本頁面由 Cloud Translation API 翻譯而成。

訓練分類或迴歸模型

本頁說明如何使用 Google Cloud 控制台或 Vertex AI API，從表格資料集訓練分類或迴歸模型。

事前準備

訓練模型前，請務必完成下列步驟：

訓練模型

Google Cloud 控制台

在 Google Cloud 控制台的 Vertex AI 專區，前往「Datasets」頁面。

前往「資料集」頁面
按一下要用來訓練模型的資料集名稱，開啟詳細資料頁面。
如果資料類型使用註解集，請選取要用於這個模型的註解集。
按一下「訓練新模型」。
選取「其他」。

在「訓練新模型」頁面中，完成下列步驟：

選取模型訓練方法。
- AutoML 適用於多種用途。
按一下「繼續」。
輸入新模型的顯示名稱。
選取目標欄。

目標欄是模型要預測的值。

進一步瞭解目標資料欄規定。
選用：如要將測試資料集匯出至 BigQuery，請勾選「將測試資料集匯出至 BigQuery」，並提供資料表名稱。
選用：如要選擇如何將資料分割為訓練、測試和驗證集，請開啟「進階選項」。您可以選擇下列資料分割選項：
- 隨機 (預設)：Vertex AI 會隨機選取與各資料集相關聯的資料列。根據預設，Vertex AI 會選取 80% 的資料列做為訓練集、10% 做為驗證集，以及 10% 做為測試集。
- 手動：Vertex AI 會根據資料分割欄中的值，為每個資料集選取資料列。提供資料分割欄的名稱。
- 按照時間排序：Vertex AI 會根據時間欄中的時間戳記分割資料。提供時間資料欄的名稱。
進一步瞭解資料分割。
按一下「繼續」。
選用：按一下「產生統計資料」。產生統計資料會填入「轉換」下拉式選單。
在「訓練選項」頁面中，檢查資料欄清單，並從訓練中排除不應用於訓練模型的資料欄。
檢查所選轉換是否適用於納入的功能，以及是否允許無效資料，並進行任何必要更新。

進一步瞭解轉換和無效資料。
如要指定權重欄，或變更預設的最佳化目標，請開啟「進階選項」並選取所需選項。

進一步瞭解權重資料欄和最佳化目標。
按一下「繼續」。

在「運算與定價」視窗中，設定如下：

輸入模型訓練時數上限。

這項設定可協助您設定訓練費用上限。建立新模型時還會涉及其他作業，因此實際經過的時間可能會比這個值長。

建議的訓練時間與訓練資料大小有關。下表顯示依列數建議的訓練時間範圍；大量欄也會增加所需的訓練時間。

資料列	建議的訓練時間
小於 100,000	1-3 小時
100,000 - 1,000,000	1-6 小時
1,000,000 - 10,000,000	1-12 小時
超過 10,000,000	3 到 24 小時

如要瞭解訓練定價，請參閱定價頁面。

按一下「開始訓練」。
模型訓練可能需要數小時，視資料大小和複雜度而定，以及您是否指定訓練預算。您可以關閉這個分頁，稍後再返回查看。模型訓練完成後，您會收到電子郵件通知。

Cloud Storage 或 BigQuery 中的表格訓練資料不會匯入 Vertex AI。(從本機檔案匯入時，檔案會匯入 Cloud Storage。)建立含有表格資料的資料集時，資料會與該資料集建立關聯。在建立資料集後，您對 Cloud Storage 或 BigQuery 中資料來源所做的變更，會併入後續使用該資料集訓練的模型。模型訓練開始時，系統會建立資料集快照。

API

選取表格資料類型目標。

分類

選取語言或環境的分頁標籤：

REST

您可以使用 trainingPipelines.create 指令訓練模型。

訓練模型。

使用任何要求資料之前，請先替換以下項目：

LOCATION：您的區域。
PROJECT：您的專案 ID。
TRAININGPIPELINE_DISPLAY_NAME：為這項作業建立的訓練管道顯示名稱。
TARGET_COLUMN：您希望模型預測的資料欄 (值)。
WEIGHT_COLUMN：(選用) 權重欄。瞭解詳情。
TRAINING_BUDGET：模型訓練時間上限，以毫節點時數為單位 (1,000 毫節點時數等於 1 節點時數)。
OPTIMIZATION_OBJECTIVE：只有在不想為預測類型使用預設最佳化目標時，才需要提供這項資訊。瞭解詳情。
TRANSFORMATION_TYPE：系統會為用於訓練模型的每個資料欄提供轉換類型。瞭解詳情。
COLUMN_NAME：具有指定轉換類型的資料欄名稱。必須指定用於訓練模型的所有資料欄。
MODEL_DISPLAY_NAME：新訓練模型的顯示名稱。
DATASET_ID：訓練資料集的 ID。
您可以提供 Split 物件來控制資料分割。如要瞭解如何控制資料分割，請參閱「使用 REST 控制資料分割」。
PROJECT_NUMBER：系統自動為專案產生的專案編號

