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    Train an image classification model

    This page shows you how to train an AutoML classification model from an image dataset using either the Google Cloud console or the Vertex AI API.

    Train an AutoML model

    Google Cloud console

    1. In the Google Cloud console, in the Vertex AI section, go to the Datasetspage.

      Go to the Datasets page

    2. Click the name of the dataset you want to use to train your model to open its details page.

    3. Click Train new model.

    4. For the training method, select AutoML .

    5. Click Continue .

    6. Enter a name for the model.

    7. If you want manually set how your training data is split, expand Advanced options and select a data split option. Learn more .

    8. Click Start Training .

      Model training can take many hours, depending on the size and complexity of your data and your training budget, if you specified one. You can close this tab and return to it later. You will receive an email when your model has completed training.

    API

    Select the tab below for your objective:

    Classification

    Select the tab below for your language or environment:

    REST

    Before using any of the request data, make the following replacements:

    • LOCATION : Region where dataset is located and Model is created. For example, us-central1 .
    • PROJECT : Your project ID .
    • TRAININGPIPELINE_DISPLAYNAME : Required. A display name for the trainingPipeline.
    • DATASET_ID : The ID number for the dataset to use for training.
    • fractionSplit : Optional. One of several possible ML use split options for your data. For fractionSplit , values must sum to 1. For example:
      • {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
    • MODEL_DISPLAYNAME * : A display name for the model uploaded (created) by the TrainingPipeline.
    • MODEL_DESCRIPTION * : A description for the model.
    • modelToUpload.labels * : Any set of key-value pairs to organize your models. For example:
      • "env": "prod"
      • "tier": "backend"
    • MODELTYPE † : The type of Cloud-hosted model to train. The options are:
      • CLOUD (default)
    • NODE_HOUR_BUDGET † : The actual training cost will be equal or less than this value. For Cloud models the budget must be: 8,000 - 800,000 milli node hours (inclusive). The default value is 192,000 which represents one day in wall time, assuming 8 nodes are used.
    • PROJECT_NUMBER : Your project's automatically generated project number
    * Schema file's description you specify in trainingTaskDefinition describes the use of this field.
    † Schema file you specify in trainingTaskDefinition declares and describes this field.

    HTTP method and URL:

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

    Request JSON body:

    {
  "displayName": " TRAININGPIPELINE_DISPLAYNAME 
",
  "inputDataConfig": {
    "datasetId": " DATASET_ID 
",
    "fractionSplit": {
      "trainingFraction": " DECIMAL 
",
      "validationFraction": " DECIMAL 
",
      "testFraction": " DECIMAL 
"
    }
  },
  "modelToUpload": {
    "displayName": " MODEL_DISPLAYNAME 
",
    "description": " MODEL_DESCRIPTION 
",
    "labels": {
      " KEY 
": " VALUE 
"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "multiLabel": " false 
",
    "modelType": [" MODELTYPE 
"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET 
}
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    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

    Save the request body in a file named request.json , and execute the following command:

    $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

    The response contains information about specifications as well as the TRAININGPIPELINE_ID .

    Java

    Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      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. Model 
. ExportFormat 
 
 ; 
 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. AutoMlImageClassificationInputs 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1.schema.trainingjob.definition. AutoMlImageClassificationInputs 
.ModelType 
 ; 
 import 
  
 com.google.rpc. Status 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 CreateTrainingPipelineImageClassificationSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 trainingPipelineDisplayName 
  
 = 
  
 "YOUR_TRAINING_PIPELINE_DISPLAY_NAME" 
 ; 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 datasetId 
  
 = 
  
 "YOUR_DATASET_ID" 
 ; 
  
 String 
  
 modelDisplayName 
  
 = 
  
 "YOUR_MODEL_DISPLAY_NAME" 
 ; 
  
 createTrainingPipelineImageClassificationSample 
 ( 
  
 project 
 , 
  
 trainingPipelineDisplayName 
 , 
  
 datasetId 
 , 
  
 modelDisplayName 
 ); 
  
 } 
  
 static 
  
 void 
  
 createTrainingPipelineImageClassificationSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 trainingPipelineDisplayName 
 , 
  
 String 
  
 datasetId 
 , 
  
 String 
  
 modelDisplayName 
 ) 
  
 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" 
 ; 
  
 String 
  
 trainingTaskDefinition 
  
 = 
  
 "gs://google-cloud-aiplatform/schema/trainingjob/definition/" 
  
 + 
  
 "automl_image_classification_1.0.0.yaml" 
 ; 
  
  LocationName 
 
  
 locationName 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 ); 
  
  AutoMlImageClassificationInputs 
 
  
 autoMlImageClassificationInputs 
  
 = 
  
  AutoMlImageClassificationInputs 
 
 . 
 newBuilder 
 () 
  
 . 
 setModelType 
 ( 
 ModelType 
 . 
 CLOUD 
 ) 
  
 . 
 setMultiLabel 
 ( 
 false 
 ) 
  
 . 
 setBudgetMilliNodeHours 
 ( 
 8000 
 ) 
  
 . 
 setDisableEarlyStopping 
 ( 
 false 
 ) 
  
