Class PredictionServiceClient (2.1.23)

public class PredictionServiceClient implements BackgroundResource

Service Description: AutoML Prediction API.

On any input that is documented to expect a string parameter in snake_case or kebab-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:


 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:


 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)

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 problems:

Image Classification
Image Object Detection
Video Classification
Video Object Tracking * Text Extraction
Tables

Sample code:


 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)

Image Classification
Image Object Detection
Video Classification
Video Object Tracking * Text Extraction
Tables

Sample code:


 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>` Required. Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. For Text 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. For Image 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. For Image 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) No more than this number of bounding boxes will be produced per image. Default is 100, the requested value may be limited by server. For Video 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. 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. The default is "false". `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. 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. The default is "false". For Tables: feature_imp<span>ortan</span>ce - (boolean) Whether feature importance should be populated in the returned TablesAnnotations. The default is false. For Video 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) No more than this number of bounding boxes will be returned per frame. Default is 100, the requested value may be limited by server. `min_bounding_box_size` - (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size will be 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)

Image Classification
Image Object Detection
Video Classification
Video Object Tracking * Text Extraction
Tables

Sample code:


 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>` Required. Additional domain-specific parameters for the predictions, any string must be up to 25000 characters long. For Text 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. For Image 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. For Image 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) No more than this number of bounding boxes will be produced per image. Default is 100, the requested value may be limited by server. For Video 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. 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. The default is "false". `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. 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. The default is "false". For Tables: feature_imp<span>ortan</span>ce - (boolean) Whether feature importance should be populated in the returned TablesAnnotations. The default is false. For Video 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) No more than this number of bounding boxes will be returned per frame. Default is 100, the requested value may be limited by server. `min_bounding_box_size` - (float) Only bounding boxes with shortest edge at least that long as a relative value of video frame size will be returned. Value in 0 to 1 range. Default is 0.

Returns

Type	Description
OperationFuture<BatchPredictResult,OperationMetadata>

batchPredictCallable()

public final UnaryCallable<BatchPredictRequest,Operation> batchPredictCallable()

Image Classification
Image Object Detection
Video Classification
Video Object Tracking * Text Extraction
Tables

Sample code:


 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()

Image Classification
Image Object Detection
Video Classification
Video Object Tracking * Text Extraction
Tables

Sample code:


 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()

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 will be directly returned in the response. Available for following ML problems, and their expected request payloads:

Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded.
Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded.
Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded.
Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING

prediction_type.

Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded.

Sample code:


 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. For Image 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. * For Image 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) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server. For Tables: feature_imp<span>ortan</span>ce - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false.

Returns

Type	Description
PredictResponse

predict(PredictRequest request)

public final PredictResponse predict(PredictRequest request)

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

Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded.
Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded.
Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded.
Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING

prediction_type.

Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded.

Sample code:


 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 will be directly returned in the response. Available for following ML problems, and their expected request payloads:

Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded.
Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded.
Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded.
Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING

prediction_type.

Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded.

Sample code:


 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. For Image 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. * For Image 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) No more than this number of bounding boxes will be returned in the response. Default is 100, the requested value may be limited by server. For Tables: feature_imp<span>ortan</span>ce - (boolean) Whether feature importance should be populated in the returned TablesAnnotation. The default is false.

Returns

Type	Description
PredictResponse

predictCallable()

public final UnaryCallable<PredictRequest,PredictResponse> predictCallable()

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

Image Classification - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Image Object Detection - Image in .JPEG, .GIF or .PNG format, image_bytes up to 30MB.
Text Classification - TextSnippet, content up to 60,000 characters, UTF-8 encoded.
Text Extraction - TextSnippet, content up to 30,000 characters, UTF-8 NFC encoded.
Translation - TextSnippet, content up to 25,000 characters, UTF-8 encoded.
Tables - Row, with column values matching the columns of the model, up to 5MB. Not available for FORECASTING

prediction_type.

Text Sentiment - TextSnippet, content up 500 characters, UTF-8 encoded.

Sample code:


 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()