HTTP 方法和網址：

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

JSON 要求主體：

{
    "displayName": "TRAININGPIPELINE_DISPLAY_NAME",
    "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tabular_1.0.0.yaml",
    "trainingTaskInputs": {
        "targetColumn": "TARGET_COLUMN",
        "weightColumn": "WEIGHT_COLUMN",
        "predictionType": "classification",
        "trainBudgetMilliNodeHours": TRAINING_BUDGET,
        "optimizationObjective": "OPTIMIZATION_OBJECTIVE",
        "transformations": [
            {"TRANSFORMATION_TYPE_1":  {"column_name" : "COLUMN_NAME_1"} },
            {"TRANSFORMATION_TYPE_2":  {"column_name" : "COLUMN_NAME_2"} },
            ...
    },
    "modelToUpload": {"displayName": "MODEL_DISPLAY_NAME"},
    "inputDataConfig": {
      "datasetId": "DATASET_ID",
    }
}

如要傳送要求，請展開以下其中一個選項：

curl (Linux、macOS 或 Cloud Shell)

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI，或使用 Cloud Shell，自動登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

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

PowerShell (Windows)

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

$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-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

您應該會收到如下的 JSON 回應：

{
  "name": "projects/PROJECT_NUMBER/locations/us-central1/trainingPipelines/4567",
  "displayName": "myModelName",
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tabular_1.0.0.yaml",
  "modelToUpload": {
    "displayName": "myModelName"
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-08-18T01:22:57.479336Z",
  "updateTime": "2020-08-18T01:22:57.479336Z"
}

Java

在試用這個範例之前，請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。詳情請參閱 Vertex AI Java API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。


import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.FilterSplit;
import com.google.cloud.aiplatform.v1.FractionSplit;
import com.google.cloud.aiplatform.v1.InputDataConfig;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredefinedSplit;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import com.google.cloud.aiplatform.v1.TimestampSplit;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation.AutoTransformation;
import com.google.rpc.Status;
import java.io.IOException;
import java.util.ArrayList;

public class CreateTrainingPipelineTabularClassificationSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String modelDisplayName = "YOUR_DATASET_DISPLAY_NAME";
    String datasetId = "YOUR_DATASET_ID";
    String targetColumn = "TARGET_COLUMN";
    createTrainingPipelineTableClassification(project, modelDisplayName, datasetId, targetColumn);
  }

  static void createTrainingPipelineTableClassification(
      String project, String modelDisplayName, String datasetId, String targetColumn)
      throws IOException {
    PipelineServiceSettings pipelineServiceSettings =
        PipelineServiceSettings.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 (PipelineServiceClient pipelineServiceClient =
        PipelineServiceClient.create(pipelineServiceSettings)) {
      String location = "us-central1";
      LocationName locationName = LocationName.of(project, location);
      String trainingTaskDefinition =
          "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml";

      // Set the columns used for training and their data types
      Transformation transformation1 =
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("sepal_width").build())
              .build();
      Transformation transformation2 =
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("sepal_length").build())
              .build();
      Transformation transformation3 =
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("petal_length").build())
              .build();
      Transformation transformation4 =
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("petal_width").build())
              .build();

      ArrayList<Transformation> transformationArrayList = new ArrayList<>();
      transformationArrayList.add(transformation1);
      transformationArrayList.add(transformation2);
      transformationArrayList.add(transformation3);
      transformationArrayList.add(transformation4);

      AutoMlTablesInputs autoMlTablesInputs =
          AutoMlTablesInputs.newBuilder()
              .setTargetColumn(targetColumn)
              .setPredictionType("classification")
              .addAllTransformations(transformationArrayList)
              .setTrainBudgetMilliNodeHours(8000)
              .build();

      FractionSplit fractionSplit =
          FractionSplit.newBuilder()
              .setTrainingFraction(0.8)
              .setValidationFraction(0.1)
              .setTestFraction(0.1)
              .build();

      InputDataConfig inputDataConfig =
          InputDataConfig.newBuilder()
              .setDatasetId(datasetId)
              .setFractionSplit(fractionSplit)
              .build();
      Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();

      TrainingPipeline trainingPipeline =
          TrainingPipeline.newBuilder()
              .setDisplayName(modelDisplayName)
              .setTrainingTaskDefinition(trainingTaskDefinition)
              .setTrainingTaskInputs(ValueConverter.toValue(autoMlTablesInputs))
              .setInputDataConfig(inputDataConfig)
              .setModelToUpload(modelToUpload)
              .build();

      TrainingPipeline trainingPipelineResponse =
          pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);

      System.out.println("Create Training Pipeline Tabular Classification Response");
      System.out.format("\tName: %s\n", trainingPipelineResponse.getName());
      System.out.format("\tDisplay Name: %s\n", trainingPipelineResponse.getDisplayName());
      System.out.format(
          "\tTraining Task Definition: %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
      System.out.format(
          "\tTraining Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
      System.out.format(
          "\tTraining Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());

      System.out.format("\tState: %s\n", trainingPipelineResponse.getState());
      System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
      System.out.format("\tStart Time: %s\n", trainingPipelineResponse.getStartTime());
      System.out.format("\tEnd Time: %s\n", trainingPipelineResponse.getEndTime());
      System.out.format("\tUpdate Time: %s\n", trainingPipelineResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", trainingPipelineResponse.getLabelsMap());

      InputDataConfig inputDataConfigResponse = trainingPipelineResponse.getInputDataConfig();
      System.out.println("\tInput Data Config");
      System.out.format("\t\tDataset Id: %s\n", inputDataConfigResponse.getDatasetId());
      System.out.format(
          "\t\tAnnotations Filter: %s\n", inputDataConfigResponse.getAnnotationsFilter());

