def test_model_combiner_grpc(self, namespace, s2i_python_version):
create_push_s2i_image(s2i_python_version, "one", "grpc")
create_push_s2i_image(s2i_python_version, "two", "grpc")
create_push_s2i_image(s2i_python_version, "combiner", "grpc")
retry_run(
f"kubectl apply -f ../resources/tags_combiner_grpc.json -n {namespace}"
)
wait_for_status("mymodel-tags-combiner", namespace)
wait_for_rollout("mymodel-tags-combiner", namespace)
r = initial_grpc_request("mymodel-tags-combiner", namespace)
arr = np.array([[1, 2, 3]])
r = grpc_request_ambassador("mymodel-tags-combiner",
namespace,
API_AMBASSADOR,
data=arr)
res = json.loads(json_format.MessageToJson(r))
logging.info(res)
# assert r.status_code == 200
assert res["data"]["ndarray"] == [["model-1"], ["model-2"]]
assert res["meta"]["tags"] == {
"combiner": "yes",
"common": 2,
"model-1": "yes",
"model-2": "yes",
}
run(
f"kubectl delete -f ../resources/tags_combiner_grpc.json -n {namespace}",
shell=True,
)
def test_xgboost_v2(self, namespace):
deploy_model(
"xgboost",
namespace=namespace,
protocol="kfserving",
model_implementation="XGBOOST_SERVER",
model_uri="gs://seldon-models/xgboost/iris",
)
wait_for_status("xgboost", namespace)
wait_for_rollout("xgboost", namespace)
time.sleep(1)
logging.warning("Initial request")
r = v2_protocol.inference_request(
deployment_name="xgboost",
model_name="model",
namespace=namespace,
payload={
"inputs": [
{
"name": "input-0",
"shape": [1, 4],
"datatype": "FP32",
"data": [[0.1, 0.2, 0.3, 0.4]],
}
]
},
)
assert len(r["outputs"]) > 0
def test_model_graph_rest(self, namespace, s2i_python_version):
create_push_s2i_image(s2i_python_version, "one", "rest")
create_push_s2i_image(s2i_python_version, "two", "rest")
retry_run(
f"kubectl apply -f ../resources/tags_graph_rest.json -n {namespace}"
)
wait_for_status("mymodel-tags-graph", namespace)
wait_for_rollout("mymodel-tags-graph", namespace)
r = initial_rest_request("mymodel-tags-graph", namespace)
arr = np.array([[1, 2, 3]])
r = rest_request_ambassador("mymodel-tags-graph",
namespace,
API_AMBASSADOR,
data=arr)
res = r.json()
logging.info(res)
assert r.status_code == 200
assert res["data"]["ndarray"] == ["model-2"]
assert res["meta"]["tags"] == {
"common": 2,
"model-1": "yes",
"model-2": "yes"
}
run(
f"kubectl delete -f ../resources/tags_graph_rest.json -n {namespace}",
shell=True,
)
def test_model_rest_non200(self, namespace, s2i_python_version):
create_push_s2i_image(s2i_python_version, "model", "rest_non200")
retry_run(
f"kubectl apply -f ../resources/s2i_python_model_non200.json -n {namespace}"
)
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
r = initial_rest_request("mymodel", namespace)
arr = np.array([[1, 2, 3]])
r = rest_request_ambassador("mymodel",
namespace,
API_AMBASSADOR,
data=arr)
res = r.json()
logging.warning(res)
assert r.status_code == 500
assert r.json()["status"]["code"] == 500
assert (
r.json()["status"]["info"] ==
"Internal service call failed calling http://localhost:9000/predict status code 400"
)
run(
f"kubectl delete -f ../resources/s2i_python_model_non200.json -n {namespace}",
shell=True,
)
def test_combiner_rest(self, s2i_python_version):
namespace = "s2i-test-combiner-rest"
retry_run(f"kubectl create namespace {namespace}")
create_push_s2i_image(s2i_python_version, "model", "rest")
create_push_s2i_image(s2i_python_version, "combiner", "rest")
retry_run(
f"kubectl apply -f ../resources/s2i_python_combiner.json -n {namespace}"
)
wait_for_status("mycombiner", namespace)
wait_for_rollout("mycombiner", namespace)
r = initial_rest_request("mycombiner", namespace)
arr = np.array([[1, 2, 3]])
r = rest_request_ambassador("mycombiner",
namespace,
API_AMBASSADOR,
data=arr)
res = r.json()
logging.warning(res)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["shape"] == [1, 3]
assert r.json()["data"]["tensor"]["values"] == [3, 4, 5]
run(
f"kubectl delete -f ../resources/s2i_python_combiner.json -n {namespace}",
shell=True,
)
