管理模型

本页面介绍如何使用 AutoML Tables 部署、取消部署、列出、删除和获取自定义模型的相关信息。

如需了解如何训练新模型，请参阅训练模型。

部署模型

训练模型后，您必须先部署模型，然后才能使用该模型请求在线预测。可以从未部署的模型请求批量预测。

部署模型会产生费用。如需了解详情，请参阅价格页面。

控制台

  1. 转到 Google Cloud 控制台中的 AutoML Tables 页面。

    转到 AutoML Tables 页面

  2. 在左侧导航窗格中，选择模型标签页，然后选择区域。

  3. 在要部署的模型的更多操作菜单中，点击部署模型。

    部署的“更多操作”菜单

REST

使用 models.deploy 方法部署模型。

在使用任何请求数据之前，请先进行以下替换：

  • endpoint：全球位置为 automl.googleapis.com，欧盟地区为 eu-automl.googleapis.com。
  • project-id：您的 Google Cloud 项目 ID。
  • location：资源的位置：全球位置为 us-central1，欧盟位置为 eu。
  • model-id：要部署的模型的 ID。例如，TBL543。

HTTP 方法和网址： 

POST https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id:deploy

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d "" \
    "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id:deploy"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -Uri "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id:deploy" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/292381/locations/us-central1/operations/TBL543",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1beta1.OperationMetadata",
    "createTime": "2019-12-26T19:21:00.550021Z",
    "updateTime": "2019-12-26T19:21:00.550021Z",
    "worksOn": [
      "projects/292381/locations/us-central1/models/TBL543"
    ],
    "deployModelDetails": {},
    "state": "RUNNING"
  }
}

部署模型是一项长时间运行的操作。您可以轮询操作状态或等待操作返回。了解详情。

Java

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.DeployModelRequest;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.cloud.automl.v1beta1.OperationMetadata;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class DeployModel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    deployModel(projectId, modelId);
  }

  // Deploy a model for prediction
  static void deployModel(String projectId, String modelId)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      DeployModelRequest request =
          DeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
      OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);

      future.get();
      System.out.println("Model deployment finished");
    }
  }
}

Node.js

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

const automl = require('@google-cloud/automl');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to deploy model.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL4704590352927948800";

// Get the full path of the model.
const modelFullId = client.modelPath(projectId, computeRegion, modelId);

// Deploy a model with the deploy model request.
client
  .deployModel({name: modelFullId})
  .then(responses => {
    const response = responses[0];
    console.log('Deployment Details:');
    console.log(`\tName: ${response.name}`);
    console.log('\tMetadata:');
    console.log(`\t\tType Url: ${response.metadata.typeUrl}`);
    console.log(`\tDone: ${response.done}`);
  })
  .catch(err => {
    console.error(err);
  });

Python

AutoML Tables 的客户端库包含其他 Python 方法，这些方法使用 AutoML Tables API 进行简化。这些方法按名称而不是 ID 来引用数据集和模型。您的数据集和模型的名称必须是唯一的。如需了解详情，请参阅客户端参考。

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# Deploy model
response = client.deploy_model(model_display_name=model_display_name)

# synchronous check of operation status.
print(f"Model deployed. {response.result()}")

取消部署模型

您必须先部署模型，然后才能请求在线预测。如果您不再需要使用模型进行在线预测，可以取消部署该模型以免产生部署费用。

如需了解部署费用，请参阅价格页面。

控制台

  1. 转到 Google Cloud 控制台中的 AutoML Tables 页面。

    转到 AutoML Tables 页面

  2. 在左侧导航窗格中，选择模型标签页，然后选择区域。

  3. 在要取消部署的模型的更多操作菜单中，点击移除部署。

    移除部署的“更多操作”菜单

REST

您可以使用 models.undeploy 方法取消部署模型。

在使用任何请求数据之前，请先进行以下替换：

  • endpoint：全球位置为 automl.googleapis.com，欧盟地区为 eu-automl.googleapis.com。
  • project-id：您的 Google Cloud 项目 ID。
  • location：资源的位置：全球位置为 us-central1，欧盟位置为 eu。
  • model-id：要取消部署的模型的 ID。例如，TBL543。

