def test_proto_lowlevel(): 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.Predict(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 test_model_str_data_identity(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/predict?json={"strData":"my data"}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["strData"] == "my data" 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"]
def test_model_metadata_ok(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get( '/predict?json={"data": {"names": ["input"], "ndarray": ["data"]}}') assert rv.status_code == 200 assert json.loads(rv.data)["data"]["ndarray"] == ["data"] rv = client.get("/metadata") assert rv.status_code == 200
def test_model_bin_data_nparray(): user_object = UserObject(ret_nparray=True) seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() encoded = base64.b64encode(b"1234").decode("utf-8") rv = client.get('/predict?json={"binData":"' + encoded + '"}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["data"]["tensor"]["values"] == [1, 2, 3] 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"]
def test_transformer_output_ok(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/transform-output?json={"data":{"ndarray":[1]}}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 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"]["ndarray"] == [1]
def test_feedback_v01_ok(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() payload = { "request": {"data": {"names": ["a", "b"], "ndarray": [[1, 2]]}}, "reward": 1.0, } rv = client.post("/api/v0.1/feedback", json=payload) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200
def test_model_bin_data(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() bdata = b"123" bdata_base64 = base64.b64encode(bdata).decode("utf-8") rv = client.get('/predict?json={"binData":"' + bdata_base64 + '"}') j = json.loads(rv.data) assert rv.status_code == 200 assert j["binData"] == bdata_base64 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"]
def test_aggreate_ok_seldon_messages(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/aggregate?json={"seldonMessages":[{"data":{"ndarray":[1]}}]}') logging.info(rv) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 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"]["ndarray"] == [1]
def test_model_ok(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/predict?json={"data":{"names":["a","b"],"ndarray":[[1,2]]}}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 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"]["names"] == ["t:0", "t:1"] assert j["data"]["ndarray"] == [[1.0, 2.0]]
def test_model_error_status_code(): class ErrorUserObject: def predict(self, X, features_names, **kwargs): raise SeldonMicroserviceException("foo", status_code=403) user_object = ErrorUserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() uo = UserObject() rv = client.get('/predict?json={"strData":"my data"}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 403
def test_transform_input_gets_meta(): user_object = UserObject(ret_meta=True) seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get( '/transform-input?json={"meta":{"puid": "abc"},"data":{"ndarray":[]}}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["meta"]["tags"] == {"inc_meta": {"puid": "abc"}} assert j["meta"]["metrics"][0]["key"] == user_object.metrics()[0]["key"] assert j["meta"]["metrics"][0]["value"] == user_object.metrics( )[0]["value"]
def test_seldon_metrics_endpoint(cls): 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 = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() 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() == "" rv = client.get( '/predict?json={"data": {"names": ["input"], "ndarray": ["data"]}}') 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_seldon_runtime_data_send_feedback(cls): user_object = cls() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/send-feedback?json={"reward": 42}') assert rv.status_code == 200 j = json.loads(rv.data) assert j["meta"]["tags"] == EXPECTED_TAGS data = seldon_metrics.data[os.getpid()] verify_seldon_metrics(data, 1, [0.0202], FEEDBACK_METRIC_METHOD_TAG) expected_base_tags = {"method": FEEDBACK_METRIC_METHOD_TAG} base_tags_key = SeldonMetrics._generate_tags_key(expected_base_tags) assert data["COUNTER", "seldon_api_model_feedback_reward", base_tags_key] == { "value": 42.0, "tags": expected_base_tags, } rv = client.get('/send-feedback?json={"reward": 42}') assert rv.status_code == 200 data = seldon_metrics.data[os.getpid()] verify_seldon_metrics(data, 2, [0.0202, 0.0202], FEEDBACK_METRIC_METHOD_TAG) assert data["COUNTER", "seldon_api_model_feedback_reward", base_tags_key] == { "value": 84.0, "tags": expected_base_tags, }
def test_unimplemented_transform_output_raw(): class CustomObject: def transform_output(self, X, features_names, **kwargs): return X * 2 user_object = CustomObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/transform-output?json={"data":{"ndarray":[1]}}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["data"]["ndarray"] == [2.0]
