def Predict(self, **kwargs):
return prediction_pb2.SeldonMessage(strData="predict")
def predict_grpc(self, request):
arr = np.array([9, 9])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
return request
def Route(selfself, **kwargs):
return prediction_pb2.SeldonMessage(strData="route")
def mocked_requests_post_success(url, *args, **kwargs):
data = np.random.rand(1, 1)
datadef = array_to_grpc_datadef("tensor", data)
request = prediction_pb2.SeldonMessage(data=datadef)
json = seldon_message_to_json(request)
return MockResponse(json, 200, text="{}")
def TransformOutput(selfself, **kwargs):
return prediction_pb2.SeldonMessage(strData="transform-output")
def construct_response(
user_model: SeldonComponent, is_request: bool,
client_request: prediction_pb2.SeldonMessage,
client_raw_response: Union[np.ndarray, str, bytes, dict]
) -> prediction_pb2.SeldonMessage:
"""
Parameters
----------
user_model
Client user class
is_request
Whether this is part of the request flow as opposed to the response flow
client_request
The request received
client_raw_response
The raw client response from their model
Returns
-------
A SeldonMessage proto response
"""
data_type = client_request.WhichOneof("data_oneof")
meta = prediction_pb2.Meta()
meta_json: Dict = {}
tags = client_custom_tags(user_model)
if tags:
meta_json["tags"] = tags
metrics = client_custom_metrics(user_model)
if metrics:
meta_json["metrics"] = metrics
if client_request.meta:
if client_request.meta.puid:
meta_json["puid"] = client_request.meta.puid
json_format.ParseDict(meta_json, meta)
if isinstance(client_raw_response, np.ndarray) or isinstance(
client_raw_response, list):
client_raw_response = np.array(client_raw_response)
if is_request:
names = client_feature_names(user_model, client_request.data.names)
else:
names = client_class_names(user_model, client_raw_response)
if data_type == "data": # If request is using defaultdata then return what was sent if is numeric response else ndarray
if np.issubdtype(client_raw_response.dtype, np.number):
default_data_type = client_request.data.WhichOneof(
"data_oneof")
else:
default_data_type = "ndarray"
else: # If numeric response return as tensor else return as ndarray
if np.issubdtype(client_raw_response.dtype, np.number):
default_data_type = "tensor"
else:
default_data_type = "ndarray"
data = array_to_grpc_datadef(default_data_type, client_raw_response,
names)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
elif isinstance(client_raw_response, str):
return prediction_pb2.SeldonMessage(strData=client_raw_response,
meta=meta)
elif isinstance(client_raw_response, dict):
jsonDataResponse = ParseDict(client_raw_response,
prediction_pb2.SeldonMessage().jsonData)
return prediction_pb2.SeldonMessage(jsonData=jsonDataResponse,
meta=meta)
elif isinstance(client_raw_response, (bytes, bytearray)):
return prediction_pb2.SeldonMessage(binData=client_raw_response,
meta=meta)
else:
raise SeldonMicroserviceException(
"Unknown data type returned as payload:" + client_raw_response)
def route_raw(self, request):
arr = np.array([1])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(1, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
return request
def test_proto_seldon_metrics_endpoint(cls, client_gets_metrics):
def _match_label(line):
_data, value = line.split()
name, labels = _data.split()[0].split("{")
labels = labels[:-1]
return name, value, eval(f"dict({labels})")
def _iterate_metrics(text):
for line in text.split("\n"):
if not line or line[0] == "#":
continue
yield _match_label(line)
user_object = cls()
seldon_metrics = SeldonMetrics()
app = SeldonModelGRPC(user_object, seldon_metrics)
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=np.array([1, 2])))
request = prediction_pb2.SeldonMessage(data=datadef)
metrics_app = get_metrics_microservice(seldon_metrics)
metrics_client = metrics_app.test_client()
rv = metrics_client.get("/metrics")
assert rv.status_code == 200
assert rv.data.decode() == ""
resp = app.Predict(request, None)
j = json.loads(json_format.MessageToJson(resp))
assert j["data"] == {
"names": ["t:0"],
"tensor": {
"shape": [2, 1],
"values": [1.0, 2.0]
},
}
assert j["meta"]["tags"] == EXPECTED_TAGS
assert ("metrics" in j["meta"]) == client_gets_metrics
rv = metrics_client.get("/metrics")
text = rv.data.decode()
timer_present = False
for name, value, labels in _iterate_metrics(text):
if name == "mytimer_bucket":
timer_present = True
if name == "mycounter_total":
assert value == "1.0"
assert labels["worker_id"] == str(os.getpid())
if name == "mygauge":
assert value == "100.0"
assert labels["worker_id"] == str(os.getpid())
if name == "customtag":
assert value == "200.0"
assert labels["mytag"] == "mytagvalue"
assert timer_present
def transform_output_raw(self, X):
arr = np.array([9, 9])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
return request
