Cria um job de ajuste de hiperparâmetro para o pacote Python usando o método create_ hiperparâmetro_tuning_job.
Mais informações
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Exemplo de código
Java
Antes de testar esse exemplo, siga as instruções de configuração para Java no Guia de início rápido da Vertex AI sobre como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Java.
Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.
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
Antes de testar essa amostra, siga as instruções de configuração para Python Guia de início rápido da Vertex AI: como usar bibliotecas de cliente. Para mais informações, consulte a documentação de referência da API Vertex AI para Python.
Para autenticar na Vertex AI, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.
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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