software.amazon.awssdk.services.sagemaker.model

Class TransformInput

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.TransformInput

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<TransformInput.Builder,TransformInput>

@Generated(value="software.amazon.awssdk:codegen")
public final class TransformInput
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<TransformInput.Builder,TransformInput>

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

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	TransformInput.Builder

Method Summary

Modifier and Type	Method and Description
static TransformInput.Builder	builder()
CompressionType	compressionType() If your transform data is compressed, specify the compression type.
String	compressionTypeAsString() If your transform data is compressed, specify the compression type.
String	contentType() The multipurpose internet mail extension (MIME) type of the data.
TransformDataSource	dataSource() Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
List<SdkField<?>>	sdkFields()
static Class<? extends TransformInput.Builder>	serializableBuilderClass()
SplitType	splitType() The method to use to split the transform job's data files into smaller batches.
String	splitTypeAsString() The method to use to split the transform job's data files into smaller batches.
TransformInput.Builder	toBuilder()
String	toString() Returns a string representation of this object.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

dataSource

public TransformDataSource dataSource()

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

Returns:

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

contentType

public 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.

Returns:

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.

compressionType

public 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.

If the service returns an enum value that is not available in the current SDK version, compressionType will return CompressionType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from compressionTypeAsString().

Returns:

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.

See Also:

CompressionType

compressionTypeAsString

public String compressionTypeAsString()

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.

If the service returns an enum value that is not available in the current SDK version, compressionType will return CompressionType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from compressionTypeAsString().

Returns:

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.

See Also:

CompressionType

splitType

public 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.

If the service returns an enum value that is not available in the current SDK version, splitType will return SplitType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from splitTypeAsString().

Returns:

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.

See Also:

SplitType

splitTypeAsString

public String splitTypeAsString()

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.

If the service returns an enum value that is not available in the current SDK version, splitType will return SplitType.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from splitTypeAsString().

Returns:

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.

See Also:

SplitType

toBuilder

public TransformInput.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<TransformInput.Builder,TransformInput>

builder

public static TransformInput.Builder builder()

serializableBuilderClass

public static Class<? extends TransformInput.Builder> serializableBuilderClass()

hashCode

public int hashCode()

Overrides:

hashCode in class Object

equals

public boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

getValueForField

public <T> Optional<T> getValueForField(String fieldName,
                                        Class<T> clazz)

sdkFields

public List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo