tracer_interceptor = server_interceptor.OpenCensusServerInterceptor(
AlwaysOnSampler())
app = App(thread_pool=futures.ThreadPoolExecutor(max_workers=10),
interceptors=(tracer_interceptor, ))
@app.method(name='say')
def say(request: InvokeMethodRequest) -> InvokeMethodResponse:
tracer = Tracer(sampler=AlwaysOnSampler())
with tracer.span(name='say') as span:
data = request.text()
span.add_annotation('Request length', len=len(data))
print(request.metadata, flush=True)
print(request.text(), flush=True)
return InvokeMethodResponse(b'SAY', "text/plain; charset=UTF-8")
@app.method(name='sleep')
def sleep(request: InvokeMethodRequest) -> InvokeMethodResponse:
tracer = Tracer(sampler=AlwaysOnSampler())
with tracer.span(name='sleep') as _:
time.sleep(2)
print(request.metadata, flush=True)
print(request.text(), flush=True)
return InvokeMethodResponse(b'SLEEP', "text/plain; charset=UTF-8")
app.run(3001)
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)
app.run(50051)
import time
from uuid import uuid4
from context import WorkflowContext
from dotenv import load_dotenv
from dapr.clients.grpc.client import DaprClient
import json
load_dotenv()
step_name = "step_3_consume"
pubsub_name = "redispubsub"
topic_name = "longRunningTasks"
app = App()
@app.subscribe(pubsub_name=pubsub_name, topic=topic_name)
def longRunningTaskFinished(event: v1.Event) -> None:
time.sleep(5)
print(
f"{step_name}: Long running task finished at {datetime.datetime.now().isoformat()}",
flush=True)
app.stop()
with WorkflowContext(step_name) as context:
with DaprClient() as d:
app.run(20001)
