StartMLDataProcessingJobRequest (AWS SDK for Java

java.lang.Object
- com.amazonaws.AmazonWebServiceRequest
- - com.amazonaws.services.neptunedata.model.StartMLDataProcessingJobRequest

All Implemented Interfaces:: HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class StartMLDataProcessingJobRequest
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

StartMLDataProcessingJobRequest()

Constructors
Constructor and Description
`StartMLDataProcessingJobRequest()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`StartMLDataProcessingJobRequest`	`clone()` Creates a shallow clone of this object for all fields except the handler context.
`boolean`	`equals(Object obj)`
`String`	`getConfigFileName()` A data specification file that describes how to load the exported graph data for training.
`String`	`getId()` A unique identifier for the new job.
`String`	`getInputDataS3Location()` The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
`String`	`getModelType()` One of the two model types that Neptune ML currently supports: heterogeneous graph models ( `heterogeneous`), and knowledge graph (`kge`).
`String`	`getNeptuneIamRoleArn()` The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
`String`	`getPreviousDataProcessingJobId()` The job ID of a completed data processing job run on an earlier version of the data.
`String`	`getProcessedDataS3Location()` The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
`String`	`getProcessingInstanceType()` The type of ML instance used during data processing.
`Integer`	`getProcessingInstanceVolumeSizeInGB()` The disk volume size of the processing instance.
`Integer`	`getProcessingTimeOutInSeconds()` Timeout in seconds for the data processing job.
`String`	`getS3OutputEncryptionKMSKey()` The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job.
`String`	`getSagemakerIamRoleArn()` The ARN of an IAM role for SageMaker execution.
`List<String>`	`getSecurityGroupIds()` The VPC security group IDs.
`List<String>`	`getSubnets()` The IDs of the subnets in the Neptune VPC.
`String`	`getVolumeEncryptionKMSKey()` The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job.
`int`	`hashCode()`
`void`	`setConfigFileName(String configFileName)` A data specification file that describes how to load the exported graph data for training.
`void`	`setId(String id)` A unique identifier for the new job.
`void`	`setInputDataS3Location(String inputDataS3Location)` The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
`void`	`setModelType(String modelType)` One of the two model types that Neptune ML currently supports: heterogeneous graph models ( `heterogeneous`), and knowledge graph (`kge`).
`void`	`setNeptuneIamRoleArn(String neptuneIamRoleArn)` The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
`void`	`setPreviousDataProcessingJobId(String previousDataProcessingJobId)` The job ID of a completed data processing job run on an earlier version of the data.
`void`	`setProcessedDataS3Location(String processedDataS3Location)` The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
`void`	`setProcessingInstanceType(String processingInstanceType)` The type of ML instance used during data processing.
`void`	`setProcessingInstanceVolumeSizeInGB(Integer processingInstanceVolumeSizeInGB)` The disk volume size of the processing instance.
`void`	`setProcessingTimeOutInSeconds(Integer processingTimeOutInSeconds)` Timeout in seconds for the data processing job.
`void`	`setS3OutputEncryptionKMSKey(String s3OutputEncryptionKMSKey)` The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job.
`void`	`setSagemakerIamRoleArn(String sagemakerIamRoleArn)` The ARN of an IAM role for SageMaker execution.
`void`	`setSecurityGroupIds(Collection<String> securityGroupIds)` The VPC security group IDs.
`void`	`setSubnets(Collection<String> subnets)` The IDs of the subnets in the Neptune VPC.
`void`	`setVolumeEncryptionKMSKey(String volumeEncryptionKMSKey)` The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job.
`String`	`toString()` Returns a string representation of this object.
`StartMLDataProcessingJobRequest`	`withConfigFileName(String configFileName)` A data specification file that describes how to load the exported graph data for training.
`StartMLDataProcessingJobRequest`	`withId(String id)` A unique identifier for the new job.
`StartMLDataProcessingJobRequest`	`withInputDataS3Location(String inputDataS3Location)` The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
`StartMLDataProcessingJobRequest`	`withModelType(String modelType)` One of the two model types that Neptune ML currently supports: heterogeneous graph models ( `heterogeneous`), and knowledge graph (`kge`).
`StartMLDataProcessingJobRequest`	`withNeptuneIamRoleArn(String neptuneIamRoleArn)` The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.
`StartMLDataProcessingJobRequest`	`withPreviousDataProcessingJobId(String previousDataProcessingJobId)` The job ID of a completed data processing job run on an earlier version of the data.
`StartMLDataProcessingJobRequest`	`withProcessedDataS3Location(String processedDataS3Location)` The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
`StartMLDataProcessingJobRequest`	`withProcessingInstanceType(String processingInstanceType)` The type of ML instance used during data processing.
`StartMLDataProcessingJobRequest`	`withProcessingInstanceVolumeSizeInGB(Integer processingInstanceVolumeSizeInGB)` The disk volume size of the processing instance.
`StartMLDataProcessingJobRequest`	`withProcessingTimeOutInSeconds(Integer processingTimeOutInSeconds)` Timeout in seconds for the data processing job.
`StartMLDataProcessingJobRequest`	`withS3OutputEncryptionKMSKey(String s3OutputEncryptionKMSKey)` The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job.
`StartMLDataProcessingJobRequest`	`withSagemakerIamRoleArn(String sagemakerIamRoleArn)` The ARN of an IAM role for SageMaker execution.
`StartMLDataProcessingJobRequest`	`withSecurityGroupIds(Collection<String> securityGroupIds)` The VPC security group IDs.
`StartMLDataProcessingJobRequest`	`withSecurityGroupIds(String... securityGroupIds)` The VPC security group IDs.
`StartMLDataProcessingJobRequest`	`withSubnets(Collection<String> subnets)` The IDs of the subnets in the Neptune VPC.
`StartMLDataProcessingJobRequest`	`withSubnets(String... subnets)` The IDs of the subnets in the Neptune VPC.
`StartMLDataProcessingJobRequest`	`withVolumeEncryptionKMSKey(String volumeEncryptionKMSKey)` The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job.