HTTP 方法和网址： 

POST https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id:undeploy

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X POST \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    -H "Content-Type: application/json; charset=utf-8" \
    -d "" \
    "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id:undeploy"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method POST `
    -Headers $headers `
    -Uri "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id:undeploy" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/292381/locations/us-central1/operations/TBL543",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1beta1.OperationMetadata",
    "createTime": "2019-12-26T19:19:21.579163Z",
    "updateTime": "2019-12-26T19:19:21.579163Z",
    "worksOn": [
      "projects/292381/locations/us-central1/models/TBL543"
    ],
    "undeployModelDetails": {},
    "state": "RUNNING"
  }
}

Java

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.cloud.automl.v1beta1.OperationMetadata;
import com.google.cloud.automl.v1beta1.UndeployModelRequest;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class UndeployModel {

  static void undeployModel() throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    undeployModel(projectId, modelId);
  }

  // Undeploy a model from prediction
  static void undeployModel(String projectId, String modelId)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);
      UndeployModelRequest request =
          UndeployModelRequest.newBuilder().setName(modelFullId.toString()).build();
      OperationFuture<Empty, OperationMetadata> future = client.undeployModelAsync(request);

      future.get();
      System.out.println("Model undeployment finished");
    }
  }
}

Node.js

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

const automl = require('@google-cloud/automl');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to undelpoy model.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL4704590352927948800";

// Get the full path of the model.
const modelFullId = client.modelPath(projectId, computeRegion, modelId);

// Undeploy a model with the undeploy model request.
client
  .undeployModel({name: modelFullId})
  .then(responses => {
    const response = responses[0];
    console.log('Undeployment Details:');
    console.log(`\tName: ${response.name}`);
    console.log('\tMetadata:');
    console.log(`\t\tType Url: ${response.metadata.typeUrl}`);
    console.log(`\tDone: ${response.done}`);
  })
  .catch(err => {
    console.error(err);
  });

Python

AutoML Tables 的客户端库包含其他 Python 方法，这些方法使用 AutoML Tables API 进行简化。这些方法按名称而不是 ID 来引用数据集和模型。您的数据集和模型的名称必须是唯一的。如需了解详情，请参阅客户端参考。

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# Undeploy model
response = client.undeploy_model(model_display_name=model_display_name)

# synchronous check of operation status.
print(f"Model undeployed. {response.result()}")

获取模型的相关信息

训练完成后，您可以获取新创建的模型的相关信息。

控制台

  1. 转到 Google Cloud 控制台中的 AutoML Tables 页面。

    转到 AutoML Tables 页面

  2. 在左侧导航窗格中选择模型标签页，然后选择要查看其信息的模型。

  3. 选择训练标签页。

    您将看到模型的概要指标，例如精确率和召回率。

    经过训练的模型的概要指标

    若在评估模型质量方面需要帮助，请参阅评估模型。

REST

您可以使用 models.get 方法获取model的相关信息。

在使用任何请求数据之前，请先进行以下替换：

  • endpoint：全球位置为 automl.googleapis.com，欧盟地区为 eu-automl.googleapis.com。
  • project-id：您的 Google Cloud 项目 ID。
  • location：资源的位置：全球位置为 us-central1，欧盟位置为 eu。
  • model-id：您要获取相关信息的模型的 ID。例如，TBL543。

HTTP 方法和网址： 

GET https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

Java

如果资源位于欧盟区域，您必须明确设置端点。了解详情。


import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.Model;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.cloud.automl.v1beta1.TablesModelColumnInfo;
import io.grpc.StatusRuntimeException;
import java.io.IOException;
import java.text.DateFormat;
import java.text.SimpleDateFormat;

public class TablesGetModel {

  public static void main(String[] args) throws IOException, StatusRuntimeException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String region = "YOUR_REGION";
    String modelId = "YOUR_MODEL_ID";
    getModel(projectId, region, modelId);
  }

  // Demonstrates using the AutoML client to get model details.
  public static void getModel(String projectId, String computeRegion, String modelId)
      throws IOException, StatusRuntimeException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {

      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, computeRegion, modelId);

      // Get complete detail of the model.
      Model model = client.getModel(modelFullId);

      // Display the model information.
      System.out.format("Model name: %s%n", model.getName());
      System.out.format(
          "Model Id: %s\n", model.getName().split("/")[model.getName().split("/").length - 1]);
      System.out.format("Model display name: %s%n", model.getDisplayName());
      System.out.format("Dataset Id: %s%n", model.getDatasetId());
      System.out.println("Tables Model Metadata: ");
      System.out.format(
          "\tTraining budget: %s%n", model.getTablesModelMetadata().getTrainBudgetMilliNodeHours());
      System.out.format(
          "\tTraining cost: %s%n", model.getTablesModelMetadata().getTrainBudgetMilliNodeHours());

      DateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd'T'HH:mm:ss.SSSZ");
      String createTime =
          dateFormat.format(new java.util.Date(model.getCreateTime().getSeconds() * 1000));
      System.out.format("Model create time: %s%n", createTime);

      System.out.format("Model deployment state: %s%n", model.getDeploymentState());

      // Get features of top importance
      for (TablesModelColumnInfo info :
          model.getTablesModelMetadata().getTablesModelColumnInfoList()) {
        System.out.format(
            "Column: %s - Importance: %.2f%n",
            info.getColumnDisplayName(), info.getFeatureImportance());
      }
    }
  }
}

Node.js

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

const automl = require('@google-cloud/automl');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to get model details.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL4704590352927948800";

// Get the full path of the model.
const modelFullId = client.modelPath(projectId, computeRegion, modelId);

// Get complete detail of the model.
client
  .getModel({name: modelFullId})
  .then(responses => {
    const model = responses[0];

    // Display the model information.
    console.log(`Model name: ${model.name}`);
    console.log(`Model Id: ${model.name.split('/').pop(-1)}`);
    console.log(`Model display name: ${model.displayName}`);
    console.log(`Dataset Id: ${model.datasetId}`);
    console.log('Tables model metadata: ');
    console.log(
      `\tTraining budget: ${model.tablesModelMetadata.trainBudgetMilliNodeHours}`
    );
    console.log(
      `\tTraining cost: ${model.tablesModelMetadata.trainCostMilliNodeHours}`
    );
    console.log(`Model deployment state: ${model.deploymentState}`);
  })
  .catch(err => {
    console.error(err);
  });

Python

AutoML Tables 的客户端库包含其他 Python 方法，这些方法使用 AutoML Tables API 进行简化。这些方法按名称而不是 ID 来引用数据集和模型。您的数据集和模型的名称必须是唯一的。如需了解详情，请参阅客户端参考。

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# Get complete detail of the model.
model = client.get_model(model_display_name=model_display_name)

# Retrieve deployment state.
if model.deployment_state == automl.Model.DeploymentState.DEPLOYED:
    deployment_state = "deployed"
else:
    deployment_state = "undeployed"

# get features of top importance
feat_list = [
    (column.feature_importance, column.column_display_name)
    for column in model.tables_model_metadata.tables_model_column_info
]
feat_list.sort(reverse=True)
if len(feat_list) < 10:
    feat_to_show = len(feat_list)
else:
    feat_to_show = 10

# Display the model information.
print(f"Model name: {model.name}")
print("Model id: {}".format(model.name.split("/")[-1]))
print(f"Model display name: {model.display_name}")
print("Features of top importance:")
for feat in feat_list[:feat_to_show]:
    print(feat)
print(f"Model create time: {model.create_time}")
print(f"Model deployment state: {deployment_state}")

列出模型

一个项目可以包含大量模型，这些模型可能是利用相同或不同的数据集训练的。

控制台

如需使用 Google Cloud 控制台查看可用模型的列表，请点击左侧导航栏中的模型标签页，然后选择区域。

REST

要使用 API 查看可用模型的列表，请使用 models.list 方法。

在使用任何请求数据之前，请先进行以下替换：

  • endpoint：全球位置为 automl.googleapis.com，欧盟地区为 eu-automl.googleapis.com。
  • project-id：您的 Google Cloud 项目 ID。
  • location：资源的位置：全球位置为 us-central1，欧盟位置为 eu。

HTTP 方法和网址： 

GET https://endpoint/v1beta1/projects/project-id/locations/location/models

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X GET \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    "https://endpoint/v1beta1/projects/project-id/locations/location/models"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method GET `
    -Headers $headers `
    -Uri "https://endpoint/v1beta1/projects/project-id/locations/location/models" | Select-Object -Expand Content
此方法会为所选位置和项目中的每个模型返回完整的模型对象。

Java

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.ListModelsRequest;
import com.google.cloud.automl.v1beta1.LocationName;
import com.google.cloud.automl.v1beta1.Model;
import java.io.IOException;

class ListModels {

  static void listModels() throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    listModels(projectId);
  }

