使用 create_hyperparameter_tuning_job 方法创建超参数调节作业。
代码示例
Java
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Java 设置说明执行操作。如需了解详情，请参阅 Vertex AI Java API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
import com.google.cloud.aiplatform.v1.AcceleratorType;
import com.google.cloud.aiplatform.v1.ContainerSpec;
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.StudySpec;
import com.google.cloud.aiplatform.v1.WorkerPoolSpec;
import java.io.IOException;
public class CreateHyperparameterTuningJobSample {
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 containerImageUri = "CONTAINER_IMAGE_URI";
createHyperparameterTuningJobSample(project, displayName, containerImageUri);
}
static void createHyperparameterTuningJobSample(
String project, String displayName, String containerImageUri) 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)) {
StudySpec.MetricSpec metric0 =
StudySpec.MetricSpec.newBuilder()
.setMetricId("accuracy")
.setGoal(StudySpec.MetricSpec.GoalType.MAXIMIZE)
.build();
StudySpec.ParameterSpec.DoubleValueSpec doubleValueSpec =
StudySpec.ParameterSpec.DoubleValueSpec.newBuilder()
.setMinValue(0.001)
.setMaxValue(0.1)
.build();
StudySpec.ParameterSpec parameter0 =
StudySpec.ParameterSpec.newBuilder()
// Learning rate.
.setParameterId("lr")
.setDoubleValueSpec(doubleValueSpec)
.build();
StudySpec studySpec =
StudySpec.newBuilder().addMetrics(metric0).addParameters(parameter0).build();
MachineSpec machineSpec =
MachineSpec.newBuilder()
.setMachineType("n1-standard-4")
.setAcceleratorType(AcceleratorType.NVIDIA_TESLA_K80)
.setAcceleratorCount(1)
.build();
ContainerSpec containerSpec =
ContainerSpec.newBuilder().setImageUri(containerImageUri).build();
WorkerPoolSpec workerPoolSpec0 =
WorkerPoolSpec.newBuilder()
.setMachineSpec(machineSpec)
.setReplicaCount(1)
.setContainerSpec(containerSpec)
.build();
CustomJobSpec trialJobSpec =
CustomJobSpec.newBuilder().addWorkerPoolSpecs(workerPoolSpec0).build();
HyperparameterTuningJob hyperparameterTuningJob =
HyperparameterTuningJob.newBuilder()
.setDisplayName(displayName)
.setMaxTrialCount(2)
.setParallelTrialCount(1)
.setMaxFailedTrialCount(1)
.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());
}
}
}
Node.js
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Node.js 设置说明执行操作。如需了解详情，请参阅 Vertex AI Node.js API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
/**
* TODO(developer): Uncomment these variables before running the sample.
* (Not necessary if passing values as arguments)
*/
/*
const displayName = 'YOUR HYPERPARAMETER TUNING JOB;
const containerImageUri = 'TUNING JOB CONTAINER URI;
const project = 'YOUR PROJECT ID';
const location = 'us-central1';
*/
// Imports the Google Cloud Pipeline Service Client library
const {JobServiceClient} = require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const jobServiceClient = new JobServiceClient(clientOptions);
async function createHyperParameterTuningJob() {
// Configure the parent resource
const parent = `projects/${project}/locations/${location}`;
// Create the hyperparameter tuning job configuration
const hyperparameterTuningJob = {
displayName,
maxTrialCount: 2,
parallelTrialCount: 1,
maxFailedTrialCount: 1,
studySpec: {
metrics: [
{
metricId: 'accuracy',
goal: 'MAXIMIZE',
},
],
parameters: [
{
parameterId: 'lr',
doubleValueSpec: {
minValue: 0.001,
maxValue: 0.1,
},
},
],
},
trialJobSpec: {
workerPoolSpecs: [
{
machineSpec: {
machineType: 'n1-standard-4',
acceleratorType: 'NVIDIA_TESLA_K80',
acceleratorCount: 1,
},
replicaCount: 1,
containerSpec: {
imageUri: containerImageUri,
command: [],
args: [],
},
},
],
},
};
const [response] = await jobServiceClient.createHyperparameterTuningJob({
parent,
hyperparameterTuningJob,
});
console.log('Create hyperparameter tuning job response:');
console.log(`\tDisplay name: ${response.displayName}`);
console.log(`\tTuning job resource name: ${response.name}`);
console.log(`\tJob status: ${response.state}`);
}
createHyperParameterTuningJob();Python
在尝试此示例之前，请按照《Vertex AI 快速入门：使用客户端库》中的 Python 设置说明执行操作。如需了解详情，请参阅 Vertex AI Python API 参考文档。
如需向 Vertex AI 进行身份验证，请设置应用默认凭据。 如需了解详情，请参阅为本地开发环境设置身份验证。
from google.cloud import aiplatform
def create_hyperparameter_tuning_job_sample(
project: str,
display_name: str,
container_image_uri: 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)
hyperparameter_tuning_job = {
"display_name": display_name,
"max_trial_count": 2,
"parallel_trial_count": 1,
"max_failed_trial_count": 1,
"study_spec": {
"metrics": [
{
"metric_id": "accuracy",
"goal": aiplatform.gapic.StudySpec.MetricSpec.GoalType.MAXIMIZE,
}
],
"parameters": [
{
# Learning rate.
"parameter_id": "lr",
"double_value_spec": {"min_value": 0.001, "max_value": 0.1},
},
],
},
"trial_job_spec": {
"worker_pool_specs": [
{
"machine_spec": {
"machine_type": "n1-standard-4",
"accelerator_type": aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80,
"accelerator_count": 1,
},
"replica_count": 1,
"container_spec": {
"image_uri": container_image_uri,
"command": [],
"args": [],
},
}
]
},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_hyperparameter_tuning_job(
parent=parent, hyperparameter_tuning_job=hyperparameter_tuning_job
)
print("response:", response)
后续步骤
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