 . 
 build 
 (); 
  
  InputDataConfig 
 
  
 trainingInputDataConfig 
  
 = 
  
  InputDataConfig 
 
 . 
 newBuilder 
 (). 
  setDatasetId 
 
 ( 
 datasetId 
 ). 
 build 
 (); 
  
  Model 
 
  
 model 
  
 = 
  
  Model 
 
 . 
 newBuilder 
 (). 
 setDisplayName 
 ( 
 modelDisplayName 
 ). 
 build 
 (); 
  
  TrainingPipeline 
 
  
 trainingPipeline 
  
 = 
  
  TrainingPipeline 
 
 . 
 newBuilder 
 () 
  
 . 
 setDisplayName 
 ( 
 trainingPipelineDisplayName 
 ) 
  
 . 
  setTrainingTaskDefinition 
 
 ( 
 trainingTaskDefinition 
 ) 
  
 . 
  setTrainingTaskInputs 
 
 ( 
  ValueConverter 
 
 . 
  toValue 
 
 ( 
 autoMlImageClassificationInputs 
 )) 
  
 . 
  setInputDataConfig 
 
 ( 
 trainingInputDataConfig 
 ) 
  
 . 
  setModelToUpload 
 
 ( 
 model 
 ) 
  
 . 
 build 
 (); 
  
  TrainingPipeline 
 
  
 trainingPipelineResponse 
  
 = 
  
 pipelineServiceClient 
 . 
 createTrainingPipeline 
 ( 
 locationName 
 , 
  
 trainingPipeline 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Create Training Pipeline Image Classification Response" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Display Name: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getDisplayName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Training Task Definition %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getTrainingTaskDefinition 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Training Task Inputs: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getTrainingTaskInputs 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Training Task Metadata: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getTrainingTaskMetadata 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "State: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getState 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Create Time: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getCreateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "StartTime %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getStartTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "End Time: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getEndTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Update Time: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getUpdateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Labels: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getLabelsMap 
 
 ()); 
  
  InputDataConfig 
 
  
 inputDataConfig 
  
 = 
  
 trainingPipelineResponse 
 . 
  getInputDataConfig 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Input Data Config" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Dataset Id: %s" 
 , 
  
 inputDataConfig 
 . 
  getDatasetId 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Annotations Filter: %s\n" 
 , 
  
 inputDataConfig 
 . 
  getAnnotationsFilter 
 
 ()); 
  
  FractionSplit 
 
  
 fractionSplit 
  
 = 
  
 inputDataConfig 
 . 
  getFractionSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Fraction Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Fraction: %s\n" 
 , 
  
 fractionSplit 
 . 
  getTrainingFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Validation Fraction: %s\n" 
 , 
  
 fractionSplit 
 . 
  getValidationFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Test Fraction: %s\n" 
 , 
  
 fractionSplit 
 . 
  getTestFraction 
 
 ()); 
  
  FilterSplit 
 
  
 filterSplit 
  
 = 
  
 inputDataConfig 
 . 
  getFilterSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Filter Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Filter: %s\n" 
 , 
  
 filterSplit 
 . 
  getTrainingFilter 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Validation Filter: %s\n" 
 , 
  
 filterSplit 
 . 
  getValidationFilter 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Test Filter: %s\n" 
 , 
  
 filterSplit 
 . 
  getTestFilter 
 
 ()); 
  
  PredefinedSplit 
 
  
 predefinedSplit 
  
 = 
  
 inputDataConfig 
 . 
  getPredefinedSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Predefined Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Key: %s\n" 
 , 
  
 predefinedSplit 
 . 
  getKey 
 
 ()); 
  
  TimestampSplit 
 
  
 timestampSplit 
  
 = 
  
 inputDataConfig 
 . 
  getTimestampSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Timestamp Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Fraction: %s\n" 
 , 
  
 timestampSplit 
 . 
  getTrainingFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Validation Fraction: %s\n" 
 , 
  
 timestampSplit 
 . 
  getValidationFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Test Fraction: %s\n" 
 , 
  
 timestampSplit 
 . 
  getTestFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Key: %s\n" 
 , 
  
 timestampSplit 
 . 
  getKey 
 
 ()); 
  
  Model 
 
  
 modelResponse 
  
 = 
  
 trainingPipelineResponse 
 . 
  getModelToUpload 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model To Upload" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 modelResponse 
 . 
  getName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Display Name: %s\n" 
 , 
  
 modelResponse 
 . 
  getDisplayName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Description: %s\n" 
 , 
  
 modelResponse 
 . 
  getDescription 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Metadata Schema Uri: %s\n" 
 , 
  
 modelResponse 
 . 
  getMetadataSchemaUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Metadata: %s\n" 
 , 
  
 modelResponse 
 . 
  getMetadata 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Pipeline: %s\n" 
 , 
  
 modelResponse 
 . 
  getTrainingPipeline 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Artifact Uri: %s\n" 
 , 
  
 modelResponse 
 . 
  getArtifactUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Supported Deployment Resources Types: %s\n" 
 , 
  
 modelResponse 
 . 
  getSupportedDeploymentResourcesTypesList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Supported Input Storage Formats: %s\n" 
 , 
  
 modelResponse 
 . 
  getSupportedInputStorageFormatsList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Supported Output Storage Formats: %s\n" 
 , 
  
 modelResponse 
 . 
  getSupportedOutputStorageFormatsList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Create Time: %s\n" 
 , 
  
 modelResponse 
 . 
  getCreateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Update Time: %s\n" 
 , 
  
 modelResponse 
 . 
  getUpdateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Labels: %sn\n" 
 , 
  
 modelResponse 
 . 
  getLabelsMap 
 
 ()); 
  
