使用预测方法获取自定义训练模型的预测。
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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.EndpointName;
import com.google.cloud.aiplatform.v1.PredictRequest;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.protobuf.ListValue;
import com.google.protobuf.Value;
import com.google.protobuf.util.JsonFormat;
import java.io.IOException;
import java.util.List;
public class PredictCustomTrainedModelSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String instance = "[{ “feature_column_a”: “value”, “feature_column_b”: “value”}]";
String project = "YOUR_PROJECT_ID";
String endpointId = "YOUR_ENDPOINT_ID";
predictCustomTrainedModel(project, endpointId, instance);
}
static void predictCustomTrainedModel(String project, String endpointId, String instance)
throws IOException {
PredictionServiceSettings predictionServiceSettings =
PredictionServiceSettings.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 (PredictionServiceClient predictionServiceClient =
PredictionServiceClient.create(predictionServiceSettings)) {
String location = "us-central1";
EndpointName endpointName = EndpointName.of(project, location, endpointId);
ListValue.Builder listValue = ListValue.newBuilder();
JsonFormat.parser().merge(instance, listValue);
List<Value> instanceList = listValue.getValuesList();
PredictRequest predictRequest =
PredictRequest.newBuilder()
.setEndpoint(endpointName.toString())
.addAllInstances(instanceList)
.build();
PredictResponse predictResponse = predictionServiceClient.predict(predictRequest);
System.out.println("Predict Custom Trained model Response");
System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());
System.out.println("Predictions");
for (Value prediction : predictResponse.getPredictionsList()) {
System.out.format("\tPrediction: %s\n", prediction);
}
}
}
}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 filename = "YOUR_PREDICTION_FILE_NAME";
// const endpointId = "YOUR_ENDPOINT_ID";
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const util = require('util');
const {readFile} = require('fs');
const readFileAsync = util.promisify(readFile);
// Imports the Google Cloud Prediction Service Client library
const {PredictionServiceClient} = require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const predictionServiceClient = new PredictionServiceClient(clientOptions);
async function predictCustomTrainedModel() {
// Configure the parent resource
const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
const parameters = {
structValue: {
fields: {},
},
};
const instanceDict = await readFileAsync(filename, 'utf8');
const instanceValue = JSON.parse(instanceDict);
const instance = {
structValue: {
fields: {
Age: {stringValue: instanceValue['Age']},
Balance: {stringValue: instanceValue['Balance']},
Campaign: {stringValue: instanceValue['Campaign']},
Contact: {stringValue: instanceValue['Contact']},
Day: {stringValue: instanceValue['Day']},
Default: {stringValue: instanceValue['Default']},
Deposit: {stringValue: instanceValue['Deposit']},
Duration: {stringValue: instanceValue['Duration']},
Housing: {stringValue: instanceValue['Housing']},
Job: {stringValue: instanceValue['Job']},
Loan: {stringValue: instanceValue['Loan']},
MaritalStatus: {stringValue: instanceValue['MaritalStatus']},
Month: {stringValue: instanceValue['Month']},
PDays: {stringValue: instanceValue['PDays']},
POutcome: {stringValue: instanceValue['POutcome']},
Previous: {stringValue: instanceValue['Previous']},
},
},
};
const instances = [instance];
const request = {
endpoint,
instances,
parameters,
};
// Predict request
const [response] = await predictionServiceClient.predict(request);
console.log('Predict custom trained model response');
console.log(`\tDeployed model id : ${response.deployedModelId}`);
const predictions = response.predictions;
console.log('\tPredictions :');
for (const prediction of predictions) {
console.log(`\t\tPrediction : ${JSON.stringify(prediction)}`);
}
}
predictCustomTrainedModel();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 typing import Dict, List, Union
from google.cloud import aiplatform
from google.protobuf import json_format
from google.protobuf.struct_pb2 import Value
def predict_custom_trained_model_sample(
project: str,
endpoint_id: str,
instances: Union[Dict, List[Dict]],
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
"""
`instances` can be either single instance of type dict or a list
of instances.
"""
# 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.PredictionServiceClient(client_options=client_options)
# The format of each instance should conform to the deployed model's prediction input schema.
instances = instances if isinstance(instances, list) else [instances]
instances = [
json_format.ParseDict(instance_dict, Value()) for instance_dict in instances
]
parameters_dict = {}
parameters = json_format.ParseDict(parameters_dict, Value())
endpoint = client.endpoint_path(
project=project, location=location, endpoint=endpoint_id
)
response = client.predict(
endpoint=endpoint, instances=instances, parameters=parameters
)
print("response")
print(" deployed_model_id:", response.deployed_model_id)
# The predictions are a google.protobuf.Value representation of the model's predictions.
predictions = response.predictions
for prediction in predictions:
print(" prediction:", dict(prediction))
后续步骤
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