获取图片对象检测模型的预测结果

本页面介绍如何使用 Google Cloud 控制台或 Vertex AI API 通过图片对象检测模型获取在线(实时)预测和批量预测

在线预测与批量预测之间的区别

在线预测是向模型端点发出的同步请求。如果您要发出请求以响应应用输入,或者在其他需要及时推理的情况下,可以使用在线预测。

批量预测是异步请求。您可以直接从模型资源请求批量预测,而无需将模型部署到端点。对于图片数据,如果您不需要获得即时响应并且希望通过一个请求处理累积数据,则可以使用批量预测。

获取在线预测结果

将模型部署到端点

您必须先将模型部署到端点,然后才能使用该模型执行在线预测。部署模型会将物理资源与模型相关联,以便以低延迟方式执行在线预测。

您可以将多个模型部署到一个端点,也可以将一个模型部署到多个端点。如需详细了解部署模型的方法和使用场景,请参阅部署模型简介

请使用以下方法之一部署模型:

Google Cloud 控制台

  1. 在 Google Cloud 控制台的 Vertex AI 部分中,转到模型页面。

    转到“模型”页面

  2. 点击要部署的模型名称以打开其详情页面。

  3. 选择部署和测试标签页。

    如果模型已部署到任何端点,部署模型 (Deploy your model) 部分中会列出这些端点。

  4. 点击部署到端点

  5. 如需将模型部署到新的端点,请选择创建新端点并为新端点提供名称。如需将模型部署到现有端点,请选择 添加到现有端点,然后从下拉列表中选择端点。

    您可以将多个模型添加到一个端点,也可以将一个模型添加到多个端点。了解详情

  6. 如果您将模型部署到已部署有一个或多个模型的现有端点,则必须为要部署的模型和已部署模型更新流量拆分,以使所有百分比的总和为 100%。

  7. 选择 AutoML 图片,然后按如下方式进行配置:

    1. 如果您要将模型部署到新端点,请接受 100 的流量拆分值。否则,请为端点上所有模型调整流量拆分值,使它们的总和为 100。

    2. 输入要为模型提供的计算节点数量

      这是此模型始终可用的节点数。即使没有预测流量,您也要为节点付费。请参阅价格页面

    3. 了解如何更改预测日志记录的默认设置

    4. 仅限分类模型(可选):在可解释性选项部分,选择 为此模型启用特征归因以启用 Vertex Explainable AI。接受现有的可视化设置或选择新值,然后点击完成

      部署配置有 Vertex Explainable AI 的 AutoML 图片分类模型并使用可解释性进行预测均为可选操作。在部署时启用 Vertex Explainable AI 会产生基于已部署节点数量和部署时间的额外费用。如需了解详情,请参阅价格

    5. 为模型点击完成,当所有流量分配百分比均正确无误时,点击继续

      随即将显示在其中部署模型的区域。此区域必须是您在其中创建模型的区域。

    6. 点击部署,将模型部署到端点。

API

使用 Vertex AI API 部署模型时,请完成以下步骤:

  1. 根据需要创建端点。
  2. 获取端点 ID。
  3. 将模型部署到端点。

创建端点

如果要将模型部署到现有端点,您可以跳过此步骤。

gcloud

以下示例使用 gcloud ai endpoints create 命令

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

替换以下内容:

  • LOCATION_ID:您在其中使用 Vertex AI 的区域。
  • ENDPOINT_NAME:端点的显示名称。

Google Cloud CLI 工具可能需要几秒钟才能创建端点。

REST

在使用任何请求数据之前,请先进行以下替换:

  • LOCATION_ID:您的区域。
  • PROJECT_ID:您的项目 ID
  • ENDPOINT_NAME:端点的显示名称。

HTTP 方法和网址:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/endpoints

请求 JSON 正文:

{
  "display_name": "ENDPOINT_NAME"
}

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/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"
    }
  }
}
您可以轮询操作状态,直到响应包含 "done": true

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.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

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 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

如需了解如何安装或更新 Python,请参阅安装 Python 版 Vertex AI SDK。如需了解详情,请参阅 Python API 参考文档

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

检索端点 ID

您需要端点 ID 才能部署模型。

gcloud

以下示例使用 gcloud ai endpoints list 命令

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

替换以下内容:

