Channel (AWS SDK for Java

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

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class Channel
extends Object
implements Serializable, Cloneable, StructuredPojo
```
A channel is a named input source that training algorithms can consume.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

Channel()

Constructors
Constructor and Description
`Channel()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Channel`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getChannelName()` The name of the channel.
`String`	`getCompressionType()` If training data is compressed, the compression type.
`String`	`getContentType()` The MIME type of the data.
`DataSource`	`getDataSource()` The location of the channel data.
`String`	`getInputMode()` (Optional) The input mode to use for the data channel in a training job.
`String`	`getRecordWrapperType()`
`ShuffleConfig`	`getShuffleConfig()` A configuration for a shuffle option for input data in a channel.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setChannelName(String channelName)` The name of the channel.
`void`	`setCompressionType(String compressionType)` If training data is compressed, the compression type.
`void`	`setContentType(String contentType)` The MIME type of the data.
`void`	`setDataSource(DataSource dataSource)` The location of the channel data.
`void`	`setInputMode(String inputMode)` (Optional) The input mode to use for the data channel in a training job.
`void`	`setRecordWrapperType(String recordWrapperType)`
`void`	`setShuffleConfig(ShuffleConfig shuffleConfig)` A configuration for a shuffle option for input data in a channel.
`String`	`toString()` Returns a string representation of this object.
`Channel`	`withChannelName(String channelName)` The name of the channel.
`Channel`	`withCompressionType(CompressionType compressionType)` If training data is compressed, the compression type.
`Channel`	`withCompressionType(String compressionType)` If training data is compressed, the compression type.
`Channel`	`withContentType(String contentType)` The MIME type of the data.
`Channel`	`withDataSource(DataSource dataSource)` The location of the channel data.
`Channel`	`withInputMode(String inputMode)` (Optional) The input mode to use for the data channel in a training job.
`Channel`	`withInputMode(TrainingInputMode inputMode)` (Optional) The input mode to use for the data channel in a training job.
`Channel`	`withRecordWrapperType(RecordWrapper recordWrapperType)`
`Channel`	`withRecordWrapperType(String recordWrapperType)`
`Channel`	`withShuffleConfig(ShuffleConfig shuffleConfig)` A configuration for a shuffle option for input data in a channel.

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

- Constructor Detail
  - Channel
```
public Channel()
```
- Method Detail
  - setChannelName
```
public void setChannelName(String channelName)
```
    The name of the channel.
    
    Parameters:
    
    channelName - The name of the channel.
  - getChannelName
```
public String getChannelName()
```
    The name of the channel.
    
    Returns:
    
    The name of the channel.
  - withChannelName
```
public Channel withChannelName(String channelName)
```
    The name of the channel.
    
    Parameters:
    
    channelName - The name of the channel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setDataSource
```
public void setDataSource(DataSource dataSource)
```
    The location of the channel data.
    
    Parameters:
    
    dataSource - The location of the channel data.
  - getDataSource
```
public DataSource getDataSource()
```
    The location of the channel data.
    
    Returns:
    
    The location of the channel data.
  - withDataSource
```
public Channel withDataSource(DataSource dataSource)
```
    The location of the channel data.
    
    Parameters:
    
    dataSource - The location of the channel data.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setContentType
```
public void setContentType(String contentType)
```
    The MIME type of the data.
    
    Parameters:
    
    contentType - The MIME type of the data.
  - getContentType
```
public String getContentType()
```
    The MIME type of the data.
    
    Returns:
    
    The MIME type of the data.
  - withContentType
```
public Channel withContentType(String contentType)
```
    The MIME type of the data.
    
    Parameters:
    
    contentType - The MIME type of the data.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCompressionType
```
public void setCompressionType(String compressionType)
```
    If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    Parameters:
    
    compressionType - If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    See Also:
    
    CompressionType
  - getCompressionType
```
public String getCompressionType()
```
    If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    Returns:
    
    If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    See Also:
    
    CompressionType
  - withCompressionType
```
public Channel withCompressionType(String compressionType)
```
    If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    Parameters:
    
    compressionType - If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CompressionType
  - withCompressionType
```
public Channel withCompressionType(CompressionType compressionType)
```
    If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    Parameters:
    
    compressionType - If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    CompressionType
  - setRecordWrapperType
```
public void setRecordWrapperType(String recordWrapperType)
```
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    Parameters:
    
    recordWrapperType -
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    See Also:
    
    RecordWrapper
  - getRecordWrapperType
```
public String getRecordWrapperType()
```
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    Returns:
    
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    See Also:
    
    RecordWrapper
  - withRecordWrapperType
```
public Channel withRecordWrapperType(String recordWrapperType)
```
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    Parameters:
    
    recordWrapperType -
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    RecordWrapper
  - withRecordWrapperType
```
public Channel withRecordWrapperType(RecordWrapper recordWrapperType)
```
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    Parameters:
    
    recordWrapperType -
    Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.
    
    In File mode, leave this field unset or set it to None.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    RecordWrapper
  - setInputMode
```
public void setInputMode(String inputMode)
```
    (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    Parameters:
    
    inputMode - (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    See Also:
    
    TrainingInputMode
  - getInputMode
```
public String getInputMode()
```
    (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    Returns:
    
    (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    See Also:
    
    TrainingInputMode
  - withInputMode
```
public Channel withInputMode(String inputMode)
```
    (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    Parameters:
    
    inputMode - (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TrainingInputMode
  - withInputMode
```
public Channel withInputMode(TrainingInputMode inputMode)
```
    (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    Parameters:
    
    inputMode - (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode.
    
    To use a model for incremental training, choose File input model.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TrainingInputMode
  - setShuffleConfig
```
public void setShuffleConfig(ShuffleConfig shuffleConfig)
```
    A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
    
    For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
    
    Parameters:
    
    shuffleConfig - A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
    
    For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
  - getShuffleConfig
```
public ShuffleConfig getShuffleConfig()
```
    A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
    
    For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
    
    Returns:
    
    A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
    
    For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
  - withShuffleConfig
```
public Channel withShuffleConfig(ShuffleConfig shuffleConfig)
```
    A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
    
    For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
    
    Parameters:
    
    shuffleConfig - A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value.
    
    For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
    
    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 Channel 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 - 1.12.773

Class Channel

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Channel

Method Detail

setChannelName

getChannelName

withChannelName

setDataSource

getDataSource

withDataSource

setContentType

getContentType

withContentType

setCompressionType

getCompressionType

withCompressionType

withCompressionType

setRecordWrapperType

getRecordWrapperType

withRecordWrapperType

withRecordWrapperType

setInputMode

getInputMode

withInputMode

withInputMode

setShuffleConfig

getShuffleConfig

withShuffleConfig

toString

equals

hashCode

clone

marshall