Get predictions from a image classification model

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This page shows you how to get online (real-time) predictions and batch predictions from your image classification models using the Google Cloud console or the Vertex AI API.

Difference between online and batch predictions

Online predictions are synchronous requests made to a model endpoint. Use online predictions when you are making requests in response to application input or in situations that require timely inference.

Batch predictions are asynchronous requests. You request batch predictions directly from the model resource without needing to deploy the model to an endpoint. For image data, use batch predictions when you don't require an immediate response and want to process accumulated data by using a single request.

Get online predictions

Deploy a model to an endpoint

You must deploy a model to an endpoint before that model can be used to serve online predictions. Deploying a model associates physical resources with the model so it can serve online predictions with low latency.

You can deploy more than one model to an endpoint, and you can deploy a model to more than one endpoint. For more information about options and use cases for deploying models, see About deploying models.

Use one of the following methods to deploy a model:

Google Cloud console

  1. In the Google Cloud console, in the Vertex AI section, go to the Models page.

    Go to the Models page

  2. Click the name of the model you want to deploy to open its details page.

  3. Select the Deploy & Test tab.

    If your model is already deployed to any endpoints, they are listed in the Deploy your model section.

  4. Click Deploy to endpoint.

  5. To deploy your model to a new endpoint, select Create new endpoint and provide a name for the new endpoint. To deploy your model to an existing endpoint, select Add to existing endpoint and select the endpoint from the drop-down list.

    You can add more than one model to an endpoint, and you can add a model to more than one endpoint. Learn more.

  6. If you deploy your model to an existing endpoint that has one or more models deployed to it, you must update the Traffic split percentage for the model you are deploying and the already deployed models so that all of the percentages add up to 100%.

  7. Select AutoML Image and configure as follows:

    1. If you're deploying your model to a new endpoint, accept 100 for the Traffic split. Otherwise, adjust the traffic split values for all models on the endpoint so they add up to 100.

    2. Enter the Number of compute nodes you want to provide for your model.

      This is the number of nodes available to this model at all times. You are charged for the nodes, even without prediction traffic. See the pricing page.

    3. Learn how to change the default settings for prediction logging.

    4. Classification models only (optional): In the Explainability options section, select Enable feature attributions for this model to enable Vertex Explainable AI. Accept existing visualization settings or choose new values and click Done.

      Deploying AutoML image classification models with Vertex Explainable AI configured and performing predictions with explanations is optional. Enabling Vertex Explainable AI at deploy time will incur additional costs based on deployed node count and deployment time. See Pricing for more information.

    5. Click Done for your model, and when all the Traffic split percentages are correct, click Continue.

      The region where your model will be deployed is displayed. This must be the region where your model was created.

    6. Click Deploy to deploy your model to the endpoint.

API

When you deploy a model using the Vertex AI API, you complete the following steps:

  1. Create an endpoint if needed.
  2. Get the endpoint ID.
  3. Deploy the model to the endpoint.

Create an endpoint

If you are deploying a model to an existing endpoint, you can skip this step.

gcloud CLI

The following example uses the gcloud ai endpoints create command:

gcloud ai endpoints create \
  --region=LOCATION \
  --display-name=ENDPOINT_NAME

Replace the following:

  • LOCATION_ID: The region where you are using Vertex AI.
  • ENDPOINT_NAME: Display name for the endpoint.

The Google Cloud CLI tool might take a few seconds to create the endpoint.

REST

Before using any of the request data, make the following replacements:

  • LOCATION: Your region.
  • PROJECT: Your project ID.
  • ENDPOINT_NAME: Display name for the endpoint.
  • PROJECT_NUMBER: Project number for your project

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/endpoints

Request JSON body:

{
  "display_name": "ENDPOINT_NAME"
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION/endpoints/ENDPOINT_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.CreateEndpointOperationMetadata",
    "genericMetadata": {
      "createTime": "2020-11-05T17:45:42.812656Z",
      "updateTime": "2020-11-05T17:45:42.812656Z"
    }
  }
}
You can poll for the status of the operation until the response includes "done": true.

