Train an AutoML Edge model using the Vertex AI API

importcom.google.cloud.aiplatform.v1.DeployedModelRef ;
importcom.google.cloud.aiplatform.v1.EnvVar ;
importcom.google.cloud.aiplatform.v1.FilterSplit ;
importcom.google.cloud.aiplatform.v1.FractionSplit ;
importcom.google.cloud.aiplatform.v1.InputDataConfig ;
importcom.google.cloud.aiplatform.v1.Model ;
importcom.google.cloud.aiplatform.v1.ModelContainerSpec ;
importcom.google.cloud.aiplatform.v1.PipelineServiceClient ;
importcom.google.cloud.aiplatform.v1.PipelineServiceSettings ;
importcom.google.cloud.aiplatform.v1.Port ;
importcom.google.cloud.aiplatform.v1.PredefinedSplit ;
importcom.google.cloud.aiplatform.v1.PredictSchemata ;
importcom.google.cloud.aiplatform.v1.TimestampSplit ;
importcom.google.cloud.aiplatform.v1.TrainingPipeline ;
importcom.google.cloud.aiplatform.v1.TrainingPipelineName ;
importcom.google.rpc.Status ;
importjava.io.IOException;
publicclass GetTrainingPipelineSample{
publicstaticvoidmain(String[]args)throwsIOException{
// TODO(developer): Replace these variables before running the sample.
Stringproject="YOUR_PROJECT_ID";
StringtrainingPipelineId="YOUR_TRAINING_PIPELINE_ID";
getTrainingPipeline(project,trainingPipelineId);
}
staticvoidgetTrainingPipeline(Stringproject,StringtrainingPipelineId)throwsIOException{
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)){
Stringlocation="us-central1";
TrainingPipelineName trainingPipelineName=
TrainingPipelineName .of(project,location,trainingPipelineId);
TrainingPipeline trainingPipelineResponse=
pipelineServiceClient.getTrainingPipeline(trainingPipelineName);
System.out.println("Get Training Pipeline 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 inputDataConfig=trainingPipelineResponse.getInputDataConfig ();
System.out.println("\tInput Data Config");
System.out.format("\t\tDataset Id: %s\n",inputDataConfig.getDatasetId ());
System.out.format("\t\tAnnotations Filter: %s\n",inputDataConfig.getAnnotationsFilter ());
FractionSplit fractionSplit=inputDataConfig.getFractionSplit ();
System.out.println("\t\tFraction Split");
System.out.format("\t\t\tTraining Fraction: %s\n",fractionSplit.getTrainingFraction ());
System.out.format("\t\t\tValidation Fraction: %s\n",fractionSplit.getValidationFraction ());
System.out.format("\t\t\tTest Fraction: %s\n",fractionSplit.getTestFraction ());
FilterSplit filterSplit=inputDataConfig.getFilterSplit ();
System.out.println("\t\tFilter Split");
System.out.format("\t\t\tTraining Filter: %s\n",filterSplit.getTrainingFilter ());
System.out.format("\t\t\tValidation Filter: %s\n",filterSplit.getValidationFilter ());
System.out.format("\t\t\tTest Filter: %s\n",filterSplit.getTestFilter ());
PredefinedSplit predefinedSplit=inputDataConfig.getPredefinedSplit ();
System.out.println("\t\tPredefined Split");
System.out.format("\t\t\tKey: %s\n",predefinedSplit.getKey ());
TimestampSplit timestampSplit=inputDataConfig.getTimestampSplit ();
System.out.println("\t\tTimestamp Split");
System.out.format("\t\t\tTraining Fraction: %s\n",timestampSplit.getTrainingFraction ());
System.out.format("\t\t\tTest Fraction: %s\n",timestampSplit.getTestFraction ());
System.out.format("\t\t\tValidation Fraction: %s\n",timestampSplit.getValidationFraction ());
System.out.format("\t\t\tKey: %s\n",timestampSplit.getKey ());
Model modelResponse=trainingPipelineResponse.getModelToUpload ();
System.out.println("\t\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\tLabels: %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 ());
}
}
}

fromgoogle.cloudimport aiplatform
defget_training_pipeline_sample(
 project: str,
 training_pipeline_id: str,
 location: str = "us-central1",
 api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
 # The AI Platform services require regional API endpoints.
 client_options = {"api_endpoint": api_endpoint}
 # Initialize client that will be used to create and send requests.
 # This client only needs to be created once, and can be reused for multiple requests.
 client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
 name = client.training_pipeline_path(
 project=project, location=location, training_pipeline=training_pipeline_id
 )
 response = client.get_training_pipeline(name=name)
 print("response:", response)

