Automating the setup of SageMaker Studio Custom Images

Automating the setup of SageMaker Studio Custom Images

Step-by-step guide to continuous delivery of Custom Images in a Studio domain. Let's explore it with us now.

Step-by-step guide to continuous delivery of Custom Images in a Studio domain

Amazon SageMaker Studio is a fully integrated IDE unifying the tools needed for managing your ML projects and collaborating with your team members.

Alongside providing pre-built images for running your notebooks, SageMaker Studio allows you to create containers with your favourite libraries and attach them as custom imagesto your domain.

In most large enterprises, ML platform administrators will manage those custom images to ensure only approved libraries are used by the Studio users. This can represent operational overhead for admins, if done manually.

In this post I show how you can automate a Studio domain setup by implementing simple continuous delivery for your custom images.

We will create a custom image based on Tensorflow 2.5.0 and attach it to a Studio domain. We will use CodeCommit, CodeBuild, and CodePipeline for continuous delivery of the custom image. Feel free to use any CI/CD tool of your choice in your environment. The stack is packaged in a CloudFormation template so you can repeat the setup on demand.

Visiting Bringing your own custom container image to Amazon SageMaker Studio notebooks, the SageMaker Studio Custom Image Samples, and the CodePipeline Tutorials could be a good start if those things sound new to you.

machine-learning sagemaker aws tensorflow

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