Methods inherited from class java.lang.Object
getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - StartMLDataProcessingJobRequest
```
public StartMLDataProcessingJobRequest()
```
- Method Detail
  - setId
```
public void setId(String id)
```
    A unique identifier for the new job. The default is an autogenerated UUID.
    
    Parameters:
    
    id - A unique identifier for the new job. The default is an autogenerated UUID.
  - getId
```
public String getId()
```
    A unique identifier for the new job. The default is an autogenerated UUID.
    
    Returns:
    
    A unique identifier for the new job. The default is an autogenerated UUID.
  - withId
```
public StartMLDataProcessingJobRequest withId(String id)
```
    A unique identifier for the new job. The default is an autogenerated UUID.
    
    Parameters:
    
    id - A unique identifier for the new job. The default is an autogenerated UUID.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setPreviousDataProcessingJobId
```
public void setPreviousDataProcessingJobId(String previousDataProcessingJobId)
```
    The job ID of a completed data processing job run on an earlier version of the data.
    
    Parameters:
    
    previousDataProcessingJobId - The job ID of a completed data processing job run on an earlier version of the data.
  - getPreviousDataProcessingJobId
```
public String getPreviousDataProcessingJobId()
```
    The job ID of a completed data processing job run on an earlier version of the data.
    
    Returns:
    
    The job ID of a completed data processing job run on an earlier version of the data.
  - withPreviousDataProcessingJobId
```
public StartMLDataProcessingJobRequest withPreviousDataProcessingJobId(String previousDataProcessingJobId)
```
    The job ID of a completed data processing job run on an earlier version of the data.
    