      FractionSplit fractionSplitResponse = inputDataConfigResponse.getFractionSplit();
      System.out.println("\t\tFraction Split");
      System.out.format(
          "\t\t\tTraining Fraction: %s\n", fractionSplitResponse.getTrainingFraction());
      System.out.format(
          "\t\t\tValidation Fraction: %s\n", fractionSplitResponse.getValidationFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", fractionSplitResponse.getTestFraction());

      FilterSplit filterSplit = inputDataConfigResponse.getFilterSplit();
      System.out.println("\t\tFilter Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", filterSplit.getTrainingFilter());
      System.out.format("\t\t\tValidation Fraction: %s\n", filterSplit.getValidationFilter());
      System.out.format("\t\t\tTest Fraction: %s\n", filterSplit.getTestFilter());

      PredefinedSplit predefinedSplit = inputDataConfigResponse.getPredefinedSplit();
      System.out.println("\t\tPredefined Split");
      System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());

      TimestampSplit timestampSplit = inputDataConfigResponse.getTimestampSplit();
      System.out.println("\t\tTimestamp Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());
      System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());

      Model modelResponse = trainingPipelineResponse.getModelToUpload();
      System.out.println("\tModel To Upload");
      System.out.format("\t\tName: %s\n", modelResponse.getName());
      System.out.format("\t\tDisplay Name: %s\n", modelResponse.getDisplayName());
      System.out.format("\t\tDescription: %s\n", modelResponse.getDescription());
      System.out.format("\t\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("\t\tMeta Data: %s\n", modelResponse.getMetadata());
      System.out.format("\t\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("\t\tArtifact Uri: %s\n", modelResponse.getArtifactUri());

      System.out.format(
          "\t\tSupported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList().toString());
      System.out.format(
          "\t\tSupported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList().toString());
      System.out.format(
          "\t\tSupported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList().toString());

      System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
      System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("\t\tLables: %s\n", modelResponse.getLabelsMap());
      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();

      System.out.println("\tPredict Schemata");
      System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format(
          "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format(
          "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (Model.ExportFormat supportedExportFormat :
          modelResponse.getSupportedExportFormatsList()) {
        System.out.println("\tSupported Export Format");
        System.out.format("\t\tId: %s\n", supportedExportFormat.getId());
      }
      ModelContainerSpec containerSpec = modelResponse.getContainerSpec();

      System.out.println("\tContainer Spec");
      System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
      System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
      System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
      System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
      System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());

      for (EnvVar envVar : containerSpec.getEnvList()) {
        System.out.println("\t\tEnv");
        System.out.format("\t\t\tName: %s\n", envVar.getName());
        System.out.format("\t\t\tValue: %s\n", envVar.getValue());
      }

      for (Port port : containerSpec.getPortsList()) {
        System.out.println("\t\tPort");
        System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("\tDeployed Model");
        System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }

      Status status = trainingPipelineResponse.getError();
      System.out.println("\tError");
      System.out.format("\t\tCode: %s\n", status.getCode());
      System.out.format("\t\tMessage: %s\n", status.getMessage());
    }
  }
}

Node.js

在試用這個範例之前，請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。詳情請參閱 Vertex AI Node.js API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const datasetId = 'YOUR_DATASET_ID';
// const modelDisplayName = 'YOUR_MODEL_DISPLAY_NAME';
// const trainingPipelineDisplayName = 'YOUR_TRAINING_PIPELINE_DISPLAY_NAME';
// const targetColumn = 'YOUR_TARGET_COLUMN';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {definition} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.trainingjob;

// Imports the Google Cloud Pipeline Service Client library
const {PipelineServiceClient} = aiplatform.v1;
// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const pipelineServiceClient = new PipelineServiceClient(clientOptions);

async function createTrainingPipelineTablesClassification() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  const transformations = [
    {auto: {column_name: 'sepal_width'}},
    {auto: {column_name: 'sepal_length'}},
    {auto: {column_name: 'petal_length'}},
    {auto: {column_name: 'petal_width'}},
  ];
  const trainingTaskInputsObj = new definition.AutoMlTablesInputs({
    targetColumn: targetColumn,
    predictionType: 'classification',
    transformations: transformations,
    trainBudgetMilliNodeHours: 8000,
    disableEarlyStopping: false,
    optimizationObjective: 'minimize-log-loss',
  });
  const trainingTaskInputs = trainingTaskInputsObj.toValue();

  const modelToUpload = {displayName: modelDisplayName};
  const inputDataConfig = {
    datasetId: datasetId,
    fractionSplit: {
      trainingFraction: 0.8,
      validationFraction: 0.1,
      testFraction: 0.1,
    },
  };
  const trainingPipeline = {
    displayName: trainingPipelineDisplayName,
    trainingTaskDefinition:
      'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml',
    trainingTaskInputs,
    inputDataConfig,
    modelToUpload,
  };
  const request = {
    parent,
    trainingPipeline,
  };

  // Create training pipeline request
  const [response] =
    await pipelineServiceClient.createTrainingPipeline(request);

  console.log('Create training pipeline tabular classification response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createTrainingPipelineTablesClassification();

