![]() Under the hood, Python’s multiprocessing package spins up a new python process for each core of the processor. Thus, to speed up our Python script we can utilize multiprocessing. We are only using 5% of our true processing power! The Python script will then run to completion. The operating system will assign the process to a single one of those cores: Figure 2: Without multiprocessing, your OpenCV program may not be efficiently using all cores or processors available on your machine. Now, let’s assume we launch our Python script. This figure is meant to visualize the 3 GHz Intel Xeon W on my iMac Pro - note how the processor has a total of 20 cores. This 3GHz Intel Xeon W processor is being underutilized. By default, Python scripts use a single process. To see how we’re underutilizing our processor, consider the following image: Figure 1: Multiprocessing with OpenCV and Python. That’s all fine and good - but we are only utilizing a small amount of our true processing power. The OS then allows the Python script to run on the processor core until completion.The operating system assigns the Python program to a single core of the processor.via the OS however, for the sake of simplicity, let’s assume: There are entire books dedicated to multiprocessing, operating systems, and how processes are scheduled, assigned, removed, deleted, etc. How the actual Python process itself is assigned to a CPU core is dependent on how the operating system handles (1) process scheduling and (2) assigning system vs. When you launch your Python project, the python binary launches a Python interpreter (i.e., the “Python process”). The vast majority of projects and applications you have implemented are (very likely) single-threaded. ![]() Let’s get started! Why use multiprocessing for processing a dataset of images?
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