def mytopic(event: v1.Event) -> TopicEventResponse:
global should_retry
data = json.loads(event.Data())
print(
f'Subscriber received: id={data["id"]}, message="{data["message"]}", content_type="{event.content_type}"',
flush=True)
if should_retry:
should_retry = False # we only retry once in this example
return TopicEventResponse('retry')
return TopicEventResponse('success')
def mytopic(event: v1.Event) -> None:
X = json.loads(event.Data()).get('X')
Y = json.loads(event.Data()).get('Y')
# convert numpy array to tensor in shape of input size
x = torch.from_numpy(np.asarray(X).reshape(-1, 1)).float()
y = torch.from_numpy(np.asarray(Y).reshape(-1, 1)).float()
# Define Optimizer and Loss Function
optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
loss_func = torch.nn.MSELoss()
inputs = Variable(x)
outputs = Variable(y)
for i in range(25):
prediction = net(inputs)
loss = loss_func(prediction, outputs)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % 5 == 0:
# plot and show learning process
plt.cla()
plt.scatter(x.data.numpy(), y.data.numpy())
plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=2)
plt.text(0.5,
0,
'Loss=%.4f' % loss.data.numpy(),
fontdict={
'size': 10,
'color': 'red'
})
plt.pause(0.1)
# display(fig)
# make_dot(net)
for param in net.parameters():
print(param)
def mytopic(event: v1.Event) -> None:
data = json.loads(event.Data())
print(f'Subscriber received: id={data["id"]}, message="{data["message"]}", content_type="{event.content_type}"',flush=True)
def mytopic(event: v1.Event) -> None:
print(event.Data(), flush=True)
def mytopic(event: v1.Event) -> None:
data = json.loads(event.Data())
print(
f'Subscriber received: value="{data["value"]}", timestamp = "{data["timestamp"]}", topic="{data["topic"]}", content_type="{event.content_type}"',
flush=True)
def local_subscribe(event: v1.Event) -> None:
"""Receives event/data from local component and forwards to remotes."""
data = str(event.Data(), encoding='utf-8')
print(f"Data received from local: {data}")
send_request(data)