使用 create_dataset 方法创建数据集。
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.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.CreateDatasetOperationMetadata;
import com.google.cloud.aiplatform.v1.Dataset;
import com.google.cloud.aiplatform.v1.DatasetServiceClient;
import com.google.cloud.aiplatform.v1.DatasetServiceSettings;
import com.google.cloud.aiplatform.v1.LocationName;
import java.io.IOException;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
public class CreateDatasetSample {
public static void main(String[] args)
throws IOException, InterruptedException, ExecutionException, TimeoutException {
// TODO(developer): Replace these variables before running the sample.
String project = "YOUR_PROJECT_ID";
String datasetDisplayName = "YOUR_DATASET_DISPLAY_NAME";
String metadataSchemaUri = "YOUR_METADATA_SCHEMA_URI";
createDatasetSample(project, datasetDisplayName, metadataSchemaUri);
}
static void createDatasetSample(
String project, String datasetDisplayName, String metadataSchemaUri)
throws IOException, InterruptedException, ExecutionException, TimeoutException {
DatasetServiceSettings datasetServiceSettings =
DatasetServiceSettings.newBuilder()
.setEndpoint("us-central1-aiplatform.googleapis.com:443")
.build();
// 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 (DatasetServiceClient datasetServiceClient =
DatasetServiceClient.create(datasetServiceSettings)) {
String location = "us-central1";
LocationName locationName = LocationName.of(project, location);
Dataset dataset =
Dataset.newBuilder()
.setDisplayName(datasetDisplayName)
.setMetadataSchemaUri(metadataSchemaUri)
.build();
OperationFuture<Dataset, CreateDatasetOperationMetadata> datasetFuture =
datasetServiceClient.createDatasetAsync(locationName, dataset);
System.out.format("Operation name: %s\n", datasetFuture.getInitialFuture().get().getName());
System.out.println("Waiting for operation to finish...");
Dataset datasetResponse = datasetFuture.get(300, TimeUnit.SECONDS);
System.out.println("Create Dataset Response");
System.out.format("Name: %s\n", datasetResponse.getName());
System.out.format("Display Name: %s\n", datasetResponse.getDisplayName());
System.out.format("Metadata Schema Uri: %s\n", datasetResponse.getMetadataSchemaUri());
System.out.format("Metadata: %s\n", datasetResponse.getMetadata());
System.out.format("Create Time: %s\n", datasetResponse.getCreateTime());
System.out.format("Update Time: %s\n", datasetResponse.getUpdateTime());
System.out.format("Labels: %s\n", datasetResponse.getLabelsMap());
}
}
}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 datasetDisplayName = 'YOUR_DATASET_DISPLAY_NAME';
// const metadataSchemaUri = 'YOUR_METADATA_SCHEMA_URI';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// Imports the Google Cloud Dataset Service Client library
const {DatasetServiceClient} = require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const datasetServiceClient = new DatasetServiceClient(clientOptions);
async function createDataset() {
// Configure the parent resource
const parent = `projects/${project}/locations/${location}`;
// Configure the dataset resource
const dataset = {
displayName: datasetDisplayName,
metadataSchemaUri: metadataSchemaUri,
};
const request = {
parent,
dataset,
};
// Create Dataset Request
const [response] = await datasetServiceClient.createDataset(request);
console.log(`Long running operation : ${response.name}`);
// Wait for operation to complete
const [createDatasetResponse] = await response.promise();
console.log('Create dataset response');
console.log(`\tName : ${createDatasetResponse.name}`);
console.log(`\tDisplay name : ${createDatasetResponse.displayName}`);
console.log(
`\tMetadata schema uri : ${createDatasetResponse.metadataSchemaUri}`
);
console.log(
`\tMetadata : ${JSON.stringify(createDatasetResponse.metadata)}`
);
console.log(`\tCreate time : ${createDatasetResponse.createTime}`);
console.log(`\tUpdate time : ${createDatasetResponse.updateTime}`);
console.log(`\tLabels : ${JSON.stringify(createDatasetResponse.labels)}`);
}
createDataset();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_dataset_sample(
project: str,
display_name: str,
metadata_schema_uri: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
timeout: int = 300,
):
# 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.DatasetServiceClient(client_options=client_options)
dataset = {
"display_name": display_name,
"metadata_schema_uri": metadata_schema_uri,
}
parent = f"projects/{project}/locations/{location}"
response = client.create_dataset(parent=parent, dataset=dataset)
print("Long running operation:", response.operation.name)
create_dataset_response = response.result(timeout=timeout)
print("create_dataset_response:", create_dataset_response)
后续步骤
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