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opis

  1. uv jako izolacja zalezności
  2. wykorzystanie framework Flower AI jako core implementacji, z właśną implementacją dodatkowych scenariuszy

SuperLink

run superlink server:

flower-superlink --insecure

Load the server:

flwr run . local-deployment --stream

SuperNode

command 1:

flower-supernode \
     --insecure \
     --superlink 127.0.0.1:9092 \
     --clientappio-api-address 127.0.0.1:9094 \
     --node-config "partition-id=0 num-partitions=2"

command 2:

flower-supernode \
     --insecure \
     --superlink 127.0.0.1:9092 \
     --clientappio-api-address 127.0.0.1:9095 \
     --node-config "partition-id=1 num-partitions=2"

truffle cheat sheet

const instance = await TrainOrch.deployed();

const sessionId = await instance.currentSession();
sessionId.toString()
instance.sessions(0)
instance.getTrainers(sessionId);

let accounts = await web3.eth.getAccounts()
web3.eth.getBalance(accounts[0])

flow Solidity:

Server - createNewSession
Client - regusterForTraining
Client - Funder - fundSessionRewards
Server - startSession
off-chain - learning process
Server - completeTrainingSession
Server - distributeRewwards
Server - createNewSession

flow client - register:

publish key hash
register for a training
train
wait for reward

flow client - funder

publish key hash
fund a model
wait for training results

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Flower AI, end-to-end, Ethereum

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