pepeai-for-omarg's Introduction

#IMPORTANT MESSAGE : This repo will be moving into the -OMARG-AIR-AID repository to be adapted via the in repo OS.

pepeai-testnet

experimental testnet for decentralized p2p AI processing

Setting up the environment:

Nvidia Driver:

sudo apt-get update && sudo apt-get install nvidia-driver-530

Docker:

Linux
Windows
OSX

sudo docker run -p 31330:31330 --ipc host --gpus all --volume petals-cache:/cache --rm     learningathome/petals:main python -m petals.cli.run_server bigscience/bloomz-petals --initial_peers /ip4/54.164.52.79/tcp/8989/p2p/QmR7G6GZzeQeJoXfTovp8PJdJ7qxKYFwXUa2fp9p6BYdDi --port 31330

Or install manually:

Anaconda Install:

wget https://repo.anaconda.com/archive/Anaconda3-2023.03-1-Linux-x86_64.sh
chmod +x ./Anaconda3-2023.03-1-Linux-x86_64.sh
./Anaconda3-2023.03-1-Linux-x86_64.sh

Answer yes to base env setup.

conda create -n pepeaitestnet python=3.10
conda activate pepeaitestnet
conda install pytorch pytorch-cuda=11.7 -c pytorch -c nvidia
pip install -r requirements.txt

Running a Node:

PepeAI net seperates processing by PepeCoin Nodes. Each node requires at least 1 GPU w/ 8GB+ or more of memory.

bloom model layers distrubuted across fleet of pepecoin nodes
will support various models as they are updated for pepecoin ai net

Start GPU Processing Node:

ensure environment is setup as indicated above
If you have more than 1 GPU, set the GPU index before starting the node
you can run multiple nodes under screen

export CUDA_VISIBLE_DEVICES=0  # Insert the GPU index here, counting from zero
python -m petals.cli.run_server bigscience/bloomz-petals --initial_peers /ip4/54.164.52.79/tcp/8989/p2p/QmR7G6GZzeQeJoXfTovp8PJdJ7qxKYFwXUa2fp9p6BYdDi

Demo Chatbot: