テキスト感情分析モデルから予測を取得する

 import com.google.api.gax.longrunning.OperationFuture; import com.google.api.gax.longrunning.OperationTimedPollAlgorithm; import com.google.api.gax.retrying.RetrySettings; import com.google.cloud.aiplatform.v1.AutomaticResources; import com.google.cloud.aiplatform.v1.DedicatedResources; import com.google.cloud.aiplatform.v1.DeployModelOperationMetadata; import com.google.cloud.aiplatform.v1.DeployModelResponse; import com.google.cloud.aiplatform.v1.DeployedModel; import com.google.cloud.aiplatform.v1.EndpointName; import com.google.cloud.aiplatform.v1.EndpointServiceClient; import com.google.cloud.aiplatform.v1.EndpointServiceSettings; import com.google.cloud.aiplatform.v1.MachineSpec; import com.google.cloud.aiplatform.v1.ModelName; import com.google.cloud.aiplatform.v1.stub.EndpointServiceStubSettings; import java.io.IOException; import java.util.HashMap; import java.util.Map; import java.util.concurrent.ExecutionException; import java.util.concurrent.TimeUnit; import java.util.concurrent.TimeoutException; import org.threeten.bp.Duration;  public class DeployModelSample {    public static void main(String[] args)       throws IOException, InterruptedException, ExecutionException, TimeoutException {     // TODO(developer): Replace these variables before running the sample.     String project = "YOUR_PROJECT_ID";     String deployedModelDisplayName = "YOUR_DEPLOYED_MODEL_DISPLAY_NAME";     String endpointId = "YOUR_ENDPOINT_NAME";     String modelId = "YOUR_MODEL_ID";     int timeout = 900;     deployModelSample(project, deployedModelDisplayName, endpointId, modelId, timeout);   }    static void deployModelSample(       String project,       String deployedModelDisplayName,       String endpointId,       String modelId,       int timeout)       throws IOException, InterruptedException, ExecutionException, TimeoutException {      // Set long-running operations (LROs) timeout     final OperationTimedPollAlgorithm operationTimedPollAlgorithm =         OperationTimedPollAlgorithm.create(             RetrySettings.newBuilder()                 .setInitialRetryDelay(Duration.ofMillis(5000L))                 .setRetryDelayMultiplier(1.5)                 .setMaxRetryDelay(Duration.ofMillis(45000L))                 .setInitialRpcTimeout(Duration.ZERO)                 .setRpcTimeoutMultiplier(1.0)                 .setMaxRpcTimeout(Duration.ZERO)                 .setTotalTimeout(Duration.ofSeconds(timeout))                 .build());      EndpointServiceStubSettings.Builder endpointServiceStubSettingsBuilder =         EndpointServiceStubSettings.newBuilder();     endpointServiceStubSettingsBuilder         .deployModelOperationSettings()         .setPollingAlgorithm(operationTimedPollAlgorithm);     EndpointServiceStubSettings endpointStubSettings = endpointServiceStubSettingsBuilder.build();     EndpointServiceSettings endpointServiceSettings =         EndpointServiceSettings.create(endpointStubSettings);     endpointServiceSettings =         endpointServiceSettings.toBuilder()             .setEndpoint("us-central1-aiplatform.googleapis.com:443")             .build();      // Initialize client that will be used to send requests. This client only needs to be created     // once, and can be reused for multiple requests. After completing all of your requests, call     // the "close" method on the client to safely clean up any remaining background resources.     try (EndpointServiceClient endpointServiceClient =         EndpointServiceClient.create(endpointServiceSettings)) {       String location = "us-central1";       EndpointName endpointName = EndpointName.of(project, location, endpointId);       // key '0' assigns traffic for the newly deployed model       // Traffic percentage values must add up to 100       // Leave dictionary empty if endpoint should not accept any traffic       Map<String, Integer> trafficSplit = new HashMap<>();       trafficSplit.put("0", 100);       ModelName modelName = ModelName.of(project, location, modelId);       AutomaticResources automaticResourcesInput =           AutomaticResources.newBuilder().setMinReplicaCount(1).setMaxReplicaCount(1).build();       DeployedModel deployedModelInput =           DeployedModel.newBuilder()               .setModel(modelName.toString())               .setDisplayName(deployedModelDisplayName)               .setAutomaticResources(automaticResourcesInput)               .build();        OperationFuture<DeployModelResponse, DeployModelOperationMetadata> deployModelResponseFuture =           endpointServiceClient.deployModelAsync(endpointName, deployedModelInput, trafficSplit);       System.out.format(           "Operation name: %s\n", deployModelResponseFuture.getInitialFuture().get().getName());       System.out.println("Waiting for operation to finish...");       DeployModelResponse deployModelResponse = deployModelResponseFuture.get(20, TimeUnit.MINUTES);        System.out.println("Deploy Model Response");       DeployedModel deployedModel = deployModelResponse.getDeployedModel();       System.out.println("\tDeployed Model");       System.out.format("\t\tid: %s\n", deployedModel.getId());       System.out.format("\t\tmodel: %s\n", deployedModel.getModel());       System.out.format("\t\tDisplay Name: %s\n", deployedModel.getDisplayName());       System.out.format("\t\tCreate Time: %s\n", deployedModel.getCreateTime());        DedicatedResources dedicatedResources = deployedModel.getDedicatedResources();       System.out.println("\t\tDedicated Resources");       System.out.format("\t\t\tMin Replica Count: %s\n", dedicatedResources.getMinReplicaCount());        MachineSpec machineSpec = dedicatedResources.getMachineSpec();       System.out.println("\t\t\tMachine Spec");       System.out.format("\t\t\t\tMachine Type: %s\n", machineSpec.getMachineType());       System.out.format("\t\t\t\tAccelerator Type: %s\n", machineSpec.getAcceleratorType());       System.out.format("\t\t\t\tAccelerator Count: %s\n", machineSpec.getAcceleratorCount());        AutomaticResources automaticResources = deployedModel.getAutomaticResources();       System.out.println("\t\tAutomatic Resources");       System.out.format("\t\t\tMin Replica Count: %s\n", automaticResources.getMinReplicaCount());       System.out.format("\t\t\tMax Replica Count: %s\n", automaticResources.getMaxReplicaCount());     }   } }