Java

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Java API reference documentation.


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.CreateEndpointOperationMetadata;
import com.google.cloud.aiplatform.v1.Endpoint;
import com.google.cloud.aiplatform.v1.EndpointServiceClient;
import com.google.cloud.aiplatform.v1.EndpointServiceSettings;
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 CreateEndpointSample {

  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 endpointDisplayName = "YOUR_ENDPOINT_DISPLAY_NAME";
    createEndpointSample(project, endpointDisplayName);
  }

  static void createEndpointSample(String project, String endpointDisplayName)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    EndpointServiceSettings endpointServiceSettings =
        EndpointServiceSettings.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 (EndpointServiceClient endpointServiceClient =
        EndpointServiceClient.create(endpointServiceSettings)) {
      String location = "us-central1";
      LocationName locationName = LocationName.of(project, location);
      Endpoint endpoint = Endpoint.newBuilder().setDisplayName(endpointDisplayName).build();

      OperationFuture<Endpoint, CreateEndpointOperationMetadata> endpointFuture =
          endpointServiceClient.createEndpointAsync(locationName, endpoint);
      System.out.format("Operation name: %s\n", endpointFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      Endpoint endpointResponse = endpointFuture.get(300, TimeUnit.SECONDS);

      System.out.println("Create Endpoint Response");
      System.out.format("Name: %s\n", endpointResponse.getName());
      System.out.format("Display Name: %s\n", endpointResponse.getDisplayName());
      System.out.format("Description: %s\n", endpointResponse.getDescription());
      System.out.format("Labels: %s\n", endpointResponse.getLabelsMap());
      System.out.format("Create Time: %s\n", endpointResponse.getCreateTime());
      System.out.format("Update Time: %s\n", endpointResponse.getUpdateTime());
    }
  }
}

Node.js

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Node.js API reference documentation.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const endpointDisplayName = 'YOUR_ENDPOINT_DISPLAY_NAME';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

// Imports the Google Cloud Endpoint Service Client library
const {EndpointServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const endpointServiceClient = new EndpointServiceClient(clientOptions);

async function createEndpoint() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;
  const endpoint = {
    displayName: endpointDisplayName,
  };
  const request = {
    parent,
    endpoint,
  };

  // Get and print out a list of all the endpoints for this resource
  const [response] = await endpointServiceClient.createEndpoint(request);
  console.log(`Long running operation : ${response.name}`);

  // Wait for operation to complete
  await response.promise();
  const result = response.result;

  console.log('Create endpoint response');
  console.log(`\tName : ${result.name}`);
  console.log(`\tDisplay name : ${result.displayName}`);
  console.log(`\tDescription : ${result.description}`);
  console.log(`\tLabels : ${JSON.stringify(result.labels)}`);
  console.log(`\tCreate time : ${JSON.stringify(result.createTime)}`);
  console.log(`\tUpdate time : ${JSON.stringify(result.updateTime)}`);
}
createEndpoint();

Python

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Python API reference documentation.

def create_endpoint_sample(
    project: str,
    display_name: str,
    location: str,
):
    aiplatform.init(project=project, location=location)

    endpoint = aiplatform.Endpoint.create(
        display_name=display_name,
        project=project,
        location=location,
    )

    print(endpoint.display_name)
    print(endpoint.resource_name)
    return endpoint

Retrieve the endpoint ID

You need the endpoint ID to deploy the model.

gcloud CLI

The following example uses the gcloud ai endpoints list command:

gcloud ai endpoints list \
  --region=LOCATION \
  --filter=display_name=ENDPOINT_NAME

Replace the following:

  • LOCATION_ID: The region where you are using Vertex AI.
  • ENDPOINT_NAME: Display name for the endpoint.

Note the number that appears in the ENDPOINT_ID column. Use this ID in the following step.

