Efficiently Pooling Objects in a Parallel Process Pool with Python

Discover how to optimize parallel processing using `multiprocessing` in Python, sharing mutable objects among processes without excessive cloning.
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Efficiently Pooling Objects in a Parallel Process Pool with Python

When working with Python, especially within the realm of data processing or machine learning, the need for efficient parallel processing can become pivotal. A common challenge arises when you need to share mutable objects across multiple processes without creating numerous copies, which can be both resource and time-consuming. In this guide, we will explore a solution to efficiently manage mutable objects in a parallel process pool.

The Problem

Imagine you have a function that uses a mutable object to perform calculations, as shown in the example below:

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You would typically use a loop to execute this function multiple times:

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However, to speed up the process, you want to leverage Python's multiprocessing library to perform multiple calls of fun in parallel. The challenge? You need to share a mutable object without unnecessarily cloning it for every input pair.

The Naive Approach

A straightforward but inefficient method would be to create a deep copy of the mutable object for each input:

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While this method works, it is wasteful in terms of CPU and memory, particularly when dealing with large objects—like Keras models in the case of neural networks.

The Efficient Solution

Here’s a more efficient approach that allows you to manage processes manually, ensuring that only one object is created per process instead of for every input pair. Let's break down the solution:

Step 1: Create a Mutable Object Class

First, we define our mutable object:

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Step 2: Extend the Process Class

Next, we subclass the Process class. This custom class manages its own mutable object:

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Step 3: Implement the Worker Logic

Inside the run method of our Worker class, we process tasks from the task queue:

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Step 4: Set Up Queues and Spawn Workers

We need to set up our queues for tasks and results, and then spawn multiple worker processes:

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Step 5: Retrieve Results

After assigning tasks, we wait for them to complete and retrieve the results:

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Conclusion

This approach efficiently pools mutable objects in a parallel processing environment, reducing the overhead caused by excessive copying. Though the results may not maintain the original input order, indexing tasks allows for easy sorting later if needed.

If your processing needs to run indefinitely, consider implementing callbacks to handle results dynamically. A potential structure for this involves encapsulating your logic in another class, such as a TaskRunner, to manage state elegantly.

Implementing this efficient multiprocessing with shared mutable objects can vastly improve your application's performance, particularly when dealing with large datasets or complex models.

Feel free to explore this method in your parallel processing tasks, and optimize your Python applications!