https://github.com/clementsan/object_detection_lambda
Object detection using AWS Lambda
https://github.com/clementsan/object_detection_lambda
ai aws aws-lambda computer-vision deep-learning gradio huggingface huggingface-spaces object-detection
Last synced: 3 months ago
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Object detection using AWS Lambda
- Host: GitHub
- URL: https://github.com/clementsan/object_detection_lambda
- Owner: clementsan
- Created: 2024-12-23T18:09:00.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-13T20:34:03.000Z (over 1 year ago)
- Last Synced: 2025-03-29T12:15:23.076Z (over 1 year ago)
- Topics: ai, aws, aws-lambda, computer-vision, deep-learning, gradio, huggingface, huggingface-spaces, object-detection
- Language: Python
- Homepage:
- Size: 236 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
---
title: Object Detection Lambda
emoji: 🌖
colorFrom: purple
colorTo: green
sdk: gradio
sdk_version: 5.5.0
app_file: app.py
pinned: false
short_description: Object detection Lambda
---
# Object detection via AWS Lambda
[](https://www.python.org/downloads/)
[](https://hub.docker.com/repository/docker/cvachet/object-detection-lambda)
[](https://github.com/psf/black)
[](https://github.com/pylint-dev/pylint)
**Aim: AI-driven object detection task**
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/object_detection_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. Build and run the Docker container
Step 1 - Building the docker image
bash
> docker build -t object-detection-lambda .
Step 2 - Running the docker container locally
bash
> docker run --name object-detection-lambda-cont -p 8080:8080 object-detection-lambda
### 1.2. 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
### 1.3. Execution via command line:
Example of a prediction request
bash
> encoded_image=$(base64 -i ./tests/data/boats.jpg)
> curl -X POST "http://localhost:8080/2015-03-31/functions/function/invocations" \
> -H "Content-Type: application/json" \
> -d '{"body": "'"$encoded_image"'", "isBase64Encoded": true, "model":"yolos-small"}'
python
> python3 inference_api.py \
> --api http://localhost:8080/2015-03-31/functions/function/invocations \
> --file ./tests/data/boats.jpg \
> --model yolos-small
## 2. Deployment to AWS
### 2.1. Pushing the docker container to AWS ECR
Steps:
- Create new ECR Repository via aws console
Example: ```object-detection-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 object-detection-lambda:latest .dkr.ecr..amazonaws.com/object-detection-lambda:latest
- Push docker image to ECR
> docker push .dkr.ecr..amazonaws.com/object-detection-lambda:latest
[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
**Steps**:
- Create function from container image
Example name: ```object-detection```
- Notes: the API endpoint will use the ```lambda_function.py``` file and ```lambda_hander``` function
- Test the lambda via the AWS console
Advanced notes:
- Steps to update the Lambda function with latest container via aws cli:
> aws lambda update-function-code --function-name object-detection --image-uri .dkr.ecr..amazonaws.com/object-detection-lambda:latest
### 2.3. Creating a REST API via API Gateway
**Steps**:
- Create a new ```Rest API``` (e.g. ```object-detection-api```)
- Add a new resource to the API (e.g. ```/detect```)
- Add a ```POST``` method to the resource
- Integrate the Lambda function to the API
- Notes: currently using proxy integration option unchecked
- Deploy API with a specific stage (e.g. ```dev``` stage)
Example AWS API Endpoint:
```https://.execute-api..amazonaws.com/dev/detect```
### 2.4. Execution for deployed model
Example of a prediction request
bash
> encoded_image=$(base64 -i ./tests/data/boats.jpg)
> curl -X POST "https://.execute-api..amazonaws.com/dev/detect" \
> -H "Content-Type: application/json" \
> -d '{"body": "'"$encoded_image"'", "isBase64Encoded": true, "model":"yolos-small"}'
python
> python3 inference_api.py \
> --api https://.execute-api..amazonaws.com/dev/detect \
> --file ./tests/data/boats.jpg \
> --model yolos-small
## 3. Deployment to Hugging Face
This web application is available on Hugging Face
Hugging Face space URL:
https://huggingface.co/spaces/cvachet/object_detection_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/object-detection-lambda