Python

如要瞭解如何安裝或更新 Python 適用的 Vertex AI SDK，請參閱「安裝 Python 適用的 Vertex AI SDK」。詳情請參閱 Python API 參考說明文件。

def create_training_pipeline_tabular_classification_sample(
    project: str,
    display_name: str,
    dataset_id: str,
    location: str = "us-central1",
    model_display_name: str = None,
    target_column: str = "target_column",
    training_fraction_split: float = 0.8,
    validation_fraction_split: float = 0.1,
    test_fraction_split: float = 0.1,
    budget_milli_node_hours: int = 8000,
    disable_early_stopping: bool = False,
    sync: bool = True,
):
    aiplatform.init(project=project, location=location)

    tabular_classification_job = aiplatform.AutoMLTabularTrainingJob(
        display_name=display_name, optimization_prediction_type="classification"
    )

    my_tabular_dataset = aiplatform.TabularDataset(dataset_name=dataset_id)

    model = tabular_classification_job.run(
        dataset=my_tabular_dataset,
        target_column=target_column,
        training_fraction_split=training_fraction_split,
        validation_fraction_split=validation_fraction_split,
        test_fraction_split=test_fraction_split,
        budget_milli_node_hours=budget_milli_node_hours,
        model_display_name=model_display_name,
        disable_early_stopping=disable_early_stopping,
        sync=sync,
    )

    model.wait()

    print(model.display_name)
    print(model.resource_name)
    print(model.uri)
    return model

迴歸

選取語言或環境的分頁標籤：

REST

您可以使用 trainingPipelines.create 指令訓練模型。

訓練模型。

使用任何要求資料之前，請先替換以下項目：

LOCATION：您的區域。
PROJECT：。
TRAININGPIPELINE_DISPLAY_NAME：為這項作業建立的訓練管道顯示名稱。
TARGET_COLUMN：您希望模型預測的資料欄 (值)。
WEIGHT_COLUMN：(選用) 權重欄。瞭解詳情。
TRAINING_BUDGET：模型訓練時間上限，以毫節點時數為單位 (1,000 毫節點時數等於 1 節點時數)。
OPTIMIZATION_OBJECTIVE：只有在不想為預測類型使用預設最佳化目標時，才需要提供這項資訊。瞭解詳情。
TRANSFORMATION_TYPE：系統會為用於訓練模型的每個資料欄提供轉換類型。瞭解詳情。
COLUMN_NAME：具有指定轉換類型的資料欄名稱。必須指定用於訓練模型的所有資料欄。
MODEL_DISPLAY_NAME：新訓練模型的顯示名稱。
DATASET_ID：訓練資料集的 ID。
您可以提供 Split 物件來控制資料分割。如要瞭解如何控制資料分割，請參閱「使用 REST 控制資料分割」。
PROJECT_NUMBER：系統自動為專案產生的專案編號

HTTP 方法和網址：

POST https://LOCATION-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines

JSON 要求主體：

{
    "displayName": "TRAININGPIPELINE_DISPLAY_NAME",
    "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tabular_1.0.0.yaml",
    "trainingTaskInputs": {
        "targetColumn": "TARGET_COLUMN",
        "weightColumn": "WEIGHT_COLUMN",
        "predictionType": "regression",
        "trainBudgetMilliNodeHours": TRAINING_BUDGET,
        "optimizationObjective": "OPTIMIZATION_OBJECTIVE",
        "transformations": [
            {"TRANSFORMATION_TYPE_1":  {"column_name" : "COLUMN_NAME_1"} },
            {"TRANSFORMATION_TYPE_2":  {"column_name" : "COLUMN_NAME_2"} },
            ...
    },
    "modelToUpload": {"displayName": "MODEL_DISPLAY_NAME"},
    "inputDataConfig": {
      "datasetId": "DATASET_ID",
    }
}

如要傳送要求，請展開以下其中一個選項：

curl (Linux、macOS 或 Cloud Shell)

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

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

PowerShell (Windows)

注意： 下列指令假設您已執行 gcloud init 或 gcloud auth login，透過使用者帳戶登入 gcloud CLI。您可以執行 gcloud auth list 查看目前有效的帳戶。

將要求主體儲存在名為 request.json 的檔案中，然後執行下列指令：

$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-aiplatform.googleapis.com/v1/projects/PROJECT/locations/LOCATION/trainingPipelines" | Select-Object -Expand Content

您應該會收到如下的 JSON 回應：

{
  "name": "projects/PROJECT_NUMBER/locations/us-central1/trainingPipelines/4567",
  "displayName": "myModelName",
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tabular_1.0.0.yaml",
  "modelToUpload": {
    "displayName": "myModelName"
  },
  "state": "PIPELINE_STATE_PENDING",
  "createTime": "2020-08-18T01:22:57.479336Z",
  "updateTime": "2020-08-18T01:22:57.479336Z"
}

Java

在試用這個範例之前，請先按照Java使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。詳情請參閱 Vertex AI Java API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。


import com.google.cloud.aiplatform.util.ValueConverter;
import com.google.cloud.aiplatform.v1.DeployedModelRef;
import com.google.cloud.aiplatform.v1.EnvVar;
import com.google.cloud.aiplatform.v1.FilterSplit;
import com.google.cloud.aiplatform.v1.FractionSplit;
import com.google.cloud.aiplatform.v1.InputDataConfig;
import com.google.cloud.aiplatform.v1.LocationName;
import com.google.cloud.aiplatform.v1.Model;
import com.google.cloud.aiplatform.v1.ModelContainerSpec;
import com.google.cloud.aiplatform.v1.PipelineServiceClient;
import com.google.cloud.aiplatform.v1.PipelineServiceSettings;
import com.google.cloud.aiplatform.v1.Port;
import com.google.cloud.aiplatform.v1.PredefinedSplit;
import com.google.cloud.aiplatform.v1.PredictSchemata;
import com.google.cloud.aiplatform.v1.TimestampSplit;
import com.google.cloud.aiplatform.v1.TrainingPipeline;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation.AutoTransformation;
import com.google.cloud.aiplatform.v1.schema.trainingjob.definition.AutoMlTablesInputs.Transformation.TimestampTransformation;
import com.google.rpc.Status;
import java.io.IOException;
import java.util.ArrayList;

