get_model メソッドを使用して、モデルを取得します。
もっと見る
このコードサンプルを含む詳細なドキュメントについては、以下をご覧ください。
コードサンプル
Java
このサンプルを試す前に、Vertex AI クイックスタート: クライアント ライブラリの使用にある Java の設定手順を完了してください。詳細については、Vertex AI Java API のリファレンス ドキュメントをご覧ください。
Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。
import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.Model.ExportFormat;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.ModelName;
import com.google.cloud.aiplatform.v1.ModelServiceClient;
import com.google.cloud.aiplatform.v1.ModelServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import java.io.IOException;
public class GetModelSample {
public static void main(String[] args) throws IOException {
// TODO(developer): Replace these variables before running the sample.
String project = "YOUR_PROJECT_ID";
String modelId = "YOUR_MODEL_ID";
getModelSample(project, modelId);
}
static void getModelSample(String project, String modelId) throws IOException {
ModelServiceSettings modelServiceSettings =
ModelServiceSettings.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 (ModelServiceClient modelServiceClient = ModelServiceClient.create(modelServiceSettings)) {
String location = "us-central1";
ModelName modelName = ModelName.of(project, location, modelId);
Model modelResponse = modelServiceClient.getModel(modelName);
System.out.println("Get Model response");
System.out.format("\tName: %s\n", modelResponse.getName());
System.out.format("\tDisplay Name: %s\n", modelResponse.getDisplayName());
System.out.format("\tDescription: %s\n", modelResponse.getDescription());
System.out.format("\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
System.out.format("\tMetadata: %s\n", modelResponse.getMetadata());
System.out.format("\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
System.out.format("\tArtifact Uri: %s\n", modelResponse.getArtifactUri());
System.out.format(
"\tSupported Deployment Resources Types: %s\n",
modelResponse.getSupportedDeploymentResourcesTypesList());
System.out.format(
"\tSupported Input Storage Formats: %s\n",
modelResponse.getSupportedInputStorageFormatsList());
System.out.format(
"\tSupported Output Storage Formats: %s\n",
modelResponse.getSupportedOutputStorageFormatsList());
System.out.format("\tCreate Time: %s\n", modelResponse.getCreateTime());
System.out.format("\tUpdate Time: %s\n", modelResponse.getUpdateTime());
System.out.format("\tLabels: %s\n", modelResponse.getLabelsMap());
PredictSchemata predictSchemata = modelResponse.getPredictSchemata();
System.out.println("\tPredict Schemata");
System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
System.out.format(
"\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
System.out.format(
"\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());
for (ExportFormat exportFormat : modelResponse.getSupportedExportFormatsList()) {
System.out.println("\tSupported Export Format");
System.out.format("\t\tId: %s\n", exportFormat.getId());
}
ModelContainerSpec containerSpec = modelResponse.getContainerSpec();
System.out.println("\tContainer Spec");
System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());
for (EnvVar envVar : containerSpec.getEnvList()) {
System.out.println("\t\tEnv");
System.out.format("\t\t\tName: %s\n", envVar.getName());
System.out.format("\t\t\tValue: %s\n", envVar.getValue());
}
for (Port port : containerSpec.getPortsList()) {
System.out.println("\t\tPort");
System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
}
for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
System.out.println("\tDeployed Model");
System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
}
}
}
}
Node.js
このサンプルを試す前に、Vertex AI クイックスタート: クライアント ライブラリの使用にある Node.js の設定手順を完了してください。詳細については、Vertex AI Node.js API のリファレンス ドキュメントをご覧ください。
Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。
/**
* TODO(developer): Uncomment these variables before running the sample.\
* (Not necessary if passing values as arguments)
*/
// const modelId = 'YOUR_MODEL_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// Imports the Google Cloud Model Service Client library
const {ModelServiceClient} = require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint
const clientOptions = {
apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
const modelServiceClient = new ModelServiceClient(clientOptions);
async function getModel() {
// Configure the parent resource
const name = `projects/${project}/locations/${location}/models/${modelId}`;
const request = {
name,
};
// Get and print out a list of all the endpoints for this resource
const [response] = await modelServiceClient.getModel(request);
console.log('Get model response');
console.log(`\tName : ${response.name}`);
console.log(`\tDisplayName : ${response.displayName}`);
console.log(`\tDescription : ${response.description}`);
console.log(`\tMetadata schema uri : ${response.metadataSchemaUri}`);
console.log(`\tMetadata : ${JSON.stringify(response.metadata)}`);
console.log(`\tTraining pipeline : ${response.trainingPipeline}`);
console.log(`\tArtifact uri : ${response.artifactUri}`);
console.log(
`\tSupported deployment resource types : \
${response.supportedDeploymentResourceTypes}`
);
console.log(
`\tSupported input storage formats : \
${response.supportedInputStorageFormats}`
);
console.log(
`\tSupported output storage formats : \
${response.supportedOutputStoragFormats}`
);
console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
console.log(`\tUpdate time : ${JSON.stringify(response.updateTime)}`);
console.log(`\tLabels : ${JSON.stringify(response.labels)}`);
const predictSchemata = response.predictSchemata;
console.log('\tPredict schemata');
console.log(`\tInstance schema uri : ${predictSchemata.instanceSchemaUri}`);
console.log(
`\tParameters schema uri : ${predictSchemata.prametersSchemaUri}`
);
console.log(
`\tPrediction schema uri : ${predictSchemata.predictionSchemaUri}`
);
const [supportedExportFormats] = response.supportedExportFormats;
console.log('\tSupported export formats');
console.log(`\t${supportedExportFormats}`);
const containerSpec = response.containerSpec;
console.log('\tContainer Spec');
if (!containerSpec) {
console.log(`\t\t${JSON.stringify(containerSpec)}`);
console.log('\t\tImage uri : {}');
console.log('\t\tCommand : {}');
console.log('\t\tArgs : {}');
console.log('\t\tPredict route : {}');
console.log('\t\tHealth route : {}');
console.log('\t\tEnv');
console.log('\t\t\t{}');
console.log('\t\tPort');
console.log('\t\t{}');
} else {
console.log(`\t\t${JSON.stringify(containerSpec)}`);
console.log(`\t\tImage uri : ${containerSpec.imageUri}`);
console.log(`\t\tCommand : ${containerSpec.command}`);
console.log(`\t\tArgs : ${containerSpec.args}`);
console.log(`\t\tPredict route : ${containerSpec.predictRoute}`);
console.log(`\t\tHealth route : ${containerSpec.healthRoute}`);
const env = containerSpec.env;
console.log('\t\tEnv');
console.log(`\t\t\t${JSON.stringify(env)}`);
const ports = containerSpec.ports;
console.log('\t\tPort');
console.log(`\t\t\t${JSON.stringify(ports)}`);
}
const [deployedModels] = response.deployedModels;
console.log('\tDeployed models');
console.log('\t\t', deployedModels);
}
getModel();
Python
このサンプルを試す前に、Vertex AI クイックスタート: クライアント ライブラリの使用にある Python の設定手順を完了してください。詳細については、Vertex AI Python API のリファレンス ドキュメントをご覧ください。
Vertex AI に対する認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証を設定するをご覧ください。
from google.cloud import aiplatform
def get_model_sample(
project: str,
model_id: 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.ModelServiceClient(client_options=client_options)
name = client.model_path(project=project, location=location, model=model_id)
response = client.get_model(name=name)
print("response:", response)
次のステップ
他の Google Cloud プロダクトに関連するコードサンプルの検索およびフィルタ検索を行うには、Google Cloud のサンプルをご覧ください。