/**  * TODO(developer): Uncomment these variables before running the sample.\  * (Not necessary if passing values as arguments)  */  // const modelId = "YOUR_MODEL_ID"; // const endpointId = 'YOUR_ENDPOINT_ID'; // const deployedModelDisplayName = 'YOUR_DEPLOYED_MODEL_DISPLAY_NAME'; // const project = 'YOUR_PROJECT_ID'; // const location = 'YOUR_PROJECT_LOCATION';  const modelName = `projects/${project}/locations/${location}/models/${modelId}`; const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`; // Imports the Google Cloud Endpoint Service Client library const {EndpointServiceClient} = require('@google-cloud/aiplatform');  // Specifies the location of the api endpoint: const clientOptions = {   apiEndpoint: 'us-central1-aiplatform.googleapis.com', };  // Instantiates a client const endpointServiceClient = new EndpointServiceClient(clientOptions);  async function deployModel() {   // Configure the parent resource   // key '0' assigns traffic for the newly deployed model   // Traffic percentage values must add up to 100   // Leave dictionary empty if endpoint should not accept any traffic   const trafficSplit = {0: 100};   const deployedModel = {     // format: 'projects/{project}/locations/{location}/models/{model}'     model: modelName,     displayName: deployedModelDisplayName,     automaticResources: {minReplicaCount: 1, maxReplicaCount: 1},   };   const request = {     endpoint,     deployedModel,     trafficSplit,   };    // Get and print out a list of all the endpoints for this resource   const [response] = await endpointServiceClient.deployModel(request);   console.log(`Long running operation : ${response.name}`);    // Wait for operation to complete   await response.promise();   const result = response.result;    console.log('Deploy model response');   const modelDeployed = result.deployedModel;   console.log('\tDeployed model');   if (!modelDeployed) {     console.log('\t\tId : {}');     console.log('\t\tModel : {}');     console.log('\t\tDisplay name : {}');     console.log('\t\tCreate time : {}');      console.log('\t\tDedicated resources');     console.log('\t\t\tMin replica count : {}');     console.log('\t\t\tMachine spec {}');     console.log('\t\t\t\tMachine type : {}');     console.log('\t\t\t\tAccelerator type : {}');     console.log('\t\t\t\tAccelerator count : {}');      console.log('\t\tAutomatic resources');     console.log('\t\t\tMin replica count : {}');     console.log('\t\t\tMax replica count : {}');   } else {     console.log(`\t\tId : ${modelDeployed.id}`);     console.log(`\t\tModel : ${modelDeployed.model}`);     console.log(`\t\tDisplay name : ${modelDeployed.displayName}`);     console.log(`\t\tCreate time : ${modelDeployed.createTime}`);      const dedicatedResources = modelDeployed.dedicatedResources;     console.log('\t\tDedicated resources');     if (!dedicatedResources) {       console.log('\t\t\tMin replica count : {}');       console.log('\t\t\tMachine spec {}');       console.log('\t\t\t\tMachine type : {}');       console.log('\t\t\t\tAccelerator type : {}');       console.log('\t\t\t\tAccelerator count : {}');     } else {       console.log(         `\t\t\tMin replica count : \           ${dedicatedResources.minReplicaCount}`       );       const machineSpec = dedicatedResources.machineSpec;       console.log('\t\t\tMachine spec');       console.log(`\t\t\t\tMachine type : ${machineSpec.machineType}`);       console.log(         `\t\t\t\tAccelerator type : ${machineSpec.acceleratorType}`       );       console.log(         `\t\t\t\tAccelerator count : ${machineSpec.acceleratorCount}`       );     }      const automaticResources = modelDeployed.automaticResources;     console.log('\t\tAutomatic resources');     if (!automaticResources) {       console.log('\t\t\tMin replica count : {}');       console.log('\t\t\tMax replica count : {}');     } else {       console.log(         `\t\t\tMin replica count : \           ${automaticResources.minReplicaCount}`       );       console.log(         `\t\t\tMax replica count : \           ${automaticResources.maxReplicaCount}`       );     }   } } deployModel();

