Class PredictionServiceAsyncClient (1.14.0)

Version 1.14.0

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PredictionServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.prediction_service.transports.base.PredictionServiceTransport] = '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 online predictions and explanations.

Inheritance

builtins.object > PredictionServiceAsyncClient

Properties

transport

Returns the transport used by the client instance.

Returns

Type	Description
PredictionServiceTransport	The transport used by the client instance.

Methods

PredictionServiceAsyncClient

PredictionServiceAsyncClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Union[str, google.cloud.aiplatform_v1.services.prediction_service.transports.base.PredictionServiceTransport] = '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 prediction service client.

Parameters

Name	Description
credentials	Optional[google.auth.credentials.Credentials] The authorization credentials to attach to requests. These credentials identify the application to the service; if none are specified, the client will attempt to ascertain the credentials from the environment.
transport	Union[str, `.PredictionServiceTransport`] 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 transport instance is provided. (1) The api_endpoint property can be used to override the default endpoint provided by the client. GOOGLE_API_USE_MTLS_ENDPOINT environment variable can also be used to override the endpoint: "always" (always use the default mTLS endpoint), "never" (always use the default regular endpoint) and "auto" (auto switch to the default mTLS endpoint if client certificate is present, this is the default value). However, the api_endpoint property takes precedence if provided. (2) If GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "true", then the client_cert_source property can be used to provide client certificate for mutual TLS transport. If not provided, the default SSL client certificate will be used if present. If GOOGLE_API_USE_CLIENT_CERTIFICATE is "false" or not set, no client certificate will be used.

Exceptions

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.

endpoint_path

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

Returns a fully-qualified endpoint string.

explain

explain(request: Optional[Union[google.cloud.aiplatform_v1.types.prediction_service.ExplainRequest, dict]] = None, *, endpoint: Optional[str] = None, instances: Optional[Sequence[google.protobuf.struct_pb2.Value]] = None, parameters: Optional[google.protobuf.struct_pb2.Value] = None, deployed_model_id: 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]] = ())

Perform an online explanation.

If xref_deployed_model_id is specified, the corresponding DeployModel must have xref_explanation_spec populated. If xref_deployed_model_id is not specified, all DeployedModels must have xref_explanation_spec populated. Only deployed AutoML tabular Models have explanation_spec.

from google.cloud import aiplatform_v1

async def sample_explain():
    # Create a client
    client = aiplatform_v1.PredictionServiceAsyncClient()

    # Initialize request argument(s)
    instances = aiplatform_v1.Value()
    instances.null_value = "NULL_VALUE"

    request = aiplatform_v1.ExplainRequest(
        endpoint="endpoint_value",
        instances=instances,
    )

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

    # Handle the response
    print(response)

Parameters

Name	Description
request	Union[google.cloud.aiplatform_v1.types.ExplainRequest, dict] The request object. Request message for PredictionService.Explain.
endpoint	`str` Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint} This corresponds to the endpoint field on the request instance; if request is provided, this should not be set.
instances	:class:`Sequence[google.protobuf.struct_pb2.Value]` Required. The instances that are the input to the explanation call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the explanation call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri. This corresponds to the instances field on the request instance; if request is provided, this should not be set.
parameters	`google.protobuf.struct_pb2.Value` The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri. This corresponds to the parameters field on the request instance; if request is provided, this should not be set.
deployed_model_id	`str` If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split. This corresponds to the deployed_model_id field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns

Type	Description
google.cloud.aiplatform_v1.types.ExplainResponse	Response message for PredictionService.Explain.

from_service_account_file

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

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

Parameter

Name	Description
filename	str The path to the service account private key json file.

Returns

Type	Description
PredictionServiceAsyncClient	The constructed client.

from_service_account_info

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

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

Parameter

Name	Description
info	dict The service account private key info.

Returns

Type	Description
PredictionServiceAsyncClient	The constructed client.

from_service_account_json

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

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

Parameter

Name	Description
filename	str The path to the service account private key json file.

Returns

Type	Description
PredictionServiceAsyncClient	The constructed client.

get_mtls_endpoint_and_cert_source

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

Return the API endpoint and client cert source for mutual TLS.

The client cert source is determined in the following order: (1) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is not "true", the client cert source is None. (2) if client_options.client_cert_source is provided, use the provided one; if the default client cert source exists, use the default one; otherwise the client cert source is None.

The API endpoint is determined in the following order: (1) if client_options.api_endpoint if provided, use the provided one. (2) if GOOGLE_API_USE_CLIENT_CERTIFICATE environment variable is "always", use the default mTLS endpoint; if the environment variabel is "never", use the default API endpoint; otherwise if client cert source exists, use the default mTLS endpoint, otherwise use the default API endpoint.

More details can be found at https://google.aip.dev/auth/4114.

Parameter

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

Exceptions

Type	Description
google.auth.exceptions.MutualTLSChannelError	If any errors happen.

