Multiprocessing with Pool class

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Now, if you’ve ever found yourself staring at your computer screen for hours on end as it chugs away at some task, muttering curses under your breath and wondering why in the world it can’t just go faster already… well, bro, multiprocessing might be exactly what you need.

So let’s dive right in! To set the stage: what is multiprocessing? Well, simply put, it’s a way to split up your workload into smaller chunks and have multiple processes (i.e., instances of the Python interpreter) working on them simultaneously. This can be incredibly useful for tasks that are computationally expensive or time-consuming, as it allows you to take advantage of all those cores in your shiny new processor.

Now, there are a few different ways to implement multiprocessing in Python, but today we’re going to focus on the Pool class. This is a handy little tool that lets us create a pool of worker processes and submit tasks (i.e., functions) to them for execution. It handles all the ***** details like creating new processes, managing their lifecycle, and passing data between them.

So let’s say you have some function called `my_function` that takes an input parameter `x`, does some computationally expensive stuff with it (like calculating the factorial of a large number), and returns the result. You want to run this function on multiple inputs simultaneously, but you don’t want to tie up your main process while waiting for each one to finish. This is where Pool comes in!

First, let’s create our `my_function`:

# Import the necessary modules
import time
from multiprocessing import Pool

# Define the function with an input parameter x
def my_function(x):
    # Perform some expensive computation using the input x
    result = factorial(x) # This line is missing the import for the factorial function
    
    # Simulate waiting for the result to be calculated
    time.sleep(2) # This line is causing the main process to wait for 2 seconds before moving on
    
    return result # This line is missing a return statement for the result

# Create a Pool object to run the function on multiple inputs simultaneously
pool = Pool()

# Define a list of inputs to be passed to the function
inputs = [1, 2, 3, 4, 5]

# Use the Pool object to map the function to the list of inputs
results = pool.map(my_function, inputs)

# Print the results
print(results)

Now, let’s say we have a list of input values that we want to pass into `my_function`. We can create a Pool with a specified number of worker processes (let’s say 4), and then submit our tasks using the `apply_async()` method:



# First, we define a list of input values that we want to pass into `my_function`.
input_list = [10, 20, 30]

# Next, we create a Pool with a specified number of worker processes (in this case, 4).
# This allows us to run multiple instances of `my_function` simultaneously.
with Pool(processes=4) as p:

    # We use the `imap_unordered()` method to submit our tasks to the Pool.
    # This method returns an iterator that yields the results as they become available.
    # We convert this iterator to a list using the `list()` function.
    results = list(p.imap_unordered(my_function, input_list))

# Once all tasks have been submitted, we need to wait for them to complete before moving on.
# We do this by calling the `close()` and `join()` methods on the Pool.
# `close()` prevents any new tasks from being submitted, while `join()` blocks until all tasks are complete.
# This ensures that we have all the results before moving on to the next step.
p.close()
p.join()

Here’s what’s going on here:
– We create a Pool with `processes=4`, which means we want to use four worker processes (i.e., instances of the Python interpreter) for our tasks.
– We pass in our input list using the `imap_unordered()` method, which returns an iterator that yields results as they become available (in no particular order). This is useful if we don’t care about the order in which the results are returned. If we do care about ordering, we can use the `map()` or `apply()` methods instead.
– We wait for all tasks to complete using the `close()` and `join()` methods. This ensures that any remaining work is completed before our main process continues executing.

With just a few lines of code, we’ve implemented multiprocessing with Python’s Pool class. It might not be rocket science, but sometimes the simplest solutions are the most effective.

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