@Generated(value="com.amazonaws:aws-java-sdk-code-generator") public class CreateAutoMLJobV2Request extends AmazonWebServiceRequest implements Serializable, Cloneable
NOOP| Constructor and Description |
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CreateAutoMLJobV2Request() |
| Modifier and Type | Method and Description |
|---|---|
CreateAutoMLJobV2Request |
clone()
Creates a shallow clone of this object for all fields except the handler context.
|
boolean |
equals(Object obj) |
List<AutoMLJobChannel> |
getAutoMLJobInputDataConfig()
An array of channel objects describing the input data and their location.
|
String |
getAutoMLJobName()
Identifies an Autopilot job.
|
AutoMLJobObjective |
getAutoMLJobObjective()
Specifies a metric to minimize or maximize as the objective of a job.
|
AutoMLProblemTypeConfig |
getAutoMLProblemTypeConfig()
Defines the configuration settings of one of the supported problem types.
|
AutoMLDataSplitConfig |
getDataSplitConfig()
This structure specifies how to split the data into train and validation datasets.
|
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 |
getRoleArn()
The ARN of the role that is used to access the data.
|
AutoMLSecurityConfig |
getSecurityConfig()
The security configuration for traffic encryption or Amazon VPC settings.
|
List<Tag> |
getTags()
An array of key-value pairs.
|
int |
hashCode() |
void |
setAutoMLJobInputDataConfig(Collection<AutoMLJobChannel> autoMLJobInputDataConfig)
An array of channel objects describing the input data and their location.
|
void |
setAutoMLJobName(String autoMLJobName)
Identifies an Autopilot job.
|
void |
setAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective)
Specifies a metric to minimize or maximize as the objective of a job.
|
void |
setAutoMLProblemTypeConfig(AutoMLProblemTypeConfig autoMLProblemTypeConfig)
Defines the configuration settings of one of the supported problem types.
|
void |
setDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig)
This structure specifies how to split the data into train and validation datasets.
|
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 |
setRoleArn(String roleArn)
The ARN of the role that is used to access the data.
|
void |
setSecurityConfig(AutoMLSecurityConfig securityConfig)
The security configuration for traffic encryption or Amazon VPC settings.
|
void |
setTags(Collection<Tag> tags)
An array of key-value pairs.
|
String |
toString()
Returns a string representation of this object.
|
CreateAutoMLJobV2Request |
withAutoMLJobInputDataConfig(AutoMLJobChannel... autoMLJobInputDataConfig)
An array of channel objects describing the input data and their location.
|
CreateAutoMLJobV2Request |
withAutoMLJobInputDataConfig(Collection<AutoMLJobChannel> autoMLJobInputDataConfig)
An array of channel objects describing the input data and their location.
|
CreateAutoMLJobV2Request |
withAutoMLJobName(String autoMLJobName)
Identifies an Autopilot job.
|
CreateAutoMLJobV2Request |
withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective)
Specifies a metric to minimize or maximize as the objective of a job.
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CreateAutoMLJobV2Request |
withAutoMLProblemTypeConfig(AutoMLProblemTypeConfig autoMLProblemTypeConfig)
Defines the configuration settings of one of the supported problem types.
|
CreateAutoMLJobV2Request |
withDataSplitConfig(AutoMLDataSplitConfig dataSplitConfig)
This structure specifies how to split the data into train and validation datasets.
|
CreateAutoMLJobV2Request |
withModelDeployConfig(ModelDeployConfig modelDeployConfig)
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
|
CreateAutoMLJobV2Request |
withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
|
CreateAutoMLJobV2Request |
withRoleArn(String roleArn)
The ARN of the role that is used to access the data.
|
CreateAutoMLJobV2Request |
withSecurityConfig(AutoMLSecurityConfig securityConfig)
The security configuration for traffic encryption or Amazon VPC settings.
|
CreateAutoMLJobV2Request |
withTags(Collection<Tag> tags)
An array of key-value pairs.
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CreateAutoMLJobV2Request |
withTags(Tag... tags)
An array of key-value pairs.
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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, withSdkRequestTimeoutpublic void setAutoMLJobName(String autoMLJobName)
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case insensitive.public String getAutoMLJobName()
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
public CreateAutoMLJobV2Request withAutoMLJobName(String autoMLJobName)
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case insensitive.public List<AutoMLJobChannel> getAutoMLJobInputDataConfig()
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 CreateAutoMLJob 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.