REST

Before using any of the request data, make the following replacements:

  • LOCATION: Your region.
  • PROJECT: Your project ID.
  • ENDPOINT_NAME: Display name for the endpoint.
  • PROJECT_NUMBER: Project number for your project

HTTP method and URL:

GET https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/endpoints?filter=display_name=ENDPOINT_NAME

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "endpoints": [
    {
      "name": "projects/PROJECT_NUMBER/locations/us-central1/endpoints/ENDPOINT_ID",
      "displayName": "ENDPOINT_NAME",
      "etag": "AMEw9yPz5pf4PwBHbRWOGh0PcAxUdjbdX2Jm3QO_amguy3DbZGP5Oi_YUKRywIE-BtLx",
      "createTime": "2020-04-17T18:31:11.585169Z",
      "updateTime": "2020-04-17T18:35:08.568959Z"
    }
  ]
}
Note the ENDPOINT_ID.

Deploy the model

Select the tab below for your language or environment:

gcloud CLI

The following examples use the gcloud ai endpoints deploy-model command.

The following example deploys a Model to an Endpoint without splitting traffic between multiple DeployedModel resources:

Before using any of the command data below, make the following replacements:

  • ENDPOINT_ID: The ID for the endpoint.
  • LOCATION_ID: The region where you are using Vertex AI.
  • MODEL_ID: The ID for the model to be deployed.
  • DEPLOYED_MODEL_NAME: A name for the DeployedModel. You can use the display name of the Model for the DeployedModel as well.
  • MIN_REPLICA_COUNT: The minimum number of nodes for this deployment. The node count can be increased or decreased as required by the prediction load, up to the maximum number of nodes, but will never fall below this number.
  • MAX_REPLICA_COUNT: The maximum number of nodes for this deployment. The node count can be increased or decreased as required by the prediction load, but will never exceed the maximum. If you omit the --max-replica-count flag, then maximum number of nodes is set to the value of --min-replica-count.

Execute the gcloud ai endpoints deploy-model command:

Linux, macOS, or Cloud Shell

gcloud ai endpoints deploy-model ENDPOINT_ID\
  --region=LOCATION \
  --model=MODEL_ID \
  --display-name=DEPLOYED_MODEL_NAME \
  --min-replica-count=MIN_REPLICA_COUNT \
  --max-replica-count=MAX_REPLICA_COUNT \
  --traffic-split=0=100

Windows (PowerShell)

gcloud ai endpoints deploy-model ENDPOINT_ID`
  --region=LOCATION `
  --model=MODEL_ID `
  --display-name=DEPLOYED_MODEL_NAME `
  --min-replica-count=MIN_REPLICA_COUNT `
  --max-replica-count=MAX_REPLICA_COUNT `
  --traffic-split=0=100

Windows (cmd.exe)

gcloud ai endpoints deploy-model ENDPOINT_ID^
  --region=LOCATION ^
  --model=MODEL_ID ^
  --display-name=DEPLOYED_MODEL_NAME ^
  --min-replica-count=MIN_REPLICA_COUNT ^
  --max-replica-count=MAX_REPLICA_COUNT ^
  --traffic-split=0=100
 

Splitting traffic

The --traffic-split=0=100 flag in the preceding examples sends 100% of prediction traffic that the Endpoint receives to the new DeployedModel, which is represented by the temporary ID 0. If your Endpoint already has other DeployedModel resources, then you can split traffic between the new DeployedModel and the old ones. For example, to send 20% of traffic to the new DeployedModel and 80% to an older one, run the following command.

Before using any of the command data below, make the following replacements:

  • OLD_DEPLOYED_MODEL_ID: the ID of the existing DeployedModel.

Execute the gcloud ai endpoints deploy-model command:

Linux, macOS, or Cloud Shell

gcloud ai endpoints deploy-model ENDPOINT_ID\
  --region=LOCATION \
  --model=MODEL_ID \
  --display-name=DEPLOYED_MODEL_NAME \ 
  --min-replica-count=MIN_REPLICA_COUNT \
  --max-replica-count=MAX_REPLICA_COUNT \
  --traffic-split=0=20,OLD_DEPLOYED_MODEL_ID=80

Windows (PowerShell)

gcloud ai endpoints deploy-model ENDPOINT_ID`
  --region=LOCATION `
  --model=MODEL_ID `
  --display-name=DEPLOYED_MODEL_NAME \ 
  --min-replica-count=MIN_REPLICA_COUNT `
  --max-replica-count=MAX_REPLICA_COUNT `
  --traffic-split=0=20,OLD_DEPLOYED_MODEL_ID=80

Windows (cmd.exe)

gcloud ai endpoints deploy-model ENDPOINT_ID^
  --region=LOCATION ^
  --model=MODEL_ID ^
  --display-name=DEPLOYED_MODEL_NAME \ 
  --min-replica-count=MIN_REPLICA_COUNT ^
  --max-replica-count=MAX_REPLICA_COUNT ^
  --traffic-split=0=20,OLD_DEPLOYED_MODEL_ID=80
 

REST

Deploy the model.

Before using any of the request data, make the following replacements:

  • LOCATION_ID: The region where you are using Vertex AI.
  • PROJECT: Your project ID.
  • ENDPOINT_ID: The ID for the endpoint.
  • MODEL_ID: The ID for the model to be deployed.
  • DEPLOYED_MODEL_NAME: A name for the DeployedModel. You can use the display name of the Model for the DeployedModel as well.
  • MIN_REPLICA_COUNT: The minimum number of nodes for this deployment. The node count can be increased or decreased as required by the prediction load, up to the maximum number of nodes, but will never fall below this number.
  • MAX_REPLICA_COUNT: The maximum number of nodes for this deployment. The node count can be increased or decreased as required by the prediction load, but will never exceed the maximum.
  • TRAFFIC_SPLIT_THIS_MODEL: The percentage of the prediction traffic to this endpoint to be routed to the model being deployed with this operation. Defaults to 100. All traffic percentages must add up to 100. Learn more about traffic splits.
  • DEPLOYED_MODEL_ID_N: Optional. If other models are deployed to this endpoint, you must update their traffic split percentages so that all percentages add up to 100.
  • TRAFFIC_SPLIT_MODEL_N: The traffic split percentage value for the deployed model id key.
  • PROJECT_NUMBER: Project number for your project

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/endpoints/ENDPOINT_ID:deployModel

Request JSON body:

{
  "deployedModel": {
    "model": "projects/PROJECT/locations/us-central1/models/MODEL_ID",
    "displayName": "DEPLOYED_MODEL_NAME",
    "automaticResources": {
       "minReplicaCount": MIN_REPLICA_COUNT,
       "maxReplicaCount": MAX_REPLICA_COUNT
     }
  },
  "trafficSplit": {
    "0": TRAFFIC_SPLIT_THIS_MODEL,
    "DEPLOYED_MODEL_ID_1": TRAFFIC_SPLIT_MODEL_1,
    "DEPLOYED_MODEL_ID_2": TRAFFIC_SPLIT_MODEL_2
  },
}

To send your request, expand one of these options:

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_ID/locations/LOCATION/endpoints/ENDPOINT_ID/operations/OPERATION_ID",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.aiplatform.v1.DeployModelOperationMetadata",
    "genericMetadata": {
      "createTime": "2020-10-19T17:53:16.502088Z",
      "updateTime": "2020-10-19T17:53:16.502088Z"
    }
  }
}

Java

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Java API reference documentation.


import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.aiplatform.v1.AutomaticResources;
import com.google.cloud.aiplatform.v1.DedicatedResources;
import com.google.cloud.aiplatform.v1.DeployModelOperationMetadata;
import com.google.cloud.aiplatform.v1.DeployModelResponse;
import com.google.cloud.aiplatform.v1.DeployedModel;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.EndpointServiceClient;
import com.google.cloud.aiplatform.v1.EndpointServiceSettings;
import com.google.cloud.aiplatform.v1.MachineSpec;
import com.google.cloud.aiplatform.v1.ModelName;
import java.io.IOException;
import java.util.HashMap;
import java.util.Map;
import java.util.concurrent.ExecutionException;
import java.util.concurrent.TimeUnit;
import java.util.concurrent.TimeoutException;

public class DeployModelSample {

  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 deployedModelDisplayName = "YOUR_DEPLOYED_MODEL_DISPLAY_NAME";
    String endpointId = "YOUR_ENDPOINT_NAME";
    String modelId = "YOUR_MODEL_ID";
    deployModelSample(project, deployedModelDisplayName, endpointId, modelId);
  }

  static void deployModelSample(
      String project, String deployedModelDisplayName, String endpointId, String modelId)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {
    EndpointServiceSettings endpointServiceSettings =
        EndpointServiceSettings.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 (EndpointServiceClient endpointServiceClient =
        EndpointServiceClient.create(endpointServiceSettings)) {
      String location = "us-central1";
      EndpointName endpointName = EndpointName.of(project, location, endpointId);
      // key '0' assigns traffic for the newly deployed model
      // Traffic percentage values must add up to 100
      // Leave dictionary empty if endpoint should not accept any traffic
      Map<String, Integer> trafficSplit = new HashMap<>();
      trafficSplit.put("0", 100);
      ModelName modelName = ModelName.of(project, location, modelId);
      AutomaticResources automaticResourcesInput =
          AutomaticResources.newBuilder().setMinReplicaCount(1).setMaxReplicaCount(1).build();
      DeployedModel deployedModelInput =
          DeployedModel.newBuilder()
              .setModel(modelName.toString())
              .setDisplayName(deployedModelDisplayName)
              .setAutomaticResources(automaticResourcesInput)
              .build();

      OperationFuture<DeployModelResponse, DeployModelOperationMetadata> deployModelResponseFuture =
          endpointServiceClient.deployModelAsync(endpointName, deployedModelInput, trafficSplit);
      System.out.format(
          "Operation name: %s\n", deployModelResponseFuture.getInitialFuture().get().getName());
      System.out.println("Waiting for operation to finish...");
      DeployModelResponse deployModelResponse = deployModelResponseFuture.get(20, TimeUnit.MINUTES);

      System.out.println("Deploy Model Response");
      DeployedModel deployedModel = deployModelResponse.getDeployedModel();
      System.out.println("\tDeployed Model");
      System.out.format("\t\tid: %s\n", deployedModel.getId());
      System.out.format("\t\tmodel: %s\n", deployedModel.getModel());
      System.out.format("\t\tDisplay Name: %s\n", deployedModel.getDisplayName());
      System.out.format("\t\tCreate Time: %s\n", deployedModel.getCreateTime());

      DedicatedResources dedicatedResources = deployedModel.getDedicatedResources();
      System.out.println("\t\tDedicated Resources");
      System.out.format("\t\t\tMin Replica Count: %s\n", dedicatedResources.getMinReplicaCount());

      MachineSpec machineSpec = dedicatedResources.getMachineSpec();
      System.out.println("\t\t\tMachine Spec");
      System.out.format("\t\t\t\tMachine Type: %s\n", machineSpec.getMachineType());
      System.out.format("\t\t\t\tAccelerator Type: %s\n", machineSpec.getAcceleratorType());
      System.out.format("\t\t\t\tAccelerator Count: %s\n", machineSpec.getAcceleratorCount());

      AutomaticResources automaticResources = deployedModel.getAutomaticResources();
      System.out.println("\t\tAutomatic Resources");
      System.out.format("\t\t\tMin Replica Count: %s\n", automaticResources.getMinReplicaCount());
      System.out.format("\t\t\tMax Replica Count: %s\n", automaticResources.getMaxReplicaCount());
    }
  }
}

Node.js

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Node.js API reference documentation.

