本文档适用于 AutoML Vision,它与 Vertex AI 有所不同。如果您使用的是 Vertex AI,请参阅 Vertex AI 文档

部署模型

初始模型部署

部署对象检测模型会产生费用。如需了解详情,请参阅价格页面

创建(训练)模型后,您必须先部署模型,然后才能在线(或同步)调用模型。

现在,如果您还需要其他在线预测能力,还可以更新模型部署。

网页界面

  1. 打开 AutoML Vision Object Detection 界面,然后选择左侧导航栏中的模型标签页以显示可用的模型。

    如需查看其他项目的模型,请从标题栏右上角的下拉列表中选择该项目。

  2. 选择要用于标记图片的模型所对应的行。
  3. 选择标题栏正下方的测试和使用标签页。
  4. 从模型名称下方的横幅中选择部署模型,以打开模型部署选项窗口。

    “测试和使用”模型页面 部署弹出式菜单

    在该窗口中,您可以选择要在其中部署模型的节点数,以及查看每秒可用的预测查询次数 (QPS)。

  5. 选择部署以开始部署模型。

    模型部署
  6. 模型部署完成后,您会收到电子邮件通知。

    “部署已完成”电子邮件

REST 和命令行

在使用任何请求数据之前,请先进行以下替换:

  • project-id:您的 GCP 项目 ID。
  • model-id:您的模型的 ID(从创建模型时返回的响应中获取)。此 ID 是模型名称的最后一个元素。 例如:
    • 模型名称:projects/project-id/locations/location-id/models/IOD4412217016962778756
    • 模型 ID:IOD4412217016962778756

字段注意事项

  • nodeCount - 需要在其上部署模型的节点的数量。值必须介于 1 到 100 之间,包括 1 和 100。节点是机器资源的抽象,可处理模型的 qps_per_node 中指定的每秒在线预测查询次数 (QPS)。

HTTP 方法和网址:

POST https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:deploy

请求 JSON 正文:

{
  "imageObjectDetectionModelDeploymentMetadata": {
    "nodeCount": 2
  }
}

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 application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:deploy"

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 application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:deploy" | Select-Object -Expand Content

您应该会看到类似如下所示的输出。可以使用操作 ID 来获取任务的状态。如需查看示例,请参阅处理长时间运行的操作

{
  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-08-07T22:00:20.692109Z",
    "updateTime": "2019-08-07T22:00:20.692109Z",
    "deployModelDetails": {}
  }
}

您可以通过以下 HTTP 方法和网址获取操作的状态:

GET https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/operations/operation-id

操作完成的状态将类似于以下内容:

{
  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-06-21T16:47:21.704674Z",
    "updateTime": "2019-06-21T17:01:00.802505Z",
    "deployModelDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.protobuf.Empty"
  }
}

Go

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"
)

// deployModel deploys a model.
func deployModel(w io.Writer, projectID string, location string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "TRL123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.DeployModelRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
	}

	op, err := client.DeployModel(ctx, req)
	if err != nil {
		return fmt.Errorf("DeployModel: %v", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	if err := op.Wait(ctx); err != nil {
		return fmt.Errorf("Wait: %v", err)
	}

	fmt.Fprintf(w, "Model deployed.\n")

	return nil
}

Java

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.DeployModelRequest;
import com.google.cloud.automl.v1.ModelName;
import com.google.cloud.automl.v1.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

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function deployModel() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
  };

  const [operation] = await client.deployModel(request);

  // Wait for operation to complete.
  const [response] = await operation.promise();
  console.log(`Model deployment finished. ${response}`);
}

deployModel();

Python

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)
response = client.deploy_model(name=model_full_id)

print(f"Model deployment finished. {response.result()}")

其他语言

C#: 请按照客户端库页面上的 C# 设置说明操作,然后访问 .NET 版 AutoML Vision Object Detection 参考文档。

PHP: 请按照客户端库页面上的 PHP 设置说明操作,然后访问 PHP 版 AutoML Vision Object Detection 参考文档。

Ruby 版: 请按照客户端库页面上的 Ruby 设置说明操作,然后访问 Ruby 版 AutoML Vision Object Detection 参考文档。

更新模型的节点编号

如果您拥有经过训练的已部署模型,可以更新部署模型的节点数量,以响应您的特定流量。例如,如果您遇到的每秒查询次数 (QPS) 高于预期。

您可以在不必首先取消部署的情况下更改此节点数量。更新部署将更改节点数量,而不会中断您处理的预测流量。

网页界面

  1. AutoML Vision Object Detection UI中,选择左侧导航栏中的模型标签页(带有灯泡图标)以显示可用的模型。

    如需查看其他项目的模型,请从标题栏右上角的下拉列表中选择该项目。

  2. 选择已部署且经过训练的模型。
  3. 选择标题栏正下方的测试和使用标签页。
  4. 页面顶部的框中将显示一条消息,其内容是“您的模型已部署,可用于在线预测请求”。选择此文本旁边的更新部署选项。

    更新部署按钮的图片
  5. 在打开的更新部署窗口中,从列表中选择用于部署模型的新节点编号。节点编号显示其估算的每秒预测查询次数 (QPS)。更新部署弹出式窗口的图片
  6. 从列表中选择新节点编号后,选择更新部署以更新用于部署模型的节点编号。

    选择新节点编号后的更新部署窗口
  7. 您将返回到测试和使用窗口,此时您会看到显示“正在部署模型…”的文本框。 模型部署
  8. 模型成功部署到新节点编号后,您会在与项目关联的地址处收到一封电子邮件。

