Numba can compile in “no Python” mode only with a subset of Python. Eg classes is limited and is still experimental. Also I think string manipulation is slow but the doc has details.
If you want to specify the type (for example for aot or just because you want to make it clear) then the call signature is less flexible.
In short, pick any random Python library, you’d find there are very few places you can jit accelerate something effectively. It is for numeric.
Even for numerical code, it is more like writing C functions than say C++ (with classes etc).
But it does makes accelerating vectorized code very easy. Even if you have a function that uses Numpy, it is likely you can speed it up using Numba with a decorator only.
But when it doesn’t work, it might often be not very clear why you can’t until you get some experience.
The ahead of time compilation output is... Well.. let's say difficult to package _properly_ (compare it to Cython where it's well supported and documented). That makes it useless for production, unless you want to ship giant containers with compilers etc
In theory, a compiler toolchain is not required since Numba already comes with LLVM, i.e. for JIT compilation, no additional compiler is necessary.
In the past, that was also possible for AOT compilation [1], but that technique broke during some update and it seems like there is no one left who knows how to fix this.
What??? Numba has more usage in the AI/ML community than Cython has ever had by anyone, ever.
"Fits very few use cases" LOL okay without numba there's no UMAP and HDBScan and those are pretty popular and important libraries that come to mind just off the top of my head...
Also, claiming Cython is well documented also gets a huge LOL from me as someone whose actually written a bit of Cython.
I assume the parent comment was talking about the context of computations where numba is supposed to be a drop-in for wherever numpy is used.
And I agree that it's not actually usable everywhere, since the support for numpy's feature set is actually quite limited, especially around multidimensional arrays. I had to effectively rewrite my logic to make use of numba. Still it is pretty worth it imo, given how it can add parallelism for free. And conforming to numbas allowed subset of numpy usually results in simpler and more efficient code. In my case I ended up having to work around the lack of support for multidimensional arrays but ended up with a more efficient solution relying on low dimensional arrays being broadcasted, reducing a lot of duplicate computations
KolenCh|4 years ago
If you want to specify the type (for example for aot or just because you want to make it clear) then the call signature is less flexible.
In short, pick any random Python library, you’d find there are very few places you can jit accelerate something effectively. It is for numeric.
Even for numerical code, it is more like writing C functions than say C++ (with classes etc).
But it does makes accelerating vectorized code very easy. Even if you have a function that uses Numpy, it is likely you can speed it up using Numba with a decorator only.
But when it doesn’t work, it might often be not very clear why you can’t until you get some experience.
korijn|4 years ago
johndough|4 years ago
In the past, that was also possible for AOT compilation [1], but that technique broke during some update and it seems like there is no one left who knows how to fix this.
[1] https://stackoverflow.com/a/42198101
Der_Einzige|4 years ago
"Fits very few use cases" LOL okay without numba there's no UMAP and HDBScan and those are pretty popular and important libraries that come to mind just off the top of my head...
Also, claiming Cython is well documented also gets a huge LOL from me as someone whose actually written a bit of Cython.
sockpuppet69|4 years ago
CornCobs|4 years ago
And I agree that it's not actually usable everywhere, since the support for numpy's feature set is actually quite limited, especially around multidimensional arrays. I had to effectively rewrite my logic to make use of numba. Still it is pretty worth it imo, given how it can add parallelism for free. And conforming to numbas allowed subset of numpy usually results in simpler and more efficient code. In my case I ended up having to work around the lack of support for multidimensional arrays but ended up with a more efficient solution relying on low dimensional arrays being broadcasted, reducing a lot of duplicate computations
henrydark|4 years ago