What is the process of performing multiprocessing in FastAPI?

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FastAPI does not have built-in support for multiprocessing, but it can easily be implemented using Python's multiprocessing module. Here are the general steps for performing multiprocessing in FastAPI:

1. Import the necessary modules, including FastAPI and the multiprocessing module.
2. Create a FastAPI app object and any necessary routes.
3. Define the function that will be executed in parallel. This can be a simple function that performs a computation or a more complex function that takes inputs, performs computations, and returns results.
4. Create a multiprocessing Pool object that will manage the parallel workers.
5. Define a function that will run each worker. This function should call the function defined in step 3 with the appropriate inputs and return the result.
6. Use the Pool object to map the worker function to a list of inputs. This will start the parallel workers and return a list of results.
7. Use the FastAPI app object to define a route that will call the multiprocessing function and return the results to the client.

Note that there are many additional details and considerations that will depend on the specific problem being solved and the environment in which the multiprocessing is being performed. Some important considerations include data serialization, load balancing, synchronization, and error handling.