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_proto_seldon_metrics_aggregate(cls, client_gets_metrics):
user_object = cls()
seldon_metrics = SeldonMetrics()
app = SeldonModelGRPC(user_object, seldon_metrics)
arr1 = np.array([1, 2])
datadef1 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr1))
arr2 = np.array([3, 4])
datadef2 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr2))
msg1 = prediction_pb2.SeldonMessage(data=datadef1)
msg2 = prediction_pb2.SeldonMessage(data=datadef2)
request = prediction_pb2.SeldonMessageList(seldonMessages=[msg1, msg2])
resp = app.Aggregate(request, None)
assert ("metrics" in json.loads(
json_format.MessageToJson(resp))["meta"]) == client_gets_metrics
data = seldon_metrics.data[os.getpid()]
verify_seldon_metrics(data, 1, [0.0202], AGGREGATE_METRIC_METHOD_TAG)
resp = app.Aggregate(request, None)
assert ("metrics" in json.loads(
json_format.MessageToJson(resp))["meta"]) == client_gets_metrics
data = seldon_metrics.data[os.getpid()]
verify_seldon_metrics(data, 2, [0.0202, 0.0202],
AGGREGATE_METRIC_METHOD_TAG)
def test_aggregate_proto_ok():
user_object = UserObject()
seldon_metrics = SeldonMetrics()
app = SeldonModelGRPC(user_object, seldon_metrics)
arr1 = np.array([1, 2])
datadef1 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr1)
)
arr2 = np.array([3, 4])
datadef2 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr2)
)
msg1 = prediction_pb2.SeldonMessage(data=datadef1)
msg2 = prediction_pb2.SeldonMessage(data=datadef2)
request = prediction_pb2.SeldonMessageList(seldonMessages=[msg1, msg2])
resp = app.Aggregate(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
logging.info(j)
assert j["meta"]["tags"] == {"mytag": 1}
# add default type
assert j["meta"]["metrics"][0]["key"] == user_object.metrics()[0]["key"]
assert j["meta"]["metrics"][0]["value"] == user_object.metrics()[0]["value"]
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [1, 2]
def test_aggregate_proto_combines_metrics():
user_object = UserObject()
seldon_metrics = SeldonMetrics()
app = SeldonModelGRPC(user_object, seldon_metrics)
arr1 = np.array([1, 2])
meta1 = prediction_pb2.Meta()
json_format.ParseDict(
{
"metrics": [{
"key": "request_gauge_1",
"type": "GAUGE",
"value": 100
}]
}, meta1)
datadef1 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr1))
arr2 = np.array([3, 4])
meta2 = prediction_pb2.Meta()
json_format.ParseDict(
{
"metrics": [{
"key": "request_gauge_2",
"type": "GAUGE",
"value": 200
}]
}, meta2)
datadef2 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr2))
msg1 = prediction_pb2.SeldonMessage(data=datadef1, meta=meta1)
msg2 = prediction_pb2.SeldonMessage(data=datadef2, meta=meta2)
request = prediction_pb2.SeldonMessageList(seldonMessages=[msg1, msg2])
resp = app.Aggregate(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
logging.info(j)
assert j["meta"]["tags"] == {"mytag": 1}
assert j["meta"]["metrics"][0]["key"] == "request_gauge_1"
assert j["meta"]["metrics"][0]["value"] == 100
assert j["meta"]["metrics"][1]["key"] == "request_gauge_2"
assert j["meta"]["metrics"][1]["value"] == 200
assert j["meta"]["metrics"][2]["key"] == user_object.metrics()[0]["key"]
assert j["meta"]["metrics"][2]["value"] == user_object.metrics(
)[0]["value"]
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [1, 2]
def test_proto_seldon_runtime_data_route(cls, client_gets_metrics):
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)
resp = app.Route(request, None)
j = json.loads(json_format.MessageToJson(resp))
assert j["data"] == {
"names": ["t:0"],
"tensor": {"shape": [1, 1], "values": [22.0]},
}
assert j["meta"]["tags"] == EXPECTED_TAGS
assert ("metrics" in j["meta"]) == client_gets_metrics
data = seldon_metrics.data[os.getpid()]
verify_seldon_metrics(data, 1, [0.0202], ROUTER_METRIC_METHOD_TAG)
resp = app.Route(request, None)
j = json.loads(json_format.MessageToJson(resp))
assert j["data"] == {
"names": ["t:0"],
"tensor": {"shape": [1, 1], "values": [22.0]},
}
assert j["meta"]["tags"] == EXPECTED_TAGS
assert ("metrics" in j["meta"]) == client_gets_metrics
data = seldon_metrics.data[os.getpid()]
verify_seldon_metrics(data, 2, [0.0202, 0.0202], ROUTER_METRIC_METHOD_TAG)
def route_grpc(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_route(cls):
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)
app.Route(request, None)
data = seldon_metrics.data[os.getpid()]
assert data["GAUGE", "mygauge"]["value"] == 100
assert data["GAUGE", "customtag"]["value"] == 200
assert data["GAUGE", "customtag"]["tags"] == {"mytag": "mytagvalue"}
assert data["COUNTER", "mycounter"]["value"] == 1
assert np.allclose(
np.histogram([20.2 / 1000], BINS)[0], data["TIMER",
"mytimer"]["value"][0])
assert np.allclose(data["TIMER", "mytimer"]["value"][1], 0.0202)
app.Route(request, None)
data = seldon_metrics.data[os.getpid()]
assert data["GAUGE", "mygauge"]["value"] == 100
