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ModelServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
A service for managing Vertex AI's machine learning Models.
Inheritance
builtins.object > ModelServiceAsyncClientProperties
transport
Returns the transport used by the client instance.
Type | Description |
ModelServiceTransport | The transport used by the client instance. |
Methods
ModelServiceAsyncClient
ModelServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.model_service.transports.base.ModelServiceTransport] = 'grpc_asyncio', client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
Instantiates the model service client.
Name | Description |
credentials |
Optional[google.auth.credentials.Credentials]
The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment. |
transport |
Union[str, `.ModelServiceTransport`]
The transport to use. If set to None, a transport is chosen automatically. |
client_options |
ClientOptions
Custom options for the client. It won't take effect if a |
Type | Description |
google.auth.exceptions.MutualTlsChannelError | If mutual TLS transport creation failed for any reason. |
batch_import_model_evaluation_slices
batch_import_model_evaluation_slices(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.BatchImportModelEvaluationSlicesRequest, dict]] = None, *, parent: Optional[str] = None, model_evaluation_slices: Optional[Sequence[google.cloud.aiplatform_v1.types.model_evaluation_slice.ModelEvaluationSlice]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Imports a list of externally generated ModelEvaluationSlice.
from google.cloud import aiplatform_v1
async def sample_batch_import_model_evaluation_slices():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.BatchImportModelEvaluationSlicesRequest(
parent="parent_value",
)
# Make the request
response = await client.batch_import_model_evaluation_slices(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesRequest, dict]
The request object. Request message for ModelService.BatchImportModelEvaluationSlices |
parent |
`str`
Required. The name of the parent ModelEvaluation resource. Format: |
model_evaluation_slices |
:class:`Sequence[google.cloud.aiplatform_v1.types.ModelEvaluationSlice]`
Required. Model evaluation slice resource to be imported. This corresponds to the |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.BatchImportModelEvaluationSlicesResponse | Response message for ModelService.BatchImportModelEvaluationSlices |
cancel_operation
cancel_operation(request: Optional[google.longrunning.operations_pb2.CancelOperationRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
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
.
Name | Description |
request |
`.operations_pb2.CancelOperationRequest`
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
common_billing_account_path
common_billing_account_path(billing_account: str)
Returns a fully-qualified billing_account string.
common_folder_path
common_folder_path(folder: str)
Returns a fully-qualified folder string.
common_location_path
common_location_path(project: str, location: str)
Returns a fully-qualified location string.
common_organization_path
common_organization_path(organization: str)
Returns a fully-qualified organization string.
common_project_path
common_project_path(project: str)
Returns a fully-qualified project string.
delete_model
delete_model(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.DeleteModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Deletes a Model.
A model cannot be deleted if any xref_Endpoint resource has a xref_DeployedModel based on the model in its xref_deployed_models field.
from google.cloud import aiplatform_v1
async def sample_delete_model():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.DeleteModelRequest(
name="name_value",
)
# Make the request
operation = client.delete_model(request=request)
print("Waiting for operation to complete...")
response = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.DeleteModelRequest, dict]
The request object. Request message for ModelService.DeleteModel. |
name |
`str`
Required. The name of the Model resource to be deleted. Format: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_model_version
delete_model_version(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.DeleteModelVersionRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Deletes a Model version.
Model version can only be deleted if there are no [DeployedModels][] created from it. Deleting the only version in the Model is not allowed. Use xref_DeleteModel for deleting the Model instead.
from google.cloud import aiplatform_v1
async def sample_delete_model_version():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.DeleteModelVersionRequest(
name="name_value",
)
# Make the request
operation = client.delete_model_version(request=request)
print("Waiting for operation to complete...")
response = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.DeleteModelVersionRequest, dict]
The request object. Request message for ModelService.DeleteModelVersion. |
name |
`str`
Required. The name of the model version to be deleted, with a version ID explicitly included. Example: |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be `google.protobuf.empty_pb2.Empty` A generic empty message that you can re-use to avoid defining duplicated empty messages in your APIs. A typical example is to use it as the request or the response type of an API method. For instance: service Foo { rpc Bar(google.protobuf.Empty) returns (google.protobuf.Empty); } |
delete_operation
delete_operation(request: Optional[google.longrunning.operations_pb2.DeleteOperationRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Deletes a 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
.
