Class PredictionServiceClient (2.5.3)

Version 2.5.3

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public class PredictionServiceClient implements BackgroundResource

Service Description: A service for online predictions and explanations.

This class provides the ability to make remote calls to the backing service through method calls that map to API methods. Sample code to get started:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   List<Value> instances = new ArrayList<>();
   Value parameters = Value.newBuilder().build();
   PredictResponse response = predictionServiceClient.predict(endpoint, instances, parameters);
 }
 

Note: close() needs to be called on the PredictionServiceClient object to clean up resources such as threads. In the example above, try-with-resources is used, which automatically calls close().

The surface of this class includes several types of Java methods for each of the API's methods:

1. A "flattened" method. With this type of method, the fields of the request type have been converted into function parameters. It may be the case that not all fields are available as parameters, and not every API method will have a flattened method entry point.
2. A "request object" method. This type of method only takes one parameter, a request object, which must be constructed before the call. Not every API method will have a request object method.
3. A "callable" method. This type of method takes no parameters and returns an immutable API callable object, which can be used to initiate calls to the service.

See the individual methods for example code.

Many parameters require resource names to be formatted in a particular way. To assist with these names, this class includes a format method for each type of name, and additionally a parse method to extract the individual identifiers contained within names that are returned.

This class can be customized by passing in a custom instance of PredictionServiceSettings to create(). For example:

To customize credentials:

 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);
 

To customize the endpoint:

 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);
 

Please refer to the GitHub repository's samples for more quickstart code snippets.

Inheritance

java.lang.Object > PredictionServiceClient

Implements

BackgroundResource

Inherited Members

Object.clone()

Object.equals(Object)

Object.finalize()

Object.getClass()

Object.hashCode()

Object.notify()

Object.notifyAll()

Object.toString()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Constructors

PredictionServiceClient(PredictionServiceSettings settings)

protected PredictionServiceClient(PredictionServiceSettings settings)

Constructs an instance of PredictionServiceClient, using the given settings. This is protected so that it is easy to make a subclass, but otherwise, the static factory methods should be preferred.

Parameter

Name	Description
settings	PredictionServiceSettings

PredictionServiceClient(PredictionServiceStub stub)

protected PredictionServiceClient(PredictionServiceStub stub)

Parameter

Name	Description
stub	PredictionServiceStub

Methods

awaitTermination(long duration, TimeUnit unit)

public boolean awaitTermination(long duration, TimeUnit unit)

Parameters

Name	Description
duration	long
unit	TimeUnit

Returns

Type	Description
boolean

Exceptions

Type	Description
InterruptedException

close()

public final void close()

create()

public static final PredictionServiceClient create()

Constructs an instance of PredictionServiceClient with default settings.

Returns

Type	Description
PredictionServiceClient

Exceptions

Type	Description
IOException

create(PredictionServiceSettings settings)

public static final PredictionServiceClient create(PredictionServiceSettings settings)

Constructs an instance of PredictionServiceClient, using the given settings. The channels are created based on the settings passed in, or defaults for any settings that are not set.

Parameter

Name	Description
settings	PredictionServiceSettings

Returns

Type	Description
PredictionServiceClient

Exceptions

Type	Description
IOException

create(PredictionServiceStub stub)

public static final PredictionServiceClient create(PredictionServiceStub stub)

Constructs an instance of PredictionServiceClient, using the given stub for making calls. This is for advanced usage - prefer using create(PredictionServiceSettings).

Parameter

Name	Description
stub	PredictionServiceStub

Returns

Type	Description
PredictionServiceClient

explain(EndpointName endpoint, List<Value> instances, Value parameters, String deployedModelId)

public final ExplainResponse explain(EndpointName endpoint, List<Value> instances, Value parameters, String deployedModelId)

Perform an online explanation.

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   List<Value> instances = new ArrayList<>();
   Value parameters = Value.newBuilder().build();
   String deployedModelId = "deployedModelId-1817547906";
   ExplainResponse response =
       predictionServiceClient.explain(endpoint, instances, parameters, deployedModelId);
 }
 

Parameters

Name	Description
endpoint	EndpointName Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
instances	List<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 PredictSchemata's instance_schema_uri.
parameters	Value The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.
deployedModelId	String If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

Returns

Type	Description
ExplainResponse

explain(ExplainRequest request)

public final ExplainResponse explain(ExplainRequest request)

Perform an online explanation.

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   ExplainRequest request =
       ExplainRequest.newBuilder()
           .setEndpoint(EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString())
           .addAllInstances(new ArrayList<Value>())
           .setParameters(Value.newBuilder().build())
           .setExplanationSpecOverride(ExplanationSpecOverride.newBuilder().build())
           .setDeployedModelId("deployedModelId-1817547906")
           .build();
   ExplainResponse response = predictionServiceClient.explain(request);
 }
 

Parameter

Name	Description
request	ExplainRequest The request object containing all of the parameters for the API call.

Returns

Type	Description
ExplainResponse

explain(String endpoint, List<Value> instances, Value parameters, String deployedModelId)

public final ExplainResponse explain(String endpoint, List<Value> instances, Value parameters, String deployedModelId)

Perform an online explanation.

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   String endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString();
   List<Value> instances = new ArrayList<>();
   Value parameters = Value.newBuilder().build();
   String deployedModelId = "deployedModelId-1817547906";
   ExplainResponse response =
       predictionServiceClient.explain(endpoint, instances, parameters, deployedModelId);
 }
 

Parameters

Name	Description
endpoint	String Required. The name of the Endpoint requested to serve the explanation. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
instances	List<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 PredictSchemata's instance_schema_uri.
parameters	Value The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.
deployedModelId	String If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding Endpoint.traffic_split.

