def test_search_obj(self):
hook = sy.TorchHook()
hook.local_worker.is_client_worker = False
x = sy.Var(sy.FloatTensor(
[-2, -1, 0, 1, 2, 3])).set_id('#boston_housing #target #dataset')
y = sy.Var(sy.FloatTensor(
[-2, -1, 0, 1, 2, 3])).set_id('#boston_housing #input #dataset')
hook.local_worker.is_client_worker = True
assert len(hook.local_worker.search("#boston_housing")) == 2
assert len(hook.local_worker.search(["#boston_housing",
"#target"])) == 1
def test_set_id(self):
init_state = hook.local_worker.is_client_worker
hook.local_worker.is_client_worker = False
x = torch.FloatTensor([-2, -1, 0, 1, 2, 3]).set_id('bobs tensor')
assert x.id == 'bobs tensor'
assert x.child.id == 'bobs tensor'
assert x.id in hook.local_worker._objects
assert list(x.child.old_ids)[0] in hook.local_worker._objects
assert list(x.child.old_ids)[0] != x.id
x = sy.Var(sy.FloatTensor([-2, -1, 0, 1, 2, 3])).set_id('bobs variable')
assert x.id == 'bobs variable'
assert x.child.id == 'bobs variable'
assert x.id in hook.local_worker._objects
assert list(x.child.old_ids)[0] in hook.local_worker._objects
assert list(x.child.old_ids)[0] != x.id
]))
# create a couple workers
bob = sy.VirtualWorker(id="bob")
alice = sy.VirtualWorker(id="alice")
secure_worker = sy.VirtualWorker(id="secure_worker")
#bob.add_workers([alice, secure_worker])
#alice.add_workers([bob, secure_worker])
#secure_worker.add_workers([alice, bob])
train_distributed_dataset = []
for batch_idx, (data, target) in enumerate(train_loader):
if batch_idx > 4: break
data = sy.Var(data)
target = sy.Var(target.long())
data.send(bob)
target.send(bob)
train_distributed_dataset.append((data, target))
#bobs_model = model.copy().send(bob)
#alices_model = model.copy().send(alice)
bobs_model = Net()
bobs_opt = optim.SGD(params=bobs_model.parameters(), lr=0.1)
#alices_opt = optim.SGD(params=alices_model.parameters(),lr=0.1)
bobs_model = bobs_model.send(bob)