Examples with MachineSpec com.google.cloud.aiplatform.v1.MachineSpec used on opensource projects

Example 1 with MachineSpec

use of com.google.cloud.aiplatform.v1.MachineSpec in project java-aiplatform by googleapis.

the class CreateHyperparameterTuningJobSample method createHyperparameterTuningJobSample.

static void createHyperparameterTuningJobSample(String project, String displayName, String containerImageUri) throws IOException {
    JobServiceSettings settings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    String location = "us-central1";
    // the "close" method on the client to safely clean up any remaining background resources.
    try (JobServiceClient client = JobServiceClient.create(settings)) {
        StudySpec.MetricSpec metric0 = StudySpec.MetricSpec.newBuilder().setMetricId("accuracy").setGoal(StudySpec.MetricSpec.GoalType.MAXIMIZE).build();
        StudySpec.ParameterSpec.DoubleValueSpec doubleValueSpec = StudySpec.ParameterSpec.DoubleValueSpec.newBuilder().setMinValue(0.001).setMaxValue(0.1).build();
        StudySpec.ParameterSpec parameter0 = StudySpec.ParameterSpec.newBuilder().setParameterId("lr").setDoubleValueSpec(doubleValueSpec).build();
        StudySpec studySpec = StudySpec.newBuilder().addMetrics(metric0).addParameters(parameter0).build();
        MachineSpec machineSpec = MachineSpec.newBuilder().setMachineType("n1-standard-4").setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80).setAcceleratorCount(1).build();
        ContainerSpec containerSpec = ContainerSpec.newBuilder().setImageUri(containerImageUri).build();
        WorkerPoolSpec workerPoolSpec0 = WorkerPoolSpec.newBuilder().setMachineSpec(machineSpec).setReplicaCount(1).setContainerSpec(containerSpec).build();
        CustomJobSpec trialJobSpec = CustomJobSpec.newBuilder().addWorkerPoolSpecs(workerPoolSpec0).build();
        HyperparameterTuningJob hyperparameterTuningJob = HyperparameterTuningJob.newBuilder().setDisplayName(displayName).setMaxTrialCount(2).setParallelTrialCount(1).setMaxFailedTrialCount(1).setStudySpec(studySpec).setTrialJobSpec(trialJobSpec).build();
        LocationName parent = LocationName.of(project, location);
        HyperparameterTuningJob response = client.createHyperparameterTuningJob(parent, hyperparameterTuningJob);
        System.out.format("response: %s\n", response);
        System.out.format("Name: %s\n", response.getName());
    }
}

Example 2 with MachineSpec

use of com.google.cloud.aiplatform.v1.MachineSpec in project java-aiplatform by googleapis.

the class CreateTrainingPipelineCustomTrainingManagedDatasetSample method createTrainingPipelineCustomTrainingManagedDatasetSample.

