def list_model_metric_metas(
self,
model_name: Text,
project_name: Optional[Text] = None
) -> Tuple[int, Text, Union[None, MetricMeta, List[MetricMeta]]]:
"""
List of model metric metadata filter by model name and project name for Metric Center.
:param model_name: Name of the model associated with the registered metric meta.
:param project_name: Name of the project associated with the registered metric meta.
:return: List of :py:class:`ai_flow.meta.metric_meta.MetricMeta` objects.
"""
request = ListModelMetricMetasRequest(
model_name=model_name, project_name=stringValue(project_name))
response = self.metric_stub.listModelMetricMetas(request)
if 0 == response.return_code:
repeated_metric_meta_proto = response.metric_metas
if 1 == len(repeated_metric_meta_proto):
metric_meta = proto_to_metric_meta(
repeated_metric_meta_proto[0])
return response.return_code, response.return_msg, metric_meta
else:
res = []
for metric_meta_proto in repeated_metric_meta_proto:
res.append(proto_to_metric_meta(metric_meta_proto))
return response.return_code, response.return_msg, res
else:
return response.return_code, response.return_msg, None
def get_dataset_metric_meta(self, dataset_id: int) -> Tuple[int, Text, Union[None, MetricMeta, List[MetricMeta]]]:
request = metric_service_pb2.GetDataSetMetricMetaRequest(dataset_id=dataset_id)
response = self.metric_stub.getDatasetMetricMeta(request)
if 0 == response.return_code:
repeated_metric_meta_proto = response.metric_meta
if 1 == len(repeated_metric_meta_proto):
metric_meta = proto_to_metric_meta(repeated_metric_meta_proto[0])
return response.return_code, response.return_msg, metric_meta
else:
res = []
for metric_meta_proto in repeated_metric_meta_proto:
res.append(proto_to_metric_meta(metric_meta_proto))
return response.return_code, response.return_msg, res
else:
return response.return_code, response.return_msg, None
def get_model_metric_meta(self, model_name, model_version) \
-> Tuple[int, Text, Union[None, MetricMeta, List[MetricMeta]]]:
request = metric_service_pb2.GetModelMetricMetaRequest(model_name=model_name, model_version=model_version)
response = self.metric_stub.getModelMetricMeta(request)
if 0 == response.return_code:
repeated_metric_meta_proto = response.metric_meta
if 1 == len(repeated_metric_meta_proto):
metric_meta = proto_to_metric_meta(repeated_metric_meta_proto[0])
return response.return_code, response.return_msg, metric_meta
else:
res = []
for metric_meta_proto in repeated_metric_meta_proto:
res.append(proto_to_metric_meta(metric_meta_proto))
return response.return_code, response.return_msg, res
else:
return response.return_code, response.return_msg, None
def get_metric_meta(self, name: Text) -> Tuple[int, Text, Union[None, MetricMeta]]:
request = metric_service_pb2.GetMetricMetaRequest(metric_name=name)
response = self.metric_stub.getMetricMeta(request)
if 0 == response.return_code:
metric_meta_proto = response.metric_meta
metric_meta = proto_to_metric_meta(metric_meta_proto)
return response.return_code, response.return_msg, metric_meta
else:
return response.return_code, response.return_msg, None
def updateMetricMeta(self, request, context):
metric_meta_proto = request.metric_meta
metric_meta = proto_to_metric_meta(metric_meta_proto)
res_metric_meta = self.store.update_metric_meta(
metric_meta.metric_name, metric_meta.metric_desc,
metric_meta.project_name, metric_meta.dataset_name,
metric_meta.model_name, metric_meta.job_name,
metric_meta.start_time, metric_meta.end_time, metric_meta.uri,
metric_meta.tags, metric_meta.properties)
return _warp_metric_meta_response(res_metric_meta)
def register_metric_meta(
self,
metric_name: Text,
metric_type: MetricType,
project_name: Text,
metric_desc: Optional[Text] = None,
dataset_name: Optional[Text] = None,
model_name: Optional[Text] = None,
job_name: Optional[Text] = None,
start_time: int = None,
end_time: int = None,
uri: Optional[Text] = None,
tags: Optional[Text] = None,
properties: Properties = None
) -> Tuple[int, Text, Optional[MetricMeta]]:
"""
Register metric metadata in Metric Center.
