create_hyperparameter_tuning_job 메서드를 사용하여 Python 패키지를 위한 초매개변수 조정 작업을 만듭니다.
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코드 샘플
Java
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Java 설정 안내를 따르세요. 자세한 내용은 Vertex AI Java API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
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_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());
}
}
}
Python
이 샘플을 사용해 보기 전에 Vertex AI 빠른 시작: 클라이언트 라이브러리 사용의 Python 설정 안내를 따르세요. 자세한 내용은 Vertex AI Python API 참고 문서를 참조하세요.
Vertex AI에 인증하려면 애플리케이션 기본 사용자 인증 정보를 설정합니다. 자세한 내용은 로컬 개발 환경의 인증 설정을 참조하세요.
from google.cloud import aiplatform
def create_hyperparameter_tuning_job_python_package_sample(
project: str,
display_name: str,
executor_image_uri: str,
package_uri: str,
python_module: 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.JobServiceClient(client_options=client_options)
# study_spec
metric = {
"metric_id": "val_rmse",
"goal": aiplatform.gapic.StudySpec.MetricSpec.GoalType.MINIMIZE,
}
conditional_parameter_decay = {
"parameter_spec": {
"parameter_id": "decay",
"double_value_spec": {"min_value": 1e-07, "max_value": 1},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [32, 64]},
}
conditional_parameter_learning_rate = {
"parameter_spec": {
"parameter_id": "learning_rate",
"double_value_spec": {"min_value": 1e-07, "max_value": 1},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
},
"parent_discrete_values": {"values": [4, 8, 16]},
}
parameter = {
"parameter_id": "batch_size",
"discrete_value_spec": {"values": [4, 8, 16, 32, 64, 128]},
"scale_type": aiplatform.gapic.StudySpec.ParameterSpec.ScaleType.UNIT_LINEAR_SCALE,
"conditional_parameter_specs": [
conditional_parameter_decay,
conditional_parameter_learning_rate,
],
}
# trial_job_spec
machine_spec = {
"machine_type": "n1-standard-4",
"accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
"accelerator_count": 1,
}
worker_pool_spec = {
"machine_spec": machine_spec,
"replica_count": 1,
"python_package_spec": {
"executor_image_uri": executor_image_uri,
"package_uris": [package_uri],
"python_module": python_module,
"args": [],
},
}
# hyperparameter_tuning_job
hyperparameter_tuning_job = {
"display_name": display_name,
"max_trial_count": 4,
"parallel_trial_count": 2,
"study_spec": {
"metrics": [metric],
"parameters": [parameter],
"algorithm": aiplatform.gapic.StudySpec.Algorithm.RANDOM_SEARCH,
},
"trial_job_spec": {"worker_pool_specs": [worker_pool_spec]},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_hyperparameter_tuning_job(
parent=parent, hyperparameter_tuning_job=hyperparameter_tuning_job
)
print("response:", response)
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