    Parameters:
    
    previousDataProcessingJobId - The job ID of a completed data processing job run on an earlier version of the data.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setInputDataS3Location
```
public void setInputDataS3Location(String inputDataS3Location)
```
    The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
    
    Parameters:
    
    inputDataS3Location - The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
  - getInputDataS3Location
```
public String getInputDataS3Location()
```
    The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
    
    Returns:
    
    The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
  - withInputDataS3Location
```
public StartMLDataProcessingJobRequest withInputDataS3Location(String inputDataS3Location)
```
    The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
    
    Parameters:
    
    inputDataS3Location - The URI of the Amazon S3 location where you want SageMaker to download the data needed to run the data processing job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setProcessedDataS3Location
```
public void setProcessedDataS3Location(String processedDataS3Location)
```
    The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
    
    Parameters:
    
    processedDataS3Location - The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
  - getProcessedDataS3Location
```
public String getProcessedDataS3Location()
```
    The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
    
    Returns:
    
    The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
  - withProcessedDataS3Location
```
public StartMLDataProcessingJobRequest withProcessedDataS3Location(String processedDataS3Location)
```
    The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
    
    Parameters:
    
    processedDataS3Location - The URI of the Amazon S3 location where you want SageMaker to save the results of a data processing job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setSagemakerIamRoleArn
```
public void setSagemakerIamRoleArn(String sagemakerIamRoleArn)
```
    The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.
    
    Parameters:
    
    sagemakerIamRoleArn - The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.
  - getSagemakerIamRoleArn
```
public String getSagemakerIamRoleArn()
```
    The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.
    
    Returns:
    
    The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.
  - withSagemakerIamRoleArn
```
public StartMLDataProcessingJobRequest withSagemakerIamRoleArn(String sagemakerIamRoleArn)
```
    The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.
    
    Parameters:
    
    sagemakerIamRoleArn - The ARN of an IAM role for SageMaker execution. This must be listed in your DB cluster parameter group or an error will occur.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setNeptuneIamRoleArn
```
public void setNeptuneIamRoleArn(String neptuneIamRoleArn)
```
    The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.
    
    Parameters:
    
    neptuneIamRoleArn - The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.
  - getNeptuneIamRoleArn
```
public String getNeptuneIamRoleArn()
```
    The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.
    
    Returns:
    
    The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.
  - withNeptuneIamRoleArn
```
public StartMLDataProcessingJobRequest withNeptuneIamRoleArn(String neptuneIamRoleArn)
```
    The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.
    
    Parameters:
    
    neptuneIamRoleArn - The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf. This must be listed in your DB cluster parameter group or an error will occur.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setProcessingInstanceType
```
public void setProcessingInstanceType(String processingInstanceType)
```
    The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.
    
    Parameters:
    
    processingInstanceType - The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.
  - getProcessingInstanceType
```
public String getProcessingInstanceType()
```
    The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.
    
    Returns:
    
    The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.
  - withProcessingInstanceType
```
public StartMLDataProcessingJobRequest withProcessingInstanceType(String processingInstanceType)
```
    The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.
    
    Parameters:
    
    processingInstanceType - The type of ML instance used during data processing. Its memory should be large enough to hold the processed dataset. The default is the smallest ml.r5 type whose memory is ten times larger than the size of the exported graph data on disk.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setProcessingInstanceVolumeSizeInGB
```
public void setProcessingInstanceVolumeSizeInGB(Integer processingInstanceVolumeSizeInGB)
```
    The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML chooses the volume size automatically based on the data size.
    
    Parameters:
    
    processingInstanceVolumeSizeInGB - The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML chooses the volume size automatically based on the data size.
  - getProcessingInstanceVolumeSizeInGB
```
public Integer getProcessingInstanceVolumeSizeInGB()
```
    The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML chooses the volume size automatically based on the data size.
    
    Returns:
    
    The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML chooses the volume size automatically based on the data size.
  - withProcessingInstanceVolumeSizeInGB
```
public StartMLDataProcessingJobRequest withProcessingInstanceVolumeSizeInGB(Integer processingInstanceVolumeSizeInGB)
```
    The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML chooses the volume size automatically based on the data size.
    