run(f"kubectl delete namespace {namespace}", shell=True)
def test_model_rest_non200(self, s2i_python_version):
namespace = "s2i-test-model-rest-non200"
retry_run(f"kubectl create namespace {namespace}")
create_push_s2i_image(s2i_python_version, "model", "rest_non200")
retry_run(
f"kubectl apply -f ../resources/s2i_python_model_non200.json -n {namespace}"
)
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
r = initial_rest_request("mymodel", namespace)
arr = np.array([[1, 2, 3]])
r = rest_request_ambassador("mymodel",
namespace,
API_AMBASSADOR,
data=arr)
res = r.json()
logging.warning(res)
assert r.status_code == 200
assert r.json()["status"]["code"] == 400
assert r.json()["status"]["reason"] == "exception message"
assert r.json()["status"]["info"] == "exception caught"
assert r.json()["status"]["status"] == "FAILURE"
run(
f"kubectl delete -f ../resources/s2i_python_model_non200.json -n {namespace}",
shell=True,
)
run(f"kubectl delete namespace {namespace}", shell=True)
def test_rolling_update_deployment(namespace, from_deployment, to_deployment):
from_file_path = to_resources_path(from_deployment)
retry_run(f"kubectl apply -f {from_file_path} -n {namespace}")
# Note that this is not yet parametrised!
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
logging.warning("Initial request")
r = initial_rest_request("mymodel", namespace)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
to_file_path = to_resources_path(to_deployment)
retry_run(f"kubectl apply -f {to_file_path} -n {namespace}")
r = initial_rest_request("mymodel", namespace)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
i = 0
for i in range(100):
r = rest_request_ambassador("mymodel", namespace, API_AMBASSADOR)
assert r.status_code == 200
res = r.json()
assert (res["data"]["tensor"]["values"] == [
1.0, 2.0, 3.0, 4.0
]) or (res["data"]["tensor"]["values"] == [5.0, 6.0, 7.0, 8.0])
if (not r.status_code == 200) or (res["data"]["tensor"]["values"]
== [5.0, 6.0, 7.0, 8.0]):
break
time.sleep(1)
assert i < 100
run(f"kubectl delete -f {from_file_path} -n {namespace}", shell=True)
run(f"kubectl delete -f {to_file_path} -n {namespace}", shell=True)
def test_rolling_update9(self, namespace, api_gateway):
if api_gateway == API_ISTIO_GATEWAY:
retry_run(
f"kubectl create -f ../resources/seldon-gateway.yaml -n {namespace}"
)
retry_run(
f"kubectl apply -f ../resources/graph1svc.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace, expected_deployments=2)
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
retry_run(
f"kubectl apply -f ../resources/graph5svc.json -n {namespace}")
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
i = 0
for i in range(50):
r = rest_request_ambassador("mymodel", namespace, api_gateway)
assert r.status_code == 200
res = r.json()
assert res["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
time.sleep(1)
assert i == 49
logging.warning("Success for test_rolling_update9")
run(f"kubectl delete -f ../resources/graph1svc.json -n {namespace}",
shell=True)
run(f"kubectl delete -f ../resources/graph5svc.json -n {namespace}",
shell=True)
def test_sklearn_v2(self, namespace):
deploy_model(
"sklearn",
namespace=namespace,
protocol="kfserving",
model_implementation="SKLEARN_SERVER",
model_uri="gs://seldon-models/sklearn/iris",
)
wait_for_status("sklearn", namespace)
wait_for_rollout("sklearn", namespace)
time.sleep(1)
logging.warning("Initial request")
r = v2_protocol.inference_request(
model_name="sklearn",
namespace=namespace,
payload={
"inputs": [{
"name": "input-0",
"shape": [1, 4],
"datatype": "FP32",
"data": [[0.1, 0.2, 0.3, 0.4]],
}]
},
)
assert r.status_code == 200
def test_sklearn(self, namespace):
spec = "../../servers/sklearnserver/samples/iris.yaml"
retry_run(f"kubectl apply -f {spec} -n {namespace}")
wait_for_status("sklearn", namespace)
wait_for_rollout("sklearn", namespace)
time.sleep(1)
logging.warning("Initial request")
r = initial_rest_request("sklearn",
namespace,
data=[[0.1, 0.2, 0.3, 0.4]],
dtype="ndarray")