def test_unimplemented_predict_raw(): class CustomObject: def predict(self, X, features_names, **kwargs): return X * 2 user_object = CustomObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/predict?json={"data":{"names":["a","b"],"ndarray":[[1,2]]}}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["data"]["ndarray"] == [[2.0, 4.0]]
def test_transform_input_passes_through_tags(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get( '/transform-input?json={"meta":{"tags":{"foo":"bar"}},"data":{"ndarray":[]}}' ) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["meta"]["tags"] == {"foo": "bar", "mytag": 1} assert j["meta"]["metrics"][0]["key"] == user_object.metrics()[0]["key"] assert j["meta"]["metrics"][0]["value"] == user_object.metrics( )[0]["value"]
def test_requestPath_2nd_node_ok(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get( '/predict?json={"meta":{"requestPath":{"earlier-node": "earlier-image"}},"data":{"names":["a","b"],"ndarray":[[1,2]]}}' ) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["meta"]["requestPath"] == { "my-test-model": "my-test-model-image", "earlier-node": "earlier-image", }
def test_unimplemented_route_raw(): class CustomObject: def route(self, X, features_names): return 53 user_object = CustomObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get('/route?json={"data":{"ndarray":[2]}}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["data"]["ndarray"] == [[53]]
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_unimplemented_feedback_raw(): class CustomObject: def feedback(self, features, feature_names, reward, truth): logging.info("Feedback called") user_object = CustomObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get( '/send-feedback?json={"request":{"data":{"ndarray":[]}},"reward":1.0}') j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200
def test_proto_tftensor_ok(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = SeldonModelGRPC(user_object, seldon_metrics) arr = np.array([1, 2]) datadef = prediction_pb2.DefaultData(tftensor=tf.make_tensor_proto(arr)) request = prediction_pb2.SeldonMessage(data=datadef) resp = app.Predict(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"] == user_object.metrics()[0]["key"] assert j["meta"]["metrics"][0]["value"] == user_object.metrics()[0]["value"] arr2 = tf.make_ndarray(resp.data.tftensor) assert np.array_equal(arr, arr2)
def test_unimplemented_aggregate_raw(): class CustomObject: def aggregate(self, Xs, features_names): return sum(Xs) * 2 user_object = CustomObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get( '/aggregate?json={"seldonMessages":[{"data":{"ndarray":[1]}},{"data":{"ndarray":[2]}}]}' ) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 assert j["data"]["ndarray"] == [6.0]
def test_model_passes_through_metrics(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.get( '/predict?json={"meta":{"metrics":[{"key":"request_gauge","type":"GAUGE","value":100}]},"data":{"ndarray":[]}}' ) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 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"]
def test_proto_seldon_runtime_data_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) 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 data = seldon_metrics.data[os.getpid()] verify_seldon_metrics(data, 1, [0.0202], AGGREGATE_METRIC_METHOD_TAG) resp = app.Aggregate(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 data = seldon_metrics.data[os.getpid()] verify_seldon_metrics(data, 2, [0.0202, 0.0202], AGGREGATE_METRIC_METHOD_TAG)
def test_model_lowlevel_multi_form_data_strData_ok(): user_object = UserObjectLowLevelWithPredictRaw("strData") seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.post( "/predict", data={ "meta": '{"puid":"1234"}', "strData": (f"./tests/resources/test.txt", "test.txt"), }, content_type="multipart/form-data", ) j = json.loads(rv.data) assert rv.status_code == 200 assert j["meta"]["puid"] == "1234" assert (j["data"]["ndarray"][0] == "this is test file for testing multipart/form-data input\n")
def test_aggreate_ok_bindata(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() bdata = b"123" bdata_base64 = base64.b64encode(bdata).decode("utf-8") rv = client.get('/aggregate?json={"seldonMessages":[{"binData":"' + bdata_base64 + '"},{"binData":"' + bdata_base64 + '"}]}') logging.info(rv) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 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["binData"] == bdata_base64
def test_model_multi_form_data_ok(): user_object = UserObject() seldon_metrics = SeldonMetrics() app = get_rest_microservice(user_object, seldon_metrics) client = app.test_client() rv = client.post( "/predict", data={"data": '{"names":["a","b"],"ndarray":[[1,2]]}'}, content_type="multipart/form-data", ) j = json.loads(rv.data) logging.info(j) assert rv.status_code == 200 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"]["names"] == ["t:0", "t:1"] assert j["data"]["ndarray"] == [[1.0, 2.0]]
def test_proto_feedback(): 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() 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)
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_transform_output_proto_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)) 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["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]