DJL Spring Boot Demo

This repository contains example code demonstrating how to use the Deep Java Library (DJL) with Spring Boot and the DJL Spring Boot Starter. It covers MXNet-based object detection inference with platform specific DJL libraries that can be consumed using DJL Spring Boot Starter dependencies.

Building the backend

Examples use Gradle as the build tool. It is a multi-project build. Before building, ensure that the DJL Spring Boot starter BOM is in your local maven repository. For more information, see the DJL Spring Boot Starter repo. You need to check out this repository and run ./mvnw install.

To build the DJL Spring Boot microservice, run the following command:

./gradlew :djl-spring-boot-app:bootJar

This command detects the operating system on the system where the build is running and uses it for platform dependency resolution.

Platform specific builds (for CI):

./gradlew :djl-spring-boot-app:bootJar -P osclassifier=linux-x86_64
./gradlew :djl-spring-boot-app:bootJar -P osclassifier=osx-x86_64
./gradlew :djl-spring-boot-app:bootJar -P osclassifier=win-x86_64

The produced artifacts will have the classifier in the name of the spring boot uber jar, e.g.

djl-spring-boot-app/build/libs/djl-spring-boot-app-0.0.1-SNAPSHOT-linux-x86_64.jar
djl-spring-boot-app/build/libs/djl-spring-boot-app-0.0.1-SNAPSHOT-osx-x86_64.jar

Running the backend

Running the app requires the following: The S3 bucket (djl-demo by default) must be specified in djl-spring-boot-app/src/main/resources/application.properties. The bucket is expected to have two prefixes: inbox, where input is located, and outbox, where results are placed. AWS_ACCESS_KEY and AWS_SECRET_KEY set as environment variables for region us-east-1. The IAM user must have read permissions for the inbox and write permissions for the outbox.

Run the following command to start the backend based on the created JAR file, sample command for macOS:

java -jar djl-spring-boot-app/build/libs/djl-spring-boot-app-0.0.1-SNAPSHOT-osx-x86_64.jar

Alternatively you can use Gradle for execution, sample command for Windows:

gradlew :djl-spring-boot-app:bootRun -P osclassifier=win-x86_64

Deploying to PCF

Make sure your manifest.yml file contains valid S3 access credentials. You can specify your own bucket in [application.properties] (djl-spring-boot-app/src/main/resources/application.properties) and web application.properties.

Use the following command to deploy:

cf login
cf push -f manifest.yml -p build/libs/djl-spring-boot-app-0.0.1-SNAPSHOT-linux-x86_64.jar

Check the logs:

cd logs djl-demo --recent

Deploying to EKS (Amazon Elastic Kubernetes Service)

The instructions in this section may be generalized for any vanilla Kubernetes cluster, however they contain EKS specific features, such as IAM Roles integration for Kubernetes Service Accounts (IRSA).

Deployment to EKS is implemented in such a way that credentials to access remote resources such as S3 buckets from the pod are not stored anywhere on the cluster, instead we will provision a role with the right IAM policy and use it instead.

We will use eksctl tool to provision an EKS cluster for demonstration purposes. You also need to install kubectl – A command line tool for working with Kubernetes clusters.

Before you start, make sure you have AWS CLI installed and proper credentials to access your AWS account. Steps 2, 4 and 5 are only needed if you would like to protect access to your S3 bucket properly (it is NOT a good idea to open up an bucket with read/write access to the world).

1. Create EKS Cluster

   eksctl create cluster --name=djl-demo --nodes=2

2. Create an OIDC Identity Provider (for IRSA)

   eksctl utils associate-iam-oidc-provider --cluster djl-demo --approve
   

   More info: EKS Workshop

3. Create djl-demo-<youraccount> S3 bucket. We will use two prefixes: inbox for incoming images and outbox for outgoing processed images with detected objects. The bucket name must be unique so in this example we add a suffix for your AWS account number which should replace the placeholder <YOUR_AWS_ACCOUNT>.

   aws s3 mb s3://djl-demo-<YOUR_AWS_ACCOUNT>

   Note: this will create a bucket that will require proper authorization. Objects in the bucket will not be accessible publicly.

