部署模型

初始模型部署

创建(训练)模型后,您必须先部署模型,然后才能对其进行在线(或同步)调用。

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. 导航至标题栏下方的测试和使用标签页。
  2. 选择部署模型按钮。随即将打开一个新部署选项窗口。 “测试和使用”模型页面
  3. 在新打开的部署选项窗口中,指定要在其中部署模型的节点数。 每个节点每秒可支持一定数量的预测查询 (QPS)。

    对于大多数实验性数据流量来说,一个节点时通常能够满足需要

    部署弹出式菜单
  4. 选择部署以开始部署模型。

    模型部署
  5. 模型部署操作完成后,您会收到电子邮件通知。

REST 和命令行

在使用下面的任何请求数据之前,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:

{
  "imageClassificationModelDeploymentMetadata": {
    "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();

PHP

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。

use Google\Cloud\AutoMl\V1\AutoMlClient;

/** Uncomment and populate these variables in your code */
// $projectId = '[Google Cloud Project ID]';
// $location = 'us-central1';
// $modelId = 'my_model_id_123';

$client = new AutoMlClient();

try {
    // get full path of model
    $formattedName = $client->modelName(
        $projectId,
        $location,
        $modelId
    );

    $operationResponse = $client->deployModel($formattedName);
    $operationResponse->pollUntilComplete();
    if ($operationResponse->operationSucceeded()) {
        $result = $operationResponse->getResult();
        printf('Model deployed.' . PHP_EOL);
    } else {
        $error = $operationResponse->getError();
        // handleError($error)
    }
} finally {
    $client->close();
}

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()}")

Ruby

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。

require "google/cloud/automl"

project_id = "YOUR_PROJECT_ID"
model_id = "YOUR_MODEL_ID"

client = Google::Cloud::AutoML.auto_ml

# Get the full path of the dataset
model_full_id = client.model_path project: project_id,
                                  location: "us-central1",
                                  model: model_id

operation = client.deploy_model name: model_full_id

# Wait until the long running operation is done
operation.wait_until_done!

puts "Model deployment finished."

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. Vision Dashboard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. 选择已部署的经过训练的模型。
  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。

在使用下面的任何请求数据之前,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:

{
  "imageClassificationModelDeploymentMetadata": {
    "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"
)

// visionClassificationDeployModelWithNodeCount deploys a model with node count.
func visionClassificationDeployModelWithNodeCount(w io.Writer, projectID string, location string, modelID string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// modelID := "ICN123456789..."

	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_ImageClassificationModelDeploymentMetadata{
			ImageClassificationModelDeploymentMetadata: &automlpb.ImageClassificationModelDeploymentMetadata{
				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.ImageClassificationModelDeploymentMetadata;
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 VisionClassificationDeployModelNodeCount {

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

  // Deploy a model for prediction with a specified node count (can be used to redeploy a model)
  static void visionClassificationDeployModelNodeCount(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);
      ImageClassificationModelDeploymentMetadata metadata =
          ImageClassificationModelDeploymentMetadata.newBuilder().setNodeCount(2).build();
      DeployModelRequest request =
          DeployModelRequest.newBuilder()
              .setName(modelFullId.toString())
              .setImageClassificationModelDeploymentMetadata(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),
    imageClassificationModelDeploymentMetadata: {
      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();

PHP

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。

use Google\Cloud\AutoMl\V1\AutoMlClient;
use Google\Cloud\AutoMl\V1\ImageClassificationModelDeploymentMetadata;

/** Uncomment and populate these variables in your code */
// $projectId = '[Google Cloud Project ID]';
// $location = 'us-central1';
// $modelId = 'my_model_id_123';

$client = new AutoMlClient();

try {
    // get full path of model
    $formattedName = $client->modelName(
        $projectId,
        $location,
        $modelId
    );

    // set prediction node count
    $metadata = (new ImageClassificationModelDeploymentMetadata())
        ->setNodeCount(2);
    $args = ['imageClassificationModelDeploymentMetadata' => $metadata];

    $operationResponse = $client->deployModel($formattedName, $args);
    $operationResponse->pollUntilComplete();
    if ($operationResponse->operationSucceeded()) {
        $result = $operationResponse->getResult();
        printf('Model deployed.' . PHP_EOL);
    } else {
        $error = $operationResponse->getError();
        // handleError($error)
    }
} finally {
    $client->close();
}

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#imageclassificationmodeldeploymentmetadata
metadata = automl.ImageClassificationModelDeploymentMetadata(node_count=2)

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

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

Ruby

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。

require "google/cloud/automl"

project_id = "YOUR_PROJECT_ID"
model_id = "YOUR_MODEL_ID"

client = Google::Cloud::AutoML.auto_ml

# Get the full path of the model.
model_full_id = client.model_path project: project_id,
                                  location: "us-central1",
                                  model: model_id
metadata = { node_count: 2 }

operation = client.deploy_model name: model_full_id,
                                mage_classification_model_deployment_metadata: metadata

# Wait until the long running operation is done
operation.wait_until_done!

puts "Model deployment finished."