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

Example 1 with ParameterSpec

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

the class CreateHyperparameterTuningJobPythonPackageSample method createHyperparameterTuningJobPythonPackageSample.

static void createHyperparameterTuningJobPythonPackageSample(String project, String displayName, String executorImageUri, String packageUri, String pythonModule) 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)) {
        // study spec
        MetricSpec metric = MetricSpec.newBuilder().setMetricId("val_rmse").setGoal(GoalType.MINIMIZE).build();
        // decay
        DoubleValueSpec doubleValueSpec = DoubleValueSpec.newBuilder().setMinValue(1e-07).setMaxValue(1).build();
        ParameterSpec parameterDecaySpec = ParameterSpec.newBuilder().setParameterId("decay").setDoubleValueSpec(doubleValueSpec).setScaleType(ScaleType.UNIT_LINEAR_SCALE).build();
        Double[] decayValues = { 32.0, 64.0 };
        DiscreteValueCondition discreteValueDecay = DiscreteValueCondition.newBuilder().addAllValues(Arrays.asList(decayValues)).build();
        ConditionalParameterSpec conditionalParameterDecay = ConditionalParameterSpec.newBuilder().setParameterSpec(parameterDecaySpec).setParentDiscreteValues(discreteValueDecay).build();
        // learning rate
        ParameterSpec parameterLearningSpec = ParameterSpec.newBuilder().setParameterId("learning_rate").setDoubleValueSpec(// Use the same min/max as for decay
        doubleValueSpec).setScaleType(ScaleType.UNIT_LINEAR_SCALE).build();
        Double[] learningRateValues = { 4.0, 8.0, 16.0 };
        DiscreteValueCondition discreteValueLearning = DiscreteValueCondition.newBuilder().addAllValues(Arrays.asList(learningRateValues)).build();
        ConditionalParameterSpec conditionalParameterLearning = ConditionalParameterSpec.newBuilder().setParameterSpec(parameterLearningSpec).setParentDiscreteValues(discreteValueLearning).build();
        // batch size
        Double[] batchSizeValues = { 4.0, 8.0, 16.0, 32.0, 64.0, 128.0 };
        DiscreteValueSpec discreteValueSpec = DiscreteValueSpec.newBuilder().addAllValues(Arrays.asList(batchSizeValues)).build();
        ParameterSpec parameter = ParameterSpec.newBuilder().setParameterId("batch_size").setDiscreteValueSpec(discreteValueSpec).setScaleType(ScaleType.UNIT_LINEAR_SCALE).addConditionalParameterSpecs(conditionalParameterDecay).addConditionalParameterSpecs(conditionalParameterLearning).build();
        // trial_job_spec
        MachineSpec machineSpec = MachineSpec.newBuilder().setMachineType("n1-standard-4").setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80).setAcceleratorCount(1).build();
        PythonPackageSpec pythonPackageSpec = PythonPackageSpec.newBuilder().setExecutorImageUri(executorImageUri).addPackageUris(packageUri).setPythonModule(pythonModule).build();
        WorkerPoolSpec workerPoolSpec = WorkerPoolSpec.newBuilder().setMachineSpec(machineSpec).setReplicaCount(1).setPythonPackageSpec(pythonPackageSpec).build();
        StudySpec studySpec = StudySpec.newBuilder().addMetrics(metric).addParameters(parameter).setAlgorithm(StudySpec.Algorithm.RANDOM_SEARCH).build();
        CustomJobSpec trialJobSpec = CustomJobSpec.newBuilder().addWorkerPoolSpecs(workerPoolSpec).build();
        // hyperparameter_tuning_job
        HyperparameterTuningJob hyperparameterTuningJob = HyperparameterTuningJob.newBuilder().setDisplayName(displayName).setMaxTrialCount(4).setParallelTrialCount(2).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());
    }
}