4. Create IAM Policy to access the S3 bucket that you created (will be used for pods deployed in the demo):

   • Create a copy of the docs/AccesDjlBucketPolicyTemplate.json as AccessDjlBucketPolicy.json replacing <YOUR_BUCKET_NAME> with your actual bucket name.

   cp docs/AccessDjlBucketPolicyTemplate.json AccessDjlBucketPolicy.json
   # edit your AccessDjlBucketPolicy.json file
   aws iam create-policy --policy-name AccessDjlBucket --policy-document file://AccessDjlBucketPolicy.json

   • You can get the ARN on of the created policy with the following command:

    aws iam list-policies --query 'Policies[?PolicyName==`AccessDjlBucket`].Arn'

5. Create a service account for the backend service:

   Note: Adjust the namespace if you plan to deploy to a namespace different from default.

   eksctl create iamserviceaccount \
       --name djl-backend-account \
       --namespace default \
       --cluster djl-demo \
       --attach-policy-arn <ARN_OF_POLICY_OBTAINED_IN_STEP_4> \
       --approve \
       --override-existing-serviceaccounts

6. In this example we will use Amazon ECR private repository to push images. The repository will need to be created upfront.

   aws ecr create-repository --repository-name djl-spring-boot-app

7. Build and push the container image for the API app to an accessible container (Docker) registry.

   • You must be properly authenticated to push images to Amazon ECR. For more info see this doc.

   aws ecr get-login-password --region <YOUR_REGION> | docker login --username AWS --password-stdin <YOUR_AWS_ACCOUNT>.dkr.ecr.<YOUR_REGION>.amazonaws.com

   • Assuming you forked this repository, modify the jib section of the djl-spring-boot-app/build.gradle.kts to reflect your settings (replace the placeholders):

   jib {
       from.image = "adoptopenjdk/openjdk13:debian"
       to.image = "<YOUR_AWS_ACCOUNT>.dkr.ecr.<YOUR_REGION>.amazonaws.com/djl-spring-boot-app"
       to.tags = versionTags
   } 

   • From the root directory:

   ./gradlew djl-spring-boot-app:bootjar
   ./gradlew djl-spring-boot-app:jib

   The above will push the image to the Amazon ECR container registry and output the image/tag pair that you will need for the subsequent steps.

8. Deploy the application:

   • Modify the provided docs/deployment-template.yaml and specify your image:tag produced in step 7

   • Run the command below to deploy the application and create a load balancer to access it over HTTP (don't use this approach in production without TLS in place):

   # assuming you saved the modified deployment-template.yaml in the current directory as deployment.yaml
   kubectl apply -f deployment.yaml

   You can set the -n YOUR_NAMESPACE flag on the command if you created the service account in a different namespace.

   Note: this deployment is leveraging the service account djl-backend-account created in the previous steps. If you don't need a service account (e.g. in case you modified the app to read from local storage), then remove the
   service account association in the template (serviceAccountName: djl-backend-account).

9. Test your api:

   • Get the load balancer public DNS name by listing the created service

   # ensure the pod is in running state
   kubectl get po 
   # get the loadbalancer URL
   kubectl get svc djl-app

   • Upload a file to your S3 bucket inbox folder

       curl -O https://resources.djl.ai/images/kitten.jpg
       aws s3 cp kitten.jpg s3://<YOUR_BUCKET_NAME>/inbox/kitten.jpg

   • Test with curl

       curl -v "http://<YOUR_LOAD_BALANCER_DNS>/inference?file=kitten.jpg&generateOutputImage=true"

   • Get the output file from s3 outbox folder (at present .png extension is always appended)

       aws s3 ls s3://<YOUR_BUCKET_NAME>/outbox/
       aws s3 cp s3://<YOUR_BUCKET_NAME>/outbox/kitten.jpg.png

This completes the EKS deployment portion of the application.

The EKS deployment of the API can be modified to scale up and down based on demand and spin up pods as needed based on HPA. The recommended approach to deploy any workloads on EKS is to use GipOps approach such as FluxCD or ArgoCD.

Frontend Web Application

Web Application

License

This project is licensed under the Apache-2.0 License.