static void createTrainingPipelineCustomTrainingManagedDatasetSample(String project, String displayName, String modelDisplayName, String datasetId, String annotationSchemaUri, String trainingContainerSpecImageUri, String modelContainerSpecImageUri, String baseOutputUriPrefix) throws IOException {
    PipelineServiceSettings settings = PipelineServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    String location = "us-central1";
    // the "close" method on the client to safely clean up any remaining background resources.
    try (PipelineServiceClient client = PipelineServiceClient.create(settings)) {
        JsonArray jsonArgs = new JsonArray();
        jsonArgs.add("--model-dir=$(AIP_MODEL_DIR)");
        // training_task_inputs
        JsonObject jsonTrainingContainerSpec = new JsonObject();
        jsonTrainingContainerSpec.addProperty("imageUri", trainingContainerSpecImageUri);
        // AIP_MODEL_DIR is set by the service according to baseOutputDirectory.
        jsonTrainingContainerSpec.add("args", jsonArgs);
        JsonObject jsonMachineSpec = new JsonObject();
        jsonMachineSpec.addProperty("machineType", "n1-standard-8");
        JsonObject jsonTrainingWorkerPoolSpec = new JsonObject();
        jsonTrainingWorkerPoolSpec.addProperty("replicaCount", 1);
        jsonTrainingWorkerPoolSpec.add("machineSpec", jsonMachineSpec);
        jsonTrainingWorkerPoolSpec.add("containerSpec", jsonTrainingContainerSpec);
        JsonArray jsonWorkerPoolSpecs = new JsonArray();
        jsonWorkerPoolSpecs.add(jsonTrainingWorkerPoolSpec);
        JsonObject jsonBaseOutputDirectory = new JsonObject();
        jsonBaseOutputDirectory.addProperty("outputUriPrefix", baseOutputUriPrefix);
        JsonObject jsonTrainingTaskInputs = new JsonObject();
        jsonTrainingTaskInputs.add("workerPoolSpecs", jsonWorkerPoolSpecs);
        jsonTrainingTaskInputs.add("baseOutputDirectory", jsonBaseOutputDirectory);
        Value.Builder trainingTaskInputsBuilder = Value.newBuilder();
        JsonFormat.parser().merge(jsonTrainingTaskInputs.toString(), trainingTaskInputsBuilder);
        Value trainingTaskInputs = trainingTaskInputsBuilder.build();
        // model_to_upload
        ModelContainerSpec modelContainerSpec = ModelContainerSpec.newBuilder().setImageUri(modelContainerSpecImageUri).build();
        Model model = Model.newBuilder().setDisplayName(modelDisplayName).setContainerSpec(modelContainerSpec).build();
        GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(baseOutputUriPrefix).build();
        // input_data_config
        InputDataConfig inputDataConfig = InputDataConfig.newBuilder().setDatasetId(datasetId).setAnnotationSchemaUri(annotationSchemaUri).setGcsDestination(gcsDestination).build();
        // training_task_definition
        String customTaskDefinition = "gs://google-cloud-aiplatform/schema/trainingjob/definition/custom_task_1.0.0.yaml";
        TrainingPipeline trainingPipeline = TrainingPipeline.newBuilder().setDisplayName(displayName).setInputDataConfig(inputDataConfig).setTrainingTaskDefinition(customTaskDefinition).setTrainingTaskInputs(trainingTaskInputs).setModelToUpload(model).build();
        LocationName parent = LocationName.of(project, location);
        TrainingPipeline response = client.createTrainingPipeline(parent, trainingPipeline);
        System.out.format("response: %s\n", response);
        System.out.format("Name: %s\n", response.getName());
    }
}

Example 3 with MachineSpec

use of com.google.cloud.aiplatform.v1.MachineSpec in project java-aiplatform by googleapis.

the class CreateBatchPredictionJobVideoClassificationSample method createBatchPredictionJobVideoClassification.

