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)