This video explains the `multiprocessing` module in Python.
This article introduces practical tips for writing safe and efficient parallel processing code using the `multiprocessing` module.
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🕐 Video contents
00:00 Start
00:33 The `multiprocessing` module in Python / Basics: Why use `multiprocessing`?
00:56 - Simple usage of `Process`
01:43 - Simple parallelization with `Pool` (high-level API)
02:24 - Interprocess communication: Producer/Consumer pattern using `Queue`
03:12 - Shared memory: `Value` and `Array`
04:08 - Advanced sharing: Shared objects (dicts, lists) with `Manager`
04:53 - Synchronization mechanisms: How to use `Lock` and `Semaphore`
05:36 - Differences between `fork` on UNIX and behavior on Windows
06:22 - Practical example: Benchmarking CPU-bound workloads (comparison)
07:11 - Important basic rules
08:24 - Practical design advice
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