Name | Description |
request |
`.operations_pb2.DeleteOperationRequest`
The request object. Request message for |
retry |
google.api_core.retry.Retry
Designation of what errors, if any, should be retried. |
timeout |
float
The timeout for this request. |
metadata |
Sequence[Tuple[str, str]]
Strings which should be sent along with the request as metadata. |
endpoint_path
endpoint_path(project: str, location: str, endpoint: str)
Returns a fully-qualified endpoint string.
export_model
export_model(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ExportModelRequest, dict]] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.aiplatform_v1.types.model_service.ExportModelRequest.OutputConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Exports a trained, exportable Model to a location specified by the user. A Model is considered to be exportable if it has at least one [supported export format][google.cloud.aiplatform.v1.Model.supported_export_formats].
from google.cloud import aiplatform_v1
async def sample_export_model():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.ExportModelRequest(
name="name_value",
)
# Make the request
operation = client.export_model(request=request)
print("Waiting for operation to complete...")
response = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ExportModelRequest, dict]
The request object. Request message for ModelService.ExportModel. |
name |
`str`
Required. The resource name of the Model to export. The resource name may contain version id or version alias to specify the version, if no version is specified, the default version will be exported. This corresponds to the |
output_config |
OutputConfig
Required. The desired output location and configuration. This corresponds to the |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be ExportModelResponse Response message of ModelService.ExportModel operation. |
from_service_account_file
from_service_account_file(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
ModelServiceAsyncClient | The constructed client. |
from_service_account_info
from_service_account_info(info: dict, *args, **kwargs)
Creates an instance of this client using the provided credentials info.
Name | Description |
info |
dict
The service account private key info. |
Type | Description |
ModelServiceAsyncClient | The constructed client. |
from_service_account_json
from_service_account_json(filename: str, *args, **kwargs)
Creates an instance of this client using the provided credentials file.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
ModelServiceAsyncClient | The constructed client. |
get_iam_policy
get_iam_policy(request: Optional[google.iam.v1.iam_policy_pb2.GetIamPolicyRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets the IAM access control policy for a function.
Returns an empty policy if the function exists and does not have a policy set.
Name | Description |
request |
`.iam_policy_pb2.GetIamPolicyRequest`
The request object. Request message for |
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. |
Type | Description |
`.policy_pb2.Policy` | Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A ``Policy`` is a collection of ``bindings``. A ``binding`` binds one or more ``members`` to a single ``role``. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A ``role`` is a named list of permissions (defined by IAM or configured by users). A ``binding`` can optionally specify a ``condition``, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource. **JSON Example** :: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": ["user:eve@example.com"], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01t00:00:00.000z')",="" }="" }="" ]="" }="" **yaml="" example**="" ::="" bindings:="" -="" members:="" -="" user:mike@example.com="" -="" group:admins@example.com="" -="" domain:google.com="" -="" serviceaccount:my-project-id@appspot.gserviceaccount.com="" role:="" roles/resourcemanager.organizationadmin="" -="" members:="" -="" user:eve@example.com="" role:="" roles/resourcemanager.organizationviewer="" condition:="" title:="" expirable="" access="" description:="" does="" not="" grant="" access="" after="" sep="" 2020="" expression:="" request.time="">< timestamp('2020-10-01t00:00:00.000z')="" for="" a="" description="" of="" iam="" and="" its="" features,="" see="" the="" `iam="" developer's="" guide=""> |
get_location
get_location(request: Optional[google.cloud.location.locations_pb2.GetLocationRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets information about a location.
Name | Description |
request |
`.location_pb2.GetLocationRequest`
The request object. Request message for |
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. |
Type | Description |
`.location_pb2.Location` | Location object. |
get_model
get_model(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.GetModelRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets a Model.
from google.cloud import aiplatform_v1
async def sample_get_model():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.GetModelRequest(
name="name_value",
)
# Make the request
response = await client.get_model(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetModelRequest, dict]
The request object. Request message for ModelService.GetModel. |
name |
`str`
Required. The name of the Model resource. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Model | A trained machine learning Model. |
get_model_evaluation
get_model_evaluation(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.GetModelEvaluationRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets a ModelEvaluation.
from google.cloud import aiplatform_v1
async def sample_get_model_evaluation():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.GetModelEvaluationRequest(
name="name_value",
)
# Make the request
response = await client.get_model_evaluation(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetModelEvaluationRequest, dict]
The request object. Request message for ModelService.GetModelEvaluation. |
name |
`str`
Required. The name of the ModelEvaluation resource. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.ModelEvaluation | A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data. |
get_model_evaluation_slice
get_model_evaluation_slice(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.GetModelEvaluationSliceRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets a ModelEvaluationSlice.
