Wow, just... wow. I wonder why they didn't utilize (multiple) GPUs instead? I would guess it would be far more efficient in all aspects. Especially now that there is TensorFlow & co.
Wow, just... wow. I wonder why they didn't utilize (multiple) GPUs instead? I would guess it would be far more efficient in all aspects. Especially now that there is TensorFlow & co.
I'm not sure if we're planning to use GPUs for future iterations of algorithm testing; the GPUs are installed in hardware serving production traffic so it's unlikely to be used for experimental stuff like this. That said, a bunch of us just got Dell XPS-15s with the NVIDIA GTX 1050 so we can probably have a go on our local machines to get a feel for performance characteristics... watch this space, I guess!
It's not a neural network, so everything would be hand coded TF operations. It's unclear how that would be quicker than gradient decent.
I'd be more interested to know why they didn't try the learning-to-rank implementations in XGB or LightGBM?
I imagine the reason for using CPU was that they would throw it out anyway, so there was no need to make an ideal solution - and they already had those 56 CPU cores.