  // List the models available in the specified location
  static void listModels(String projectId) throws IOException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");

      // Create list models request.
      ListModelsRequest listModelsRequest =
          ListModelsRequest.newBuilder()
              .setParent(projectLocation.toString())
              .setFilter("")
              .build();

      // List all the models available in the region by applying filter.
      System.out.println("List of models:");
      for (Model model : client.listModels(listModelsRequest).iterateAll()) {
        // Display the model information.
        System.out.format("Model name: %s%n", model.getName());
        // To get the model id, you have to parse it out of the `name` field. As models Ids are
        // required for other methods.
        // Name Format: `projects/{project_id}/locations/{location_id}/models/{model_id}`
        String[] names = model.getName().split("/");
        String retrievedModelId = names[names.length - 1];
        System.out.format("Model id: %s%n", retrievedModelId);
        System.out.format("Model display name: %s%n", model.getDisplayName());
        System.out.println("Model create time:");
        System.out.format("\tseconds: %s%n", model.getCreateTime().getSeconds());
        System.out.format("\tnanos: %s%n", model.getCreateTime().getNanos());
        System.out.format("Model deployment state: %s%n", model.getDeploymentState());
      }
    }
  }
}

Node.js

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

const automl = require('@google-cloud/automl');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to list all models.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const filter_ = '[FILTER_EXPRESSIONS]' e.g., "tablesModelMetadata:*";

// A resource that represents Google Cloud Platform location.
const projectLocation = client.locationPath(projectId, computeRegion);

// List all the models available in the region by applying filter.
client
  .listModels({parent: projectLocation, filter: filter})
  .then(responses => {
    const model = responses[0];

    // Display the model information.
    console.log('List of models:');
    for (let i = 0; i < model.length; i++) {
      console.log(`\nModel name: ${model[i].name}`);
      console.log(`Model Id: ${model[i].name.split('/').pop(-1)}`);
      console.log(`Model display name: ${model[i].displayName}`);
      console.log(`Dataset Id: ${model[i].datasetId}`);
      console.log('Tables model metadata:');
      console.log(
        `\tTraining budget: ${model[i].tablesModelMetadata.trainBudgetMilliNodeHours}`
      );
      console.log(
        `\tTraining cost: ${model[i].tablesModelMetadata.trainCostMilliNodeHours}`
      );
      console.log(`Model deployment state: ${model[i].deploymentState}`);
    }
  })
  .catch(err => {
    console.error(err);
  });

Python

AutoML Tables 的客户端库包含其他 Python 方法，这些方法使用 AutoML Tables API 进行简化。这些方法按名称而不是 ID 来引用数据集和模型。您的数据集和模型的名称必须是唯一的。如需了解详情，请参阅客户端参考。

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# filter = 'DATASET_DISPLAY_NAME_HERE'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# List all the models available in the region by applying filter.
response = client.list_models(filter=filter)

print("List of models:")
for model in response:
    # Retrieve deployment state.
    if model.deployment_state == automl.Model.DeploymentState.DEPLOYED:
        deployment_state = "deployed"
    else:
        deployment_state = "undeployed"

    # Display the model information.
    print(f"Model name: {model.name}")
    print("Model id: {}".format(model.name.split("/")[-1]))
    print(f"Model display name: {model.display_name}")
    metadata = model.tables_model_metadata
    print(
        "Target column display name: {}".format(
            metadata.target_column_spec.display_name
        )
    )
    print(
        "Training budget in node milli hours: {}".format(
            metadata.train_budget_milli_node_hours
        )
    )
    print(
        "Training cost in node milli hours: {}".format(
            metadata.train_cost_milli_node_hours
        )
    )
    print(f"Model create time: {model.create_time}")
    print(f"Model deployment state: {deployment_state}")
    print("\n")

删除模型

删除模型会将该模型从项目中永久移除。

控制台

  1. 在 AutoML Tables 界面中，点击左侧导航菜单中的模型标签页并选择区域，以显示该区域的可用模型的列表。

  2. 点击待删除行最右侧的三点状菜单，然后选择删除模型。

  3. 在确认对话框中点击删除。

REST

您可以使用 models.delete 方法删除模型。

在使用任何请求数据之前，请先进行以下替换：

  • endpoint：全球位置为 automl.googleapis.com，欧盟地区为 eu-automl.googleapis.com。
  • project-id：您的 Google Cloud 项目 ID。
  • location：资源的位置：全球位置为 us-central1，欧盟位置为 eu。
  • model-id：要删除的模型的 ID。例如，TBL543。

