AI-Horde VS Pytorch

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

PyTorch also has some support for them, but it's quite incomplete and has many issues so that it is basically unusable. And its future development is also unclear. https://github.com/pytorch/pytorch/issues/60832

TensorFlow

TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.

> does PyTorch have a similar concept

of course https://github.com/pytorch/pytorch/blob/main/torch/utils/_py...

fyi should work on most 40xx[1]

[1] https://github.com/pytorch/pytorch/issues/119638#issuecommen...

Sure, right here: https://github.com/pytorch/pytorch/blob/main/torch/autograd/...

Here's the documentation: https://pytorch.org/tutorials/intermediate/forward_ad_usage....

> When an input, which we call “primal”, is associated with a “direction” tensor, which we call “tangent”, the resultant new tensor object is called a “dual tensor” for its connection to dual numbers[0].

*My post explains Dot, Matrix and Element-wise multiplication in PyTorch.

In PyTorch with @, dot() or matmul():

import torch # we use PyTorch: https://pytorch.org data = torch.tensor(encode(text), dtype=torch.long) print(data.shape, data.dtype) print(data[:1000]) # the 1000 characters we looked at earlier will to the GPT look like this

AI's Open Embrace Artificial intelligence (AI) and machine learning (ML) are increasingly leveraging open-source frameworks like TensorFlow [https://www.tensorflow.org/] and PyTorch [https://pytorch.org/]. This democratization of AI tools is driving innovation and lowering entry barriers across industries.