In Python machine learning, how do I parallelize workloads?

In Python machine learning, you can parallelize workloads to improve performance and efficiency. One of the common ways to achieve this is by utilizing libraries such as multiprocessing, joblib, and concurrent.futures. These libraries allow you to run multiple tasks in parallel, leveraging multiple CPU cores.

Here’s a simple example demonstrating how to use the concurrent.futures module to parallelize a computation task:

import concurrent.futures import time def compute_square(n): time.sleep(1) # Simulate a time-consuming task return n * n if __name__ == '__main__': numbers = [1, 2, 3, 4, 5] with concurrent.futures.ProcessPoolExecutor() as executor: results = list(executor.map(compute_square, numbers)) print(results) # Output: [1, 4, 9, 16, 25]

Python machine learning parallel workloads multiprocessing joblib concurrent.futures

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