Introduction

For the hands-on AI project, we will use Twitter sentiment analyst to demonstrate full-stack machine learning inference.

The idea of the project is to get metadata from Twitter URL and sentiment analyst content. After that, we will aggregate sentiment text from the individual user to analyze more sentiment for each user.

First, make sure you copy the following file and edit its contents:

cp .env_template .env
vim .env

Local setup

Download model transfomer to local data

pip install transformers
python generate_torch_script.py
cp -r twitter-roberta-base-sentiment/* data/*
rm -rf twitter-roberta-base-sentiment # Remove nonuse model for faster build time

Set redis_host enviroment to redis

Once you have the .env setup, run the following script to init docker-compose service:

make build-local # Using sudo if your docker require sudo access

When done, you should have listed:

Service is on http://localhost:2000/

Access the swagger docs in http://localhost:2000/docs

Infrastructure setup

Variables:

• PROJECT_NAME Any arbitrary project name. Use 'echo' if you don't have any preference.
• AWS_DEFAULT_REGION Your preferred AWS region
• AWS_ACCOUNT_ID Your account ID as you see here
• KEY_PAIR Name of Key Pair you'd like to use to setup the infrastructure. Find it here

Once you have the .env setup, run the following script to initialize VPC, ECR/ECS and App Service.

make build-infra

When done, you should have listed:

Bastion endpoint:
54.65.206.60
Public endpoint:
http://clust-LoadB-4RPWCBUJAH83-1023823123.ap-southeast-1.elb.amazonaws.com

Access the above load balancer and make sure that you have output like this:

$ curl -i "http://clust-LoadB-4RPWCBUJAH83-1023823123.ap-southeast-1.elb.amazonaws.com"
HTTP/1.1 200 OK
Date: Mon, 18 Jul 2022 03:29:51 GMT
Content-Type: application/json
Content-Length: 49
Connection: keep-alive
server: uvicorn

{"statusCode":200,"body":"{\"message\": \"OK\"}"}%

Congrats! You're successfully done create AI service!

For more service detail, go to my blog