public class CreateTrainingPipelineTabularRegressionSample {

  public static void main(String[] args) throws IOException {
    // TODO(developer): Replace these variables before running the sample.
    String project = "YOUR_PROJECT_ID";
    String modelDisplayName = "YOUR_DATASET_DISPLAY_NAME";
    String datasetId = "YOUR_DATASET_ID";
    String targetColumn = "TARGET_COLUMN";
    createTrainingPipelineTableRegression(project, modelDisplayName, datasetId, targetColumn);
  }

  static void createTrainingPipelineTableRegression(
      String project, String modelDisplayName, String datasetId, String targetColumn)
      throws IOException {
    PipelineServiceSettings pipelineServiceSettings =
        PipelineServiceSettings.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 (PipelineServiceClient pipelineServiceClient =
        PipelineServiceClient.create(pipelineServiceSettings)) {
      String location = "us-central1";
      LocationName locationName = LocationName.of(project, location);
      String trainingTaskDefinition =
          "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml";

      // Set the columns used for training and their data types
      ArrayList<Transformation> tranformations = new ArrayList<>();
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("STRING_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("INTEGER_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("FLOAT_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("FLOAT_5000unique_REPEATED"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("NUMERIC_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("BOOLEAN_2unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setTimestamp(
                  TimestampTransformation.newBuilder()
                      .setColumnName("TIMESTAMP_1unique_NULLABLE")
                      .setInvalidValuesAllowed(true))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("DATE_1unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(AutoTransformation.newBuilder().setColumnName("TIME_1unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setTimestamp(
                  TimestampTransformation.newBuilder()
                      .setColumnName("DATETIME_1unique_NULLABLE")
                      .setInvalidValuesAllowed(true))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(
                  AutoTransformation.newBuilder()
                      .setColumnName("STRUCT_NULLABLE.STRING_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(
                  AutoTransformation.newBuilder()
                      .setColumnName("STRUCT_NULLABLE.INTEGER_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(
                  AutoTransformation.newBuilder()
                      .setColumnName("STRUCT_NULLABLE.FLOAT_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(
                  AutoTransformation.newBuilder()
                      .setColumnName("STRUCT_NULLABLE.FLOAT_5000unique_REQUIRED"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(
                  AutoTransformation.newBuilder()
                      .setColumnName("STRUCT_NULLABLE.FLOAT_5000unique_REPEATED"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(
                  AutoTransformation.newBuilder()
                      .setColumnName("STRUCT_NULLABLE.NUMERIC_5000unique_NULLABLE"))
              .build());
      tranformations.add(
          Transformation.newBuilder()
              .setAuto(
                  AutoTransformation.newBuilder()
                      .setColumnName("STRUCT_NULLABLE.TIMESTAMP_1unique_NULLABLE"))
              .build());

      AutoMlTablesInputs trainingTaskInputs =
          AutoMlTablesInputs.newBuilder()
              .addAllTransformations(tranformations)
              .setTargetColumn(targetColumn)
              .setPredictionType("regression")
              .setTrainBudgetMilliNodeHours(8000)
              .setDisableEarlyStopping(false)
              // supported regression optimisation objectives: minimize-rmse,
              // minimize-mae, minimize-rmsle
              .setOptimizationObjective("minimize-rmse")
              .build();

      FractionSplit fractionSplit =
          FractionSplit.newBuilder()
              .setTrainingFraction(0.8)
              .setValidationFraction(0.1)
              .setTestFraction(0.1)
              .build();

      InputDataConfig inputDataConfig =
          InputDataConfig.newBuilder()
              .setDatasetId(datasetId)
              .setFractionSplit(fractionSplit)
              .build();
      Model modelToUpload = Model.newBuilder().setDisplayName(modelDisplayName).build();

      TrainingPipeline trainingPipeline =
          TrainingPipeline.newBuilder()
              .setDisplayName(modelDisplayName)
              .setTrainingTaskDefinition(trainingTaskDefinition)
              .setTrainingTaskInputs(ValueConverter.toValue(trainingTaskInputs))
              .setInputDataConfig(inputDataConfig)
              .setModelToUpload(modelToUpload)
              .build();

      TrainingPipeline trainingPipelineResponse =
          pipelineServiceClient.createTrainingPipeline(locationName, trainingPipeline);

      System.out.println("Create Training Pipeline Tabular Regression Response");
      System.out.format("\tName: %s\n", trainingPipelineResponse.getName());
      System.out.format("\tDisplay Name: %s\n", trainingPipelineResponse.getDisplayName());
      System.out.format(
          "\tTraining Task Definition: %s\n", trainingPipelineResponse.getTrainingTaskDefinition());
      System.out.format(
          "\tTraining Task Inputs: %s\n", trainingPipelineResponse.getTrainingTaskInputs());
      System.out.format(
          "\tTraining Task Metadata: %s\n", trainingPipelineResponse.getTrainingTaskMetadata());

