ModelService¶

async delete_model(request: Optional[Union[DeleteModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a Model.

A model cannot be deleted if any [Endpoint][google.cloud.aiplatform.v1beta1.Endpoint] resource has a [DeployedModel][google.cloud.aiplatform.v1beta1.DeployedModel] based on the model in its [deployed_models][google.cloud.aiplatform.v1beta1.Endpoint.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_v1beta1

async def sample_delete_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteModelRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_model(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteModelRequest, dict]]) – The request object. Request message for [ModelService.DeleteModel][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

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);

}

Return type:

async delete_model_version(request: Optional[Union[DeleteModelVersionRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

Deletes a Model version.

Model version can only be deleted if there are no [DeployedModels][google.cloud.aiplatform.v1beta1.DeployedModel] created from it. Deleting the only version in the Model is not allowed. Use [DeleteModel][google.cloud.aiplatform.v1beta1.ModelService.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_v1beta1

async def sample_delete_model_version():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.DeleteModelVersionRequest(
        name="name_value",
    )

    # Make the request
    operation = client.delete_model_version(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.DeleteModelVersionRequest, dict]]) – The request object. Request message for [ModelService.DeleteModelVersion][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

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);

}

Return type:

async delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

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:

request (DeleteOperationRequest) – The request object. Request message for DeleteOperation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

None

static endpoint_path(project: str, location: str, endpoint: str) → str¶: Returns a fully-qualified endpoint string.

async export_model(request: Optional[Union[ExportModelRequest, dict]] = None, *, name: Optional[str] = None, output_config: Optional[OutputConfig] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

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.v1beta1.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_v1beta1

async def sample_export_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ExportModelRequest(
        name="name_value",
    )

    # Make the request
    operation = client.export_model(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ExportModelRequest, dict]]) – The request object. Request message for [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.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_v1beta1.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_async.AsyncRetry) – 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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.ExportModelResponse Response message of: [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.ModelService.ExportModel] operation.

Return type:

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]¶

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelServiceAsyncClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]¶

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

Parameters:

info (dict) – The service account private key info.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelServiceAsyncClient

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

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelServiceAsyncClient

async get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

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:

request (GetIamPolicyRequest) – The request object. Request message for GetIamPolicy method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

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.

Return type:

Policy

async get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location[source]¶

Gets information about a location.

Parameters:

request (GetLocationRequest) – The request object. Request message for GetLocation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Location object.

Return type:

Location

async get_model(request: Optional[Union[GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Model[source]¶

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_v1beta1

async def sample_get_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetModelRequest, dict]]) – The request object. Request message for [ModelService.GetModel][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

A trained machine learning Model.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelEvaluation

async get_model_evaluation(request: Optional[Union[GetModelEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelEvaluation[source]¶

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_v1beta1

async def sample_get_model_evaluation():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelEvaluationRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model_evaluation(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetModelEvaluationRequest, dict]]) – The request object. Request message for [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

A collection of metrics calculated by comparing Model’s predictions on all of the test data against annotations from the test data.

Return type:

async get_model_evaluation_slice(request: Optional[Union[GetModelEvaluationSliceRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelEvaluationSlice[source]¶

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_v1beta1

async def sample_get_model_evaluation_slice():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelEvaluationSliceRequest(
        name="name_value",
    )

    # Make the request
    response = await client.get_model_evaluation_slice(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.GetModelEvaluationSliceRequest, dict]]) – The request object. Request message for [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

A collection of metrics calculated by comparing Model’s predictions on a slice of the test data against ground truth annotations.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelEvaluationSlice

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)[source]¶

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.

Parameters:

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns:

returns the API endpoint and the: client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

async get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Gets the latest state of a long-running operation.

Parameters:

request (GetOperationRequest) – The request object. Request message for GetOperation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

An Operation object.

Return type:

Operation

classmethod get_transport_class(label: Optional[str] = None) → Type[ModelServiceTransport]¶

Returns an appropriate transport class.