  PredictSchemata 
 
  
 predictSchemata 
  
 = 
  
 modelResponse 
 . 
  getPredictSchemata 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Predict Schemata" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Instance Schema Uri: %s\n" 
 , 
  
 predictSchemata 
 . 
  getInstanceSchemaUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Parameters Schema Uri: %s\n" 
 , 
  
 predictSchemata 
 . 
  getParametersSchemaUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Prediction Schema Uri: %s\n" 
 , 
  
 predictSchemata 
 . 
  getPredictionSchemaUri 
 
 ()); 
  
 for 
  
 ( 
  ExportFormat 
 
  
 exportFormat 
  
 : 
  
 modelResponse 
 . 
  getSupportedExportFormatsList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Supported Export Format" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Id: %s\n" 
 , 
  
 exportFormat 
 . 
 getId 
 ()); 
  
 } 
  
  ModelContainerSpec 
 
  
 modelContainerSpec 
  
 = 
  
 modelResponse 
 . 
  getContainerSpec 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Container Spec" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Image Uri: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getImageUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Command: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getCommandList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Args: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getArgsList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Predict Route: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getPredictRoute 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Health Route: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getHealthRoute 
 
 ()); 
  
 for 
  
 ( 
  EnvVar 
 
  
 envVar 
  
 : 
  
 modelContainerSpec 
 . 
  getEnvList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Env" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 envVar 
 . 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Value: %s\n" 
 , 
  
 envVar 
 . 
 getValue 
 ()); 
  
 } 
  
 for 
  
 ( 
  Port 
 
  
 port 
  
 : 
  
 modelContainerSpec 
 . 
  getPortsList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Port" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Container Port: %s\n" 
 , 
  
 port 
 . 
 getContainerPort 
 ()); 
  
 } 
  
 for 
  
 ( 
  DeployedModelRef 
 
  
 deployedModelRef 
  
 : 
  
 modelResponse 
 . 
  getDeployedModelsList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Deployed Model" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Endpoint: %s\n" 
 , 
  
 deployedModelRef 
 . 
 getEndpoint 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Deployed Model Id: %s\n" 
 , 
  
 deployedModelRef 
 . 
 getDeployedModelId 
 ()); 
  
 } 
  
  Status 
 
  
 status 
  
 = 
  
 trainingPipelineResponse 
 . 
  getError 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Error" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Code: %s\n" 
 , 
  
 status 
 . 
  getCode 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Message: %s\n" 
 , 
  
 status 
 . 
  getMessage 
 
 ()); 
  
 } 
  
 } 
 }

    Node.js

    Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      /** 
 * TODO(developer): Uncomment these variables before running the sample. 
 * (Not necessary if passing values as arguments) 
 */ 
 /* 
 const datasetId = 'YOUR DATASET'; 
 const modelDisplayName = 'NEW MODEL NAME; 
 const trainingPipelineDisplayName = 'NAME FOR TRAINING PIPELINE'; 
 const project = 'YOUR PROJECT ID'; 
 const location = 'us-central1'; 
 */ 
 // Imports the Google Cloud Pipeline Service Client library 
 const 
  
 aiplatform 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ); 
 const 
  
 { 
 definition 
 } 
  
 = 
  
 aiplatform 
 . 
 protos 
 . 
 google 
 . 
 cloud 
 . 
 aiplatform 
 . 
 v1 
 . 
 schema 
 . 
 trainingjob 
 ; 
 const 
  
 ModelType 
  
 = 
  
 definition 
 . 
 AutoMlImageClassificationInputs 
 . 
 ModelType 
 ; 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 'us-central1-aiplatform.googleapis.com' 
 , 
 }; 
 // Instantiates a client 
 const 
  
 { 
 PipelineServiceClient 
 } 
  
 = 
  
 aiplatform 
 . 
 v1 
 ; 
 const 
  
 pipelineServiceClient 
  
 = 
  
 new 
  
  PipelineServiceClient 
 
 ( 
 clientOptions 
 ); 
 async 
  
 function 
  
 createTrainingPipelineImageClassification 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 ` 
 ; 
  
 // Values should match the input expected by your model. 
  
 const 
  
 trainingTaskInputsMessage 
  
 = 
  
 new 
  
 definition 
 . 
 AutoMlImageClassificationInputs 
 ({ 
  
 multiLabel 
 : 
  
 true 
 , 
  
 modelType 
 : 
  
 ModelType 
 . 
 CLOUD 
 , 
  
 budgetMilliNodeHours 
 : 
  
