TransformInput (AWS SDK for Java

java.lang.Object
- com.amazonaws.services.sagemaker.model.TransformInput

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class TransformInput
extends Object
implements Serializable, Cloneable, StructuredPojo
```
Describes the input source of a transform job and the way the transform job consumes it.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

TransformInput()

Constructors
Constructor and Description
`TransformInput()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`TransformInput`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getCompressionType()` If your transform data is compressed, specify the compression type.
`String`	`getContentType()` The multipurpose internet mail extension (MIME) type of the data.
`TransformDataSource`	`getDataSource()` Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
`String`	`getSplitType()` The method to use to split the transform job's data files into smaller batches.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setCompressionType(String compressionType)` If your transform data is compressed, specify the compression type.
`void`	`setContentType(String contentType)` The multipurpose internet mail extension (MIME) type of the data.
`void`	`setDataSource(TransformDataSource dataSource)` Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
`void`	`setSplitType(String splitType)` The method to use to split the transform job's data files into smaller batches.
`String`	`toString()` Returns a string representation of this object.
`TransformInput`	`withCompressionType(CompressionType compressionType)` If your transform data is compressed, specify the compression type.
`TransformInput`	`withCompressionType(String compressionType)` If your transform data is compressed, specify the compression type.
`TransformInput`	`withContentType(String contentType)` The multipurpose internet mail extension (MIME) type of the data.
`TransformInput`	`withDataSource(TransformDataSource dataSource)` Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
`TransformInput`	`withSplitType(SplitType splitType)` The method to use to split the transform job's data files into smaller batches.
`TransformInput`	`withSplitType(String splitType)` The method to use to split the transform job's data files into smaller batches.

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

- Constructor Detail
  - TransformInput
```
public TransformInput()
```
- Method Detail
  - setDataSource
```
public void setDataSource(TransformDataSource dataSource)
```
    Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
    
    Parameters:
    
    dataSource - Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
  - getDataSource
```
public TransformDataSource getDataSource()
```
    Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
    
    Returns:
    
    Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
  - withDataSource
```
public TransformInput withDataSource(TransformDataSource dataSource)
```
    Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
    
    Parameters:
    
    dataSource - Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setContentType
```
public void setContentType(String contentType)
```
    The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
    
    Parameters:
    
    contentType - The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
  - getContentType
```
public String getContentType()
```
    The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
    
    Returns:
    
    The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
  - withContentType
```
public TransformInput withContentType(String contentType)
```
    The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
    
    Parameters:
    
    contentType - The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCompressionType
```
public void setCompressionType(String compressionType)
```
    If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    Parameters:
    
    compressionType - If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    See Also:
    
    CompressionType
  - getCompressionType
```
public String getCompressionType()
```
    If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    Returns:
    
    If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    See Also:
    
    CompressionType
  - withCompressionType
```
public TransformInput withCompressionType(String compressionType)
```
    If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    Parameters:
    
    compressionType - If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CompressionType
  - withCompressionType
```
public TransformInput withCompressionType(CompressionType compressionType)
```
    If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    Parameters:
    
    compressionType - If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CompressionType
  - setSplitType
```
public void setSplitType(String splitType)
```
    The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    - RecordIO
    - TFRecord
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    Parameters:
    splitType - The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    
    RecordIO
    
    TFRecord
    
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    See Also:
    
    SplitType
  - getSplitType
```
public String getSplitType()
```
    The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    - RecordIO
    - TFRecord
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    Returns:
    The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    
    RecordIO
    
    TFRecord
    
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    See Also:
    
    SplitType
  - withSplitType
```
public TransformInput withSplitType(String splitType)
```
    The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    - RecordIO
    - TFRecord
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    Parameters:
    splitType - The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    
    RecordIO
    
    TFRecord
    
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    SplitType
  - withSplitType
```
public TransformInput withSplitType(SplitType splitType)
```
    The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    - RecordIO
    - TFRecord
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    Parameters:
    splitType - The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:
    
    RecordIO
    
    TFRecord
    
    When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.
    
    Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord.
    
    For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    SplitType
  - 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 TransformInput clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

AWS SDK for Java 1.x API Reference - 1.12.772

Class TransformInput

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

TransformInput

Method Detail

setDataSource

getDataSource

withDataSource

setContentType

getContentType

withContentType

setCompressionType

getCompressionType

withCompressionType

withCompressionType

setSplitType

getSplitType

withSplitType

withSplitType

toString

equals

hashCode

clone

marshall