def json_to_seldon_messages(message_json: Dict) -> prediction_pb2.SeldonMessageList: message_proto = prediction_pb2.SeldonMessageList() try: json_format.ParseDict(message_json, message_proto) return message_proto except json_format.ParseError as pbExc: raise SeldonMicroserviceException("Invalid JSON: " + str(pbExc))
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_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_bin_data(): user_object = UserObject() app = SeldonModelGRPC(user_object) binData = b"\0\1" msg1 = prediction_pb2.SeldonMessage(binData=binData) request = prediction_pb2.SeldonMessageList(seldonMessages=[msg1]) resp = app.Aggregate(request, None) assert resp.binData == binData
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_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_proto_seldon_metrics_aggregate(cls): 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]) app.Aggregate(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.Aggregate(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_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]