---
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
[](https://www.python.org/downloads/)
[](https://hub.docker.com/repository/docker/cvachet/iris-classification-lambda)
[](https://github.com/psf/black)
**Aims:** Categorization of different species of iris flowers (Setosa, Versicolor, and Virginica)
based on measurements of physical characteristics (sepals and petals).
**Method:** Use of Decision Tree Classifier
**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](https://huggingface.co/spaces/cvachet/iris_classification_lambda
)!
----
**Table of contents:**
- [Local development](#1-local-development)
- [AWS deployment](#2-deployment-to-aws)
- [Hugging Face deployment](#3-deployment-to-hugging-face)
- [Docker Hub deployment](#4-deployment-to-docker-hub)
----
## 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
<details>
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 <aws_region> | docker login --username AWS --password-stdin <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com
- Tag local docker image with the Amazon ECR registry and repository
> docker tag iris-classification-lambda:latest <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com/iris-classification-lambda:latest
- Push docker image to ECR
> docker push <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com/iris-classification-lambda:latest
</details>
[Link to AWS ECR Documention](https://docs.aws.amazon.com/AmazonECR/latest/userguide/docker-push-ecr-image.html)
### 2.2. Creating and testing a Lambda function
<details>
**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 <aws_account_id>.dkr.ecr.<aws_region>.amazonaws.com/iris-classification-lambda:latest
</details>
### 2.3. Creating an API via API Gateway
<details>
**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)
</details>
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://<api_id>.execute-api.<aws_region>.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://<api_id>.execute-api.<aws_region>.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
## 4. Deployment to Docker Hub
This web application is available on Docker Hub as a docker image
URL:
https://hub.docker.com/r/cvachet/iris-classification-lambda