Miners accept requests from the validator containing a model state dict and return a gradient over that model with respect to a dataset. The gradients are aggregated on the validator and applied to the model. We use a GPT2 model architecture and the Falcon Refined Web dataset. The optimizer exists only on the validator side. Miner are free to compute gradients in any manner.
TODO: introduce a ranking method.
python -m pip install -r requirements.txtFirst run your miners
# Miner script.
# python src/miner.py
#
# Miner wallet name
# --wallet.name miners Miner wallet name
#
# Miner hotkey must be distinct per miner
# --wallet.hotkey M1
#
# Select a device (different for each miner), if you dont have a GPU pass 'cpu'
# --device cuda:1
#
# Each miner must have a separate port here (also different for each miner)
# --.port 8091
# Run first miner
python src/miner.py --wallet.name miners --wallet.hotkey M1 --device cuda:1 --axon.port 8091
# Run your second miner
python src/miner.py --wallet.name miners --wallet.hotkey M2 --device cuda:2 --axon.port 8092 Second run your validator/trainer on the same machine.
#
# Script name:
# python src/validator.py
#
# The validator wallet name:
# --wallet.name validator
#
# The validator hotkey name:
# --wallet.hotkey default
#
# The validator device, different that miners:
# --device cuda:0
#
# Enter the ports for each of the miners you are running:
# --axons 8091 8092
# Run the validator
python src/validator.py --wallet.name validator --wallet.hotkey default --device cuda:0 --axons 8091 8092This repository is licensed under the MIT License.
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