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]
