importcom.google.api.gax.longrunning.OperationFuture;importcom.google.api.gax.longrunning.OperationTimedPollAlgorithm;importcom.google.api.gax.retrying.RetrySettings;importcom.google.cloud.aiplatform.v1.AutomaticResources;importcom.google.cloud.aiplatform.v1.DedicatedResources;importcom.google.cloud.aiplatform.v1.DeployModelOperationMetadata;importcom.google.cloud.aiplatform.v1.DeployModelResponse;importcom.google.cloud.aiplatform.v1.DeployedModel;importcom.google.cloud.aiplatform.v1.EndpointName;importcom.google.cloud.aiplatform.v1.EndpointServiceClient;importcom.google.cloud.aiplatform.v1.EndpointServiceSettings;importcom.google.cloud.aiplatform.v1.MachineSpec;importcom.google.cloud.aiplatform.v1.ModelName;importcom.google.cloud.aiplatform.v1.stub.EndpointServiceStubSettings;importjava.io.IOException;importjava.util.HashMap;importjava.util.Map;importjava.util.concurrent.ExecutionException;importjava.util.concurrent.TimeUnit;importjava.util.concurrent.TimeoutException;importorg.threeten.bp.Duration;publicclassDeployModelSample{publicstaticvoidmain(String[]args)throwsIOException,InterruptedException,ExecutionException,TimeoutException{// TODO(developer): Replace these variables before running the sample.Stringproject="YOUR_PROJECT_ID";StringdeployedModelDisplayName="YOUR_DEPLOYED_MODEL_DISPLAY_NAME";StringendpointId="YOUR_ENDPOINT_NAME";StringmodelId="YOUR_MODEL_ID";inttimeout=900;deployModelSample(project,deployedModelDisplayName,endpointId,modelId,timeout);}staticvoiddeployModelSample(Stringproject,StringdeployedModelDisplayName,StringendpointId,StringmodelId,inttimeout)throwsIOException,InterruptedException,ExecutionException,TimeoutException{// Set long-running operations (LROs) timeoutfinalOperationTimedPollAlgorithmoperationTimedPollAlgorithm=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.BuilderendpointServiceStubSettingsBuilder=EndpointServiceStubSettings.newBuilder();endpointServiceStubSettingsBuilder.deployModelOperationSettings().setPollingAlgorithm(operationTimedPollAlgorithm);EndpointServiceStubSettingsendpointStubSettings=endpointServiceStubSettingsBuilder.build();EndpointServiceSettingsendpointServiceSettings=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(EndpointServiceClientendpointServiceClient=EndpointServiceClient.create(endpointServiceSettings)){Stringlocation="us-central1";EndpointNameendpointName=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 trafficMap<String,Integer>trafficSplit=newHashMap<>();trafficSplit.put("0",100);ModelNamemodelName=ModelName.of(project,location,modelId);AutomaticResourcesautomaticResourcesInput=AutomaticResources.newBuilder().setMinReplicaCount(1).setMaxReplicaCount(1).build();DeployedModeldeployedModelInput=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...");DeployModelResponsedeployModelResponse=deployModelResponseFuture.get(20,TimeUnit.MINUTES);System.out.println("Deploy Model Response");DeployedModeldeployedModel=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());DedicatedResourcesdedicatedResources=deployedModel.getDedicatedResources();System.out.println("\t\tDedicated Resources");System.out.format("\t\t\tMin Replica Count: %s\n",dedicatedResources.getMinReplicaCount());MachineSpecmachineSpec=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());AutomaticResourcesautomaticResources=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';constmodelName=`projects/${project}/locations/${location}/models/${modelId}`;constendpoint=`projects/${project}/locations/${location}/endpoints/${endpointId}`;// Imports the Google Cloud Endpoint Service Client libraryconst{EndpointServiceClient}=require('@google-cloud/aiplatform');// Specifies the location of the api endpoint:constclientOptions={apiEndpoint:'us-central1-aiplatform.googleapis.com',};// Instantiates a clientconstendpointServiceClient=newEndpointServiceClient(clientOptions);asyncfunctiondeployModel(){// 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 trafficconsttrafficSplit={0:100};constdeployedModel={// format: 'projects/{project}/locations/{location}/models/{model}'model:modelName,displayName:deployedModelDisplayName,automaticResources:{minReplicaCount:1,maxReplicaCount:1},};constrequest={endpoint,deployedModel,trafficSplit,};// Get and print out a list of all the endpoints for this resourceconst[response]=awaitendpointServiceClient.deployModel(request);console.log(`Long running operation :${response.name}`);// Wait for operation to completeawaitresponse.promise();constresult=response.result;console.log('Deploy model response');constmodelDeployed=result.deployedModel;console.log('\tDeployed model');if(!modelDeployed){console.log('\t\tId : {}');console.log('\t\tModel : {}');console.log('\t\tDisplay name : {}');console.log('\t\tCreate time : {}');console.log('\t\tDedicated resources');console.log('\t\t\tMin replica count : {}');console.log('\t\t\tMachine spec {}');console.log('\t\t\t\tMachine type : {}');console.log('\t\t\t\tAccelerator type : {}');console.log('\t\t\t\tAccelerator count : {}');console.log('\t\tAutomatic resources');console.log('\t\t\tMin replica count : {}');console.log('\t\t\tMax replica count : {}');}else{console.log(`\t\tId :${modelDeployed.id}`);console.log(`\t\tModel :${modelDeployed.model}`);console.log(`\t\tDisplay name :${modelDeployed.displayName}`);console.log(`\t\tCreate time :${modelDeployed.createTime}`);constdedicatedResources=modelDeployed.dedicatedResources;console.log('\t\tDedicated resources');if(!dedicatedResources){console.log('\t\t\tMin replica count : {}');console.log('\t\t\tMachine spec {}');console.log('\t\t\t\tMachine type : {}');console.log('\t\t\t\tAccelerator type : {}');console.log('\t\t\t\tAccelerator count : {}');}else{console.log(`\t\t\tMin replica count : \${dedicatedResources.minReplicaCount}`);constmachineSpec=dedicatedResources.machineSpec;console.log('\t\t\tMachine spec');console.log(`\t\t\t\tMachine type :${machineSpec.machineType}`);console.log(`\t\t\t\tAccelerator type :${machineSpec.acceleratorType}`);console.log(`\t\t\t\tAccelerator count :${machineSpec.acceleratorCount}`);}constautomaticResources=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();

