A while ago at $work, we had a performance issue with one of our core Python libraries. This particular library forms the backbone of our 3D processing pipeline. It’s a rather big and complex library which uses NumPy and other scientific Python packages to do a wide range of mathematical and geometrical operations. Our system also has to work on-prem with limited CPU resources, and while at first
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