:param metric_name: Name of registered metric meta. This is expected to be unique in the backend store.
:param metric_type: Type of registered metric meta.
:param project_name: Name of the project associated with the registered metric meta.
:param metric_desc: (Optional) Description of registered metric meta.
:param dataset_name: (Optional) Name of the dataset associated with the registered metric meta.
:param model_name: (Optional) Name of the model associated with the registered metric meta.
:param job_name: (Optional) Name of the job associated with the registered metric meta.
:param start_time: (Optional) Start time of registered metric meta.
:param end_time: (Optional) End time of registered metric meta.
:param uri: (Optional) Uri of registered metric meta.
:param tags: (Optional) Tags of registered metric meta.
:param properties: (Optional) Properties of registered metric meta.
:return: A single :py:class:`ai_flow.meta.metric_meta.MetricMeta` object.
"""
request = MetricMetaRequest(metric_meta=MetricMetaProto(
metric_name=stringValue(metric_name),
metric_type=MetricTypeProto.Value(metric_type.value),
metric_desc=stringValue(metric_desc),
project_name=stringValue(project_name),
dataset_name=stringValue(dataset_name),
model_name=stringValue(model_name),
job_name=stringValue(job_name),
start_time=int64Value(start_time),
end_time=int64Value(end_time),
uri=stringValue(uri),
tags=stringValue(tags),
properties=properties))
response = self.metric_stub.registerMetricMeta(request)
if 0 == response.return_code:
metric_meta_proto = response.metric_meta
metric_meta = proto_to_metric_meta(metric_meta_proto)
return response.return_code, response.return_msg, metric_meta
else:
return response.return_code, response.return_msg, None
def registerMetricMeta(self, request, context):
metric_meta_proto = request.metric_meta
metric_meta = proto_to_metric_meta(metric_meta_proto)
res_metric_meta = self.store.register_metric_meta(
metric_meta.name, metric_meta.dataset_id, metric_meta.model_name,
metric_meta.model_version, metric_meta.job_id,
metric_meta.start_time, metric_meta.end_time,
metric_meta.metric_type, metric_meta.uri, metric_meta.tags,
metric_meta.metric_description, metric_meta.properties)
return _warp_metric_meta_response(res_metric_meta)
def get_metric_meta(
self,
metric_name: Text) -> Tuple[int, Text, Union[None, MetricMeta]]:
"""
Get metric metadata detail filter by metric name for Metric Center.
:param metric_name: Name of registered metric meta. This is expected to be unique in the backend store.
:return: A single :py:class:`ai_flow.meta.metric_meta.MetricMeta` object.
"""
request = MetricNameRequest(metric_name=metric_name)
response = self.metric_stub.getMetricMeta(request)
if 0 == response.return_code:
metric_meta_proto = response.metric_meta
metric_meta = proto_to_metric_meta(metric_meta_proto)
return response.return_code, response.return_msg, metric_meta
else:
return response.return_code, response.return_msg, None
def update_metric_meta(self,
uuid: int,
name: Text = None,
dataset_id: int = None,
model_name: Optional[Text] = None,
model_version: Optional[Text] = None,
job_id: int = None,
start_time: int = None,
end_time: int = None,
metric_type: MetricType = MetricType.DATASET,
uri: Text = None,
tags: Text = None,
metric_description: Text = None,
properties: Properties = None,
) -> Tuple[int, Text, Optional[MetricMeta]]:
pb_metric_type = message_pb2.MetricTypeProto.Value(metric_type.value)
request = metric_service_pb2.MetricMetaRequest(
metric_meta=message_pb2.MetricMetaProto(
uuid=uuid,
name=stringValue(name),
dataset_id=int64Value(dataset_id),
model_name=stringValue(model_name),
model_version=stringValue(model_version),
job_id=int64Value(job_id),
start_time=int64Value(start_time),
end_time=int64Value(end_time),
metric_type=pb_metric_type,
uri=stringValue(uri),
tags=stringValue(tags),
metric_description=stringValue(metric_description),
properties=properties)
)
response = self.metric_stub.updateMetricMeta(request)
if 0 == response.return_code:
metric_meta_proto = response.metric_meta
metric_meta = proto_to_metric_meta(metric_meta_proto)
return response.return_code, response.return_msg, metric_meta
else:
return response.return_code, response.return_msg, None