Update container configuration - Amazon SageMaker AI

Update container configuration

You can bring custom Docker images into your machine learning workflows. A key aspect of customizing these images is configuring the container configurations, or ContainerConfig. The following page provides an example on how to configure your ContainerConfig.

An entrypoint is the command or script that runs when the container starts. Custom entrypoints enable you to set up your environment, initialize services, or perform any necessary setup before your application launches.

This example provides instructions on how to configure a custom entrypoint, for your JupyterLab application, using the AWS CLI. This example assumes that you have already created a custom image and domain. For instructions, see Attach your custom image to your domain.

  1. First set your variables for the AWS CLI commands that follow.

    APP_IMAGE_CONFIG_NAME=app-image-config-name
ENTRYPOINT_FILE=entrypoint-file-name
ENV_KEY=environment-key
ENV_VALUE=environment-value
REGION=aws-region
DOMAIN_ID=domain-id
IMAGE_NAME=custom-image-name
IMAGE_VERSION=custom-image-version

    • app-image-config-name is the name of your application image configuration.

    • entrypoint-file-name is the name of your container's entrypoint script. For example, entrypoint.sh.

    • environment-key is the name of your environment variable.

    • environment-value is the value assigned to your environment variable.

    • aws-region is the AWS Region of your Amazon SageMaker AI domain. You can find this at the top right of any AWS console page.

    • domain-id is your domain ID. To view your domains, see View domains.

    • custom-image-name is the name of your custom image. To view your custom image details, see View custom image details (console).

      If you followed the instructions in Attach your custom image to your domain, you may want to use the same image name you used in that process.

    • custom-image-version is the version number of your custom image. This should be an integer, representing the version of your image. To view your custom image details, see View custom image details (console).

  2. Use the CreateAppImageConfig API to create an image configuration.

    aws sagemaker create-app-image-config \
    --region ${REGION} \
    --app-image-config-name "${APP_IMAGE_CONFIG_NAME}" \
    --jupyter-lab-app-image-config "ContainerConfig = {
        ContainerEntrypoint = "${ENTRYPOINT_FILE}", 
        ContainerEnvironmentVariables = {
            "${ENV_KEY}"="${ENV_VALUE}"
        }
    }"

  3. Use the UpdateDomain API to update the default settings for your domain. This will attach the custom image as well as the application image configuration. 

    aws sagemaker update-domain \
    --region ${REGION} \
    --domain-id "${DOMAIN_ID}" \
    --default-user-settings "{
        \"JupyterLabAppSettings\": {
            \"CustomImages\": [
                {
                    \"ImageName\": \"${IMAGE_NAME}\",
                    \"ImageVersionNumber\": ${IMAGE_VERSION},
                    \"AppImageConfigName\": \"${APP_IMAGE_CONFIG_NAME}\"
                }
            ]
        }
    }"