透過自訂訓練模型取得線上推論

 import com.google.cloud.aiplatform.v1.EndpointName; import com.google.cloud.aiplatform.v1.PredictRequest; import com.google.cloud.aiplatform.v1.PredictResponse; import com.google.cloud.aiplatform.v1.PredictionServiceClient; import com.google.cloud.aiplatform.v1.PredictionServiceSettings; import com.google.protobuf.ListValue; import com.google.protobuf.Value; import com.google.protobuf.util.JsonFormat; import java.io.IOException; import java.util.List;  public class PredictCustomTrainedModelSample {   public static void main(String[] args) throws IOException {     // TODO(developer): Replace these variables before running the sample.     String instance = "[{ “feature_column_a”: “value”, “feature_column_b”: “value”}]";     String project = "YOUR_PROJECT_ID";     String endpointId = "YOUR_ENDPOINT_ID";     predictCustomTrainedModel(project, endpointId, instance);   }    static void predictCustomTrainedModel(String project, String endpointId, String instance)       throws IOException {     PredictionServicPredictionServiceSettingsceSettings =         PredictionServicPredictionServiceSettings          .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 (PredictionServicPredictionServiceClientceClient =         PredictionServicPredictionServiceClientonServiceSettings)) {       String location = "us-central1";       EndpointName endEndpointNameEndpointName.of(EndpointNameation, endpointId);        ListValue.BuildeListValueue = ListValue.newBuiListValue     JsonFormat.parseJsonFormatinstance, listValue);       List<Value> instanListValuelistValue.getValuesList();        PredictRequest pPredictRequest=           PredictRequest.nPredictRequest            .setEndpoint(endpointName.toSendpointName.toString().addAllInstances(instanceList)               .build();       PredictResponse PredictResponse = predictionServiceClient.predict(predictRequest);        System.out.println("Predict Custom Trained model Response");       System.out.format("\tDeployed Model Id: %s\n", predictResponse.predictResponse.getDeployedModelId()out.println("Predictions");       for (Value predictionValueedictResponse.predictResponse.getPredictionsList()em.out.format("\tPrediction: %s\n", prediction);       }     }   } }

/**  * TODO(developer): Uncomment these variables before running the sample.\  * (Not necessary if passing values as arguments)  */  // const filename = "YOUR_PREDICTION_FILE_NAME"; // const endpointId = "YOUR_ENDPOINT_ID"; // const project = 'YOUR_PROJECT_ID'; // const location = 'YOUR_PROJECT_LOCATION'; const util = require('util'); const {readFile} = require('fs'); const readFileAsync = util.promisify(readFile);  // Imports the Google Cloud Prediction Service Client library const {PredictionServiceClient} = require('@google-cloud/aiplatform');  // 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 predictCustomTrainedModel() {   // Configure the parent resource   const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;   const parameters = {     structValue: {       fields: {},     },   };   const instanceDict = await readFileAsync(filename, 'utf8');   const instanceValue = JSON.parse(instanceDict);   const instance = {     structValue: {       fields: {         Age: {stringValue: instanceValue['Age']},         Balance: {stringValue: instanceValue['Balance']},         Campaign: {stringValue: instanceValue['Campaign']},         Contact: {stringValue: instanceValue['Contact']},         Day: {stringValue: instanceValue['Day']},         Default: {stringValue: instanceValue['Default']},         Deposit: {stringValue: instanceValue['Deposit']},         Duration: {stringValue: instanceValue['Duration']},         Housing: {stringValue: instanceValue['Housing']},         Job: {stringValue: instanceValue['Job']},         Loan: {stringValue: instanceValue['Loan']},         MaritalStatus: {stringValue: instanceValue['MaritalStatus']},         Month: {stringValue: instanceValue['Month']},         PDays: {stringValue: instanceValue['PDays']},         POutcome: {stringValue: instanceValue['POutcome']},         Previous: {stringValue: instanceValue['Previous']},       },     },   };    const instances = [instance];   const request = {     endpoint,     instances,     parameters,   };    // Predict request   const [response] = await predictionServiceClient.predict(request);    console.log('Predict custom trained model response');   console.log(`\tDeployed model id : ${response.deployedModelId}`);   const predictions = response.predictions;   console.log('\tPredictions :');   for (const prediction of predictions) {     console.log(`\t\tPrediction : ${JSON.stringify(prediction)}`);   } } predictCustomTrainedModel();

def endpoint_predict_sample(     project: str, location: str, instances: list, endpoint: str ):     aiplatform.init(project=project, location=location)      endpoint = aiplatform.Endpoint(endpoint)      prediction = endpoint.predict(instances=instances)     print(prediction)     return prediction  

from google.cloud import aiplatform_v1   def sample_raw_predict():     # Create a client     client = aiplatform_v1.PredictionServiceClient()      # Initialize request argument(s)     request = aiplatform_v1.RawPredictRequest(         endpoint="endpoint_value",     )      # Make the request     response = client.raw_predict(request=request)      # Handle the response     print(response) 

def explain_tabular_sample(     project: str, location: str, endpoint_id: str, instance_dict: Dict ):      aiplatform.init(project=project, location=location)      endpoint = aiplatform.Endpoint(endpoint_id)      response = endpoint.explain(instances=[instance_dict], parameters={})      for explanation in response.explanations:         print(" explanation")         # Feature attributions.         attributions = explanation.attributions         for attribution in attributions:             print("  attribution")             print("   baseline_output_value:", attribution.baseline_output_value)             print("   instance_output_value:", attribution.instance_output_value)             print("   output_display_name:", attribution.output_display_name)             print("   approximation_error:", attribution.approximation_error)             print("   output_name:", attribution.output_name)             output_index = attribution.output_index             for output_index in output_index:                 print("   output_index:", output_index)      for prediction in response.predictions:         print(prediction)