Class PredictionServiceClient (2.30.0)

Service Description: AutoML Prediction API.

On any input that is documented to expect a string parameter in snake_case or dash-case, either of those cases is accepted.

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:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
   ExamplePayload payload = ExamplePayload.newBuilder().build();
   Map<String, String> params = new HashMap<>();
   PredictResponse response = predictionServiceClient.predict(name, payload, params);
 }

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:

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.
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.
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:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder()
         .setCredentialsProvider(FixedCredentialsProvider.create(myCredentials))
         .build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);

To customize the endpoint:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newBuilder().setEndpoint(myEndpoint).build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);

To use REST (HTTP1.1/JSON) transport (instead of gRPC) for sending and receiving requests over the wire:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 PredictionServiceSettings predictionServiceSettings =
     PredictionServiceSettings.newHttpJsonBuilder().build();
 PredictionServiceClient predictionServiceClient =
     PredictionServiceClient.create(predictionServiceSettings);

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

Inheritance

java.lang.Object > PredictionServiceClient

Static Methods

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`

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`

batchPredictAsync(BatchPredictRequest request)

public final OperationFuture<BatchPredictResult,OperationMetadata> batchPredictAsync(BatchPredictRequest request)

Perform a batch prediction. Unlike the online Predict, batch prediction result won't be immediately available in the response. Instead, a long running operation object is returned. User can poll the operation result via GetOperation method. Once the operation is done, BatchPredictResult is returned in the response field. Available for following ML scenarios:

AutoML Vision Classification
AutoML Vision Object Detection
AutoML Video Intelligence Classification
AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
AutoML Natural Language Entity Extraction
AutoML Natural Language Sentiment Analysis
AutoML Tables

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   BatchPredictRequest request =
       BatchPredictRequest.newBuilder()
           .setName(ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]").toString())
           .setInputConfig(BatchPredictInputConfig.newBuilder().build())
           .setOutputConfig(BatchPredictOutputConfig.newBuilder().build())
           .putAllParams(new HashMap<String, String>())
           .build();
   BatchPredictResult response = predictionServiceClient.batchPredictAsync(request).get();
 }

Parameter
Name	Description
`request`	`BatchPredictRequest` The request object containing all of the parameters for the API call.

Returns
Type	Description
`OperationFuture<BatchPredictResult,OperationMetadata>`

batchPredictAsync(ModelName name, BatchPredictInputConfig inputConfig, BatchPredictOutputConfig outputConfig, Map<String,String> params)

public final OperationFuture<BatchPredictResult,OperationMetadata> batchPredictAsync(ModelName name, BatchPredictInputConfig inputConfig, BatchPredictOutputConfig outputConfig, Map<String,String> params)

AutoML Vision Classification
AutoML Vision Object Detection
AutoML Video Intelligence Classification
AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
AutoML Natural Language Entity Extraction
AutoML Natural Language Sentiment Analysis
AutoML Tables

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
   BatchPredictInputConfig inputConfig = BatchPredictInputConfig.newBuilder().build();
   BatchPredictOutputConfig outputConfig = BatchPredictOutputConfig.newBuilder().build();
   Map<String, String> params = new HashMap<>();
   BatchPredictResult response =
       predictionServiceClient.batchPredictAsync(name, inputConfig, outputConfig, params).get();
 }

Parameters
Name	Description
`name`	`ModelName` Required. Name of the model requested to serve the batch prediction.
`inputConfig`	`BatchPredictInputConfig` Required. The input configuration for batch prediction.
`outputConfig`	`BatchPredictOutputConfig` Required. The Configuration specifying where output predictions should be written.
`params`	`Map<String,String>` Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. AutoML Natural Language Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5. AutoML Vision Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. AutoML Vision Object Detection `score_threshold` : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server. AutoML Video Intelligence Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for a video, it will only produce results that have at least this confidence score. The default is 0.5. `segment_classification` : (boolean) Set to true to request segment-level classification. AutoML Video Intelligence returns labels and their confidence scores for the entire segment of the video that user specified in the request configuration. The default is true. `shot_classification` : (boolean) Set to true to request shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. The default is false. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. `1s_interval_classification` : (boolean) Set to true to request classification for a video at one-second intervals. AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. The default is false. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. AutoML Video Intelligence Object Tracking `score_threshold` : (float) When Model detects objects on video frames, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server. `min_bounding_box_size` : (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size are returned. Value in 0 to 1 range. Default is 0.

