Welcome to this comprehensive tutorial on Python’s threading module. Today, we delve into the exciting world of multithreaded programming and learn how to leverage the power of Python to perform concurrent tasks. This skill is a game-changer, taking your Python programming skills up a notch and opening up incredible possibilities, particularly in game development and Artificial Intelligence.
Table of contents
What is Python threading?
The Python threading module is one part of Python’s insights into concurrent programming – the ability of your program to do many things at once. Python’s threading allows your program to manage its own operation, breaking it into smaller, manageable tasks that can run simultaneously.
What is it for?
In both game development and AI programming, there’s a need for programs to handle multiple tasks at once. For example, imagine a game situation where your character is running, jumping, and shooting all at the same time. These actions need to occur concurrently, and that’s where Python threading comes in.
Why should I learn it?
Learning about the threading module is essential if you want to develop complex applications, especially games and AI programs. Mastering Python threading equips you with the ability to create efficient programs that utilize system resources in an optimal way, thereby enhancing your program’s performance.
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Getting Started with Python Threading
Let’s jump right in and get our hands dirty with Python threading. Here are some basic examples that will take you through creating and starting threads, using common methods, and understanding synchronization and deadlock situations.
1. Creating and Starting Threads
First, we need to import the threading module. This is a built-in module in Python, so you don’t need to install anything:
import threading
Next, let’s identify our current thread:
print(threading.current_thread())
You’ll get an output like this:
2. Working With Multiple Threads
Let’s create a simple function that we can use for our threading experiment:
def square_numbers():
for i in range(5):
print("The square of", i, "is", i*i)In main, we’ll first call square_numbers in a conventional way and check our current thread:
if __name__ == "__main__":
square_numbers()3. Creating Threads Using Threading Module
Now that we have our function, square_numbers, let’s create a new thread to execute this function:
thread = threading.Thread(target=square_numbers)
This creates a new thread, which we can then start with:
thread.start()
4. Waiting for Threads to Complete
Additionally, you can make your main thread wait until your created thread is done with:
thread.join()
This method blocks the calling thread (your main thread) until the thread whose join() method is called is terminated – either normally or through an unhandled exception or until the optional timeout occurs.
In the next section, we’ll review advanced threading techniques, including synchronization and deadlock prevention.
Advanced Python Threading Techniques
In this section, we’ll dive deeper into threading by presenting some more complex examples. You’ll learn about using threading with instances, synchronization, and how to avoid deadlocks.
1. Using Threading with Instances
When you’re working with objects you’ll need a way to thread instance methods. You can accomplish this by passing both the target function and the object itself in the thread initialization.
class MyClass:
def square_numbers(self):
for i in range(5):
print("The square of", i, "is", i*i)
my_object = MyClass()
thread = threading.Thread(target=my_object.square_numbers)
thread.start()2. Using arguments in the function with threading
If your target function requires arguments, you can pass them in the thread initialisation using the args property.
def function_with_args(arg1, arg2):
print("arg1 is: ", arg1)
print("arg2 is: ", arg2)
thread = threading.Thread(target=function_with_args, args=(5,"Hello"))
thread.start()3. Synchronization
When multiple threads are modifying a shared data object, you might encounter inconsistent results. Synchronization helps prevent such inconsistencies by allowing only one thread to access the shared data at a time.
class SharedObject:
def __init__(self):
self.shared_data = 0
self.lock = threading.Lock()
def increment_shared_data(self):
with self.lock:
self.shared_data += 1
print("The shared data is now: ", self.shared_data)
shared_object = SharedObject()
thread1 = threading.Thread(target=shared_object.increment_shared_data)
thread2 = threading.Thread(target=shared_object.increment_shared_data)
thread1.start()
thread2.start()4. Avoiding Deadlocks
To avoid deadlocks, always make sure that the acquired lock is released, even if an error occurs. Python’s threading library has a construct known as a condition variable that can be used for this purpose.
lock = threading.Lock()
try:
lock.acquire()
# Do something...
finally:
lock.release()Using these methods and understanding how to work with Python’s threading module can optimize your programs, especially when your tasks are I/O bound, enabling you to develop more efficient and performant applications.
Where to go next?
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Conclusion
Adding concurrency to your Python programming toolbox can open new doors in your coding journey. With Python threading, you can devise efficient programs that run multiple tasks concurrently, unleashing the true power of your CPU. As we have demonstrated, Python threading is your friend when you tackle work related to game and AI programming or just want to level up your Python skills.
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