HTTP 方法和网址： 

DELETE https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id

如需发送请求，请选择以下方式之一：

curl

执行以下命令： 

curl -X DELETE \
    -H "Authorization: Bearer $(gcloud auth print-access-token)" \
    -H "x-goog-user-project: project-id" \
    "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id"

PowerShell

执行以下命令： 

$cred = gcloud auth print-access-token
$headers = @{ "Authorization" = "Bearer $cred"; "x-goog-user-project" = "project-id" }

Invoke-WebRequest `
    -Method DELETE `
    -Headers $headers `
    -Uri "https://endpoint/v1beta1/projects/project-id/locations/location/models/model-id" | Select-Object -Expand Content

您应该收到类似以下内容的 JSON 响应：

{
  "name": "projects/29452381/locations/us-central1/operations/TBL543",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1beta1.OperationMetadata",
    "createTime": "2019-12-26T17:19:50.684850Z",
    "updateTime": "2019-12-26T17:19:50.684850Z",
    "deleteDetails": {},
    "worksOn": [
      "projects/29452381/locations/us-central1/models/TBL543"
    ],
    "state": "DONE"
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

删除模型是一项长时间运行的操作。您可以轮询操作状态或等待操作返回。了解详情。

Java

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

import com.google.cloud.automl.v1beta1.AutoMlClient;
import com.google.cloud.automl.v1beta1.ModelName;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class DeleteModel {

  public static void main(String[] args)
      throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String modelId = "YOUR_MODEL_ID";
    deleteModel(projectId, modelId);
  }

  // Delete a model
  static void deleteModel(String projectId, String modelId)
      throws IOException, ExecutionException, InterruptedException {
    // 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 (AutoMlClient client = AutoMlClient.create()) {
      // Get the full path of the model.
      ModelName modelFullId = ModelName.of(projectId, "us-central1", modelId);

      // Delete a model.
      Empty response = client.deleteModelAsync(modelFullId).get();

      System.out.println("Model deletion started...");
      System.out.println(String.format("Model deleted. %s", response));
    }
  }
}

Node.js

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

const automl = require('@google-cloud/automl');
const client = new automl.v1beta1.AutoMlClient();

/**
 * Demonstrates using the AutoML client to delete a model.
 * TODO(developer): Uncomment the following lines before running the sample.
 */
// const projectId = '[PROJECT_ID]' e.g., "my-gcloud-project";
// const computeRegion = '[REGION_NAME]' e.g., "us-central1";
// const modelId = '[MODEL_ID]' e.g., "TBL4704590352927948800";

// Get the full path of the model.
const modelFullId = client.modelPath(projectId, computeRegion, modelId);

// Delete a model.
client
  .deleteModel({name: modelFullId})
  .then(responses => {
    const operation = responses[0];
    return operation.promise();
  })
  .then(responses => {
    // The final result of the operation.
    const operationDetails = responses[2];

    // Get the Model delete details.
    console.log('Model delete details:');
    console.log('\tOperation details:');
    console.log(`\t\tName: ${operationDetails.name}`);
    console.log(`\tDone: ${operationDetails.done}`);
  })
  .catch(err => {
    console.error(err);
  });

Python

AutoML Tables 的客户端库包含其他 Python 方法，这些方法使用 AutoML Tables API 进行简化。这些方法按名称而不是 ID 来引用数据集和模型。您的数据集和模型的名称必须是唯一的。如需了解详情，请参阅客户端参考。

如果资源位于欧盟区域，您必须明确设置端点。了解详情。

# TODO(developer): Uncomment and set the following variables
# project_id = 'PROJECT_ID_HERE'
# compute_region = 'COMPUTE_REGION_HERE'
# model_display_name = 'MODEL_DISPLAY_NAME_HERE'

from google.cloud import automl_v1beta1 as automl

client = automl.TablesClient(project=project_id, region=compute_region)

# Undeploy model
response = client.delete_model(model_display_name=model_display_name)

# synchronous check of operation status.
print(f"Model deleted. {response.result()}")

后续步骤