def aggregate(
user_model: Any, request: Union[prediction_pb2.SeldonMessageList, List,
Dict]
) -> Union[prediction_pb2.SeldonMessage, List, Dict]:
"""
Aggregate a list of payloads
Parameters
----------
user_model
A Seldon user model
request
SeldonMessage proto
Returns
-------
Aggregated SeldonMessage proto
"""
is_proto = isinstance(request, prediction_pb2.SeldonMessageList)
if hasattr(user_model, "aggregate_rest"):
logger.warning(
"aggregate_rest is deprecated. Please use aggregate_raw")
return user_model.aggregate_rest(request)
elif hasattr(user_model, "aggregate_grpc"):
logger.warning(
"aggregate_grpc is deprecated. Please use aggregate_raw")
return user_model.aggregate_grpc(request)
else:
if hasattr(user_model, "aggregate_raw"):
try:
return user_model.aggregate_raw(request)
except SeldonNotImplementedError:
pass
if is_proto:
features_list = []
names_list = []
for msg in request.seldonMessages:
(features, meta, datadef,
data_type) = extract_request_parts(msg)
features_list.append(features)
names_list.append(datadef.names)
client_response = client_aggregate(user_model, features_list,
names_list)
return construct_response(user_model, False,
request.seldonMessages[0],
client_response)
else:
features_list = []
names_list = []
if isinstance(request, list):
msgs = request
elif "seldonMessages" in request and isinstance(
request["seldonMessages"], list):
msgs = request["seldonMessages"]
else:
raise SeldonMicroserviceException(
f"Invalid request data type: {request}")
for msg in msgs:
(features, meta, datadef,
data_type) = extract_request_parts_json(msg)
class_names = datadef[
"names"] if datadef and "names" in datadef else []
features_list.append(features)
names_list.append(class_names)
client_response = client_aggregate(user_model, features_list,
names_list)
return construct_response_json(user_model, False, msgs[0],
client_response)
def aggregate(
user_model: Any,
request: Union[prediction_pb2.SeldonMessageList, List, Dict],
seldon_metrics: SeldonMetrics,
) -> Union[prediction_pb2.SeldonMessage, List, Dict]:
"""
Aggregate a list of payloads
Parameters
----------
user_model
A Seldon user model
request
SeldonMessage proto
seldon_metrics
A SeldonMetrics instance
Returns
-------
Aggregated SeldonMessage proto
"""
def merge_meta(meta_list):
tags = {}
for meta in meta_list:
if meta:
tags.update(meta.get("tags", {}))
return {"tags": tags}
def merge_metrics(meta_list, custom_metrics):
metrics = []
for meta in meta_list:
if meta:
metrics.extend(meta.get("metrics", []))
metrics.extend(custom_metrics)
return metrics
is_proto = isinstance(request, prediction_pb2.SeldonMessageList)
if hasattr(user_model, "aggregate_rest"):
logger.warning(
"aggregate_rest is deprecated. Please use aggregate_raw")
return user_model.aggregate_rest(request)
elif hasattr(user_model, "aggregate_grpc"):
logger.warning(
"aggregate_grpc is deprecated. Please use aggregate_raw")
return user_model.aggregate_grpc(request)
else:
if hasattr(user_model, "aggregate_raw"):
try:
response = user_model.aggregate_raw(request)
handle_raw_custom_metrics(response, seldon_metrics, is_proto,
AGGREGATE_METRIC_METHOD_TAG)
return response
except SeldonNotImplementedError:
pass
if is_proto:
features_list = []
names_list = []
meta_list = []
for msg in request.seldonMessages:
(features, meta, datadef,
data_type) = extract_request_parts(msg)
features_list.append(features)
names_list.append(datadef.names)
meta_list.append(meta)
client_response = client_aggregate(user_model, features_list,
names_list)
metrics = client_custom_metrics(
user_model,
seldon_metrics,
AGGREGATE_METRIC_METHOD_TAG,
client_response.metrics,
)
return construct_response(
user_model,
False,
request.seldonMessages[0],
client_response.data,
merge_meta(meta_list),
merge_metrics(meta_list, metrics),
client_response.tags,
)
else:
features_list = []
names_list = []
if isinstance(request, list):
msgs = request
elif "seldonMessages" in request and isinstance(
request["seldonMessages"], list):
msgs = request["seldonMessages"]
else:
raise SeldonMicroserviceException(
f"Invalid request data type: {request}")
meta_list = []
for msg in msgs:
(features, meta, datadef,
data_type) = extract_request_parts_json(msg)
class_names = datadef[
"names"] if datadef and "names" in datadef else []
features_list.append(features)
names_list.append(class_names)
meta_list.append(meta)
client_response = client_aggregate(user_model, features_list,
names_list)
metrics = client_custom_metrics(
user_model,
seldon_metrics,
AGGREGATE_METRIC_METHOD_TAG,
client_response.metrics,
)
return construct_response_json(
user_model,
False,
msgs[0],
client_response.data,
merge_meta(meta_list),
merge_metrics(meta_list, metrics),
client_response.tags,
)
