com.amazonaws.services.sagemaker.model.TransformInput Maven / Gradle / Ivy
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/*
* Copyright 2019-2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
*
* Describes the input source of a transform job and the way the transform job consumes it.
*
*
* @see AWS API
* Documentation
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class TransformInput implements Serializable, Cloneable, StructuredPojo {
/**
*
* Describes the location of the channel data, which is, the S3 location of the input data that the model can
* consume.
*
*/
private TransformDataSource dataSource;
/**
*
* 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.
*
*/
private String contentType;
/**
*
* 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.
*
*/
private String compressionType;
/**
*
* 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.
*
*
*/
private String splitType;
/**
*
* Describes the location of the channel data, which is, the S3 location of the input data that the model can
* consume.
*
*
* @param dataSource
* Describes the location of the channel data, which is, the S3 location of the input data that the model can
* consume.
*/
public void setDataSource(TransformDataSource dataSource) {
this.dataSource = dataSource;
}
/**
*
* Describes the location of the channel data, which is, the S3 location of the input data that the model can
* consume.
*
*
* @return Describes the location of the channel data, which is, the S3 location of the input data that the model
* can consume.
*/
public TransformDataSource getDataSource() {
return this.dataSource;
}
/**
*
* Describes the location of the channel data, which is, the S3 location of the input data that the model can
* consume.
*
*
* @param dataSource
* Describes the location of the channel data, which is, the S3 location of the input data that the model can
* consume.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TransformInput withDataSource(TransformDataSource dataSource) {
setDataSource(dataSource);
return this;
}
/**
*
* 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.
*
*
* @param 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.
*/
public void setContentType(String contentType) {
this.contentType = 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.
*
*
* @return 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.
*/
public String getContentType() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public TransformInput withContentType(String contentType) {
setContentType(contentType);
return this;
}
/**
*
* 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.
*
*
* @param 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.
* @see CompressionType
*/
public void setCompressionType(String compressionType) {
this.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.
*
*
* @return 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 CompressionType
*/
public String getCompressionType() {
return this.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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see CompressionType
*/
public TransformInput withCompressionType(String compressionType) {
setCompressionType(compressionType);
return this;
}
/**
*
* 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.
*
*
* @param 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.
* @return Returns a reference to this object so that method calls can be chained together.
* @see CompressionType
*/
public TransformInput withCompressionType(CompressionType compressionType) {
this.compressionType = compressionType.toString();
return this;
}
/**
*
* 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.
*
*
*
* @param 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.
*
* @see SplitType
*/
public void setSplitType(String splitType) {
this.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.
*
*
*
* @return 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 SplitType
*/
public String getSplitType() {
return this.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.
*
*
*
* @param 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.
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see SplitType
*/
public TransformInput withSplitType(String splitType) {
setSplitType(splitType);
return this;
}
/**
*
* 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.
*
*
*
* @param 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.
*
* @return Returns a reference to this object so that method calls can be chained together.
* @see SplitType
*/
public TransformInput withSplitType(SplitType splitType) {
this.splitType = splitType.toString();
return this;
}
/**
* 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.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getDataSource() != null)
sb.append("DataSource: ").append(getDataSource()).append(",");
if (getContentType() != null)
sb.append("ContentType: ").append(getContentType()).append(",");
if (getCompressionType() != null)
sb.append("CompressionType: ").append(getCompressionType()).append(",");
if (getSplitType() != null)
sb.append("SplitType: ").append(getSplitType());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof TransformInput == false)
return false;
TransformInput other = (TransformInput) obj;
if (other.getDataSource() == null ^ this.getDataSource() == null)
return false;
if (other.getDataSource() != null && other.getDataSource().equals(this.getDataSource()) == false)
return false;
if (other.getContentType() == null ^ this.getContentType() == null)
return false;
if (other.getContentType() != null && other.getContentType().equals(this.getContentType()) == false)
return false;
if (other.getCompressionType() == null ^ this.getCompressionType() == null)
return false;
if (other.getCompressionType() != null && other.getCompressionType().equals(this.getCompressionType()) == false)
return false;
if (other.getSplitType() == null ^ this.getSplitType() == null)
return false;
if (other.getSplitType() != null && other.getSplitType().equals(this.getSplitType()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getDataSource() == null) ? 0 : getDataSource().hashCode());
hashCode = prime * hashCode + ((getContentType() == null) ? 0 : getContentType().hashCode());
hashCode = prime * hashCode + ((getCompressionType() == null) ? 0 : getCompressionType().hashCode());
hashCode = prime * hashCode + ((getSplitType() == null) ? 0 : getSplitType().hashCode());
return hashCode;
}
@Override
public TransformInput clone() {
try {
return (TransformInput) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.TransformInputMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}