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)), )