  • LOCATION_ID:您在其中使用 Vertex AI 的区域。
  • ENDPOINT_NAME:端点的显示名称。

请注意 ENDPOINT_ID 列中显示的数字。请在以下步骤中使用此 ID。

REST

在使用任何请求数据之前,请先进行以下替换:

  • LOCATION_ID:您在其中使用 Vertex AI 的区域。
  • PROJECT_ID:您的项目 ID
  • ENDPOINT_NAME:端点的显示名称。

HTTP 方法和网址:

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

如需发送您的请求,请展开以下选项之一:

您应该收到类似以下内容的 JSON 响应:

{
  "endpoints": [
    {
      "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/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"
    }
  ]
}
请记下 ENDPOINT_ID

部署模型

在下面选择您的语言或环境对应的标签页:

gcloud

以下示例使用 gcloud ai endpoints deploy-model 命令

以下示例将 Model 部署到 Endpoint,但没有在多个 DeployedModel 资源之间拆分流量:

在使用下面的命令数据之前,请先进行以下替换:

  • ENDPOINT_ID:端点的 ID。
  • LOCATION_ID:您在其中使用 Vertex AI 的区域。
  • MODEL_ID:要部署的模型的 ID。
  • DEPLOYED_MODEL_NAMEDeployedModel 的名称。您还可以将 Model 的显示名用于 DeployedModel
  • MIN_REPLICA_COUNT:此部署的最小节点数。 节点数可根据预测负载的需要而增加或减少,直至达到节点数上限并且绝不会少于此节点数。
  • MAX_REPLICA_COUNT:此部署的节点数上限。 节点数可根据预测负载的需要而增加或减少,直至达到此节点数并且绝不会少于节点数下限。如果您省略 --max-replica-count 标志,则节点数上限将设置为 --min-replica-count 的值。

执行 gcloud ai endpoints deploy-model 命令:

Linux、macOS 或 Cloud Shell

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

拆分流量

上述示例中的 --traffic-split=0=100 标志会将 Endpoint 接收的 100% 预测流量发送到新 DeployedModel(使用临时 ID 0 表示)。如果您的 Endpoint 已有其他 DeployedModel 资源,那么您可以在新 DeployedModel 和旧资源之间拆分流量。例如,如需将 20% 的流量发送到新 DeployedModel,将 80% 发送到旧版本,请运行以下命令。

在使用下面的命令数据之前,请先进行以下替换:

  • OLD_DEPLOYED_MODEL_ID:现有 DeployedModel 的 ID。

执行 gcloud ai endpoints deploy-model 命令:

Linux、macOS 或 Cloud Shell

gcloud ai endpoints deploy-model ENDPOINT_ID\
  --region=LOCATION_ID \
  --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_ID `
  --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_ID ^
  --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

部署此模型。

在使用任何请求数据之前,请先进行以下替换:

  • LOCATION_ID:您在其中使用 Vertex AI 的区域。
  • PROJECT_ID:您的项目 ID
  • ENDPOINT_ID:端点的 ID。
  • MODEL_ID:要部署的模型的 ID。
  • DEPLOYED_MODEL_NAMEDeployedModel 的名称。您还可以将 Model 的显示名用于 DeployedModel
  • MIN_REPLICA_COUNT:此部署的最小节点数。 节点数可根据预测负载的需要而增加或减少,直至达到节点数上限并且绝不会少于此节点数。
  • MAX_REPLICA_COUNT:此部署的节点数上限。 节点数可根据预测负载的需要而增加或减少,直至达到此节点数并且绝不会少于节点数下限。
  • TRAFFIC_SPLIT_THIS_MODEL:流向此端点的要路由到使用此操作部署的模型的预测流量百分比。默认值为 100。所有流量百分比之和必须为 100。详细了解流量拆分
  • DEPLOYED_MODEL_ID_N:可选。如果将其他模型部署到此端点,您必须更新其流量拆分百分比,以便所有百分比之和等于 100。
  • TRAFFIC_SPLIT_MODEL_N:已部署模型 ID 密钥的流量拆分百分比值。
  • PROJECT_NUMBER:自动生成的项目编号

HTTP 方法和网址:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/endpoints/ENDPOINT_ID:deployModel

请求 JSON 正文:

