def test_connect_close(hook, start_proc):
kwargs = {"id": "fed", "host": "localhost", "port": 8763, "hook": hook}
process_remote_worker = start_proc(WebsocketServerWorker, **kwargs)
time.sleep(0.1)
kwargs = {"id": "fed", "host": "localhost", "port": 8763, "hook": hook}
local_worker = WebsocketClientWorker(**kwargs)
x = torch.tensor([1, 2, 3])
x_ptr = x.send(local_worker)
assert local_worker.objects_count_remote() == 1
local_worker.close()
time.sleep(0.1)
local_worker.connect()
assert local_worker.objects_count_remote() == 1
x_val = x_ptr.get()
assert (x_val == x).all()
local_worker.ws.shutdown()
time.sleep(0.1)
process_remote_worker.terminate()
optimizer=optimizer,
batch_size=batch_size,
optimizer_args=optimizer_args,
epochs=5,
shuffle=shuffle)
arw = {"host":"10.0.0.1","hook":hook}
h1 = WebsocketClientWorker(id="h1",port=8778,**arw)
train_config.send(h1)
message = h1.create_message_execute_command(command_name="start_monitoring",command_owner="self")
serialized_message = syft.serde.serialize(message)
h1._recv_msg(serialized_message)
time.sleep(3)
for epoch in range(10):
time.sleep(3)
loss = h1.fit(dataset_key="train") # ask alice to train using "xor" dataset
print("-" * 50)
print("Iteration %s: h1's loss: %s" % (epoch, loss))
message = h1.create_message_execute_command(command_name="stop_monitoring",command_owner="self")
serialized_message = syft.serde.serialize(message)
h1._recv_msg(serialized_message)
new_model = train_config.model_ptr.get()
h1.close()