2.10. Code Speed¶
This section will show you some ways to speed up or track the performance of your Python code.
2.10.1. Concurrently Execute Tasks on Separate CPUs¶
If you want to concurrently execute tasks on separate CPUs to run faster, consider using joblib.Parallel. It allows you to easily execute several tasks at once, with each task using its own processor.
from joblib import Parallel, delayed
import multiprocessing
def add_three(num: int):
return num + 3
num_cores = multiprocessing.cpu_count()
results = Parallel(n_jobs=num_cores)(delayed(add_three)(i) for i in range(10))
results
[3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
2.10.2. Compare The Execution Time Between 2 Functions¶
If you want to compare the execution time between 2 functions, try timeit.timeit. You can also specify the number of times you want to rerun your function to get a better estimation of the time.
import time
import timeit
def func():
"""comprehension"""
l = [i for i in range(10_000)]
def func2():
"""list range"""
l = list(range(10_000))
expSize = 1000
time1 = timeit.timeit(func, number=expSize)
time2 = timeit.timeit(func2, number=expSize)
print(time1/time2)
2.6299518653018685
From the result, we can see that it is faster to use list range than to use list comprehension on average.