Returns
Type	Description
`OperationFuture<BatchPredictResult,OperationMetadata>`

batchPredictAsync(String name, BatchPredictInputConfig inputConfig, BatchPredictOutputConfig outputConfig, Map<String,String> params)

public final OperationFuture<BatchPredictResult,OperationMetadata> batchPredictAsync(String name, BatchPredictInputConfig inputConfig, BatchPredictOutputConfig outputConfig, Map<String,String> params)

AutoML Vision Classification
AutoML Vision Object Detection
AutoML Video Intelligence Classification
AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
AutoML Natural Language Entity Extraction
AutoML Natural Language Sentiment Analysis
AutoML Tables

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   String name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]").toString();
   BatchPredictInputConfig inputConfig = BatchPredictInputConfig.newBuilder().build();
   BatchPredictOutputConfig outputConfig = BatchPredictOutputConfig.newBuilder().build();
   Map<String, String> params = new HashMap<>();
   BatchPredictResult response =
       predictionServiceClient.batchPredictAsync(name, inputConfig, outputConfig, params).get();
 }

Parameters
Name	Description
`name`	`String` Required. Name of the model requested to serve the batch prediction.
`inputConfig`	`BatchPredictInputConfig` Required. The input configuration for batch prediction.
`outputConfig`	`BatchPredictOutputConfig` Required. The Configuration specifying where output predictions should be written.
`params`	`Map<String,String>` Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. AutoML Natural Language Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for a text snippet, it will only produce results that have at least this confidence score. The default is 0.5. AutoML Vision Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. AutoML Vision Object Detection `score_threshold` : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server. AutoML Video Intelligence Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for a video, it will only produce results that have at least this confidence score. The default is 0.5. `segment_classification` : (boolean) Set to true to request segment-level classification. AutoML Video Intelligence returns labels and their confidence scores for the entire segment of the video that user specified in the request configuration. The default is true. `shot_classification` : (boolean) Set to true to request shot-level classification. AutoML Video Intelligence determines the boundaries for each camera shot in the entire segment of the video that user specified in the request configuration. AutoML Video Intelligence then returns labels and their confidence scores for each detected shot, along with the start and end time of the shot. The default is false. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. `1s_interval_classification` : (boolean) Set to true to request classification for a video at one-second intervals. AutoML Video Intelligence returns labels and their confidence scores for each second of the entire segment of the video that user specified in the request configuration. The default is false. WARNING: Model evaluation is not done for this classification type, the quality of it depends on training data, but there are no metrics provided to describe that quality. AutoML Video Intelligence Object Tracking `score_threshold` : (float) When Model detects objects on video frames, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` : (int64) The maximum number of bounding boxes returned per image. The default is 100, the number of bounding boxes returned might be limited by the server. `min_bounding_box_size` : (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size are returned. Value in 0 to 1 range. Default is 0.

Returns
Type	Description
`OperationFuture<BatchPredictResult,OperationMetadata>`

batchPredictCallable()

public final UnaryCallable<BatchPredictRequest,Operation> batchPredictCallable()

AutoML Vision Classification
AutoML Vision Object Detection
AutoML Video Intelligence Classification
AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
AutoML Natural Language Entity Extraction
AutoML Natural Language Sentiment Analysis
AutoML Tables

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   BatchPredictRequest request =
       BatchPredictRequest.newBuilder()
           .setName(ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]").toString())
           .setInputConfig(BatchPredictInputConfig.newBuilder().build())
           .setOutputConfig(BatchPredictOutputConfig.newBuilder().build())
           .putAllParams(new HashMap<String, String>())
           .build();
   ApiFuture<Operation> future =
       predictionServiceClient.batchPredictCallable().futureCall(request);
   // Do something.
   Operation response = future.get();
 }