importcom.google.cloud.aiplatform.v1.DeployedModelRef ;
importcom.google.cloud.aiplatform.v1.EnvVar ;
importcom.google.cloud.aiplatform.v1.Model ;
importcom.google.cloud.aiplatform.v1.Model.ExportFormat ;
importcom.google.cloud.aiplatform.v1.ModelContainerSpec ;
importcom.google.cloud.aiplatform.v1.ModelName ;
importcom.google.cloud.aiplatform.v1.ModelServiceClient ;
importcom.google.cloud.aiplatform.v1.ModelServiceSettings ;
importcom.google.cloud.aiplatform.v1.Port ;
importcom.google.cloud.aiplatform.v1.PredictSchemata ;
importjava.io.IOException;
publicclass GetModelSample{
publicstaticvoidmain(String[]args)throwsIOException{
// TODO(developer): Replace these variables before running the sample.
Stringproject="YOUR_PROJECT_ID";
StringmodelId="YOUR_MODEL_ID";
getModelSample(project,modelId);
}
staticvoidgetModelSample(Stringproject,StringmodelId)throwsIOException{
ModelServiceSettings modelServiceSettings=
ModelServiceSettings .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(ModelServiceClient modelServiceClient=ModelServiceClient .create(modelServiceSettings)){
Stringlocation="us-central1";
ModelName modelName=ModelName .of(project,location,modelId);
Model modelResponse=modelServiceClient.getModel(modelName);
System.out.println("Get Model response");
System.out.format("\tName: %s\n",modelResponse.getName ());
System.out.format("\tDisplay Name: %s\n",modelResponse.getDisplayName ());
System.out.format("\tDescription: %s\n",modelResponse.getDescription ());
System.out.format("\tMetadata Schema Uri: %s\n",modelResponse.getMetadataSchemaUri ());
System.out.format("\tMetadata: %s\n",modelResponse.getMetadata ());
System.out.format("\tTraining Pipeline: %s\n",modelResponse.getTrainingPipeline ());
System.out.format("\tArtifact Uri: %s\n",modelResponse.getArtifactUri ());
System.out.format(
"\tSupported Deployment Resources Types: %s\n",
modelResponse.getSupportedDeploymentResourcesTypesList ());
System.out.format(
"\tSupported Input Storage Formats: %s\n",
modelResponse.getSupportedInputStorageFormatsList ());
System.out.format(
"\tSupported Output Storage Formats: %s\n",
modelResponse.getSupportedOutputStorageFormatsList ());
System.out.format("\tCreate Time: %s\n",modelResponse.getCreateTime ());
System.out.format("\tUpdate Time: %s\n",modelResponse.getUpdateTime ());
System.out.format("\tLabels: %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(ExportFormat exportFormat:modelResponse.getSupportedExportFormatsList ()){
System.out.println("\tSupported Export Format");
System.out.format("\t\tId: %s\n",exportFormat.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());
}
}
}
}

/**
 * TODO(developer): Uncomment these variables before running the sample.\
 * (Not necessary if passing values as arguments)
 */
// const modelId = 'YOUR_MODEL_ID';
// const project = 'YOUR_PROJECT_ID';
// const location = 'YOUR_PROJECT_LOCATION';
// Imports the Google Cloud Model Service Client library
const{ModelServiceClient}=require('@google-cloud/aiplatform');
// Specifies the location of the api endpoint
constclientOptions={
apiEndpoint:'us-central1-aiplatform.googleapis.com',
};
// Instantiates a client
constmodelServiceClient=newModelServiceClient (clientOptions);
asyncfunctiongetModel(){
// Configure the parent resource
constname=`projects/${project}/locations/${location}/models/${modelId}`;
constrequest={
name,
};
// Get and print out a list of all the endpoints for this resource
const[response]=awaitmodelServiceClient.getModel(request);
console.log('Get model response');
console.log(`\tName : ${response.name}`);
console.log(`\tDisplayName : ${response.displayName}`);
console.log(`\tDescription : ${response.description}`);
console.log(`\tMetadata schema uri : ${response.metadataSchemaUri}`);
console.log(`\tMetadata : ${JSON.stringify(response.metadata)}`);
console.log(`\tTraining pipeline : ${response.trainingPipeline}`);
console.log(`\tArtifact uri : ${response.artifactUri}`);
console.log(
`\tSupported deployment resource types : \
${response.supportedDeploymentResourceTypes}`
);
console.log(
`\tSupported input storage formats : \
${response.supportedInputStorageFormats}`
);
console.log(
`\tSupported output storage formats : \
${response.supportedOutputStoragFormats}`
);
console.log(`\tCreate time : ${JSON.stringify(response.createTime)}`);
console.log(`\tUpdate time : ${JSON.stringify(response.updateTime)}`);
console.log(`\tLabels : ${JSON.stringify(response.labels)}`);
constpredictSchemata=response.predictSchemata;
console.log('\tPredict schemata');
console.log(`\tInstance schema uri : ${predictSchemata.instanceSchemaUri}`);
console.log(
`\tParameters schema uri : ${predictSchemata.prametersSchemaUri}`
);
console.log(
`\tPrediction schema uri : ${predictSchemata.predictionSchemaUri}`
);
const[supportedExportFormats]=response.supportedExportFormats;
console.log('\tSupported export formats');
console.log(`\t${supportedExportFormats}`);
constcontainerSpec=response.containerSpec;
console.log('\tContainer Spec');
if(!containerSpec){
console.log(`\t\t${JSON.stringify(containerSpec)}`);
console.log('\t\tImage uri : {}');
console.log('\t\tCommand : {}');
console.log('\t\tArgs : {}');
console.log('\t\tPredict route : {}');
console.log('\t\tHealth route : {}');
console.log('\t\tEnv');
console.log('\t\t\t{}');
console.log('\t\tPort');
console.log('\t\t{}');
}else{
console.log(`\t\t${JSON.stringify(containerSpec)}`);
console.log(`\t\tImage uri : ${containerSpec.imageUri}`);
console.log(`\t\tCommand : ${containerSpec.command}`);
console.log(`\t\tArgs : ${containerSpec.args}`);
console.log(`\t\tPredict route : ${containerSpec.predictRoute}`);
console.log(`\t\tHealth route : ${containerSpec.healthRoute}`);
constenv=containerSpec.env;
console.log('\t\tEnv');
console.log(`\t\t\t${JSON.stringify(env)}`);
constports=containerSpec.ports;
console.log('\t\tPort');
console.log(`\t\t\t${JSON.stringify(ports)}`);
}
const[deployedModels]=response.deployedModels;
console.log('\tDeployed models');
console.log('\t\t',deployedModels);
}
getModel();

def get_model_sample(project: str, location: str, model_name: str):
 aiplatform.init(project=project, location=location)
 model = aiplatform.Model(model_name=model_name)
 print(model.display_name)
 print(model.resource_name)
 return model