3 ms·
It was hyped and probably are used in even more places (in my previous work i even wrote a full Julia based parsers etc.) But still i didn't see much advantage
by komuher 6y ago
It was hyped and probably are used in even more places (in my previous work i even wrote a full Julia based parsers etc.) But still i didn't see much advantage of writing in Julia now 2 years and even maybe 1 year ago there were few (before numba and other JIT python stuff was working fine).
Right now for me its just l'art pour l'ar you almost never get performance like in C/Nim/RUST and you don't even have much good libs in the community (compared to python thier main rival).
- BadInformatics 6y agoAs someone who uses TF/PyTorch/JAX and numba for their day-to-day DL work, I've been watching Julia's post-hype developments in this area intently. Julia has probably the most vibrant ML ecosystem of all the non-Python languages (remind me when someone has implemented a transformers library for Arraymancer [1]), and absolutely smokes Python in certain niches (e.g. neural ODEs and more under the SciML [2] umbrella, I'd argue Python is "lagging hard" there). Numba can work for speeding up simple operations, but is rife with limitations that prevent optimizing more idiomatic Python code and is an absolute pain to debug. Likewise, trying to decipher or even catch errors from deep in the C++ bowels of most modern DL frameworks is an exercise of frustration. Python may still be the best language for non-cutting edge deep learning, but we ought to consider whether we've overfit our algorithms to the limitations of the current system [3]. [1] https://github.com/mratsim/Arraymancer https://github.com/mratsim/Arraymancer [2] https://sciml.ai/ https://sciml.ai/ [3] https://news.ycombinator.com/item?id=20301619 https://news.ycombinator.com/item?id=20301619