Returns
Type	Description
`UnaryCallable<BatchPredictRequest,Operation>`

batchPredictOperationCallable()

public final OperationCallable<BatchPredictRequest,BatchPredictResult,OperationMetadata> batchPredictOperationCallable()

AutoML Vision Classification
AutoML Vision Object Detection
AutoML Video Intelligence Classification
AutoML Video Intelligence Object Tracking * AutoML Natural Language Classification
AutoML Natural Language Entity Extraction
AutoML Natural Language Sentiment Analysis
AutoML Tables

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   BatchPredictRequest request =
       BatchPredictRequest.newBuilder()
           .setName(ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]").toString())
           .setInputConfig(BatchPredictInputConfig.newBuilder().build())
           .setOutputConfig(BatchPredictOutputConfig.newBuilder().build())
           .putAllParams(new HashMap<String, String>())
           .build();
   OperationFuture<BatchPredictResult, OperationMetadata> future =
       predictionServiceClient.batchPredictOperationCallable().futureCall(request);
   // Do something.
   BatchPredictResult response = future.get();
 }

Returns
Type	Description
`OperationCallable<BatchPredictRequest,BatchPredictResult,OperationMetadata>`

close()

public final void close()

getHttpJsonOperationsClient()

public final OperationsClient getHttpJsonOperationsClient()

Returns the OperationsClient that can be used to query the status of a long-running operation returned by another API method call.

Returns
Type	Description
`OperationsClient`

getOperationsClient()

public final OperationsClient getOperationsClient()

Returns the OperationsClient that can be used to query the status of a long-running operation returned by another API method call.

Returns
Type	Description
`OperationsClient`

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(ModelName name, ExamplePayload payload, Map<String,String> params)

public final PredictResponse predict(ModelName name, ExamplePayload payload, Map<String,String> params)

Perform an online prediction. The prediction result is directly returned in the response. Available for following ML scenarios, and their expected request payloads:

AutoML Vision Classification

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Vision Object Detection

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Natural Language Classification

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Natural Language Entity Extraction

A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document in .PDF, .TIF or .TIFF format with size upto 20MB.

AutoML Natural Language Sentiment Analysis

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Translation

A TextSnippet up to 25,000 characters, UTF-8 encoded.

AutoML Tables

A row with column values matching the columns of the model, up to 5MB. Not available for FORECASTING prediction_type.

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   ModelName name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]");
   ExamplePayload payload = ExamplePayload.newBuilder().build();
   Map<String, String> params = new HashMap<>();
   PredictResponse response = predictionServiceClient.predict(name, payload, params);
 }

Parameters
Name	Description
`name`	`ModelName` Required. Name of the model requested to serve the prediction.
`payload`	`ExamplePayload` Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
`params`	`Map<String,String>` Additional domain-specific parameters, any string must be up to 25000 characters long. AutoML Vision Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. AutoML Vision Object Detection `score_threshold` : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` : (int64) The maximum number of bounding boxes returned. The default is 100. The number of returned bounding boxes might be limited by the server. AutoML Tables `feature_importance` : (boolean) Whether feature_importance is populated in the returned list of TablesAnnotation objects. The default is false.

Returns
Type	Description
`PredictResponse`

predict(PredictRequest request)

public final PredictResponse predict(PredictRequest request)

Perform an online prediction. The prediction result is directly returned in the response. Available for following ML scenarios, and their expected request payloads:

AutoML Vision Classification

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Vision Object Detection

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Natural Language Classification

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Natural Language Entity Extraction

A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document in .PDF, .TIF or .TIFF format with size upto 20MB.

AutoML Natural Language Sentiment Analysis

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Translation

A TextSnippet up to 25,000 characters, UTF-8 encoded.

AutoML Tables

A row with column values matching the columns of the model, up to 5MB. Not available for FORECASTING prediction_type.

