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A guide on how to deploy and monitor an application built on AWS using Atlassian and third-party tools.
To demonstrate how to develop, deploy, and manage applications using Jira Software and various connected tools, our team created ImageLabeller, a simple demo application built on AWS that uses machine learning to apply labels to images.
AWS demo application | ImageLabeller
Image labeller is a simple demo application that uses machine learning to apply labels to images. It uses AWS Lambda, AWS DynamoDB, AWS S3, AWS SageMaker, AWS API Gateway, AWS CloudWatch, and AWS CloudFormation, and is written in Go, and Python. This guide covers how to use Atlassian Open DevOps tools to deploy, and monitor a subset of these components.
How it works
A user invokes AWS Lambda SubmitImage, passing a valid image URL as a parameter. SubmitImage downloads the image, uploads it to an AWS S3 bucket open-devops-images-<region>, inserts a new tuple into an AWS DynamoDB table ImageLabels, and returns an image ID token to the user. The image ID token is used to correlated the image in open-devops-images-<region>, and the tuple in ImageLabels.
The AWS Lambda InvokeLabeller has an AWS DynamoDB event trigger. InvokeLabeller downloads the corresponding image from open-devops-images-<region>, calls a pre-trained AWS SageMaker Inception V3 model to generate image labels, and updates the tuple in ImageLabels when a new tuple is inserted into ImageLabels.
The user invokes AWS Lambda GetImageLabel, passing the image ID token as a parameter. GetImageLabel looks up the labels associated with the image ID token in ImageLabels, and returns them to the user. The user gets a NOT_CLASSIFIED label if the AWS SageMaker model hasn’t finished processing an image.
Image labeller is deployed to five environments. A Test environment in us-west-1, a Staging environment in us-east-2, and three Production environments in us-west-2, us-east-1, and ca-central-1.
Public facing Github repositories with Image Labeller code
How to use this guide
The AWS S3 buckets and AWS DynamoDB tables that are part of ImageLabeller are deployed using AWS CloudFormation templates. This guide provides CI/CD configuration information for how to deploy the AWS S3 buckets. The process for deploying the AWS DynamoDB tables is almost identical, and is left as an exercise for you to complete on your own.
There are three AWS Lambda in ImageLabeller: SubmitImage, InvokeLabeller, and GetImageLabel. This guide provides CI/CD configuration information for how to deploy SubmitImage. The process for deploying GetImageLabel is almost identical, and is also left for you to complete on your own.
InvokeLabeller is a python based AWS Lambda, and requires minor updating before it can be published. Specifically, InvokeLabeller is dependent on AWS SageMaker configuration which is done through the AWS console.
The system tests included in the ImageLabeller project test ImageLabeller as a whole. The tests will fail if any component of ImageLabeller is not present or working as expected. SubmitImage, GetImageLabel, and InvokeLabeller deployments all execute the system tests as part of their respective CI/CD pipelines.
For the initial deployment of SubmitImage, GetImageLabel, and InvokeLabeller you need to comment out system test execution steps. Once the deployments have successfully run once, you can uncomment the system test execution steps, and redeploy.
Order to deploy components
Deploy AWS SageMaker pre-trained model manually through the AWS console
Deploy AWS infrastructure repositories
Deploy AWS Lambdas
Deploy System Tests
Now that you understand what ImageLabeller is, it’s time to actually deploy it. The first step is to setup AWS SageMaker. Click here to get started!
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