static void createBatchPredictionJobVideoClassification(String batchPredictionDisplayName, String modelId, String gcsSourceUri, String gcsDestinationOutputUriPrefix, String project) throws IOException {
    JobServiceSettings jobServiceSettings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // the "close" method on the client to safely clean up any remaining background resources.
    try (JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings)) {
        String location = "us-central1";
        LocationName locationName = LocationName.of(project, location);
        VideoClassificationPredictionParams modelParamsObj = VideoClassificationPredictionParams.newBuilder().setConfidenceThreshold(((float) 0.5)).setMaxPredictions(10000).setSegmentClassification(true).setShotClassification(true).setOneSecIntervalClassification(true).build();
        Value modelParameters = ValueConverter.toValue(modelParamsObj);
        ModelName modelName = ModelName.of(project, location, modelId);
        GcsSource.Builder gcsSource = GcsSource.newBuilder();
        gcsSource.addUris(gcsSourceUri);
        InputConfig inputConfig = InputConfig.newBuilder().setInstancesFormat("jsonl").setGcsSource(gcsSource).build();
        GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
        OutputConfig outputConfig = OutputConfig.newBuilder().setPredictionsFormat("jsonl").setGcsDestination(gcsDestination).build();
        BatchPredictionJob batchPredictionJob = BatchPredictionJob.newBuilder().setDisplayName(batchPredictionDisplayName).setModel(modelName.toString()).setModelParameters(modelParameters).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
        BatchPredictionJob batchPredictionJobResponse = jobServiceClient.createBatchPredictionJob(locationName, batchPredictionJob);
        System.out.println("Create Batch Prediction Job Video Classification Response");
        System.out.format("\tName: %s\n", batchPredictionJobResponse.getName());
        System.out.format("\tDisplay Name: %s\n", batchPredictionJobResponse.getDisplayName());
        System.out.format("\tModel %s\n", batchPredictionJobResponse.getModel());
        System.out.format("\tModel Parameters: %s\n", batchPredictionJobResponse.getModelParameters());
        System.out.format("\tState: %s\n", batchPredictionJobResponse.getState());
        System.out.format("\tCreate Time: %s\n", batchPredictionJobResponse.getCreateTime());
        System.out.format("\tStart Time: %s\n", batchPredictionJobResponse.getStartTime());
        System.out.format("\tEnd Time: %s\n", batchPredictionJobResponse.getEndTime());
        System.out.format("\tUpdate Time: %s\n", batchPredictionJobResponse.getUpdateTime());
        System.out.format("\tLabels: %s\n", batchPredictionJobResponse.getLabelsMap());
        InputConfig inputConfigResponse = batchPredictionJobResponse.getInputConfig();
        System.out.println("\tInput Config");
        System.out.format("\t\tInstances Format: %s\n", inputConfigResponse.getInstancesFormat());
        GcsSource gcsSourceResponse = inputConfigResponse.getGcsSource();
        System.out.println("\t\tGcs Source");
        System.out.format("\t\t\tUris %s\n", gcsSourceResponse.getUrisList());
        BigQuerySource bigQuerySource = inputConfigResponse.getBigquerySource();
        System.out.println("\t\tBigquery Source");
        System.out.format("\t\t\tInput_uri: %s\n", bigQuerySource.getInputUri());
        OutputConfig outputConfigResponse = batchPredictionJobResponse.getOutputConfig();
        System.out.println("\tOutput Config");
        System.out.format("\t\tPredictions Format: %s\n", outputConfigResponse.getPredictionsFormat());
        GcsDestination gcsDestinationResponse = outputConfigResponse.getGcsDestination();
        System.out.println("\t\tGcs Destination");
        System.out.format("\t\t\tOutput Uri Prefix: %s\n", gcsDestinationResponse.getOutputUriPrefix());
        BigQueryDestination bigQueryDestination = outputConfigResponse.getBigqueryDestination();
        System.out.println("\t\tBig Query Destination");
        System.out.format("\t\t\tOutput Uri: %s\n", bigQueryDestination.getOutputUri());
        BatchDedicatedResources batchDedicatedResources = batchPredictionJobResponse.getDedicatedResources();
        System.out.println("\tBatch Dedicated Resources");
        System.out.format("\t\tStarting Replica Count: %s\n", batchDedicatedResources.getStartingReplicaCount());
        System.out.format("\t\tMax Replica Count: %s\n", batchDedicatedResources.getMaxReplicaCount());
        MachineSpec machineSpec = batchDedicatedResources.getMachineSpec();
        System.out.println("\t\tMachine Spec");
        System.out.format("\t\t\tMachine Type: %s\n", machineSpec.getMachineType());
        System.out.format("\t\t\tAccelerator Type: %s\n", machineSpec.getAcceleratorType());
        System.out.format("\t\t\tAccelerator Count: %s\n", machineSpec.getAcceleratorCount());
        ManualBatchTuningParameters manualBatchTuningParameters = batchPredictionJobResponse.getManualBatchTuningParameters();
        System.out.println("\tManual Batch Tuning Parameters");
        System.out.format("\t\tBatch Size: %s\n", manualBatchTuningParameters.getBatchSize());
        OutputInfo outputInfo = batchPredictionJobResponse.getOutputInfo();
        System.out.println("\tOutput Info");
        System.out.format("\t\tGcs Output Directory: %s\n", outputInfo.getGcsOutputDirectory());
        System.out.format("\t\tBigquery Output Dataset: %s\n", outputInfo.getBigqueryOutputDataset());
        Status status = batchPredictionJobResponse.getError();
        System.out.println("\tError");
        System.out.format("\t\tCode: %s\n", status.getCode());
        System.out.format("\t\tMessage: %s\n", status.getMessage());
        List<Any> details = status.getDetailsList();
        for (Status partialFailure : batchPredictionJobResponse.getPartialFailuresList()) {
            System.out.println("\tPartial Failure");
            System.out.format("\t\tCode: %s\n", partialFailure.getCode());
            System.out.format("\t\tMessage: %s\n", partialFailure.getMessage());
            List<Any> partialFailureDetailsList = partialFailure.getDetailsList();
        }
        ResourcesConsumed resourcesConsumed = batchPredictionJobResponse.getResourcesConsumed();
        System.out.println("\tResources Consumed");
        System.out.format("\t\tReplica Hours: %s\n", resourcesConsumed.getReplicaHours());
        CompletionStats completionStats = batchPredictionJobResponse.getCompletionStats();
        System.out.println("\tCompletion Stats");
        System.out.format("\t\tSuccessful Count: %s\n", completionStats.getSuccessfulCount());
        System.out.format("\t\tFailed Count: %s\n", completionStats.getFailedCount());
        System.out.format("\t\tIncomplete Count: %s\n", completionStats.getIncompleteCount());
    }
}

