I am a heavy user of Python and its scientific libraries (numpy, etc.), and although I know about the GIL, I have to add, that for us (we do a lot of scientific

I am a heavy user of Python and its scientific libraries (numpy, etc.), and although I know about the GIL, I have to add, that for us (we do a lot of scientific code-prototyping to evaluate remote sensing processing methods) the GIL hasn't been a problem so far. E.g. in the remote sensing and earth observation domain you can simply divide your problem (e.g. semantic segmentation) into (maybe over-lapping) subproblems (via e.g. tiling) and start separate processes for each image processing tool-chain. Granted you may not utilize your resources to the full extent by only applying multiprocessing (and ignoring threading), but in my experience you can solve a lot of problems by simply applying map-reduce-like programs and optimizing for throughput.