Understanding multiprocessing Manager Behavior: Are Object Attributes Shared?

Explore how Python's `multiprocessing` manager handles object attributes and why understanding this is key to effective parallel processing.
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Understanding multiprocessing Manager Behavior: Are Object Attributes Shared?

When working with Python's multiprocessing library, many users face a common confusion: how are object attributes managed across processes? Specifically, if I register a class with a Manager, are the arguments tailored or modified across different processes? This question is crucial for anyone looking to leverage multiple processes effectively, especially as it can impact performance and functionality.

In this guide, we will explore how multiprocessing managers handle class instances, why you might not see shared attributes as expected, and what modifications may be necessary to achieve your desired functionality.

The Basics of Multiprocessing Managers

The multiprocessing library allows you to create multiple processes in Python, enabling parallel execution of tasks. A Manager provides a way to create shared objects that can be easily transmitted between processes. However, it’s important to know how these shared objects behave, especially when it comes to their attributes.

Situation Overview

Consider the following code snippet which leverages a custom SharedType class to share data between processes:

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

In this scenario, the goal is to observe how attributes custom_arg and builtin_arg are shared among processes.

The Reality of Attribute Sharing

Despite the ability to use a multiprocessing manager, Python does not allow an object to exist simultaneously in multiple processes. Instead, the manager creates a server process associated with the specified class instance, such as SharedType.

The Proxy Mechanism

Here's how the communication works:

When you create a shared object (shared_obj) with the manager, what you actually receive is a proxy object.

Proxies send and receive data through pickling, meaning they serialize the data to send it over inter-process communication (IPC) channels to the server.

When a method is called on the proxy, its arguments are pickled, sent to the server, and a new object is created in the server process.

Why Attributes Don't Behave as Expected

When attempting to modify custom_arg and builtin_arg, the changes made don’t affect the SharedType instance’s attributes on the server. This is because when you call methods like get_custom_arg():

The method returns a copy of the object, not a reference to the original.

Thus, operations on that returned copy do not reflect back on the original object stored in the manager.

Example Explained

In the initial example, the output showed shared characteristics only for the count attribute because it was updated using the accum method directly via the proxy. In contrast, the lists returned by get_custom_arg() and get_builtin_arg() were independently manipulated, leading to no change in the actual lists handled by the SharedType instance:

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

How to Properly Share Complex Attributes

To have shared attributes effectively:

Return Proxies: Modify your get_custom_arg() and get_builtin_arg() methods to return proxies. You will also have to register the relevant classes (like CustomArg).

Using Manager: Initialize lists or mutable objects through the manager so that operations on these objects reflect in the main shared instance.

Synchronization Requirement: Using shared objects still necessitates synchronization mechanisms to prevent race conditions, particularly when multiple processes may modify shared attributes at the same time.

Example Solution Steps

To create a functional code, consider the following revisions:

Modify the attributes to utilize Manager methods.

Ensure methods return proxies instead of copies.

[[See Video t