      System.out.format("\tState: %s\n", trainingPipelineResponse.getState());
      System.out.format("\tCreate Time: %s\n", trainingPipelineResponse.getCreateTime());
      System.out.format("\tStart Time: %s\n", trainingPipelineResponse.getStartTime());
      System.out.format("\tEnd Time: %s\n", trainingPipelineResponse.getEndTime());
      System.out.format("\tUpdate Time: %s\n", trainingPipelineResponse.getUpdateTime());
      System.out.format("\tLabels: %s\n", trainingPipelineResponse.getLabelsMap());

      InputDataConfig inputDataConfigResponse = trainingPipelineResponse.getInputDataConfig();
      System.out.println("\tInput Data Config");
      System.out.format("\t\tDataset Id: %s\n", inputDataConfigResponse.getDatasetId());
      System.out.format(
          "\t\tAnnotations Filter: %s\n", inputDataConfigResponse.getAnnotationsFilter());

      FractionSplit fractionSplitResponse = inputDataConfigResponse.getFractionSplit();
      System.out.println("\t\tFraction Split");
      System.out.format(
          "\t\t\tTraining Fraction: %s\n", fractionSplitResponse.getTrainingFraction());
      System.out.format(
          "\t\t\tValidation Fraction: %s\n", fractionSplitResponse.getValidationFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", fractionSplitResponse.getTestFraction());

      FilterSplit filterSplit = inputDataConfigResponse.getFilterSplit();
      System.out.println("\t\tFilter Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", filterSplit.getTrainingFilter());
      System.out.format("\t\t\tValidation Fraction: %s\n", filterSplit.getValidationFilter());
      System.out.format("\t\t\tTest Fraction: %s\n", filterSplit.getTestFilter());

      PredefinedSplit predefinedSplit = inputDataConfigResponse.getPredefinedSplit();
      System.out.println("\t\tPredefined Split");
      System.out.format("\t\t\tKey: %s\n", predefinedSplit.getKey());

      TimestampSplit timestampSplit = inputDataConfigResponse.getTimestampSplit();
      System.out.println("\t\tTimestamp Split");
      System.out.format("\t\t\tTraining Fraction: %s\n", timestampSplit.getTrainingFraction());
      System.out.format("\t\t\tValidation Fraction: %s\n", timestampSplit.getValidationFraction());
      System.out.format("\t\t\tTest Fraction: %s\n", timestampSplit.getTestFraction());
      System.out.format("\t\t\tKey: %s\n", timestampSplit.getKey());

      Model modelResponse = trainingPipelineResponse.getModelToUpload();
      System.out.println("\tModel To Upload");
      System.out.format("\t\tName: %s\n", modelResponse.getName());
      System.out.format("\t\tDisplay Name: %s\n", modelResponse.getDisplayName());
      System.out.format("\t\tDescription: %s\n", modelResponse.getDescription());
      System.out.format("\t\tMetadata Schema Uri: %s\n", modelResponse.getMetadataSchemaUri());
      System.out.format("\t\tMeta Data: %s\n", modelResponse.getMetadata());
      System.out.format("\t\tTraining Pipeline: %s\n", modelResponse.getTrainingPipeline());
      System.out.format("\t\tArtifact Uri: %s\n", modelResponse.getArtifactUri());

      System.out.format(
          "\t\tSupported Deployment Resources Types: %s\n",
          modelResponse.getSupportedDeploymentResourcesTypesList().toString());
      System.out.format(
          "\t\tSupported Input Storage Formats: %s\n",
          modelResponse.getSupportedInputStorageFormatsList().toString());
      System.out.format(
          "\t\tSupported Output Storage Formats: %s\n",
          modelResponse.getSupportedOutputStorageFormatsList().toString());

      System.out.format("\t\tCreate Time: %s\n", modelResponse.getCreateTime());
      System.out.format("\t\tUpdate Time: %s\n", modelResponse.getUpdateTime());
      System.out.format("\t\tLables: %s\n", modelResponse.getLabelsMap());
      PredictSchemata predictSchemata = modelResponse.getPredictSchemata();

      System.out.println("\tPredict Schemata");
      System.out.format("\t\tInstance Schema Uri: %s\n", predictSchemata.getInstanceSchemaUri());
      System.out.format(
          "\t\tParameters Schema Uri: %s\n", predictSchemata.getParametersSchemaUri());
      System.out.format(
          "\t\tPrediction Schema Uri: %s\n", predictSchemata.getPredictionSchemaUri());

      for (Model.ExportFormat supportedExportFormat :
          modelResponse.getSupportedExportFormatsList()) {
        System.out.println("\tSupported Export Format");
        System.out.format("\t\tId: %s\n", supportedExportFormat.getId());
      }
      ModelContainerSpec containerSpec = modelResponse.getContainerSpec();

