Class ModelServiceClient (1.66.0) 

ModelServiceClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport, typing.Callable[[...], google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport]]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

A service for managing Vertex AI's machine learning Models.

Properties

api_endpoint

Return the API endpoint used by the client instance.

Returns
Type Description
str The API endpoint used by the client instance.

transport

Returns the transport used by the client instance.

Returns
Type Description
ModelServiceTransport The transport used by the client instance.

universe_domain

Return the universe domain used by the client instance.

Returns
Type Description
str The universe domain used by the client instance.

Methods

ModelServiceClient

ModelServiceClient(*, credentials: typing.Optional[google.auth.credentials.Credentials] = None, transport: typing.Optional[typing.Union[str, google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport, typing.Callable[[...], google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport]]] = None, client_options: typing.Optional[typing.Union[google.api_core.client_options.ClientOptions, dict]] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)

Instantiates the model service client.

Parameters
Name Description
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.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If mutual TLS transport creation failed for any reason.

__exit__

__exit__(type, value, traceback)

Releases underlying transport's resources.

batch_import_evaluated_annotations

batch_import_evaluated_annotations(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.BatchImportEvaluatedAnnotationsRequest,
            dict,
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    evaluated_annotations: typing.Optional[
        typing.MutableSequence[
            google.cloud.aiplatform_v1.types.evaluated_annotation.EvaluatedAnnotation
        ]
    ] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
    google.cloud.aiplatform_v1.types.model_service.BatchImportEvaluatedAnnotationsResponse
)

Imports a list of externally generated EvaluatedAnnotations.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.BatchImportEvaluatedAnnotationsRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.BatchImportEvaluatedAnnotationsResponse Response message for ModelService.BatchImportEvaluatedAnnotations

batch_import_model_evaluation_slices

batch_import_model_evaluation_slices(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.BatchImportModelEvaluationSlicesRequest,
            dict,
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    model_evaluation_slices: typing.Optional[
        typing.MutableSequence[
            google.cloud.aiplatform_v1.types.model_evaluation_slice.ModelEvaluationSlice
        ]
    ] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
    google.cloud.aiplatform_v1.types.model_service.BatchImportModelEvaluationSlicesResponse
)

Imports a list of externally generated ModelEvaluationSlice.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesResponse Response message for ModelService.BatchImportModelEvaluationSlices

cancel_operation

cancel_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.CancelOperationRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None

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.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.

common_billing_account_path

common_billing_account_path(billing_account: str) -> str

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str) -> str

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str) -> str

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str) -> str

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str) -> str

Returns a fully-qualified project string.

copy_model

copy_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.CopyModelRequest, dict
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    source_model: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

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 xref_Model.metadata content to be region-agnostic, as well as making sure that any resources (e.g. files) it depends on remain accessible.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.CopyModelRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be CopyModelResponse Response message of ModelService.CopyModel operation.

delete_model

delete_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.DeleteModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Deletes a Model.

A model cannot be deleted if any xref_Endpoint resource has a xref_DeployedModel based on the model in its xref_deployed_models field.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteModelRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be 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); }

delete_model_version

delete_model_version(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.DeleteModelVersionRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Deletes a Model version.

Model version can only be deleted if there are no xref_DeployedModels created from it. Deleting the only version in the Model is not allowed. Use xref_DeleteModel for deleting the Model instead.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.DeleteModelVersionRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be 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); }

delete_operation

delete_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.DeleteOperationRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> None

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.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.

endpoint_path

endpoint_path(project: str, location: str, endpoint: str) -> str

Returns a fully-qualified endpoint string.

export_model

export_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.ExportModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    output_config: typing.Optional[
        google.cloud.aiplatform_v1.types.model_service.ExportModelRequest.OutputConfig
    ] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

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].

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ExportModelRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be ExportModelResponse Response message of ModelService.ExportModel operation.

from_service_account_file

from_service_account_file(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
ModelServiceClient The constructed client.

from_service_account_info

from_service_account_info(info: dict, *args, **kwargs)

Creates an instance of this client using the provided credentials info.

Parameter
Name Description
info dict

The service account private key info.
Returns
Type Description
ModelServiceClient The constructed client.

from_service_account_json

from_service_account_json(filename: str, *args, **kwargs)

Creates an instance of this client using the provided credentials file.

Parameter
Name Description
filename str

The path to the service account private key json file.
Returns
Type Description
ModelServiceClient The constructed client.

get_iam_policy

get_iam_policy(
    request: typing.Optional[google.iam.v1.iam_policy_pb2.GetIamPolicyRequest] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.policy_pb2.Policy

Gets the IAM access control policy for a function.