{
  "deployedModel": {
    "model": "projects/PROJECT_ID/locations/LOCATION_ID/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
  },
}

如需发送您的请求,请展开以下选项之一:

您应会收到如下所示的 JSON 响应:

{
  "name": "projects/PROJECT_ID/locations/LOCATION_ID/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

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.api.gax.longrunning.OperationFuture;
import com.google.api.gax.longrunning.OperationTimedPollAlgorithm;
import com.google.api.gax.retrying.RetrySettings;
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 com.google.cloud.aiplatform.v1.stub.EndpointServiceStubSettings;
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;
import org.threeten.bp.Duration;

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";
    int timeout = 900;
    deployModelSample(project, deployedModelDisplayName, endpointId, modelId, timeout);
  }

  static void deployModelSample(
      String project,
      String deployedModelDisplayName,
      String endpointId,
      String modelId,
      int timeout)
      throws IOException, InterruptedException, ExecutionException, TimeoutException {

    // Set long-running operations (LROs) timeout
    final OperationTimedPollAlgorithm operationTimedPollAlgorithm =
        OperationTimedPollAlgorithm.create(
            RetrySettings.newBuilder()
                .setInitialRetryDelay(Duration.ofMillis(5000L))
                .setRetryDelayMultiplier(1.5)
                .setMaxRetryDelay(Duration.ofMillis(45000L))
                .setInitialRpcTimeout(Duration.ZERO)
                .setRpcTimeoutMultiplier(1.0)
                .setMaxRpcTimeout(Duration.ZERO)
                .setTotalTimeout(Duration.ofSeconds(timeout))
                .build());

    EndpointServiceStubSettings.Builder endpointServiceStubSettingsBuilder =
        EndpointServiceStubSettings.newBuilder();
    endpointServiceStubSettingsBuilder
        .deployModelOperationSettings()
        .setPollingAlgorithm(operationTimedPollAlgorithm);
    EndpointServiceStubSettings endpointStubSettings = endpointServiceStubSettingsBuilder.build();
    EndpointServiceSettings endpointServiceSettings =
        EndpointServiceSettings.create(endpointStubSettings);
    endpointServiceSettings =
        endpointServiceSettings.toBuilder()
            .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

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 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

如需了解如何安装或更新 Python,请参阅安装 Python 版 Vertex AI SDK。如需了解详情,请参阅 Python API 参考文档

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

了解如何更改预测日志记录的默认设置

获取操作状态

某些请求会启动需要一些时间才能完成的长时间运行的操作。这些请求会返回操作名称,您可以使用该名称查看操作状态或取消操作。Vertex AI 提供辅助方法来调用长时间运行的操作。如需了解详情,请参阅使用长时间运行的操作

使用已部署的模型进行在线预测

如需进行在线预测,请向模型提交一个或多个测试项进行分析,模型会返回基于模型目标的结果。如需详细了解预测结果,请参阅解读结果页面。

控制台

使用 Google Cloud 控制台请求在线预测。您的模型必须部署到端点。

  1. 在 Google Cloud 控制台的 Vertex AI 部分中,转到模型页面。

    转到“模型”页面

  2. 从模型列表中,点击要向其请求预测的模型的名称。

  3. 选择部署和测试标签页。

  4. 测试模型部分下,添加测试项以请求预测。

    用于图片目标的 AutoML 模型要求您上传图片才能请求预测。

    如需了解局部特征重要性,请参阅获取说明

    预测完成后,Vertex AI 会在控制台中返回结果。

API

使用 Vertex AI API 来请求在线预测。您的模型必须部署到端点。

图片数据类型目标包括分类和对象检测。

Edge 模型预测:当您使用 AutoML 图片 Edge 模型进行预测时,必须先将任何非 JPEG 预测文件转换为 JPEG 文件,然后再发送预测请求。如需查看 Python 预处理函数示例,请参阅适用于 Google Cloud AutoML API 的 Python 客户端代码库

gcloud

  1. 创建名为 request.json 且包含以下内容的文件:

    {
      "instances": [{
        "content": "CONTENT"
      }],
      "parameters": {
        "confidenceThreshold": THRESHOLD_VALUE,
        "maxPredictions": MAX_PREDICTIONS
      }
    }
    

