ハイパーパラメータ チューニング ジョブを作成する

import com.google.cloud.aiplatform.v1.AcceleratorType;
import com.google.cloud.aiplatform.v1.CustomJobSpec;
import com.google.cloud.aiplatform.v1.HyperparameterTuningJob;
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.MachineSpec;
import com.google.cloud.aiplatform.v1.PythonPackageSpec;
import com.google.cloud.aiplatform.v1.StudySpec;
import com.google.cloud.aiplatform.v1.StudySpec.MetricSpec;
import com.google.cloud.aiplatform.v1.StudySpec.MetricSpec.GoalType;
import com.google.cloud.aiplatform.v1.StudySpec.ParameterSpec;
import com.google.cloud.aiplatform.v1.StudySpec.ParameterSpec.ConditionalParameterSpec;
import com.google.cloud.aiplatform.v1.StudySpec.ParameterSpec.ConditionalParameterSpec.DiscreteValueCondition;
import com.google.cloud.aiplatform.v1.StudySpec.ParameterSpec.DiscreteValueSpec;
import com.google.cloud.aiplatform.v1.StudySpec.ParameterSpec.DoubleValueSpec;
import com.google.cloud.aiplatform.v1.StudySpec.ParameterSpec.ScaleType;
import com.google.cloud.aiplatform.v1.WorkerPoolSpec;
import java.io.IOException;
import java.util.Arrays;

public class CreateHyperparameterTuningJobPythonPackageSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "PROJECT";
    String displayName = "DISPLAY_NAME";
    String executorImageUri = "EXECUTOR_IMAGE_URI";
    String packageUri = "PACKAGE_URI";
    String pythonModule = "PYTHON_MODULE";
    createHyperparameterTuningJobPythonPackageSample(
        project, displayName, executorImageUri, packageUri, pythonModule);
  }

  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";

    // 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)) {
      // 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(doubleValueSpec) // Use the same min/max as for decay
              .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_T4)
              .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());
    }
  }
}

from google.cloud import aiplatform

from google.cloud.aiplatform import hyperparameter_tuning as hpt


def create_hyperparameter_tuning_job_sample(
    project: str,
    location: str,
    staging_bucket: str,
    display_name: str,
    container_uri: str,
):
    aiplatform.init(project=project, location=location, staging_bucket=staging_bucket)

    worker_pool_specs = [
        {
            "machine_spec": {
                "machine_type": "n1-standard-4",
                "accelerator_type": "NVIDIA_TESLA_K80",
                "accelerator_count": 1,
            },
            "replica_count": 1,
            "container_spec": {
                "image_uri": container_uri,
                "command": [],
                "args": [],
            },
        }
    ]

    custom_job = aiplatform.CustomJob(
        display_name='custom_job',
        worker_pool_specs=worker_pool_specs,
    )

    hpt_job = aiplatform.HyperparameterTuningJob(
        display_name=display_name,
        custom_job=custom_job,
        metric_spec={
            'loss': 'minimize',
        },
        parameter_spec={
            'lr': hpt.DoubleParameterSpec(min=0.001, max=0.1, scale='log'),
            'units': hpt.IntegerParameterSpec(min=4, max=128, scale='linear'),
            'activation': hpt.CategoricalParameterSpec(values=['relu', 'selu']),
            'batch_size': hpt.DiscreteParameterSpec(values=[128, 256], scale='linear')
        },
        max_trial_count=128,
        parallel_trial_count=8,
        labels={'my_key': 'my_value'},
    )

    hpt_job.run()

    print(hpt_job.resource_name)
    return hpt_job

import com.google.cloud.aiplatform.v1.HyperparameterTuningJob;
import com.google.cloud.aiplatform.v1.HyperparameterTuningJobName;
import com.google.cloud.aiplatform.v1.JobServiceClient;
import com.google.cloud.aiplatform.v1.JobServiceSettings;
import java.io.IOException;

public class GetHyperparameterTuningJobSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "PROJECT";
    String hyperparameterTuningJobId = "HYPERPARAMETER_TUNING_JOB_ID";
    getHyperparameterTuningJobSample(project, hyperparameterTuningJobId);
  }

  static void getHyperparameterTuningJobSample(String project, String hyperparameterTuningJobId)
      throws IOException {
    JobServiceSettings settings =
        JobServiceSettings.newBuilder()
            .setEndpoint("us-central1-aiplatform.googleapis.com:443")
            .build();
    String location = "us-central1";

    // 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)) {
      HyperparameterTuningJobName name =
          HyperparameterTuningJobName.of(project, location, hyperparameterTuningJobId);
      HyperparameterTuningJob response = client.getHyperparameterTuningJob(name);
      System.out.format("response: %s\n", response);
    }
  }
}

# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from google.cloud import aiplatform


def get_hyperparameter_tuning_job_sample(
    project: str,
    hyperparameter_tuning_job_id: str,
    location: str = "us-central1",
):

    aiplatform.init(project=project, location=location)

    hpt_job = aiplatform.HyperparameterTuningJob.get(
        resource_name=hyperparameter_tuning_job_id,
    )

    return hpt_job

from google.cloud import aiplatform


def cancel_hyperparameter_tuning_job_sample(
    project: str,
    hyperparameter_tuning_job_id: str,
    location: str = "us-central1",
):

    aiplatform.init(project=project, location=location)

    hpt_job = aiplatform.HyperparameterTuningJob.get(
        resource_name=hyperparameter_tuning_job_id,
    )

    hpt_job.cancel()

from google.cloud import aiplatform


def delete_hyperparameter_tuning_job_sample(
    project: str,
    hyperparameter_tuning_job_id: str,
    location: str = "us-central1",
):

    aiplatform.init(project=project, location=location)

    hpt_job = aiplatform.HyperparameterTuningJob.get(
        resource_name=hyperparameter_tuning_job_id,
    )

    hpt_job.delete()