      System.out.println("\tContainer Spec");
      System.out.format("\t\tImage Uri: %s\n", containerSpec.getImageUri());
      System.out.format("\t\tCommand: %s\n", containerSpec.getCommandList());
      System.out.format("\t\tArgs: %s\n", containerSpec.getArgsList());
      System.out.format("\t\tPredict Route: %s\n", containerSpec.getPredictRoute());
      System.out.format("\t\tHealth Route: %s\n", containerSpec.getHealthRoute());

      for (EnvVar envVar : containerSpec.getEnvList()) {
        System.out.println("\t\tEnv");
        System.out.format("\t\t\tName: %s\n", envVar.getName());
        System.out.format("\t\t\tValue: %s\n", envVar.getValue());
      }

      for (Port port : containerSpec.getPortsList()) {
        System.out.println("\t\tPort");
        System.out.format("\t\t\tContainer Port: %s\n", port.getContainerPort());
      }

      for (DeployedModelRef deployedModelRef : modelResponse.getDeployedModelsList()) {
        System.out.println("\tDeployed Model");
        System.out.format("\t\tEndpoint: %s\n", deployedModelRef.getEndpoint());
        System.out.format("\t\tDeployed Model Id: %s\n", deployedModelRef.getDeployedModelId());
      }

      Status status = trainingPipelineResponse.getError();
      System.out.println("\tError");
      System.out.format("\t\tCode: %s\n", status.getCode());
      System.out.format("\t\tMessage: %s\n", status.getMessage());
    }
  }
}

Node.js

在試用這個範例之前，請先按照Node.js使用用戶端程式庫的 Vertex AI 快速入門中的操作說明進行設定。詳情請參閱 Vertex AI Node.js API 參考說明文件。

如要向 Vertex AI 進行驗證，請設定應用程式預設憑證。詳情請參閱「為本機開發環境設定驗證」。

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */

// const datasetId = 'YOUR_DATASET_ID';
// const modelDisplayName = 'YOUR_MODEL_DISPLAY_NAME';
// const trainingPipelineDisplayName = 'YOUR_TRAINING_PIPELINE_DISPLAY_NAME';
// const targetColumn = 'YOUR_TARGET_COLUMN';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
const aiplatform = require('@google-cloud/aiplatform');
const {definition} =
  aiplatform.protos.google.cloud.aiplatform.v1.schema.trainingjob;

// Imports the Google Cloud Pipeline Service Client library
const {PipelineServiceClient} = aiplatform.v1;
// Specifies the location of the api endpoint
const clientOptions = {
  apiEndpoint: 'us-central1-aiplatform.googleapis.com',
};

// Instantiates a client
const pipelineServiceClient = new PipelineServiceClient(clientOptions);

async function createTrainingPipelineTablesRegression() {
  // Configure the parent resource
  const parent = `projects/${project}/locations/${location}`;

  const transformations = [
    {auto: {column_name: 'STRING_5000unique_NULLABLE'}},
    {auto: {column_name: 'INTEGER_5000unique_NULLABLE'}},
    {auto: {column_name: 'FLOAT_5000unique_NULLABLE'}},
    {auto: {column_name: 'FLOAT_5000unique_REPEATED'}},
    {auto: {column_name: 'NUMERIC_5000unique_NULLABLE'}},
    {auto: {column_name: 'BOOLEAN_2unique_NULLABLE'}},
    {
      timestamp: {
        column_name: 'TIMESTAMP_1unique_NULLABLE',
        invalid_values_allowed: true,
      },
    },
    {auto: {column_name: 'DATE_1unique_NULLABLE'}},
    {auto: {column_name: 'TIME_1unique_NULLABLE'}},
    {
      timestamp: {
        column_name: 'DATETIME_1unique_NULLABLE',
        invalid_values_allowed: true,
      },
    },
    {auto: {column_name: 'STRUCT_NULLABLE.STRING_5000unique_NULLABLE'}},
    {auto: {column_name: 'STRUCT_NULLABLE.INTEGER_5000unique_NULLABLE'}},
    {auto: {column_name: 'STRUCT_NULLABLE.FLOAT_5000unique_NULLABLE'}},
    {auto: {column_name: 'STRUCT_NULLABLE.FLOAT_5000unique_REQUIRED'}},
    {auto: {column_name: 'STRUCT_NULLABLE.FLOAT_5000unique_REPEATED'}},
    {auto: {column_name: 'STRUCT_NULLABLE.NUMERIC_5000unique_NULLABLE'}},
    {auto: {column_name: 'STRUCT_NULLABLE.BOOLEAN_2unique_NULLABLE'}},
    {auto: {column_name: 'STRUCT_NULLABLE.TIMESTAMP_1unique_NULLABLE'}},
  ];

  const trainingTaskInputsObj = new definition.AutoMlTablesInputs({
    transformations,
    targetColumn,
    predictionType: 'regression',
    trainBudgetMilliNodeHours: 8000,
    disableEarlyStopping: false,
    optimizationObjective: 'minimize-rmse',
  });
  const trainingTaskInputs = trainingTaskInputsObj.toValue();

  const modelToUpload = {displayName: modelDisplayName};
  const inputDataConfig = {
    datasetId: datasetId,
    fractionSplit: {
      trainingFraction: 0.8,
      validationFraction: 0.1,
      testFraction: 0.1,
    },
  };
  const trainingPipeline = {
    displayName: trainingPipelineDisplayName,
    trainingTaskDefinition:
      'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_tables_1.0.0.yaml',
    trainingTaskInputs,
    inputDataConfig,
    modelToUpload,
  };
  const request = {
    parent,
    trainingPipeline,
  };

  // Create training pipeline request
  const [response] =
    await pipelineServiceClient.createTrainingPipeline(request);

  console.log('Create training pipeline tabular regression response');
  console.log(`Name : ${response.name}`);
  console.log('Raw response:');
  console.log(JSON.stringify(response, null, 2));
}
createTrainingPipelineTablesRegression();

