Manager module in python

Multiprocessing Manager With a Custom Class

You can use manager to with custom Python classes by creating a Manager class that extends the BaseManager class, registering the custom class with the manager, then using the manager to create a shared instance of the custom class.

In this tutorial you will discover how to use a multiprocessing manager with a custom class in Python.

Need Manager to Share Custom Class

A manager in the multiprocessing module provides a way to create Python objects that can be shared easily between processes.

Managers provide a way to create data which can be shared between different processes, including sharing over a network between processes running on different machines. A manager object controls a server process which manages shared objects. Other processes can access the shared objects by using proxies.

— multiprocessing — Process-based parallelism

A manager creates a server process that hosts a centralized version of objects that can be shared among multiple processes.

The objects are not shared directly. Instead, the manager creates a proxy object for each object that it manages and the proxy objects are shared among processes.

The proxy objects are used and operate just like the original objects, except that they serialize data, synchronize and coordinate with the centralized version of the object hosted in the manager server process.

A proxy is an object which refers to a shared object which lives (presumably) in a different process. […] A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy).

— multiprocessing — Process-based parallelism

This makes managers a process-safe and preferred way to share simple data structures like lists and dicts among processes.

They are also a preferred way to share concurrency primitives among processes, specifically among workers in a multiprocessing pool.

You can learn more about multiprocessing managers in the tutorial:

We may need to use a Manager to host our own custom Python objects.

This may be because our custom classes cannot be pickled and therefore cannot be shared directly with child processes or are perhaps challenging to make process-safe.

How can we use a multiprocessing manager to share our own custom Python objects among multiple processes?

Run your loops using all CPUs, download my FREE book to learn how.

How to Use a Manager Create a Custom Class

We can use a manager to create and manage a centralized version of a custom class.

This requires a few steps:

1. Defining a custom class.
2. Defining a custom Manager class that extends BaseManager.
3. Registering the custom class with the custom manager.
4. Creating an instance of the custom manager.
5. Creating an instance of the custom class from the manager instance.

Let’s take a closer look at each step in turn.

1. Define a Custom Class

The first step is to define our custom class.

This is just a normal Python class. No special Manager-specific changes are required.

The class should be serializable, e.g. picklable.

If we expect the class to be accessed and modified from multiple processes concurrently, then it is a good idea to protect any internal state using a mutex lock.

Otherwise, you run the risk of race conditions that may leave the object’s internal state in an inconsistent and unknown condition.

You can learn more about race conditions in objects hosted by a manager in the tutorial:

2. Define a Custom Manager

We must define a custom manager in order to use the manager to create and manage custom classes.

The custom class does not need to override the constructor or any methods, nor define any functionality.

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Multiprocessing Manager Example in Python

You can share objects among processes using a manager.

In this tutorial you will discover how to use managers to share access to centralized Python objects.

What Is a Manager

A manager in the multiprocessing module provides a way to create Python objects that can be shared easily between processes.

Managers provide a way to create data which can be shared between different processes, including sharing over a network between processes running on different machines. A manager object controls a server process which manages shared objects. Other processes can access the shared objects by using proxies.

— multiprocessing — Process-based parallelism

A manager creates a server process that hosts a centralized version of objects that can be shared among multiple processes.

The objects are not shared directly. Instead, the manager creates a proxy object for each object that it manages and the proxy objects are shared among processes.

The proxy objects are used and operate just like the original objects, except that they serialize data, synchronize and coordinate with the centralized version of the object hosted in the manager server process.

A proxy is an object which refers to a shared object which lives (presumably) in a different process. […] A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy).

— multiprocessing — Process-based parallelism

This makes managers a process-safe and preferred way to share simple data structures like lists and dicts among processes.

They are also a preferred way to share concurrency primitives among processes, specifically among workers in a multiprocessing pool.

You can learn more about multiprocessing managers in the tutorial:

Next, let’s look at how we might create and use a manager.

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How to Create and Use a Manager

A multiprocessing.Manager provides a way to create a centralized version of a Python object hosted on a server process.

Once created, it returns proxy objects that allow other processes to interact with the centralized objects automatically behind the scenes.

The multiprocessing.Manager provides the full multiprocessing API, allowing Python objects and concurrency primitives to be shared among processes.

This includes Python objects we may want to share, such as:

It include shared ctypes for primitive values, such as:

It includes concurrency primitives for synchronizing and coordinating processes, such as:

It even includes patterns and process-safe data structures, such as:

First, we must create a manager instance.

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Multiprocessing Manager Share Queue in Python

You can use a Manager to create a hosted Queue object and share it via proxy objects with multiple child processes.

In this tutorial you will discover how to share a queue using a manager in Python.

Need Manager to Share a Queue

A manager in the multiprocessing module provides a way to create Python objects that can be shared easily between processes.

Managers provide a way to create data which can be shared between different processes, including sharing over a network between processes running on different machines. A manager object controls a server process which manages shared objects. Other processes can access the shared objects by using proxies.

— multiprocessing — Process-based parallelism

A manager creates a server process that hosts a centralized version of objects that can be shared among multiple processes.

The objects are not shared directly. Instead, the manager creates a proxy object for each object that it manages and the proxy objects are shared among processes.

The proxy objects are used and operate just like the original objects, except that they serialize data, synchronize and coordinate with the centralized version of the object hosted in the manager server process.

A proxy is an object which refers to a shared object which lives (presumably) in a different process. […] A proxy object has methods which invoke corresponding methods of its referent (although not every method of the referent will necessarily be available through the proxy).

— multiprocessing — Process-based parallelism

This makes managers a process-safe and preferred way to share Python objects among processes.

You can learn more about multiprocessing managers in the tutorial:

When using multiprocessing, we may need to share a Queue with child processes.

This may be for many reasons, such as:

• Child processes need to put results to the queue.
• Child processes need to get results from the queue.

We may not be able to share the Queue directly with the child processes.

This may be because a queue object cannot be pickled, and we may have to pickle objects when sending them to a child process such as via a Pipe, via another Queue or as an argument to a task to the Pool class.

How can we use a multiprocessing Manager to share a Queue with child processes?

Run your loops using all CPUs, download my FREE book to learn how.

Manager for Sharing a Sharing Queue

A queue is a data structure on which items can be added by a call to put() and from which items can be retrieved by a call to get().

The multiprocessing.Queue provides a first-in, first-out FIFO queue, which means that the items are retrieved from the queue in the order they were added. The first items added to the queue will be the first items retrieved. This is opposed to other queue types such as last-in, first-out and priority queues.

You can learn more about how to use the process-safe Queue in the tutorial:

Managers are provided via the multiprocessing.Manager class, which creates and returns a multiprocessing.managers.SyncManager instance.

The SyncManager allows a suite of Python objects to be created and managed by default, including a Queue.

Specifically the SyncManager provides two registered Queue objects, they are:

As such, we can create a Manager instance and use it directly to create a Queue object.

This will create a hosted version of the Queue in the Manager‘s server process and return proxy objects for the Queue that can be pickled and shared with child processes.

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