Streamlit On Fargate: Hosting Scalable AI Workflows With AWS Bedrock And CDK

Deploying Python-based frontends for AI applications has become increasingly streamlined with tools like Streamlit and AWS services. This article explores how to host scalable AI workflows using Streamlit, AWS Fargate, and the Cloud Development Kit (CDK) while integrating AWS Bedrock for AI-driven queries.

You Should Know:

Setting Up Streamlit with AWS ECS and Fargate
AWS Fargate eliminates the need to manage servers, allowing you to deploy containerized applications effortlessly. Below are the key steps and commands to deploy a Streamlit app using ECS Fargate:

Prerequisites:

AWS CLI configured (aws configure)
Docker installed (sudo apt-get install docker.io)
CDK installed (npm install -g aws-cdk)

Steps:

1. Create a Dockerfile for Streamlit:

FROM python:3.9-slim
WORKDIR /app
COPY . .
RUN pip install streamlit boto3
CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

2. Build and Push to ECR:

aws ecr create-repository --repository-name streamlit-app
docker build -t streamlit-app .
aws ecr get-login-password | docker login --username AWS --password-stdin YOUR_ACCOUNT_ID.dkr.ecr.REGION.amazonaws.com
docker tag streamlit-app:latest YOUR_ACCOUNT_ID.dkr.ecr.REGION.amazonaws.com/streamlit-app:latest
docker push YOUR_ACCOUNT_ID.dkr.ecr.REGION.amazonaws.com/streamlit-app:latest

3. Deploy Using AWS CDK:

from aws_cdk import (
aws_ecs as ecs,
aws_ec2 as ec2,
aws_ecs_patterns as ecs_patterns,
core
)

class StreamlitStack(core.Stack):
def <strong>init</strong>(self, scope: core.Construct, id: str, kwargs) -> None:
super().<strong>init</strong>(scope, id, kwargs)

vpc = ec2.Vpc(self, "StreamlitVPC", max_azs=2)
cluster = ecs.Cluster(self, "StreamlitCluster", vpc=vpc)

ecs_patterns.ApplicationLoadBalancedFargateService(
self, "StreamlitService",
cluster=cluster,
task_image_options=ecs_patterns.ApplicationLoadBalancedTaskImageOptions(
image=ecs.ContainerImage.from_ecr_repository("YOUR_ECR_REPO"),
container_port=8501
),
public_load_balancer=True
)

2. Integrating AWS Bedrock for AI Queries

AWS Bedrock provides foundational AI models for generative tasks. Use boto3 to interact with Bedrock:

import boto3
import json

bedrock = boto3.client('bedrock-runtime', region_name='us-west-2')

def query_bedrock(prompt):
response = bedrock.invoke_model(
body=json.dumps({"prompt": prompt}),
modelId="anthropic.claude-v2"
)
return json.loads(response['body'].read())

3. Scaling and Monitoring

Auto-Scaling: Configure ECS Service Auto Scaling via CDK.

CloudWatch Logs: Stream logs using:

aws logs tail /ecs/StreamlitService --follow

What Undercode Say:

Deploying Streamlit on Fargate with AWS Bedrock provides a serverless, scalable AI frontend solution. Key takeaways:
– Use CDK for IaC (Infrastructure as Code) to automate deployments.
– Fargate removes server management overhead.
– Bedrock enables seamless AI integration.
– Monitor using CloudWatch and ECS metrics.

For further reading, check the original article: Streamlit on Fargate: Hosting Scalable AI Workflows with AWS Bedrock and CDK.