Membuat tugas penyesuaian hyperparameter untuk paket python menggunakan metode create_hyperparameter_tuning_job.
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Java
Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Java di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Java Vertex AI.
Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
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
Sebelum mencoba contoh ini, ikuti petunjuk penyiapan Python di Panduan memulai Vertex AI menggunakan library klien. Untuk mengetahui informasi selengkapnya, lihat Dokumentasi referensi API Python Vertex AI.
Untuk melakukan autentikasi ke Vertex AI, siapkan Kredensial Default Aplikasi. Untuk mengetahui informasi selengkapnya, baca Menyiapkan autentikasi untuk lingkungan pengembangan lokal.
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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