Parameters:: label – The name of the desired transport. If none is provided, then the first transport in the registry is used.
Returns:: The transport class to use.

async import_model_evaluation(request: Optional[Union[ImportModelEvaluationRequest, dict]] = None, *, parent: Optional[str] = None, model_evaluation: Optional[ModelEvaluation] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelEvaluation[source]¶

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_v1beta1

async def sample_import_model_evaluation():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ImportModelEvaluationRequest(
        parent="parent_value",
    )

    # Make the request
    response = await client.import_model_evaluation(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ImportModelEvaluationRequest, dict]]) – The request object. Request message for [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1beta1.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_v1beta1.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_async.AsyncRetry) – 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:

A collection of metrics calculated by comparing Model’s predictions on all of the test data against annotations from the test data.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelEvaluation

async list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse[source]¶

Lists information about the supported locations for this service.

Parameters:

request (ListLocationsRequest) – The request object. Request message for ListLocations method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for ListLocations method.

Return type:

ListLocationsResponse

async list_model_evaluation_slices(request: Optional[Union[ListModelEvaluationSlicesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelEvaluationSlicesAsyncPager[source]¶

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_v1beta1

async def sample_list_model_evaluation_slices():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelEvaluationSlicesRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_model_evaluation_slices(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListModelEvaluationSlicesRequest, dict]]) – The request object. Request message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluationSlices].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelEvaluationSlicesAsyncPager

async list_model_evaluations(request: Optional[Union[ListModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelEvaluationsAsyncPager[source]¶

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_v1beta1

async def sample_list_model_evaluations():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelEvaluationsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_model_evaluations(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListModelEvaluationsRequest, dict]]) – The request object. Request message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluations].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelEvaluationsAsyncPager

async list_model_versions(request: Optional[Union[ListModelVersionsRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelVersionsAsyncPager[source]¶

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_v1beta1

async def sample_list_model_versions():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelVersionsRequest(
        name="name_value",
    )

    # Make the request
    page_result = client.list_model_versions(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListModelVersionsRequest, dict]]) – The request object. Request message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.ModelService.ListModelVersions]

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelVersionsAsyncPager

async list_models(request: Optional[Union[ListModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelsAsyncPager[source]¶

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_v1beta1

async def sample_list_models():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ListModelsRequest(
        parent="parent_value",
    )

    # Make the request
    page_result = client.list_models(request=request)

    # Handle the response
    async for response in page_result:
        print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.ListModelsRequest, dict]]) – The request object. Request message for [ModelService.ListModels][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

Response message for: [ModelService.ListModels][google.cloud.aiplatform.v1beta1.ModelService.ListModels]

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelsAsyncPager

async list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse[source]¶

Lists operations that match the specified filter in the request.

Parameters:

request (ListOperationsRequest) – The request object. Request message for ListOperations method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for ListOperations method.

Return type:

ListOperationsResponse

async merge_version_aliases(request: Optional[Union[MergeVersionAliasesRequest, dict]] = None, *, name: Optional[str] = None, version_aliases: Optional[MutableSequence[str]] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Model[source]¶

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_v1beta1

async def sample_merge_version_aliases():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.MergeVersionAliasesRequest(
        name="name_value",
        version_aliases=['version_aliases_value1', 'version_aliases_value2'],
    )

    # Make the request
    response = await client.merge_version_aliases(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.MergeVersionAliasesRequest, dict]]) – The request object. Request message for [ModelService.MergeVersionAliases][google.cloud.aiplatform.v1beta1.ModelService.MergeVersionAliases].
name (str) –
Required. The name of the model version to merge aliases, with a version ID explicitly included.

Example: projects/{project}/locations/{location}/models/{model}@1234

This corresponds to the name field on the request instance; if request is provided, this should not be set.
version_aliases (MutableSequence[str]) –
Required. The set of version aliases to merge. The alias should be at most 128 characters, and match [a-z][a-zA-Z0-9-]{0,126}[a-z-0-9]. Add the - prefix to an alias means removing that alias from the version. - is NOT counted in the 128 characters. Example: -golden means removing the golden alias from the version.

