Automating ML Image Builds with AWS CodePipeline and Amazon ECR

Overview

This blog explores how to streamline and automate the process of building and storing Docker images for machine learning (ML) applications using AWS services. We will focus on integrating AWS CodePipeline for automation and Amazon ECR for container storage. This automation helps teams reduce manual steps, eliminate errors, and accelerate the ML deployment cycle.

Introduction

Building Docker images is an essential but often repetitive task in production-grade machine learning workflows. Manual builds can delay deployments, introduce inconsistencies, and complicate team collaboration. Automating this step ensures faster, consistent, and more reliable deployments.

AWS provides a suite of services, AWS CodePipeline, AWS CodeBuild, and Amazon ECR, that can work together to automate Docker image creation and storage. In this blog, we will walk through setting up a complete CI/CD pipeline that automatically builds ML Docker images and pushes them to Amazon ECR whenever there are updates to your code.

Architecture Overview

• Source: A version-controlled repository (GitHub, AWS CodeCommit, or an Amazon S3 bucket) that contains your ML project and Dockerfile.
• AWS CodePipeline: Orchestrates the automation, tracking source changes, and triggering builds.
• AWS CodeBuild: Compiles and packages the Docker image.
• Amazon ECR (Elastic Container Registry): A managed container image registry that stores the output images for use in services like Amazon ECS or Amazon EKS.

AWS CodePipeline

Orchestrates the entire CI/CD process by automating the workflow from source to build and optionally to deployment.

AWS CodePipeline ensures that every code update triggers the pipeline automatically. It helps maintain a consistent, repeatable build process and can integrate with manual approval steps, ensuring controlled and auditable deployments, which is especially important for production ML models.

AWS CodeBuild

A fully managed build service that compiles source code, runs unit tests, and builds Docker images.

AWS CodeBuild is responsible for building Docker images based on your Dockerfile and buildspec.yml. It handles dependencies and environment configuration and securely pushes images to Amazon ECR, eliminating the need to run builds locally or manage your build servers.

Amazon Elastic Container Registry (Amazon ECR)

A fully managed container image registry for storing, managing, and deploying Docker images.

Amazon ECR provides secure, scalable storage for ML Docker images. Once AWS CodeBuild builds an image, it’s pushed to Amazon ECR, from where it can be pulled by Amazon ECS, Amazon EKS, or other container services for deployment, ensuring consistency and version control across environments.

Amazon S3

Scalable object storage used to store source code, datasets, or artifacts.

If GitHub or AWS CodeCommit isn’t used, the ML project (including the Dockerfile and source code) can be uploaded to an Amazon S3 bucket. AWS CodePipeline can use this bucket as a source stage, making it a simple and cost-effective option for storing code for CI/CD pipelines.

Why Automate ML Image Builds?

• Faster Iteration: Every update triggers a fresh image build automatically.
• Consistency: Ensures that all environments use identical Docker images.
• Security: Amazon ECR offers encryption, versioning, and access control.
• Scalability: Supports scalable and team-friendly workflows without manual interference.

Steps in Automating ML Image Builds with AWS CodePipeline and Amazon ECR

Step 1: Prepare Your ML Codebase

Ensure you have all the files and the dockerfile required to build the image in the S3 bucket.

Example of dockerfile:

Step 2: Create an Amazon ECR Repository

Note: Replace <repo-name> and <region-name> with your actual values.

Step 3: Add buildspec.yml for AWS CodeBuild

Note: Replace <AWS_ACCOUNT_ID> and $AWS_REGION with your AWS details.

Step 4: Set Up AWS CodeBuild

• Create a build project in AWS CodeBuild.
• Use a managed image with Docker pre-installed.
• Link your source code repository.
• Attach a service role that allows access to Amazon ECR.

Step 5: Configure AWS CodePipeline

• Source Stage: Choose your Amazon S3 bucket.
• Build Stage: Attach the AWS CodeBuild project you created.

Use Case

Imagine you’re developing a machine learning pipeline using MLflow. You want the Docker image to update every time your code changes. With this setup:

• Developers push code changes to the repo.
• AWS CodePipeline picks up the change.
• AWS CodeBuild builds a new image and pushes it to Amazon ECR.
• Amazon EKS pulls the latest image for deployment.

Conclusion

Drop a query if you have any questions regarding Amazon ECR and we will get back to you quickly.

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