leela-zero VS hivemind

https://github.com/leela-zero/leela-zero but it is not user friendly imo

Yeah, see https://github.com/leela-zero/leela-zero/pull/883 for the discussions near the origin of this idea, which Leela Zero was the first to use many years ago. KataGo's implementation is a bit different in minor ways, but still based on the same mathematical idea. https://github.com/lightvector/KataGo/blob/master/cpp/search/searchhelpers.cpp#L482

Totally agree. I don't even know what benefit they'd get at this point from keeping some parts locked up.

Anyway if you want something runnable Leela has a nice reimplementation: https://github.com/leela-zero/leela-zero

See https://github.com/leela-zero/leela-zero/issues/2445 for an example with Leela Zero failing to see an atari, even *with* tons of search. This is a similar issue - neural nets have a hard time perceiving things that depend sensitively on large areas when unusual shapes are involved.

(https://github.com/leela-zero/leela-zero/issues/2273)

I think it's Leela https://github.com/leela-zero/leela-zero

There is Leela Zero

Yeah, it looks like I need to recompile Leela Zero with a 9x9 board size? https://github.com/leela-zero/leela-zero/pull/928 (and https://github.com/leela-zero/leela-zero/issues/2613)

https://github.com/learning-at-home/hivemind is also relevant

I really hope you'll join me, for the Petals support, at least! A single docker-compose.yml file is all we need, for now. If we are able to find enough people willing to host some smaller models, perhaps we could expand into the Hivemind, and create our own, custom foundation model one day?

Decentralized deep learning: https://github.com/learning-at-home/hivemind

https://github.com/learning-at-home/hivemind - same people behind it, was made before petals I think.

not if we rise to meet that challenge. here's a few tools that facilitate AI research in the face of an advanced persistent threat: Hivemind- a distributed Pytorch framework

yeah, there's Hivemind. and there's research wrt how to chunk out training workload so it can be scaled up. not sure why there's commentary that latency issues would limit this sort of enterprise, the architecture typically isn't designed for liveness. other subfields of distributed training/inference include zero-knowledge machine learning. besides all of that, there's also adversarial computation like SafetyNets and refereed delegation of computation.

We already have the software for it. There are some projects, but the one I'm most familiar with is https://github.com/learning-at-home/hivemind for training and it's sister project https://petals.ml/ for running large models distributed.

I'm not entirely how the approach they're using works [0], but I study federated learning and one of the highly-cited survey papers has several chapters (5 and 6 in particular) addressing potential attacks, failure modes, and bias [1].

0: https://github.com/learning-at-home/hivemind

1: https://arxiv.org/abs/1912.04977

The Hivemind project is just that

https://github.com/learning-at-home/hivemind