https://github.com/ivangolt/ml_service_fastapi_celery_rabbitmq_redis

https://github.com/ivangolt/ml_service_fastapi_celery_rabbitmq_redis

Last synced: 6 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/ivangolt/ml_service_fastapi_celery_rabbitmq_redis
• Owner: ivangolt
• License: mit
• Created: 2024-06-23T11:11:45.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-06-23T11:38:57.000Z (5 months ago)
• Last Synced: 2024-06-23T12:42:39.660Z (5 months ago)
• Language: Python
• Size: 73.2 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Model Service Api

Example The ML service is a web application that provides an API for interacting with a machine learning model. It allows users to send queries with prediction data and get results back.

**Startup logic:**

When launched, the application initializes FastAPI, which handles HTTP requests. The app also connects to the machine learning model and loads it into memory for use in making predictions.

## Operational logic

- Client sends HTTP request with text data in json to FastAPI asynchronous endpoint.

- FastAPI receives the request, validates it and creates a new Celery task.

- Celery places the task in the queue of the RabbitMQ broker running "under the bonnet".

- The Celery Worker retrieves the task from the RabbitMQ queue, then sends the request to the ML model, receives the response, and returns the result.

- Redis is used to cache intermediate results and speed up repeated queries with the same data.

- The response is returned via RabbitMQ to FastAPI, which sends it back to the client as an HTTP response.

```

.

├── .docker

│   └── Dockerfile              # Docker image with app

├── docker-compose.yml          # Docker container managing

├── pyproject.toml              # Dependencies

└── src

    ├── app.py                  # Main app, FastAPI initializing

    ├── api                     # Package with API routes

    │   ├── __init__.py

    │   └── routes              # Package with API routes

    │       ├── __init__.py

    │       ├── healthcheck.py  # Route to check the srvice status

    │       ├── predict.py      # Route for model predictions

    │       └── router.py       # Main router

    ├── celery                  # Package with data models

    │   ├── __init__.py

    │   ├── celery_init.py      # Celery initializing

    │   └── worker.py           # Celery worker

    └── services                # Package with ML model

        ├── __init__.py

        ├── model.py            # ML model with prediction

        └── utils.py            # Supporting utilities

```

## Getting started with Docker Compose

`docker-compose up --build`

Web-server on

`http://localhost:8000`

UI on

`http://localhost:8000/docs`

## Running local

`pip install --no-cache-dir poetry`

`poetry install --no-dev`

`poetry run uvicorn src.app:app --host localhost --port 8000`