 8000 
 , 
  
 disableEarlyStopping 
 : 
  
 false 
 , 
  
 }); 
  
 const 
  
 trainingTaskInputs 
  
 = 
  
 trainingTaskInputsMessage 
 . 
 toValue 
 (); 
  
 const 
  
 trainingTaskDefinition 
  
 = 
  
 'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml' 
 ; 
  
 const 
  
 modelToUpload 
  
 = 
  
 { 
 displayName 
 : 
  
 modelDisplayName 
 }; 
  
 const 
  
 inputDataConfig 
  
 = 
  
 { 
 datasetId 
 }; 
  
 const 
  
 trainingPipeline 
  
 = 
  
 { 
  
 displayName 
 : 
  
 trainingPipelineDisplayName 
 , 
  
 trainingTaskDefinition 
 , 
  
 trainingTaskInputs 
 , 
  
 inputDataConfig 
 , 
  
 modelToUpload 
 , 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
 parent 
 , 
  
 trainingPipeline 
 }; 
  
 // Create training pipeline request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 pipelineServiceClient 
 . 
 createTrainingPipeline 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 'Create training pipeline image classification response' 
 ); 
  
 console 
 . 
 log 
 ( 
 `Name : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 'Raw response:' 
 ); 
  
 console 
 . 
 log 
 ( 
 JSON 
 . 
 stringify 
 ( 
 response 
 , 
  
 null 
 , 
  
 2 
 )); 
 } 
 createTrainingPipelineImageClassification 
 ();

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      def 
  
 create_training_pipeline_image_classification_sample 
 ( 
  
 project 
 : 
  
 str 
 , 
  
 location 
 : 
  
 str 
 , 
  
 display_name 
 : 
  
 str 
 , 
  
 dataset_id 
 : 
  
 str 
 , 
  
 model_display_name 
 : 
  
 Optional 
 [ 
 str 
 ] 
  
 = 
  
 None 
 , 
  
 model_type 
 : 
  
 str 
  
 = 
  
 "CLOUD" 
 , 
  
 multi_label 
 : 
  
 bool 
  
 = 
  
 False 
 , 
  
 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 
 ) 
  
 job 
  
 = 
  
 aiplatform 
 . 
 AutoMLImageTrainingJob 
 ( 
  
 display_name 
 = 
 display_name 
 , 
  
 model_type 
 = 
 model_type 
 , 
  
 prediction_type 
 = 
 "classification" 
 , 
  
 multi_label 
 = 
 multi_label 
 , 
  
 ) 
  
 my_image_ds 
  
 = 
  
 aiplatform 
 . 
 ImageDataset 
 ( 
 dataset_id 
 ) 
  
 model 
  
 = 
  
 job 
 . 
 run 
 ( 
  
 dataset 
 = 
 my_image_ds 
 , 
  
 model_display_name 
 = 
 model_display_name 
 , 
  
 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 
 , 
  
 disable_early_stopping 
 = 
 disable_early_stopping 
 , 
  
 sync 
 = 
 sync 
 , 
  
 ) 
  
 model 
 . 
 wait 
 () 
  
 print 
 ( 
 model 
 . 
 display_name 
 ) 
  
 print 
 ( 
 model 
 . 
 resource_name 
 ) 
  
 print 
 ( 
 model 
 . 
 uri 
 ) 
  
 return 
  
 model

    Classification

    Select the tab below for your language or environment:

    REST

    Before using any of the request data, make the following replacements:

    • LOCATION : Region where dataset is located and Model is created. For example, us-central1 .
    • PROJECT : .
    • TRAININGPIPELINE_DISPLAYNAME : Required. A display name for the trainingPipeline.
    • DATASET_ID : The ID number for the dataset to use for training.
    • fractionSplit : Optional. One of several possible ML use split options for your data. For fractionSplit , values must sum to 1. For example:
      • {"trainingFraction": "0.7","validationFraction": "0.15","testFraction": "0.15"}
    • MODEL_DISPLAYNAME * : A display name for the model uploaded (created) by the TrainingPipeline.
    • MODEL_DESCRIPTION * : A description for the model.
    • modelToUpload.labels * : Any set of key-value pairs to organize your models. For example:
      • "env": "prod"
      • "tier": "backend"
    • MODELTYPE † : The type of Cloud-hosted model to train. The options are:
      • CLOUD (default)
    • NODE_HOUR_BUDGET † : The actual training cost will be equal or less than this value. For Cloud models the budget must be: 8,000 - 800,000 milli node hours (inclusive). The default value is 192,000 which represents one day in wall time, assuming 8 nodes are used.
    • PROJECT_NUMBER : Your project's automatically generated project number
    * Schema file's description you specify in trainingTaskDefinition describes the use of this field.
    † Schema file you specify in trainingTaskDefinition declares and describes this field.

