# -*- coding: utf-8 -*- # Copyright 2025 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from collections import OrderedDict from http import HTTPStatus import json import logging as std_logging import os import re from typing import ( Dict, Callable, Mapping, MutableMapping, MutableSequence, Optional, Sequence, Tuple, Type, Union, cast, ) import warnings from google.cloud.aiplatform_v1 import gapic_version as package_version from google.api_core import client_options as client_options_lib from google.api_core import exceptions as core_exceptions from google.api_core import gapic_v1 from google.api_core import retry as retries from google.auth import credentials as ga_credentials # type: ignore from google.auth.transport import mtls # type: ignore from google.auth.transport.grpc import SslCredentials # type: ignore from google.auth.exceptions import MutualTLSChannelError # type: ignore from google.oauth2 import service_account # type: ignore import google.protobuf try: OptionalRetry = Union[retries.Retry, gapic_v1.method._MethodDefault, None] except AttributeError: # pragma: NO COVER OptionalRetry = Union[retries.Retry, object, None] # type: ignore try: from google.api_core import client_logging # type: ignore CLIENT_LOGGING_SUPPORTED = True # pragma: NO COVER except ImportError: # pragma: NO COVER CLIENT_LOGGING_SUPPORTED = False _LOGGER = std_logging.getLogger(__name__) from google.api_core import operation as gac_operation # type: ignore from google.api_core import operation_async # type: ignore from google.cloud.aiplatform_v1.services.model_service import pagers from google.cloud.aiplatform_v1.types import deployed_model_ref from google.cloud.aiplatform_v1.types import encryption_spec from google.cloud.aiplatform_v1.types import evaluated_annotation from google.cloud.aiplatform_v1.types import explanation from google.cloud.aiplatform_v1.types import model from google.cloud.aiplatform_v1.types import model as gca_model from google.cloud.aiplatform_v1.types import model_evaluation from google.cloud.aiplatform_v1.types import model_evaluation as gca_model_evaluation from google.cloud.aiplatform_v1.types import model_evaluation_slice from google.cloud.aiplatform_v1.types import model_service from google.cloud.aiplatform_v1.types import operation as gca_operation from google.cloud.location import locations_pb2 # type: ignore from google.iam.v1 import iam_policy_pb2 # type: ignore from google.iam.v1 import policy_pb2 # type: ignore from google.longrunning import operations_pb2 # type: ignore from google.protobuf import empty_pb2 # type: ignore from google.protobuf import field_mask_pb2 # type: ignore from google.protobuf import struct_pb2 # type: ignore from google.protobuf import timestamp_pb2 # type: ignore from .transports.base import ModelServiceTransport, DEFAULT_CLIENT_INFO from .transports.grpc import ModelServiceGrpcTransport from .transports.grpc_asyncio import ModelServiceGrpcAsyncIOTransport from .transports.rest import ModelServiceRestTransport try: from .transports.rest_asyncio import AsyncModelServiceRestTransport HAS_ASYNC_REST_DEPENDENCIES = True except ImportError as e: # pragma: NO COVER HAS_ASYNC_REST_DEPENDENCIES = False ASYNC_REST_EXCEPTION = e class ModelServiceClientMeta(type): """Metaclass for the ModelService client. This provides class-level methods for building and retrieving support objects (e.g. transport) without polluting the client instance objects. """ _transport_registry = OrderedDict() # type: Dict[str, Type[ModelServiceTransport]] _transport_registry["grpc"] = ModelServiceGrpcTransport _transport_registry["grpc_asyncio"] = ModelServiceGrpcAsyncIOTransport _transport_registry["rest"] = ModelServiceRestTransport if HAS_ASYNC_REST_DEPENDENCIES: # pragma: NO COVER _transport_registry["rest_asyncio"] = AsyncModelServiceRestTransport def get_transport_class( cls, label: Optional[str] = None, ) -> Type[ModelServiceTransport]: """Returns an appropriate transport class. Args: label: The name of the desired transport. If none is provided, then the first transport in the registry is used. Returns: The transport class to use. """ # If a specific transport is requested, return that one. if ( label == "rest_asyncio" and not HAS_ASYNC_REST_DEPENDENCIES ): # pragma: NO COVER raise ASYNC_REST_EXCEPTION if label: return cls._transport_registry[label] # No transport is requested; return the default (that is, the first one # in the dictionary). return next(iter(cls._transport_registry.values())) class ModelServiceClient(metaclass=ModelServiceClientMeta): """A service for managing Vertex AI's machine learning Models.""" @staticmethod def _get_default_mtls_endpoint(api_endpoint): """Converts api endpoint to mTLS endpoint. Convert "*.sandbox.googleapis.com" and "*.googleapis.com" to "*.mtls.sandbox.googleapis.com" and "*.mtls.googleapis.com" respectively. Args: api_endpoint (Optional[str]): the api endpoint to convert. Returns: str: converted mTLS api endpoint. """ if not api_endpoint: return api_endpoint mtls_endpoint_re = re.compile( r"(?P[^.]+)(?P\.mtls)?(?P\.sandbox)?(?P\.googleapis\.com)?" ) m = mtls_endpoint_re.match(api_endpoint) name, mtls, sandbox, googledomain = m.groups() if mtls or not googledomain: return api_endpoint if sandbox: return api_endpoint.replace( "sandbox.googleapis.com", "mtls.sandbox.googleapis.com" ) return api_endpoint.replace(".googleapis.com", ".mtls.googleapis.com") # Note: DEFAULT_ENDPOINT is deprecated. Use _DEFAULT_ENDPOINT_TEMPLATE instead. DEFAULT_ENDPOINT = "aiplatform.googleapis.com" DEFAULT_MTLS_ENDPOINT = _get_default_mtls_endpoint.__func__( # type: ignore DEFAULT_ENDPOINT ) _DEFAULT_ENDPOINT_TEMPLATE = "aiplatform.{UNIVERSE_DOMAIN}" _DEFAULT_UNIVERSE = "googleapis.com" @staticmethod def _use_client_cert_effective(): """Returns whether client certificate should be used for mTLS if the google-auth version supports should_use_client_cert automatic mTLS enablement. Alternatively, read from the GOOGLE_API_USE_CLIENT_CERTIFICATE env var. Returns: bool: whether client certificate should be used for mTLS Raises: ValueError: (If using a version of google-auth without should_use_client_cert and GOOGLE_API_USE_CLIENT_CERTIFICATE is set to an unexpected value.) """ # check if google-auth version supports should_use_client_cert for automatic mTLS enablement if hasattr(mtls, "should_use_client_cert"): # pragma: NO COVER return mtls.should_use_client_cert() else: # pragma: NO COVER # if unsupported, fallback to reading from env var use_client_cert_str = os.getenv( "GOOGLE_API_USE_CLIENT_CERTIFICATE", "false" ).lower() if use_client_cert_str not in ("true", "false"): raise ValueError( "Environment variable `GOOGLE_API_USE_CLIENT_CERTIFICATE` must be" " either `true` or `false`" ) return use_client_cert_str == "true" @classmethod def from_service_account_info(cls, info: dict, *args, **kwargs): """Creates an instance of this client using the provided credentials info. Args: info (dict): The service account private key info. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: ModelServiceClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_info(info) kwargs["credentials"] = credentials return cls(*args, **kwargs) @classmethod def from_service_account_file(cls, filename: str, *args, **kwargs): """Creates an instance of this client using the provided credentials file. Args: filename (str): The path to the service account private key json file. args: Additional arguments to pass to the constructor. kwargs: Additional arguments to pass to the constructor. Returns: ModelServiceClient: The constructed client. """ credentials = service_account.Credentials.from_service_account_file(filename) kwargs["credentials"] = credentials return cls(*args, **kwargs) from_service_account_json = from_service_account_file @property def transport(self) -> ModelServiceTransport: """Returns the transport used by the client instance. Returns: ModelServiceTransport: The transport used by the client instance. """ return self._transport @staticmethod def endpoint_path( project: str, location: str, endpoint: str, ) -> str: """Returns a fully-qualified endpoint string.""" return "projects/{project}/locations/{location}/endpoints/{endpoint}".format( project=project, location=location, endpoint=endpoint, ) @staticmethod def parse_endpoint_path(path: str) -> Dict[str, str]: """Parses a endpoint path into its component segments.""" m = re.match( r"^projects/(?P.+?)/locations/(?P.+?)/endpoints/(?P.+?)$", path, ) return m.groupdict() if m else {} @staticmethod def model_path( project: str, location: str, model: str, ) -> str: """Returns a fully-qualified model string.""" return "projects/{project}/locations/{location}/models/{model}".format( project=project, location=location, model=model, ) @staticmethod def parse_model_path(path: str) -> Dict[str, str]: """Parses a model path into its component segments.""" m = re.match( r"^projects/(?P.+?)/locations/(?P.+?)/models/(?P.+?)$", path, ) return m.groupdict() if m else {} @staticmethod def model_evaluation_path( project: str, location: str, model: str, evaluation: str, ) -> str: """Returns a fully-qualified model_evaluation string.""" return "projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}".format( project=project, location=location, model=model, evaluation=evaluation, ) @staticmethod def parse_model_evaluation_path(path: str) -> Dict[str, str]: """Parses a model_evaluation path into its component segments.""" m = re.match( r"^projects/(?P.