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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. |
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); } The JSON representation for Empty is empty JSON object {}. |
endpoint_path
endpoint_path(project: str, location: str, endpoint: str)
Returns a fully-qualified endpoint string.
export_model
export_model(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.ExportModelRequest, dict]] = None, *, name: Optional[str] = None, output_config: Optional[google.cloud.aiplatform_v1.types.model_service.ExportModelRequest.OutputConfig] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Exports a trained, exportable Model to a location specified by the user. A Model is considered to be exportable if it has at least one [supported export format][google.cloud.aiplatform.v1.Model.supported_export_formats].
from google.cloud import aiplatform_v1
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. 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_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_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_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_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. |
model_evaluation_path
model_evaluation_path(project: str, location: str, model: str, evaluation: str)
Returns a fully-qualified model_evaluation string.
model_evaluation_slice_path
model_evaluation_slice_path(
project: str, location: str, model: str, evaluation: str, slice: str
)
Returns a fully-qualified model_evaluation_slice string.
model_path
model_path(project: str, location: str, model: str)
Returns a fully-qualified model string.
parse_common_billing_account_path
parse_common_billing_account_path(path: str)
Parse a billing_account path into its component segments.
parse_common_folder_path
parse_common_folder_path(path: str)
Parse a folder path into its component segments.
parse_common_location_path
parse_common_location_path(path: str)
Parse a location path into its component segments.
parse_common_organization_path
parse_common_organization_path(path: str)
Parse a organization path into its component segments.
parse_common_project_path
parse_common_project_path(path: str)
Parse a project path into its component segments.
parse_endpoint_path
parse_endpoint_path(path: str)
Parses a endpoint path into its component segments.
parse_model_evaluation_path
parse_model_evaluation_path(path: str)
Parses a model_evaluation path into its component segments.
parse_model_evaluation_slice_path
parse_model_evaluation_slice_path(path: str)
Parses a model_evaluation_slice path into its component segments.
parse_model_path
parse_model_path(path: str)
Parses a model path into its component segments.
parse_training_pipeline_path
parse_training_pipeline_path(path: str)
Parses a training_pipeline path into its component segments.
training_pipeline_path
training_pipeline_path(project: str, location: str, training_pipeline: str)
Returns a fully-qualified training_pipeline string.
update_model
update_model(request: Optional[Union[google.cloud.aiplatform_v1.types.model_service.UpdateModelRequest, dict]] = None, *, model: Optional[google.cloud.aiplatform_v1.types.model.Model] = None, update_mask: Optional[google.protobuf.field_mask_pb2.FieldMask] = None, retry: Union[google.api_core.retry.Retry, google.api_core.gapic_v1.method._MethodDefault] = <_MethodDefault._DEFAULT_VALUE: <object object>>, timeout: Optional[float] = None, metadata: Sequence[Tuple[str, str]] = ())
Updates a Model.
from google.cloud import aiplatform_v1
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. |