Example 4 with MachineSpec

use of com.google.cloud.aiplatform.v1.MachineSpec in project java-aiplatform by googleapis.

the class CreateBatchPredictionJobVideoObjectTrackingSample method batchPredictionJobVideoObjectTracking.

static void batchPredictionJobVideoObjectTracking(String batchPredictionDisplayName, String modelId, String gcsSourceUri, String gcsDestinationOutputUriPrefix, String project) throws IOException {
    JobServiceSettings jobServiceSettings = JobServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    // the "close" method on the client to safely clean up any remaining background resources.
    try (JobServiceClient jobServiceClient = JobServiceClient.create(jobServiceSettings)) {
        String location = "us-central1";
        LocationName locationName = LocationName.of(project, location);
        ModelName modelName = ModelName.of(project, location, modelId);
        VideoObjectTrackingPredictionParams modelParamsObj = VideoObjectTrackingPredictionParams.newBuilder().setConfidenceThreshold(((float) 0.5)).build();
        Value modelParameters = ValueConverter.toValue(modelParamsObj);
        GcsSource.Builder gcsSource = GcsSource.newBuilder();
        gcsSource.addUris(gcsSourceUri);
        InputConfig inputConfig = InputConfig.newBuilder().setInstancesFormat("jsonl").setGcsSource(gcsSource).build();
        GcsDestination gcsDestination = GcsDestination.newBuilder().setOutputUriPrefix(gcsDestinationOutputUriPrefix).build();
        OutputConfig outputConfig = OutputConfig.newBuilder().setPredictionsFormat("jsonl").setGcsDestination(gcsDestination).build();
        BatchPredictionJob batchPredictionJob = BatchPredictionJob.newBuilder().setDisplayName(batchPredictionDisplayName).setModel(modelName.toString()).setModelParameters(modelParameters).setInputConfig(inputConfig).setOutputConfig(outputConfig).build();
        BatchPredictionJob batchPredictionJobResponse = jobServiceClient.createBatchPredictionJob(locationName, batchPredictionJob);
        System.out.println("Create Batch Prediction Job Video Object Tracking Response");
        System.out.format("\tName: %s\n", batchPredictionJobResponse.getName());
        System.out.format("\tDisplay Name: %s\n", batchPredictionJobResponse.getDisplayName());
        System.out.format("\tModel %s\n", batchPredictionJobResponse.getModel());
        System.out.format("\tModel Parameters: %s\n", batchPredictionJobResponse.getModelParameters());
        System.out.format("\tState: %s\n", batchPredictionJobResponse.getState());
        System.out.format("\tCreate Time: %s\n", batchPredictionJobResponse.getCreateTime());
        System.out.format("\tStart Time: %s\n", batchPredictionJobResponse.getStartTime());
        System.out.format("\tEnd Time: %s\n", batchPredictionJobResponse.getEndTime());
        System.out.format("\tUpdate Time: %s\n", batchPredictionJobResponse.getUpdateTime());
        System.out.format("\tLabels: %s\n", batchPredictionJobResponse.getLabelsMap());
        InputConfig inputConfigResponse = batchPredictionJobResponse.getInputConfig();
        System.out.println("\tInput Config");
        System.out.format("\t\tInstances Format: %s\n", inputConfigResponse.getInstancesFormat());
        GcsSource gcsSourceResponse = inputConfigResponse.getGcsSource();
        System.out.println("\t\tGcs Source");
        System.out.format("\t\t\tUris %s\n", gcsSourceResponse.getUrisList());
        BigQuerySource bigQuerySource = inputConfigResponse.getBigquerySource();
        System.out.println("\t\tBigquery Source");
        System.out.format("\t\t\tInput_uri: %s\n", bigQuerySource.getInputUri());
        OutputConfig outputConfigResponse = batchPredictionJobResponse.getOutputConfig();
        System.out.println("\tOutput Config");
        System.out.format("\t\tPredictions Format: %s\n", outputConfigResponse.getPredictionsFormat());
        GcsDestination gcsDestinationResponse = outputConfigResponse.getGcsDestination();
        System.out.println("\t\tGcs Destination");
        System.out.format("\t\t\tOutput Uri Prefix: %s\n", gcsDestinationResponse.getOutputUriPrefix());
        BigQueryDestination bigQueryDestination = outputConfigResponse.getBigqueryDestination();
        System.out.println("\t\tBig Query Destination");
        System.out.format("\t\t\tOutput Uri: %s\n", bigQueryDestination.getOutputUri());
        BatchDedicatedResources batchDedicatedResources = batchPredictionJobResponse.getDedicatedResources();
        System.out.println("\tBatch Dedicated Resources");
        System.out.format("\t\tStarting Replica Count: %s\n", batchDedicatedResources.getStartingReplicaCount());
        System.out.format("\t\tMax Replica Count: %s\n", batchDedicatedResources.getMaxReplicaCount());
        MachineSpec machineSpec = batchDedicatedResources.getMachineSpec();
        System.out.println("\t\tMachine Spec");
        System.out.format("\t\t\tMachine Type: %s\n", machineSpec.getMachineType());
        System.out.format("\t\t\tAccelerator Type: %s\n", machineSpec.getAcceleratorType());
        System.out.format("\t\t\tAccelerator Count: %s\n", machineSpec.getAcceleratorCount());
        ManualBatchTuningParameters manualBatchTuningParameters = batchPredictionJobResponse.getManualBatchTuningParameters();
        System.out.println("\tManual Batch Tuning Parameters");
        System.out.format("\t\tBatch Size: %s\n", manualBatchTuningParameters.getBatchSize());
        OutputInfo outputInfo = batchPredictionJobResponse.getOutputInfo();
        System.out.println("\tOutput Info");
        System.out.format("\t\tGcs Output Directory: %s\n", outputInfo.getGcsOutputDirectory());
        System.out.format("\t\tBigquery Output Dataset: %s\n", outputInfo.getBigqueryOutputDataset());
        Status status = batchPredictionJobResponse.getError();
        System.out.println("\tError");
        System.out.format("\t\tCode: %s\n", status.getCode());
        System.out.format("\t\tMessage: %s\n", status.getMessage());
        List<Any> details = status.getDetailsList();
        for (Status partialFailure : batchPredictionJobResponse.getPartialFailuresList()) {
            System.out.println("\tPartial Failure");
            System.out.format("\t\tCode: %s\n", partialFailure.getCode());
            System.out.format("\t\tMessage: %s\n", partialFailure.getMessage());
            List<Any> partialFailureDetailsList = partialFailure.getDetailsList();
        }
        ResourcesConsumed resourcesConsumed = batchPredictionJobResponse.getResourcesConsumed();
        System.out.println("\tResources Consumed");
        System.out.format("\t\tReplica Hours: %s\n", resourcesConsumed.getReplicaHours());
        CompletionStats completionStats = batchPredictionJobResponse.getCompletionStats();
        System.out.println("\tCompletion Stats");
        System.out.format("\t\tSuccessful Count: %s\n", completionStats.getSuccessfulCount());
        System.out.format("\t\tFailed Count: %s\n", completionStats.getFailedCount());
        System.out.format("\t\tIncomplete Count: %s\n", completionStats.getIncompleteCount());
    }
}

