[naturalgradient]

I would just want to comment that while this is true in principle, it's also slightly misleading because it does not include how much tuning and testing is necessary until one gets to this result.

Determining the scale needed, fiddling with the state/action/reward model, massively parallel hyper-parameter tuning.

I may be overestimating but I would reckon with hyper-parameter tuning and all that was easily in the 7-8 figure range for retail cost.

This is slightly frustrating in an academic environment when people tout results for just a few days of training (even with much smaller resources, say 16 gpus and 512 CPUs) when the cost of getting there is just not practical, especially for timing reasons. E.g. if an experiment runs 5 days, it doesn't matter that it doesnt use large scale resources, because realistically you need 100s of runs to evaluate a new technique and get it to the point of publishing the result, so you can only do that on a reasonable time scale if you actually have at least 10x the resources needed to run it.

Sorry, slightly off topic, but it's becoming a more and more salient point from the point of academic RL users.

[boulos]

I hear you. I would say that this work is tantamount to what would normally be a giant NSF grant.

Depending on your institution, this is precisely why we (and other providers) give out credits though. Similar to Intel/NVIDIA/Dell donating hardware historically, we understand we need to help support academia.

[naturalgradient]

Yes, thank you for that by the way, did not want to diminish your efforts. Just wanted to point out that papers are often misleading about how many resources are needed to get to the point of running the result. I have received significant amounts of money from Google, full disclosure.

[scoggs]

That's so awesome. Thanks for the exchange you two had. I love seeing the technology permeate through it's different causeways to become a useful and tangible product for more and more people. It's a thing of beauty to watch unfold each and every time, to me.

[abhgh]

This is a very good point. While the final model might be a weekend of training, getting there is a lot more iterations/work.