PredictionServiceClient (Google Cloud Vertex AI 2.8.4 API)

java.lang.Object
- com.google.cloud.aiplatform.v1.PredictionServiceClient

All Implemented Interfaces:

com.google.api.gax.core.BackgroundResource, AutoCloseable
```
@Generated(value="by gapic-generator-java")
public class PredictionServiceClient
extends Object
implements com.google.api.gax.core.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.

Constructor Summary

Constructors
Modifier	Constructor and Description
`protected`	`PredictionServiceClient(PredictionServiceSettings settings)` Constructs an instance of PredictionServiceClient, using the given settings.
`protected`	`PredictionServiceClient(PredictionServiceStub stub)`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`boolean`	`awaitTermination(long duration, TimeUnit unit)`
`void`	`close()`
`static PredictionServiceClient`	`create()` Constructs an instance of PredictionServiceClient with default settings.
`static PredictionServiceClient`	`create(PredictionServiceSettings settings)` Constructs an instance of PredictionServiceClient, using the given settings.
`static PredictionServiceClient`	`create(PredictionServiceStub stub)` Constructs an instance of PredictionServiceClient, using the given stub for making calls.
`ExplainResponse`	`explain(EndpointName endpoint, List<com.google.protobuf.Value> instances, com.google.protobuf.Value parameters, String deployedModelId)` Perform an online explanation.
`ExplainResponse`	`explain(ExplainRequest request)` Perform an online explanation.
`ExplainResponse`	`explain(String endpoint, List<com.google.protobuf.Value> instances, com.google.protobuf.Value parameters, String deployedModelId)` Perform an online explanation.
`com.google.api.gax.rpc.UnaryCallable<ExplainRequest,ExplainResponse>`	`explainCallable()` Perform an online explanation.
`PredictionServiceSettings`	`getSettings()`
`PredictionServiceStub`	`getStub()`
`boolean`	`isShutdown()`
`boolean`	`isTerminated()`
`PredictResponse`	`predict(EndpointName endpoint, List<com.google.protobuf.Value> instances, com.google.protobuf.Value parameters)` Perform an online prediction.
`PredictResponse`	`predict(PredictRequest request)` Perform an online prediction.
`PredictResponse`	`predict(String endpoint, List<com.google.protobuf.Value> instances, com.google.protobuf.Value parameters)` Perform an online prediction.
`com.google.api.gax.rpc.UnaryCallable<PredictRequest,PredictResponse>`	`predictCallable()` Perform an online prediction.
`com.google.api.HttpBody`	`rawPredict(EndpointName endpoint, com.google.api.HttpBody httpBody)` Perform an online prediction with an arbitrary HTTP payload.
`com.google.api.HttpBody`	`rawPredict(RawPredictRequest request)` Perform an online prediction with an arbitrary HTTP payload.
`com.google.api.HttpBody`	`rawPredict(String endpoint, com.google.api.HttpBody httpBody)` Perform an online prediction with an arbitrary HTTP payload.
`com.google.api.gax.rpc.UnaryCallable<RawPredictRequest,com.google.api.HttpBody>`	`rawPredictCallable()` Perform an online prediction with an arbitrary HTTP payload.
`void`	`shutdown()`
`void`	`shutdownNow()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - PredictionServiceClient
```
protected PredictionServiceClient(PredictionServiceSettings settings)
                           throws IOException
```
    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.
    
    Throws:
    
    IOException
  - PredictionServiceClient
```
@BetaApi(value="A restructuring of stub classes is planned, so this may break in the future")
protected PredictionServiceClient(PredictionServiceStub stub)
```
- Method Detail
  - create
```
public static final PredictionServiceClient create()
                                            throws IOException
```
    Constructs an instance of PredictionServiceClient with default settings.
    
    Throws:
    
    IOException
  - create
```
public static final PredictionServiceClient create(PredictionServiceSettings settings)
                                            throws IOException
```
    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.
    
    Throws:
    
    IOException
  - create
```
@BetaApi(value="A restructuring of stub classes is planned, so this may break in the future")
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).
  - getSettings
```
public final PredictionServiceSettings getSettings()
```
  - getStub
```
@BetaApi(value="A restructuring of stub classes is planned, so this may break in the future")
public PredictionServiceStub getStub()
```
  - predict
```
public final PredictResponse predict(EndpointName endpoint,
                                     List<com.google.protobuf.Value> instances,
                                     com.google.protobuf.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:
    
    endpoint - Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
    
    instances - 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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
    
    parameters - 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][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - predict
```
public final PredictResponse predict(String endpoint,
                                     List<com.google.protobuf.Value> instances,
                                     com.google.protobuf.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:
    
    endpoint - Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
    
    instances - 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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
    
    parameters - 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][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - predict
```
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);
 }
 
```
    Parameters:
    
    request - The request object containing all of the parameters for the API call.
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - predictCallable
```
public final com.google.api.gax.rpc.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<PredictResponse> future =
       predictionServiceClient.predictCallable().futureCall(request);
   // Do something.
   PredictResponse response = future.get();
 }
 
