[sestep]

Hey Eric, great to see you've now published this! I know we chatted about this briefly last year, but it would be awesome to see how the performance of jax-js compares against that of other autodiff tools on a broader and more standard set of benchmarks: https://github.com/gradbench/gradbench

[ekzhang]

For sure! It looks like this is benchmarking the autodiff cpu time, not the actual kernels though, which (correct me if I’m wrong) isn’t really relevant for an ML library — it’s more for if you have a really complex scientific expression

[sestep]

Nope, both are measured! In fact, the time to do the autodiff transformation isn't even reflected in the charts shown on the README and the website; those charts only show the time to actually run the computations.

[ekzhang]

Hm okay, seems like an interesting set of benchmarks — let me know if there’s anything I can do to help make jax-js more compatible with your docker setup

[sestep]

It should be fairly straightforward; feel free to open a PR following the instructions in CONTRIBUTING.md :)

[ekzhang]

I don’t think this is straightforward but it may be a skill issue on my part. It would require dockerizing headless Chrome with WebGPU support and dynamically injecting custom bundled JavaScript into the page, then extracting the results with Chrome IPC

[sestep]

Ahh no you're right, I forgot about the difficulties for GPU specifically; apologies for my overly curt earlier message. More accurately: I think this is definitely possible (Troels and I have talked a bit about this previously) and I'd be happy to work together if this is something you're interested in. I probably won't work on this if you're not interested on your end, though.