Interface TransformInput.Builder

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

  • Method Details

    • dataSource

      TransformInput.Builder dataSource(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.

    • dataSource

      default TransformInput.Builder dataSource(Consumer<TransformDataSource.Builder> dataSource)

      Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.

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

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

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

    • contentType

      TransformInput.Builder contentType(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.

    • compressionType

      TransformInput.Builder compressionType(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

      TransformInput.Builder compressionType(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:

    • splitType

      TransformInput.Builder splitType(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

      TransformInput.Builder splitType(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: