Get a model

Gets a model using the get_model method.

Explore further

For detailed documentation that includes this code sample, see the following:

Code sample

Java

Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Java API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.


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

Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Node.js API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

/**
 * 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

Before trying this sample, follow the Python setup instructions in the Vertex AI quickstart using client libraries. For more information, see the Vertex AI Python API reference documentation.

To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment.

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)

What's next

To search and filter code samples for other Google Cloud products, see the Google Cloud sample browser.