Class ModelServiceClient (1.13.1) 

ModelServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = 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.

Inheritance

builtins.object > ModelServiceClient

Properties

transport

Returns the transport used by the client instance.

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

Methods

ModelServiceClient

ModelServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = 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 Union[str, ModelServiceTransport]

The transport to use. If set to None, a transport is chosen automatically.
client_options google.api_core.client_options.ClientOptions

Custom options for the client. It won't take effect if a transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "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). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS 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.
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.

common_billing_account_path

common_billing_account_path(billing_account: str)

Returns a fully-qualified billing_account string.

common_folder_path

common_folder_path(folder: str)

Returns a fully-qualified folder string.

common_location_path

common_location_path(project: str, location: str)

Returns a fully-qualified location string.

common_organization_path

common_organization_path(organization: str)

Returns a fully-qualified organization string.

common_project_path

common_project_path(project: str)

Returns a fully-qualified project string.

delete_model

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

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.

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); } The JSON representation for Empty is empty JSON object {}.

endpoint_path

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

Returns a fully-qualified endpoint string.

export_model

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

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].

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. 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_model

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

Gets a Model.

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} 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: Optional[Union[google.cloud.aiplatform_v1.types.model_service.GetModelEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Gets a ModelEvaluation.

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

Gets a ModelEvaluationSlice.

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: Optional[google.api_core.client_options.ClientOptions] = None,
)

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 variabel 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.

import_model_evaluation

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

Imports an externally generated ModelEvaluation.

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_model_evaluation_slices

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

Lists ModelEvaluationSlices in a ModelEvaluation.

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: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ListModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())

Lists ModelEvaluations in a Model.

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_models

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

Lists Models in a Location.

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.

model_evaluation_path

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

Returns a fully-qualified model_evaluation_slice string.

model_path

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

Returns a fully-qualified model string.

parse_common_billing_account_path

parse_common_billing_account_path(path: str)

Parse a billing_account path into its component segments.

parse_common_folder_path

parse_common_folder_path(path: str)

Parse a folder path into its component segments.

parse_common_location_path

parse_common_location_path(path: str)

Parse a location path into its component segments.

parse_common_organization_path

parse_common_organization_path(path: str)

Parse a organization path into its component segments.

parse_common_project_path

parse_common_project_path(path: str)

Parse a project path into its component segments.

parse_endpoint_path

parse_endpoint_path(path: str)

Parses a endpoint path into its component segments.

parse_model_evaluation_path

parse_model_evaluation_path(path: str)

Parses a model_evaluation path into its component segments.

parse_model_evaluation_slice_path

parse_model_evaluation_slice_path(path: str)

Parses a model_evaluation_slice path into its component segments.

parse_model_path

parse_model_path(path: str)

Parses a model path into its component segments.

parse_training_pipeline_path

parse_training_pipeline_path(path: str)

Parses a training_pipeline path into its component segments.

training_pipeline_path

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

Returns a fully-qualified training_pipeline string.

update_model

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

Updates a Model.

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

Uploads a Model artifact into Vertex AI.

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.