def test_model_template_app_grpc_tags(tracing):
with start_microservice(
join(dirname(__file__), "model-template-app"), tracing=tracing, grpc=True
):
data = np.array([[1, 2]])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=data.shape, values=data.flatten())
)
meta = prediction_pb2.Meta()
json_format.ParseDict({"tags": {"foo": "bar"}}, meta)
request = prediction_pb2.SeldonMessage(data=datadef, meta=meta)
channel = grpc.insecure_channel("0.0.0.0:5000")
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.Predict(request=request)
assert response.data.tensor.shape[0] == 1
assert response.data.tensor.shape[1] == 2
assert response.data.tensor.values[0] == 1
assert response.data.tensor.values[1] == 2
assert response.meta.tags["foo"].string_value == "bar"
def test_model_template_app_grpc(tracing):
with start_microservice(join(dirname(__file__), "model-template-app"),
tracing=tracing,
grpc=True):
data = np.array([[1, 2]])
datadef = prediction_pb2.DefaultData(tensor=prediction_pb2.Tensor(
shape=data.shape, values=data.flatten()))
request = prediction_pb2.SeldonMessage(data=datadef)
channel = grpc.insecure_channel("0.0.0.0:5000")
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.Predict(request=request)
assert response.data.tensor.shape[0] == 1
assert response.data.tensor.shape[1] == 2
assert response.data.tensor.values[0] == 1
assert response.data.tensor.values[1] == 2
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
feedback = prediction_pb2.Feedback(request=request, reward=1.0)
response = stub.SendFeedback(request=request)
def test_model_template_app_grpc_metrics(microservice):
data = np.array([[1, 2]])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=data.shape, values=data.flatten())
)
meta = prediction_pb2.Meta()
json_format.ParseDict(
{"metrics": [{"key": "mygauge", "type": "GAUGE", "value": 100}]}, meta
)
request = prediction_pb2.SeldonMessage(data=datadef, meta=meta)
channel = grpc.insecure_channel("0.0.0.0:5000")
stub = prediction_pb2_grpc.ModelStub(channel)
response = retry_method(stub.Predict, kwargs=dict(request=request))
assert response.data.tensor.shape[0] == 1
assert response.data.tensor.shape[1] == 2
assert response.data.tensor.values[0] == 1
assert response.data.tensor.values[1] == 2
assert response.meta.metrics[0].key == "mygauge"
assert response.meta.metrics[0].value == 100
def run_predict(args):
contract = json.load(open(args.contract, 'r'))
contract = unfold_contract(contract)
feature_names = [feature["name"] for feature in contract["features"]]
REST_url = "http://" + args.host + ":" + str(args.port) + "/predict"
for i in range(args.n_requests):
batch = generate_batch(contract, args.batch_size, 'features')
if args.prnt:
print('-' * 40)
print("SENDING NEW REQUEST:")
if not args.grpc and not args.fbs:
REST_request = gen_REST_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(REST_request)
t1 = time()
response = requests.post(REST_url,
data={
"json": json.dumps(REST_request),
"isDefault": True
})
t2 = time()
jresp = response.json()
if args.prnt:
print("RECEIVED RESPONSE:")
print(jresp)
print()
print("Time " + str(t2 - t1))
elif args.grpc:
GRPC_request = gen_GRPC_request(batch,
features=feature_names,
tensor=args.tensor)
if args.prnt:
print(GRPC_request)
channel = grpc.insecure_channel('{}:{}'.format(
args.host, args.port))
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.Predict(GRPC_request)
if args.prnt:
print("RECEIVED RESPONSE:")
print(response)
print()
elif args.fbs:
import socket
import struct
from seldon_core.tester_flatbuffers import NumpyArrayToSeldonRPC, SeldonRPCToNumpyArray
data = NumpyArrayToSeldonRPC(batch, feature_names)
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((args.host, args.port))
totalsent = 0
MSGLEN = len(data)
print("Will send", MSGLEN, "bytes")
while totalsent < MSGLEN:
sent = s.send(data[totalsent:])
if sent == 0:
raise RuntimeError("socket connection broken")
totalsent = totalsent + sent
data = s.recv(4)
obj = struct.unpack('<i', data)
len_msg = obj[0]
data = s.recv(len_msg)
arr = SeldonRPCToNumpyArray(data)
print(arr)
def run_send_feedback(args):
contract = json.load(open(args.contract, 'r'))
contract = unfold_contract(contract)
feature_names = [feature["name"] for feature in contract["features"]]
response_names = [feature["name"] for feature in contract["targets"]]
REST_url = "http://" + args.host + ":" + str(args.port) + "/send-feedback"
for i in range(args.n_requests):
batch = generate_batch(contract, args.batch_size, 'features')
response = generate_batch(contract, args.batch_size, 'targets')
if args.prnt:
print('-' * 40)
print("SENDING NEW REQUEST:")
if not args.grpc and not args.fbs:
REST_request = gen_REST_request(batch,
features=feature_names,
tensor=args.tensor)
REST_response = gen_REST_request(response,
features=response_names,
tensor=args.tensor)
reward = 1.0
REST_feedback = {
"request": REST_request,
"response": REST_response,
"reward": reward
}
if args.prnt:
print(REST_feedback)
t1 = time()
response = requests.post(REST_url,
data={"json": json.dumps(REST_feedback)})
t2 = time()
if args.prnt:
print("Time " + str(t2 - t1))
print(response)
elif args.grpc:
GRPC_request = gen_GRPC_request(batch,
features=feature_names,
tensor=args.tensor)
GRPC_response = gen_GRPC_request(response,
features=response_names,
tensor=args.tensor)
reward = 1.0
GRPC_feedback = prediction_pb2.Feedback(request=GRPC_request,
response=GRPC_response,
reward=reward)
if args.prnt:
print(GRPC_feedback)
channel = grpc.insecure_channel('{}:{}'.format(
args.host, args.port))
stub = prediction_pb2_grpc.ModelStub(channel)
response = stub.SendFeedback(GRPC_feedback)
if args.prnt:
print("RECEIVED RESPONSE:")
print()
