As 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 pr

As 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