CreateAutoMLJob 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.
public void setAutoMLJobInputDataConfig(Collection<AutoMLJobChannel> autoMLJobInputDataConfig)
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 CreateAutoMLJob 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.
autoMLJobInputDataConfig - 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 CreateAutoMLJob 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.
public CreateAutoMLJobV2Request withAutoMLJobInputDataConfig(AutoMLJobChannel... autoMLJobInputDataConfig)
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 CreateAutoMLJob 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.
NOTE: This method appends the values to the existing list (if any). Use
setAutoMLJobInputDataConfig(java.util.Collection) or
withAutoMLJobInputDataConfig(java.util.Collection) if you want to override the existing values.
autoMLJobInputDataConfig - 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 CreateAutoMLJob 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.
public CreateAutoMLJobV2Request withAutoMLJobInputDataConfig(Collection<AutoMLJobChannel> autoMLJobInputDataConfig)
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 CreateAutoMLJob 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.
autoMLJobInputDataConfig - 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 CreateAutoMLJob 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.
public void setOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an
AutoML job.public AutoMLOutputDataConfig getOutputDataConfig()
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
public CreateAutoMLJobV2Request withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an
AutoML job.public void setAutoMLProblemTypeConfig(AutoMLProblemTypeConfig autoMLProblemTypeConfig)
Defines the configuration settings of one of the supported problem types.
autoMLProblemTypeConfig - Defines the configuration settings of one of the supported problem types.public AutoMLProblemTypeConfig getAutoMLProblemTypeConfig()
Defines the configuration settings of one of the supported problem types.
public CreateAutoMLJobV2Request withAutoMLProblemTypeConfig(AutoMLProblemTypeConfig autoMLProblemTypeConfig)
Defines the configuration settings of one of the supported problem types.
autoMLProblemTypeConfig - Defines the configuration settings of one of the supported problem types.public void setRoleArn(String roleArn)
The ARN of the role that is used to access the data.
roleArn - The ARN of the role that is used to access the data.public String getRoleArn()
The ARN of the role that is used to access the data.
public CreateAutoMLJobV2Request withRoleArn(String roleArn)
The ARN of the role that is used to access the data.
roleArn - The ARN of the role that is used to access the data.public List<Tag> getTags()
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.
public void setTags(Collection<Tag> 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.
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.public CreateAutoMLJobV2Request withTags(Tag... 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.
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.
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.public CreateAutoMLJobV2Request withTags(Collection<Tag> 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.
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.public void setSecurityConfig(AutoMLSecurityConfig securityConfig)
The security configuration for traffic encryption or Amazon VPC settings.
securityConfig - The security configuration for traffic encryption or Amazon VPC settings.public AutoMLSecurityConfig getSecurityConfig()
The security configuration for traffic encryption or Amazon VPC settings.
public CreateAutoMLJobV2Request withSecurityConfig(AutoMLSecurityConfig securityConfig)
The security configuration for traffic encryption or Amazon VPC settings.
securityConfig - The security configuration for traffic encryption or Amazon VPC settings.public void setAutoMLJobObjective(AutoMLJobObjective 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.
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.
public AutoMLJobObjective getAutoMLJobObjective()
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.
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.
public CreateAutoMLJobV2Request withAutoMLJobObjective(AutoMLJobObjective 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.
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.
public void setModelDeployConfig(ModelDeployConfig modelDeployConfig)
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
modelDeployConfig - Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.public ModelDeployConfig getModelDeployConfig()
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
public CreateAutoMLJobV2Request withModelDeployConfig(ModelDeployConfig modelDeployConfig)
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
modelDeployConfig - Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.public void setDataSplitConfig(AutoMLDataSplitConfig 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
CreateAutoMLJob, 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.
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
CreateAutoMLJob, 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.
public AutoMLDataSplitConfig getDataSplitConfig()
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
CreateAutoMLJob, 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.
The validation and training datasets must contain the same headers. For jobs created by calling
CreateAutoMLJob, 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.
public CreateAutoMLJobV2Request withDataSplitConfig(AutoMLDataSplitConfig 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
CreateAutoMLJob, 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.
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
CreateAutoMLJob, 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.
public String toString()
toString in class ObjectObject.toString()public CreateAutoMLJobV2Request clone()
AmazonWebServiceRequestclone in class AmazonWebServiceRequestObject.clone()