+?)/locations/(?P.+?)/models/(?P.+?)/evaluations/(?P.+?)$", path, ) return m.groupdict() if m else {} @staticmethod def model_evaluation_slice_path( project: str, location: str, model: str, evaluation: str, slice: str, ) -> str: """Returns a fully-qualified model_evaluation_slice string.""" return "projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}".format( project=project, location=location, model=model, evaluation=evaluation, slice=slice, ) @staticmethod def parse_model_evaluation_slice_path(path: str) -> Dict[str, str]: """Parses a model_evaluation_slice path into its component segments.""" m = re.match( r"^projects/(?P.+?)/locations/(?P.+?)/models/(?P.+?)/evaluations/(?P.+?)/slices/(?P.+?)$", path, ) return m.groupdict() if m else {} @staticmethod def pipeline_job_path( project: str, location: str, pipeline_job: str, ) -> str: """Returns a fully-qualified pipeline_job string.""" return "projects/{project}/locations/{location}/pipelineJobs/{pipeline_job}".format( project=project, location=location, pipeline_job=pipeline_job, ) @staticmethod def parse_pipeline_job_path(path: str) -> Dict[str, str]: """Parses a pipeline_job path into its component segments.""" m = re.match( r"^projects/(?P.+?)/locations/(?P.+?)/pipelineJobs/(?P.+?)$", path, ) return m.groupdict() if m else {} @staticmethod def training_pipeline_path( project: str, location: str, training_pipeline: str, ) -> str: """Returns a fully-qualified training_pipeline string.""" return "projects/{project}/locations/{location}/trainingPipelines/{training_pipeline}".format( project=project, location=location, training_pipeline=training_pipeline, ) @staticmethod def parse_training_pipeline_path(path: str) -> Dict[str, str]: """Parses a training_pipeline path into its component segments.""" m = re.match( r"^projects/(?P.+?)/locations/(?P.+?)/trainingPipelines/(?P.+?)$", path, ) return m.groupdict() if m else {} @staticmethod def common_billing_account_path( billing_account: str, ) -> str: """Returns a fully-qualified billing_account string.""" return "billingAccounts/{billing_account}".format( billing_account=billing_account, ) @staticmethod def parse_common_billing_account_path(path: str) -> Dict[str, str]: """Parse a billing_account path into its component segments.""" m = re.match(r"^billingAccounts/(?P.+?)$", path) return m.groupdict() if m else {} @staticmethod def common_folder_path( folder: str, ) -> str: """Returns a fully-qualified folder string.""" return "folders/{folder}".format( folder=folder, ) @staticmethod def parse_common_folder_path(path: str) -> Dict[str, str]: """Parse a folder path into its component segments.""" m = re.match(r"^folders/(?P.+?)$", path) return m.groupdict() if m else {} @staticmethod def common_organization_path( organization: str, ) -> str: """Returns a fully-qualified organization string.""" return "organizations/{organization}".format( organization=organization, ) @staticmethod def parse_common_organization_path(path: str) -> Dict[str, str]: """Parse a organization path into its component segments.""" m = re.match(r"^organizations/(?P.+?)$", path) return m.groupdict() if m else {} @staticmethod def common_project_path( project: str, ) -> str: """Returns a fully-qualified project string.""" return "projects/{project}".format( project=project, ) @staticmethod def parse_common_project_path(path: str) -> Dict[str, str]: """Parse a project path into its component segments.""" m = re.match(r"^projects/(?P.+?)$", path) return m.groupdict() if m else {} @staticmethod def common_location_path( project: str, location: str, ) -> str: """Returns a fully-qualified location string.""" return "projects/{project}/locations/{location}".format( project=project, location=location, ) @staticmethod def parse_common_location_path(path: str) -> Dict[str, str]: """Parse a location path into its component segments.""" m = re.match(r"^projects/(?P.+?)/locations/(?P.+?)$", path) return m.groupdict() if m else {} @classmethod def get_mtls_endpoint_and_cert_source( cls, client_options: Optional[client_options_lib.ClientOptions] = None ): """Deprecated. Return the API endpoint and client cert source for mutual TLS. The client cert source is determined in the following order: (1) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is not "true", the client cert source is None. (2) if `client_options.client_cert_source` is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None. The API endpoint is determined in the following order: (1) if `client_options.api_endpoint` if provided, use the provided one. (2) if `GOOGLE_API_USE_CLIENT_CERTIFICATE` environment variable is "always", use the default mTLS endpoint; if the environment variable is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint. More details can be found at https://google.aip.dev/auth/4114. Args: client_options (google.api_core.client_options.ClientOptions): Custom options for the client. Only the `api_endpoint` and `client_cert_source` properties may be used in this method. Returns: Tuple[str, Callable[[], Tuple[bytes, bytes]]]: returns the API endpoint and the client cert source to use. Raises: google.auth.exceptions.MutualTLSChannelError: If any errors happen. """ warnings.warn( "get_mtls_endpoint_and_cert_source is deprecated. Use the api_endpoint property instead.", DeprecationWarning, ) if client_options is None: client_options = client_options_lib.ClientOptions() use_client_cert = ModelServiceClient._use_client_cert_effective() use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto") if use_mtls_endpoint not in ("auto", "never", "always"): raise MutualTLSChannelError( "Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`" ) # Figure out the client cert source to use. client_cert_source = None if use_client_cert: if client_options.client_cert_source: client_cert_source = client_options.client_cert_source elif mtls.has_default_client_cert_source(): client_cert_source = mtls.default_client_cert_source() # Figure out which api endpoint to use. if client_options.api_endpoint is not None: api_endpoint = client_options.api_endpoint elif use_mtls_endpoint == "always" or ( use_mtls_endpoint == "auto" and client_cert_source ): api_endpoint = cls.DEFAULT_MTLS_ENDPOINT else: api_endpoint = cls.DEFAULT_ENDPOINT return api_endpoint, client_cert_source @staticmethod def _read_environment_variables(): """Returns the environment variables used by the client. Returns: Tuple[bool, str, str]: returns the GOOGLE_API_USE_CLIENT_CERTIFICATE, GOOGLE_API_USE_MTLS_ENDPOINT, and GOOGLE_CLOUD_UNIVERSE_DOMAIN environment variables. Raises: ValueError: If GOOGLE_API_USE_CLIENT_CERTIFICATE is not any of ["true", "false"]. google.auth.exceptions.MutualTLSChannelError: If GOOGLE_API_USE_MTLS_ENDPOINT is not any of ["auto", "never", "always"]. """ use_client_cert = ModelServiceClient._use_client_cert_effective() use_mtls_endpoint = os.getenv("GOOGLE_API_USE_MTLS_ENDPOINT", "auto").lower() universe_domain_env = os.getenv("GOOGLE_CLOUD_UNIVERSE_DOMAIN") if use_mtls_endpoint not in ("auto", "never", "always"): raise MutualTLSChannelError( "Environment variable `GOOGLE_API_USE_MTLS_ENDPOINT` must be `never`, `auto` or `always`" ) return use_client_cert, use_mtls_endpoint, universe_domain_env @staticmethod def _get_client_cert_source(provided_cert_source, use_cert_flag): """Return the client cert source to be used by the client. Args: provided_cert_source (bytes): The client certificate source provided. use_cert_flag (bool): A flag indicating whether to use the client certificate. Returns: bytes or None: The client cert source to be used by the client. """ client_cert_source = None if use_cert_flag: if provided_cert_source: client_cert_source = provided_cert_source elif mtls.has_default_client_cert_source(): client_cert_source = mtls.default_client_cert_source() return client_cert_source @staticmethod def _get_api_endpoint( api_override, client_cert_source, universe_domain, use_mtls_endpoint ): """Return the API endpoint used by the client. Args: api_override (str): The API endpoint override. If specified, this is always the return value of this function and the other arguments are not used. client_cert_source (bytes): The client certificate source used by the client. universe_domain (str): The universe domain used by the client. use_mtls_endpoint (str): How to use the mTLS endpoint, which depends also on the other parameters. Possible values are "always", "auto", or "never". Returns: str: The API endpoint to be used by the client. """ if api_override is not None: api_endpoint = api_override elif use_mtls_endpoint == "always" or ( use_mtls_endpoint == "auto" and client_cert_source ): _default_universe = ModelServiceClient._DEFAULT_UNIVERSE if universe_domain != _default_universe: raise MutualTLSChannelError( f"mTLS is not supported in any universe other than {_default_universe}." ) api_endpoint = ModelServiceClient.DEFAULT_MTLS_ENDPOINT else: api_endpoint = ModelServiceClient._DEFAULT_ENDPOINT_TEMPLATE.format( UNIVERSE_DOMAIN=universe_domain ) return api_endpoint @staticmethod def _get_universe_domain( client_universe_domain: Optional[str], universe_domain_env: Optional[str] ) -> str: """Return the universe domain used by the client. Args: client_universe_domain (Optional[str]): The universe domain configured via the client options. universe_domain_env (Optional[str]): The universe domain configured via the "GOOGLE_CLOUD_UNIVERSE_DOMAIN" environment variable. Returns: str: The universe domain to be used by the client. Raises: ValueError: If the universe domain is an empty string. """ universe_domain = ModelServiceClient._DEFAULT_UNIVERSE if client_universe_domain is not None: universe_domain = client_universe_domain elif universe_domain_env is not None: universe_domain = universe_domain_env if len(universe_domain.strip()) == 0: raise ValueError("Universe Domain cannot be an empty string.") return universe_domain def _validate_universe_domain(self): """Validates client's and credentials' universe domains are consistent. Returns: bool: True iff the configured universe domain is valid. Raises: ValueError: If the configured universe domain is not valid. """ # NOTE (b/349488459): universe validation is disabled until further notice. return True def _add_cred_info_for_auth_errors( self, error: core_exceptions.GoogleAPICallError ) -> None: """Adds credential info string to error details for 401/403/404 errors. Args: error (google.api_core.exceptions.GoogleAPICallError): The error to add the cred info. """ if error.code not in [ HTTPStatus.UNAUTHORIZED, HTTPStatus.FORBIDDEN, HTTPStatus.NOT_FOUND, ]: return cred = self._transport._credentials # get_cred_info is only available in google-auth>=2.35.0 if not hasattr(cred, "get_cred_info"): return # ignore the type check since pypy test fails when get_cred_info # is not available cred_info = cred.get_cred_info() # type: ignore if cred_info and hasattr(error._details, "append"): error._details.append(json.dumps(cred_info)) @property def api_endpoint(self): """Return the API endpoint used by the client instance. Returns: str: The API endpoint used by the client instance. """ return self._api_endpoint @property def universe_domain(self) -> str: """Return the universe domain used by the client instance. Returns: str: The universe domain used by the client instance. """ return self._universe_domain def __init__( self, *, credentials: Optional[ga_credentials.Credentials] = None, transport: Optional[ Union[str, ModelServiceTransport, Callable[..., ModelServiceTransport]] ] = None, client_options: Optional[Union[client_options_lib.ClientOptions, dict]] = None, client_info: gapic_v1.client_info.ClientInfo = DEFAULT_CLIENT_INFO, ) -> None: """Instantiates the model service client. Args: credentials (Optional[google.auth.credentials.Credentials]): The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. transport (Optional[Union[str,ModelServiceTransport,Callable[..., ModelServiceTransport]]]): The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the ModelServiceTransport constructor. If set to None, a transport is chosen automatically. client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]): Custom options for the client. 1. The ``api_endpoint`` property can be used to override the default endpoint provided by the client when ``transport`` is not explicitly provided. Only if this property is not set and ``transport`` was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value). 2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the ``client_cert_source`` property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used. 3. The ``universe_domain`` property can be used to override the default "googleapis.com" universe. Note that the ``api_endpoint`` property still takes precedence; and ``universe_domain`` is currently not supported for mTLS. client_info (google.api_core.gapic_v1.client_info.ClientInfo): The client info used to send a user-agent string along with API requests. If ``None``, then default info will be used. Generally, you only need to set this if you're developing your own client library. Raises: google.auth.exceptions.MutualTLSChannelError: If mutual TLS transport creation failed for any reason. """ self._client_options = client_options if isinstance(self._client_options, dict): self._client_options = client_options_lib.from_dict(self._client_options) if self._client_options is None: self._client_options = client_options_lib.ClientOptions() self._client_options = cast( client_options_lib.ClientOptions, self._client_options ) universe_domain_opt = getattr(self._client_options, "universe_domain", None) self._use_client_cert, self._use_mtls_endpoint, self._universe_domain_env = ( ModelServiceClient._read_environment_variables() ) self._client_cert_source = ModelServiceClient._get_client_cert_source( self._client_options.client_cert_source, self._use_client_cert ) self._universe_domain = ModelServiceClient._get_universe_domain( universe_domain_opt, self._universe_domain_env ) self._api_endpoint = None # updated below, depending on `transport` # Initialize the universe domain validation. self._is_universe_domain_valid = False if CLIENT_LOGGING_SUPPORTED: # pragma: NO COVER # Setup logging. client_logging.initialize_logging() api_key_value = getattr(self._client_options, "api_key", None) if api_key_value and credentials: raise ValueError( "client_options.api_key and credentials are mutually exclusive" ) # Save or instantiate the transport. # Ordinarily, we provide the transport, but allowing a custom transport # instance provides an extensibility point for unusual situations. transport_provided = isinstance(transport, ModelServiceTransport) if transport_provided: # transport is a ModelServiceTransport instance. if credentials or self._client_options.credentials_file or api_key_value: raise ValueError( "When providing a transport instance, " "provide its credentials directly." ) if self._client_options.scopes: raise ValueError( "When providing a transport instance, provide its scopes " "directly." ) self._transport = cast(ModelServiceTransport, transport) self._api_endpoint = self._transport.host self._api_endpoint = self._api_endpoint or ModelServiceClient._get_api_endpoint( self._client_options.api_endpoint, self._client_cert_source, self._universe_domain, self._use_mtls_endpoint, ) if not transport_provided: transport_init: Union[ Type[ModelServiceTransport], Callable[..., ModelServiceTransport] ] = ( ModelServiceClient.get_transport_class(transport) if isinstance(transport, str) or transport is None else cast(Callable[..., ModelServiceTransport], transport) ) if "rest_asyncio" in str(transport_init): unsupported_params = { "google.api_core.client_options.ClientOptions.credentials_file": self._client_options.credentials_file, "google.api_core.client_options.ClientOptions.scopes": self._client_options.scopes, "google.api_core.client_options.ClientOptions.quota_project_id": self._client_options.quota_project_id, "google.api_core.client_options.ClientOptions.client_cert_source": self._client_options.client_cert_source, "google.api_core.client_options.ClientOptions.api_audience": self._client_options.api_audience, } provided_unsupported_params = [ name for name, value in unsupported_params.items() if value is not None ] if provided_unsupported_params: raise core_exceptions.AsyncRestUnsupportedParameterError( # type: ignore f"The following provided parameters are not supported for `transport=rest_asyncio`: {', '.join(provided_unsupported_params)}" ) self._transport = transport_init( credentials=credentials, host=self._api_endpoint, client_info=client_info, ) return import google.auth._default # type: ignore if api_key_value and hasattr( google.auth._default, "get_api_key_credentials" ): credentials = google.auth._default.get_api_key_credentials( api_key_value ) # initialize with the provided callable or the passed in class self._transport = transport_init( credentials=credentials, credentials_file=self._client_options.credentials_file, host=self._api_endpoint, scopes=self._client_options.scopes, client_cert_source_for_mtls=self._client_cert_source, quota_project_id=self._client_options.quota_project_id, client_info=client_info, always_use_jwt_access=True, api_audience=self._client_options.api_audience, ) if "async" not in str(self._transport): if CLIENT_LOGGING_SUPPORTED and _LOGGER.isEnabledFor( std_logging.DEBUG ): # pragma: NO COVER _LOGGER.debug( "Created client `google.cloud.aiplatform_v1.ModelServiceClient`.", extra=( { "serviceName": "google.cloud.aiplatform.v1.ModelService", "universeDomain": getattr( self._transport._credentials, "universe_domain", "" ), "credentialsType": f"{type(self._transport._credentials).__module__}.{type(self._transport._credentials).__qualname__}", "credentialsInfo": getattr( self.transport._credentials, "get_cred_info", lambda: None, )(), } if hasattr(self._transport, "_credentials") else { "serviceName": "google.cloud.aiplatform.v1.ModelService", "credentialsType": None, } ), ) def upload_model( self, request: Optional[Union[model_service.UploadModelRequest, dict]] = None, *, parent: Optional[str] = None, model: Optional[gca_model.Model] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gac_operation.Operation: r"""Uploads a Model artifact into Vertex AI. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_upload_model(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) model = aiplatform_v1.Model() model.display_name = "display_name_value" request = aiplatform_v1.UploadModelRequest( parent="parent_value", model=model, ) # Make the request operation = client.upload_model(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.UploadModelRequest, dict]): The request object. Request message for [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel]. parent (str): Required. The resource name of the Location into which to upload the Model. Format: ``projects/{project}/locations/{location}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. model (google.cloud.aiplatform_v1.types.Model): Required. The Model to create. This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.aiplatform_v1.types.UploadModelResponse` Response message of [ModelService.UploadModel][google.cloud.aiplatform.v1.ModelService.UploadModel] operation. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent, model] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.UploadModelRequest): request = model_service.UploadModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if model is not None: request.model = model # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.upload_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Wrap the response in an operation future. response = gac_operation.from_gapic( response, self._transport.operations_client, model_service.UploadModelResponse, metadata_type=model_service.UploadModelOperationMetadata, ) # Done; return the response. return response def get_model( self, request: Optional[Union[model_service.GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model.Model: r"""Gets a Model. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_get_model(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.GetModelRequest( name="name_value", ) # Make the request response = client.get_model(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.GetModelRequest, dict]): The request object. Request message for [ModelService.GetModel][google.cloud.aiplatform.v1.ModelService.GetModel]. name (str): Required. The name of the Model resource. Format: ``projects/{project}/locations/{location}/models/{model}`` In order to retrieve a specific version of the model, also provide the version ID or version alias. Example: ``projects/{project}/locations/{location}/models/{model}@2`` or ``projects/{project}/locations/{location}/models/{model}@golden`` If no version ID or alias is specified, the "default" version will be returned. The "default" version alias is created for the first version of the model, and can be moved to other versions later on. There will be exactly one default version. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.Model: A trained machine learning Model. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.GetModelRequest): request = model_service.GetModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def list_models( self, request: Optional[Union[model_service.ListModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> pagers.ListModelsPager: r"""Lists Models in a Location. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_list_models(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.ListModelsRequest( parent="parent_value", ) # Make the request page_result = client.list_models(request=request) # Handle the response for response in page_result: print(response) Args: request (Union[google.cloud.aiplatform_v1.types.ListModelsRequest, dict]): The request object. Request message for [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels]. parent (str): Required. The resource name of the Location to list the Models from. Format: ``projects/{project}/locations/{location}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.services.model_service.pagers.ListModelsPager: Response message for [ModelService.ListModels][google.cloud.aiplatform.v1.ModelService.ListModels] Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.ListModelsRequest): request = model_service.ListModelsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_models] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelsPager( method=rpc, request=request, response=response, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def list_model_versions( self, request: Optional[Union[model_service.ListModelVersionsRequest, dict]] = None, *, name: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> pagers.ListModelVersionsPager: r"""Lists versions of the specified model. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_list_model_versions(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.ListModelVersionsRequest( name="name_value", ) # Make the request page_result = client.list_model_versions(request=request) # Handle the response for response in page_result: print(response) Args: request (Union[google.cloud.aiplatform_v1.types.ListModelVersionsRequest, dict]): The request object. Request message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1.ModelService.ListModelVersions]. name (str): Required. The name of the model to list versions for. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.services.model_service.pagers.ListModelVersionsPager: Response message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1.ModelService.ListModelVersions] Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.ListModelVersionsRequest): request = model_service.ListModelVersionsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_model_versions] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelVersionsPager( method=rpc, request=request, response=response, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def list_model_version_checkpoints( self, request: Optional[ Union[model_service.ListModelVersionCheckpointsRequest, dict] ] = None, *, name: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> pagers.ListModelVersionCheckpointsPager: r"""Lists checkpoints of the specified model version. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_list_model_version_checkpoints(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.ListModelVersionCheckpointsRequest( name="name_value", ) # Make the request page_result = client.list_model_version_checkpoints(request=request) # Handle the response for response in page_result: print(response) Args: request (Union[google.cloud.aiplatform_v1.types.ListModelVersionCheckpointsRequest, dict]): The request object. Request message for [ModelService.ListModelVersionCheckpoints][google.cloud.aiplatform.v1.ModelService.ListModelVersionCheckpoints]. name (str): Required. The name of the model version to list checkpoints for. ``projects/{project}/locations/{location}/models/{model}@{version}`` Example: ``projects/{project}/locations/{location}/models/{model}@2`` or ``projects/{project}/locations/{location}/models/{model}@golden`` If no version ID or alias is specified, the latest version will be used. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.services.model_service.pagers.ListModelVersionCheckpointsPager: Response message for [ModelService.ListModelVersionCheckpoints][google.cloud.aiplatform.v1.ModelService.ListModelVersionCheckpoints] Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.ListModelVersionCheckpointsRequest): request = model_service.ListModelVersionCheckpointsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.list_model_version_checkpoints ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelVersionCheckpointsPager( method=rpc, request=request, response=response, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def update_model( self, request: Optional[Union[model_service.UpdateModelRequest, dict]] = None, *, model: Optional[gca_model.Model] = None, update_mask: Optional[field_mask_pb2.FieldMask] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gca_model.Model: r"""Updates a Model. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_update_model(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) model = aiplatform_v1.Model() model.display_name = "display_name_value" request = aiplatform_v1.UpdateModelRequest( model=model, ) # Make the request response = client.update_model(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.UpdateModelRequest, dict]): The request object. Request message for [ModelService.UpdateModel][google.cloud.aiplatform.v1.ModelService.UpdateModel]. model (google.cloud.aiplatform_v1.types.Model): Required. The Model which replaces the resource on the server. When Model Versioning is enabled, the model.name will be used to determine whether to update the model or model version. 1. model.name with the @ value, e.g. models/123@1, refers to a version specific update. 2. model.name without the @ value, e.g. models/123, refers to a model update. 3. model.name with @-, e.g. models/123@-, refers to a model update. 4. Supported model fields: display_name, description; supported version-specific fields: version_description. Labels are supported in both scenarios. Both the model labels and the version labels are merged when a model is returned. When updating labels, if the request is for model-specific update, model label gets updated. Otherwise, version labels get updated. 5. A model name or model version name fields update mismatch will cause a precondition error. 