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const modelId = "YOUR_MODEL_ID";
// const endpointId = 'YOUR_ENDPOINT_ID';
// const deployedModelDisplayName = 'YOUR_DEPLOYED_MODEL_DISPLAY_NAME';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';

const modelName = `projects/${project}/locations/${location}/models/${modelId}`;
const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;
// Imports the Google Cloud Endpoint Service Client library
const {EndpointServiceClient} = require('@google-cloud/aiplatform');

// Specifies the location of the api endpoint:
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const endpointServiceClient = new EndpointServiceClient(clientOptions);

async function deployModel() {
  // Configure the parent resource
  // key '0' assigns traffic for the newly deployed model
  // Traffic percentage values must add up to 100
  // Leave dictionary empty if endpoint should not accept any traffic
  const trafficSplit = {0: 100};
  const deployedModel = {
    // format: 'projects/{project}/locations/{location}/models/{model}'
    model: modelName,
    displayName: deployedModelDisplayName,
    // AutoML Vision models require `automatic_resources` field
    // Other model types may require `dedicated_resources` field instead
    automaticResources: {minReplicaCount: 1, maxReplicaCount: 1},
  };
  const request = {
    endpoint,
    deployedModel,
    trafficSplit,
  };

  // Get and print out a list of all the endpoints for this resource
  const [response] = await endpointServiceClient.deployModel(request);
  console.log(`Long running operation : ${response.name}`);

  // Wait for operation to complete
  await response.promise();
  const result = response.result;

  console.log('Deploy model response');
  const modelDeployed = result.deployedModel;
  console.log('\tDeployed model');
  if (!modelDeployed) {
    console.log('\t\tId : {}');
    console.log('\t\tModel : {}');
    console.log('\t\tDisplay name : {}');
    console.log('\t\tCreate time : {}');

    console.log('\t\tDedicated resources');
    console.log('\t\t\tMin replica count : {}');
    console.log('\t\t\tMachine spec {}');
    console.log('\t\t\t\tMachine type : {}');
    console.log('\t\t\t\tAccelerator type : {}');
    console.log('\t\t\t\tAccelerator count : {}');

    console.log('\t\tAutomatic resources');
    console.log('\t\t\tMin replica count : {}');
    console.log('\t\t\tMax replica count : {}');
  } else {
    console.log(`\t\tId : ${modelDeployed.id}`);
    console.log(`\t\tModel : ${modelDeployed.model}`);
    console.log(`\t\tDisplay name : ${modelDeployed.displayName}`);
    console.log(`\t\tCreate time : ${modelDeployed.createTime}`);

    const dedicatedResources = modelDeployed.dedicatedResources;
    console.log('\t\tDedicated resources');
    if (!dedicatedResources) {
      console.log('\t\t\tMin replica count : {}');
      console.log('\t\t\tMachine spec {}');
      console.log('\t\t\t\tMachine type : {}');
      console.log('\t\t\t\tAccelerator type : {}');
      console.log('\t\t\t\tAccelerator count : {}');
    } else {
      console.log(
        `\t\t\tMin replica count : \
          ${dedicatedResources.minReplicaCount}`
      );
      const machineSpec = dedicatedResources.machineSpec;
      console.log('\t\t\tMachine spec');
      console.log(`\t\t\t\tMachine type : ${machineSpec.machineType}`);
      console.log(
        `\t\t\t\tAccelerator type : ${machineSpec.acceleratorType}`
      );
      console.log(
        `\t\t\t\tAccelerator count : ${machineSpec.acceleratorCount}`
      );
    }

    const automaticResources = modelDeployed.automaticResources;
    console.log('\t\tAutomatic resources');
    if (!automaticResources) {
      console.log('\t\t\tMin replica count : {}');
      console.log('\t\t\tMax replica count : {}');
    } else {
      console.log(
        `\t\t\tMin replica count : \
          ${automaticResources.minReplicaCount}`
      );
      console.log(
        `\t\t\tMax replica count : \
          ${automaticResources.maxReplicaCount}`
      );
    }
  }
}
deployModel();

Python

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Python API reference documentation.

def deploy_model_with_automatic_resources_sample(
    project,
    location,
    model_name: str,
    endpoint: Optional[aiplatform.Endpoint] = None,
    deployed_model_display_name: Optional[str] = None,
    traffic_percentage: Optional[int] = 0,
    traffic_split: Optional[Dict[str, int]] = None,
    min_replica_count: int = 1,
    max_replica_count: int = 1,
    metadata: Optional[Sequence[Tuple[str, str]]] = (),
    sync: bool = True,
):
    """
    model_name: A fully-qualified model resource name or model ID.
          Example: "projects/123/locations/us-central1/models/456" or
          "456" when project and location are initialized or passed.
    """

    aiplatform.init(project=project, location=location)

    model = aiplatform.Model(model_name=model_name)

    model.deploy(
        endpoint=endpoint,
        deployed_model_display_name=deployed_model_display_name,
        traffic_percentage=traffic_percentage,
        traffic_split=traffic_split,
        min_replica_count=min_replica_count,
        max_replica_count=max_replica_count,
        metadata=metadata,
        sync=sync,
    )

    model.wait()

    print(model.display_name)
    print(model.resource_name)
    return model

Learn how to change the default settings for prediction logging.

Get operation status

Some requests start long-running operations that require time to complete. These requests return an operation name, which you can use to view the operation's status or cancel the operation. Vertex AI provides helper methods to make calls against long-running operations. For more information, see Working with long-running operations.

Make an online prediction using your deployed model

To make an online prediction, submit one or more test items to a model for analysis, and the model returns results that are based on your model's objective. For more information about prediction results, see the Interpret results page.

Console

Use the Google Cloud console to request an online prediction. Your model must be deployed to an endpoint.

  1. In the Google Cloud console, in the Vertex AI section, go to the Models page.

    Go to the Models page

  2. From the list of models, click the name of the model to request predictions from.

  3. Select the Deploy & test tab.

  4. Under the Test your model section, add test items to request a prediction.

    AutoML models for image objectives require you to upload an image to request a prediction.

    For information about local feature importance, see Get explanations.

    After the prediction is complete, Vertex AI returns the results in the console.

API

Use the Vertex AI API to request an online prediction. Your model must be deployed to an endpoint.

Get batch predictions

To make a batch prediction request, you specify an input source and an output format where Vertex AI stores predictions results. Batch predictions for the AutoML image model type require an input JSON Lines file and the name of a Cloud Storage bucket to store the output.

Input data requirements

The input for batch requests specifies the items to send to your model for prediction. For image classification models, you can use a JSON Lines file to specify a list of images to make predictions about and then store the JSON Lines file in a Cloud Storage bucket. The following sample shows a single line in an input JSON Lines file:

{"content": "gs://sourcebucket/datasets/images/source_image.jpg", "mimeType": "image/jpeg"}

Request a batch prediction

For batch prediction requests, you can use the Google Cloud console or the Vertex AI API. Depending on the number of input items that you've submitted, a batch prediction task can take some time to complete.

Google Cloud console

Use the Google Cloud console to request a batch prediction.

  1. In the Google Cloud console, in the Vertex AI section, go to the Batch predictions page.

    Go to the Batch predictions page

  2. Click Create to open the New batch prediction window and complete the following steps:

    1. Enter a name for the batch prediction.
    2. For Model name, select the name of the model to use for this batch prediction.
    3. For Source path, specify the Cloud Storage location where your JSON Lines input file is located.
    4. For the Destination path, specify a Cloud Storage location where the batch prediction results are stored. The Output format is determined by your model's objective. AutoML models for image objectives output JSON Lines files.

API

Use the Vertex AI API to send batch prediction requests.

REST

Before using any of the request data, make the following replacements:

  • LOCATION: Region where Model is stored and batch prediction job is executed. For example, us-central1.
  • PROJECT: Your project ID
  • BATCH_JOB_NAME: Display name for the batch job
  • MODEL_ID: The ID for the model to use for making predictions
  • THRESHOLD_VALUE (optional): Vertex AI returns only predictions that have confidence scores with at least this value. The default is 0.0.
  • MAX_PREDICTIONS (optional): Vertex AI returns up to this many predictions starting with the predictions that have highest confidence scores. The default is 10.
  • URI: Cloud Storage URI where your input JSON Lines file is located.
  • BUCKET: Your Cloud Storage bucket
  • PROJECT_NUMBER: Project number for your project

HTTP method and URL:

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/batchPredictionJobs

Request JSON body:

{
    "displayName": "BATCH_JOB_NAME",
    "model": "projects/PROJECT/locations/LOCATION/models/MODEL_ID",
    "modelParameters": {
      "confidenceThreshold": THRESHOLD_VALUE,
      "maxPredictions": MAX_PREDICTIONS
    },
    "inputConfig": {
        "instancesFormat": "jsonl",
        "gcsSource": {
            "uris": ["URI"],
        },
    },
    "outputConfig": {
        "predictionsFormat": "jsonl",
        "gcsDestination": {
            "outputUriPrefix": "OUTPUT_BUCKET",
        },
    },
}

To send your request, choose one of these options:

curl

Save the request body in a file called request.json, and execute the following command:

curl -X POST \
-H "Authorization: Bearer $(gcloud auth print-access-token)" \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/batchPredictionJobs"

PowerShell

Save the request body in a file called request.json, and execute the following command:

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/batchPredictionJobs" | Select-Object -Expand Content

You should receive a JSON response similar to the following:

{
  "name": "projects/PROJECT_NUMBER/locations/us-central1/batchPredictionJobs/BATCH_JOB_ID",
  "displayName": "BATCH_JOB_NAME 202005291958",
  "model": "projects/PROJECT/locations/us-central1/models/MODEL_ID",
  "inputConfig": {
    "instancesFormat": "jsonl",
    "gcsSource": {
      "uris": [
        "CONTENT"
      ]
    }
  },
  "outputConfig": {
    "predictionsFormat": "jsonl",
    "gcsDestination": {
      "outputUriPrefix": "BUCKET"
    }
  },
  "state": "JOB_STATE_PENDING",
  "createTime": "2020-05-30T02:58:44.341643Z",
  "updateTime": "2020-05-30T02:58:44.341643Z",
  "modelDisplayName": "MODEL_NAME",
  "modelObjective": "MODEL_OBJECTIVE"
}

You can poll for the status of the batch job using the BATCH_JOB_ID until the job state is JOB_STATE_SUCCEEDED.

Python

To learn how to install and use the client library for Vertex AI, see Vertex AI client libraries. For more information, see the Vertex AI Python API reference documentation.

def create_batch_prediction_job_sample(
    project: str,
    location: str,
    model_resource_name: str,
    job_display_name: str,
    gcs_source: Union[str, Sequence[str]],
    gcs_destination: str,
    sync: bool = True,
):
    aiplatform.init(project=project, location=location)

    my_model = aiplatform.Model(model_resource_name)

    batch_prediction_job = my_model.batch_predict(
        job_display_name=job_display_name,
        gcs_source=gcs_source,
        gcs_destination_prefix=gcs_destination,
        sync=sync,
    )

    batch_prediction_job.wait()

    print(batch_prediction_job.display_name)
    print(batch_prediction_job.resource_name)
    print(batch_prediction_job.state)
    return batch_prediction_job

Retrieve batch prediction results

Vertex AI sends batch prediction output to your specified destination.

When a batch prediction task is complete, the output of the prediction is stored in the Cloud Storage bucket that you specified in your request.

Example batch prediction results

The following an example batch prediction results from a image classification model.

{
  "instance": {"content": "gs://bucket/image.jpg", "mimeType": "image/jpeg"},
  "prediction": {
    "ids": [1, 2],
    "displayNames": ["cat", "dog"],
    "confidences": [0.7, 0.5]
  }
}