class Aws::SageMaker::Types::TransformInput

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

@note When making an API call, you may pass TransformInput

data as a hash:

    {
      data_source: { # required
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
          s3_uri: "S3Uri", # required
        },
      },
      content_type: "ContentType",
      compression_type: "None", # accepts None, Gzip
      split_type: "None", # accepts None, Line, RecordIO, TFRecord
    }

@!attribute [rw] data_source

Describes the location of the channel data, which is, the S3
location of the input data that the model can consume.
@return [Types::TransformDataSource]

@!attribute [rw] content_type

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.
@return [String]

@!attribute [rw] compression_type

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`.
@return [String]

@!attribute [rw] split_type

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.

<note markdown="1"> 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][1] in the MXNet documentation. For more information about
`TFRecord`, see [Consuming TFRecord data][2] in the TensorFlow
documentation.

 </note>

[1]: https://mxnet.apache.org/api/faq/recordio
[2]: https://www.tensorflow.org/guide/data#consuming_tfrecord_data
@return [String]

@see docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/TransformInput AWS API Documentation

Constants

SENSITIVE