assert r.status_code == 200
r = rest_request_ambassador("sklearn", namespace, method="metadata")
assert r.status_code == 200
res = r.json()
logging.warning(res)
assert res["name"] == "iris"
assert res["versions"] == ["iris/v1"]
r = grpc_request_ambassador("sklearn",
namespace,
data=np.array([[0.1, 0.2, 0.3, 0.4]]))
res = json.loads(json_format.MessageToJson(r))
logging.info(res)
logging.warning("Success for test_prepack_sklearn")
run(f"kubectl delete -f {spec} -n {namespace}", shell=True)
def test_tracing_rest(namespace):
# Deploy model and check that is running
retry_run(
f"kubectl apply -f ../resources/graph-tracing.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
initial_rest_request("mymodel", namespace)
# We need the current pod name to find the right traces
deployment_names = get_deployment_names("mymodel", namespace)
deployment_name = deployment_names[0]
pod_names = get_pod_names(deployment_name, namespace)
pod_name = pod_names[0]
# Get traces and assert their content
traces = get_traces(pod_name,
"executor",
"predictions",
_should_retry=_is_jaeger_syncing)
assert len(traces) == 1
trace = traces[0]
processes = trace["processes"]
assert len(processes) == 2
assert_trace(trace,
expected_operations=["predictions", "/predict", "Predict"])
def test_label_update(namespace, seldon_version):
# Deploy test model
retry_run(f"kubectl apply -f ../resources/graph1.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
assert_model("mymodel", namespace, initial=True)
# Install id-scoped operator
controller_id = f"seldon-{namespace}"
# TODO: We install the new controller on the same namespace but it's not
# necessary, since it will get targeted by controllerId
retry_run(
f"helm install {controller_id} "
"../../helm-charts/seldon-core-operator "
f"--namespace {namespace} "
"--set crd.create=false "
f"--set controllerId={controller_id} "
"--wait",
attempts=2,
)
# Label model to be served by new controller
retry_run("kubectl label sdep mymodel "
f"seldon.io/controller-id={controller_id} "
f"--namespace {namespace}")
# Assert that model is still working under new id-scoped operator
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
assert_model("mymodel", namespace, initial=True)
# Delete all resources (webhooks, etc.) before deleting namespace
retry_run(f"helm delete {controller_id} --namespace {namespace}")
def test_rolling_update5(self, namespace, api_gateway):
if api_gateway == API_ISTIO_GATEWAY:
retry_run(
f"kubectl create -f ../resources/seldon-gateway.yaml -n {namespace}"
)
retry_run(f"kubectl apply -f ../resources/graph1.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
logging.warning("Initial request")
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
retry_run(f"kubectl apply -f ../resources/graph6.json -n {namespace}")
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
i = 0
for i in range(50):
r = rest_request_ambassador("mymodel", namespace, api_gateway)
assert r.status_code == 200
res = r.json()
assert (res["data"]["tensor"]["values"] == [
1.0, 2.0, 3.0, 4.0
]) or (res["data"]["tensor"]["values"] == [5.0, 6.0, 7.0, 8.0])
if (not r.status_code == 200) or (res["data"]["tensor"]["values"]
== [5.0, 6.0, 7.0, 8.0]):
break
time.sleep(1)
assert i < 100
logging.warning("Success for test_rolling_update5")
run(f"kubectl delete -f ../resources/graph1.json -n {namespace}",
shell=True)
run(f"kubectl delete -f ../resources/graph6.json -n {namespace}",
shell=True)
def test_tracing_rest(namespace):
# Deploy model and check that is running
retry_run(
f"kubectl apply -f ../resources/graph-tracing.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
initial_rest_request("mymodel", namespace)
# We need the current pod name to find the right traces
deployment_names = get_deployment_names("mymodel", namespace)
deployment_name = deployment_names[0]
pod_names = get_pod_names(deployment_name, namespace)
pod_name = pod_names[0]
print("deployment name", deployment_name, "pod name", pod_name)
