The uncertainty alone is already stopping people from making job offers this year in physics. Will the department of Energy keep funding fundamental physics research, for example? Will the NSF or NASA? No one knows. The rational response under these circumstances is to pull back on hiring.
Baratza also sell replacement parts for their grinders on their web site, and provide clear instructions on how to install them. I am delighted to support a manufacturer that builds repairable products.
But the interpretation of the journalist is the issue here. Comparing a cost optimized part (TPU v5e) against a performance optimized part (H100) and deciding that this makes Google "behind" is just incorrect.
No, it is not. That's the sparse fp8 flop number, but you need to ignore sparsity and compare bf16 flops not fp8 flops for the comparison the ancestor post is making.
Yes, we made this more formally supported recently.
We felt that Windows CPU support was important so everyone can run JAX, even if it's not always the most-accelerated version of JAX. And we got some great PRs from the community that helped fix a few open issues.
We don't support Windows GPU because we haven't had the engineer bandwidth to support it well.
We recommend WSL2 for GPU on Windows at the moment because that is a compromise: it allows CUDA support, without us having to support another release variant.
We don't release Windows GPU wheels at the moment, but that's because we're a small team and none of us use Windows personally. We welcome contributions!
(I verified that the Windows CUDA GPU support built as recently as two weeks ago, but I don't have the ability to test that it works.)
We recommend WSL2 because that's just using our existing Linux CUDA release.
My employer withheld California tax on restricted stock grants for me exactly as described here when I moved out of California years ago.
Other states even have forms for you to submit this calculation to them, see for example New York's IT203-F Schedule B. But you still have to apportion the income between states like this whether there's a form to do it on or not.
Yes, the difference between 4k and 8k text is easily noticeable for a 32" monitor, and I'm not particularly eagle-eyed. Text is sharper and finer details are better rendered. For videos and images I don't notice the difference in practice.
I had a 4k 32" monitor at work and found that it simply didn't give sharp, high resolution text, driven by either a Macbook or by a Linux box. And you wouldn't really expect that, either: a 4k 32" monitor is only ~140dpi, which is only marginally higher resolution than the ~100dpi screens we had for many years.
I think the best point of comparison for the Dell UP3218k monitor which I have is a Retina Macbook Pro screen: subjectively, it's a similar experience in terms of text sharpness and legibility (>200 DPI, glossy), just in a 32" form factor.
I suspect 8k at 32" is actually a bit higher resolution than necessary (~280dpi), but there's nothing else on the >30" high resolution monitor market other than Apple's 6k display, which is significantly more expensive.
Be aware that there's no Mac OS support for 8k displays, but Linux and Windows on a desktop with a reasonably modern NVidia GPU work great.
I love using a 32" 8k monitor for working with text/code. To me, as a software engineer who stares at code all day, higher quality text rendering is sufficient justification for 8k.
I tried playing a few games at 8k (just for fun) and found that it simply wasn't worth the frame rate hit, or really even that noticeable an improvement with the assets in the games I tried.
One nice property of 8k resolutions is that they have an integer scaling factor from both 4k (2x) and 1440p (3x), so if you have an 8k monitor you can play games at either of those resolutions with high quality scaling.
We (JAX) see JAX and Numba as mostly complementary; they have different strengths and they are focused on different things.
We haven't tried combining them yet, but we think it would be fun to explore (https://github.com/google/jax/issues/1870). For example, you could use Numba to hand write a numerical kernel that then participates in a machine learning model that uses JAX automatic differentiation.
Good catch, I missed Trax! Trax is a configuration-driven neural network framework focused on sequence model research, as a successor to Tensor2Tensor.
Comparisons are hard in general and I don't have a good answer for you right now, but keep in mind most of these libraries are from researchers openly sharing the codebases they develop for their own work. We see the role of JAX as analogous to NumPy, that is, a common substrate on which folks can build these sorts of tools.
You might also like the per-example gradients example that appears first in the JAX Github page: this is only one line of code, but important for research areas such as differential privacy.