Returns

Type	Description
Tuple[str, Callable[[], Tuple[bytes, bytes]]]	returns the API endpoint and the client cert source to use.

get_transport_class

get_transport_class()

Returns an appropriate transport class.

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_path

parse_model_path(path: str)

Parses a model path into its component segments.

predict

predict(request: Optional[Union[google.cloud.aiplatform_v1.types.prediction_service.PredictRequest, dict]] = None, *, endpoint: Optional[str] = None, instances: Optional[Sequence[google.protobuf.struct_pb2.Value]] = None, parameters: Optional[google.protobuf.struct_pb2.Value] = 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]] = ())

Perform an online prediction.

from google.cloud import aiplatform_v1

async def sample_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceAsyncClient()

    # Initialize request argument(s)
    instances = aiplatform_v1.Value()
    instances.null_value = "NULL_VALUE"

    request = aiplatform_v1.PredictRequest(
        endpoint="endpoint_value",
        instances=instances,
    )

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

    # Handle the response
    print(response)

Parameters

Name	Description
request	Union[google.cloud.aiplatform_v1.types.PredictRequest, dict] The request object. Request message for PredictionService.Predict.
endpoint	`str` Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint} This corresponds to the endpoint field on the request instance; if request is provided, this should not be set.
instances	:class:`Sequence[google.protobuf.struct_pb2.Value]` Required. The instances that are the input to the prediction call. A DeployedModel may have an upper limit on the number of instances it supports per request, and when it is exceeded the prediction call errors in case of AutoML Models, or, in case of customer created Models, the behaviour is as documented by that Model. The schema of any single instance may be specified via Endpoint's DeployedModels' [Model's][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] instance_schema_uri. This corresponds to the instances field on the request instance; if request is provided, this should not be set.
parameters	`google.protobuf.struct_pb2.Value` The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' [Model's ][google.cloud.aiplatform.v1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1.Model.predict_schemata] parameters_schema_uri. This corresponds to the parameters field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns

Type	Description
google.cloud.aiplatform_v1.types.PredictResponse	Response message for PredictionService.Predict.

raw_predict

raw_predict(request: Optional[Union[google.cloud.aiplatform_v1.types.prediction_service.RawPredictRequest, dict]] = None, *, endpoint: Optional[str] = None, http_body: Optional[google.api.httpbody_pb2.HttpBody] = 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]] = ())

Perform an online prediction with an arbitrary HTTP payload.

The response includes the following HTTP headers:

• X-Vertex-AI-Endpoint-Id: ID of the xref_Endpoint that served this prediction.

• X-Vertex-AI-Deployed-Model-Id: ID of the Endpoint's xref_DeployedModel that served this prediction.

from google.cloud import aiplatform_v1

async def sample_raw_predict():
    # Create a client
    client = aiplatform_v1.PredictionServiceAsyncClient()

    # Initialize request argument(s)
    request = aiplatform_v1.RawPredictRequest(
        endpoint="endpoint_value",
    )

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

    # Handle the response
    print(response)

Parameters

Name	Description
request	Union[google.cloud.aiplatform_v1.types.RawPredictRequest, dict] The request object. Request message for PredictionService.RawPredict.
endpoint	`str` Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint} This corresponds to the endpoint field on the request instance; if request is provided, this should not be set.
http_body	`google.api.httpbody_pb2.HttpBody` The prediction input. Supports HTTP headers and arbitrary data payload. A DeployedModel may have an upper limit on the number of instances it supports per request. When this limit it is exceeded for an AutoML model, the RawPredict method returns an error. When this limit is exceeded for a custom-trained model, the behavior varies depending on the model. You can specify the schema for each instance in the predict_schemata.instance_schema_uri field when you create a Model. This schema applies when you deploy the Model as a DeployedModel to an Endpoint and use the RawPredict method. This corresponds to the http_body field on the request instance; if request is provided, this should not be set.
retry	google.api_core.retry.Retry Designation of what errors, if any, should be retried.
timeout	float The timeout for this request.
metadata	Sequence[Tuple[str, str]] Strings which should be sent along with the request as metadata.

Returns

Type

Description

google.api.httpbody_pb2.HttpBody

Message that represents an arbitrary HTTP body. It should only be used for payload formats that can't be represented as JSON, such as raw binary or an HTML page. This message can be used both in streaming and non-streaming API methods in the request as well as the response. It can be used as a top-level request field, which is convenient if one wants to extract parameters from either the URL or HTTP template into the request fields and also want access to the raw HTTP body. Example: message GetResourceRequest { // A unique request id. string request_id = 1; // The raw HTTP body is bound to this field. google.api.HttpBody http_body = 2; } service ResourceService { rpc GetResource(GetResourceRequest) returns (google.api.HttpBody); rpc UpdateResource(google.api.HttpBody) returns (google.protobuf.Empty); } Example with streaming methods: service CaldavService { rpc GetCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); rpc UpdateCalendar(stream google.api.HttpBody) returns (stream google.api.HttpBody); } Use of this type only changes how the request and response bodies are handled, all other features will continue to work unchanged.