Class: Aws::SageMaker::Types::TransformInput

Inherits:
Struct
  • Object
show all
Includes:
Aws::Structure
Defined in:
lib/aws-sdk-sagemaker/types.rb

Overview

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
}

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

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#compression_type ⇒ String

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:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 10768

class TransformInput < Struct.new(
  :data_source,
  :content_type,
  :compression_type,
  :split_type)
  include Aws::Structure
end

#content_type ⇒ String

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:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 10768

class TransformInput < Struct.new(
  :data_source,
  :content_type,
  :compression_type,
  :split_type)
  include Aws::Structure
end

#data_source ⇒ Types::TransformDataSource

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

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 10768

class TransformInput < Struct.new(
  :data_source,
  :content_type,
  :compression_type,
  :split_type)
  include Aws::Structure
end

#split_type ⇒ String

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.

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 the RecordIO, see [Data Format][1] in the

MXNet documentation. For more information about the TFRecord, see

Consuming TFRecord data][2

in the TensorFlow documentation.

</note>

[1]: mxnet.io/architecture/note_data_loading.html#data-format [2]: www.tensorflow.org/guide/datasets#consuming_tfrecord_data

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 10768

class TransformInput < Struct.new(
  :data_source,
  :content_type,
  :compression_type,
  :split_type)
  include Aws::Structure
end