# The engine and the executor identify as different services and different
# operations against Jaeger. We need to consider both.
service = "executor"
operation = "predictions"
request_operation = "/predict"
# Get traces and assert their content
traces = get_traces(pod_name,
service,
operation,
_should_retry=_is_jaeger_syncing)
assert len(traces) == 1
trace = traces[0]
processes = trace["processes"]
assert len(processes) == 2
assert_trace(trace,
expected_operations=[operation, request_operation, "Predict"])
def test_modelmetadata_grpc(self, namespace, s2i_python_version):
create_push_s2i_image(s2i_python_version, "modelmetadata", "grpc")
retry_run(
f"kubectl apply -f ../resources/metadata_modelmetadata_grpc.yaml -n {namespace}"
)
wait_for_status("mymodel-modelmetadata", namespace)
wait_for_rollout("mymodel-modelmetadata", namespace)
r = initial_grpc_request("mymodel-modelmetadata", namespace)
r = grpc_request_ambassador_metadata(
"mymodel-modelmetadata", namespace, model_name="my-model"
)
res = json.loads(json_format.MessageToJson(r))
logging.info(res)
# Cast reference model metadata to proto and back in order to have int->float
# infamous casting in google.protobuf.Value
metadata_proto = prediction_pb2.SeldonModelMetadata()
json_format.ParseDict(
model_metadata, metadata_proto, ignore_unknown_fields=True
)
assert res == json.loads(json_format.MessageToJson(metadata_proto))
r = grpc_request_ambassador_metadata("mymodel-modelmetadata", namespace)
res = json.loads(json_format.MessageToJson(r))
logging.info(res)
graph_metadata_proto = prediction_pb2.SeldonGraphMetadata()
json_format.ParseDict(
graph_metadata_grpc, graph_metadata_proto, ignore_unknown_fields=True
)
assert res == json.loads(json_format.MessageToJson(graph_metadata_proto))
def test_rolling_deployment(namespace, api_gateway, from_deployment,
to_deployment, change):
if api_gateway == API_ISTIO_GATEWAY:
retry_run(
f"kubectl create -f ../resources/seldon-gateway.yaml -n {namespace}"
)
from_file_path = to_resources_path(from_deployment)
retry_run(f"kubectl apply -f {from_file_path} -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
assert_model("mymodel", namespace, initial=True, endpoint=api_gateway)
old_pod_name = get_pod_name_for_sdep("mymodel", namespace)[0]
to_file_path = to_resources_path(to_deployment)
def _update_model():
retry_run(f"kubectl apply -f {to_file_path} -n {namespace}")
if change:
wait_for_pod_shutdown(old_pod_name, namespace)
wait_for_status("mymodel", namespace)
time.sleep(2) # Wait a little after deployment marked Available
assert_model_during_op(_update_model,
"mymodel",
namespace,
endpoint=api_gateway)
delete_cmd = f"kubectl delete --ignore-not-found -n {namespace}"
run(f"{delete_cmd} -f {from_file_path}", shell=True)
run(f"{delete_cmd} -f {to_file_path}", shell=True)
def test_mlflow(self, namespace):
spec = "../../servers/mlflowserver/samples/elasticnet_wine.yaml"
retry_run(f"kubectl apply -f {spec} -n {namespace}")
wait_for_status("mlflow", namespace)
wait_for_rollout("mlflow", namespace)
time.sleep(1)
r = initial_rest_request(
"mlflow",
namespace,
data=[[6.3, 0.3, 0.34, 1.6, 0.049, 14, 132, 0.994, 3.3, 0.49,
9.5]],
dtype="ndarray",
names=[
"fixed acidity",
"volatile acidity",
"citric acid",
"residual sugar",
"chlorides",
"free sulfur dioxide",
"total sulfur dioxide",
"density",
"pH",
"sulphates",
"alcohol",
],
)
assert r.status_code == 200
run(f"kubectl delete -f {spec} -n {namespace}", shell=True)
def test_rolling_update7(self, namespace, api_gateway):
retry_run(f"kubectl apply -f ../resources/graph1svc.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace, expected_deployments=2)
logging.warning("Initial request")
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
retry_run(f"kubectl apply -f ../resources/graph3svc.json -n {namespace}")
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
i = 0
for i in range(100):
r = rest_request_ambassador("mymodel", namespace, api_gateway)
assert r.status_code == 200
res = r.json()
assert (res["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]) or (