from google.cloud import aiplatform_v1
async def sample_get_model_evaluation_slice():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.GetModelEvaluationSliceRequest(
name="name_value",
)
# Make the request
response = await client.get_model_evaluation_slice(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.GetModelEvaluationSliceRequest, dict]
The request object. Request message for ModelService.GetModelEvaluationSlice. |
name |
`str`
Required. The name of the ModelEvaluationSlice resource. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.ModelEvaluationSlice | A collection of metrics calculated by comparing Model's predictions on a slice of the test data against ground truth annotations. |
get_mtls_endpoint_and_cert_source
get_mtls_endpoint_and_cert_source(
client_options: Optional[google.api_core.client_options.ClientOptions] = None,
)
Return the API endpoint and client cert source for mutual TLS.
The client cert source is determined in the following order:
(1) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is not "true", the
client cert source is None.
(2) if client_options.client_cert_source
is provided, use the provided one; if the
default client cert source exists, use the default one; otherwise the client cert
source is None.
The API endpoint is determined in the following order:
(1) if client_options.api_endpoint
if provided, use the provided one.
(2) if GOOGLE_API_USE_CLIENT_CERTIFICATE
environment variable is "always", use the
default mTLS endpoint; if the environment variabel is "never", use the default API
endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise
use the default API endpoint.
More details can be found at https://google.aip.dev/auth/4114.
Name | Description |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. Only the |
Type | Description |
google.auth.exceptions.MutualTLSChannelError | If any errors happen. |
Type | Description |
Tuple[str, Callable[[], Tuple[bytes, bytes]]] | returns the API endpoint and the client cert source to use. |
get_operation
get_operation(request: Optional[google.longrunning.operations_pb2.GetOperationRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Gets the latest state of a long-running operation.
Name | Description |
request |
`.operations_pb2.GetOperationRequest`
The request object. Request message for |
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. |
Type | Description |
`.operations_pb2.Operation` | An ``Operation`` object. |
get_transport_class
get_transport_class()
Returns an appropriate transport class.
import_model_evaluation
import_model_evaluation(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ImportModelEvaluationRequest, dict]] = None, *, parent: Optional[str] = None, model_evaluation: Optional[google.cloud.aiplatform_v1.types.model_evaluation.ModelEvaluation] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Imports an externally generated ModelEvaluation.
from google.cloud import aiplatform_v1
async def sample_import_model_evaluation():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.ImportModelEvaluationRequest(
parent="parent_value",
)
# Make the request
response = await client.import_model_evaluation(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ImportModelEvaluationRequest, dict]
The request object. Request message for ModelService.ImportModelEvaluation |
parent |
`str`
Required. The name of the parent model resource. Format: |
model_evaluation |
ModelEvaluation
Required. Model evaluation resource to be imported. This corresponds to the |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.ModelEvaluation | A collection of metrics calculated by comparing Model's predictions on all of the test data against annotations from the test data. |
list_locations
list_locations(request: Optional[google.cloud.location.locations_pb2.ListLocationsRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists information about the supported locations for this service.
Name | Description |
request |
`.location_pb2.ListLocationsRequest`
The request object. Request message for |
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. |
Type | Description |
`.location_pb2.ListLocationsResponse` | Response message for ``ListLocations`` method. |
list_model_evaluation_slices
list_model_evaluation_slices(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ListModelEvaluationSlicesRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists ModelEvaluationSlices in a ModelEvaluation.
from google.cloud import aiplatform_v1
async def sample_list_model_evaluation_slices():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.ListModelEvaluationSlicesRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_model_evaluation_slices(request=request)
# Handle the response
async for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListModelEvaluationSlicesRequest, dict]
The request object. Request message for ModelService.ListModelEvaluationSlices. |
parent |
`str`
Required. The resource name of the ModelEvaluation to list the ModelEvaluationSlices from. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationSlicesAsyncPager | Response message for ModelService.ListModelEvaluationSlices. Iterating over this object will yield results and resolve additional pages automatically. |
list_model_evaluations
list_model_evaluations(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ListModelEvaluationsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists ModelEvaluations in a Model.
from google.cloud import aiplatform_v1
async def sample_list_model_evaluations():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.ListModelEvaluationsRequest(
parent="parent_value",
)
# Make the request
page_result = client.list_model_evaluations(request=request)
# Handle the response
async for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListModelEvaluationsRequest, dict]
The request object. Request message for ModelService.ListModelEvaluations. |
parent |
`str`
Required. The resource name of the Model to list the ModelEvaluations from. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelEvaluationsAsyncPager | Response message for ModelService.ListModelEvaluations. Iterating over this object will yield results and resolve additional pages automatically. |
list_model_versions
list_model_versions(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ListModelVersionsRequest, dict]] = None, *, name: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists versions of the specified model.