There is NO ordering in aliases, which means
1. The aliases returned from GetModel API might not have the exactly same order from this MergeVersionAliases API. 2) Adding and deleting the same alias in the request is not recommended, and the 2 operations will be cancelled out.
This corresponds to the version_aliases field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry_async.AsyncRetry) – 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:

A trained machine learning Model.

Return type:

static model_evaluation_path(project: str, location: str, model: str, evaluation: str) → str¶: Returns a fully-qualified model_evaluation string.

static model_evaluation_slice_path(project: str, location: str, model: str, evaluation: str, slice: str) → str¶: Returns a fully-qualified model_evaluation_slice string.

static model_path(project: str, location: str, model: str) → str¶: Returns a fully-qualified model string.

static parse_common_billing_account_path(path: str) → Dict[str, str]¶: Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str]¶: Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str]¶: Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str]¶: Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str]¶: Parse a project path into its component segments.

static parse_endpoint_path(path: str) → Dict[str, str]¶: Parses a endpoint path into its component segments.

static parse_model_evaluation_path(path: str) → Dict[str, str]¶: Parses a model_evaluation path into its component segments.

static parse_model_evaluation_slice_path(path: str) → Dict[str, str]¶: Parses a model_evaluation_slice path into its component segments.

static parse_model_path(path: str) → Dict[str, str]¶: Parses a model path into its component segments.

static parse_training_pipeline_path(path: str) → Dict[str, str]¶: Parses a training_pipeline path into its component segments.

async set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

request (SetIamPolicyRequest) – The request object. Request message for SetIamPolicy method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

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.

Return type:

Policy

async test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse[source]¶

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:

request (TestIamPermissionsRequest) – The request object. Request message for TestIamPermissions method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

static training_pipeline_path(project: str, location: str, training_pipeline: str) → str¶: Returns a fully-qualified training_pipeline string.

property transport: ModelServiceTransport¶

Returns the transport used by the client instance.

Returns:: The transport used by the client instance.
Return type:: ModelServiceTransport

property universe_domain: str¶

Return the universe domain used by the client instance.

Returns:

The universe domain used: by the client instance.

Return type:

str

async update_explanation_dataset(request: Optional[Union[UpdateExplanationDatasetRequest, dict]] = None, *, model: Optional[str] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

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_v1beta1

async def sample_update_explanation_dataset():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.UpdateExplanationDatasetRequest(
        model="model_value",
    )

    # Make the request
    operation = client.update_explanation_dataset(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateExplanationDatasetRequest, dict]]) – The request object. Request message for [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1beta1.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_async.AsyncRetry) – 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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.UpdateExplanationDatasetResponse Response message of: [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1beta1.ModelService.UpdateExplanationDataset] operation.

Return type:

async update_model(request: Optional[Union[UpdateModelRequest, dict]] = None, *, model: Optional[Model] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Model[source]¶

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_v1beta1

async def sample_update_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    model = aiplatform_v1beta1.Model()
    model.display_name = "display_name_value"

    request = aiplatform_v1beta1.UpdateModelRequest(
        model=model,
    )

    # Make the request
    response = await client.update_model(request=request)

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UpdateModelRequest, dict]]) – The request object. Request message for [ModelService.UpdateModel][google.cloud.aiplatform.v1beta1.ModelService.UpdateModel].
model (google.cloud.aiplatform_v1beta1.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_async.AsyncRetry) – 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:

A trained machine learning Model.

Return type:

async upload_model(request: Optional[Union[UploadModelRequest, dict]] = None, *, parent: Optional[str] = None, model: Optional[Model] = None, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → AsyncOperation[source]¶

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_v1beta1

async def sample_upload_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceAsyncClient()

    # Initialize request argument(s)
    model = aiplatform_v1beta1.Model()
    model.display_name = "display_name_value"

    request = aiplatform_v1beta1.UploadModelRequest(
        parent="parent_value",
        model=model,
    )

    # Make the request
    operation = client.upload_model(request=request)

    print("Waiting for operation to complete...")

    response = (await operation).result()

    # Handle the response
    print(response)

Parameters:

request (Optional[Union[google.cloud.aiplatform_v1beta1.types.UploadModelRequest, dict]]) – The request object. Request message for [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.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_v1beta1.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_async.AsyncRetry) – 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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.UploadModelResponse Response message of: [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.ModelService.UploadModel] operation.