Returns an empty policy if the function exists and does not have a policy set.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.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 __.

get_location

get_location(
    request: typing.Optional[
        google.cloud.location.locations_pb2.GetLocationRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.location.locations_pb2.Location

Gets information about a location.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.location_pb2.Location Location object.

get_model

get_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.GetModelRequest, dict
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.types.model.Model

Gets a Model.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetModelRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.Model A trained machine learning Model.

get_model_evaluation

get_model_evaluation(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.GetModelEvaluationRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.types.model_evaluation.ModelEvaluation

Gets a ModelEvaluation.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetModelEvaluationRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
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.

get_model_evaluation_slice

get_model_evaluation_slice(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.GetModelEvaluationSliceRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.types.model_evaluation_slice.ModelEvaluationSlice

Gets a ModelEvaluationSlice.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.GetModelEvaluationSliceRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
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.

get_mtls_endpoint_and_cert_source

get_mtls_endpoint_and_cert_source(
    client_options: typing.Optional[
        google.api_core.client_options.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.

Parameter
Name Description
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.
Exceptions
Type Description
google.auth.exceptions.MutualTLSChannelError If any errors happen.
Returns
Type Description
Tuple[str, Callable[[], Tuple[bytes, bytes]]] returns the API endpoint and the client cert source to use.

get_operation

get_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.GetOperationRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.Operation

Gets the latest state of a long-running operation.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.operations_pb2.Operation An Operation object.

import_model_evaluation

import_model_evaluation(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.ImportModelEvaluationRequest,
            dict,
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    model_evaluation: typing.Optional[
        google.cloud.aiplatform_v1.types.model_evaluation.ModelEvaluation
    ] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.types.model_evaluation.ModelEvaluation

Imports an externally generated ModelEvaluation.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ImportModelEvaluationRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
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.

list_locations

list_locations(
    request: typing.Optional[
        google.cloud.location.locations_pb2.ListLocationsRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.location.locations_pb2.ListLocationsResponse

Lists information about the supported locations for this service.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.location_pb2.ListLocationsResponse Response message for ListLocations method.

list_model_evaluation_slices

list_model_evaluation_slices(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.ListModelEvaluationSlicesRequest,
            dict,
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> (
    google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationSlicesPager
)

Lists ModelEvaluationSlices in a ModelEvaluation.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListModelEvaluationSlicesRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationSlicesPager Response message for ModelService.ListModelEvaluationSlices. Iterating over this object will yield results and resolve additional pages automatically.

list_model_evaluations

list_model_evaluations(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.ListModelEvaluationsRequest,
            dict,
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationsPager

Lists ModelEvaluations in a Model.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListModelEvaluationsRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationsPager Response message for ModelService.ListModelEvaluations. Iterating over this object will yield results and resolve additional pages automatically.

list_model_versions

list_model_versions(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.ListModelVersionsRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.services.model_service.pagers.ListModelVersionsPager

Lists versions of the specified model.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListModelVersionsRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelVersionsPager Response message for ModelService.ListModelVersions Iterating over this object will yield results and resolve additional pages automatically.

list_models

list_models(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.ListModelsRequest, dict
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.services.model_service.pagers.ListModelsPager

Lists Models in a Location.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.ListModelsRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelsPager Response message for ModelService.ListModels Iterating over this object will yield results and resolve additional pages automatically.

list_operations

list_operations(
    request: typing.Optional[
        google.longrunning.operations_pb2.ListOperationsRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.ListOperationsResponse

Lists operations that match the specified filter in the request.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.operations_pb2.ListOperationsResponse Response message for ListOperations method.

merge_version_aliases

merge_version_aliases(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.MergeVersionAliasesRequest,
            dict,
        ]
    ] = None,
    *,
    name: typing.Optional[str] = None,
    version_aliases: typing.Optional[typing.MutableSequence[str]] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.types.model.Model

Merges a set of aliases for a Model version.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.MergeVersionAliasesRequest, dict]

The request object. Request message for 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 `<code>a-z][a-zA-Z0-9-]</code>{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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.Model A trained machine learning Model.

model_evaluation_path

model_evaluation_path(
    project: str, location: str, model: str, evaluation: str
) -> str

Returns a fully-qualified model_evaluation string.

model_evaluation_slice_path

model_evaluation_slice_path(
    project: str, location: str, model: str, evaluation: str, slice: str
) -> str

