Solving the NotImplementedError in a Multiprocessing Pool with Async Workers in Python

Discover how to resolve the `NotImplementedError` when using a multiprocessing pool with async workers in Python for efficient parallel processing.
---

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Multiprocessing pool with async workers

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Understanding the NotImplementedError in Python's Multiprocessing Pool

When working with Python's multiprocessing module, you might encounter some hurdles that can be quite perplexing—one of the most common being the NotImplementedError. A particularly tricky situation arises when using a Pool with asynchronous workers. This guide will delve into this issue and provide you with a clear, step-by-step solution to get your code running smoothly.

The Problem

In a scenario where you’re trying to use asynchronous workers with a multiprocessing pool, you might write code similar to the following:

[[See Video to Reveal this Text or Code Snippet]]

When you execute this code, you may receive the following error message:

[[See Video to Reveal this Text or Code Snippet]]

The Solution

To resolve this issue, we need to modify the way we handle the state of the class. The key is to implement the __getstate__ and __setstate__ methods, which will manage the serialization process when using multiprocessing. Here's how you can adjust your class definition:

Step 1: Modify the Class

Add the following two methods to your Sandbox class:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Full Working Example

Here’s how your complete code should look after incorporating these changes:

[[See Video to Reveal this Text or Code Snippet]]

Understanding the Solution

getstate: This method creates a copy of the instance's __dict__ but removes the pool attribute before returning it. This way, it ensures that whatever gets serialized does not include the unserializable parts of the object.

setstate: This method is called when an object is unpickled. It updates the object's __dict__ to the newly inferred state, effectively restoring it to its previous condition but without the pool reference.

Conclusion

By following the steps outlined in this post, you can eliminate the NotImplementedError encountered when using a multiprocessing pool with async workers in Python. The key takeaway here is to manage the state of your objects thoroughly, particularly when dealing with multiprocessing, which can involve complex serializations.

Keep experimenting with Python's multiprocessing features to harness the full power of concurrent programming in your applications. Happy coding!