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   PredictRequest request =
       PredictRequest.newBuilder()
           .setName(ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]").toString())
           .setPayload(ExamplePayload.newBuilder().build())
           .putAllParams(new HashMap<String, String>())
           .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 name, ExamplePayload payload, Map<String,String> params)

public final PredictResponse predict(String name, ExamplePayload payload, Map<String,String> params)

Perform an online prediction. The prediction result is directly returned in the response. Available for following ML scenarios, and their expected request payloads:

AutoML Vision Classification

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Vision Object Detection

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Natural Language Classification

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Natural Language Entity Extraction

A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document in .PDF, .TIF or .TIFF format with size upto 20MB.

AutoML Natural Language Sentiment Analysis

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Translation

A TextSnippet up to 25,000 characters, UTF-8 encoded.

AutoML Tables

A row with column values matching the columns of the model, up to 5MB. Not available for FORECASTING prediction_type.

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   String name = ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]").toString();
   ExamplePayload payload = ExamplePayload.newBuilder().build();
   Map<String, String> params = new HashMap<>();
   PredictResponse response = predictionServiceClient.predict(name, payload, params);
 }

Parameters
Name	Description
`name`	`String` Required. Name of the model requested to serve the prediction.
`payload`	`ExamplePayload` Required. Payload to perform a prediction on. The payload must match the problem type that the model was trained to solve.
`params`	`Map<String,String>` Additional domain-specific parameters, any string must be up to 25000 characters long. AutoML Vision Classification `score_threshold` : (float) A value from 0.0 to 1.0. When the model makes predictions for an image, it will only produce results that have at least this confidence score. The default is 0.5. AutoML Vision Object Detection `score_threshold` : (float) When Model detects objects on the image, it will only produce bounding boxes which have at least this confidence score. Value in 0 to 1 range, default is 0.5. `max_bounding_box_count` : (int64) The maximum number of bounding boxes returned. The default is 100. The number of returned bounding boxes might be limited by the server. AutoML Tables `feature_importance` : (boolean) Whether feature_importance is populated in the returned list of TablesAnnotation objects. The default is false.

Returns
Type	Description
`PredictResponse`

predictCallable()

public final UnaryCallable<PredictRequest,PredictResponse> predictCallable()

Perform an online prediction. The prediction result is directly returned in the response. Available for following ML scenarios, and their expected request payloads:

AutoML Vision Classification

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Vision Object Detection

An image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.

AutoML Natural Language Classification

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Natural Language Entity Extraction

A TextSnippet up to 10,000 characters, UTF-8 NFC encoded or a document in .PDF, .TIF or .TIFF format with size upto 20MB.

AutoML Natural Language Sentiment Analysis

A TextSnippet up to 60,000 characters, UTF-8 encoded or a document in .PDF, .TIF or .TIFF format with size upto 2MB.

AutoML Translation

A TextSnippet up to 25,000 characters, UTF-8 encoded.

AutoML Tables

A row with column values matching the columns of the model, up to 5MB. Not available for FORECASTING prediction_type.

Sample code:


 // This snippet has been automatically generated and should be regarded as a code template only.
 // It will require modifications to work:
 // - It may require correct/in-range values for request initialization.
 // - It may require specifying regional endpoints when creating the service client as shown in
 // https://cloud.google.com/java/docs/setup#configure_endpoints_for_the_client_library
 try (PredictionServiceClient predictionServiceClient = PredictionServiceClient.create()) {
   PredictRequest request =
       PredictRequest.newBuilder()
           .setName(ModelName.of("[PROJECT]", "[LOCATION]", "[MODEL]").toString())
           .setPayload(ExamplePayload.newBuilder().build())
           .putAllParams(new HashMap<String, String>())
           .build();
   ApiFuture<PredictResponse> future =
       predictionServiceClient.predictCallable().futureCall(request);
   // Do something.
   PredictResponse response = future.get();
 }

Returns
Type	Description
`UnaryCallable<PredictRequest,PredictResponse>`

shutdown()

public void shutdown()

shutdownNow()

public void shutdownNow()