from google.cloud import aiplatform_v1
async def sample_list_model_versions():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.ListModelVersionsRequest(
name="name_value",
)
# Make the request
page_result = client.list_model_versions(request=request)
# Handle the response
async for response in page_result:
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListModelVersionsRequest, dict]
The request object. Request message for ModelService.ListModelVersions. |
name |
`str`
Required. The name of the model to list versions for. This corresponds to the |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelVersionsAsyncPager | Response message for ModelService.ListModelVersions Iterating over this object will yield results and resolve additional pages automatically. |
list_models
list_models(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ListModelsRequest, dict]] = None, *, parent: Optional[str] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists Models in a Location.
from google.cloud import aiplatform_v1
async def sample_list_models():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.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)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.ListModelsRequest, dict]
The request object. Request message for ModelService.ListModels. |
parent |
`str`
Required. The resource name of the Location to list the Models from. Format: |
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. |
Type | Description |
google.cloud.aiplatform_v1.services.model_service.pagers.ListModelsAsyncPager | Response message for ModelService.ListModels Iterating over this object will yield results and resolve additional pages automatically. |
list_operations
list_operations(request: Optional[google.longrunning.operations_pb2.ListOperationsRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Lists operations that match the specified filter in the request.
Name | Description |
request |
`.operations_pb2.ListOperationsRequest`
The request object. Request message for |
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. |
Type | Description |
`.operations_pb2.ListOperationsResponse` | Response message for ``ListOperations`` method. |
merge_version_aliases
merge_version_aliases(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.MergeVersionAliasesRequest, dict]] = None, *, name: Optional[str] = None, version_aliases: Optional[Sequence[str]] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Merges a set of aliases for a Model version.
from google.cloud import aiplatform_v1
async def sample_merge_version_aliases():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
request = aiplatform_v1.MergeVersionAliasesRequest(
name="name_value",
version_aliases=['version_aliases_value_1', 'version_aliases_value_2'],
)
# Make the request
response = await client.merge_version_aliases(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.MergeVersionAliasesRequest, dict]
The request object. Request message for ModelService.MergeVersionAliases. |
name |
`str`
Required. The name of the model version to merge aliases, with a version ID explicitly included. Example: |
version_aliases |
:class:`Sequence[str]`
Required. The set of version aliases to merge. The alias should be at most 128 characters, and match |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Model | A trained machine learning Model. |
model_evaluation_path
model_evaluation_path(project: str, location: str, model: str, evaluation: str)
Returns a fully-qualified model_evaluation string.
model_evaluation_slice_path
model_evaluation_slice_path(
project: str, location: str, model: str, evaluation: str, slice: str
)
Returns a fully-qualified model_evaluation_slice string.
model_path
model_path(project: str, location: str, model: str)
Returns a fully-qualified model string.
parse_common_billing_account_path
parse_common_billing_account_path(path: str)
Parse a billing_account path into its component segments.
parse_common_folder_path
parse_common_folder_path(path: str)
Parse a folder path into its component segments.
parse_common_location_path
parse_common_location_path(path: str)
Parse a location path into its component segments.
parse_common_organization_path
parse_common_organization_path(path: str)
Parse a organization path into its component segments.
parse_common_project_path
parse_common_project_path(path: str)
Parse a project path into its component segments.
parse_endpoint_path
parse_endpoint_path(path: str)
Parses a endpoint path into its component segments.
parse_model_evaluation_path
parse_model_evaluation_path(path: str)
Parses a model_evaluation path into its component segments.
parse_model_evaluation_slice_path
parse_model_evaluation_slice_path(path: str)
Parses a model_evaluation_slice path into its component segments.
parse_model_path
parse_model_path(path: str)
Parses a model path into its component segments.
parse_training_pipeline_path
parse_training_pipeline_path(path: str)
Parses a training_pipeline path into its component segments.
set_iam_policy
set_iam_policy(request: Optional[google.iam.v1.iam_policy_pb2.SetIamPolicyRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Sets the IAM access control policy on the specified function.
Replaces any existing policy.