    请替换以下内容:

    • CONTENTbase64 编码的图片内容。
    • THRESHOLD_VALUE(可选):模型仅返回置信度分数至少为此值的预测结果。
    • MAX_PREDICTIONS(可选):模型返回具有最高置信度分数的预测结果的数量上限。
  2. 运行以下命令:

    gcloud ai endpoints predict ENDPOINT_ID \
      --region=LOCATION_ID \
      --json-request=request.json

    替换以下内容:

    • ENDPOINT_ID:端点的 ID。
    • LOCATION_ID:您在其中使用 Vertex AI 的区域。

REST

在使用任何请求数据之前,请先进行以下替换:

  • LOCATION_ID:端点所在的区域。例如 us-central1
  • PROJECT_ID:您的项目 ID
  • ENDPOINT_ID:端点的 ID。
  • CONTENTbase64 编码的图片内容。
  • THRESHOLD_VALUE(可选):模型仅返回置信度分数至少为此值的预测结果。
  • MAX_PREDICTIONS(可选):模型返回具有最高置信度分数的预测结果的数量上限。

HTTP 方法和网址:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/endpoints/ENDPOINT_ID:predict

请求 JSON 正文:

{
  "instances": [{
    "content": "CONTENT"
  }],
  "parameters": {
    "confidenceThreshold": THRESHOLD_VALUE,
    "maxPredictions": MAX_PREDICTIONS
  }
}

如需发送请求,请选择以下方式之一:

curl

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

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

PowerShell

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

$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_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/endpoints/ENDPOINT_ID:predict" | Select-Object -Expand Content

您应会收到如下所示的 JSON 响应:

{
  "predictions": [
    {
      "confidences": [
        0.975873291,
        0.972160876,
        0.879488528,
        0.866532683,
        0.686478078
      ],
      "displayNames": [
        "Salad",
        "Salad",
        "Tomato",
        "Tomato",
        "Salad"
      ],
      "ids": [
        "7517774415476555776",
        "7517774415476555776",
        "2906088397049167872",
        "2906088397049167872",
        "7517774415476555776"
      ],
      "bboxes": [
        [
          0.0869686604,
          0.977020741,
          0.395135701,
          1
        ],
        [
          0,
          0.488701463,
          0.00157663226,
          0.512249
        ],
        [
          0.361617863,
          0.509664357,
          0.772928834,
          0.914706349
        ],
        [
          0.310678929,
          0.45781514,
          0.565507233,
          0.711237729
        ],
        [
          0.584359646,
          1,
          0.00116168708,
          0.130817384
        ]
      ]
    }
  ],
  "deployedModelId": "3860570043075002368"
}

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.util.ValueConverter;
import com.google.cloud.aiplatform.v1.EndpointName;
import com.google.cloud.aiplatform.v1.PredictResponse;
import com.google.cloud.aiplatform.v1.PredictionServiceClient;
import com.google.cloud.aiplatform.v1.PredictionServiceSettings;
import com.google.cloud.aiplatform.v1.schema.predict.instance.ImageObjectDetectionPredictionInstance;
import com.google.cloud.aiplatform.v1.schema.predict.params.ImageObjectDetectionPredictionParams;
import com.google.cloud.aiplatform.v1.schema.predict.prediction.ImageObjectDetectionPredictionResult;
import com.google.protobuf.Value;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.Base64;
import java.util.List;

public class PredictImageObjectDetectionSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String fileName = "YOUR_IMAGE_FILE_PATH";
    String endpointId = "YOUR_ENDPOINT_ID";
    predictImageObjectDetection(project, fileName, endpointId);
  }

  static void predictImageObjectDetection(String project, String fileName, String endpointId)
      throws IOException {
    PredictionServiceSettings settings =
        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(settings)) {
      String location = "us-central1";
      EndpointName endpointName = EndpointName.of(project, location, endpointId);

      byte[] contents = Base64.getEncoder().encode(Files.readAllBytes(Paths.get(fileName)));
      String content = new String(contents, StandardCharsets.UTF_8);

      ImageObjectDetectionPredictionParams params =
          ImageObjectDetectionPredictionParams.newBuilder()
              .setConfidenceThreshold((float) (0.5))
              .setMaxPredictions(5)
              .build();

