metadata

title: IRIS Classification Lambda
emoji: 🏢
colorFrom: indigo
colorTo: blue
sdk: gradio
sdk_version: 5.5.0
app_file: app.py
pinned: false
short_description: IRIS Classification Lambda

IRIS classification task with AWS Lambda

Aims: Categorization of different species of iris flowers (Setosa, Versicolor, and Virginica) based on measurements of physical characteristics (sepals and petals).

Architecture:

Front-end: user interface via Gradio library
Back-end: use of AWS Lambda function to run deployed ML model

You can try out our deployed Hugging Face Space!

Table of contents:

Local development
AWS deployment
Hugging Face deployment

1. Local development

1.1 Training the ML model

bash

python train.py

1.2. Docker container

Building the docker image

bash

docker build -t iris-classification-lambda .

Running the docker container

bash

docker run --name iris-classification-lambda-cont -p 8080:8080 iris-classification-lambda

1.3. Execution via command line

Example of a prediction request

bash

curl -X POST "http://localhost:8080/2015-03-31/functions/function/invocations" -H "Content-Type: application/json" -d '{"features": [[6.5, 3.0, 5.8, 2.2], [6.1, 2.8, 4.7, 1.2]]}'

python

python3 inference_api.py --url http://localhost:8080/2015-03-31/functions/function/invocations -d '{"features": [[6.5, 3.0, 5.8, 2.2], [6.1, 2.8, 4.7, 1.2]]}'

1.4. Execution via user interface

Use of Gradio library for web interface

Note: The environment variable AWS_API should point to the local container

export AWS_API=http://localhost:8080

Command line for execution:

python3 app.py

The Gradio web application should now be accessible at http://localhost:7860

2. Deployment to AWS

2.1. Pushing the docker container to AWS ECR

Steps:

Create new ECR Repository via aws console

Example: iris-classification-lambda

Optional for aws cli configuration (to run above commands):

aws configure
Authenticate Docker client to the Amazon ECR registry

aws ecr get-login-password --region | docker login --username AWS --password-stdin .dkr.ecr..amazonaws.com
Tag local docker image with the Amazon ECR registry and repository

docker tag iris-classification-lambda:latest .dkr.ecr..amazonaws.com/iris-classification-lambda:latest
Push docker image to ECR

docker push .dkr.ecr..amazonaws.com/iris-classification-lambda:latest

Link to AWS ECR Documention

2.2. Creating and testing a Lambda function

Steps:

Create function from container image

Example name: iris-classification

Notes: the API endpoint will use the lambda_function.py file and lambda_hander function
Test the lambda via the AWS console

Example JSON object:

{
    "features": [[6.5, 3.0, 5.8, 2.2], [6.1, 2.8, 4.7, 1.2]]
}

Advanced notes:

Steps to update the Lambda function with latest container via aws cli:

aws lambda update-function-code --function-name iris-classification --image-uri .dkr.ecr..amazonaws.com/iris-classification-lambda:latest

2.3. Creating an API via API Gateway

Steps:

Create a new Rest API (e.g. iris-classification-api)
Add a new resource to the API (e.g. /classify)
Add a POST method to the resource
Integrate the Lambda function to the API
- Notes: using proxy integration option unchecked
Deploy API with a specific stage (e.g. test stage)

Example AWS API Endpoint: https://<api_id>.execute-api.<aws_region>.amazonaws.com/test/classify

2.4. Execution for deployed model

Example of a prediction request

bash

curl -X POST "https://.execute-api..amazonaws.com/test/classify" -H "Content-Type: application/json" -d '{"features": [[6.5, 3.0, 5.8, 2.2], [6.1, 2.8, 4.7, 1.2]]}'

python

python3 inference_api.py --url https://.execute-api..amazonaws.com/test/classify -d '{"features": [[6.5, 3.0, 5.8, 2.2], [6.1, 2.8, 4.7, 1.2]]}'

3. Deployment to Hugging Face

This web application is available on Hugging Face

Hugging Face space URL: https://huggingface.co/spaces/cvachet/iris_classification_lambda

Note: This space uses the ML model deployed on AWS Lambda