Returns a fully-qualified model_evaluation_slice string.

model_path

model_path(project: str, location: str, model: str) -> str

Returns a fully-qualified model string.

parse_common_billing_account_path

parse_common_billing_account_path(path: str) -> typing.Dict[str, str]

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str) -> typing.Dict[str, str]

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str) -> typing.Dict[str, str]

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str) -> typing.Dict[str, str]

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str) -> typing.Dict[str, str]

Parse a project path into its component segments.

parse_endpoint_path

parse_endpoint_path(path: str) -> typing.Dict[str, str]

Parses a endpoint path into its component segments.

parse_model_evaluation_path

parse_model_evaluation_path(path: str) -> typing.Dict[str, str]

Parses a model_evaluation path into its component segments.

parse_model_evaluation_slice_path

parse_model_evaluation_slice_path(path: str) -> typing.Dict[str, str]

Parses a model_evaluation_slice path into its component segments.

parse_model_path

parse_model_path(path: str) -> typing.Dict[str, str]

Parses a model path into its component segments.

parse_pipeline_job_path

parse_pipeline_job_path(path: str) -> typing.Dict[str, str]

Parses a pipeline_job path into its component segments.

parse_training_pipeline_path

parse_training_pipeline_path(path: str) -> typing.Dict[str, str]

Parses a training_pipeline path into its component segments.

pipeline_job_path

pipeline_job_path(project: str, location: str, pipeline_job: str) -> str

Returns a fully-qualified pipeline_job string.

set_iam_policy

set_iam_policy(
    request: typing.Optional[google.iam.v1.iam_policy_pb2.SetIamPolicyRequest] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.policy_pb2.Policy

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.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 __.

test_iam_permissions

test_iam_permissions(
    request: typing.Optional[
        google.iam.v1.iam_policy_pb2.TestIamPermissionsRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.iam.v1.iam_policy_pb2.TestIamPermissionsResponse

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.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.iam_policy_pb2.TestIamPermissionsResponse Response message for TestIamPermissions method.

training_pipeline_path

training_pipeline_path(project: str, location: str, training_pipeline: str) -> str

Returns a fully-qualified training_pipeline string.

update_explanation_dataset

update_explanation_dataset(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.UpdateExplanationDatasetRequest,
            dict,
        ]
    ] = None,
    *,
    model: typing.Optional[str] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Incrementally update the dataset used for an examples model.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateExplanationDatasetRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be UpdateExplanationDatasetResponse Response message of ModelService.UpdateExplanationDataset operation.

update_model

update_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.UpdateModelRequest, dict
        ]
    ] = None,
    *,
    model: typing.Optional[google.cloud.aiplatform_v1.types.model.Model] = None,
    update_mask: typing.Optional[google.protobuf.field_mask_pb2.FieldMask] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.cloud.aiplatform_v1.types.model.Model

Updates a Model.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UpdateModelRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.cloud.aiplatform_v1.types.Model A trained machine learning Model.

upload_model

upload_model(
    request: typing.Optional[
        typing.Union[
            google.cloud.aiplatform_v1.types.model_service.UploadModelRequest, dict
        ]
    ] = None,
    *,
    parent: typing.Optional[str] = None,
    model: typing.Optional[google.cloud.aiplatform_v1.types.model.Model] = None,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.api_core.operation.Operation

Uploads a Model artifact into Vertex AI.

# 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)
Parameters
Name Description
request Union[google.cloud.aiplatform_v1.types.UploadModelRequest, dict]

The request object. Request message for 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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
google.api_core.operation.Operation An object representing a long-running operation. The result type for the operation will be UploadModelResponse Response message of ModelService.UploadModel operation.

wait_operation

wait_operation(
    request: typing.Optional[
        google.longrunning.operations_pb2.WaitOperationRequest
    ] = None,
    *,
    retry: typing.Optional[
        typing.Union[
            google.api_core.retry.retry_unary.Retry,
            google.api_core.gapic_v1.method._MethodDefault,
        ]
    ] = _MethodDefault._DEFAULT_VALUE,
    timeout: typing.Union[float, object] = _MethodDefault._DEFAULT_VALUE,
    metadata: typing.Sequence[typing.Tuple[str, str]] = ()
) -> google.longrunning.operations_pb2.Operation

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.

Parameters
Name Description
request .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, str]]

Strings which should be sent along with the request as metadata.
Returns
Type Description
.operations_pb2.Operation An Operation object.