Returns

Type	Description
ExplainResponse

explainCallable()

public final UnaryCallable<ExplainRequest,ExplainResponse> explainCallable()

Perform an online explanation.

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   ExplainRequest request =
       ExplainRequest.newBuilder()
           .setEndpoint(EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString())
           .addAllInstances(new ArrayList<Value>())
           .setParameters(Value.newBuilder().build())
           .setExplanationSpecOverride(ExplanationSpecOverride.newBuilder().build())
           .setDeployedModelId("deployedModelId-1817547906")
           .build();
   ApiFuture

Returns

Type	Description
UnaryCallable<ExplainRequest,ExplainResponse>

getSettings()

public final PredictionServiceSettings getSettings()

Returns

Type	Description
PredictionServiceSettings

getStub()

public PredictionServiceStub getStub()

Returns

Type	Description
PredictionServiceStub

isShutdown()

public boolean isShutdown()

Returns

Type	Description
boolean

isTerminated()

public boolean isTerminated()

Returns

Type	Description
boolean

predict(EndpointName endpoint, List<Value> instances, Value parameters)

public final PredictResponse predict(EndpointName endpoint, List<Value> instances, Value parameters)

Perform an online prediction.

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   List<Value> instances = new ArrayList<>();
   Value parameters = Value.newBuilder().build();
   PredictResponse response = predictionServiceClient.predict(endpoint, instances, parameters);
 }
 

Parameters

Name	Description
endpoint	EndpointName Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
instances	List<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 PredictSchemata's instance_schema_uri.
parameters	Value The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

Returns

Type	Description
PredictResponse

predict(PredictRequest request)

public final PredictResponse predict(PredictRequest request)

Perform an online prediction.

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   PredictRequest request =
       PredictRequest.newBuilder()
           .setEndpoint(EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString())
           .addAllInstances(new ArrayList<Value>())
           .setParameters(Value.newBuilder().build())
           .build();
   PredictResponse response = predictionServiceClient.predict(request);
 }
 

Parameter

Name	Description
request	PredictRequest The request object containing all of the parameters for the API call.

Returns

Type	Description
PredictResponse

predict(String endpoint, List<Value> instances, Value parameters)

public final PredictResponse predict(String endpoint, List<Value> instances, Value parameters)

Perform an online prediction.

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   String endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString();
   List<Value> instances = new ArrayList<>();
   Value parameters = Value.newBuilder().build();
   PredictResponse response = predictionServiceClient.predict(endpoint, instances, parameters);
 }
 

Parameters

Name	Description
endpoint	String Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
instances	List<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 PredictSchemata's instance_schema_uri.
parameters	Value The parameters that govern the prediction. The schema of the parameters may be specified via Endpoint's DeployedModels' Model's PredictSchemata's parameters_schema_uri.

Returns

Type	Description
PredictResponse

predictCallable()

public final UnaryCallable<PredictRequest,PredictResponse> predictCallable()

Perform an online prediction.

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   PredictRequest request =
       PredictRequest.newBuilder()
           .setEndpoint(EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString())
           .addAllInstances(new ArrayList<Value>())
           .setParameters(Value.newBuilder().build())
           .build();
   ApiFuture

Returns

Type	Description
UnaryCallable<PredictRequest,PredictResponse>

rawPredict(EndpointName endpoint, HttpBody httpBody)

public final HttpBody rawPredict(EndpointName endpoint, HttpBody httpBody)

Perform an online prediction with an arbitrary HTTP payload.

The response includes the following HTTP headers:

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

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   EndpointName endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]");
   HttpBody httpBody = HttpBody.newBuilder().build();
   HttpBody response = predictionServiceClient.rawPredict(endpoint, httpBody);
 }
 

Parameters

Name	Description
endpoint	EndpointName Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
httpBody	com.google.api.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.

Returns

Type	Description
com.google.api.HttpBody

rawPredict(RawPredictRequest request)

public final HttpBody rawPredict(RawPredictRequest request)

Perform an online prediction with an arbitrary HTTP payload.

The response includes the following HTTP headers:

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

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   RawPredictRequest request =
       RawPredictRequest.newBuilder()
           .setEndpoint(EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString())
           .setHttpBody(HttpBody.newBuilder().build())
           .build();
   HttpBody response = predictionServiceClient.rawPredict(request);
 }
 

Parameter

Name	Description
request	RawPredictRequest The request object containing all of the parameters for the API call.

Returns

Type	Description
com.google.api.HttpBody

rawPredict(String endpoint, HttpBody httpBody)

public final HttpBody rawPredict(String endpoint, HttpBody httpBody)

Perform an online prediction with an arbitrary HTTP payload.

The response includes the following HTTP headers:

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

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   String endpoint = EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString();
   HttpBody httpBody = HttpBody.newBuilder().build();
   HttpBody response = predictionServiceClient.rawPredict(endpoint, httpBody);
 }
 

Parameters

Name	Description
endpoint	String Required. The name of the Endpoint requested to serve the prediction. Format: projects/{project}/locations/{location}/endpoints/{endpoint}
httpBody	com.google.api.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.

Returns

Type	Description
com.google.api.HttpBody

rawPredictCallable()

public final UnaryCallable<RawPredictRequest,HttpBody> rawPredictCallable()

Perform an online prediction with an arbitrary HTTP payload.

The response includes the following HTTP headers:

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

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

Sample code:

 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   RawPredictRequest request =
       RawPredictRequest.newBuilder()
           .setEndpoint(EndpointName.of("[PROJECT]", "[LOCATION]", "[ENDPOINT]").toString())
           .setHttpBody(HttpBody.newBuilder().build())
           .build();
   ApiFuture

Returns

Type	Description
UnaryCallable<RawPredictRequest,com.google.api.HttpBody>

shutdown()

public void shutdown()

shutdownNow()

public void shutdownNow()