Return type:

google.cloud.aiplatform_v1beta1.types.BatchImportEvaluatedAnnotationsResponse

async wait_operation(request: Optional[WaitOperationRequest] = None, *, retry: Optional[Union[AsyncRetry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

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:

request (WaitOperationRequest) – The request object. Request message for WaitOperation method.
retry (google.api_core.retry_async.AsyncRetry) – 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:

An Operation object.

Return type:

Operation

class google.cloud.aiplatform_v1beta1.services.model_service.ModelServiceClient(*, credentials: ~typing.Optional[~google.auth.credentials.Credentials] = None, transport: ~typing.Optional[~typing.Union[str, ~google.cloud.aiplatform_v1beta1.services.model_service.transports.base.ModelServiceTransport, ~typing.Callable[[...], ~google.cloud.aiplatform_v1beta1.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>)[source]¶

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

Instantiates the model service client.

Parameters:

credentials (Optional[google.auth.credentials.Credentials]) – The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.
transport (Optional[Union[str,ModelServiceTransport,Callable[..., ModelServiceTransport]]]) – The transport to use, or a Callable that constructs and returns a new transport. If a Callable is given, it will be called with the same set of initialization arguments as used in the ModelServiceTransport constructor. If set to None, a transport is chosen automatically.
client_options (Optional[Union[google.api_core.client_options.ClientOptions, dict]]) –
Custom options for the client.

1. The api_endpoint property can be used to override the default endpoint provided by the client when transport is not explicitly provided. Only if this property is not set and transport was not explicitly provided, the endpoint is determined by the GOOGLE_API_USE_MTLS_ENDPOINT environment variable, which have one of the following values: “always” (always use the default mTLS endpoint), “never” (always use the default regular endpoint) and “auto” (auto-switch to the default mTLS endpoint if client certificate is present; this is the default value).

2. If the GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is “true”, then the client_cert_source property can be used to provide a client certificate for mTLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is “false” or not set, no client certificate will be used.

3. The universe_domain property can be used to override the default “googleapis.com” universe. Note that the api_endpoint property still takes precedence; and universe_domain is currently not supported for mTLS.
client_info (google.api_core.gapic_v1.client_info.ClientInfo) – The client info used to send a user-agent string along with API requests. If None, then default info will be used. Generally, you only need to set this if you’re developing your own client library.

Raises:

google.auth.exceptions.MutualTLSChannelError – If mutual TLS transport creation failed for any reason.

__exit__(type, value, traceback)[source]¶: Releases underlying transport’s resources.

Warning

ONLY use as a context manager if the transport is NOT shared with other clients! Exiting the with block will CLOSE the transport and may cause errors in other clients!

property api_endpoint¶

Return the API endpoint used by the client instance.

Returns:: The API endpoint used by the client instance.
Return type:: str

batch_import_evaluated_annotations(request: Optional[Union[BatchImportEvaluatedAnnotationsRequest, dict]] = None, *, parent: Optional[str] = None, evaluated_annotations: Optional[MutableSequence[EvaluatedAnnotation]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchImportEvaluatedAnnotationsResponse[source]¶

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_v1beta1

def sample_batch_import_evaluated_annotations():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.BatchImportEvaluatedAnnotationsRequest(
        parent="parent_value",
    )

    # Make the request
    response = client.batch_import_evaluated_annotations(request=request)

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.BatchImportEvaluatedAnnotationsRequest, dict]) – The request object. Request message for [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1beta1.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_v1beta1.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:

Response message for: [ModelService.BatchImportEvaluatedAnnotations][google.cloud.aiplatform.v1beta1.ModelService.BatchImportEvaluatedAnnotations]