res["data"]["tensor"]["values"] == [5.0, 6.0, 7.0, 8.0]
)
if (not r.status_code == 200) or (
res["data"]["tensor"]["values"] == [5.0, 6.0, 7.0, 8.0]
):
break
time.sleep(1)
assert i < 100
logging.warning("Success for test_rolling_update7")
run(f"kubectl delete -f ../resources/graph1svc.json -n {namespace}", shell=True)
run(f"kubectl delete -f ../resources/graph3svc.json -n {namespace}", shell=True)
def test_namespace_update(namespace, seldon_version):
# Deploy test model
retry_run(f"kubectl apply -f ../resources/graph1.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
assert_model("mymodel", namespace, initial=True)
# Label namespace to deploy a single operator
retry_run(
f"kubectl label namespace {namespace} seldon.io/controller-id={namespace}"
)
def _install_namespace_scoped():
# Install on the current namespace
retry_run(
"helm install seldon "
"../../helm-charts/seldon-core-operator "
f"--namespace {namespace} "
"--set crd.create=false "
"--set singleNamespace=true "
"--wait",
attempts=2,
)
# Assert that model is still working under new namespaced version
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
assert_model_during_op(_install_namespace_scoped, "mymodel", namespace)
def test_model_rest(s2i_image, namespace):
deploy_model("mymodel", namespace=namespace, model_image=s2i_image)
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
r = initial_rest_request("mymodel", namespace)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["shape"] == [1]
assert r.json()["data"]["tensor"]["values"] == [1.0]
def test_xss_header(namespace):
sdep_name = "mymodel"
sdep_path = to_resources_path("graph-echo.json")
retry_run(f"kubectl apply -f {sdep_path} -n {namespace}")
wait_for_status(sdep_name, namespace)
wait_for_rollout(sdep_name, namespace)
res = initial_rest_request(sdep_name, namespace)
assert "X-Content-Type-Options" in res.headers
assert res.headers["X-Content-Type-Options"] == "nosniff"
def test_xgboost(self, namespace):
spec = "../../servers/xgboostserver/samples/iris.yaml"
retry_run(f"kubectl apply -f {spec} -n {namespace}")
wait_for_status("xgboost", namespace)
wait_for_rollout("xgboost", namespace)
time.sleep(1)
logging.warning("Initial request")
r = initial_rest_request(
"xgboost", namespace, data=[[0.1, 0.2, 0.3, 0.4]], dtype="ndarray"
)
assert r.status_code == 200
logging.warning("Success for test_prepack_xgboost")
run(f"kubectl delete -f {spec} -n {namespace}", shell=True)
def test_api_version(namespace, apiVersion):
command = ("helm install mymodel ../../helm-charts/seldon-single-model "
f"--set apiVersion={apiVersion} "
f"--set model.image=seldonio/fixed-model:0.1 "
f"--namespace {namespace}")
run(command, shell=True, check=True)
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
assert_model("mymodel", namespace, initial=True)
run("helm delete mymodel", shell=True)
def test_namespace_operator(self, namespace):
retry_run(
f"helm install seldon ../../helm-charts/seldon-core-operator --namespace {namespace} --set executor.enabled=true --set istio.enabled=true --set istio.gateway=seldon-gateway --set certManager.enabled=false --set crd.create=false --set singleNamespace=true"
)
retry_run(f"kubectl apply -f ../resources/graph1.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
logging.warning("Initial request")
r = initial_rest_request("mymodel", namespace, endpoint=API_AMBASSADOR)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
logging.warning("Success for test_namespace_operator")
run(f"kubectl delete -f ../resources/graph1.json -n {namespace}", shell=True)
run(f"helm uninstall seldon -n {namespace}", shell=True)
def test_rolling_update9(self, api_gateway):
if api_gateway == API_AMBASSADOR:
ns_suffix = "ambas"
else:
ns_suffix = "istio"
namespace = "test-rolling-update-9" + ns_suffix
retry_run(f"kubectl create namespace {namespace}")
if api_gateway == API_ISTIO_GATEWAY:
retry_run(
f"kubectl create -f ../resources/seldon-gateway.yaml -n {namespace}"
)
retry_run(
f"kubectl apply -f ../resources/graph1svc.json -n {namespace}")
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace, expected_deployments=2)
