Note: This page isn't rendering properly on iPhone
edit: seems the JS is written in the straightforward, idiomatic JS way without employing speed hacks or typed arrays. So that I guess explains some of it.
Ran in Chrome profiler, spent most time in these forward/backward functions https://github.com/DanRuta/jsNet/blob/master/dev/js/ConvLaye...
Once you've decided that you do care about those other things, then getting the best performance you can is great and deeplearnjs is very useful.
But if what you care about most is performance, then using JS is not the way to go. I'm guessing deeplearnjs is an order of magnitude slower (at least) for training a modern convnet relative to cudnn or MKL. And I don't think it's currently possible to use multiple GPUs? Although you could imagine some crazy distributed asynchronous training running in browsers all over the world.
This is NOT a fair and honest comparison.
The WebAssembly implementation pegs 100% of one CPU core (as monitored in htop) on my system until it is completed.
The JS button sits between 66.2%-66.8% and 19-26% of one core depending on whether the tab is focused or not.
The JS version does not use Web Workers. I can, however, see two references to setTimeout(). This leads me to assume that the JS version is being slowed down so that the UI does not lock up.
Completely understandable, but patently dishonest, as there is no mention of this fact on the webpage.
The JS version should be reimplemented so it can run at 100% speed.
I'm focusing more on a WebGL version, but once that's done, it should be a fairer comparison.
I'm currently most focused on designing and implementing a WebGL version (partially working FC forward shader, so far), between uni assignments, trying to see if I can get it working nicely together with WebAssembly (need to figure out the best way to create the contexts with an off-screen canvas).
The JS version uses setTimeout to stop locking up the browser, and is something old, which I'm about to change, in favour of using WebWorkers, and (optionally) collecting error data in an array, for displaying charts at the end of training, instead of during. That should come out in version 3.3, unless the GPU stuff comes out first, in v4.0.
I didn't expect the link would get posted somewhere, so the library versions used were pretty old, haha, but I'll update them now. I've also added a note about the setTimeout thing, which, again, will be removed soon.
The repo is here, if anyone was interested: https://github.com/DanRuta/jsNet