      ImageObjectDetectionPredictionInstance instance =
          ImageObjectDetectionPredictionInstance.newBuilder().setContent(content).build();

      List<Value> instances = new ArrayList<>();
      instances.add(ValueConverter.toValue(instance));

      PredictResponse predictResponse =
          predictionServiceClient.predict(endpointName, instances, ValueConverter.toValue(params));
      System.out.println("Predict Image Object Detection Response");
      System.out.format("\tDeployed Model Id: %s\n", predictResponse.getDeployedModelId());

      System.out.println("Predictions");
      for (Value prediction : predictResponse.getPredictionsList()) {

        ImageObjectDetectionPredictionResult.Builder resultBuilder =
            ImageObjectDetectionPredictionResult.newBuilder();

        ImageObjectDetectionPredictionResult result =
            (ImageObjectDetectionPredictionResult)
                ValueConverter.fromValue(resultBuilder, prediction);

        for (int i = 0; i < result.getIdsCount(); i++) {
          System.out.printf("\tDisplay name: %s\n", result.getDisplayNames(i));
          System.out.printf("\tConfidences: %f\n", result.getConfidences(i));
          System.out.printf("\tIDs: %d\n", result.getIds(i));
          System.out.printf("\tBounding boxes: %s\n", result.getBboxes(i));
        }
      }
    }
  }
}

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 aiplatform = require('@google-cloud/aiplatform');
const {instance, params, prediction} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.predict;

// Imports the Google Cloud Prediction Service Client library
const {PredictionServiceClient} = aiplatform.v1;

// 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 predictImageObjectDetection() {
  // Configure the endpoint resource
  const endpoint = `projects/${project}/locations/${location}/endpoints/${endpointId}`;

  const parametersObj = new params.ImageObjectDetectionPredictionParams({
    confidenceThreshold: 0.5,
    maxPredictions: 5,
  });
  const parameters = parametersObj.toValue();

  const fs = require('fs');
  const image = fs.readFileSync(filename, 'base64');
  const instanceObj = new instance.ImageObjectDetectionPredictionInstance({
    content: image,
  });

  const instanceVal = instanceObj.toValue();
  const instances = [instanceVal];
  const request = {
    endpoint,
    instances,
    parameters,
  };

  // Predict request
  const [response] = await predictionServiceClient.predict(request);

  console.log('Predict image object detection response');
  console.log(`\tDeployed model id : ${response.deployedModelId}`);
  const predictions = response.predictions;
  console.log('Predictions :');
  for (const predictionResultVal of predictions) {
    const predictionResultObj =
      prediction.ImageObjectDetectionPredictionResult.fromValue(
        predictionResultVal
      );
    for (const [i, label] of predictionResultObj.displayNames.entries()) {
      console.log(`\tDisplay name: ${label}`);
      console.log(`\tConfidences: ${predictionResultObj.confidences[i]}`);
      console.log(`\tIDs: ${predictionResultObj.ids[i]}`);
      console.log(`\tBounding boxes: ${predictionResultObj.bboxes[i]}\n\n`);
    }
  }
}
predictImageObjectDetection();

Python

如需了解如何安装或更新 Python,请参阅安装 Python 版 Vertex AI SDK。如需了解详情,请参阅 Python API 参考文档

import base64

from google.cloud import aiplatform
from google.cloud.aiplatform.gapic.schema import predict

def predict_image_object_detection_sample(
    project: str,
    endpoint_id: str,
    filename: 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.PredictionServiceClient(client_options=client_options)
    with open(filename, "rb") as f:
        file_content = f.read()

    # The format of each instance should conform to the deployed model's prediction input schema.
    encoded_content = base64.b64encode(file_content).decode("utf-8")
    instance = predict.instance.ImageObjectDetectionPredictionInstance(
        content=encoded_content,
    ).to_value()
    instances = [instance]
    # See gs://google-cloud-aiplatform/schema/predict/params/image_object_detection_1.0.0.yaml for the format of the parameters.
    parameters = predict.params.ImageObjectDetectionPredictionParams(
        confidence_threshold=0.5,
        max_predictions=5,
    ).to_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)
    # See gs://google-cloud-aiplatform/schema/predict/prediction/image_object_detection_1.0.0.yaml for the format of the predictions.
    predictions = response.predictions
    for prediction in predictions:
        print(" prediction:", dict(prediction))

