def get_tempo_artifacts(
artifacts_folder: str) -> Tuple[Pipeline, Model, Model]:
sklearn_model = Model(
name="test-iris-sklearn",
platform=ModelFramework.SKLearn,
local_folder=f"{artifacts_folder}/{SKLearnFolder}",
uri="s3://tempo/basic/sklearn",
description="SKLearn Iris classification model",
)
xgboost_model = Model(
name="test-iris-xgboost",
platform=ModelFramework.XGBoost,
local_folder=f"{artifacts_folder}/{XGBFolder}",
uri="s3://tempo/basic/xgboost",
description="XGBoost Iris classification model",
)
@pipeline(
name="classifier",
uri="s3://tempo/basic/pipeline",
local_folder=f"{artifacts_folder}/classifier",
models=PipelineModels(sklearn=sklearn_model, xgboost=xgboost_model),
description=
"A pipeline to use either an sklearn or xgboost model for Iris classification",
)
def classifier(payload: np.ndarray) -> Tuple[np.ndarray, str]:
res1 = classifier.models.sklearn(input=payload)
print(res1)
if res1[0] == 1:
return res1, SKLearnTag
else:
return classifier.models.xgboost(input=payload), XGBoostTag
return classifier, sklearn_model, xgboost_model
def inference_pipeline(
sklearn_model, runtime: SeldonDockerRuntime,
pipeline_conda_yaml: str) -> Generator[Pipeline, None, None]:
@pipeline(
name="inference-pipeline",
models=PipelineModels(sklearn=sklearn_model),
local_folder=os.path.dirname(pipeline_conda_yaml),
)
async def _pipeline(payload: np.ndarray) -> np.ndarray:
res1 = await _pipeline.models.sklearn(payload)
if res1[0][0] > 0.7:
return res1
return res1.sum(keepdims=True)
_pipeline.save(save_env=True)
runtime.deploy(_pipeline)
runtime.wait_ready(_pipeline)
time.sleep(8)
yield _pipeline
try:
runtime.undeploy(_pipeline)
except docker.errors.NotFound:
# TODO: Should undeploy be idempotent as well?
# Ignore if the model has already been undeployed
pass
def create_explainer(model: Model):
@pipeline(
name="income-explainer",
uri="s3://tempo/explainer/pipeline",
local_folder=os.path.join(ARTIFACTS_FOLDER, EXPLAINER_FOLDER),
models=PipelineModels(sklearn=model),
)
class ExplainerPipeline(object):
def __init__(self):
pipeline = self.get_tempo()
models_folder = pipeline.details.local_folder
explainer_path = os.path.join(models_folder, "explainer.dill")
with open(explainer_path, "rb") as f:
self.explainer = dill.load(f)
def update_predict_fn(self, x):
if np.argmax(self.models.sklearn(x).shape) == 0:
self.explainer.predictor = self.models.sklearn
self.explainer.samplers[0].predictor = self.models.sklearn
else:
self.explainer.predictor = ArgmaxTransformer(self.models.sklearn)
self.explainer.samplers[0].predictor = ArgmaxTransformer(self.models.sklearn)
@predictmethod
def explain(self, payload: np.ndarray, parameters: dict) -> str:
print("Explain called with ", parameters)
self.update_predict_fn(payload)
explanation = self.explainer.explain(payload, **parameters)
return explanation.to_json()
# explainer = ExplainerPipeline()
# return sklearn_model, explainer
return ExplainerPipeline
def create_svc_cls(outlier, model):
@pipeline(
name="cifar10-service",
protocol=KFServingV2Protocol(),
uri="s3://tempo/outlier/cifar10/svc",
local_folder=os.path.join(ARTIFACTS_FOLDER, "svc"),
models=PipelineModels(outlier=outlier, cifar10=model),
)
class Cifar10Svc(object):
@predictmethod
def predict(self, payload: np.ndarray) -> np.ndarray:
r = self.models.outlier(payload=payload)
if r["data"]["is_outlier"][0]:
return np.array([])
else:
return self.models.cifar10(payload)
return Cifar10Svc
def get_tempo_artifacts(
sklearn_local_path: str,
xgboost_local_path: str,
classifier_local_path: str,
sklearn_url: str = "",
xgboost_url: str = "",
classifier_url: str = "",
) -> Tuple[Pipeline, Model, Model]:
sklearn_model = Model(
name="test-iris-sklearn",
platform=ModelFramework.SKLearn,
local_folder=sklearn_local_path,
uri=sklearn_url,
description="An SKLearn Iris classification model",
)
xgboost_model = Model(
name="test-iris-xgboost",
platform=ModelFramework.XGBoost,
local_folder=xgboost_local_path,
uri=xgboost_url,
description="An XGBoost Iris classification model",
)
@pipeline(
name="classifier",
uri=classifier_url,
local_folder=classifier_local_path,
models=PipelineModels(sklearn=sklearn_model, xgboost=xgboost_model),
description=
"A pipeline to use either an sklearn or xgboost model for Iris classification",
)
def classifier(payload: np.ndarray) -> Tuple[np.ndarray, str]:
res1 = classifier.models.sklearn(input=payload)
if res1[0] == 1:
return res1, SKLearnTag
else:
return classifier.models.xgboost(input=payload), XGBoostTag
return classifier, sklearn_model, xgboost_model