Example 5 with MachineSpec

use of com.google.cloud.aiplatform.v1.MachineSpec in project java-aiplatform by googleapis.

the class DeployModelCustomTrainedModelSample method deployModelCustomTrainedModelSample.

static void deployModelCustomTrainedModelSample(String project, String endpointId, String model, String deployedModelDisplayName) throws IOException, ExecutionException, InterruptedException {
    EndpointServiceSettings settings = EndpointServiceSettings.newBuilder().setEndpoint("us-central1-aiplatform.googleapis.com:443").build();
    String location = "us-central1";
    // the "close" method on the client to safely clean up any remaining background resources.
    try (EndpointServiceClient client = EndpointServiceClient.create(settings)) {
        MachineSpec machineSpec = MachineSpec.newBuilder().setMachineType("n1-standard-2").build();
        DedicatedResources dedicatedResources = DedicatedResources.newBuilder().setMinReplicaCount(1).setMachineSpec(machineSpec).build();
        String modelName = ModelName.of(project, location, model).toString();
        DeployedModel deployedModel = DeployedModel.newBuilder().setModel(modelName).setDisplayName(deployedModelDisplayName).setDedicatedResources(dedicatedResources).build();
        // 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);
        EndpointName endpoint = EndpointName.of(project, location, endpointId);
        OperationFuture<DeployModelResponse, DeployModelOperationMetadata> response = client.deployModelAsync(endpoint, deployedModel, trafficSplit);
        // You can use OperationFuture.getInitialFuture to get a future representing the initial
        // response to the request, which contains information while the operation is in progress.
        System.out.format("Operation name: %s\n", response.getInitialFuture().get().getName());
        // OperationFuture.get() will block until the operation is finished.
        DeployModelResponse deployModelResponse = response.get();
        System.out.format("deployModelResponse: %s\n", deployModelResponse);
    }
}