1818from typing import Dict , List , NamedTuple , Optional , Sequence , Tuple , Union
1919
2020from google .api_core import operation
21+ from google .api_core import exceptions as api_exceptions
2122from google .auth import credentials as auth_credentials
2223
24+ from google .cloud import aiplatform
2325from google .cloud .aiplatform import base
2426from google .cloud .aiplatform import compat
2527from google .cloud .aiplatform import explain
@@ -119,6 +121,33 @@ def __init__(
119121 credentials = credentials ,
120122 )
121123
124+ @property
125+ def traffic_split (self ) -> Dict [str , int ]:
126+ """A map from a DeployedModel's ID to the percentage of this Endpoint's
127+ traffic that should be forwarded to that DeployedModel.
128+
129+ If a DeployedModel's ID is not listed in this map, then it receives no traffic.
130+
131+ The traffic percentage values must add up to 100, or map must be empty if
132+ the Endpoint is to not accept any traffic at a moment.
133+ """
134+ self ._sync_gca_resource ()
135+ return dict (self ._gca_resource .traffic_split )
136+
137+ @property
138+ def network (self ) -> Optional [str ]:
139+ """The full name of the Google Compute Engine
140+ [network](https://cloud.google.com/vpc/docs/vpc#networks) to which this
141+ Endpoint should be peered.
142+
143+ Takes the format `projects/{project}/global/networks/{network}`. Where
144+ {project} is a project number, as in `12345`, and {network} is a network name.
145+
146+ Private services access must already be configured for the network. If left
147+ unspecified, the Endpoint is not peered with any network.
148+ """
149+ return getattr (self ._gca_resource , "network" )
150+
122151 @classmethod
123152 def create (
124153 cls ,
@@ -1211,12 +1240,13 @@ class Model(base.VertexAiResourceNounWithFutureManager):
12111240 _delete_method = "delete_model"
12121241
12131242 @property
1214- def uri (self ):
1215- """Uri of the model."""
1216- return self ._gca_resource .artifact_uri
1243+ def uri (self ) -> Optional [str ]:
1244+ """Path to the directory containing the Model artifact and any of its
1245+ supporting files. Not present for AutoML Models."""
1246+ return self ._gca_resource .artifact_uri or None
12171247
12181248 @property
1219- def description (self ):
1249+ def description (self ) -> str :
12201250 """Description of the model."""
12211251 return self ._gca_resource .description
12221252
@@ -1240,6 +1270,98 @@ def supported_export_formats(
12401270 for export_format in self ._gca_resource .supported_export_formats
12411271 }
12421272
1273+ @property
1274+ def supported_deployment_resources_types (
1275+ self ,
1276+ ) -> List [aiplatform .gapic .Model .DeploymentResourcesType ]:
1277+ """List of deployment resource types accepted for this Model.
1278+
1279+ When this Model is deployed, its prediction resources are described by
1280+ the `prediction_resources` field of the objects returned by
1281+ `Endpoint.list_models()`. Because not all Models support all resource
1282+ configuration types, the configuration types this Model supports are
1283+ listed here.
1284+
1285+ If no configuration types are listed, the Model cannot be
1286+ deployed to an `Endpoint` and does not support online predictions
1287+ (`Endpoint.predict()` or `Endpoint.explain()`). Such a Model can serve
1288+ predictions by using a `BatchPredictionJob`, if it has at least one entry
1289+ each in `Model.supported_input_storage_formats` and
1290+ `Model.supported_output_storage_formats`."""
1291+ return list (self ._gca_resource .supported_deployment_resources_types )
1292+
1293+ @property
1294+ def supported_input_storage_formats (self ) -> List [str ]:
1295+ """The formats this Model supports in the `input_config` field of a
1296+ `BatchPredictionJob`. If `Model.predict_schemata.instance_schema_uri`
1297+ exists, the instances should be given as per that schema.
1298+
1299+ [Read the docs for more on batch prediction formats](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions#batch_request_input)
1300+
1301+ If this Model doesn't support any of these formats it means it cannot be
1302+ used with a `BatchPredictionJob`. However, if it has
1303+ `supported_deployment_resources_types`, it could serve online predictions
1304+ by using `Endpoint.predict()` or `Endpoint.explain()`.
1305+ """
1306+ return list (self ._gca_resource .supported_input_storage_formats )
1307+
1308+ @property
1309+ def supported_output_storage_formats (self ) -> List [str ]:
1310+ """The formats this Model supports in the `output_config` field of a
1311+ `BatchPredictionJob`.
1312+
1313+ If both `Model.predict_schemata.instance_schema_uri` and
1314+ `Model.predict_schemata.prediction_schema_uri` exist, the predictions
1315+ are returned together with their instances. In other words, the
1316+ prediction has the original instance data first, followed by the actual
1317+ prediction content (as per the schema).
1318+
1319+ [Read the docs for more on batch prediction formats](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)
1320+
1321+ If this Model doesn't support any of these formats it means it cannot be
1322+ used with a `BatchPredictionJob`. However, if it has
1323+ `supported_deployment_resources_types`, it could serve online predictions
1324+ by using `Endpoint.predict()` or `Endpoint.explain()`.
1325+ """
1326+ return list (self ._gca_resource .supported_output_storage_formats )
1327+
1328+ @property
1329+ def predict_schemata (self ) -> Optional [aiplatform .gapic .PredictSchemata ]:
1330+ """The schemata that describe formats of the Model's predictions and
1331+ explanations, if available."""
1332+ return getattr (self ._gca_resource , "predict_schemata" )
1333+
1334+ @property
1335+ def training_job (self ) -> Optional ["aiplatform.training_jobs._TrainingJob" ]:
1336+ """The TrainingJob that uploaded this Model, if any.
1337+
1338+ Raises:
1339+ api_core.exceptions.NotFound: If the Model's training job resource
1340+ cannot be found on the Vertex service.
1341+ """
1342+ job_name = getattr (self ._gca_resource , "training_pipeline" )
1343+
1344+ if not job_name :
1345+ return None
1346+
1347+ try :
1348+ return aiplatform .training_jobs ._TrainingJob ._get_and_return_subclass (
1349+ resource_name = job_name ,
1350+ project = self .project ,
1351+ location = self .location ,
1352+ credentials = self .credentials ,
1353+ )
1354+ except api_exceptions .NotFound :
1355+ raise api_exceptions .NotFound (
1356+ f"The training job used to create this model could not be found: { job_name }
1357+ )
1358+
1359+ @property
1360+ def container_spec (self ) -> Optional [aiplatform .gapic .ModelContainerSpec ]:
1361+ """The specification of the container that is to be used when deploying
1362+ this Model. Not present for AutoML Models."""
1363+ return getattr (self ._gca_resource , "container_spec" )
1364+
12431365 def __init__ (
12441366 self ,
12451367 model_name : str ,
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