com.amazonaws.services.sagemaker.model

Class CreateAutoMLJobRequest

java.lang.Object

com.amazonaws.AmazonWebServiceRequest

com.amazonaws.services.sagemaker.model.CreateAutoMLJobRequest

All Implemented Interfaces:

HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

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

See Also:

AWS API Documentation, Serialized Form

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

NOOP

Constructor Summary

Constructors

Constructor and Description
CreateAutoMLJobRequest()

Method Summary

Modifier and Type	Method and Description
CreateAutoMLJobRequest	clone() Creates a shallow clone of this object for all fields except the handler context.
boolean	equals(Object obj)
AutoMLJobConfig	getAutoMLJobConfig() A collection of settings used to configure an AutoML job.
String	getAutoMLJobName() Identifies an Autopilot job.
AutoMLJobObjective	getAutoMLJobObjective() Defines the objective metric used to measure the predictive quality of an AutoML job.
Boolean	getGenerateCandidateDefinitionsOnly() Generates possible candidates without training the models.
List<AutoMLChannel>	getInputDataConfig() An array of channel objects that describes the input data and its location.
ModelDeployConfig	getModelDeployConfig() Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
AutoMLOutputDataConfig	getOutputDataConfig() Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
String	getProblemType() Defines the type of supervised learning available for the candidates.
String	getRoleArn() The ARN of the role that is used to access the data.
List<Tag>	getTags() Each tag consists of a key and an optional value.
int	hashCode()
Boolean	isGenerateCandidateDefinitionsOnly() Generates possible candidates without training the models.
void	setAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig) A collection of settings used to configure an AutoML job.
void	setAutoMLJobName(String autoMLJobName) Identifies an Autopilot job.
void	setAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective) Defines the objective metric used to measure the predictive quality of an AutoML job.
void	setGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.
void	setInputDataConfig(Collection<AutoMLChannel> inputDataConfig) An array of channel objects that describes the input data and its location.
void	setModelDeployConfig(ModelDeployConfig modelDeployConfig) Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
void	setOutputDataConfig(AutoMLOutputDataConfig outputDataConfig) Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
void	setProblemType(String problemType) Defines the type of supervised learning available for the candidates.
void	setRoleArn(String roleArn) The ARN of the role that is used to access the data.
void	setTags(Collection<Tag> tags) Each tag consists of a key and an optional value.
String	toString() Returns a string representation of this object.
CreateAutoMLJobRequest	withAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig) A collection of settings used to configure an AutoML job.
CreateAutoMLJobRequest	withAutoMLJobName(String autoMLJobName) Identifies an Autopilot job.
CreateAutoMLJobRequest	withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective) Defines the objective metric used to measure the predictive quality of an AutoML job.
CreateAutoMLJobRequest	withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.
CreateAutoMLJobRequest	withInputDataConfig(AutoMLChannel... inputDataConfig) An array of channel objects that describes the input data and its location.
CreateAutoMLJobRequest	withInputDataConfig(Collection<AutoMLChannel> inputDataConfig) An array of channel objects that describes the input data and its location.
CreateAutoMLJobRequest	withModelDeployConfig(ModelDeployConfig modelDeployConfig) Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
CreateAutoMLJobRequest	withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig) Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
CreateAutoMLJobRequest	withProblemType(ProblemType problemType) Defines the type of supervised learning available for the candidates.
CreateAutoMLJobRequest	withProblemType(String problemType) Defines the type of supervised learning available for the candidates.
CreateAutoMLJobRequest	withRoleArn(String roleArn) The ARN of the role that is used to access the data.
CreateAutoMLJobRequest	withTags(Collection<Tag> tags) Each tag consists of a key and an optional value.
CreateAutoMLJobRequest	withTags(Tag... tags) Each tag consists of a key and an optional value.

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, wait, wait, wait

Constructor Detail

CreateAutoMLJobRequest

public CreateAutoMLJobRequest()

Method Detail

setAutoMLJobName

public void setAutoMLJobName(String autoMLJobName)

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

Parameters:

autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

getAutoMLJobName

public String getAutoMLJobName()

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

Returns:

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

withAutoMLJobName

public CreateAutoMLJobRequest withAutoMLJobName(String autoMLJobName)

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

Parameters:

autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

Returns:

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

getInputDataConfig

public List<AutoMLChannel> getInputDataConfig()

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

Returns:

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

setInputDataConfig

public void setInputDataConfig(Collection<AutoMLChannel> inputDataConfig)

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

Parameters:

inputDataConfig - An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

withInputDataConfig

public CreateAutoMLJobRequest withInputDataConfig(AutoMLChannel... inputDataConfig)

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

NOTE: This method appends the values to the existing list (if any). Use setInputDataConfig(java.util.Collection) or withInputDataConfig(java.util.Collection) if you want to override the existing values.

