Custom images - Amazon SageMaker AI
Health check and URL for applicationsDockerfile examples
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Custom images

If you need functionality that is different than what's provided by SageMaker distribution, you can bring your own image with your custom extensions and packages. You can also use it to personalize the JupyterLab UI for your own branding or compliance needs.

The following page will provide JupyterLab-specific information and templates to create your own custom SageMaker AI images. This is meant to supplement the Amazon SageMaker Studio information and instructions on creating your own SageMaker AI image and bringing your own image to Studio. To learn about custom Amazon SageMaker AI images and how to bring your own image to Studio, see Bring your own image (BYOI).

Health check and URL for applications

  • Base URL – The base URL for the BYOI application must be jupyterlab/default. You can only have one application and it must always be named default.

  • HealthCheck API – SageMaker AI uses the health check endpoint at port 8888 to check the health of the JupyterLab application. jupyterlab/default/api/status is the endpoint for the health check.

  • Home/Default URL – The /opt/.sagemakerinternal and /opt/ml directories that are used by Amazon. The metadata file in /opt/ml contains metadata about resources such as DomainId.

  • Authentication – To enable authentication for your users, turn off the Jupyter notebooks token or password based authentication and allow all origins.

Dockerfile examples

The following examples are Dockerfiles that meets the above information and Custom image specifications.

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.

Example AL2023 Dockerfile

The following is an example AL2023 Dockerfile that meets the above information and Custom image specifications.

FROM public.ecr.aws/amazonlinux/amazonlinux:2023

ARG NB_USER="sagemaker-user"
ARG NB_UID=1000
ARG NB_GID=100

# Install Python3, pip, and other dependencies
RUN yum install -y \
    python3 \
    python3-pip \
    python3-devel \
    gcc \
    shadow-utils && \
    useradd --create-home --shell /bin/bash --gid "${NB_GID}" --uid ${NB_UID} ${NB_USER} && \
    yum clean all

RUN python3 -m pip install --no-cache-dir \
    'jupyterlab>=4.0.0,<5.0.0' \
    urllib3 \
    jupyter-activity-monitor-extension \
    --ignore-installed

# Verify versions
RUN python3 --version && \
    jupyter lab --version

USER ${NB_UID}
CMD jupyter lab --ip 0.0.0.0 --port 8888 \
    --ServerApp.base_url="/jupyterlab/default" \
    --ServerApp.token='' \
    --ServerApp.allow_origin='*'
Example Amazon SageMaker Distribution Dockerfile

The following is a example Amazon SageMaker Distribution Dockerfile that meets the above information and Custom image specifications.

FROM public.ecr.aws/sagemaker/sagemaker-distribution:latest-cpu
ARG NB_USER="sagemaker-user"
ARG NB_UID=1000
ARG NB_GID=100

ENV MAMBA_USER=$NB_USER

USER root

RUN apt-get update
RUN micromamba install sagemaker-inference --freeze-installed --yes --channel conda-forge --name base

USER $MAMBA_USER

ENTRYPOINT ["jupyter-lab"]
CMD ["--ServerApp.ip=0.0.0.0", "--ServerApp.port=8888", "--ServerApp.allow_origin=*", "--ServerApp.token=''", "--ServerApp.base_url=/jupyterlab/default"]