Description:
This 9-video course offers a lab-only exploration and introduction to the libraries available in Python to run tasks asynchronously by using both processes and threads. To take this course, you should have prior knowledge of how to spawn and manage processes in Python. First, you will learn how to significantly improve the performance and responsiveness of your application by running them concurrently, then learn how to create a process pool, and how to use multiprocessing to execute tasks in parallel. Next, you will learn to use multithreading to run chunks of a task at one time, and to switch between the chunks regularly. Learners will then examine the concurrent.futures module, which contains objects to run threads and processes in an asynchronous manner, and to monitor their progress while they are still executing. Continue by learning how to use ThreadPoolExecutor, available in the concurrent.futures module. Finally, you examine the asyncio module in Python, which provides lightweight mechanisms for asynchronous executions of tasks.
Target Audience:
Duration: 01:02
Description:
This course is a lab-only exploration of the creation and management of processes in Python to speed up the execution of your programs. In this 10-video course, learners will use Jupyter notebooks to execute all programs demonstrated. First, you will learn how to create initialized threads, and how to do the same with processes in Python. Then you will examine different thread-safe data structures in Python to implement queues, stacks, and priority queues. Next, you will learn how to use Python for synchronization mechanisms and inter-process communication, and will see a comparison of processes to threads. You will learn to use Python's built-in queue data structure for multithreaded applications, and how to implement multiprocessing. Continue by learning how processes are created and executed and how they differ from threads when one is using shared resources. You will explore how to use a manager class, or service process manager to share any Python types. Finally, you will examine the available mechanisms in Python for communication and synchronization between processes.
Target Audience:
Duration: 01:17
Description:
This course offers an in-depth exploration of the creation and management of concurrent threads in Python. In its 14 videos, you will learn how to significantly improve the performance and responsiveness of your apps by using concurrent threads. Begin by examining how threads are created in Python from their initialization to their execution; then learn how to use the various synchronization mechanisms such as locks, semaphores, and events. Next, you will examine how concurrent execution could occur in two ways: multithreading and parallel. You will learn to use multithreading to run chunks of each task at one time, and then switch between them regularly. You will learn multiprocessing of threads by executing tasks in parallel. Learners will examine concurrent execution of threads, and some of the issues that arise when these threads are not synchronized. Finally, you will examine several threads of synchronization mechanisms available in Python such as locks, semaphores, events, and conditions and explore the properties and use cases for each of these objects.
Target Audience:
Duration: 01:42
Description:
Explore the general theory of concurrent programming, and examine how to have multiple tasks active at any given point in time. This 14-video course offers an in-depth examination of concurrent programming by using the Python programming language. First, learners will examine the two main forms of concurrent programming, multithreading and multiprocessing, and examine their differences and use cases. Next, you will examine executing multitask sequentially, and with multithreading to save time, and how to use multiprocessing to manage a collection of tasks efficiently. Continue by exploring challenges that programmers encounter when adopting concurrency such as synchronization issues and deadlocks, and how to address these issues. You will examine issues that arise when writing concurrent code, and you will learn how to fix these by using the built-in objects available in Python. Finally, this course examines several of the objects available in the Python language such as queues and pools, which simplify the task of building multithreading and multiprocessing applications.
Target Audience:
Duration: 01:30