FastAPI: Implementing Multiprocessing in Python

In this tutorial, we will learn how to do multiprocessing in FastAPI using Python. FastAPI is a modern web framework for building APIs with Python. Multiprocessing is a technique in which multiple processes run simultaneously on a multi-core machine, which can help in improving the performance of the application.

To use multiprocessing in FastAPI, we will first need to install the necessary packages. You can install FastAPI and Uvicorn, which is a lightning-fast ASGI server using the following command:

pip install fastapi uvicorn

Next, we will create a simple FastAPI application to demonstrate multiprocessing. Create a new Python file called main.py and add the following code:

from fastapi import FastAPI
import multiprocessing

app = FastAPI()

def cpu_bound_task(x):
    return x * x

@app.get("/square/{number}")
async def square(number: int):
    # Create a Pool of processes
    pool = multiprocessing.Pool()

    # Apply the function to the number in parallel
    result = pool.apply(cpu_bound_task, args=(number,))

    # Close the pool of processes
    pool.close()

    return {"result": result}

In this code snippet, we define a CPU-bound task cpu_bound_task that squares a given number. We create a FastAPI application and define a route /square/{number} that takes an integer number as input and returns the square of that number. Inside the route handler, we create a pool of processes using multiprocessing.Pool() and apply the CPU-bound task to the number in parallel using pool.apply(). Finally, we close the pool of processes using pool.close().

To run the FastAPI application, you can use Uvicorn with the following command:

uvicorn main:app --reload

Now, you can access the API at http://127.0.0.1:8000/square/{number} where {number} is the integer number you want to square. For example, you can access http://127.0.0.1:8000/square/5 to get the square of 5.

This is a simple example of how you can use multiprocessing in FastAPI to improve the performance of your application by running CPU-bound tasks in parallel. You can modify the code and add more CPU-bound tasks to parallelize them and utilize the multi-core machine effectively.

Overall, multiprocessing in FastAPI can help in handling multiple requests concurrently and improve the response time of the application. It is a powerful technique that can be used to boost the performance of your FastAPI application.