```
  - rawPredict
```
public final com.google.api.HttpBody rawPredict(EndpointName endpoint,
                                                com.google.api.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][google.cloud.aiplatform.v1.Endpoint] that served this prediction.
    - `X-Vertex-AI-Deployed-Model-Id`: ID of the Endpoint's [DeployedModel][google.cloud.aiplatform.v1.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:
    
    endpoint - Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
    
    httpBody - The prediction input. Supports HTTP headers and arbitrary data payload.
    A [DeployedModel][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.PredictionService.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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] field when you create a [Model][google.cloud.aiplatform.v1.Model]. This schema applies when you deploy the `Model` as a `DeployedModel` to an [Endpoint][google.cloud.aiplatform.v1.Endpoint] and use the `RawPredict` method.
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - rawPredict
```
public final com.google.api.HttpBody rawPredict(String endpoint,
                                                com.google.api.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][google.cloud.aiplatform.v1.Endpoint] that served this prediction.
    - `X-Vertex-AI-Deployed-Model-Id`: ID of the Endpoint's [DeployedModel][google.cloud.aiplatform.v1.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:
    
    endpoint - Required. The name of the Endpoint requested to serve the prediction. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
    
    httpBody - The prediction input. Supports HTTP headers and arbitrary data payload.
    A [DeployedModel][google.cloud.aiplatform.v1.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][google.cloud.aiplatform.v1.PredictionService.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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri] field when you create a [Model][google.cloud.aiplatform.v1.Model]. This schema applies when you deploy the `Model` as a `DeployedModel` to an [Endpoint][google.cloud.aiplatform.v1.Endpoint] and use the `RawPredict` method.
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - rawPredict
```
public final com.google.api.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][google.cloud.aiplatform.v1.Endpoint] that served this prediction.
    - `X-Vertex-AI-Deployed-Model-Id`: ID of the Endpoint's [DeployedModel][google.cloud.aiplatform.v1.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);
 }
 
```
    Parameters:
    
    request - The request object containing all of the parameters for the API call.
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - rawPredictCallable
```
public final com.google.api.gax.rpc.UnaryCallable<RawPredictRequest,com.google.api.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][google.cloud.aiplatform.v1.Endpoint] that served this prediction.
    - `X-Vertex-AI-Deployed-Model-Id`: ID of the Endpoint's [DeployedModel][google.cloud.aiplatform.v1.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<HttpBody> future = predictionServiceClient.rawPredictCallable().futureCall(request);
   // Do something.
   HttpBody response = future.get();
 }
 
```
  - explain
```
public final ExplainResponse explain(EndpointName endpoint,
                                     List<com.google.protobuf.Value> instances,
                                     com.google.protobuf.Value parameters,
                                     String deployedModelId)
```
    Perform an online explanation.
    If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is specified, the corresponding DeployModel must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] populated. If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is not specified, all DeployedModels must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.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:
    
    endpoint - Required. The name of the Endpoint requested to serve the explanation. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
    
    instances - 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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
    
    parameters - 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][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].
    
    deployedModelId - If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding [Endpoint.traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split].
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - explain
```
public final ExplainResponse explain(String endpoint,
                                     List<com.google.protobuf.Value> instances,
                                     com.google.protobuf.Value parameters,
                                     String deployedModelId)
```
    Perform an online explanation.
    If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is specified, the corresponding DeployModel must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] populated. If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is not specified, all DeployedModels must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.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:
    
    endpoint - Required. The name of the Endpoint requested to serve the explanation. Format: `projects/{project}/locations/{location}/endpoints/{endpoint}`
    
    instances - 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][google.cloud.aiplatform.v1.PredictSchemata.instance_schema_uri].
    
    parameters - 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][google.cloud.aiplatform.v1.PredictSchemata.parameters_schema_uri].
    
    deployedModelId - If specified, this ExplainRequest will be served by the chosen DeployedModel, overriding [Endpoint.traffic_split][google.cloud.aiplatform.v1.Endpoint.traffic_split].
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - explain
```
public final ExplainResponse explain(ExplainRequest request)
```
    Perform an online explanation.
    If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is specified, the corresponding DeployModel must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] populated. If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is not specified, all DeployedModels must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.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);
 }
 
```
    Parameters:
    
    request - The request object containing all of the parameters for the API call.
    
    Throws:
    
    com.google.api.gax.rpc.ApiException - if the remote call fails
  - explainCallable
```
public final com.google.api.gax.rpc.UnaryCallable<ExplainRequest,ExplainResponse> explainCallable()
```
    Perform an online explanation.
    If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is specified, the corresponding DeployModel must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.explanation_spec] populated. If [deployed_model_id][google.cloud.aiplatform.v1.ExplainRequest.deployed_model_id] is not specified, all DeployedModels must have [explanation_spec][google.cloud.aiplatform.v1.DeployedModel.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<ExplainResponse> future =
       predictionServiceClient.explainCallable().futureCall(request);
   // Do something.
   ExplainResponse response = future.get();
 }
 
```
  - close
```
public final void close()
```
    Specified by:
    
    close in interface AutoCloseable
  - shutdown
```
public void shutdown()
```
    Specified by:
    
    shutdown in interface com.google.api.gax.core.BackgroundResource
  - isShutdown
```
public boolean isShutdown()
```
    Specified by:
    
    isShutdown in interface com.google.api.gax.core.BackgroundResource
  - isTerminated
```
public boolean isTerminated()
```
    Specified by:
    
    isTerminated in interface com.google.api.gax.core.BackgroundResource
  - shutdownNow
```
public void shutdownNow()
```
    Specified by:
    
    shutdownNow in interface com.google.api.gax.core.BackgroundResource
  - awaitTermination
```
public boolean awaitTermination(long duration,
                                TimeUnit unit)
                         throws InterruptedException
```
    Specified by:
    
    awaitTermination in interface com.google.api.gax.core.BackgroundResource
    
    Throws:
    
    InterruptedException

Class PredictionServiceClient

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

PredictionServiceClient

PredictionServiceClient

Method Detail

create

create

create

getSettings

getStub

predict

predict

predict

predictCallable

rawPredict

rawPredict

rawPredict

rawPredictCallable

explain

explain

explain

explainCallable

close

shutdown

isShutdown

isTerminated

shutdownNow

awaitTermination