进行批量预测

如需发出批量预测请求,请指定输入源和 Vertex AI 存储预测结果所采用的输出格式。 AutoML 图片模型类型的批量预测需要输入 JSON 行文件和存储输出的 Cloud Storage 存储桶的名称。

输入数据要求

批量请求的输入指定要发送到模型进行预测的内容。对于图片对象检测模型,您可以使用 JSON 行文件指定用于进行预测的图片列表,然后将 JSON 行文件存储在 Cloud Storage 存储桶中。以下示例显示了输入 JSON 行文件中的一行:

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

请求批量预测

对于批量预测请求,您可以使用 Google Cloud 控制台或 Vertex AI API。批量预测任务可能需要一些时间才能完成,具体取决于提交的输入数据项数量。

Google Cloud 控制台

使用 Google Cloud 控制台请求批量预测。

  1. 在 Google Cloud 控制台的 Vertex AI 部分中,前往批量预测页面。

    前往“批量预测”页面

  2. 点击创建以打开新建批量预测窗口,完成以下步骤:

    1. 输入批量预测的名称。
    2. 对于模型名称,选择要用于此批量预测的模型的名称。
    3. 对于来源路径,指定 JSON 行输入文件所在的 Cloud Storage 位置。
    4. 对于目标路径,指定存储批量预测结果的 Cloud Storage 位置。输出格式取决于模型的目标。用于图片目标的 AutoML 模型会输出 JSON 行文件。

API

使用 Vertex AI API 发送批量预测请求。

REST

在使用任何请求数据之前,请先进行以下替换:

  • LOCATION_ID:存储模型和执行批量预测作业的区域。例如 us-central1
  • PROJECT_ID:您的项目 ID
  • BATCH_JOB_NAME:批量作业的显示名
  • MODEL_ID:用于执行预测的模型的 ID
  • THRESHOLD_VALUE(可选):Vertex AI 仅返回置信度分数至少为此值的预测。默认值为 0.0
  • MAX_PREDICTIONS(可选):从具有最高置信度分数的预测开始,Vertex AI 返回的预测的数量上限。默认值为 10
  • URI:输入 JSON 行文件所在的 Cloud Storage URI。
  • BUCKET:您的 Cloud Storage 存储桶
  • PROJECT_NUMBER:自动生成的项目编号

HTTP 方法和网址:

POST https://LOCATION_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/batchPredictionJobs

请求 JSON 正文:

{
    "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",
        },
    },
}

如需发送请求,请选择以下方式之一:

curl

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

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

PowerShell

将请求正文保存在名为 request.json 的文件中,然后执行以下命令:

$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_ID-aiplatform.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION_ID/batchPredictionJobs" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应:

{
  "name": "projects/PROJECT_NUMBER/locations/LOCATION_ID/batchPredictionJobs/BATCH_JOB_ID",
  "displayName": "BATCH_JOB_NAME",
  "model": "projects/PROJECT_ID/locations/LOCATION_ID/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"
}

您可以使用 BATCH_JOB_ID 轮询批量作业的状态,直到作业 stateJOB_STATE_SUCCEEDED

Python

如需了解如何安装或更新 Python,请参阅安装 Python 版 Vertex AI SDK。如需了解详情,请参阅 Python API 参考文档

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

检索批量预测结果

Vertex AI 将批量预测输出发送到您指定的目标位置。

批量预测任务完成后,预测的输出存储在您在请求中指定的 Cloud Storage 存储桶中。

批量预测结果示例

以下示例演示了来自图片对象检测模型的批量预测结果。

重要提示 :边界框的指定方式如下:

"bboxes": [ [xMin, xMax, yMin, yMax], ...]

其中,xMinxMax 是最小和最大 x 值, yMinyMax 分别是最小和最大 y 值。

{
  "instance": {"content": "gs://bucket/image.jpg", "mimeType": "image/jpeg"},
  "prediction": {
    "ids": [1, 2],
    "displayNames": ["cat", "dog"],
    "bboxes":  [
      [0.1, 0.2, 0.3, 0.4],
      [0.2, 0.3, 0.4, 0.5]
    ],
    "confidences": [0.7, 0.5]
  }
}