Project overview

This project is an asynchron messages that allows you to create, update, delete and list machine learning generated images using 🧨Diffusers

In this example you can see a fast demo using pretrained model runwayml/stable-diffusion-v1-5:

FastAPI

This project uses FastAPI as the main framework to create the API. The three endpoints implemented are:

• health: this endpoint allows you to check the health of the API.
• image_tasks: this endpoint allows you to create, and retrieve image creation tasks.
• images: this endpoint allows you get an image given a task id. This endpoint acts as a proxy to the image service.

This OpenAPi shows the three endpoints implemented.

Celery

This well known library is used to create the tasks that are going to be executed by the workers. The tasks are created by the image_tasks endpoint and are executed by the workers. The workers are going to create the images and store them in the image service.

Redis

This project uses Redis as the message broker to send the tasks to the workers. The workers are going to get the tasks from the message broker and execute them.

Flower

This service is used to monitor the Celery workers. It is a simple web based tool that allows you to monitor the tasks that are being executed by the workers. It is a simple tool that allows you to monitor the tasks that are being executed by the workers.

MongoDB

This project uses MongoDB as the main database to store the tasks and images information. An example of task document is:

{
  "_id": "887c90ed-192d-4de8-a381-d643a989639c",
  "prompt": "una gallina sobre una mesa",
  "status": "completed",
  "generation_steps": 50,
  "url": "http://0.0.0.0:8000/ai-platform/v1/images/887c90ed-192d-4de8-a381-d643a989639c"
}

Project local setup

API setup

Clone the project:

git clone https:/ssoto/ai-platform-api.git api
cd api

You are going to need to have Docker installed in your machine or something similar like Rancher to run the project.

Then start compiling proper images and running the project:

docker-compose build  --build-arg ENV=docker workers api 
docker-compose up -d

When all the services are up and running you can access localhost API

If you want to monitor the workers you can use Flower:

docker-compose up -d flower

Workers setup, without containers

This guide shows how to run the workers locally, not in a container.

To run the celery workers you need to install python 3.11 and poetry:

python -m pip install poetry
poetry install

Once you have the project installed you can run the workers with the following commands:

export ENV=local
celery -A ai_platform.task_queue.main worker --queues default --pool solo -n worker-01 -l INFO 

You need to set the ENV variable to local to run the workers locally.

Workers setup, with containers

CAVEAT: The workers are not going to work properly in a container because you will need high cpu or GPU to run the workers. The workers are going to be very slow in a container.

To run the workers in a container you need to build the worker image and run the container:

docker build --build-arg ENV=docker workers
docker compose up -d worker --scale worker=2