defdeploy_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)returnmodel

importcom.google.cloud.aiplatform.util.ValueConverter;importcom.google.cloud.aiplatform.v1.EndpointName;importcom.google.cloud.aiplatform.v1.PredictResponse;importcom.google.cloud.aiplatform.v1.PredictionServiceClient;importcom.google.cloud.aiplatform.v1.PredictionServiceSettings;importcom.google.cloud.aiplatform.v1.schema.predict.instance.ImageObjectDetectionPredictionInstance;importcom.google.cloud.aiplatform.v1.schema.predict.params.ImageObjectDetectionPredictionParams;importcom.google.cloud.aiplatform.v1.schema.predict.prediction.ImageObjectDetectionPredictionResult;importcom.google.protobuf.Value;importjava.io.IOException;importjava.nio.charset.StandardCharsets;importjava.nio.file.Files;importjava.nio.file.Paths;importjava.util.ArrayList;importjava.util.Base64;importjava.util.List;publicclassPredictImageObjectDetectionSample{publicstaticvoidmain(String[]args)throwsIOException{// TODO(developer): Replace these variables before running the sample.Stringproject="YOUR_PROJECT_ID";StringfileName="YOUR_IMAGE_FILE_PATH";StringendpointId="YOUR_ENDPOINT_ID";predictImageObjectDetection(project,fileName,endpointId);}staticvoidpredictImageObjectDetection(Stringproject,StringfileName,StringendpointId)throwsIOException{PredictionServiceSettingssettings=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(PredictionServiceClientpredictionServiceClient=PredictionServiceClient.create(settings)){Stringlocation="us-central1";EndpointNameendpointName=EndpointName.of(project,location,endpointId);byte[]contents=Base64.getEncoder().encode(Files.readAllBytes(Paths.get(fileName)));Stringcontent=newString(contents,StandardCharsets.UTF_8);ImageObjectDetectionPredictionParamsparams=ImageObjectDetectionPredictionParams.newBuilder().setConfidenceThreshold((float)(0.5)).setMaxPredictions(5).build();ImageObjectDetectionPredictionInstanceinstance=ImageObjectDetectionPredictionInstance.newBuilder().setContent(content).build();List<Value>instances=newArrayList<>();instances.add(ValueConverter.toValue(instance));PredictResponsepredictResponse=predictionServiceClient.predict(endpointName,instances,ValueConverter.toValue(params));System.out.println("Predict Image Object Detection Response");System.out.format("\tDeployed Model Id: %s\n",predictResponse.getDeployedModelId());System.out.println("Predictions");for(Valueprediction:predictResponse.getPredictionsList()){ImageObjectDetectionPredictionResult.BuilderresultBuilder=ImageObjectDetectionPredictionResult.newBuilder();ImageObjectDetectionPredictionResultresult=(ImageObjectDetectionPredictionResult)ValueConverter.fromValue(resultBuilder,prediction);for(inti=0;i <result.getIdsCount();i++){System.out.printf("\tDisplay name: %s\n",result.getDisplayNames(i));System.out.printf("\tConfidences: %f\n",result.getConfidences(i));System.out.printf("\tIDs: %d\n",result.getIds(i));System.out.printf("\tBounding boxes: %s\n",result.getBboxes(i));}}}}}

