As we all know, the technically best solution always wins.
The future of Deep Learning frameworks
21–30 of 113 posts
Re: The future of Deep Learning frameworks
#22[flagged]
Re: The future of Deep Learning frameworks
#23PyTorch beat Tensorflow because it was much easier to use for research. Jax is much harder to use for exploratory research than PyTorch, due to requiring a fixed shape computation graph, which makes implementing many custom model architectures very difficult.
Jax's advantages shine when it comes to parallelizing a new architecture across multiple GPU/TPUs, which it makes much easier than PyTorch (no need for custom cuda/networking code). Needing to scale up a new architecture across many GPUs is however not a common use-case, and most teams that have the resources for large-scale multi-gpu training also have the resources for specialised engineers to do it in PyTorch.
Re: The future of Deep Learning frameworks
#24I wish dex-lang [1] had gotten more traction. It’s JAX without the limitations that come from being a Python DSL.
But ML researchers apparently don’t want to touch anything that doesn’t look exactly like Python.
Re: The future of Deep Learning frameworks
#25i like pytorch because all the academia release their code with it ive never even heard of jax nor will i have the skills to use it i literally just want to know two things: 1) how much vram 2) how to run it on pytorch
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Re: The future of Deep Learning frameworks
#26[flagged]
[deleted]
Re: The future of Deep Learning frameworks
#27This is such a hyperbolic headline it's hard to be interested in reading the actual article.
All blanket statements are false.
Re: The future of Deep Learning frameworks
#28i like pytorch because all the academia release their code with it ive never even heard of jax nor will i have the skills to use it i literally just want to know two things: 1) how much vram 2) how to run it on pytorch
Jax is a competing computational framework that does something similar to PyTorch, so both of your questions don't really make sense.
Re: The future of Deep Learning frameworks
#29PyTorch is the javascript of ML. sadly "worse is better" software has better survival characteristics even when there is consensus that technology X is theoretically better
I don't think the comparison is fair. Imo PyTorch has the cleanest abstractions, which is the reason it is so popular. People can do quick prototyping without having to spend too much time figuring out the engineering details that make their hardware run it.
Re: The future of Deep Learning frameworks
#30I wish dex-lang [1] had gotten more traction. It’s JAX without the limitations that come from being a Python DSL. But ML researchers apparently don’t want to touch anything that doesn’t look exactly like Python. [1]: https://github.com/google-research/dex-lang
It seems like an experimental research language.
Julia also competes in this domain from a more practical standpoint and has less limitations than JAX as I understand it, but is less mature and still working on getting wider traction.