Скачать или смотреть Run some task in parallel and others sequentially using python

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In this tutorial, we will explore how to run tasks in parallel and sequentially using Python. Running tasks in parallel can significantly improve the performance of your programs, especially when dealing with CPU-intensive or I/O-bound operations. We will use the concurrent.futures module, which provides a high-level interface for asynchronously executing callables.
Before we begin, make sure you have Python installed on your system. You can download it from python.org.
The concurrent.futures module in Python provides a high-level interface for asynchronously executing functions. It has two classes that are commonly used for parallel execution: ThreadPoolExecutor and ProcessPoolExecutor. In this tutorial, we will focus on the ThreadPoolExecutor.
Let's define two simple tasks, one simulating a CPU-bound task and the other simulating an I/O-bound task.
Now, let's use the ThreadPoolExecutor to run the tasks in parallel.
This code submits the tasks to the thread pool and retrieves the results when they are completed.
If you have tasks that need to be executed sequentially, you can simply call the functions one after another.
In this tutorial, we explored how to run tasks in parallel and sequentially using Python's concurrent.futures module. Depending on the nature of your tasks, you can choose to run them concurrently to improve performance or sequentially if the order of execution matters.
Remember to adapt the number of threads or processes based on your specific use case and system resources.
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