def deploy_model_with_automatic_resources_sample(     project,     location,     model_name: str,     endpoint: Optional[aiplatform.Endpoint] = None,     deployed_model_display_name: Optional[str] = None,     traffic_percentage: Optional[int] = 0,     traffic_split: Optional[Dict[str, int]] = None,     min_replica_count: int = 1,     max_replica_count: int = 1,     metadata: Optional[Sequence[Tuple[str, str]]] = (),     sync: bool = True, ):     """     model_name: A fully-qualified model resource name or model ID.           Example: "projects/123/locations/us-central1/models/456" or           "456" when project and location are initialized or passed.     """      aiplatform.init(project=project, location=location)      model = aiplatform.Model(model_name=model_name)      model.deploy(         endpoint=endpoint,         deployed_model_display_name=deployed_model_display_name,         traffic_percentage=traffic_percentage,         traffic_split=traffic_split,         min_replica_count=min_replica_count,         max_replica_count=max_replica_count,         metadata=metadata,         sync=sync,     )      model.wait()      print(model.display_name)     print(model.resource_name)     return model  

 import com.google.cloud.aiplatform.v1.EndpointName; import com.google.cloud.aiplatform.v1.PredictResponse; import com.google.cloud.aiplatform.v1.PredictionServiceClient; import com.google.cloud.aiplatform.v1.PredictionServiceSettings; import com.google.gson.JsonObject; import com.google.protobuf.Value; import com.google.protobuf.util.JsonFormat; import java.io.IOException; import java.util.ArrayList; import java.util.List;  public class PredictTextSentimentAnalysisSample {    public static void main(String[] args) throws IOException {     // TODO(developer): Replace these variables before running the sample.     String project = "YOUR_PROJECT_ID";     String content = "YOUR_TEXT_CONTENT";     String endpointId = "YOUR_ENDPOINT_ID";      predictTextSentimentAnalysis(project, content, endpointId);   }    static void predictTextSentimentAnalysis(String project, String content, String endpointId)       throws IOException {     PredictionServiceSettings predictionServiceSettings =         PredictionServiceSettings.newBuilder()             .setEndpoint("us-central1-aiplatform.googleapis.com:443")             .build();      // Initialize client that will be used to send requests. This client only needs to be created     // once, and can be reused for multiple requests. After completing all of your requests, call     // the "close" method on the client to safely clean up any remaining background resources.     try (PredictionServiceClient predictionServiceClient =         PredictionServiceClient.create(predictionServiceSettings)) {       String location = "us-central1";        // Use JsonObject to ensure safe serialization of the content; handles characters like `"`.       JsonObject contentJsonObject = new JsonObject();       contentJsonObject.addProperty("content", content);        EndpointName endpointName = EndpointName.of(project, location, endpointId);        Value parameter = Value.newBuilder().setNumberValue(0).setNumberValue(5).build();       Value.Builder instance = Value.newBuilder();       JsonFormat.parser().merge(contentJsonObject.toString(), instance);        List<Value> instances = new ArrayList<>();       instances.add(instance.build());        PredictResponse predictResponse =           predictionServiceClient.predict(endpointName, instances, parameter);       System.out.println("Predict Text Sentiment Analysis Response");       System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());        System.out.println("Predictions");       for (Value prediction : predictResponse.getPredictionsList()) {         System.out.format("\tPrediction: %s\n", prediction);       }     }   } }

