Read up about how Message Queues work

[srikharshashi]

We will be using Redis Queues in our application to make it just a little bit better to serve more requests and decouple some of the requests

To create a queue for processing inference tasks in a separate thread or process in a Flask server, you can use a task queue like Celery or RQ (Redis Queue).

Here's an example of how you might set this up using RQ:

1. Install RQ and Redis: RQ requires Redis to function, so you'll need to install both of these. You can install them using pip and your package manager of choice, respectively.

pip install rq
sudo apt-get install redis-server  # Or equivalent for your OS

2. Create your task: The task is the function that will be run in the background. In your case, this will be the function that performs the inference.

def perform_inference(data):
    # Perform inference and save results
    results = model.predict(data)
    save_results(results)
    return results

3. Queue your task: When you receive a request to perform inference, you'll queue the task and then immediately return a response to the user.

from flask import Flask, request, jsonify
from rq import Queue

app = Flask(__name__)
q = Queue(connection='redis://localhost:6379')

@app.route('/inference', methods=['POST'])
def inference():
    data = request.get_json()
    task = q.enqueue(perform_inference, data)
    return jsonify({'task_id': task.get_id()}), 202  # 202 Accepted status code

4. Run a worker: A worker is a separate process that will run your tasks. You can start a worker from the command line.

rqworker

With this setup, when a user makes a request to perform inference, the request will be immediately queued and the user will receive a response with the ID of the task. The task will then be picked up by the worker and run in the background.

You can also create an endpoint to check the status of a task or to retrieve the results of a completed task.

@app.route('/task/<task_id>', methods=['GET'])
def task(task_id):
    task = Task.fetch(task_id, connection='redis://localhost:6379')
    if task.is_finished:
        results = load_results(task_id)
        return jsonify(results)
    else:
        return jsonify({'status': task.status}), 202