Package software.amazon.awssdk.services.sagemaker.model

Interface Channel.Builder

All Superinterfaces:
Buildable, CopyableBuilder<Channel.Builder,Channel>, SdkBuilder<Channel.Builder,Channel>, SdkPojo
Enclosing class:
Channel
public static interface Channel.Builder extends SdkPojo, CopyableBuilder<Channel.Builder,Channel>

  • Method Details

    • channelName

      Channel.Builder channelName(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.

    • dataSource

      Channel.Builder dataSource(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.

    • dataSource

      default Channel.Builder dataSource(Consumer<DataSource.Builder> dataSource)

      The location of the channel data.

      This is a convenience method that creates an instance of the DataSource.Builder avoiding the need to create one manually via DataSource.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to dataSource(DataSource).

      Parameters:
      dataSource - a consumer that will call methods on DataSource.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also:

    • contentType

      Channel.Builder contentType(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.

    • compressionType

      Channel.Builder compressionType(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

      Channel.Builder compressionType(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:

    • recordWrapperType

      Channel.Builder recordWrapperType(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:

    • recordWrapperType

      Channel.Builder recordWrapperType(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:

    • inputMode

      Channel.Builder inputMode(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:

    • inputMode

      Channel.Builder inputMode(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:

    • shuffleConfig

      Channel.Builder shuffleConfig(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.

    • shuffleConfig

      default Channel.Builder shuffleConfig(Consumer<ShuffleConfig.Builder> 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.

      This is a convenience method that creates an instance of the ShuffleConfig.Builder avoiding the need to create one manually via ShuffleConfig.builder().

      When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to shuffleConfig(ShuffleConfig).

      Parameters:
      shuffleConfig - a consumer that will call methods on ShuffleConfig.Builder
      Returns:
      Returns a reference to this object so that method calls can be chained together.
      See Also: