What is Multiprocessing: Python For AI Explained

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Multiple interconnected computer processors symbolizing multiprocessing

In the realm of computer programming, multiprocessing is a term that refers to the ability of a system to support more than one processor at the same time. Applications in a multiprocessing system are broken to smaller routines that run independently. The operating system allocates these threads to the processors improving performance and making the system run faster. The concept of multiprocessing is widely used in various fields, including artificial intelligence (AI), where it is used to train and run complex models more efficiently.

Python, a high-level, interpreted programming language, is a popular choice among AI developers due to its simplicity and vast library support. Python’s multiprocessing module allows for the creation and management of separate processes, each with its own Python interpreter. This feature is particularly beneficial for CPU-intensive tasks, such as machine learning model training, where utilizing multiple processors can significantly reduce computation time.

Understanding Multiprocessing

Multiprocessing involves the use of two or more CPUs within a single computer system. The main advantage of multiprocessing is that it can process multiple tasks at the same time, rather than sequentially, thus improving overall system throughput. The idea is based on the concept that two heads are better than one; in multiprocessing, each CPU handles a different task, so the operating system can handle more tasks in the same amount of time.

In the context of Python, multiprocessing refers to the ability of a program to leverage multiple cores of a machine. This is achieved by running multiple processes concurrently, with each process running on a different core. Unlike threading, where tasks are executed in a shared memory space, each process in multiprocessing has its own memory space, which enhances the stability of the program.

Types of Multiprocessing

There are two main types of multiprocessing: symmetric and asymmetric. In symmetric multiprocessing, each processor runs tasks in the same operating system. This means that the system is easier to manage, as each processor can perform tasks when system demand is high. It also means that if one processor fails, the system can continue to operate.

On the other hand, asymmetric multiprocessing involves each processor being assigned a specific task. This means that some processors can be dedicated to specific functions, improving efficiency. However, it also means that if one processor fails, it could cause the entire system to crash, as other processors may not be able to handle its task.

Python’s Multiprocessing Module

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Python’s multiprocessing module allows developers to create processes, and offers both local and remote concurrency. The module uses subprocesses instead of threads, bypassing the Global Interpreter Lock and making it possible to leverage multiple processors. This module offers a way to work around the limitations of Python’s threading capabilities, providing a means to improve the performance of CPU-bound tasks.

The multiprocessing module provides a number of classes for managing concurrent processes. These include Process, Queue, Pipe, Lock, and others. Each of these classes provides specific functionality for managing and coordinating processes. For example, the Process class is used to create and manage individual processes, while the Queue and Pipe classes provide ways to communicate between processes.

Creating a Process

To create a process in Python, you instantiate a Process object and then call its start() method. The start() method begins the process and Python’s scheduler decides when it will execute. After the process is started, you can either let it run independently or wait for it to finish using the join() method.

Here’s an example of creating a process:

from multiprocessing import Process

def print_func(continent='Asia'):
    print('The name of continent is : ', continent)

if __name__ == "__main__":  # confirms that the code is under main function
    names = ['America', 'Europe', 'Africa']
    procs = []
    proc = Process(target=print_func)  # instantiating without any argument
    procs.append(proc)
    proc.start()

    # instantiating process with arguments
    for name in names:
        # print(name)
        proc = Process(target=print_func, args=(name,))
        procs.append(proc)
        proc.start()

    # complete the processes
    for proc in procs:
        proc.join()

Communicating Between Processes

Python’s multiprocessing module provides several ways to communicate between processes. The two most common ways are through shared memory and server process. Shared memory allows maximum speed and convenience of communication, as it can be done at the speed of memory access. However, it requires synchronization between processes to avoid conflicts. The server process allows multiple processes to communicate over a network, but it is slower than shared memory because it needs to serialize and de-serialize data.

Here’s an example of using a Queue to communicate between two processes:

from multiprocessing import Process, Queue

def print_func(continent='Asia'):
    print('The name of continent is : ', continent)

def square(numbers, queue):
    for i in numbers:
        queue.put(i * i)

def make_negative(numbers, queue):
    for i in numbers:
        queue.put(-1 * i)

if __name__ == "__main__":
    numbers = range(1, 6)
    q = Queue()

    p1 = Process(target=square, args=(numbers, q))
    p2 = Process(target=make_negative, args=(numbers, q))

    p1.start()
    p2.start()

    p1.join()
    p2.join()

    while not q.empty():
        print(q.get())

Multiprocessing in AI

AI, particularly machine learning, involves a lot of heavy computation, which can be parallelized for performance. Training a machine learning model involves performing many independent computations simultaneously. This is where multiprocessing comes in – you can distribute the computations across multiple cores to speed up the training process.

For example, if you’re training a neural network, you can distribute the computation of each layer across multiple cores. This is known as model parallelism. Another common technique is data parallelism, where you distribute the data to different cores and each core trains the model on a different subset of the data.

Python Libraries for Multiprocessing in AI

There are several Python libraries that simplify multiprocessing in AI. These libraries provide high-level abstractions for distributing computations across multiple cores.

One such library is Joblib, a tool for parallelizing Python functions. Joblib provides a simple way to distribute tasks over several cores. Another library is Ray, a high-performance distributed execution engine. Ray allows you to easily write distributed applications, and it integrates with many popular AI libraries.

Here’s an example of using Joblib to parallelize the training of a machine learning model:

from joblib import Parallel, delayed
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification

# Generate a binary classification dataset.
X, y = make_classification(n_samples=1000, n_features=20,
                            n_informative=2, n_redundant=10,
                            random_state=42)

# Instantiate a random forest classifier.
clf = RandomForestClassifier(n_estimators=10, random_state=42)

# Perform cross-validation in parallel
scores = Parallel(n_jobs=-1)(
    delayed(cross_val_score)(clf, X, y, cv=5) for _ in range(10)
)

Conclusion

Multiprocessing is a powerful feature in Python that allows for the execution of multiple processes concurrently. This is particularly useful in the field of AI, where heavy computations can be distributed across multiple cores to improve performance. Python’s multiprocessing module, along with libraries like Joblib and Ray, make it easier to implement multiprocessing in your AI projects.

However, multiprocessing comes with its own challenges, such as the need for synchronization and the potential for process contention. Therefore, it’s important to understand the underlying concepts and trade-offs before implementing multiprocessing in your applications.

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