provides instructions for Docker CLI Install for Studio Classic SageMaker Images which are Debian-Bullseye based. Do cat /etc/os-release to verify the OS in App Image terminal.
  • Applicable Studio AppTypes/Images:
    • Amazon SageMaker Studio Classic [Kernel Gateway Applications]
      • Applicable Images: Base Python 3.0, Base Python 2.0, Data Science 3.0, Data Science 2.0, SparkAnalytics 2.0, SparkAnalytics 1.0.
• SageMaker Classic - Ubuntu-Focal Docker CLI Install Directions: This script provides instructions for Docker CLI Install for Studio Classic SageMaker Images which are Ubuntu-Focal based. Do cat /etc/os-release to verify the OS in App Image terminal.
  • Applicable Studio AppTypes/Images:
    • Amazon SageMaker Studio Classic [Kernel Gateway Applications]
      • Applicable Images: All currently supported Pytorch/Tensorflow Framework based Studio Images here.