    Parameters:
    
    processingInstanceVolumeSizeInGB - The disk volume size of the processing instance. Both input data and processed data are stored on disk, so the volume size must be large enough to hold both data sets. The default is 0. If not specified or 0, Neptune ML chooses the volume size automatically based on the data size.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setProcessingTimeOutInSeconds
```
public void setProcessingTimeOutInSeconds(Integer processingTimeOutInSeconds)
```
    Timeout in seconds for the data processing job. The default is 86,400 (1 day).
    
    Parameters:
    
    processingTimeOutInSeconds - Timeout in seconds for the data processing job. The default is 86,400 (1 day).
  - getProcessingTimeOutInSeconds
```
public Integer getProcessingTimeOutInSeconds()
```
    Timeout in seconds for the data processing job. The default is 86,400 (1 day).
    
    Returns:
    
    Timeout in seconds for the data processing job. The default is 86,400 (1 day).
  - withProcessingTimeOutInSeconds
```
public StartMLDataProcessingJobRequest withProcessingTimeOutInSeconds(Integer processingTimeOutInSeconds)
```
    Timeout in seconds for the data processing job. The default is 86,400 (1 day).
    
    Parameters:
    
    processingTimeOutInSeconds - Timeout in seconds for the data processing job. The default is 86,400 (1 day).
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setModelType
```
public void setModelType(String modelType)
```
    One of the two model types that Neptune ML currently supports: heterogeneous graph models ( heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.
    
    Parameters:
    
    modelType - One of the two model types that Neptune ML currently supports: heterogeneous graph models ( heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.
  - getModelType
```
public String getModelType()
```
    One of the two model types that Neptune ML currently supports: heterogeneous graph models ( heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.
    
    Returns:
    
    One of the two model types that Neptune ML currently supports: heterogeneous graph models ( heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.
  - withModelType
```
public StartMLDataProcessingJobRequest withModelType(String modelType)
```
    One of the two model types that Neptune ML currently supports: heterogeneous graph models ( heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.
    
    Parameters:
    
    modelType - One of the two model types that Neptune ML currently supports: heterogeneous graph models ( heterogeneous), and knowledge graph (kge). The default is none. If not specified, Neptune ML chooses the model type automatically based on the data.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setConfigFileName
```
public void setConfigFileName(String configFileName)
```
    A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.
    
    Parameters:
    
    configFileName - A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.
  - getConfigFileName
```
public String getConfigFileName()
```
    A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.
    
    Returns:
    
    A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.
  - withConfigFileName
```
public StartMLDataProcessingJobRequest withConfigFileName(String configFileName)
```
    A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.
    
    Parameters:
    
    configFileName - A data specification file that describes how to load the exported graph data for training. The file is automatically generated by the Neptune export toolkit. The default is training-data-configuration.json.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getSubnets
```
public List<String> getSubnets()
```
    The IDs of the subnets in the Neptune VPC. The default is None.
    
    Returns:
    
    The IDs of the subnets in the Neptune VPC. The default is None.
  - setSubnets
```
public void setSubnets(Collection<String> subnets)
```
    The IDs of the subnets in the Neptune VPC. The default is None.
    
    Parameters:
    
    subnets - The IDs of the subnets in the Neptune VPC. The default is None.
  - withSubnets
```
public StartMLDataProcessingJobRequest withSubnets(String... subnets)
```
    The IDs of the subnets in the Neptune VPC. The default is None.
    
    NOTE: This method appends the values to the existing list (if any). Use setSubnets(java.util.Collection) or withSubnets(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    subnets - The IDs of the subnets in the Neptune VPC. The default is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withSubnets
```
public StartMLDataProcessingJobRequest withSubnets(Collection<String> subnets)
```
    The IDs of the subnets in the Neptune VPC. The default is None.
    
    Parameters:
    
    subnets - The IDs of the subnets in the Neptune VPC. The default is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - getSecurityGroupIds
```
public List<String> getSecurityGroupIds()
```
    The VPC security group IDs. The default is None.
    