Name | Description |
request |
`.iam_policy_pb2.SetIamPolicyRequest`
The request object. Request message for |
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. |
Type | Description |
`.policy_pb2.Policy` | Defines an Identity and Access Management (IAM) policy. It is used to specify access control policies for Cloud Platform resources. A ``Policy`` is a collection of ``bindings``. A ``binding`` binds one or more ``members`` to a single ``role``. Members can be user accounts, service accounts, Google groups, and domains (such as G Suite). A ``role`` is a named list of permissions (defined by IAM or configured by users). A ``binding`` can optionally specify a ``condition``, which is a logic expression that further constrains the role binding based on attributes about the request and/or target resource. **JSON Example** :: { "bindings": [ { "role": "roles/resourcemanager.organizationAdmin", "members": [ "user:mike@example.com", "group:admins@example.com", "domain:google.com", "serviceAccount:my-project-id@appspot.gserviceaccount.com" ] }, { "role": "roles/resourcemanager.organizationViewer", "members": ["user:eve@example.com"], "condition": { "title": "expirable access", "description": "Does not grant access after Sep 2020", "expression": "request.time < timestamp('2020-10-01t00:00:00.000z')",="" }="" }="" ]="" }="" **yaml="" example**="" ::="" bindings:="" -="" members:="" -="" user:mike@example.com="" -="" group:admins@example.com="" -="" domain:google.com="" -="" serviceaccount:my-project-id@appspot.gserviceaccount.com="" role:="" roles/resourcemanager.organizationadmin="" -="" members:="" -="" user:eve@example.com="" role:="" roles/resourcemanager.organizationviewer="" condition:="" title:="" expirable="" access="" description:="" does="" not="" grant="" access="" after="" sep="" 2020="" expression:="" request.time="">< timestamp('2020-10-01t00:00:00.000z')="" for="" a="" description="" of="" iam="" and="" its="" features,="" see="" the="" `iam="" developer's="" guide=""> |
test_iam_permissions
test_iam_permissions(request: Optional[google.iam.v1.iam_policy_pb2.TestIamPermissionsRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
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.
Name | Description |
request |
`.iam_policy_pb2.TestIamPermissionsRequest`
The request object. Request message for |
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. |
Type | Description |
`.iam_policy_pb2.TestIamPermissionsResponse` | Response message for ``TestIamPermissions`` method. |
training_pipeline_path
training_pipeline_path(project: str, location: str, training_pipeline: str)
Returns a fully-qualified training_pipeline string.
update_model
update_model(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.UpdateModelRequest, dict]] = None, *, model: Optional[google.cloud.aiplatform_v1.types.model.Model] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Updates a Model.
from google.cloud import aiplatform_v1
async def sample_update_model():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
model = aiplatform_v1.Model()
model.display_name = "display_name_value"
request = aiplatform_v1.UpdateModelRequest(
model=model,
)
# Make the request
response = await client.update_model(request=request)
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.UpdateModelRequest, dict]
The request object. Request message for ModelService.UpdateModel. |
model |
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 |
update_mask |
`google.protobuf.field_mask_pb2.FieldMask`
Required. The update mask applies to the resource. For the |
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. |
Type | Description |
google.cloud.aiplatform_v1.types.Model | A trained machine learning Model. |
upload_model
upload_model(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.UploadModelRequest, dict]] = None, *, parent: Optional[str] = None, model: Optional[google.cloud.aiplatform_v1.types.model.Model] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Uploads a Model artifact into Vertex AI.
from google.cloud import aiplatform_v1
async def sample_upload_model():
# Create a client
client = aiplatform_v1.ModelServiceAsyncClient()
# Initialize request argument(s)
model = aiplatform_v1.Model()
model.display_name = "display_name_value"
request = aiplatform_v1.UploadModelRequest(
parent="parent_value",
model=model,
)
# Make the request
operation = client.upload_model(request=request)
print("Waiting for operation to complete...")
response = await operation.result()
# Handle the response
print(response)
Name | Description |
request |
Union[google.cloud.aiplatform_v1.types.UploadModelRequest, dict]
The request object. Request message for ModelService.UploadModel. |
parent |
`str`
Required. The resource name of the Location into which to upload the Model. Format: |
model |
Model
Required. The Model to create. This corresponds to the |
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. |
Type | Description |
google.api_core.operation_async.AsyncOperation | An object representing a long-running operation. The result type for the operation will be UploadModelResponse Response message of ModelService.UploadModel operation. |
wait_operation
wait_operation(request: Optional[google.longrunning.operations_pb2.WaitOperationRequest] = None, *, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
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
.
Name | Description |
request |
`.operations_pb2.WaitOperationRequest`
The request object. Request message for |
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. |
Type | Description |
`.operations_pb2.Operation` | An ``Operation`` object. |