Python

如要瞭解如何安裝或更新 Python 適用的 Vertex AI SDK，請參閱「安裝 Python 適用的 Vertex AI SDK」。詳情請參閱 Python API 參考說明文件。

def create_training_pipeline_tabular_regression_sample(
    project: str,
    display_name: str,
    dataset_id: str,
    location: str = "us-central1",
    model_display_name: str = "my_model",
    target_column: str = "target_column",
    training_fraction_split: float = 0.8,
    validation_fraction_split: float = 0.1,
    test_fraction_split: float = 0.1,
    budget_milli_node_hours: int = 8000,
    disable_early_stopping: bool = False,
    sync: bool = True,
):
    aiplatform.init(project=project, location=location)

    tabular_regression_job = aiplatform.AutoMLTabularTrainingJob(
        display_name=display_name, optimization_prediction_type="regression"
    )

    my_tabular_dataset = aiplatform.TabularDataset(dataset_name=dataset_id)

    model = tabular_regression_job.run(
        dataset=my_tabular_dataset,
        target_column=target_column,
        training_fraction_split=training_fraction_split,
        validation_fraction_split=validation_fraction_split,
        test_fraction_split=test_fraction_split,
        budget_milli_node_hours=budget_milli_node_hours,
        model_display_name=model_display_name,
        disable_early_stopping=disable_early_stopping,
        sync=sync,
    )

    model.wait()

    print(model.display_name)
    print(model.resource_name)
    print(model.uri)
    return model

使用 REST 控制資料分割

您可以控制訓練資料在訓練、驗證和測試集之間的分割方式。使用 Vertex AI API 時，請使用 Split 物件判斷資料分割。Split 物件可做為其中一種物件類型，納入 inputDataConfig 物件中，每種物件類型都提供不同的訓練資料分割方式。

分割資料的方法取決於資料類型：

FractionSplit：
- TRAINING_FRACTION：用於訓練集的訓練資料比例。
- VALIDATION_FRACTION：用於驗證集的訓練資料比例。
- TEST_FRACTION：用於測試集的訓練資料比例。
如果指定任何分數，請指定所有分數。分數總和必須為 1.0。瞭解詳情。
```
"fractionSplit": {
"trainingFraction": TRAINING_FRACTION,
"validationFraction": VALIDATION_FRACTION,
"testFraction": TEST_FRACTION
},
```
PredefinedSplit：
- DATA_SPLIT_COLUMN：包含資料分割值的資料欄 (TRAIN、VALIDATION、TEST)。
使用分割欄手動指定每個資料列的資料分割。瞭解詳情。
```
"predefinedSplit": {
  "key": DATA_SPLIT_COLUMN
},
```
TimestampSplit：
- TRAINING_FRACTION：用於訓練集的訓練資料百分比。預設值為 0.80。
- VALIDATION_FRACTION：用於驗證集的訓練資料百分比。預設值為 0.10。
- TEST_FRACTION：用於測試集的訓練資料百分比。預設值為 0.10。
- TIME_COLUMN：包含時間戳記的資料欄。
如果指定任何分數，請指定所有分數。分數加總必須為 1.0。瞭解詳情。
```
"timestampSplit": {
  "trainingFraction": TRAINING_FRACTION,
  "validationFraction": VALIDATION_FRACTION,
  "testFraction": TEST_FRACTION,
  "key": TIME_COLUMN
}
```

分類或迴歸模型的最佳化目標

訓練模型時，Vertex AI 會根據模型類型和目標資料欄所用的資料類型，選取預設最佳化目標。

分類模型最適合用於：

最佳化目標	API 值	目標的適用情境
AUC ROC	`maximize-au-roc`	盡量增加接收者操作特徵 (ROC) 曲線下的面積。區分類別。二元分類的預設值。
對數損失	`minimize-log-loss`	盡可能保持推論機率的準確度。多元分類唯一支援的目標。
AUC PR	`maximize-au-prc`	盡量增加精確度和喚回度曲線下的面積。可最佳化少用類別的推論結果。
維持特定喚回度時的精確度	`maximize-precision-at-recall`	在特定召回值下，盡量提高精確度。
維持特定精確度時的喚回度	`maximize-recall-at-precision`	以特定精確度值為目標，盡量提高喚回度。

迴歸模型最適合用於：

最佳化目標	API 值	目標的適用情境
均方根誤差	`minimize-rmse`	盡量減少均方根誤差 (RMSE)。準確擷取更多極端值。預設值。
平均絕對誤差	`minimize-mae`	盡量減少平均絕對誤差 (MAE)。將極端值視為對模型影響較小的離群值。
均方根對數誤差	`minimize-rmsle`	盡量減少均方根對數誤差 (RMSLE)。依據相對大小 (而非絕對值) 懲罰誤差，如果預測值和實際值可能相當大，這個做法就非常實用。

後續步驟

評估模型。
瞭解如何匯出模型。

訓練分類或迴歸模型 透過集合功能整理內容 你可以依據偏好儲存及分類內容。

事前準備

訓練模型

Google Cloud 控制台

API

分類

REST

curl (Linux、macOS 或 Cloud Shell)

PowerShell (Windows)

Java

Node.js

Python

迴歸

REST

curl (Linux、macOS 或 Cloud Shell)

PowerShell (Windows)

Java

Node.js

Python

使用 REST 控制資料分割

分類或迴歸模型的最佳化目標

後續步驟

訓練分類或迴歸模型