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PacktPub - Concurrent and Parallel Programming in Python

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Added 3 years ago by xHOBBiTx in Other
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PacktPub - Concurrent and Parallel Programming in Python (Size: 1.7 GB)
  001. Introduction to Writing Asynchronous Programs.mp4 44.7 MB
  001. Multiprocessing Introduction.mp4 33.6 MB
  001. Threading, Multiprocessing, Async Introduction.mp4 59.3 MB
  002. Asynchronous Tasks.mp4 26.1 MB
  002. Multiprocessing Queues.mp4 36.6 MB
  002. Threading in Python.mp4 65.6 MB
  003. Async Gather Method.mp4 30.7 MB
  003. Creating a Threading Class.mp4 53.7 MB
  003. Multiprocessing Pool.mp4 44.1 MB
  004. Creating a Wikipedia Reader.mp4 84.1 MB
  004. Multiprocessing Pool Map Multiple Arguments.mp4 18.1 MB
  004. Using Async Timeouts.mp4 13.1 MB
  005. Creating Asynchronous For Loops.mp4 12.8 MB
  005. Creating a Yahoo Finance Reader.mp4 86.6 MB
  005. Multiprocessing Multiple Varying Arguments.mp4 16.4 MB
  006. Multiprocessing Checking Elements in List in Certain Ranges.mp4 29.4 MB
  006. Queues and Master Scheduler.mp4 67.6 MB
  006. Using Asynchronous Libraries.mp4 48.1 MB
  007. Creating a Postgres Worker.mp4 94.5 MB
  007. The Async Wait Statement.mp4 41.2 MB
  008. Combining Async and Multiprocessing.mp4 51.4 MB
  008. Integrating the Postgres Worker.mp4 111.3 MB
  009. Yaml File Introduction.mp4 88.8 MB
  010. Creating a Yaml Reader.mp4 161.6 MB
  011. Improving Our Wiki Worker.mp4 153.2 MB
  012. Improving All Workers and Adding Monitoring.mp4 146.3 MB
  013. Final Program Cleanup.mp4 34.1 MB
  014. Locking.mp4 55.7 MB
  ▲ 28 total files

Description


PacktPub – Concurrent and Parallel Programming in Python

English | Tutorial | Size: 1.67 GB





In a big data project, a plethora of information is retrieved, big numbers are crunched on our machine, or both. If the coding is sequential or synchronous, our application will struggle to execute. Two mechanisms to alleviate such bottlenecks are concurrency and parallelism. In Python, concurrency is represented by threading, whereas multiprocessing achieves parallelism. This course begins with an introduction about potential programming speed bottlenecks and solving them. You will delve into Python concepts and create a Wikipedia Reader, Yahoo Finance Reader, Queues, and Master Scheduler. You will build a multi-threaded program to grab data from the Internet and parse and save them into a local database. Implement multiprocessing in Python, which lets us use multiple CPUs in our code. Learn about threading, multiprocessing, asynchronous wait, locking, multiprocessing queues, Pool Map Multiple Arguments, writing asynchronous programs, and combining async and multiprocessing. Upon completion, we can spread our workload over all cores available on the used machine. We will combine both elements, multiprocessing with asynchronous programming, to maximize benefit and CPU resource usage and minimize the time spent waiting for IO responses.

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