Return type:

batch_import_model_evaluation_slices(request: Optional[Union[BatchImportModelEvaluationSlicesRequest, dict]] = None, *, parent: Optional[str] = None, model_evaluation_slices: Optional[MutableSequence[ModelEvaluationSlice]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → BatchImportModelEvaluationSlicesResponse[source]¶

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_v1beta1

def sample_batch_import_model_evaluation_slices():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.BatchImportModelEvaluationSlicesRequest(
        parent="parent_value",
    )

    # Make the request
    response = client.batch_import_model_evaluation_slices(request=request)

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.BatchImportModelEvaluationSlicesRequest, dict]) – The request object. Request message for [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1beta1.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_v1beta1.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:

Response message for: [ModelService.BatchImportModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.BatchImportModelEvaluationSlices]

Return type:

google.cloud.aiplatform_v1beta1.types.BatchImportModelEvaluationSlicesResponse

cancel_operation(request: Optional[CancelOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

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:

request (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.

Returns:

None

static common_billing_account_path(billing_account: str) → str[source]¶: Returns a fully-qualified billing_account string.

static common_folder_path(folder: str) → str[source]¶: Returns a fully-qualified folder string.

static common_location_path(project: str, location: str) → str[source]¶: Returns a fully-qualified location string.

static common_organization_path(organization: str) → str[source]¶: Returns a fully-qualified organization string.

static common_project_path(project: str) → str[source]¶: Returns a fully-qualified project string.

copy_model(request: Optional[Union[CopyModelRequest, dict]] = None, *, parent: Optional[str] = None, source_model: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Copies an already existing Vertex AI Model into the specified Location. The source Model must exist in the same Project. When copying custom Models, the users themselves are responsible for [Model.metadata][google.cloud.aiplatform.v1beta1.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_v1beta1

def sample_copy_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.CopyModelRequest, dict]) – The request object. Request message for [ModelService.CopyModel][google.cloud.aiplatform.v1beta1.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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.CopyModelResponse Response message of: [ModelService.CopyModel][google.cloud.aiplatform.v1beta1.ModelService.CopyModel] operation.

Return type:

delete_model(request: Optional[Union[DeleteModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a Model.

A model cannot be deleted if any [Endpoint][google.cloud.aiplatform.v1beta1.Endpoint] resource has a [DeployedModel][google.cloud.aiplatform.v1beta1.DeployedModel] based on the model in its [deployed_models][google.cloud.aiplatform.v1beta1.Endpoint.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_v1beta1

def sample_delete_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.DeleteModelRequest, dict]) – The request object. Request message for [ModelService.DeleteModel][google.cloud.aiplatform.v1beta1.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:

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);

}

Return type:

delete_model_version(request: Optional[Union[DeleteModelVersionRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Deletes a Model version.

Model version can only be deleted if there are no [DeployedModels][google.cloud.aiplatform.v1beta1.DeployedModel] created from it. Deleting the only version in the Model is not allowed. Use [DeleteModel][google.cloud.aiplatform.v1beta1.ModelService.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_v1beta1

def sample_delete_model_version():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.DeleteModelVersionRequest, dict]) – The request object. Request message for [ModelService.DeleteModelVersion][google.cloud.aiplatform.v1beta1.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:

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);

}

Return type:

delete_operation(request: Optional[DeleteOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → None[source]¶

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:

request (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.

Returns:

None

static endpoint_path(project: str, location: str, endpoint: str) → str[source]¶: Returns a fully-qualified endpoint string.

export_model(request: Optional[Union[ExportModelRequest, dict]] = None, *, name: Optional[str] = None, output_config: Optional[OutputConfig] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

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.v1beta1.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_v1beta1

def sample_export_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ExportModelRequest, dict]) – The request object. Request message for [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.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_v1beta1.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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.ExportModelResponse Response message of: [ModelService.ExportModel][google.cloud.aiplatform.v1beta1.ModelService.ExportModel] operation.