6. One request cannot update both the model and the version fields. You must update them separately. This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. update_mask (google.protobuf.field_mask_pb2.FieldMask): Required. The update mask applies to the resource. For the ``FieldMask`` definition, see [google.protobuf.FieldMask][google.protobuf.FieldMask]. This corresponds to the ``update_mask`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.Model: A trained machine learning Model. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [model, update_mask] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.UpdateModelRequest): request = model_service.UpdateModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model if update_mask is not None: request.update_mask = update_mask # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.update_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata( (("model.name", request.model.name),) ), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def update_explanation_dataset( self, request: Optional[ Union[model_service.UpdateExplanationDatasetRequest, dict] ] = None, *, model: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gac_operation.Operation: r"""Incrementally update the dataset used for an examples model. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_update_explanation_dataset(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.UpdateExplanationDatasetRequest( model="model_value", ) # Make the request operation = client.update_explanation_dataset(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.UpdateExplanationDatasetRequest, dict]): The request object. Request message for [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1.ModelService.UpdateExplanationDataset]. model (str): Required. The resource name of the Model to update. Format: ``projects/{project}/locations/{location}/models/{model}`` This corresponds to the ``model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.aiplatform_v1.types.UpdateExplanationDatasetResponse` Response message of [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1.ModelService.UpdateExplanationDataset] operation. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [model] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.UpdateExplanationDatasetRequest): request = model_service.UpdateExplanationDatasetRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if model is not None: request.model = model # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.update_explanation_dataset ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("model", request.model),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Wrap the response in an operation future. response = gac_operation.from_gapic( response, self._transport.operations_client, model_service.UpdateExplanationDatasetResponse, metadata_type=model_service.UpdateExplanationDatasetOperationMetadata, ) # Done; return the response. return response def delete_model( self, request: Optional[Union[model_service.DeleteModelRequest, dict]] = None, *, name: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gac_operation.Operation: r"""Deletes a Model. A model cannot be deleted if any [Endpoint][google.cloud.aiplatform.v1.Endpoint] resource has a [DeployedModel][google.cloud.aiplatform.v1.DeployedModel] based on the model in its [deployed_models][google.cloud.aiplatform.v1.Endpoint.deployed_models] field. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_delete_model(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.DeleteModelRequest( name="name_value", ) # Make the request operation = client.delete_model(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.DeleteModelRequest, dict]): The request object. Request message for [ModelService.DeleteModel][google.cloud.aiplatform.v1.ModelService.DeleteModel]. name (str): Required. The name of the Model resource to be deleted. Format: ``projects/{project}/locations/{location}/models/{model}`` This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.DeleteModelRequest): request = model_service.DeleteModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.delete_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Wrap the response in an operation future. response = gac_operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=gca_operation.DeleteOperationMetadata, ) # Done; return the response. return response def delete_model_version( self, request: Optional[Union[model_service.DeleteModelVersionRequest, dict]] = None, *, name: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gac_operation.Operation: r"""Deletes a Model version. Model version can only be deleted if there are no [DeployedModels][google.cloud.aiplatform.v1.DeployedModel] created from it. Deleting the only version in the Model is not allowed. Use [DeleteModel][google.cloud.aiplatform.v1.ModelService.DeleteModel] for deleting the Model instead. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_delete_model_version(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.DeleteModelVersionRequest( name="name_value", ) # Make the request operation = client.delete_model_version(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.DeleteModelVersionRequest, dict]): The request object. Request message for [ModelService.DeleteModelVersion][google.cloud.aiplatform.v1.ModelService.DeleteModelVersion]. name (str): Required. The name of the model version to be deleted, with a version ID explicitly included. Example: ``projects/{project}/locations/{location}/models/{model}@1234`` This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.DeleteModelVersionRequest): request = model_service.DeleteModelVersionRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.delete_model_version] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Wrap the response in an operation future. response = gac_operation.from_gapic( response, self._transport.operations_client, empty_pb2.Empty, metadata_type=gca_operation.DeleteOperationMetadata, ) # Done; return the response. return response def merge_version_aliases( self, request: Optional[Union[model_service.MergeVersionAliasesRequest, dict]] = None, *, name: Optional[str] = None, version_aliases: Optional[MutableSequence[str]] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model.Model: r"""Merges a set of aliases for a Model version. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_merge_version_aliases(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.MergeVersionAliasesRequest( name="name_value", version_aliases=['version_aliases_value1', 'version_aliases_value2'], ) # Make the request response = client.merge_version_aliases(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.MergeVersionAliasesRequest, dict]): The request object. Request message for [ModelService.MergeVersionAliases][google.cloud.aiplatform.v1.ModelService.MergeVersionAliases]. name (str): Required. The name of the model version to merge aliases, with a version ID explicitly included. Example: ``projects/{project}/locations/{location}/models/{model}@1234`` This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. version_aliases (MutableSequence[str]): Required. The set of version aliases to merge. The alias should be at most 128 characters, and match ``[a-z][a-zA-Z0-9-]{0,126}[a-z-0-9]``. Add the ``-`` prefix to an alias means removing that alias from the version. ``-`` is NOT counted in the 128 characters. Example: ``-golden`` means removing the ``golden`` alias from the version. There is NO ordering in aliases, which means 1) The aliases returned from GetModel API might not have the exactly same order from this MergeVersionAliases API. 2) Adding and deleting the same alias in the request is not recommended, and the 2 operations will be cancelled out. This corresponds to the ``version_aliases`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.Model: A trained machine learning Model. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name, version_aliases] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.MergeVersionAliasesRequest): request = model_service.MergeVersionAliasesRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name if version_aliases is not None: request.version_aliases = version_aliases # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.merge_version_aliases] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def export_model( self, request: Optional[Union[model_service.ExportModelRequest, dict]] = None, *, name: Optional[str] = None, output_config: Optional[model_service.ExportModelRequest.OutputConfig] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gac_operation.Operation: r"""Exports a trained, exportable Model to a location specified by the user. A Model is considered to be exportable if it has at least one [supported export format][google.cloud.aiplatform.v1.Model.supported_export_formats]. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_export_model(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.ExportModelRequest( name="name_value", ) # Make the request operation = client.export_model(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.ExportModelRequest, dict]): The request object. Request message for [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel]. name (str): Required. The resource name of the Model to export. The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be exported. This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. output_config (google.cloud.aiplatform_v1.types.ExportModelRequest.OutputConfig): Required. The desired output location and configuration. This corresponds to the ``output_config`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.aiplatform_v1.types.ExportModelResponse` Response message of [ModelService.ExportModel][google.cloud.aiplatform.v1.ModelService.ExportModel] operation. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name, output_config] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.ExportModelRequest): request = model_service.ExportModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name if output_config is not None: request.output_config = output_config # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.export_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Wrap the response in an operation future. response = gac_operation.from_gapic( response, self._transport.operations_client, model_service.ExportModelResponse, metadata_type=model_service.ExportModelOperationMetadata, ) # Done; return the response. return response def copy_model( self, request: Optional[Union[model_service.CopyModelRequest, dict]] = None, *, parent: Optional[str] = None, source_model: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gac_operation.Operation: r"""Copies an already existing Vertex AI Model into the specified Location. The source Model must exist in the same Project. When copying custom Models, the users themselves are responsible for [Model.metadata][google.cloud.aiplatform.v1.Model.metadata] content to be region-agnostic, as well as making sure that any resources (e.g. files) it depends on remain accessible. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_copy_model(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.CopyModelRequest( model_id="model_id_value", parent="parent_value", source_model="source_model_value", ) # Make the request operation = client.copy_model(request=request) print("Waiting for operation to complete...") response = operation.result() # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.CopyModelRequest, dict]): The request object. Request message for [ModelService.CopyModel][google.cloud.aiplatform.v1.ModelService.CopyModel]. parent (str): Required. The resource name of the Location into which to copy the Model. Format: ``projects/{project}/locations/{location}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. source_model (str): Required. The resource name of the Model to copy. That Model must be in the same Project. Format: ``projects/{project}/locations/{location}/models/{model}`` This corresponds to the ``source_model`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.api_core.operation.Operation: An object representing a long-running operation. The result type for the operation will be :class:`google.cloud.aiplatform_v1.types.CopyModelResponse` Response message of [ModelService.CopyModel][google.cloud.aiplatform.v1.ModelService.CopyModel] operation. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent, source_model] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.CopyModelRequest): request = model_service.CopyModelRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if source_model is not None: request.source_model = source_model # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.copy_model] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Wrap the response in an operation future. response = gac_operation.from_gapic( response, self._transport.operations_client, model_service.CopyModelResponse, metadata_type=model_service.CopyModelOperationMetadata, ) # Done; return the response. return response def import_model_evaluation( self, request: Optional[ Union[model_service.ImportModelEvaluationRequest, dict] ] = None, *, parent: Optional[str] = None, model_evaluation: Optional[gca_model_evaluation.ModelEvaluation] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> gca_model_evaluation.ModelEvaluation: r"""Imports an externally generated ModelEvaluation. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_import_model_evaluation(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.ImportModelEvaluationRequest( parent="parent_value", ) # Make the request response = client.import_model_evaluation(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.ImportModelEvaluationRequest, dict]): The request object. Request message for [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1.ModelService.ImportModelEvaluation] parent (str): Required. The name of the parent model resource. Format: ``projects/{project}/locations/{location}/models/{model}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. model_evaluation (google.cloud.aiplatform_v1.types.ModelEvaluation): Required. Model evaluation resource to be imported. This corresponds to the ``model_evaluation`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.ModelEvaluation: A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent, model_evaluation] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.ImportModelEvaluationRequest): request = model_service.ImportModelEvaluationRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if model_evaluation is not None: request.model_evaluation = model_evaluation # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.import_model_evaluation] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def batch_import_model_evaluation_slices( self, request: Optional[ Union[model_service.BatchImportModelEvaluationSlicesRequest, dict] ] = None, *, parent: Optional[str] = None, model_evaluation_slices: Optional[ MutableSequence[model_evaluation_slice.ModelEvaluationSlice] ] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model_service.BatchImportModelEvaluationSlicesResponse: r"""Imports a list of externally generated ModelEvaluationSlice. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_batch_import_model_evaluation_slices(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.BatchImportModelEvaluationSlicesRequest( parent="parent_value", ) # Make the request response = client.batch_import_model_evaluation_slices(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesRequest, dict]): The request object. Request message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.BatchImportModelEvaluationSlices] parent (str): Required. The name of the parent ModelEvaluation resource. Format: ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. model_evaluation_slices (MutableSequence[google.cloud.aiplatform_v1.types.ModelEvaluationSlice]): Required. Model evaluation slice resource to be imported. This corresponds to the ``model_evaluation_slices`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesResponse: Response message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.BatchImportModelEvaluationSlices] """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent, model_evaluation_slices] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance( request, model_service.BatchImportModelEvaluationSlicesRequest ): request = model_service.BatchImportModelEvaluationSlicesRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if model_evaluation_slices is not None: request.model_evaluation_slices = model_evaluation_slices # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.batch_import_model_evaluation_slices ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def batch_import_evaluated_annotations( self, request: Optional[ Union[model_service.BatchImportEvaluatedAnnotationsRequest, dict] ] = None, *, parent: Optional[str] = None, evaluated_annotations: Optional[ MutableSequence[evaluated_annotation.EvaluatedAnnotation] ] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model_service.BatchImportEvaluatedAnnotationsResponse: r"""Imports a list of externally generated EvaluatedAnnotations. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_batch_import_evaluated_annotations(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.BatchImportEvaluatedAnnotationsRequest( parent="parent_value", ) # Make the request response = client.batch_import_evaluated_annotations(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.BatchImportEvaluatedAnnotationsRequest, dict]): The request object. Request message for [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1.ModelService.BatchImportEvaluatedAnnotations] parent (str): Required. The name of the parent ModelEvaluationSlice resource. Format: ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. evaluated_annotations (MutableSequence[google.cloud.aiplatform_v1.types.EvaluatedAnnotation]): Required. Evaluated annotations resource to be imported. This corresponds to the ``evaluated_annotations`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.BatchImportEvaluatedAnnotationsResponse: Response message for [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1.ModelService.BatchImportEvaluatedAnnotations] """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent, evaluated_annotations] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance( request, model_service.BatchImportEvaluatedAnnotationsRequest ): request = model_service.BatchImportEvaluatedAnnotationsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent if evaluated_annotations is not None: request.evaluated_annotations = evaluated_annotations # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.batch_import_evaluated_annotations ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def get_model_evaluation( self, request: Optional[Union[model_service.GetModelEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model_evaluation.ModelEvaluation: r"""Gets a ModelEvaluation. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_get_model_evaluation(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.GetModelEvaluationRequest( name="name_value", ) # Make the request response = client.get_model_evaluation(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.GetModelEvaluationRequest, dict]): The request object. Request message for [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1.ModelService.GetModelEvaluation]. name (str): Required. The name of the ModelEvaluation resource. Format: ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}`` This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.ModelEvaluation: A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.GetModelEvaluationRequest): request = model_service.GetModelEvaluationRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_model_evaluation] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def list_model_evaluations( self, request: Optional[ Union[model_service.ListModelEvaluationsRequest, dict] ] = None, *, parent: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> pagers.ListModelEvaluationsPager: r"""Lists ModelEvaluations in a Model. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_list_model_evaluations(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.ListModelEvaluationsRequest( parent="parent_value", ) # Make the request page_result = client.list_model_evaluations(request=request) # Handle the response for response in page_result: print(response) Args: request (Union[google.cloud.aiplatform_v1.types.ListModelEvaluationsRequest, dict]): The request object. Request message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1.ModelService.ListModelEvaluations]. parent (str): Required. The resource name of the Model to list the ModelEvaluations from. Format: ``projects/{project}/locations/{location}/models/{model}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationsPager: Response message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1.ModelService.ListModelEvaluations]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.ListModelEvaluationsRequest): request = model_service.ListModelEvaluationsRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_model_evaluations] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelEvaluationsPager( method=rpc, request=request, response=response, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def get_model_evaluation_slice( self, request: Optional[ Union[model_service.GetModelEvaluationSliceRequest, dict] ] = None, *, name: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> model_evaluation_slice.ModelEvaluationSlice: r"""Gets a ModelEvaluationSlice. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_get_model_evaluation_slice(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.GetModelEvaluationSliceRequest( name="name_value", ) # Make the request response = client.get_model_evaluation_slice(request=request) # Handle the response print(response) Args: request (Union[google.cloud.aiplatform_v1.types.GetModelEvaluationSliceRequest, dict]): The request object. Request message for [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1.ModelService.GetModelEvaluationSlice]. name (str): Required. The name of the ModelEvaluationSlice resource. Format: ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}/slices/{slice}`` This corresponds to the ``name`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.types.ModelEvaluationSlice: A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [name] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.GetModelEvaluationSliceRequest): request = model_service.GetModelEvaluationSliceRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if name is not None: request.name = name # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.get_model_evaluation_slice ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def list_model_evaluation_slices( self, request: Optional[ Union[model_service.ListModelEvaluationSlicesRequest, dict] ] = None, *, parent: Optional[str] = None, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> pagers.ListModelEvaluationSlicesPager: r"""Lists ModelEvaluationSlices in a ModelEvaluation. .. code-block:: python # This snippet has been automatically generated and should be regarded as a # code template only. # It will require modifications to work: # - It may require correct/in-range values for request initialization. # - It may require specifying regional endpoints when creating the service # client as shown in: # https://googleapis.dev/python/google-api-core/latest/client_options.html from google.cloud import aiplatform_v1 def sample_list_model_evaluation_slices(): # Create a client client = aiplatform_v1.ModelServiceClient() # Initialize request argument(s) request = aiplatform_v1.ListModelEvaluationSlicesRequest( parent="parent_value", ) # Make the request page_result = client.list_model_evaluation_slices(request=request) # Handle the response for response in page_result: print(response) Args: request (Union[google.cloud.aiplatform_v1.types.ListModelEvaluationSlicesRequest, dict]): The request object. Request message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices]. parent (str): Required. The resource name of the ModelEvaluation to list the ModelEvaluationSlices from. Format: ``projects/{project}/locations/{location}/models/{model}/evaluations/{evaluation}`` This corresponds to the ``parent`` field on the ``request`` instance; if ``request`` is provided, this should not be set. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationSlicesPager: Response message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1.ModelService.ListModelEvaluationSlices]. Iterating over this object will yield results and resolve additional pages automatically. """ # Create or coerce a protobuf request object. # - Quick check: If we got a request object, we should *not* have # gotten any keyword arguments that map to the request. flattened_params = [parent] has_flattened_params = ( len([param for param in flattened_params if param is not None]) > 0 ) if request is not None and has_flattened_params: raise ValueError( "If the `request` argument is set, then none of " "the individual field arguments should be set." ) # - Use the request object if provided (there's no risk of modifying the input as # there are no flattened fields), or create one. if not isinstance(request, model_service.ListModelEvaluationSlicesRequest): request = model_service.ListModelEvaluationSlicesRequest(request) # If we have keyword arguments corresponding to fields on the # request, apply these. if parent is not None: request.parent = parent # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[ self._transport.list_model_evaluation_slices ] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("parent", request.parent),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # This method is paged; wrap the response in a pager, which provides # an `__iter__` convenience method. response = pagers.ListModelEvaluationSlicesPager( method=rpc, request=request, response=response, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response def __enter__(self) -> "ModelServiceClient": return self def __exit__(self, type, value, traceback): """Releases underlying transport's resources. .. warning:: ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients! """ self.transport.close() def list_operations( self, request: Optional[operations_pb2.ListOperationsRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> operations_pb2.ListOperationsResponse: r"""Lists operations that match the specified filter in the request. Args: request (:class:`~.operations_pb2.ListOperationsRequest`): The request object. Request message for `ListOperations` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.operations_pb2.ListOperationsResponse: Response message for ``ListOperations`` method. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = operations_pb2.ListOperationsRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_operations] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e def get_operation( self, request: Optional[operations_pb2.GetOperationRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> operations_pb2.Operation: r"""Gets the latest state of a long-running operation. Args: request (:class:`~.operations_pb2.GetOperationRequest`): The request object. Request message for `GetOperation` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.operations_pb2.Operation: An ``Operation`` object. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = operations_pb2.GetOperationRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_operation] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e def delete_operation( self, request: Optional[operations_pb2.DeleteOperationRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> None: r"""Deletes a long-running operation. This method indicates that the client is no longer interested in the operation result. It does not cancel the operation. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Args: request (:class:`~.operations_pb2.DeleteOperationRequest`): The request object. Request message for `DeleteOperation` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: None """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = operations_pb2.DeleteOperationRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.delete_operation] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) def cancel_operation( self, request: Optional[operations_pb2.CancelOperationRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> None: r"""Starts asynchronous cancellation on a long-running operation. The server makes a best effort to cancel the operation, but success is not guaranteed. If the server doesn't support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Args: request (:class:`~.operations_pb2.CancelOperationRequest`): The request object. Request message for `CancelOperation` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: None """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = operations_pb2.CancelOperationRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.cancel_operation] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() # Send the request. rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) def wait_operation( self, request: Optional[operations_pb2.WaitOperationRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> operations_pb2.Operation: r"""Waits until the specified long-running operation is done or reaches at most a specified timeout, returning the latest state. If the operation is already done, the latest state is immediately returned. If the timeout specified is greater than the default HTTP/RPC timeout, the HTTP/RPC timeout is used. If the server does not support this method, it returns `google.rpc.Code.UNIMPLEMENTED`. Args: request (:class:`~.operations_pb2.WaitOperationRequest`): The request object. Request message for `WaitOperation` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.operations_pb2.Operation: An ``Operation`` object. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = operations_pb2.WaitOperationRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.wait_operation] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e def set_iam_policy( self, request: Optional[iam_policy_pb2.SetIamPolicyRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> policy_pb2.Policy: r"""Sets the IAM access control policy on the specified function. Replaces any existing policy. Args: request (:class:`~.iam_policy_pb2.SetIamPolicyRequest`): The request object. Request message for `SetIamPolicy` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.policy_pb2.Policy: Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A ``Policy`` is a collection of ``bindings``. A ``binding`` binds one or more ``members`` to a single ``role``. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A ``role`` is a named list of permissions (defined by IAM or configured by users). A ``binding`` can optionally specify a ``condition``, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource. **JSON Example** :: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": ["user:eve@example.com"], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ] } **YAML Example** :: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') For a description of IAM and its features, see the `IAM developer's guide `__. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = iam_policy_pb2.SetIamPolicyRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.set_iam_policy] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("resource", request.resource),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e def get_iam_policy( self, request: Optional[iam_policy_pb2.GetIamPolicyRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> policy_pb2.Policy: r"""Gets the IAM access control policy for a function. Returns an empty policy if the function exists and does not have a policy set. Args: request (:class:`~.iam_policy_pb2.GetIamPolicyRequest`): The request object. Request message for `GetIamPolicy` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.policy_pb2.Policy: Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A ``Policy`` is a collection of ``bindings``. A ``binding`` binds one or more ``members`` to a single ``role``. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A ``role`` is a named list of permissions (defined by IAM or configured by users). A ``binding`` can optionally specify a ``condition``, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource. **JSON Example** :: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": ["user:eve@example.com"], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01T00:00:00.000Z')", } } ] } **YAML Example** :: bindings: - members: - user:mike@example.com - group:admins@example.com - domain:google.com - serviceAccount:my-project-id@appspot.gserviceaccount.com role: roles/resourcemanager.organizationAdmin - members: - user:eve@example.com role: roles/resourcemanager.organizationViewer condition: title: expirable access description: Does not grant access after Sep 2020 expression: request.time < timestamp('2020-10-01T00:00:00.000Z') For a description of IAM and its features, see the `IAM developer's guide `__. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = iam_policy_pb2.GetIamPolicyRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_iam_policy] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("resource", request.resource),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e def test_iam_permissions( self, request: Optional[iam_policy_pb2.TestIamPermissionsRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> iam_policy_pb2.TestIamPermissionsResponse: r"""Tests the specified IAM permissions against the IAM access control policy for a function. If the function does not exist, this will return an empty set of permissions, not a NOT_FOUND error. Args: request (:class:`~.iam_policy_pb2.TestIamPermissionsRequest`): The request object. Request message for `TestIamPermissions` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.iam_policy_pb2.TestIamPermissionsResponse: Response message for ``TestIamPermissions`` method. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = iam_policy_pb2.TestIamPermissionsRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.test_iam_permissions] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("resource", request.resource),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e def get_location( self, request: Optional[locations_pb2.GetLocationRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> locations_pb2.Location: r"""Gets information about a location. Args: request (:class:`~.location_pb2.GetLocationRequest`): The request object. Request message for `GetLocation` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.location_pb2.Location: Location object. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = locations_pb2.GetLocationRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.get_location] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e def list_locations( self, request: Optional[locations_pb2.ListLocationsRequest] = None, *, retry: OptionalRetry = gapic_v1.method.DEFAULT, timeout: Union[float, object] = gapic_v1.method.DEFAULT, metadata: Sequence[Tuple[str, Union[str, bytes]]] = (), ) -> locations_pb2.ListLocationsResponse: r"""Lists information about the supported locations for this service. Args: request (:class:`~.location_pb2.ListLocationsRequest`): The request object. Request message for `ListLocations` method. retry (google.api_core.retry.Retry): Designation of what errors, if any, should be retried. timeout (float): The timeout for this request. metadata (Sequence[Tuple[str, Union[str, bytes]]]): Key/value pairs which should be sent along with the request as metadata. Normally, each value must be of type `str`, but for metadata keys ending with the suffix `-bin`, the corresponding values must be of type `bytes`. Returns: ~.location_pb2.ListLocationsResponse: Response message for ``ListLocations`` method. """ # Create or coerce a protobuf request object. # The request isn't a proto-plus wrapped type, # so it must be constructed via keyword expansion. if isinstance(request, dict): request = locations_pb2.ListLocationsRequest(**request) # Wrap the RPC method; this adds retry and timeout information, # and friendly error handling. rpc = self._transport._wrapped_methods[self._transport.list_locations] # Certain fields should be provided within the metadata header; # add these here. metadata = tuple(metadata) + ( gapic_v1.routing_header.to_grpc_metadata((("name", request.name),)), ) # Validate the universe domain. self._validate_universe_domain() try: # Send the request. response = rpc( request, retry=retry, timeout=timeout, metadata=metadata, ) # Done; return the response. return response except core_exceptions.GoogleAPICallError as e: self._add_cred_info_for_auth_errors(e) raise e DEFAULT_CLIENT_INFO = gapic_v1.client_info.ClientInfo( gapic_version=package_version.__version__ ) if hasattr(DEFAULT_CLIENT_INFO, "protobuf_runtime_version"): # pragma: NO COVER DEFAULT_CLIENT_INFO.protobuf_runtime_version = google.protobuf.__version__ __all__ = ("ModelServiceClient",)