/** * 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';constaiplatform=require('@google-cloud/aiplatform');const{instance,params,prediction}=aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;// Imports the Google Cloud Prediction Service Client libraryconst{PredictionServiceClient}=aiplatform.v1;// Specifies the location of the api endpointconstclientOptions={apiEndpoint:'us-central1-aiplatform.googleapis.com',};// Instantiates a clientconstpredictionServiceClient=newPredictionServiceClient(clientOptions);asyncfunctionpredictImageObjectDetection(){// Configure the endpoint resourceconstendpoint=`projects/${project}/locations/${location}/endpoints/${endpointId}`;constparametersObj=newparams.ImageObjectDetectionPredictionParams({confidenceThreshold:0.5,maxPredictions:5,});constparameters=parametersObj.toValue();constfs=require('fs');constimage=fs.readFileSync(filename,'base64');constinstanceObj=newinstance.ImageObjectDetectionPredictionInstance({content:image,});constinstanceVal=instanceObj.toValue();constinstances=[instanceVal];constrequest={endpoint,instances,parameters,};// Predict requestconst[response]=awaitpredictionServiceClient.predict(request);console.log('Predict image object detection response');console.log(`\tDeployed model id :${response.deployedModelId}`);constpredictions=response.predictions;console.log('Predictions :');for(constpredictionResultValofpredictions){constpredictionResultObj=prediction.ImageObjectDetectionPredictionResult.fromValue(predictionResultVal);for(const[i,label]ofpredictionResultObj.displayNames.entries()){console.log(`\tDisplay name:${label}`);console.log(`\tConfidences:${predictionResultObj.confidences[i]}`);console.log(`\tIDs:${predictionResultObj.ids[i]}`);console.log(`\tBounding boxes:${predictionResultObj.bboxes[i]}\n\n`);}}}predictImageObjectDetection();

importbase64fromgoogle.cloudimportaiplatformfromgoogle.cloud.aiplatform.gapic.schemaimportpredictdefpredict_image_object_detection_sample(project:str,endpoint_id:str,filename:str,location:str="us-central1",api_endpoint:str="us-central1-aiplatform.googleapis.com",):# The AI Platform services require regional API endpoints.client_options={"api_endpoint":api_endpoint}# Initialize client that will be used to create and send requests.# This client only needs to be created once, and can be reused for multiple requests.client=aiplatform.gapic.PredictionServiceClient(client_options=client_options)withopen(filename,"rb")asf:file_content=f.read()# The format of each instance should conform to the deployed model's prediction input schema.encoded_content=base64.b64encode(file_content).decode("utf-8")instance=predict.instance.ImageObjectDetectionPredictionInstance(content=encoded_content,).to_value()instances=[instance]# See gs://google-cloud-aiplatform/schema/predict/params/image_object_detection_1.0.0.yaml for the format of the parameters.parameters=predict.params.ImageObjectDetectionPredictionParams(confidence_threshold=0.5,max_predictions=5,).to_value()endpoint=client.endpoint_path(project=project,location=location,endpoint=endpoint_id)response=client.predict(endpoint=endpoint,instances=instances,parameters=parameters)print("response")print(" deployed_model_id:",response.deployed_model_id)# See gs://google-cloud-aiplatform/schema/predict/prediction/image_object_detection_1.0.0.yaml for the format of the predictions.predictions=response.predictionsforpredictioninpredictions:print(" prediction:",dict(prediction))

defcreate_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)returnbatch_prediction_job