This repository show the code created to be as a "template" to deploy applications with containers using FastAPI, Celery, Redis and RabbitMQ.
As a demo application, it was build a API service using YOLO v5 to perform object detection.
Since Yolo is a deep model which may take some time to return results, we will use Celery, Redis and RabbitMQ to control the tasks in background.
- FastAPI: high performance python framework for building APIs.
- Celery: A Task Queue with focus on real-time processing, while also supporting task scheduling.
- RabbitMQ: A message broker used to route messages between API and the workers from Celery.
- Redis: An in-memory database to store results and process status from the tasks.
The image below ilustrate the data flow from all components.
- api/app.py: expose the endpoints and send the request task to celery.
- celery_tasks/tasks.py: receive the task and send (enqueue) to workers process.
- celery_tasks/yolo.py: code to initilize and expose a method receive a picture and return the predictions.
Endpoint | Method | Description |
---|---|---|
http://localhost:8000/api/process | POST | Send one or more pictures to be processed by Yolo. Return the task_id of each task. |
http://localhost:8000/api/status/<task_id> | GET | Retrieve the status of a given task |
http://localhost:8000/api/result/<task_id> | GET | Retrieve the results of a given task |
http://localhost:8000/docs | GET | Documentation generated for each endpoint |
http://localhost:15672 | GET | RabbitMQ monitor. User: guest Password: guest. |
http://localhost | GET | Simple webapp to show how to use and display results from the API. |
Input
Form with enctype=multipart/form-data and imagens in attribute files. See the example in Ajax.
var form_data = new FormData();
files = $('#input_file_form').prop('files')
for (i = 0; i < files.length; i )
form_data.append('files', $('#input_file_form').prop('files')[i]);
$.ajax({
url: URL '/api/process',
type: "post",
data: form_data,
enctype: 'multipart/form-data',
contentType: false,
processData: false,
cache: false,
}).done(function (jsondata, textStatus, jqXHR) {
console.log(jsondata)
}).fail(function (jsondata, textStatus, jqXHR) {
console.log(jsondata)
});
Output:
[
{
"task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
"status": "PROCESSING",
"url_result": "/api/result/2b593c5c-3f0b-49c1-a145-ad613f4ecda5"
}
]
Using CURL
curl -X POST "http://localhost:8000/api/process" -H "accept: application/json" -H "Content-Type: multipart/form-data" -F "[email protected];type=image/jpeg"
Input
task_uid
Output:
{
"task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
"status": "PROCESSING",
"result": ""
}
Using CURL
curl -X GET "http://localhost/api/status/ren123/"
Input
task_id
Output (if it is done):
{
"task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
"status": "SUCCESS",
"result": {
"file_name": "static/f5956eea.jpg",
"bbox": [
{
"x": "0.4734227",
"y": "0.63320345",
"w": "0.76526415",
"h": "0.7137341",
"prob": "0.8920207",
"class": "person"
},
{
"x": "0.3752669",
"y": "0.8009622",
"w": "0.07171597",
"h": "0.38714227",
"prob": "0.89087863",
"class": "tie"
},
{
"x": "0.8268833",
"y": "0.6996484",
"w": "0.11784973",
"h": "0.5577593",
"prob": "0.28665677",
"class": "tie"
}
]
}
}
If it is processing:
{
"task_id": "2b593c5c-3f0b-49c1-a145-ad613f4ecda5",
"status": "PROCESSING",
"result": ""
}
- Clone this repository
git clone https://github.com/renatoviolin/Deploying-YOLOv5-fastapi-celery-redis-rabbitmq.git
cd Deploying-YOLOv5-fastapi-celery-redis-rabbitmq
- Install docker. If you already have, create the container with the command:
docker-compose build
- Run all containers
docker-compose up
This will start:
- rabbitmq: message broker
- redis: in-memory database
- worker: application logic (Yolo model, FastAPI and Celery)
- webapp: demo application
- Perform some requests using the integrated Swagger UI. http://localhost:8000/docs
- Open the demo webapp. http://localhost/