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() Contains CompletionCriteria and SecurityConfig.
String	getAutoMLJobName() Identifies an Autopilot job.
AutoMLJobObjective	getAutoMLJobObjective() Defines the objective of a an AutoML job.
Boolean	getGenerateCandidateDefinitionsOnly() Generates possible candidates without training a model.
List<AutoMLChannel>	getInputDataConfig() Similar to InputDataConfig supported by Tuning.
AutoMLOutputDataConfig	getOutputDataConfig() Similar to OutputDataConfig supported by Tuning.
String	getProblemType() Defines the kind of preprocessing and algorithms intended 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 a model.
void	setAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig) Contains CompletionCriteria and SecurityConfig.
void	setAutoMLJobName(String autoMLJobName) Identifies an Autopilot job.
void	setAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective) Defines the objective of a an AutoML job.
void	setGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training a model.
void	setInputDataConfig(Collection<AutoMLChannel> inputDataConfig) Similar to InputDataConfig supported by Tuning.
void	setOutputDataConfig(AutoMLOutputDataConfig outputDataConfig) Similar to OutputDataConfig supported by Tuning.
void	setProblemType(String problemType) Defines the kind of preprocessing and algorithms intended 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) Contains CompletionCriteria and SecurityConfig.
CreateAutoMLJobRequest	withAutoMLJobName(String autoMLJobName) Identifies an Autopilot job.
CreateAutoMLJobRequest	withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective) Defines the objective of a an AutoML job.
CreateAutoMLJobRequest	withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training a model.
CreateAutoMLJobRequest	withInputDataConfig(AutoMLChannel... inputDataConfig) Similar to InputDataConfig supported by Tuning.
CreateAutoMLJobRequest	withInputDataConfig(Collection<AutoMLChannel> inputDataConfig) Similar to InputDataConfig supported by Tuning.
CreateAutoMLJobRequest	withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig) Similar to OutputDataConfig supported by Tuning.
CreateAutoMLJobRequest	withProblemType(ProblemType problemType) Defines the kind of preprocessing and algorithms intended for the candidates.
CreateAutoMLJobRequest	withProblemType(String problemType) Defines the kind of preprocessing and algorithms intended 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. Must be unique to your account and is case-insensitive.

Parameters:

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

getAutoMLJobName

public String getAutoMLJobName()

Identifies an Autopilot job. Must be unique to your account and is case-insensitive.

Returns:

Identifies an Autopilot job. Must be unique to your account and is case-insensitive.

withAutoMLJobName

public CreateAutoMLJobRequest withAutoMLJobName(String autoMLJobName)

Identifies an Autopilot job. Must be unique to your account and is case-insensitive.

Parameters:

autoMLJobName - Identifies an Autopilot job. 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()

Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

Returns:

Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

setInputDataConfig

public void setInputDataConfig(Collection<AutoMLChannel> inputDataConfig)

Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

Parameters:

inputDataConfig - Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

withInputDataConfig

public CreateAutoMLJobRequest withInputDataConfig(AutoMLChannel... inputDataConfig)

Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

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 - Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

Returns:

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

withInputDataConfig

public CreateAutoMLJobRequest withInputDataConfig(Collection<AutoMLChannel> inputDataConfig)

Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

Parameters:

inputDataConfig - Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 500 rows.

Returns:

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

setOutputDataConfig

public void setOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)

Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

Parameters:

outputDataConfig - Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

getOutputDataConfig

public AutoMLOutputDataConfig getOutputDataConfig()

Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

Returns:

Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

withOutputDataConfig

public CreateAutoMLJobRequest withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)

Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

Parameters:

outputDataConfig - Similar to OutputDataConfig supported by Tuning. 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 kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

Parameters:

problemType - Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

See Also:

ProblemType

getProblemType

public String getProblemType()

Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

Returns:

Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

See Also:

ProblemType

withProblemType

public CreateAutoMLJobRequest withProblemType(String problemType)

Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

Parameters:

problemType - Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

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 kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

Parameters:

problemType - Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

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 of a an AutoML job. You provide a AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. If a metric is not specified, the most commonly used ObjectiveMetric for problem type is automaically selected.

Parameters:

autoMLJobObjective - Defines the objective of a an AutoML job. You provide a AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. If a metric is not specified, the most commonly used ObjectiveMetric for problem type is automaically selected.

getAutoMLJobObjective

public AutoMLJobObjective getAutoMLJobObjective()

Defines the objective of a an AutoML job. You provide a AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. If a metric is not specified, the most commonly used ObjectiveMetric for problem type is automaically selected.

Returns:

Defines the objective of a an AutoML job. You provide a AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. If a metric is not specified, the most commonly used ObjectiveMetric for problem type is automaically selected.

withAutoMLJobObjective

public CreateAutoMLJobRequest withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective)

Defines the objective of a an AutoML job. You provide a AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. If a metric is not specified, the most commonly used ObjectiveMetric for problem type is automaically selected.

Parameters:

autoMLJobObjective - Defines the objective of a an AutoML job. You provide a AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it. If a metric is not specified, the most commonly used ObjectiveMetric for problem type is automaically selected.

Returns:

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

setAutoMLJobConfig

public void setAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig)

Contains CompletionCriteria and SecurityConfig.

Parameters:

autoMLJobConfig - Contains CompletionCriteria and SecurityConfig.

getAutoMLJobConfig

public AutoMLJobConfig getAutoMLJobConfig()

Contains CompletionCriteria and SecurityConfig.

Returns:

Contains CompletionCriteria and SecurityConfig.

withAutoMLJobConfig

public CreateAutoMLJobRequest withAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig)

Contains CompletionCriteria and SecurityConfig.

Parameters:

autoMLJobConfig - Contains CompletionCriteria and SecurityConfig.

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 a model. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

Parameters:

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

getGenerateCandidateDefinitionsOnly

public Boolean getGenerateCandidateDefinitionsOnly()

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

Returns:

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

withGenerateCandidateDefinitionsOnly

public CreateAutoMLJobRequest withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

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

Parameters:

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

Returns:

Generates possible candidates without training a model. 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.

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()