Python unbounded generators as sub processes Parallelisation смотреть онлайн

In this tutorial, we'll explore the concept of using unbounded generators as subprocesses in Python for parallelization. Unbounded generators are a powerful feature that allows you to generate an infinite stream of data. Combining this with subprocesses can help you parallelize tasks, which can be particularly useful for tasks like data processing and computational tasks. We'll provide a step-by-step guide with code examples to demonstrate how this can be achieved.
Before we get started, make sure you have the following prerequisites:
Unbounded generators are a type of Python generator that can produce an infinite sequence of values. They are created using the yield statement and can be used in a variety of applications where data is generated on-the-fly. The key characteristic of unbounded generators is that they continue producing data until explicitly stopped.
Here's a simple example of an unbounded generator:
This generator will keep producing numbers indefinitely, and you can use it to create an infinite stream of data.
Python's multiprocessing module provides a way to execute multiple processes in parallel, which can be very useful for tasks that can be divided into smaller subtasks. To use unbounded generators in subprocesses for parallelization, you can create separate processes that consume the generator and perform tasks concurrently.
Here's a step-by-step guide to parallelizing an unbounded generator using subprocesses:
Define an unbounded generator. For this example, we'll use the infinite_counter generator from above.
Create a function that consumes data from the generator and performs some task. In this example, we'll create a function that prints the first 10 numbers from the generator:
In this code, we define the number of subprocesses (num_processes) and the number of items each process will consume (num_items_per_process). We then create a pool of subprocesses and use the apply_async method to execute the process_data function concurrently.
In this tutorial, we've explored the use of unbounded generators as subprocesses for parallelization in Python. Unbounded generators allow you to generate an infinite stream of data, while subprocesses enable you to perform tasks concurrently, making it a powerful combination for various applications, such as data processing and computation. This approach can help you harness the full potential of your CPU by efficiently utilizing multiple cores.
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