AI-Horde VS hivemind

Also the Horde for Stable Diffusion, pretty good concept: https://github.com/Haidra-Org/AI-Horde/

This is a crowdsourced distributed cluster of Image generation workers and text generation workers. If you like this service, consider joining the horde yourself!

There's AI Horde: https://github.com/db0/AI-Horde

You might be interested in the Stable Horde project : Official website: https://stablehorde.net/ GitHub page: https://github.com/db0/AI-Horde Discord server: https://discord.gg/3DxrhksKzn Related posts on Reddit: https://www.reddit.com/r/StableDiffusion/search/?q=horde&restrict_sr=1&sr_nsfw=

If its going to be AGPL that means you are allowed to use AI Hordes backend code if you wish. You can find it here : https://github.com/db0/AI-Horde/tree/KAI_DO_NOT_DELETE

The Kobold AI Horde is a community of volunteers who share their expensive computers for running AI models.

Kobold AI-Horde

I think that this is the correct github project: https://github.com/db0/AI-Horde

github: db0/AI-Horde: A crowdsourced distributed cluster for AI art and text generation (github.com)

ALSO!! If you're an ex-miner and have a lot of GPUs sitting around, consider offering them up to the Horde

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