@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.
| Constructor and Description |
|---|
Channel() |
| 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()
|
int |
hashCode() |
void |
marshall(ProtocolMarshaller 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)
|
String |
toString()
Returns a string representation of this object; useful for testing and debugging.
|
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)
|
public void setChannelName(String channelName)
The name of the channel.
channelName - The name of the channel.public String getChannelName()
The name of the channel.
public Channel withChannelName(String channelName)
The name of the channel.
channelName - The name of the channel.public void setDataSource(DataSource dataSource)
The location of the channel data.
dataSource - The location of the channel data.public DataSource getDataSource()
The location of the channel data.
public Channel withDataSource(DataSource dataSource)
The location of the channel data.
dataSource - The location of the channel data.public void setContentType(String contentType)
The MIME type of the data.
contentType - The MIME type of the data.public String getContentType()
The MIME type of the data.
public Channel withContentType(String contentType)
The MIME type of the data.
contentType - The MIME type of the data.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.
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.CompressionTypepublic 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.
None.
CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set
it to None.CompressionTypepublic 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.
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.CompressionTypepublic 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.
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.CompressionTypepublic 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 which case, Amazon 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.
recordWrapperType - Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which case, Amazon 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.
RecordWrapperpublic String getRecordWrapperType()
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which case, Amazon 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.
Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which case, Amazon 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.
RecordWrapperpublic 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 which case, Amazon 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.
recordWrapperType - Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which case, Amazon 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.
RecordWrapperpublic 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 which case, Amazon 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.
recordWrapperType - Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which case, Amazon 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.
RecordWrapperpublic 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, Amazon 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.
inputMode - (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode, Amazon 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.
TrainingInputModepublic 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, Amazon 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.
InputMode, Amazon 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.
TrainingInputModepublic 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, Amazon 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.
inputMode - (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode, Amazon 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.
TrainingInputModepublic 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, Amazon 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.
inputMode - (Optional) The input mode to use for the data channel in a training job. If you don't set a value for
InputMode, Amazon 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.
TrainingInputModepublic String toString()
toString in class ObjectObject.toString()public void marshall(ProtocolMarshaller protocolMarshaller)
marshall in interface StructuredPojoCopyright © 2018. All rights reserved.