    HTTP method and URL:

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

    Request JSON body:

    {
  "displayName": " TRAININGPIPELINE_DISPLAYNAME 
",
  "inputDataConfig": {
    "datasetId": " DATASET_ID 
",
    "fractionSplit": {
      "trainingFraction": " DECIMAL 
",
      "validationFraction": " DECIMAL 
",
      "testFraction": " DECIMAL 
"
    }
  },
  "modelToUpload": {
    "displayName": " MODEL_DISPLAYNAME 
",
    "description": " MODEL_DESCRIPTION 
",
    "labels": {
      " KEY 
": " VALUE 
"
    }
  },
  "trainingTaskDefinition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml",
  "trainingTaskInputs": {
    "multiLabel": " true 
",
    "modelType": [" MODELTYPE 
"],
    "budgetMilliNodeHours": NODE_HOUR_BUDGET 
}
}

    To send your request, choose one of these options:

    curl

    Save the request body in a file named request.json , and execute the following command:

    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

    Save the request body in a file named request.json , and execute the following command:

    $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

    The response contains information about specifications as well as the TRAININGPIPELINE_ID .

    Java

    Before trying this sample, follow the Java setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Java API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      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. Model 
. ExportFormat 
 
 ; 
 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. AutoMlImageClassificationInputs 
 
 ; 
 import 
  
 com.google.cloud.aiplatform.v1.schema.trainingjob.definition. AutoMlImageClassificationInputs 
.ModelType 
 ; 
 import 
  
 com.google.rpc. Status 
 
 ; 
 import 
  
 java.io.IOException 
 ; 
 public 
  
 class 
 CreateTrainingPipelineImageClassificationSample 
  
 { 
  
 public 
  
 static 
  
 void 
  
 main 
 ( 
 String 
 [] 
  
 args 
 ) 
  
 throws 
  
 IOException 
  
 { 
  
 // TODO(developer): Replace these variables before running the sample. 
  
 String 
  
 trainingPipelineDisplayName 
  
 = 
  
 "YOUR_TRAINING_PIPELINE_DISPLAY_NAME" 
 ; 
  
 String 
  
 project 
  
 = 
  
 "YOUR_PROJECT_ID" 
 ; 
  
 String 
  
 datasetId 
  
 = 
  
 "YOUR_DATASET_ID" 
 ; 
  
 String 
  
 modelDisplayName 
  
 = 
  
 "YOUR_MODEL_DISPLAY_NAME" 
 ; 
  
 createTrainingPipelineImageClassificationSample 
 ( 
  
 project 
 , 
  
 trainingPipelineDisplayName 
 , 
  
 datasetId 
 , 
  
 modelDisplayName 
 ); 
  
 } 
  
 static 
  
 void 
  
 createTrainingPipelineImageClassificationSample 
 ( 
  
 String 
  
 project 
 , 
  
 String 
  
 trainingPipelineDisplayName 
 , 
  
 String 
  
 datasetId 
 , 
  
 String 
  
 modelDisplayName 
 ) 
  
 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" 
 ; 
  
 String 
  
 trainingTaskDefinition 
  
 = 
  
 "gs://google-cloud-aiplatform/schema/trainingjob/definition/" 
  
 + 
  
 "automl_image_classification_1.0.0.yaml" 
 ; 
  
  LocationName 
 
  
 locationName 
  
 = 
  
  LocationName 
 
 . 
 of 
 ( 
 project 
 , 
  
 location 
 ); 
  
  AutoMlImageClassificationInputs 
 
  
 autoMlImageClassificationInputs 
  
 = 
  
  AutoMlImageClassificationInputs 
 
 . 
 newBuilder 
 () 
  
 . 
 setModelType 
 ( 
 ModelType 
 . 
 CLOUD 
 ) 
  
 . 
 setMultiLabel 
 ( 
 false 
 ) 
  
 . 
 setBudgetMilliNodeHours 
 ( 
 8000 
 ) 
  
 . 
 setDisableEarlyStopping 
 ( 
 false 
 ) 
  
 . 
 build 
 (); 
  
  InputDataConfig 
 
  
 trainingInputDataConfig 
  
 = 
  
  InputDataConfig 
 
 . 
 newBuilder 
 (). 
  setDatasetId 
 
 ( 
 datasetId 
 ). 
 build 
 (); 
  
  Model 
 
  
 model 
  
 = 
  
  Model 
 
 . 
 newBuilder 
 (). 
 setDisplayName 
 ( 
 modelDisplayName 
 ). 
 build 
 (); 
  
  TrainingPipeline 
 
  
 trainingPipeline 
  
 = 
  
  TrainingPipeline 
 
 . 
 newBuilder 
 () 
  
 . 
 setDisplayName 
 ( 
 trainingPipelineDisplayName 
 ) 
  
 . 
  setTrainingTaskDefinition 
 
 ( 
 trainingTaskDefinition 
 ) 
  
 . 
  setTrainingTaskInputs 
 
 ( 
  ValueConverter 
 
 . 
  toValue 
 
 ( 
 autoMlImageClassificationInputs 
 )) 
  
 . 
  setInputDataConfig 
 
 ( 
 trainingInputDataConfig 
 ) 
  
 . 
  setModelToUpload 
 
 ( 
 model 
 ) 
  
 . 
 build 
 (); 
  
