Data Science Garage presents a hands-on step by step tutorial: how to push a #Docker image which was built from Machine Learning (#ML) application directly in Github repository to AWS Elastic Container Registry (#AWS ECR). The provided steps are valid for any application stored in Github repository, for example for NodeJS, C++, React -based applications. The app. project must contains a Dockerfile which defines an instruction how to build a Docker image from that app.
With this tutorial you will learn how to write an YML (sometimes is called YAML) script to define your workflow. Github Action triggers this workflow file on push events in your Github repository. This approach is broadly used in Machine Learning Operations (MLOps) domain where ML engineers/ML developers builds solutions to automate application testing, deploying, monitoring and debugging. For DevOps this technique is also beneficial in designing CI/CD (Continuous Integration and Continuous Delivery) pipelines.
The parts of this video are:
0:00 - Solution scheme and theory
2:10 - Main facts about the application
3:56 - Start Setup Github action
6:31 - Create AWS User
8:01 - Handle AWS User credentials
9:39 - Create AWS ECR Repository
11:01 - Save Github Action and test workflow
12:03 - Github action test (2) - after changes made on the code
13:32 - Result: AWS ECR Images
Amazon Elastic Container Registry (Amazon ECR) is an AWS managed container image registry service that is secure, scalable, and reliable. Amazon ECR supports private repositories with resource-based permissions using AWS IAM. AWS ECR has many integration points with other AWS services, such as AWS SageMaker, AWS K8S. Additionally, you can bring your application images to AWS ECR by using MLflow API.
Official AWS ECR website: docs.aws.amazon.com/AmazonECR...
You can clone the repository used in this tutorial and replicate all the steps by yourself. Get the repository from here: github.com/vb100/github-actio...
Workflow file (main.yml) which declares steps of CI/CD pipeline: raw.githubusercontent.com/vb1...
Once you have built your Docker images on AWS ECR, you can bring your application to production in several scenarios. You can combine it with Kubernetes, or if it is a ML-based application, you can deploy it in production by enabling batch predictions (I have prepared a video tutorial for that here: • How to Deploy ML model... )
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Thank you!