Return type:

classmethod from_service_account_file(filename: str, *args, **kwargs)[source]¶

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelServiceClient

classmethod from_service_account_info(info: dict, *args, **kwargs)[source]¶

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

Parameters:

info (dict) – The service account private key info.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelServiceClient

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

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

Parameters:

filename (str) – The path to the service account private key json file.
args – Additional arguments to pass to the constructor.
kwargs – Additional arguments to pass to the constructor.

Returns:

The constructed client.

Return type:

ModelServiceClient

get_iam_policy(request: Optional[GetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

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:

request (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:

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.

Return type:

Policy

get_location(request: Optional[GetLocationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Location[source]¶

Gets information about a location.

Parameters:

request (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:

Location object.

Return type:

Location

get_model(request: Optional[Union[GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Model[source]¶

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_v1beta1

def sample_get_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_model(request=request)

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.GetModelRequest, dict]) – The request object. Request message for [ModelService.GetModel][google.cloud.aiplatform.v1beta1.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:

A trained machine learning Model.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelEvaluation

get_model_evaluation(request: Optional[Union[GetModelEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelEvaluation[source]¶

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_v1beta1

def sample_get_model_evaluation():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelEvaluationRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_model_evaluation(request=request)

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.GetModelEvaluationRequest, dict]) – The request object. Request message for [ModelService.GetModelEvaluation][google.cloud.aiplatform.v1beta1.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:

A collection of metrics calculated by comparing Model’s predictions on all of the test data against annotations from the test data.

Return type:

get_model_evaluation_slice(request: Optional[Union[GetModelEvaluationSliceRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelEvaluationSlice[source]¶

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_v1beta1

def sample_get_model_evaluation_slice():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.GetModelEvaluationSliceRequest(
        name="name_value",
    )

    # Make the request
    response = client.get_model_evaluation_slice(request=request)

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.GetModelEvaluationSliceRequest, dict]) – The request object. Request message for [ModelService.GetModelEvaluationSlice][google.cloud.aiplatform.v1beta1.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:

A collection of metrics calculated by comparing Model’s predictions on a slice of the test data against ground truth annotations.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelEvaluationSlice

classmethod get_mtls_endpoint_and_cert_source(client_options: Optional[ClientOptions] = None)[source]¶

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.

Parameters:

client_options (google.api_core.client_options.ClientOptions) – Custom options for the client. Only the api_endpoint and client_cert_source properties may be used in this method.

Returns:

returns the API endpoint and the: client cert source to use.

Return type:

Tuple[str, Callable[[], Tuple[bytes, bytes]]]

Raises:

google.auth.exceptions.MutualTLSChannelError – If any errors happen.

get_operation(request: Optional[GetOperationRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

Gets the latest state of a long-running operation.

Parameters:

request (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:

An Operation object.

Return type:

Operation

import_model_evaluation(request: Optional[Union[ImportModelEvaluationRequest, dict]] = None, *, parent: Optional[str] = None, model_evaluation: Optional[ModelEvaluation] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ModelEvaluation[source]¶

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_v1beta1

def sample_import_model_evaluation():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.ImportModelEvaluationRequest(
        parent="parent_value",
    )

    # Make the request
    response = client.import_model_evaluation(request=request)

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.ImportModelEvaluationRequest, dict]) – The request object. Request message for [ModelService.ImportModelEvaluation][google.cloud.aiplatform.v1beta1.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_v1beta1.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:

A collection of metrics calculated by comparing Model’s predictions on all of the test data against annotations from the test data.

Return type:

google.cloud.aiplatform_v1beta1.types.ModelEvaluation

list_locations(request: Optional[ListLocationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListLocationsResponse[source]¶

Lists information about the supported locations for this service.

Parameters:

request (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:

Response message for ListLocations method.

