Podcast
Questions and Answers
What is the primary purpose of the Lock class in the threading module?
What is the primary purpose of the Lock class in the threading module?
What is the main limitation of Python's threading module for CPU-bound tasks?
What is the main limitation of Python's threading module for CPU-bound tasks?
What is the purpose of the Semaphore class in the threading module?
What is the purpose of the Semaphore class in the threading module?
Which module provides a high-level interface for parallelism using threads or processes?
Which module provides a high-level interface for parallelism using threads or processes?
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What is the primary advantage of using the multiprocessing module?
What is the primary advantage of using the multiprocessing module?
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What is the purpose of the Condition class in the threading module?
What is the purpose of the Condition class in the threading module?
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What is the purpose of the RLock class in the threading module?
What is the purpose of the RLock class in the threading module?
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What is the primary advantage of using the joblib library?
What is the primary advantage of using the joblib library?
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What is the purpose of creating multiple threads in Python?
What is the purpose of creating multiple threads in Python?
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What is the primary purpose of the Thread class in the threading module?
What is the primary purpose of the Thread class in the threading module?
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Study Notes
Threading Module
- The
threadingmodule in Python allows for the creation of multiple threads of execution, which can run concurrently. - Threads are lightweight and share the same memory space, making them suitable for I/O-bound tasks.
- The
threadingmodule provides the following classes:-
Thread: represents a single thread of execution. -
Lock: a synchronization primitive for protecting shared resources. -
RLock: a reentrant lock that can be acquired multiple times by the same thread. -
Semaphore: a counter that limits the number of threads accessing a shared resource. -
Condition: a synchronization primitive that allows threads to wait for a specific condition.
-
- Example of creating a thread:
import threading
def worker():
print("Thread is working")
t = threading.Thread(target=worker)
t.start()
Parallel Processing
- Parallel processing in Python is the ability to execute multiple tasks simultaneously, taking advantage of multiple CPU cores.
- Python's Global Interpreter Lock (GIL) prevents true parallel execution of threads, making it suitable for I/O-bound tasks only.
- For CPU-bound tasks, other approaches are needed:
-
multiprocessingmodule: uses multiple processes to achieve parallelism, bypassing the GIL. -
concurrent.futuresmodule: provides a high-level interface for parallelism using threads or processes. -
jobliblibrary: provides a simple way to parallelize loops and functions.
-
- Example of parallel processing using
multiprocessing:
import multiprocessing
def worker(num):
print("Worker", num)
if __name__ == '__main__':
processes = []
for i in range(5):
p = multiprocessing.Process(target=worker, args=(i,))
processes.append(p)
p.start()
- Example of parallel processing using
concurrent.futures:
import concurrent.futures
def worker(num):
print("Worker", num)
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(worker, i) for i in range(5)]
concurrent.futures.wait(futures)
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Description
Learn about Python's threading module and how to achieve parallel processing using multiple threads and processes. Understand the limitations of Python's Global Interpreter Lock and how to bypass it.
