def xgboost_model(
xgboost_model: Model,
runtime: SeldonKubernetesRuntime) -> Generator[Model, None, None]:
xgboost_model.set_runtime(runtime)
xgboost_model.deploy()
xgboost_model.wait_ready(timeout_secs=60)
yield xgboost_model
xgboost_model.undeploy()
def xgboost_model() -> Model:
model_path = os.path.join(TESTDATA_PATH, "xgboost", "iris")
return Model(
name="test-iris-xgboost",
platform=ModelFramework.XGBoost,
uri="gs://seldon-models/xgboost/iris",
local_folder=model_path,
protocol=SeldonProtocol(),
)
def cifar10_model() -> Model:
model_path = os.path.join(TESTDATA_PATH, "tfserving", "cifar10", "resnet32")
return Model(
name="resnet32",
platform=ModelFramework.Tensorflow,
uri="gs://seldon-models/tfserving/cifar10/resnet32",
local_folder=model_path,
protocol=KFServingV1Protocol(),
)
def sklearn_mode_k8sl(
sklearn_model: Model,
runtime: SeldonKubernetesRuntime) -> Generator[Model, None, None]:
sklearn_model.set_runtime(runtime)
sklearn_model.deploy()
sklearn_model.wait_ready(timeout_secs=60)
yield sklearn_model
sklearn_model.undeploy()
def sklearn_model() -> Model:
model_path = os.path.join(TESTDATA_PATH, "sklearn", "iris")
return Model(
name="test-iris-sklearn",
platform=ModelFramework.SKLearn,
uri="gs://seldon-models/sklearn/iris",
local_folder=model_path,
protocol=SeldonProtocol(),
runtime_options=RuntimeOptions(k8s_options=KubernetesOptions(namespace="production", replicas=1)),
)