sagemaker_create_auto_ml_job_v2: Creates an Autopilot job also referred to as Autopilot...

In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

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.

See https://www.paws-r-sdk.com/docs/sagemaker_create_auto_ml_job_v2/ for full documentation.

Usage

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

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.

paws.machine.learning documentation built on Sept. 12, 2024, 6:23 a.m.