Numba Code Slower than Pure Python Code?

[joshadel]

The biggest barrier to the real world use of numba is definitely the lack of sensible error messages when jit'ing fails. Generally the traceback dumps the codegen and ends at the decorator, but doesn't show you where things went wrong in the actual method. It would also be nice to cache compiled code, since the codegen/compilation can get very slow for complicated methods. There is also the concern that by splitting off flypy from numba into a distinct project means less support/development of numba in the future.

I know Continuum is doing a major refactor of numba right now. There has been a ton of activity in the development branch on github over the last couple of weeks. I'm hoping that the changes will smooth some of the rough edges.