Woah! What visualization tool are you using?

Do you take it yourself?

I think the problem is the algorithm. BlueSky's lack of a recommendation engine means that if you're not posting all the time, your stuff doesn't get seen.

I think it's just about readability with small font, the same reason why printed newspapers use many columns.

The losses here should be marked as millions not billions, right?

Pretty much anything that you can write in high level array code like NumPy is very fast in JAX. Only intrinsically very loopy code is (relatively) slow, but JAX has excellent support for writing custom kernels in lower level languages.

AD compatible Python is at the cutting edge of performance these days with it's central role in large-scale AI training.

In my experience (mostly geophysical fluid dynamics) JAX has comparable perf to modern Fortran on CPUs, with a much easier path to GPUs and multi-device code.

Those are tiny chunks! Does that reduce max throughput for analytics use-cases compared to larger chunks?

Such exciting news!

For anyone who has tried the new sharding feature -- do you have any guidance on optimal shard sizes, if I want more flexibility in access patterns but still optimal throughput?

Hi, thanks for the mention. Here's a 7-day paywall-free link to the main feature: www.bloomberg.com/graphics/202...

This paper by Watt-Meyer et al is a good example of "Error-based learning:" agupubs.onlinelibrary.wiley.com/doi/10.1029/...

ECMWF has also done similar work on top of IFS's data assimilation system.

We have a few pre-computed climatologies in WeatherBench2: weatherbench2.readthedocs.io/en/latest/da...

We have a few other updates to share as well, which can be found in the inaugral edition of the NeuralGCM newsletter:
groups.google.com/g/neuralgcm-...

The biggest one is that NeuralGCM models are now freely available for everyone to use, including for commercial purposes!

Please reach out if you want to chat about anything related to AI modeling, NeuralGCM, JAX or Xarray. Also see Eni's poster on xarray.DataTree on Thurs: agu.confex.com/agu/agu24/me...

When I hear "ML" I tend to think of old school (i.e., scikit-learn) machine learning, which is great but much less powerful than deep learning. So I would opt for "AI weather models" though that misses quite a bit of nuance.

This diagram is accurate historically, but recently AI seems to have become synonymous with deep learning.

The bottleneck for traditional models is data movement within the CPU, not data transfer to disk -- physics based simulations do too little compute per byte (low arithmetic intensity) to fully utilize modern hardware.

AI is way better in this respect. It's easy to use lots of FLOPs on big matmuls!

Unlimited potential, zero bugs!