Create Auto Ml Job V2

sagemaker_create_auto_ml_job_v2

R Documentation

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2

Description

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.

For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.

AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.

create_auto_ml_job_v2 and describe_auto_ml_job_v2 are new versions of create_auto_ml_job and describe_auto_ml_job which offer backward compatibility.

create_auto_ml_job_v2 can manage tabular problem types identical to those of its previous version create_auto_ml_job, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a create_auto_ml_job to create_auto_ml_job_v2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.

For the list of available problem types supported by create_auto_ml_job_v2, see AutoMLProblemTypeConfig.

You can find the best-performing model after you run an AutoML job V2 by calling describe_auto_ml_job_v2.

Usage

sagemaker_create_auto_ml_job_v2(AutoMLJobName, AutoMLJobInputDataConfig,
  OutputDataConfig, AutoMLProblemTypeConfig, RoleArn, Tags,
  SecurityConfig, AutoMLJobObjective, ModelDeployConfig, DataSplitConfig,
  AutoMLComputeConfig)

Arguments

AutoMLJobName

[required] Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

AutoMLJobInputDataConfig

[required] An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the create_auto_ml_job input parameters. The supported formats depend on the problem type:

• For tabular problem types: S3Prefix, ManifestFile.

• For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

• For text classification: S3Prefix.

• For time-series forecasting: S3Prefix.

• For text generation (LLMs fine-tuning): S3Prefix.

OutputDataConfig

[required] Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

AutoMLProblemTypeConfig

[required] Defines the configuration settings of one of the supported problem types.

RoleArn

[required] The ARN of the role that is used to access the data.

Tags

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

SecurityConfig

The security configuration for traffic encryption or Amazon VPC settings.

AutoMLJobObjective

Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

• For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.

• For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

ModelDeployConfig

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

DataSplitConfig

This structure specifies how to split the data into train and validation datasets.

The validation and training datasets must contain the same headers. For jobs created by calling create_auto_ml_job, the validation dataset must be less than 2 GB in size.

This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

AutoMLComputeConfig

Specifies the compute configuration for the AutoML job V2.

Value

A list with the following syntax:

list(
  AutoMLJobArn = "string"
)

Request syntax

svc$create_auto_ml_job_v2(
  AutoMLJobName = "string",
  AutoMLJobInputDataConfig = list(
    list(
      ChannelType = "training"|"validation",
      ContentType = "string",
      CompressionType = "None"|"Gzip",
      DataSource = list(
        S3DataSource = list(
          S3DataType = "ManifestFile"|"S3Prefix"|"AugmentedManifestFile",
          S3Uri = "string"
        )
      )
    )
  ),
  OutputDataConfig = list(
    KmsKeyId = "string",
    S3OutputPath = "string"
  ),
  AutoMLProblemTypeConfig = list(
    ImageClassificationJobConfig = list(
      CompletionCriteria = list(
        MaxCandidates = 123,
        MaxRuntimePerTrainingJobInSeconds = 123,
        MaxAutoMLJobRuntimeInSeconds = 123
      )
    ),
    TextClassificationJobConfig = list(
      CompletionCriteria = list(
        MaxCandidates = 123,
        MaxRuntimePerTrainingJobInSeconds = 123,
        MaxAutoMLJobRuntimeInSeconds = 123
      ),
      ContentColumn = "string",
      TargetLabelColumn = "string"
    ),
    TimeSeriesForecastingJobConfig = list(
      FeatureSpecificationS3Uri = "string",
      CompletionCriteria = list(
        MaxCandidates = 123,
        MaxRuntimePerTrainingJobInSeconds = 123,
        MaxAutoMLJobRuntimeInSeconds = 123
      ),
      ForecastFrequency = "string",
      ForecastHorizon = 123,
      ForecastQuantiles = list(
        "string"
      ),
      Transformations = list(
        Filling = list(
          list(
            "string"
          )
        ),
        Aggregation = list(
          "sum"|"avg"|"first"|"min"|"max"
        )
      ),
      TimeSeriesConfig = list(
        TargetAttributeName = "string",
        TimestampAttributeName = "string",
        ItemIdentifierAttributeName = "string",
        GroupingAttributeNames = list(
          "string"
        )
      ),
      HolidayConfig = list(
        list(
          CountryCode = "string"
        )
      ),
      CandidateGenerationConfig = list(
        AlgorithmsConfig = list(
          list(
            AutoMLAlgorithms = list(
              "xgboost"|"linear-learner"|"mlp"|"lightgbm"|"catboost"|"randomforest"|"extra-trees"|"nn-torch"|"fastai"|"cnn-qr"|"deepar"|"prophet"|"npts"|"arima"|"ets"
            )
          )
        )
      )
    ),
    TabularJobConfig = list(
      CandidateGenerationConfig = list(
        AlgorithmsConfig = list(
          list(
            AutoMLAlgorithms = list(
              "xgboost"|"linear-learner"|"mlp"|"lightgbm"|"catboost"|"randomforest"|"extra-trees"|"nn-torch"|"fastai"|"cnn-qr"|"deepar"|"prophet"|"npts"|"arima"|"ets"
            )
          )
        )
      ),
      CompletionCriteria = list(
        MaxCandidates = 123,
        MaxRuntimePerTrainingJobInSeconds = 123,
        MaxAutoMLJobRuntimeInSeconds = 123
      ),
      FeatureSpecificationS3Uri = "string",
      Mode = "AUTO"|"ENSEMBLING"|"HYPERPARAMETER_TUNING",
      GenerateCandidateDefinitionsOnly = TRUE|FALSE,
      ProblemType = "BinaryClassification"|"MulticlassClassification"|"Regression",
      TargetAttributeName = "string",
      SampleWeightAttributeName = "string"
    ),
    TextGenerationJobConfig = list(
      CompletionCriteria = list(
        MaxCandidates = 123,
        MaxRuntimePerTrainingJobInSeconds = 123,
        MaxAutoMLJobRuntimeInSeconds = 123
      ),
      BaseModelName = "string",
      TextGenerationHyperParameters = list(
        "string"
      ),
      ModelAccessConfig = list(
        AcceptEula = TRUE|FALSE
      )
    )
  ),
  RoleArn = "string",
  Tags = list(
    list(
      Key = "string",
      Value = "string"
    )
  ),
  SecurityConfig = list(
    VolumeKmsKeyId = "string",
    EnableInterContainerTrafficEncryption = TRUE|FALSE,
    VpcConfig = list(
      SecurityGroupIds = list(
        "string"
      ),
      Subnets = list(
        "string"
      )
    )
  ),
  AutoMLJobObjective = list(
    MetricName = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"BalancedAccuracy"|"R2"|"Recall"|"RecallMacro"|"Precision"|"PrecisionMacro"|"MAE"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss"
  ),
  ModelDeployConfig = list(
    AutoGenerateEndpointName = TRUE|FALSE,
    EndpointName = "string"
  ),
  DataSplitConfig = list(
    ValidationFraction = 123.0
  ),
  AutoMLComputeConfig = list(
    EmrServerlessComputeConfig = list(
      ExecutionRoleARN = "string"
    )
  )
)