/* * Copyright 2018-2023 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; } /** *

*
* RecordIO *
*
*
* TFRecord *
*

* *

* * * @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; } /** *

*
* RecordIO *
*
*
* TFRecord *
*

* *

* * * @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 *
*

* *

* @see SplitType */ public String getSplitType() { return this.splitType; } /** *

*
* RecordIO *
*
*
* TFRecord *
*

* *

*
* RecordIO *
*
*
* TFRecord *
*

* *

* @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; } /** *

*
* RecordIO *
*
*
* TFRecord *
*

* *

*
* RecordIO *
*
*
* TFRecord *
*

* *

* @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); } }