com.amazonaws.services.sagemaker.model

Class AutoMLJobConfig

java.lang.Object

com.amazonaws.services.sagemaker.model.AutoMLJobConfig

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class AutoMLJobConfig
extends Object
implements Serializable, Cloneable, StructuredPojo

A collection of settings used for an AutoML job.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors

Constructor and Description
AutoMLJobConfig()

Method Summary

Modifier and Type	Method and Description
AutoMLJobConfig	clone()
boolean	equals(Object obj)
AutoMLCandidateGenerationConfig	getCandidateGenerationConfig() The configuration for generating a candidate for an AutoML job (optional).
AutoMLJobCompletionCriteria	getCompletionCriteria() How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
AutoMLDataSplitConfig	getDataSplitConfig() The configuration for splitting the input training dataset.
String	getMode() The method that Autopilot uses to train the data.
AutoMLSecurityConfig	getSecurityConfig() The security configuration for traffic encryption or Amazon VPC settings.
int	hashCode()
void	marshall(ProtocolMarshaller protocolMarshaller) Marshalls this structured data using the given ProtocolMarshaller.
void	setCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig) The configuration for generating a candidate for an AutoML job (optional).
void	setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
void	setDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) The configuration for splitting the input training dataset.
void	setMode(String mode) The method that Autopilot uses to train the data.
void	setSecurityConfig(AutoMLSecurityConfig securityConfig) The security configuration for traffic encryption or Amazon VPC settings.
String	toString() Returns a string representation of this object.
AutoMLJobConfig	withCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig) The configuration for generating a candidate for an AutoML job (optional).
AutoMLJobConfig	withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria) How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
AutoMLJobConfig	withDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig) The configuration for splitting the input training dataset.
AutoMLJobConfig	withMode(AutoMLMode mode) The method that Autopilot uses to train the data.
AutoMLJobConfig	withMode(String mode) The method that Autopilot uses to train the data.
AutoMLJobConfig	withSecurityConfig(AutoMLSecurityConfig securityConfig) The security configuration for traffic encryption or Amazon VPC settings.

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

AutoMLJobConfig

public AutoMLJobConfig()

Method Detail

setCompletionCriteria

public void setCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

Parameters:

completionCriteria - How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

getCompletionCriteria

public AutoMLJobCompletionCriteria getCompletionCriteria()

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

Returns:

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

withCompletionCriteria

public AutoMLJobConfig withCompletionCriteria(AutoMLJobCompletionCriteria completionCriteria)

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

Parameters:

completionCriteria - How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

Returns:

Returns a reference to this object so that method calls can be chained together.

setSecurityConfig

public void setSecurityConfig(AutoMLSecurityConfig securityConfig)

The security configuration for traffic encryption or Amazon VPC settings.

Parameters:

securityConfig - The security configuration for traffic encryption or Amazon VPC settings.

getSecurityConfig

public AutoMLSecurityConfig getSecurityConfig()

The security configuration for traffic encryption or Amazon VPC settings.

Returns:

The security configuration for traffic encryption or Amazon VPC settings.

withSecurityConfig

public AutoMLJobConfig withSecurityConfig(AutoMLSecurityConfig securityConfig)

The security configuration for traffic encryption or Amazon VPC settings.

Parameters:

securityConfig - The security configuration for traffic encryption or Amazon VPC settings.

Returns:

Returns a reference to this object so that method calls can be chained together.

setCandidateGenerationConfig

public void setCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig)

The configuration for generating a candidate for an AutoML job (optional).

Parameters:

candidateGenerationConfig - The configuration for generating a candidate for an AutoML job (optional).

getCandidateGenerationConfig

public AutoMLCandidateGenerationConfig getCandidateGenerationConfig()

The configuration for generating a candidate for an AutoML job (optional).

Returns:

The configuration for generating a candidate for an AutoML job (optional).

withCandidateGenerationConfig

public AutoMLJobConfig withCandidateGenerationConfig(AutoMLCandidateGenerationConfig candidateGenerationConfig)

The configuration for generating a candidate for an AutoML job (optional).

Parameters:

candidateGenerationConfig - The configuration for generating a candidate for an AutoML job (optional).

Returns:

Returns a reference to this object so that method calls can be chained together.

setDataSplitConfig

public void setDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig)

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

Parameters:

dataSplitConfig - The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

getDataSplitConfig

public AutoMLDataSplitConfig getDataSplitConfig()

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

Returns:

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

withDataSplitConfig

public AutoMLJobConfig withDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig)

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

Parameters:

dataSplitConfig - The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

Returns:

Returns a reference to this object so that method calls can be chained together.

setMode

public void setMode(String mode)

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Parameters:

mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

See Also:

AutoMLMode

getMode

public String getMode()

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Returns:

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

See Also:

AutoMLMode

withMode

public AutoMLJobConfig withMode(String mode)

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Parameters:

mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

AutoMLMode

withMode

public AutoMLJobConfig withMode(AutoMLMode mode)

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Parameters:

mode - The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Returns:

Returns a reference to this object so that method calls can be chained together.

See Also:

AutoMLMode

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

Returns:

A string representation of this object.

See Also:

Object.toString()

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

hashCode

public int hashCode()

Overrides:

hashCode in class Object

clone

public AutoMLJobConfig clone()

Overrides:

clone in class Object

marshall

public void marshall(ProtocolMarshaller protocolMarshaller)

Description copied from interface: StructuredPojo

Marshalls this structured data using the given ProtocolMarshaller.

Specified by:

marshall in interface StructuredPojo

Parameters:

protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.