如何使用 node.js 客户端库以编程方式从 google-cloud-automl 获取模型 ID

Question

现在我可以使用 autoML node.js 客户端库在 google-cloud-automl 上训练模型。

问：完成模型训练后，如何以编程方式获取模型 ID？

目标：我将使用该 ID 在没有 Web 界面的情况下部署模型。

尝试过：起初，我认为是在训练模型时的响应（operation.name）。 但是 operation.name 显示了projects/${projectId}/locations/${location}/operations/${operationId} ，它不包括模型 ID。 所以我不知道如何以编程方式获取模型 ID。

任何建议将不胜感激。

训练代码： https : //cloud.google.com/vision/automl/docs/train-edge

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

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

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

async function createModel() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    model: {
      displayName: displayName,
      datasetId: datasetId,
      imageClassificationModelMetadata: {
        trainBudgetMilliNodeHours: 24000,
      },
    },
  };

  // Don't wait for the LRO
  const [operation] = await client.createModel(request);
  console.log(`Training started... ${operation}`);
  console.log(`Training operation name: ${operation.name}`);
}

createModel();

部署代码来自： https : //cloud.google.com/vision/automl/docs/deploy （需要模型 ID）

/**
 * 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();

Answer 1

创建模型是一个长时间运行的操作 (LRO)，因此响应不会包含模型元数据，而是包含有关将创建模型的操作的信息：

{
  "name": "projects/project-id/locations/us-central1/operations/ICN2106290444865378475",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-10-30T20:06:08.253243Z",
    "updateTime": "2019-10-30T20:06:08.253243Z",
    "createModelDetails": {}
  }
}

您可以随时检索操作以查看它是否已完成：

/**
* TODO(developer): Uncomment these variables before running the sample.
*/
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const operationId = 'YOUR_OPERATION_ID'; // e.g. ICN2106290444865378475

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

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

async function getOperationStatus() {
  // Construct request
  const request = {
    name: `projects/${projectId}/locations/${location}/operations/${operationId}`,
  };

  const [response] = await client.operationsClient.getOperation(request);

  console.log(`Name: ${response.name}`);
  console.log(`Operation details:`);
  console.log(`${response}`);
}

getOperationStatus();

上面的 Node.js 代码来自文档的使用长时间运行的操作部分。

对于已完成的创建模型操作，您应该会看到类似于以下内容的输出：

{
  "name": "projects/project-id/locations/us-central1/operations/operation-id",
  "metadata": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.OperationMetadata",
    "createTime": "2019-07-22T18:35:06.881193Z",
    "updateTime": "2019-07-22T19:58:44.972235Z",
    "createModelDetails": {}
  },
  "done": true,
  "response": {
    "@type": "type.googleapis.com/google.cloud.automl.v1.Model",
    "name": "projects/project-id/locations/us-central1/models/model-id"
  }
}

然后，您可以从响应中获取model-id ：

console.log(response.response.name); // Full model path
console.log(response.response.name.replace(/projects\/[a-zA-Z0-9-]*\/locations\/[a-zA-Z0-9-]*\/models\//,'')); // Just the model-id