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| title: Object Detection ECS | |
| emoji: 🌖 | |
| colorFrom: purple | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 5.5.0 | |
| app_file: app.py | |
| pinned: false | |
| short_description: Object detection ECS | |
| # Object detection via ECS endpoints | |
| [](https://www.python.org/downloads/) | |
| [](https://hub.docker.com/repository/docker/cvachet/object-detection-ecs) | |
|  | |
|  | |
| **Aim: AI-driven object detection task** | |
| - Front-end: user interface via Gradio library | |
| - Back-end: use of AWS ECS endpoints to run Machine Learning models | |
| ---- | |
| **Table of contents:** | |
| - [Front-end user interface](#1-front-end-user-interface) | |
| - [Environment variables](#11-environment-variables) | |
| - [Local execution](#12-local-execution) | |
| - [Back-end Machine Learning models](#2-back-end-machine-learning-models) | |
| - [Information on ML models](#21-information-on-ml-models) | |
| - [Deployment on AWS ECS](#22-information-on-aws-ecs-deployment) | |
| - [Deployment on Hugging Face](#3-deployment-on-hugging-face) | |
| - [Deployment on Docker Hub](#4-deployment-on-docker-hub) | |
| ---- | |
| ## 1. Front-end user interface | |
| ### 1.1. Environment variables | |
| This web app uses two environment variables, corresponding to the endpoints for the deployed machine learning models | |
| (cf. [Section 2 - Back-end ML models](#2-back-end-ml-models)) | |
| Environment variables: | |
| - AWS_DETR_URL: endpoint for DETR model | |
| - AWS_YOLOS_URL: endpoint for YOLOS model | |
| ### 1.2. Local execution | |
| Use of Gradio library for web interface | |
| Command line: | |
| > python3 app.py | |
| **Note:** The Gradio app should now be accessible at http://localhost:7860 | |
| ## 2. Back-end machine learning models | |
| Machine Learning (ML) models are available on Docker Hub and have been deployed to AWS ECS (Elastic Container Service) | |
| ### 2.1. Information on ML models | |
| **Github repos:** | |
| - DETR API: https://github.com/clementsan/object_detection_detr_api | |
| - YOLOS API: https://github.com/clementsan/object_detection_yolos_api | |
| **Docker hub containers:** | |
| - DETR API: https://hub.docker.com/r/cvachet/object-detection-detr-api | |
| - YOLOS API: https://hub.docker.com/r/cvachet/object-detection-yolos-api | |
| ### 2.2 Information on AWS ECS deployment | |
| ECS: Elastic Container Service | |
| <details> | |
| Steps after docker images are available on Docker Hub | |
| ### Step 1. Create a new ECS task definition | |
| - Task name (e.g. ObjectDetectionDETRTask) | |
| - Infrastructure requirement: | |
| - Launch type: ```AWS Fargate``` | |
| - Architecture: ```Linux/X86_64``` | |
| - Task size: ```0.5 CPU, 3GB memory``` | |
| - Container: | |
| - Container name: (e.g. ```object-detection-detr```) | |
| - Image uri: point to Docker image URI (e.g. ```cvachet/object-detection-detr-api```) | |
| - Port mapping: assess port number (e.g. ```port 8000, TCP protocol```) | |
| ### Step 2. Create a new ECS cluster | |
| - Cluster name (e.g. ```ObjectDetectionCluster```) | |
| ### Step 3. Add a new service to the cluster | |
| - Compute configuration | |
| - Use capacity provider strategy (e.g. using Fargate or Fargate_spot) | |
| - Deployment configuration | |
| - Application Type: Service | |
| - Task Family: Select task definition family from prior instance (e.g. ```ObjectDetectionDETRTask```) | |
| - Assign a Service Name: (e.g. ```object-detection-detr-api```) | |
| ### Step 4. Update security group for new service | |
| - Go to Cluster -> service -> task -> configuration and networking | |
| - Click on ```Security Group``` | |
| - Adjust rules for inbound traffic (e.g. traffic only from my_ip) | |
| </details> | |
| ### 3. Deployment on Hugging Face | |
| This web application has been deployed on Hugging Face. | |
| HF Space URL: https://huggingface.co/spaces/cvachet/object_detection_ecs | |
| ### 4. Deployment on Docker Hub | |
| This web application has been deployed on Docker Hub. | |
| URL: https://hub.docker.com/r/cvachet/object-detection-ecs | |