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PredictionServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.prediction_service.transports.base.PredictionServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
A service for online predictions and explanations.
Inheritance
builtins.object > PredictionServiceClientProperties
transport
Returns the transport used by the client instance.
Type | Description |
PredictionServiceTransport | The transport used by the client instance. |
Methods
PredictionServiceClient
PredictionServiceClient(*, credentials: Optional[google.auth.credentials.Credentials] = None, transport: Optional[Union[str, google.cloud.aiplatform_v1beta1.services.prediction_service.transports.base.PredictionServiceTransport]] = None, client_options: Optional[google.api_core.client_options.ClientOptions] = None, client_info: google.api_core.gapic_v1.client_info.ClientInfo = <google.api_core.gapic_v1.client_info.ClientInfo object>)
Instantiates the prediction 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, PredictionServiceTransport]
The transport to use. If set to None, a transport is chosen automatically. |
client_options |
google.api_core.client_options.ClientOptions
Custom options for the client. It won't take effect if a |
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 |
Type | Description |
google.auth.exceptions.MutualTLSChannelError | If mutual TLS transport creation failed for any reason. |
__exit__
__exit__(type, value, traceback)
Releases underlying transport's resources.
common_billing_account_path
common_billing_account_path(billing_account: str)
Returns a fully-qualified billing_account string.
common_folder_path
common_folder_path(folder: str)
Returns a fully-qualified folder string.
common_location_path
common_location_path(project: str, location: str)
Returns a fully-qualified location string.
common_organization_path
common_organization_path(organization: str)
Returns a fully-qualified organization string.
common_project_path
common_project_path(project: str)
Returns a fully-qualified project string.
endpoint_path
endpoint_path(project: str, location: str, endpoint: str)
Returns a fully-qualified endpoint string.
explain
explain(request: Optional[Union[google.cloud.aiplatform_v1beta1.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.
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.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: |
instances |
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.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] instance_schema_uri. This corresponds to the |
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.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] parameters_schema_uri. This corresponds to the |
deployed_model_id |
str
If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split. 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_v1beta1.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.
Name | Description |
filename |
str
The path to the service account private key json file. |
Type | Description |
PredictionServiceClient | 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 |
PredictionServiceClient | 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 |
PredictionServiceClient | 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.
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. |
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_v1beta1.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.
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.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: |
instances |
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.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] instance_schema_uri. This corresponds to the |
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.v1beta1.DeployedModel.model] [PredictSchemata's][google.cloud.aiplatform.v1beta1.Model.predict_schemata] parameters_schema_uri. 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_v1beta1.types.PredictResponse | Response message for PredictionService.Predict. |
raw_predict
raw_predict(request: Optional[Union[google.cloud.aiplatform_v1beta1.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.
Name | Description |
request |
Union[google.cloud.aiplatform_v1beta1.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: |
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 |
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.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. |