REST 和命令行

最初用于部署模型的方法与更改已部署模型的节点编号的方法相同。

在使用任何请求数据之前,请先进行以下替换:

  • project-id:您的 GCP 项目 ID。
  • model-id:您的模型的 ID(从创建模型时返回的响应中获取)。此 ID 是模型名称的最后一个元素。 例如:
    • 模型名称:projects/project-id/locations/location-id/models/IOD4412217016962778756
    • 模型 ID:IOD4412217016962778756

字段注意事项

  • nodeCount - 需要在其上部署模型的节点的数量。值必须介于 1 到 100 之间,包括 1 和 100。节点是机器资源的抽象,可处理模型的 qps_per_node 中指定的每秒在线预测查询次数 (QPS)。

HTTP 方法和网址:

POST https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:deploy

请求 JSON 正文:

{
  "imageObjectDetectionModelDeploymentMetadata": {
    "nodeCount": 2
  }
}

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 application-default print-access-token) \
-H "Content-Type: application/json; charset=utf-8" \
-d @request.json \
"https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:deploy"

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 application-default print-access-token
$headers = @{ "Authorization" = "Bearer $cred" }

Invoke-WebRequest `
-Method POST `
-Headers $headers `
-ContentType: "application/json; charset=utf-8" `
-InFile request.json `
-Uri "https://automl.googleapis.com/v1/projects/project-id/locations/us-central1/models/model-id:deploy" | Select-Object -Expand Content

您应该会看到类似如下所示的输出。可以使用操作 ID 来获取任务的状态。如需查看示例,请参阅处理长时间运行的操作

{
  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-08-07T22:00:20.692109Z",
    "updateTime": "2019-08-07T22:00:20.692109Z",
    "deployModelDetails": {}
  }
}

Go

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	automlpb "google.golang.org/genproto/googleapis/cloud/automl/v1"
)

// visionObjectDetectionDeployModelWithNodeCount deploys a model with node count.
func visionObjectDetectionDeployModelWithNodeCount(w io.Writer, projectID string, location string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "IOD123456789..."

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %v", err)
	}
	defer client.Close()

	req := &automlpb.DeployModelRequest{
		Name: fmt.Sprintf("projects/%s/locations/%s/models/%s", projectID, location, modelID),
		ModelDeploymentMetadata: &automlpb.DeployModelRequest_ImageObjectDetectionModelDeploymentMetadata{
			ImageObjectDetectionModelDeploymentMetadata: &automlpb.ImageObjectDetectionModelDeploymentMetadata{
				NodeCount: 2,
			},
		},
	}

	op, err := client.DeployModel(ctx, req)
	if err != nil {
		return fmt.Errorf("DeployModel: %v", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	if err := op.Wait(ctx); err != nil {
		return fmt.Errorf("Wait: %v", err)
	}

	fmt.Fprintf(w, "Model deployed.\n")

	return nil
}

Java

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.DeployModelRequest;
import com.google.cloud.automl.v1.ImageObjectDetectionModelDeploymentMetadata;
import com.google.cloud.automl.v1.ModelName;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.protobuf.Empty;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class VisionObjectDetectionDeployModelNodeCount {

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

  // Deploy a model for prediction with a specified node count (can be used to redeploy a model)
  static void visionObjectDetectionDeployModelNodeCount(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);
      ImageObjectDetectionModelDeploymentMetadata metadata =
          ImageObjectDetectionModelDeploymentMetadata.newBuilder().setNodeCount(2).build();
      DeployModelRequest request =
          DeployModelRequest.newBuilder()
              .setName(modelFullId.toString())
              .setImageObjectDetectionModelDeploymentMetadata(metadata)
              .build();
      OperationFuture<Empty, OperationMetadata> future = client.deployModelAsync(request);

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

Node.js

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const modelId = 'YOUR_MODEL_ID';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function deployModelWithNodeCount() {
  // Construct request
  const request = {
    name: client.modelPath(projectId, location, modelId),
    imageObjectDetectionModelDeploymentMetadata: {
      nodeCount: 2,
    },
  };

  const [operation] = await client.deployModel(request);

  // Wait for operation to complete.
  const [response] = await operation.promise();
  console.log(`Model deployment finished. ${response}`);
}

deployModelWithNodeCount();

Python

在试用此示例之前,请按照客户端库页面中与此编程语言对应的设置说明执行操作。

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# model_id = "YOUR_MODEL_ID"

client = automl.AutoMlClient()
# Get the full path of the model.
model_full_id = client.model_path(project_id, "us-central1", model_id)

# node count determines the number of nodes to deploy the model on.
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#imageobjectdetectionmodeldeploymentmetadata
metadata = automl.ImageObjectDetectionModelDeploymentMetadata(node_count=2)

request = automl.DeployModelRequest(
    name=model_full_id,
    image_object_detection_model_deployment_metadata=metadata,
)
response = client.deploy_model(request=request)

print("Model deployment finished. {}".format(response.result()))

其他语言

C#: 请按照客户端库页面上的 C# 设置说明操作,然后访问 .NET 版 AutoML Vision Object Detection 参考文档。

PHP: 请按照客户端库页面上的 PHP 设置说明操作,然后访问 PHP 版 AutoML Vision Object Detection 参考文档。

Ruby 版: 请按照客户端库页面上的 Ruby 设置说明操作,然后访问 Ruby 版 AutoML Vision Object Detection 参考文档。