def _get_endpoint_features(
project: str, endpoint_id: str, features: Optional[str]
) -> List[Features]:
if not features:
url = f"projects/{project}/model-endpoints/{endpoint_id}/features"
raise MLRunNotFoundError(f"Endpoint features not found' - {url}")
parsed_features: List[dict] = json.loads(features) if features is not None else []
feature_objects = [Features(**feature) for feature in parsed_features]
return feature_objects
def get_endpoint(
access_key: str,
project: str,
endpoint_id: str,
metrics: Optional[List[str]] = None,
start: str = "now-1h",
end: str = "now",
feature_analysis: bool = False,
) -> ModelEndpoint:
"""
Returns a ModelEndpoint object with additional metrics and feature related data.
:param access_key: V3IO access key for managing user permissions
:param project: The name of the project
:param endpoint_id: The id of the model endpoint
:param metrics: A list of metrics to return for each endpoint, read more in 'TimeMetric'
:param start: The start time of the metrics
:param end: The end time of the metrics
:param feature_analysis: When True, the base feature statistics and current feature statistics will be added to
the output of the resulting object
"""
logger.info(
"Getting model endpoint record from kv",
endpoint_id=endpoint_id,
)
client = get_v3io_client(endpoint=config.v3io_api)
path = config.model_endpoint_monitoring.store_prefixes.default.format(
project=project, kind=ENDPOINTS)
_, container, path = parse_model_endpoint_store_prefix(path)
endpoint = client.kv.get(
container=container,
table_path=path,
key=endpoint_id,
access_key=access_key,
raise_for_status=RaiseForStatus.never,
)
endpoint = endpoint.output.item
if not endpoint:
raise MLRunNotFoundError(f"Endpoint {endpoint_id} not found")
labels = endpoint.get("labels")
feature_names = endpoint.get("feature_names")
feature_names = _json_loads_if_not_none(feature_names)
label_names = endpoint.get("label_names")
label_names = _json_loads_if_not_none(label_names)
feature_stats = endpoint.get("feature_stats")
feature_stats = _json_loads_if_not_none(feature_stats)
current_stats = endpoint.get("current_stats")
current_stats = _json_loads_if_not_none(current_stats)
drift_measures = endpoint.get("drift_measures")
drift_measures = _json_loads_if_not_none(drift_measures)
monitor_configuration = endpoint.get("monitor_configuration")
monitor_configuration = _json_loads_if_not_none(monitor_configuration)
endpoint = ModelEndpoint(
metadata=ModelEndpointMetadata(
project=endpoint.get("project"),
labels=_json_loads_if_not_none(labels),
uid=endpoint_id,
),
spec=ModelEndpointSpec(
function_uri=endpoint.get("function_uri"),
model=endpoint.get("model"),
model_class=endpoint.get("model_class") or None,
model_uri=endpoint.get("model_uri") or None,
feature_names=feature_names or None,
label_names=label_names or None,
stream_path=endpoint.get("stream_path") or None,
algorithm=endpoint.get("algorithm") or None,
monitor_configuration=monitor_configuration or None,
active=endpoint.get("active") or None,
),
status=ModelEndpointStatus(
state=endpoint.get("state") or None,
feature_stats=feature_stats or None,
current_stats=current_stats or None,
first_request=endpoint.get("first_request") or None,
last_request=endpoint.get("last_request") or None,
accuracy=endpoint.get("accuracy") or None,
error_count=endpoint.get("error_count") or None,
drift_status=endpoint.get("drift_status") or None,
),
)
if feature_analysis and feature_names:
endpoint_features = get_endpoint_features(
feature_names=feature_names,
feature_stats=feature_stats,
current_stats=current_stats,
)
if endpoint_features:
endpoint.status.features = endpoint_features
endpoint.status.drift_measures = drift_measures
if metrics:
endpoint_metrics = get_endpoint_metrics(
access_key=access_key,
project=project,
endpoint_id=endpoint_id,
start=start,
end=end,
metrics=metrics,
)
if endpoint_metrics:
endpoint.status.metrics = endpoint_metrics
return endpoint
def get_endpoint(
access_key: str,
project: str,
endpoint_id: str,
metrics: Optional[List[str]] = None,
start: str = "now-1h",
end: str = "now",
features: bool = False,
) -> ModelEndpointState:
"""
Returns the current state of an endpoint.