  TrainingPipeline 
 
  
 trainingPipelineResponse 
  
 = 
  
 pipelineServiceClient 
 . 
 createTrainingPipeline 
 ( 
 locationName 
 , 
  
 trainingPipeline 
 ); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Create Training Pipeline Image Classification Response" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Display Name: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getDisplayName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Training Task Definition %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getTrainingTaskDefinition 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Training Task Inputs: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getTrainingTaskInputs 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Training Task Metadata: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getTrainingTaskMetadata 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "State: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getState 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Create Time: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getCreateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "StartTime %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getStartTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "End Time: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getEndTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Update Time: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getUpdateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Labels: %s\n" 
 , 
  
 trainingPipelineResponse 
 . 
  getLabelsMap 
 
 ()); 
  
  InputDataConfig 
 
  
 inputDataConfig 
  
 = 
  
 trainingPipelineResponse 
 . 
  getInputDataConfig 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Input Data Config" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Dataset Id: %s" 
 , 
  
 inputDataConfig 
 . 
  getDatasetId 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Annotations Filter: %s\n" 
 , 
  
 inputDataConfig 
 . 
  getAnnotationsFilter 
 
 ()); 
  
  FractionSplit 
 
  
 fractionSplit 
  
 = 
  
 inputDataConfig 
 . 
  getFractionSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Fraction Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Fraction: %s\n" 
 , 
  
 fractionSplit 
 . 
  getTrainingFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Validation Fraction: %s\n" 
 , 
  
 fractionSplit 
 . 
  getValidationFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Test Fraction: %s\n" 
 , 
  
 fractionSplit 
 . 
  getTestFraction 
 
 ()); 
  
  FilterSplit 
 
  
 filterSplit 
  
 = 
  
 inputDataConfig 
 . 
  getFilterSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Filter Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Filter: %s\n" 
 , 
  
 filterSplit 
 . 
  getTrainingFilter 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Validation Filter: %s\n" 
 , 
  
 filterSplit 
 . 
  getValidationFilter 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Test Filter: %s\n" 
 , 
  
 filterSplit 
 . 
  getTestFilter 
 
 ()); 
  
  PredefinedSplit 
 
  
 predefinedSplit 
  
 = 
  
 inputDataConfig 
 . 
  getPredefinedSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Predefined Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Key: %s\n" 
 , 
  
 predefinedSplit 
 . 
  getKey 
 
 ()); 
  
  TimestampSplit 
 
  
 timestampSplit 
  
 = 
  
 inputDataConfig 
 . 
  getTimestampSplit 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Timestamp Split" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Fraction: %s\n" 
 , 
  
 timestampSplit 
 . 
  getTrainingFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Validation Fraction: %s\n" 
 , 
  
 timestampSplit 
 . 
  getValidationFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Test Fraction: %s\n" 
 , 
  
 timestampSplit 
 . 
  getTestFraction 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Key: %s\n" 
 , 
  
 timestampSplit 
 . 
  getKey 
 
 ()); 
  
  Model 
 
  
 modelResponse 
  
 = 
  
 trainingPipelineResponse 
 . 
  getModelToUpload 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Model To Upload" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 modelResponse 
 . 
  getName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Display Name: %s\n" 
 , 
  
 modelResponse 
 . 
  getDisplayName 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Description: %s\n" 
 , 
  
 modelResponse 
 . 
  getDescription 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Metadata Schema Uri: %s\n" 
 , 
  
 modelResponse 
 . 
  getMetadataSchemaUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Metadata: %s\n" 
 , 
  
 modelResponse 
 . 
  getMetadata 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Training Pipeline: %s\n" 
 , 
  
 modelResponse 
 . 
  getTrainingPipeline 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Artifact Uri: %s\n" 
 , 
  
 modelResponse 
 . 
  getArtifactUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Supported Deployment Resources Types: %s\n" 
 , 
  
 modelResponse 
 . 
  getSupportedDeploymentResourcesTypesList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Supported Input Storage Formats: %s\n" 
 , 
  
 modelResponse 
 . 
  getSupportedInputStorageFormatsList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
  
 "Supported Output Storage Formats: %s\n" 
 , 
  
 modelResponse 
 . 
  getSupportedOutputStorageFormatsList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Create Time: %s\n" 
 , 
  
 modelResponse 
 . 
  getCreateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Update Time: %s\n" 
 , 
  
 modelResponse 
 . 
  getUpdateTime 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Labels: %sn\n" 
 , 
  
 modelResponse 
 . 
  getLabelsMap 
 
 ()); 
  
  PredictSchemata 
 
  
 predictSchemata 
  
 = 
  
 modelResponse 
 . 
  getPredictSchemata 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Predict Schemata" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Instance Schema Uri: %s\n" 
 , 
  
 predictSchemata 
 . 
  getInstanceSchemaUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Parameters Schema Uri: %s\n" 
 , 
  
 predictSchemata 
 . 
  getParametersSchemaUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Prediction Schema Uri: %s\n" 
 , 
  
 predictSchemata 
 . 
  getPredictionSchemaUri 
 
 ()); 
  
 for 
  
 ( 
  ExportFormat 
 
  
 exportFormat 
  
 : 
  
 modelResponse 
 . 
  getSupportedExportFormatsList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Supported Export Format" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Id: %s\n" 
 , 
  
 exportFormat 
 . 
 getId 
 ()); 
  
