How to do multiprocessing in fastapi?

@edmond_brakus 

To implement multiprocessing in FastAPI, you can use the built-in Python multiprocessing module. Here is an example of how you can create a FastAPI endpoint that runs a function in a separate process:

1. Import the necessary modules:

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from fastapi import FastAPI
import multiprocessing

1. Create a FastAPI app instance:

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app = FastAPI()

1. Define a function that you want to run in a separate process:

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def my_function(data):
    # Your function logic here
    return data

1. Create a FastAPI endpoint that will run the function in a separate process:

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@app.post("/process-data")
async def process_data(data: str):
    # Create a multiprocessing Pool
    pool = multiprocessing.Pool()

    # Map the function to the data in a separate process
    result = pool.apply_async(my_function, args=(data,))

    # Get the result from the separate process
    processed_data = result.get()

    return {"processed_data": processed_data}

1. Run the FastAPI app:

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import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)

Now, when you send a POST request to the /process-data endpoint with some data, the function my_function will be executed in a separate process, and the processed data will be returned as a response. This allows you to take advantage of multiprocessing in FastAPI to improve performance for CPU-bound tasks.