Parameters:

inputDataConfig - An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

Returns:

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

withInputDataConfig

public CreateAutoMLJobRequest withInputDataConfig(Collection<AutoMLChannel> inputDataConfig)

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

Parameters:

inputDataConfig - An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

Returns:

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

setOutputDataConfig

public void setOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

Parameters:

outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

getOutputDataConfig

public AutoMLOutputDataConfig getOutputDataConfig()

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

Returns:

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

withOutputDataConfig

public CreateAutoMLJobRequest withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

Parameters:

outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

Returns:

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

setProblemType

public void setProblemType(String problemType)

Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

Parameters:

problemType - Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

See Also:

ProblemType

getProblemType

public String getProblemType()

Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

Returns:

Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

See Also:

ProblemType

withProblemType

public CreateAutoMLJobRequest withProblemType(String problemType)

Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

Parameters:

problemType - Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

Returns:

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

See Also:

ProblemType

withProblemType

public CreateAutoMLJobRequest withProblemType(ProblemType problemType)

Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

Parameters:

problemType - Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

Returns:

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

See Also:

ProblemType

setAutoMLJobObjective

public void setAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective)

Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

Parameters:

autoMLJobObjective - Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

getAutoMLJobObjective

public AutoMLJobObjective getAutoMLJobObjective()

Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

Returns:

Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

withAutoMLJobObjective

public CreateAutoMLJobRequest withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective)

Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

Parameters:

autoMLJobObjective - Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

Returns:

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

setAutoMLJobConfig

public void setAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig)

A collection of settings used to configure an AutoML job.

Parameters:

autoMLJobConfig - A collection of settings used to configure an AutoML job.

getAutoMLJobConfig

public AutoMLJobConfig getAutoMLJobConfig()

A collection of settings used to configure an AutoML job.

Returns:

A collection of settings used to configure an AutoML job.

withAutoMLJobConfig

public CreateAutoMLJobRequest withAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig)

A collection of settings used to configure an AutoML job.

Parameters:

autoMLJobConfig - A collection of settings used to configure an AutoML job.

Returns:

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

setRoleArn

public void setRoleArn(String roleArn)

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

Parameters:

roleArn - The ARN of the role that is used to access the data.

getRoleArn

public String getRoleArn()

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

Returns:

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

withRoleArn

public CreateAutoMLJobRequest withRoleArn(String roleArn)

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

Parameters:

roleArn - The ARN of the role that is used to access the data.

Returns:

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

setGenerateCandidateDefinitionsOnly

public void setGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Parameters:

generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

getGenerateCandidateDefinitionsOnly

public Boolean getGenerateCandidateDefinitionsOnly()

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

withGenerateCandidateDefinitionsOnly

public CreateAutoMLJobRequest withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Parameters:

generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

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

isGenerateCandidateDefinitionsOnly

public Boolean isGenerateCandidateDefinitionsOnly()

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Returns:

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

getTags

public List<Tag> getTags()

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Returns:

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

setTags

public void setTags(Collection<Tag> tags)

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Parameters:

tags - Each tag consists of a key and an optional value. Tag keys must be unique per resource.

withTags

public CreateAutoMLJobRequest withTags(Tag... tags)

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

NOTE: This method appends the values to the existing list (if any). Use setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the existing values.

Parameters:

tags - Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Returns:

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

withTags

public CreateAutoMLJobRequest withTags(Collection<Tag> tags)

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Parameters:

tags - Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Returns:

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

setModelDeployConfig

public void setModelDeployConfig(ModelDeployConfig modelDeployConfig)

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

Parameters:

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

getModelDeployConfig

public ModelDeployConfig getModelDeployConfig()

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

Returns:

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

withModelDeployConfig

public CreateAutoMLJobRequest withModelDeployConfig(ModelDeployConfig modelDeployConfig)

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

Parameters:

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

Returns:

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

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 CreateAutoMLJobRequest clone()

Description copied from class: AmazonWebServiceRequest

Creates a shallow clone of this object for all fields except the handler context. Explicitly does not clone the deep structure of the other fields in the message.

Overrides:

clone in class AmazonWebServiceRequest

See Also:

Object.clone()