:param access_key: V3IO access key for managing user permissions
:param project: The name of the project
:param endpoint_id: The id of the model endpoint
:param metrics: A list of metrics to return for each endpoint, read more in 'TimeMetric'
:param start: The start time of the metrics
:param end: The end time of the metrics
"""
verify_endpoint(project, endpoint_id)
endpoint = get_endpoint_kv_record_by_id(
access_key, project, endpoint_id, ENDPOINT_TABLE_ATTRIBUTES,
)
if not endpoint:
url = f"/projects/{project}/model-endpoints/{endpoint_id}"
raise MLRunNotFoundError(f"Endpoint {endpoint_id} not found - {url}")
endpoint_metrics = None
if metrics:
endpoint_metrics = _get_endpoint_metrics(
access_key=access_key,
project=project,
endpoint_id=endpoint_id,
start=start,
end=end,
name=metrics,
)
return ModelEndpointState(
endpoint=ModelEndpoint(
metadata=ModelEndpointMetadata(
project=endpoint.get("project"),
tag=endpoint.get("tag"),
labels=json.loads(endpoint.get("labels", "")),
),
spec=ModelEndpointSpec(
model=endpoint.get("model"),
function=endpoint.get("function"),
model_class=endpoint.get("model_class"),
),
status=ObjectStatus(state="active"),
),
first_request=endpoint.get("first_request"),
last_request=endpoint.get("last_request"),
error_count=endpoint.get("error_count"),
drift_status=endpoint.get("drift_status"),
metrics=endpoint_metrics,
)
def get_endpoint(
project: str,
endpoint_id: str,
start: str = Query(default="now-1h"),
end: str = Query(default="now"),
metrics: bool = Query(default=False),
features: bool = Query(default=False),
):
"""
Return the current state of an endpoint, meaning all additional data the is relevant to a specified endpoint.
This function also takes into account the start and end times and uses the same time-querying as v3io-frames.
"""
_verify_endpoint(project, endpoint_id)
endpoint = _get_endpoint_kv_record_by_id(
endpoint_id,
ENDPOINT_TABLE_ATTRIBUTES_WITH_FEATURES,
)
if not endpoint:
url = f"/projects/{project}/model-endpoints/{endpoint_id}"
raise MLRunNotFoundError(f"Endpoint {endpoint_id} not found - {url}")
endpoint_metrics = None
if metrics:
endpoint_metrics = _get_endpoint_metrics(
endpoint_id=endpoint_id,
start=start,
end=end,
name=["predictions", "latency"],
)
endpoint_features = None
if features:
endpoint_features = _get_endpoint_features(
project=project,
endpoint_id=endpoint_id,
features=endpoint.get("features"))
return ModelEndpointState(
endpoint=ModelEndpoint(
metadata=ModelEndpointMetadata(
project=endpoint.get("project"),
tag=endpoint.get("tag"),
labels=json.loads(endpoint.get("labels", "")),
),
spec=ModelEndpointSpec(
model=endpoint.get("model"),
function=endpoint.get("function"),
model_class=endpoint.get("model_class"),
),
status=ObjectStatus(state="active"),
),
first_request=endpoint.get("first_request"),
last_request=endpoint.get("last_request"),
error_count=endpoint.get("error_count"),
alert_count=endpoint.get("alert_count"),
drift_status=endpoint.get("drift_status"),
metrics=endpoint_metrics,
features=endpoint_features,
)