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
retry_run(
f"kubectl apply -f ../resources/graph5svc.json -n {namespace}")
r = initial_rest_request("mymodel", namespace, endpoint=api_gateway)
assert r.status_code == 200
assert r.json()["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
i = 0
for i in range(50):
r = rest_request_ambassador("mymodel", namespace, api_gateway)
assert r.status_code == 200
res = r.json()
assert (
"complex-model" in res["meta"]["requestPath"]
and res["meta"]["requestPath"]["complex-model"]
== "seldonio/fixed-model:0.1"
and res["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
) or (res["meta"]["requestPath"]["model1"]
== "seldonio/fixed-model:0.1"
and res["data"]["tensor"]["values"] == [1.0, 2.0, 3.0, 4.0]
and res["meta"]["requestPath"]["model2"]
== "seldonio/fixed-model:0.1")
if (not r.status_code == 200) or ("model1"
in res["meta"]["requestPath"]):
break
time.sleep(1)
assert i < 100
logging.warning("Success for test_rolling_update9")
run(f"kubectl delete -f ../resources/graph1svc.json -n {namespace}",
shell=True)
run(f"kubectl delete -f ../resources/graph5svc.json -n {namespace}",
shell=True)
run(f"kubectl delete namespace {namespace}", shell=True)
def _install_namespace_scoped():
# Install on the current namespace
retry_run(
"helm install seldon "
"../../helm-charts/seldon-core-operator "
f"--namespace {namespace} "
"--set crd.create=false "
"--set singleNamespace=true "
"--wait",
attempts=2,
)
# Assert that model is still working under new namespaced version
wait_for_status("mymodel", namespace)
wait_for_rollout("mymodel", namespace)
def test_xss_escaping(namespace):
sdep_name = "mymodel"
sdep_path = to_resources_path("graph-echo.json")
retry_run(f"kubectl apply -f {sdep_path} -n {namespace}")
wait_for_status(sdep_name, namespace)
wait_for_rollout(sdep_name, namespace)
payload = '<div class="div-class"></div>'
expected = '\\u003cdiv class=\\"div-class\\"\\u003e\\u003c/div\\u003e'
res = rest_request(sdep_name, namespace, data=payload, dtype="strData")
# We need to compare raw text. Otherwise, Python interprets the escaped
# sequences.
assert res.text == f'{{"meta":{{}},"strData":"{expected}"}}\n'
def test_tfserving(self, namespace):
spec = "../../servers/tfserving/samples/mnist_rest.yaml"
retry_run(f"kubectl apply -f {spec} -n {namespace}")
wait_for_status("tfserving", namespace)
wait_for_rollout("tfserving", namespace)
time.sleep(1)
logging.warning("Initial request")
r = initial_rest_request(
"tfserving",
namespace,
data=[create_random_data(784)[1].tolist()],
dtype="ndarray",
)
assert r.status_code == 200
logging.warning("Success for test_prepack_tfserving")
run(f"kubectl delete -f {spec} -n {namespace}", shell=True)
def test_sklearn(self):
namespace = "test-sklearn"
spec = "../../servers/sklearnserver/samples/iris.yaml"
retry_run(f"kubectl create namespace {namespace}")
retry_run(f"kubectl apply -f {spec} -n {namespace}")
wait_for_rollout("iris-default-4903e3c", namespace)
wait_for_status("sklearn", namespace)
time.sleep(1)
logging.warning("Initial request")
r = initial_rest_request(
"sklearn", namespace, data=[[0.1, 0.2, 0.3, 0.4]], dtype="ndarray"
)
assert r.status_code == 200
logging.warning("Success for test_prepack_sklearn")
run(f"kubectl delete -f {spec} -n {namespace}", shell=True)
run(f"kubectl delete namespace {namespace}", shell=True)
def test_abtest_model(self, namespace):
command = ("helm install myabtest ../../helm-charts/seldon-abtest "
f"--namespace {namespace}")
run(command, shell=True, check=True)
wait_for_status("myabtest", namespace)
wait_for_rollout("myabtest", namespace, expected_deployments=2)
initial_rest_request("myabtest", namespace)
logging.warning("Test Ambassador REST gateway")
r = rest_request_ambassador("myabtest", namespace, API_AMBASSADOR)
logging.warning(r.json())
assert r.status_code == 200
assert len(r.json()["data"]["tensor"]["values"]) == 1
logging.warning("Test Ambassador gRPC gateway")
logging.warning(
"WARNING SKIPPING FLAKY AMBASSADOR TEST UNTIL AMBASSADOR GRPC ISSUE FIXED.."
)
run("helm delete myabtest", shell=True)