/**  * TODO(developer): Uncomment these variables before running the sample.\  * (Not necessary if passing values as arguments)  */  // const text = "YOUR_PREDICTION_TEXT"; // const endpointId = "YOUR_ENDPOINT_ID"; // const project = 'YOUR_PROJECT_ID'; // const location = 'YOUR_PROJECT_LOCATION'; const aiplatform = require('@google-cloud/aiplatform'); const {instance, prediction} =   aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;  // Imports the Google Cloud Model Service Client library const {PredictionServiceClient} = aiplatform.v1;  // Specifies the location of the api endpoint const clientOptions = {   apiEndpoint: 'us-central1-aiplatform.googleapis.com', };  // Instantiates a client const predictionServiceClient = new PredictionServiceClient(clientOptions);  async function predictTextSentimentAnalysis() {   // Configure the endpoint resource   const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;    const instanceObj = new instance.TextSentimentPredictionInstance({     content: text,   });   const instanceVal = instanceObj.toValue();    const instances = [instanceVal];   const request = {     endpoint,     instances,   };    // Predict request   const [response] = await predictionServiceClient.predict(request);    console.log('Predict text sentiment analysis response:');   console.log(`\tDeployed model id : ${response.deployedModelId}`);    console.log('\nPredictions :');   for (const predictionResultValue of response.predictions) {     const predictionResult =       prediction.TextSentimentPredictionResult.fromValue(         predictionResultValue       );     console.log(`\tSentiment measure: ${predictionResult.sentiment}`);   } } predictTextSentimentAnalysis();

def predict_text_sentiment_analysis_sample(project, location, endpoint_id, content):      aiplatform.init(project=project, location=location)      endpoint = aiplatform.Endpoint(endpoint_id)      response = endpoint.predict(instances=[{"content": content}], parameters={})      for prediction_ in response.predictions:         print(prediction_)  