 } 
  
  ModelContainerSpec 
 
  
 modelContainerSpec 
  
 = 
  
 modelResponse 
 . 
  getContainerSpec 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Container Spec" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Image Uri: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getImageUri 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Command: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getCommandList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Args: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getArgsList 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Predict Route: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getPredictRoute 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Health Route: %s\n" 
 , 
  
 modelContainerSpec 
 . 
  getHealthRoute 
 
 ()); 
  
 for 
  
 ( 
  EnvVar 
 
  
 envVar 
  
 : 
  
 modelContainerSpec 
 . 
  getEnvList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Env" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Name: %s\n" 
 , 
  
 envVar 
 . 
 getName 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Value: %s\n" 
 , 
  
 envVar 
 . 
 getValue 
 ()); 
  
 } 
  
 for 
  
 ( 
  Port 
 
  
 port 
  
 : 
  
 modelContainerSpec 
 . 
  getPortsList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Port" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Container Port: %s\n" 
 , 
  
 port 
 . 
 getContainerPort 
 ()); 
  
 } 
  
 for 
  
 ( 
  DeployedModelRef 
 
  
 deployedModelRef 
  
 : 
  
 modelResponse 
 . 
  getDeployedModelsList 
 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Deployed Model" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Endpoint: %s\n" 
 , 
  
 deployedModelRef 
 . 
 getEndpoint 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Deployed Model Id: %s\n" 
 , 
  
 deployedModelRef 
 . 
 getDeployedModelId 
 ()); 
  
 } 
  
  Status 
 
  
 status 
  
 = 
  
 trainingPipelineResponse 
 . 
  getError 
 
 (); 
  
 System 
 . 
 out 
 . 
 println 
 ( 
 "Error" 
 ); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Code: %s\n" 
 , 
  
 status 
 . 
  getCode 
 
 ()); 
  
 System 
 . 
 out 
 . 
 format 
 ( 
 "Message: %s\n" 
 , 
  
 status 
 . 
  getMessage 
 
 ()); 
  
 } 
  
 } 
 }

    Node.js

    Before trying this sample, follow the Node.js setup instructions in the Vertex AI quickstart using client libraries . For more information, see the Vertex AI Node.js API reference documentation .

    To authenticate to Vertex AI, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      /** 
 * TODO(developer): Uncomment these variables before running the sample. 
 * (Not necessary if passing values as arguments) 
 */ 
 /* 
 const datasetId = 'YOUR DATASET'; 
 const modelDisplayName = 'NEW MODEL NAME; 
 const trainingPipelineDisplayName = 'NAME FOR TRAINING PIPELINE'; 
 const project = 'YOUR PROJECT ID'; 
 const location = 'us-central1'; 
 */ 
 // Imports the Google Cloud Pipeline Service Client library 
 const 
  
 aiplatform 
  
 = 
  
 require 
 ( 
 ' @google-cloud/aiplatform 
' 
 ); 
 const 
  
 { 
 definition 
 } 
  
 = 
  
 aiplatform 
 . 
 protos 
 . 
 google 
 . 
 cloud 
 . 
 aiplatform 
 . 
 v1 
 . 
 schema 
 . 
 trainingjob 
 ; 
 const 
  
 ModelType 
  
 = 
  
 definition 
 . 
 AutoMlImageClassificationInputs 
 . 
 ModelType 
 ; 
 // Specifies the location of the api endpoint 
 const 
  
 clientOptions 
  
 = 
  
 { 
  
 apiEndpoint 
 : 
  
 'us-central1-aiplatform.googleapis.com' 
 , 
 }; 
 // Instantiates a client 
 const 
  
 { 
 PipelineServiceClient 
 } 
  
 = 
  
 aiplatform 
 . 
 v1 
 ; 
 const 
  
 pipelineServiceClient 
  
 = 
  
 new 
  
  PipelineServiceClient 
 
 ( 
 clientOptions 
 ); 
 async 
  
 function 
  
 createTrainingPipelineImageClassification 
 () 
  
 { 
  
 // Configure the parent resource 
  
 const 
  
 parent 
  
 = 
  
 `projects/ 
 ${ 
 project 
 } 
 /locations/ 
 ${ 
 location 
 } 
 ` 
 ; 
  
 // Values should match the input expected by your model. 
  
 const 
  
 trainingTaskInputsMessage 
  
 = 
  
 new 
  
 definition 
 . 
 AutoMlImageClassificationInputs 
 ({ 
  
 multiLabel 
 : 
  
 true 
 , 
  
 modelType 
 : 
  
 ModelType 
 . 
 CLOUD 
 , 
  
 budgetMilliNodeHours 
 : 
  