    Returns:
    
    The VPC security group IDs. The default is None.
  - setSecurityGroupIds
```
public void setSecurityGroupIds(Collection<String> securityGroupIds)
```
    The VPC security group IDs. The default is None.
    
    Parameters:
    
    securityGroupIds - The VPC security group IDs. The default is None.
  - withSecurityGroupIds
```
public StartMLDataProcessingJobRequest withSecurityGroupIds(String... securityGroupIds)
```
    The VPC security group IDs. The default is None.
    
    NOTE: This method appends the values to the existing list (if any). Use setSecurityGroupIds(java.util.Collection) or withSecurityGroupIds(java.util.Collection) if you want to override the existing values.
    
    Parameters:
    
    securityGroupIds - The VPC security group IDs. The default is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - withSecurityGroupIds
```
public StartMLDataProcessingJobRequest withSecurityGroupIds(Collection<String> securityGroupIds)
```
    The VPC security group IDs. The default is None.
    
    Parameters:
    
    securityGroupIds - The VPC security group IDs. The default is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setVolumeEncryptionKMSKey
```
public void setVolumeEncryptionKMSKey(String volumeEncryptionKMSKey)
```
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.
    
    Parameters:
    
    volumeEncryptionKMSKey - The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.
  - getVolumeEncryptionKMSKey
```
public String getVolumeEncryptionKMSKey()
```
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.
    
    Returns:
    
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.
  - withVolumeEncryptionKMSKey
```
public StartMLDataProcessingJobRequest withVolumeEncryptionKMSKey(String volumeEncryptionKMSKey)
```
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.
    
    Parameters:
    
    volumeEncryptionKMSKey - The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setS3OutputEncryptionKMSKey
```
public void setS3OutputEncryptionKMSKey(String s3OutputEncryptionKMSKey)
```
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
    
    Parameters:
    
    s3OutputEncryptionKMSKey - The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
  - getS3OutputEncryptionKMSKey
```
public String getS3OutputEncryptionKMSKey()
```
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
    
    Returns:
    
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
  - withS3OutputEncryptionKMSKey
```
public StartMLDataProcessingJobRequest withS3OutputEncryptionKMSKey(String s3OutputEncryptionKMSKey)
```
    The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
    
    Parameters:
    
    s3OutputEncryptionKMSKey - The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt the output of the processing job. The default is none.
    
    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 StartMLDataProcessingJobRequest 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()

AWS SDK for Java 1.x API Reference - 1.12.629

Class StartMLDataProcessingJobRequest

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

Constructor Summary

Method Summary

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

Methods inherited from class java.lang.Object

Constructor Detail

StartMLDataProcessingJobRequest

Method Detail

setId

getId

withId

setPreviousDataProcessingJobId

getPreviousDataProcessingJobId

withPreviousDataProcessingJobId

setInputDataS3Location

getInputDataS3Location

withInputDataS3Location

setProcessedDataS3Location

getProcessedDataS3Location

withProcessedDataS3Location

setSagemakerIamRoleArn

getSagemakerIamRoleArn

withSagemakerIamRoleArn

setNeptuneIamRoleArn

getNeptuneIamRoleArn

withNeptuneIamRoleArn

setProcessingInstanceType

getProcessingInstanceType

withProcessingInstanceType

setProcessingInstanceVolumeSizeInGB

getProcessingInstanceVolumeSizeInGB

withProcessingInstanceVolumeSizeInGB

setProcessingTimeOutInSeconds

getProcessingTimeOutInSeconds

withProcessingTimeOutInSeconds

setModelType

getModelType

withModelType

setConfigFileName

getConfigFileName

withConfigFileName

getSubnets

setSubnets

withSubnets

withSubnets

getSecurityGroupIds

setSecurityGroupIds

withSecurityGroupIds

withSecurityGroupIds

setVolumeEncryptionKMSKey

getVolumeEncryptionKMSKey

withVolumeEncryptionKMSKey

setS3OutputEncryptionKMSKey

getS3OutputEncryptionKMSKey

withS3OutputEncryptionKMSKey

toString

equals

hashCode

clone