Custom image specifications - Amazon SageMaker AI (original) (raw)

The image that you specify in your Dockerfile must match the specifications in the following sections to create the image successfully.

Topics

• Running the image
• Specifications for the user and file system
• Health check and URL for applications
• Dockerfile samples

Running the image

The following configurations can be made by updating your ContainerConfig. For an example, see Update container configuration.

• Entrypoint – You can configure ContainerEntrypoint andContainerArguments that are passed to the container at runtime. We recommend configuring your entry point using ContainerConfig. See the above link for an example.
• EnvVariables – When using Studio, you can define customContainerEnvironment variables for your container. You can optionally update your environmental variables using ContainerConfig. See the above link for an example.
  SageMaker AI-specific environment variables take precedence and will override any variables with the same names. For example, SageMaker AI automatically provides environment variables prefixed withAWS_ and SAGEMAKER_ to ensure proper integration with AWS services and SageMaker AI functionality. The following are a few example SageMaker AI-specific environment variables:
  • AWS_ACCOUNT_ID
  • AWS_REGION
  • AWS_DEFAULT_REGION
  • AWS_CONTAINER_CREDENTIALS_RELATIVE_URI
  • SAGEMAKER_SPACE_NAME
  • SAGEMAKER_APP_TYPE

Specifications for the user and file system

• WorkingDirectory – The Amazon EBS volume for your space is mounted on the path /home/sagemaker-user. You can't change the mount path. Use theWORKDIR instruction to set the working directory of your image to a folder within/home/sagemaker-user.
• UID – The user ID of the Docker container. UID=1000 is a supported value. You can add sudo access to your users. The IDs are remapped to prevent a process running in the container from having more privileges than necessary.
• GID – The group ID of the Docker container. GID=100 is a supported value. You can add sudo access to your users. The IDs are remapped to prevent a process running in the container from having more privileges than necessary.
• Metadata directories – The /opt/.sagemakerinternal and/opt/ml directories that are used by AWS. The metadata file in/opt/ml contains metadata about resources such asDomainId.
  Use the following command to show the file system contents:

cat /opt/ml/metadata/resource-metadata.json  

• Logging directories – /var/log/studio are reserved for the logging directories of your applications and the extensions associated with it. We recommend that you don't use these folders in creating your image.

Health check and URL for applications

The health check and URL depend on the applications. Choose the following link associated with the application you are building the image for.

• Health check and URL for applications for Code Editor
• Health check and URL for applications for JupyterLab

Dockerfile samples

For Dockerfile samples that meet both the requirements on this page and your specific application needs, navigate to the sample Dockerfiles in the respective application's section. The following options include Amazon SageMaker Studio applications.

• Dockerfile examples for Code Editor
• Dockerfile examples for JupyterLab

Note

If you are bringing your own image to SageMaker Unified Studio, you will need to follow the Dockerfile specifications in the Amazon SageMaker Unified Studio User Guide.

Dockerfile examples for SageMaker Unified Studio can be found in Dockerfile example in the Amazon SageMaker Unified Studio User Guide.