Return type:

ListLocationsResponse

list_model_evaluation_slices(request: Optional[Union[ListModelEvaluationSlicesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelEvaluationSlicesPager[source]¶

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_v1beta1

def sample_list_model_evaluation_slices():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListModelEvaluationSlicesRequest, dict]) – The request object. Request message for [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.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:

Response message for: [ModelService.ListModelEvaluationSlices][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluationSlices].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelEvaluationSlicesPager

list_model_evaluations(request: Optional[Union[ListModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelEvaluationsPager[source]¶

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_v1beta1

def sample_list_model_evaluations():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListModelEvaluationsRequest, dict]) – The request object. Request message for [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.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:

Response message for: [ModelService.ListModelEvaluations][google.cloud.aiplatform.v1beta1.ModelService.ListModelEvaluations].

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelEvaluationsPager

list_model_versions(request: Optional[Union[ListModelVersionsRequest, dict]] = None, *, name: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelVersionsPager[source]¶

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_v1beta1

def sample_list_model_versions():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListModelVersionsRequest, dict]) – The request object. Request message for [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.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:

Response message for: [ModelService.ListModelVersions][google.cloud.aiplatform.v1beta1.ModelService.ListModelVersions]

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelVersionsPager

list_models(request: Optional[Union[ListModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListModelsPager[source]¶

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_v1beta1

def sample_list_models():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.ListModelsRequest, dict]) – The request object. Request message for [ModelService.ListModels][google.cloud.aiplatform.v1beta1.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:

Response message for: [ModelService.ListModels][google.cloud.aiplatform.v1beta1.ModelService.ListModels]

Iterating over this object will yield results and resolve additional pages automatically.

Return type:

google.cloud.aiplatform_v1beta1.services.model_service.pagers.ListModelsPager

list_operations(request: Optional[ListOperationsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → ListOperationsResponse[source]¶

Lists operations that match the specified filter in the request.

Parameters:

request (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:

Response message for ListOperations method.

Return type:

ListOperationsResponse

merge_version_aliases(request: Optional[Union[MergeVersionAliasesRequest, dict]] = None, *, name: Optional[str] = None, version_aliases: Optional[MutableSequence[str]] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Model[source]¶

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_v1beta1

def sample_merge_version_aliases():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.MergeVersionAliasesRequest, dict]) – The request object. Request message for [ModelService.MergeVersionAliases][google.cloud.aiplatform.v1beta1.ModelService.MergeVersionAliases].
name (str) –
Required. The name of the model version to merge aliases, with a version ID explicitly included.

Example: projects/{project}/locations/{location}/models/{model}@1234

This corresponds to the name field on the request instance; if request is provided, this should not be set.
version_aliases (MutableSequence[str]) –
Required. The set of version aliases to merge. The alias should be at most 128 characters, and match [a-z][a-zA-Z0-9-]{0,126}[a-z-0-9]. Add the - prefix to an alias means removing that alias from the version. - is NOT counted in the 128 characters. Example: -golden means removing the golden alias from the version.

There is NO ordering in aliases, which means
1. The aliases returned from GetModel API might not have the exactly same order from this MergeVersionAliases API. 2) Adding and deleting the same alias in the request is not recommended, and the 2 operations will be cancelled out.
This corresponds to the version_aliases field on the request instance; if request is provided, this should not be set.
retry (google.api_core.retry.Retry) – Designation of what errors, if any, should be retried.
timeout (float) – The timeout for this request.
metadata (Sequence[Tuple[str, str]]) – Strings which should be sent along with the request as metadata.

Returns:

A trained machine learning Model.

Return type:

static model_evaluation_path(project: str, location: str, model: str, evaluation: str) → str[source]¶: Returns a fully-qualified model_evaluation string.

static model_evaluation_slice_path(project: str, location: str, model: str, evaluation: str, slice: str) → str[source]¶: Returns a fully-qualified model_evaluation_slice string.

static model_path(project: str, location: str, model: str) → str[source]¶: Returns a fully-qualified model string.

static parse_common_billing_account_path(path: str) → Dict[str, str][source]¶: Parse a billing_account path into its component segments.

static parse_common_folder_path(path: str) → Dict[str, str][source]¶: Parse a folder path into its component segments.

static parse_common_location_path(path: str) → Dict[str, str][source]¶: Parse a location path into its component segments.

static parse_common_organization_path(path: str) → Dict[str, str][source]¶: Parse a organization path into its component segments.

