bad = WebsocketClientWorker(id="bad",
port=8777,
host="localhost",
hook=hook)
good = WebsocketClientWorker(id="good",
port=8778,
host="localhost",
hook=hook)
# When staring server, we already register a tensor
# object with tab "test" in the server worker
try:
print("Search test with bad")
data_pt = bad.search("test")
except ObjectNotFoundError:
print("Error: test is not found with bad worker!!!")
print("Search test with good")
data_pt = good.search("test")
print("Test is found with good worker!!!")
print("Here is the test data")
print(data_pt[0].copy().get())
# Reason:
# search the tensor object on server worker with tensor tag
# via the local client worker returns ERROR because
# set_obj() on the server side does not assign the owner attribute on
# the object.
# This leads to the fact that, when.search() is called,
import torch
from torch import optim
import syft
from syft.workers.websocket_client import WebsocketClientWorker
hook = syft.TorchHook(torch)
# create a client worker mapping to the server worker in remote machine
remote_client = WebsocketClientWorker(
host=
'192.168.0.102', # the host of remote machine, the same a the Server host
hook=hook,
id='liuwang',
port=8182)
print('>>> remote_client', remote_client)
# get the data pointers which point to the real data in remote machine for training model locally
features = remote_client.search(["toy", "features"])
labels = remote_client.search(["toy", "labels"])
print('>>> x:', features)
print('>>> y:', labels)
# a toy model
model = torch.nn.Linear(2, 1)
remote_model = model.copy().send(remote_client)
def train(x, y, N) -> torch.nn.Module:
# Training Logic
opt = optim.SGD(params=remote_model.parameters(), lr=0.1)
for iter in range(N):
# 1) erase previous gradients (if they exist)
opt.zero_grad()