def Predict(self,request,context):
features = get_data_from_proto(request)
datadef = request.data
data_type = request.WhichOneof("data_oneof")
predictions = predict(self.user_model,features,datadef.names)
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson,meta)
if isinstance(predictions, np.ndarray) or data_type == "data":
predictions = np.array(predictions)
if len(predictions.shape)>1:
class_names = get_class_names(self.user_model, predictions.shape[1])
else:
class_names = []
if data_type == "data":
default_data_type = request.data.WhichOneof("data_oneof")
else:
default_data_type = "tensor"
data = array_to_grpc_datadef(predictions, class_names, default_data_type)
return prediction_pb2.SeldonMessage(data=data,meta=meta)
else:
return prediction_pb2.SeldonMessage(binData=predictions,meta=meta)
def Predict():
request = extract_message()
sanity_check_request(request)
features = get_data_from_json(request)
names = request.get("data",{}).get("names")
predictions = predict(user_model,features,names)
# If predictions is an numpy array or we used the default data then return as numpy array
if isinstance(predictions, np.ndarray) or "data" in request:
predictions = np.array(predictions)
if len(predictions.shape)>1:
class_names = get_class_names(user_model, predictions.shape[1])
else:
class_names = []
data = array_to_rest_datadef(predictions, class_names, request.get("data",{}))
response = {"data":data,"meta":{}}
else:
response = {"binData":predictions,"meta":{}}
tags = get_custom_tags(user_model)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_model)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)
def Predict(self, request, context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
predictions = np.array(
predict(self.user_model, features, datadef.names))
if len(predictions.shape) > 1:
class_names = get_class_names(self.user_model,
predictions.shape[1])
else:
class_names = []
data = array_to_grpc_datadef(predictions, class_names,
request.data.WhichOneof("data_oneof"))
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson, meta)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
def Route():
request = extract_message()
if debug:
print("SELDON DEBUGGING")
print("Request received: ")
print(request)
sanity_check_request(request)
datadef = request.get("data")
features = rest_datadef_to_array(datadef)
routing = np.array(
[[route(user_router, features, datadef.get("names"))]])
# TODO: check that predictions is 2 dimensional
class_names = []
data = array_to_rest_datadef(routing, class_names, datadef)
response = {"data": data, "meta": {}}
tags = get_custom_tags(user_router)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_router)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)
def TransformInput():
request = extract_message()
sanity_check_request(request)
features = get_data_from_json(request)
names = request.get("data", {}).get("names")
transformed = transform_input(user_model, features, names)
# If predictions is an numpy array or we used the default data then return as numpy array
if isinstance(transformed, np.ndarray) or "data" in request:
new_feature_names = get_feature_names(user_model, names)
transformed = np.array(transformed)
data = array_to_rest_datadef(transformed, new_feature_names,
request.get("data", {}))
response = {"data": data, "meta": {}}
else:
response = {"binData": transformed, "meta": {}}
tags = get_custom_tags(user_model)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_model)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)
def TransformOutput(self, request, context):
features = get_data_from_proto(request)
datadef = request.data
data_type = request.WhichOneof("data_oneof")
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson, meta)
transformed = transform_output(self.user_model, features,
datadef.names)
if isinstance(transformed, np.ndarray) or data_type == "data":
transformed = np.array(transformed)
class_names = get_class_names(self.user_model, datadef.names)
if data_type == "data":
default_data_type = request.data.WhichOneof("data_oneof")
else:
default_data_type = "tensor"
data = array_to_grpc_datadef(transformed, class_names,
default_data_type)
return prediction_pb2.SeldonMessage(data=data, meta=meta)
else:
return prediction_pb2.SeldonMessage(binData=transformed, meta=meta)
def Route(self,request,context):
datadef = request.data
features = grpc_datadef_to_array(datadef)
routing = np.array([[route(self.user_model,features,datadef.names)]])
#TODO: check that predictions is 2 dimensional
class_names = []
data = array_to_grpc_datadef(routing, class_names, request.data.WhichOneof("data_oneof"))
# Construct meta data
meta = prediction_pb2.Meta()
metaJson = {}
tags = get_custom_tags(self.user_model)
if tags:
metaJson["tags"] = tags
metrics = get_custom_metrics(self.user_model)
if metrics:
metaJson["metrics"] = metrics
json_format.ParseDict(metaJson,meta)
return prediction_pb2.SeldonMessage(data=data,meta=meta)
def Predict():
request = extract_message()
sanity_check_request(request)
datadef = request.get("data")
features = rest_datadef_to_array(datadef)
predictions = np.array(predict(user_model,features,datadef.get("names")))
if len(predictions.shape)>1:
class_names = get_class_names(user_model, predictions.shape[1])
else:
class_names = []
data = array_to_rest_datadef(predictions, class_names, datadef)
response = {"data":data,"meta":{}}
tags = get_custom_tags(user_model)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_model)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)
def TransformOutput():
request = extract_message()
sanity_check_request(request)
features = get_data_from_json(request)
names = request.get("data", {}).get("names")
transformed = transform_output(user_model, features, names)
if isinstance(transformed, np.ndarray) or "data" in request:
new_class_names = get_class_names(user_model, names)
data = array_to_rest_datadef(transformed, new_class_names,
request.get("data", {}))
response = {"data": data, "meta": {}}
else:
response = {"binData": transformed, "meta": {}}
tags = get_custom_tags(user_model)
if tags:
response["meta"]["tags"] = tags
metrics = get_custom_metrics(user_model)
if metrics:
response["meta"]["metrics"] = metrics
return jsonify(response)