static parse_common_project_path(path: str) → Dict[str, str][source]¶: Parse a project path into its component segments.

static parse_endpoint_path(path: str) → Dict[str, str][source]¶: Parses a endpoint path into its component segments.

static parse_model_evaluation_path(path: str) → Dict[str, str][source]¶: Parses a model_evaluation path into its component segments.

static parse_model_evaluation_slice_path(path: str) → Dict[str, str][source]¶: Parses a model_evaluation_slice path into its component segments.

static parse_model_path(path: str) → Dict[str, str][source]¶: Parses a model path into its component segments.

static parse_training_pipeline_path(path: str) → Dict[str, str][source]¶: Parses a training_pipeline path into its component segments.

set_iam_policy(request: Optional[SetIamPolicyRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Policy[source]¶

Sets the IAM access control policy on the specified function.

Replaces any existing policy.

Parameters:

request (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:

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.

Return type:

Policy

test_iam_permissions(request: Optional[TestIamPermissionsRequest] = None, *, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → TestIamPermissionsResponse[source]¶

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:

request (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:

Response message for TestIamPermissions method.

Return type:

TestIamPermissionsResponse

static training_pipeline_path(project: str, location: str, training_pipeline: str) → str[source]¶: Returns a fully-qualified training_pipeline string.

property transport: ModelServiceTransport¶

Returns the transport used by the client instance.

Returns:

The transport used by the client: instance.

Return type:

ModelServiceTransport

property universe_domain: str¶

Return the universe domain used by the client instance.

Returns:: The universe domain used by the client instance.
Return type:: str

update_explanation_dataset(request: Optional[Union[UpdateExplanationDatasetRequest, dict]] = None, *, model: Optional[str] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

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_v1beta1

def sample_update_explanation_dataset():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.UpdateExplanationDatasetRequest, dict]) – The request object. Request message for [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1beta1.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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.UpdateExplanationDatasetResponse Response message of: [ModelService.UpdateExplanationDataset][google.cloud.aiplatform.v1beta1.ModelService.UpdateExplanationDataset] operation.

Return type:

update_model(request: Optional[Union[UpdateModelRequest, dict]] = None, *, model: Optional[Model] = None, update_mask: Optional[FieldMask] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Model[source]¶

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_v1beta1

def sample_update_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    model = aiplatform_v1beta1.Model()
    model.display_name = "display_name_value"

    request = aiplatform_v1beta1.UpdateModelRequest(
        model=model,
    )

    # Make the request
    response = client.update_model(request=request)

    # Handle the response
    print(response)

Parameters:

request (Union[google.cloud.aiplatform_v1beta1.types.UpdateModelRequest, dict]) – The request object. Request message for [ModelService.UpdateModel][google.cloud.aiplatform.v1beta1.ModelService.UpdateModel].
model (google.cloud.aiplatform_v1beta1.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:

A trained machine learning Model.

Return type:

upload_model(request: Optional[Union[UploadModelRequest, dict]] = None, *, parent: Optional[str] = None, model: Optional[Model] = None, retry: Optional[Union[Retry, _MethodDefault]] = _MethodDefault._DEFAULT_VALUE, timeout: Union[float, object] = _MethodDefault._DEFAULT_VALUE, metadata: Sequence[Tuple[str, str]] = ()) → Operation[source]¶

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_v1beta1

def sample_upload_model():
    # Create a client
    client = aiplatform_v1beta1.ModelServiceClient()

    # Initialize request argument(s)
    model = aiplatform_v1beta1.Model()
    model.display_name = "display_name_value"

    request = aiplatform_v1beta1.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:

request (Union[google.cloud.aiplatform_v1beta1.types.UploadModelRequest, dict]) – The request object. Request message for [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.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_v1beta1.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:

An object representing a long-running operation.

The result type for the operation will be google.cloud.aiplatform_v1beta1.types.UploadModelResponse Response message of: [ModelService.UploadModel][google.cloud.aiplatform.v1beta1.ModelService.UploadModel] operation.

Return type: