使用 import_data 方法为文本分类单个标签导入数据
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代码示例
Java
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import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.DatasetName;
import com.google.cloud.aiplatform.v1.DatasetServiceClient;
import com.google.cloud.aiplatform.v1.DatasetServiceSettings;
import com.google.cloud.aiplatform.v1.GcsSource;
import com.google.cloud.aiplatform.v1.ImportDataConfig;
import com.google.cloud.aiplatform.v1.ImportDataOperationMetadata;
import com.google.cloud.aiplatform.v1.ImportDataResponse;
import java.io.IOException;
import java.util.Collections;
import java.util.List;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;
public class ImportDataTextClassificationSingleLabelSample {
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 datasetId = "YOUR_DATASET_ID";
String gcsSourceUri =
"gs://YOUR_GCS_SOURCE_BUCKET/path_to_your_text_source/[file.csv/file.jsonl]";
importDataTextClassificationSingleLabelSample(project, datasetId, gcsSourceUri);
}
static void importDataTextClassificationSingleLabelSample(
String project, String datasetId, String gcsSourceUri)
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";
String importSchemaUri =
"gs://google-cloud-aiplatform/schema/dataset/ioformat/"
+ "text_classification_single_label_io_format_1.0.0.yaml";
GcsSource.Builder gcsSource = GcsSource.newBuilder();
gcsSource.addUris(gcsSourceUri);
DatasetName datasetName = DatasetName.of(project, location, datasetId);
List<ImportDataConfig> importDataConfigList =
Collections.singletonList(
ImportDataConfig.newBuilder()
.setGcsSource(gcsSource)
.setImportSchemaUri(importSchemaUri)
.build());
OperationFuture<ImportDataResponse, ImportDataOperationMetadata> importDataResponseFuture =
datasetServiceClient.importDataAsync(datasetName, importDataConfigList);
System.out.format(
"Operation name: %s\n", importDataResponseFuture.getInitialFuture().get().getName());
System.out.println("Waiting for operation to finish...");
ImportDataResponse importDataResponse = importDataResponseFuture.get(300, TimeUnit.SECONDS);
System.out.format(
"Import Data Text Classification Response: %s\n", importDataResponse.toString());
}
}
}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 datasetId = "YOUR_DATASET_ID";
// const gcsSourceUri = "YOUR_GCS_SOURCE_URI";
// eg. "gs://<your-gcs-bucket>/<import_source_path>/[file.csv/file.jsonl]"
// 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',
};
const datasetServiceClient = new DatasetServiceClient(clientOptions);
async function importDataTextClassificationSingleLabel() {
const name = datasetServiceClient.datasetPath(project, location, datasetId);
// Here we use only one import config with one source
const importConfigs = [
{
gcsSource: {uris: [gcsSourceUri]},
importSchemaUri:
'gs://google-cloud-aiplatform/schema/dataset/ioformat/text_classification_single_label_io_format_1.0.0.yaml',
},
];
const request = {
name,
importConfigs,
};
// Import data request
const [response] = await datasetServiceClient.importData(request);
console.log(`Long running operation : ${response.name}`);
// Wait for operation to complete
const [importDataResponse] = await response.promise();
console.log(
`Import data text classification single label response : \
${JSON.stringify(importDataResponse.result)}`
);
}
importDataTextClassificationSingleLabel();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 import_data_text_classification_single_label_sample(
project: str,
dataset_id: str,
gcs_source_uri: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
timeout: int = 1800,
):
# 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)
import_configs = [
{
"gcs_source": {"uris": [gcs_source_uri]},
"import_schema_uri": "gs://google-cloud-aiplatform/schema/dataset/ioformat/text_classification_single_label_io_format_1.0.0.yaml",
}
]
name = client.dataset_path(project=project, location=location, dataset=dataset_id)
response = client.import_data(name=name, import_configs=import_configs)
print("Long running operation:", response.operation.name)
import_data_response = response.result(timeout=timeout)
print("import_data_response:", import_data_response)
后续步骤
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