[eloisius]

I’ve got a concrete example because I just recently used numba to speed up some code. I’m working on a stereo camera object triangulation and tracking system. We wanted to make triangulation faster so I profiled the code and found a few hot spots. In some cases it was a matter of vectorizing some computations, or avoiding tearing apart numpy arrays and allocating new ones, but aside from those kinds of things, the system was spending most of its time in two functions: calculate Euclidean distance between two points and calculate distance of a point from a line (represented by two points). There wasn’t much refactoring possible because they were mostly pure numpy. I annotated them with numba’s JIT decorator and after the initial penalty of the JIT compiler, the functions are way faster.

Because my code is parallelized, I had to figure out how to trigger the compiler before forking because then each worker process had to do the compilation itself. There’s some decorator syntax to tell numba to precompile for specific numeric types, but I found it easier to just call the functions when you import them.

All told, it’s way easier than it would have been to implement this hotspot in C++ or something.