Harnessing the Power of Foundation Models with Quarkus and AWS

Introduction

Foundation models, such as OpenAI’s GPT series, have revolutionized natural language processing (NLP) tasks, offering state-of-the-art performance across various applications. Integrating these powerful models into real-world projects requires a robust infrastructure and efficient deployment strategies. In this article, we’ll explore how to leverage foundation models within a Quarkus application deployed on Amazon Web Services (AWS). We’ll provide comprehensive coding examples and step-by-step guidance to demonstrate the integration process.

Setting Up Quarkus Environment

Firstly, let’s set up our development environment with Quarkus. Ensure you have Java and Maven installed on your system. You can create a new Quarkus project using the following Maven command:

bash

mvn io.quarkus:quarkus-maven-plugin:2.7.3.Final:create \

-DprojectGroupId=com.example \

-DprojectArtifactId=quarkus-gpt \

-DclassName="com.example.GptResource" \

-Dpath="/gpt"

This command creates a new Quarkus project with a resource class GptResource mapped to the endpoint /gpt.

Integrating Foundation Models

Now, let’s integrate a foundation model into our Quarkus application. We’ll use the Hugging Face Transformers library to interact with pre-trained models. Add the following dependency to your pom.xml:

xml

<dependency>

<groupId>ai.djl</groupId>

<artifactId>ai-djl-tensorflow</artifactId>

<version>0.15.0</version>

</dependency>

With this dependency added, we can now initialize and use a GPT model within our Quarkus resource class. Here’s a simplified example:

java

import ai.djl.*;

import ai.djl.inference.Predictor;

import ai.djl.modality.nlp.SimpleVocabulary;

import ai.djl.modality.nlp.bert.BertFullTokenizer;

import ai.djl.modality.nlp.bert.BertTokenizer;

import ai.djl.modality.nlp.bert.BertTranslator;

import ai.djl.modality.nlp.bert.BertToken;

import ai.djl.modality.nlp.qa.BertQuestionAnswerTranslator;

import ai.djl.ndarray.NDArray;

import ai.djl.ndarray.NDList;

import ai.djl.ndarray.types.DataType;

import ai.djl.ndarray.types.Shape;

import ai.djl.repository.zoo.*;

import ai.djl.training.util.ProgressBar;

import ai.djl.translate.TranslateException;

import javax.ws.rs.*;
import javax.ws.rs.core.MediaType;@Path(“/gpt”)
@Produces(MediaType.APPLICATION_JSON)
@Consumes(MediaType.APPLICATION_JSON)
public class GptResource {private Predictor<String, String> predictor;public GptResource() {
try {
Criteria<String, String> criteria = Criteria.builder()
.setTypes(String.class, String.class)
.optModelUrls(“https://huggingface.co/gpt2-large/resolve”)
.optTranslator(new BertTranslator())
.optProgress(new ProgressBar())
.build();predictor = ModelZoo.loadModel(criteria).newPredictor();
} catch (ModelNotFoundException | IOException e) {
e.printStackTrace();
}
}

@POST
public String generateText(String input) {
try {
return predictor.predict(input);
} catch (TranslateException e) {
e.printStackTrace();
return “Error generating text”;
}
}
}

In this example, we initialize a GPT-2 model using the Hugging Face model zoo. We then create a method generateText to generate text based on input prompts.

Deploying Quarkus Application on AWS

Deploying our Quarkus application on AWS can be achieved using AWS Lambda and API Gateway. First, build the native executable of your Quarkus application using:

bash

./mvnw package -Pnative -Dquarkus.native.container-build=true

Then, deploy the native executable to AWS Lambda using the AWS CLI:

bash

aws lambda create-function \

--function-name quarkus-gpt \

--zip-file fileb://target/function.zip \

--handler com.example.GptResource \

--runtime provided \

--role <YOUR-LAMBDA-ROLE-ARN>

Next, create an API Gateway to expose your Lambda function as an HTTP endpoint:

bash

aws apigateway create-rest-api --name 'QuarkusGptAPI'

aws apigateway create-resource --rest-api-id <API-ID> --parent-id <PARENT-ID> --path-part 'gpt'

aws apigateway put-method --rest-api-id <API-ID> --resource-id <RESOURCE-ID> --http-method POST --authorization-type NONE

aws apigateway put-integration --rest-api-id <API-ID> --resource-id <RESOURCE-ID> --http-method POST --type AWS_PROXY --integration-http-method POST --uri arn:aws:apigateway:<REGION>:lambda:path/2015-03-31/functions/arn:aws:lambda:<REGION>:<ACCOUNT-ID>:function:quarkus-gpt/invocations

aws apigateway create-deployment --rest-api-id <API-ID> --stage-name 'prod'

Now, your Quarkus application is deployed on AWS Lambda and accessible via an API Gateway endpoint.

Conclusion

In this article, we’ve explored how to leverage foundation models, specifically GPT-based ones, with Quarkus and deploy them on AWS. By integrating foundation models into Quarkus applications, developers can easily build powerful NLP applications with minimal effort. Deploying these applications on AWS using services like Elastic Beanstalk enables scalability and reliability.

As the field of NLP continues to evolve, foundation models will play an increasingly important role in powering innovative applications. By mastering the integration of foundation models with frameworks like Quarkus and cloud platforms like AWS, developers can stay at the forefront of NLP technology and deliver cutting-edge solutions to their users.