/AWS1/CL_SGMTRANSFORMINPUT

Describes the input source of a transform job and the way the transform job consumes it.

CONSTRUCTOR

IMPORTING

Required arguments:

io_datasource TYPE REF TO /AWS1/CL_SGMTRANSFORMDATASRC /AWS1/CL_SGMTRANSFORMDATASRC

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

Optional arguments:

iv_contenttype TYPE /AWS1/SGMCONTENTTYPE /AWS1/SGMCONTENTTYPE

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.

iv_compressiontype TYPE /AWS1/SGMCOMPRESSIONTYPE /AWS1/SGMCOMPRESSIONTYPE

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.

iv_splittype TYPE /AWS1/SGMSPLITTYPE /AWS1/SGMSPLITTYPE

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.

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Queryable Attributes

DataSource

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

Accessible with the following methods

Method	Description
GET_DATASOURCE()	Getter for DATASOURCE

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.

Accessible with the following methods

Method	Description
GET_CONTENTTYPE()	Getter for CONTENTTYPE, with configurable default
ASK_CONTENTTYPE()	Getter for CONTENTTYPE w/ exceptions if field has no value
HAS_CONTENTTYPE()	Determine if CONTENTTYPE has a value

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.

Accessible with the following methods

Method	Description
GET_COMPRESSIONTYPE()	Getter for COMPRESSIONTYPE, with configurable default
ASK_COMPRESSIONTYPE()	Getter for COMPRESSIONTYPE w/ exceptions if field has no val
HAS_COMPRESSIONTYPE()	Determine if COMPRESSIONTYPE has a value

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.

Accessible with the following methods

Method	Description
GET_SPLITTYPE()	Getter for SPLITTYPE, with configurable default
ASK_SPLITTYPE()	Getter for SPLITTYPE w/ exceptions if field has no value
HAS_SPLITTYPE()	Determine if SPLITTYPE has a value