assert data["GAUGE", "customtag"]["value"] == 200
assert data["GAUGE", "customtag"]["tags"] == {"mytag": "mytagvalue"}
assert data["COUNTER", "mycounter"]["value"] == 2
assert np.allclose(
np.histogram([20.2 / 1000, 20.2 / 1000], BINS)[0],
data["TIMER", "mytimer"]["value"][0],
)
assert np.allclose(data["TIMER", "mytimer"]["value"][1], 0.0404)
def test_transform_proto_output_passes_through_metrics():
user_object = UserObject()
seldon_metrics = SeldonMetrics()
app = SeldonModelGRPC(user_object, seldon_metrics)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
meta = prediction_pb2.Meta()
json_format.ParseDict(
{"metrics": [{
"key": "request_gauge",
"type": "GAUGE",
"value": 100
}]}, meta)
request = prediction_pb2.SeldonMessage(data=datadef, meta=meta)
resp = app.TransformOutput(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
logging.info(j)
assert j["meta"]["metrics"][0]["key"] == "request_gauge"
assert j["meta"]["metrics"][0]["value"] == 100
assert j["meta"]["metrics"][1]["key"] == user_object.metrics()[0]["key"]
assert j["meta"]["metrics"][1]["value"] == user_object.metrics(
)[0]["value"]
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [1, 2]
def transform_output_grpc(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
def grpc_request_ambassador(deploymentName,
namespace,
endpoint="localhost:8004",
data_size=5,
rows=1,
data=None,
headers=None):
if data is None:
shape, arr = create_random_data(data_size, rows)
else:
shape = data.shape
arr = data.flatten()
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=shape, values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
channel = grpc.insecure_channel(endpoint)
stub = prediction_pb2_grpc.SeldonStub(channel)
if namespace is None:
metadata = [('seldon', deploymentName)]
else:
metadata = [('seldon', deploymentName), ('namespace', namespace)]
if not headers is None:
for k in headers:
metadata.append((k, headers[k]))
response = stub.Predict(request=request, metadata=metadata)
return response
def test_model_template_app_grpc_metrics(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(
{"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 grpc_request_ambassador(
deployment_name,
namespace,
endpoint="localhost:8004",
data_size=5,
rows=1,
data=None,
):
if data is None:
shape, arr = create_random_data(data_size, rows)
else:
shape = data.shape
arr = data.flatten()
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=shape, values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
channel = grpc.insecure_channel(endpoint)
stub = prediction_pb2_grpc.SeldonStub(channel)
if namespace is None:
metadata = [("seldon", deployment_name)]
else:
metadata = [("seldon", deployment_name), ("namespace", namespace)]
try:
response = stub.Predict(request=request, metadata=metadata)
channel.close()
return response
except Exception as e:
channel.close()
raise e
def array_to_grpc_datadef(array, names, data_type):
if data_type == "tensor":
datadef = prediction_pb2.DefaultData(
names=names,
tensor=prediction_pb2.Tensor(
shape=array.shape,
values=array.ravel().tolist()
)
)
elif data_type == "ndarray":
datadef = prediction_pb2.DefaultData(
names=names,
ndarray=array_to_list_value(array)
)
elif data_type == "tftensor":
datadef = prediction_pb2.DefaultData(
names=names,
tftensor=tf.make_tensor_proto(array)
)
else:
datadef = prediction_pb2.DefaultData(
names=names,
ndarray=array_to_list_value(array)
)
return datadef
def test_seldon_message_to_json():
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
dict = scu.seldon_message_to_json(request)
assert dict["data"]["tensor"]["values"] == [1, 2]
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 test_get_data_from_proto_tensor():
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
arr: np.ndarray = scu.get_data_from_proto(request)
assert arr.shape == (2, 1)
assert arr[0][0] == 1
assert arr[1][0] == 2
def test_proto_feedback_custom():
user_object = UserObjectLowLevel()
app = SeldonModelGRPC(user_object)
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)
resp = app.SendFeedback(feedback, None)
def test_aggregate_proto_lowlevel_ok():
user_object = UserObjectLowLevelGrpc()
app = SeldonModelGRPC(user_object)
arr1 = np.array([1, 2])
datadef1 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr1))
arr2 = np.array([3, 4])
datadef2 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr2))
msg1 = prediction_pb2.SeldonMessage(data=datadef1)
msg2 = prediction_pb2.SeldonMessage(data=datadef2)
request = prediction_pb2.SeldonMessageList(seldonMessages=[msg1, msg2])
resp = app.Aggregate(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [9, 9]
def test_model_template_app_grpc(microservice):
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 = 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
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 gen_GRPC_request(batch, features, tensor=True):
if tensor:
datadef = prediction_pb2.DefaultData(
names=features,
tensor=prediction_pb2.Tensor(shape=batch.shape,
values=batch.ravel().tolist()))
else:
datadef = prediction_pb2.DefaultData(
names=features, ndarray=array_to_list_value(batch))
request = prediction_pb2.SeldonMessage(data=datadef)
return request
def route_raw(self, msg):
logging.info("Route raw called")
meta = prediction_pb2.Meta()
json_format.ParseDict({"metrics": self._metrics}, meta)
arr = np.array([22])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(1, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef, meta=meta)
return request
def test_proto_lowlevel():
user_object = UserObjectLowLevelGrpc()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [9, 9]
def test_router_proto_lowlevel_raw_ok():
user_object = UserObjectLowLevelRaw()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
resp = app.Route(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
logging.info(j)
assert j["data"]["tensor"]["shape"] == [1, 1]
assert j["data"]["tensor"]["values"] == [1]
def test_transform_output_proto_lowlevel_ok():
user_object = UserObjectLowLevelGrpc()
seldon_metrics = SeldonMetrics()
app = SeldonModelGRPC(user_object, seldon_metrics)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
resp = app.TransformOutput(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
logging.info(j)
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [9, 9]
def transform_output_raw(
self, request: Union[prediction_pb2.SeldonMessage, List, Dict]
) -> Union[prediction_pb2.SeldonMessage, List, Dict]:
is_proto = isinstance(request, prediction_pb2.SeldonMessage)
arr = np.array([9, 9])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
response = prediction_pb2.SeldonMessage(data=datadef)
if is_proto:
return response
else:
return seldon_message_to_json(response)
def test_aggregate_proto_combines_tags():
user_object = UserObject()
app = SeldonModelGRPC(user_object)
arr1 = np.array([1, 2])
meta1 = prediction_pb2.Meta()
json_format.ParseDict({"tags": {"input-1": "yes", "common": 1}}, meta1)
datadef1 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr1))
arr2 = np.array([3, 4])
meta2 = prediction_pb2.Meta()
json_format.ParseDict({"tags": {"input-2": "yes", "common": 2}}, meta2)
datadef2 = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr2))
msg1 = prediction_pb2.SeldonMessage(data=datadef1, meta=meta1)
msg2 = prediction_pb2.SeldonMessage(data=datadef2, meta=meta2)
request = prediction_pb2.SeldonMessageList(seldonMessages=[msg1, msg2])
resp = app.Aggregate(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
logging.info(j)
assert j["meta"]["tags"] == {
"common": 2,
"input-1": "yes",
"input-2": "yes",
"mytag": 1,
}
# add default type
assert j["meta"]["metrics"][0]["key"] == user_object.metrics()[0]["key"]
assert j["meta"]["metrics"][0]["value"] == user_object.metrics(
)[0]["value"]
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [1, 2]
def test_proto_requestPath_ok():
user_object = UserObject()
seldon_metrics = SeldonMetrics()
app = SeldonModelGRPC(user_object, seldon_metrics)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
meta = prediction_pb2.Meta()
json_format.ParseDict({"tags": {"foo": "bar"}}, meta)
request = prediction_pb2.SeldonMessage(data=datadef, meta=meta)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
logging.info(j)
assert j["meta"]["requestPath"] == {"my-test-model": "my-test-model-image"}
def test_proto_ok():
user_object = UserObject()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
request = prediction_pb2.SeldonMessage(data=datadef)
resp = app.Predict(request, None)
jStr = json_format.MessageToJson(resp)
j = json.loads(jStr)
print(j)
assert j["meta"]["tags"] == {"mytag": 1}
assert j["meta"]["metrics"][0]["key"] == user_object.metrics()[0]["key"]
assert j["meta"]["metrics"][0]["value"] == user_object.metrics(
)[0]["value"]
assert j["data"]["tensor"]["shape"] == [2, 1]
assert j["data"]["tensor"]["values"] == [1, 2]
def test_model_template_app_grpc_tags(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({"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 = 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.tags["foo"].string_value == "bar"
def test_proto_feedback():
user_object = UserObject()
app = SeldonModelGRPC(user_object)
arr = np.array([1, 2])
datadef = prediction_pb2.DefaultData(
tensor=prediction_pb2.Tensor(shape=(2, 1), values=arr))
meta = prediction_pb2.Meta()
metaJson = {}
routing = {"1": 1}
metaJson["routing"] = routing
json_format.ParseDict(metaJson, meta)
request = prediction_pb2.SeldonMessage(data=datadef)
response = prediction_pb2.SeldonMessage(meta=meta, data=datadef)
feedback = prediction_pb2.Feedback(request=request,
response=response,
reward=1.0)
resp = app.SendFeedback(feedback, None)