import com.google.api.gax.rpc.ApiException; import com.google.cloud.aiplatform.v1.BatchPredictionJob; import com.google.cloud.aiplatform.v1.GcsDestination; import com.google.cloud.aiplatform.v1.GcsSource; import com.google.cloud.aiplatform.v1.JobServiceClient; import com.google.cloud.aiplatform.v1.JobServiceSettings; import com.google.cloud.aiplatform.v1.LocationName; import com.google.cloud.aiplatform.v1.ModelName; import java.io.IOException;  public class CreateBatchPredictionJobTextSentimentAnalysisSample {    public static void main(String[] args) throws IOException {     // TODO(developer): Replace these variables before running the sample.     String project = "PROJECT";     String location = "us-central1";     String displayName = "DISPLAY_NAME";     String modelId = "MODEL_ID";     String gcsSourceUri = "GCS_SOURCE_URI";     String gcsDestinationOutputUriPrefix = "GCS_DESTINATION_OUTPUT_URI_PREFIX";     createBatchPredictionJobTextSentimentAnalysisSample(         project, location, displayName, modelId, gcsSourceUri, gcsDestinationOutputUriPrefix);   }    static void createBatchPredictionJobTextSentimentAnalysisSample(       String project,       String location,       String displayName,       String modelId,       String gcsSourceUri,       String gcsDestinationOutputUriPrefix)       throws IOException {     // The AI Platform services require regional API endpoints.     JobServiceSettings settings =         JobServiceSettings.newBuilder()             .setEndpoint("us-central1-aiplatform.googleapis.com:443")             .build();      // Initialize client that will be used to send requests. This client only needs to be created     // once, and can be reused for multiple requests. After completing all of your requests, call     // the "close" method on the client to safely clean up any remaining background resources.     try (JobServiceClient client = JobServiceClient.create(settings)) {       try {         String modelName = ModelName.of(project, location, modelId).toString();         GcsSource gcsSource = GcsSource.newBuilder().addUris(gcsSourceUri).build();         BatchPredictionJob.InputConfig inputConfig =             BatchPredictionJob.InputConfig.newBuilder()                 .setInstancesFormat("jsonl")                 .setGcsSource(gcsSource)                 .build();         GcsDestination gcsDestination =             GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();         BatchPredictionJob.OutputConfig outputConfig =             BatchPredictionJob.OutputConfig.newBuilder()                 .setPredictionsFormat("jsonl")                 .setGcsDestination(gcsDestination)                 .build();         BatchPredictionJob batchPredictionJob =             BatchPredictionJob.newBuilder()                 .setDisplayName(displayName)                 .setModel(modelName)                 .setInputConfig(inputConfig)                 .setOutputConfig(outputConfig)                 .build();         LocationName parent = LocationName.of(project, location);         BatchPredictionJob response = client.createBatchPredictionJob(parent, batchPredictionJob);         System.out.format("response: %s\n", response);       } catch (ApiException ex) {         System.out.format("Exception: %s\n", ex.getLocalizedMessage());       }     }   } } 

/**  * TODO(developer): Uncomment these variables before running the sample.\  * (Not necessary if passing values as arguments)  */  // const batchPredictionDisplayName = 'YOUR_BATCH_PREDICTION_DISPLAY_NAME'; // const modelId = 'YOUR_MODEL_ID'; // const gcsSourceUri = 'YOUR_GCS_SOURCE_URI'; // const gcsDestinationOutputUriPrefix = 'YOUR_GCS_DEST_OUTPUT_URI_PREFIX'; //    eg. "gs://<your-gcs-bucket>/destination_path" // const project = 'YOUR_PROJECT_ID'; // const location = 'YOUR_PROJECT_LOCATION';  // Imports the Google Cloud Job Service Client library const {JobServiceClient} = require('@google-cloud/aiplatform').v1;  // Specifies the location of the api endpoint const clientOptions = {   apiEndpoint: 'us-central1-aiplatform.googleapis.com', };  // Instantiates a client const jobServiceClient = new JobServiceClient(clientOptions);  async function createBatchPredictionJobTextSentimentAnalysis() {   // Configure the parent resource   const parent = `projects/${project}/locations/${location}`;   const modelName = `projects/${project}/locations/${location}/models/${modelId}`;    const inputConfig = {     instancesFormat: 'jsonl',     gcsSource: {uris: [gcsSourceUri]},   };   const outputConfig = {     predictionsFormat: 'jsonl',     gcsDestination: {outputUriPrefix: gcsDestinationOutputUriPrefix},   };   const batchPredictionJob = {     displayName: batchPredictionDisplayName,     model: modelName,     inputConfig,     outputConfig,   };   const request = {     parent,     batchPredictionJob,   };    // Create batch prediction job request   const [response] = await jobServiceClient.createBatchPredictionJob(request);    console.log('Create batch prediction job text sentiment analysis response');   console.log(`Name : ${response.name}`);   console.log('Raw response:');   console.log(JSON.stringify(response, null, 2)); } createBatchPredictionJobTextSentimentAnalysis();

def create_batch_prediction_job_sample(     project: str,     location: str,     model_resource_name: str,     job_display_name: str,     gcs_source: Union[str, Sequence[str]],     gcs_destination: str,     sync: bool = True, ):     aiplatform.init(project=project, location=location)      my_model = aiplatform.Model(model_resource_name)      batch_prediction_job = my_model.batch_predict(         job_display_name=job_display_name,         gcs_source=gcs_source,         gcs_destination_prefix=gcs_destination,         sync=sync,     )      batch_prediction_job.wait()      print(batch_prediction_job.display_name)     print(batch_prediction_job.resource_name)     print(batch_prediction_job.state)     return batch_prediction_job