 8000 
 , 
  
 disableEarlyStopping 
 : 
  
 false 
 , 
  
 }); 
  
 const 
  
 trainingTaskInputs 
  
 = 
  
 trainingTaskInputsMessage 
 . 
 toValue 
 (); 
  
 const 
  
 trainingTaskDefinition 
  
 = 
  
 'gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_image_classification_1.0.0.yaml' 
 ; 
  
 const 
  
 modelToUpload 
  
 = 
  
 { 
 displayName 
 : 
  
 modelDisplayName 
 }; 
  
 const 
  
 inputDataConfig 
  
 = 
  
 { 
 datasetId 
 }; 
  
 const 
  
 trainingPipeline 
  
 = 
  
 { 
  
 displayName 
 : 
  
 trainingPipelineDisplayName 
 , 
  
 trainingTaskDefinition 
 , 
  
 trainingTaskInputs 
 , 
  
 inputDataConfig 
 , 
  
 modelToUpload 
 , 
  
 }; 
  
 const 
  
 request 
  
 = 
  
 { 
 parent 
 , 
  
 trainingPipeline 
 }; 
  
 // Create training pipeline request 
  
 const 
  
 [ 
 response 
 ] 
  
 = 
  
 await 
  
 pipelineServiceClient 
 . 
 createTrainingPipeline 
 ( 
 request 
 ); 
  
 console 
 . 
 log 
 ( 
 'Create training pipeline image classification response' 
 ); 
  
 console 
 . 
 log 
 ( 
 `Name : 
 ${ 
 response 
 . 
 name 
 } 
 ` 
 ); 
  
 console 
 . 
 log 
 ( 
 'Raw response:' 
 ); 
  
 console 
 . 
 log 
 ( 
 JSON 
 . 
 stringify 
 ( 
 response 
 , 
  
 null 
 , 
  
 2 
 )); 
 } 
 createTrainingPipelineImageClassification 
 ();

    Python

    To learn how to install or update the Vertex AI SDK for Python, see Install the Vertex AI SDK for Python . For more information, see the Python API reference documentation .

      def 
  
 create_training_pipeline_image_classification_sample 
 ( 
  
 project 
 : 
  
 str 
 , 
  
 location 
 : 
  
 str 
 , 
  
 display_name 
 : 
  
 str 
 , 
  
 dataset_id 
 : 
  
 str 
 , 
  
 model_display_name 
 : 
  
 Optional 
 [ 
 str 
 ] 
  
 = 
  
 None 
 , 
  
 model_type 
 : 
  
 str 
  
 = 
  
 "CLOUD" 
 , 
  
 multi_label 
 : 
  
 bool 
  
 = 
  
 False 
 , 
  
 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 
 ) 
  
 job 
  
 = 
  
 aiplatform 
 . 
 AutoMLImageTrainingJob 
 ( 
  
 display_name 
 = 
 display_name 
 , 
  
 model_type 
 = 
 model_type 
 , 
  
 prediction_type 
 = 
 "classification" 
 , 
  
 multi_label 
 = 
 multi_label 
 , 
  
 ) 
  
 my_image_ds 
  
 = 
  
 aiplatform 
 . 
 ImageDataset 
 ( 
 dataset_id 
 ) 
  
 model 
  
 = 
  
 job 
 . 
 run 
 ( 
  
 dataset 
 = 
 my_image_ds 
 , 
  
 model_display_name 
 = 
 model_display_name 
 , 
  
 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 
 , 
  
 disable_early_stopping 
 = 
 disable_early_stopping 
 , 
  
 sync 
 = 
 sync 
 , 
  
 ) 
  
 model 
 . 
 wait 
 () 
  
 print 
 ( 
 model 
 . 
 display_name 
 ) 
  
 print 
 ( 
 model 
 . 
 resource_name 
 ) 
  
 print 
 ( 
 model 
 . 
 uri 
 ) 
  
 return 
  
 model

    Control the data split using REST

    You can control how your training data is split between the training, validation, and test sets. When using the Vertex AI API, use the Split object to determine your data split. The Split object can be included in the InputConfig object as one of several object types, each of which provides a different way to split the training data. You can select one method only.

    • FractionSplit :
      • TRAINING_FRACTION : The fraction of the training data to be used for the training set.
      • VALIDATION_FRACTION : The fraction of the training data to be used for the validation set. Not used for video data.
      • TEST_FRACTION : The fraction of the training data to be used for the test set.

      If any of the fractions are specified, all must be specified. The fractions must add up to 1.0. The default values for the fractions differ depending on your data type. Learn more .

      "fractionSplit": {
  "trainingFraction": TRAINING_FRACTION 
,
  "validationFraction": VALIDATION_FRACTION 
,
  "testFraction": TEST_FRACTION 
},
    • FilterSplit :
      • TRAINING_FILTER : Data items that match this filter are used for the training set.
      • VALIDATION_FILTER : Data items that match this filter are used for the validation set. Must be "-" for video data.
      • TEST_FILTER : Data items that match this filter are used for the test set.

      These filters can be used with the ml_use label, or with any labels you apply to your data. Learn more about using the ml-use label and other labels to filter your data.

      The following example shows how to use the filterSplit object with the ml_use label, with the validation set included:

      "filterSplit": {
"trainingFilter": "labels.aiplatform.googleapis.com/ml_use=training",
"validationFilter": "labels.aiplatform.googleapis.com/ml_use=validation",
"testFilter": "labels.aiplatform.googleapis.com/ml_use=test"
}
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