Pool

Learn process pools with multiprocessing.Pool.

What is multiprocessing.Pool?

multiprocessing.Pool is a pool of processes. It distributes tasks across processes automatically; .map() applies a function to every element of a list.

For example Pool().map(func, [1, 2, 3]) distributes three items across the pool's processes and returns [func(1), func(2), func(3)]. The with Pool() as p: block closes the pool automatically. map preserves input order, so the output is deterministic. This is the most practical way to do parallel mapping.

Parallel computation with map

Pool().map(func, data) applies the function to every element of data. The process count is chosen automatically; usually it equals the CPU core count.

Important: map preserves input order. Input [1, 2, 3] gives output [func(1), func(2), func(3)]; no matter which process finishes first the order stays the same. This ensures deterministic results. If you do not use a with block you must close the pool with p.close() and p.join().

Exercise Compute squares with Pool

Import with from multiprocessing import Pool. Define a square function that returns x * x. Inside a with Pool() as p: block assign p.map(square, [1, 2, 3]) to result. Print result.

Show example solution

from multiprocessing import Pool

def square(x):
    return x * x

with Pool() as p:
    result = p.map(square, [1, 2, 3])
print(result)

Test yourself

What does Pool().map return?
```
with Pool() as p:
    result = p.map(func, [1, 2, 3])
```
- A list of results
- A queue
- A process object
- A single value
Show answer

A list of results — answer: a list of results. map lists each item's result in order.
Match Pool concepts

Pool

Process pool
map

Applies in parallel
with Pool()

Auto-closes
func

Applied function

Complete the Pool map call

with Pool() as p:
    result = p.____(square, [1, 2, 3])

Show answer

with Pool() as p:
    result = p.map(square, [1, 2, 3])

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